WorldWideScience

Sample records for learning methods specifically

  1. Learning Specific Content in Technology Education: Learning Study as a Collaborative Method in Swedish Preschool Class Using Hands-On Material

    Science.gov (United States)

    Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

    2014-01-01

    This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study…

  2. "Mastery Learning" Como Metodo Psicoeducativo para Ninos con Problemas Especificos de Aprendizaje. ("Mastery Learning" as a Psychoeducational Method for Children with Specific Learning Problems.)

    Science.gov (United States)

    Coya, Liliam de Barbosa; Perez-Coffie, Jorge

    1982-01-01

    "Mastery Learning" was compared with the "conventional" method of teaching reading skills to Puerto Rican children with specific learning disabilities. The "Mastery Learning" group showed significant gains in the cognitive and affective domains. Results suggested Mastery Learning is a more effective method of teaching…

  3. Learning a specific content in technology education : Learning Study as collaborative method in Swedish preschool class using hands-on material 

    OpenAIRE

    Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

    2014-01-01

    This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study concerns strong constructions and framed structures. This article describes how this learning study was conducted and discusses reflections made du...

  4. Defining the Undefinable: Operationalization of Methods to Identify Specific Learning Disabilities among Practicing School Psychologists

    Science.gov (United States)

    Cottrell, Joseph M.; Barrett, Courtenay A.

    2016-01-01

    Accurate and consistent identification of students with specific learning disabilities (SLDs) is crucial; however, state and district guidelines regarding identification methods lack operationalization and are inconsistent throughout the United States. In the current study, the authors surveyed 471 school psychologists about "school" SLD…

  5. Introducing the Collaborative E-Learning Design Method (CoED)

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Buus, Lillian; Nyvang, Tom

    2015-01-01

    In this chapter, a specific learning design method is introduced and explained, namely the Collaborative E-learning Design method (CoED), which has been developed through various projects in “e-Learning Lab – Centre for User Driven Innovation, Learning and Design” (Nyvang & Georgsen, 2007). We br...

  6. Conduct disorders as a result of specific learning disorders

    OpenAIRE

    VOKROJOVÁ, Nela

    2012-01-01

    This thesis focuses on relationship between specific learning disorders and conduct disorders in puberty. The theoretical part explains the basic terms apearing in the thesis such as specific learning disorders, conduct disorders, puberty and prevention of conduct disorder formation. It presents Czech and foreign research which have already been done in this and related areas. The empirical part uses a quantitative method to measure anxiety and occurrence of conduct disorders in second grade ...

  7. In silico machine learning methods in drug development.

    Science.gov (United States)

    Dobchev, Dimitar A; Pillai, Girinath G; Karelson, Mati

    2014-01-01

    Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases.

  8. IP-MLI: An Independency of Learning Materials from Platforms in a Mobile Learning using Intelligent Method

    Directory of Open Access Journals (Sweden)

    Mohammed Abdallh Otair

    2006-06-01

    Full Text Available Attempting to deliver a monolithic mobile learning system is too inflexible in view of the heterogeneous mixture of hardware and services available and the desirability of facility blended approaches to learning delivery, and how to build learning materials to run on all platforms[1]. This paper proposes a framework of mobile learning system using an intelligent method (IP-MLI . A fuzzy matching method is used to find suitable learning material design. It will provide a best matching for each specific platform type for each learner. The main contribution of the proposed method is to use software layer to insulate learning materials from device-specific features. Consequently, many versions of learning materials can be designed to work on many platform types.

  9. Adolescents with specific learning disabilities - perceptions of specific learning disabilities in the environment of secondary schools

    OpenAIRE

    Pospíšilová, Zuzana

    2012-01-01

    The thesis focuses on adolescents with specific learning disabilities in the milieu of secondary schools. It is divided into a theoretical part and an empirical part. The first part introduces a topic of specific learning disabilities in the developmental stage of adolescence. It first describes the most relevant aspects of adolescent development. The attention is then paid to typical manifestations of specific learning disabilities in adolescence, and also to secondary symptoms usually conne...

  10. 34 CFR 300.307 - Specific learning disabilities.

    Science.gov (United States)

    2010-07-01

    ... 34 Education 2 2010-07-01 2010-07-01 false Specific learning disabilities. 300.307 Section 300.307... Educational Placements Additional Procedures for Identifying Children with Specific Learning Disabilities § 300.307 Specific learning disabilities. (a) General. A State must adopt, consistent with § 300.309...

  11. Alignment-free genome tree inference by learning group-specific distance metrics.

    Science.gov (United States)

    Patil, Kaustubh R; McHardy, Alice C

    2013-01-01

    Understanding the evolutionary relationships between organisms is vital for their in-depth study. Gene-based methods are often used to infer such relationships, which are not without drawbacks. One can now attempt to use genome-scale information, because of the ever increasing number of genomes available. This opportunity also presents a challenge in terms of computational efficiency. Two fundamentally different methods are often employed for sequence comparisons, namely alignment-based and alignment-free methods. Alignment-free methods rely on the genome signature concept and provide a computationally efficient way that is also applicable to nonhomologous sequences. The genome signature contains evolutionary signal as it is more similar for closely related organisms than for distantly related ones. We used genome-scale sequence information to infer taxonomic distances between organisms without additional information such as gene annotations. We propose a method to improve genome tree inference by learning specific distance metrics over the genome signature for groups of organisms with similar phylogenetic, genomic, or ecological properties. Specifically, our method learns a Mahalanobis metric for a set of genomes and a reference taxonomy to guide the learning process. By applying this method to more than a thousand prokaryotic genomes, we showed that, indeed, better distance metrics could be learned for most of the 18 groups of organisms tested here. Once a group-specific metric is available, it can be used to estimate the taxonomic distances for other sequenced organisms from the group. This study also presents a large scale comparison between 10 methods--9 alignment-free and 1 alignment-based.

  12. e-Learning Business Research Methods

    Science.gov (United States)

    Cowie, Jonathan

    2004-01-01

    This paper outlines the development of a generic Business Research Methods course from a simple name in a box to a full e-Learning web based module. It highlights particular issues surrounding the nature of the discipline and the integration of a large number of cross faculty subject specific research methods courses into a single generic module.…

  13. Lessons learned applying CASE methods/tools to Ada software development projects

    Science.gov (United States)

    Blumberg, Maurice H.; Randall, Richard L.

    1993-01-01

    This paper describes the lessons learned from introducing CASE methods/tools into organizations and applying them to actual Ada software development projects. This paper will be useful to any organization planning to introduce a software engineering environment (SEE) or evolving an existing one. It contains management level lessons learned, as well as lessons learned in using specific SEE tools/methods. The experiences presented are from Alpha Test projects established under the STARS (Software Technology for Adaptable and Reliable Systems) project. They reflect the front end efforts by those projects to understand the tools/methods, initial experiences in their introduction and use, and later experiences in the use of specific tools/methods and the introduction of new ones.

  14. [Specific learning disabilities - from DSM-IV to DSM-5].

    Science.gov (United States)

    Schulte-Körne, Gerd

    2014-09-01

    The publication of the DSM-5 means changes in the classification and recommendations for diagnosis of specific learning disabilities. Dyslexia and dyscalculia have been reintroduced into the DSM. Three specific learning disorders - impairment in reading, impairment in the written expression, and impairment in mathematics, described by subskills - are now part of the DSM-5. Three subcomponents of the reading disorder are expressly differentiated: word reading accuracy, reading rate, and fluency and reading comprehension. Impaired subskills of the specific learning disorder with impairment in written expression are spelling accuracy, grammar and punctuation accuracy, and clarity and organization of written expression. Four subskills are found in the mathematics disorder: number sense, memorization of arithmetic facts, accurate or fluent calculation, and accurate math reasoning. Each impaired academic domain and subskill should be recorded. A description of the severity degree was also included. The diagnosis is based on a variety of methods, including medical history, clinical interview, school report, teacher evaluation, rating scales, and psychometric tests. The IQ discrepancy criterion was abandoned, though that of age or class discrepancy criterion was retained. The application of a discrepancy is recommended by 1 to 2.5 SD. All three specific developmental disorders are common (prevalence 5 %-15 %), occur early during the first years of formal schooling, and persist into adulthood.

  15. Working Memory Functioning in Children with Learning Disorders and Specific Language Impairment

    Science.gov (United States)

    Schuchardt, Kirsten; Bockmann, Ann-Katrin; Bornemann, Galina; Maehler, Claudia

    2013-01-01

    Purpose: On the basis of Baddeley's working memory model (1986), we examined working memory functioning in children with learning disorders with and without specific language impairment (SLI). We pursued the question whether children with learning disorders exhibit similar working memory deficits as children with additional SLI. Method: In…

  16. Sequence-specific procedural learning deficits in children with specific language impairment.

    Science.gov (United States)

    Hsu, Hsinjen Julie; Bishop, Dorothy V M

    2014-05-01

    This study tested the procedural deficit hypothesis of specific language impairment (SLI) by comparing children's performance in two motor procedural learning tasks and an implicit verbal sequence learning task. Participants were 7- to 11-year-old children with SLI (n = 48), typically developing age-matched children (n = 20) and younger typically developing children matched for receptive grammar (n = 28). In a serial reaction time task, the children with SLI performed at the same level as the grammar-matched children, but poorer than age-matched controls in learning motor sequences. When tested with a motor procedural learning task that did not involve learning sequential relationships between discrete elements (i.e. pursuit rotor), the children with SLI performed comparably with age-matched children and better than younger grammar-matched controls. In addition, poor implicit learning of word sequences in a verbal memory task (the Hebb effect) was found in the children with SLI. Together, these findings suggest that SLI might be characterized by deficits in learning sequence-specific information, rather than generally weak procedural learning. © 2014 The Authors. Developmental Science Published by John Wiley & Sons Ltd.

  17. Teaching Foreign Languages to Pupils with Specific Learning Disability

    OpenAIRE

    VOLDÁNOVÁ, Veronika

    2015-01-01

    This diploma thesis deals with the topic of specific learning disability. In the theoretical part I define the term specific learning disability and I mention the related terms. I deal with the history, types and causes of specific learning disability, further I describe the possibilities of diagnostics and re-education concerning specific learning disability. I also attend to the situation of a pupil in the family and school background. The main attention is especially paid to teaching forei...

  18. Comparing Three Patterns of Strengths and Weaknesses Models for the Identification of Specific Learning Disabilities

    Science.gov (United States)

    Miller, Daniel C.; Maricle, Denise E.; Jones, Alicia M.

    2016-01-01

    Processing Strengths and Weaknesses (PSW) models have been proposed as a method for identifying specific learning disabilities. Three PSW models were examined for their ability to predict expert identified specific learning disabilities cases. The Dual Discrepancy/Consistency Model (DD/C; Flanagan, Ortiz, & Alfonso, 2013) as operationalized by…

  19. Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization.

    Science.gov (United States)

    Gao, Shenghua; Tsang, Ivor Wai-Hung; Ma, Yi

    2014-02-01

    This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.

  20. SU-F-I-12: Region-Specific Dictionary Learning for Low-Dose X-Ray CT Reconstruction

    International Nuclear Information System (INIS)

    Xu, Q; Han, H; Xing, L

    2016-01-01

    Purpose: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. Methods: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region-specific dictionaries were learned from these training sets. For the low-dose CT reconstruction, a conventional reconstruction, such as filtered back-projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low-dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison. Results: Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method. Conclusion: Region-specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning

  1. SU-F-I-12: Region-Specific Dictionary Learning for Low-Dose X-Ray CT Reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Q; Han, H; Xing, L [Stanford University School of Medicine, Stanford, CA (United States)

    2016-06-15

    Purpose: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. Methods: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region-specific dictionaries were learned from these training sets. For the low-dose CT reconstruction, a conventional reconstruction, such as filtered back-projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low-dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison. Results: Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method. Conclusion: Region-specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning

  2. Greek Young Adults with Specific Learning Disabilities Seeking Learning Assessments

    Science.gov (United States)

    Bonti, Eleni; Bampalou, Christina E.; Kouimtzi, Eleni M.; Kyritsis, Zacharias

    2018-01-01

    The purpose of this study is to investigate the reasons why Greek young adults with Specific Learning Disabilities (SLD) seek learning assessments. The study sample consisted of 106 adults meeting Diagnostic and Statistical Manual of Mental Disorders criteria for SLD. Data were collected through self-report records (clinical interview) of adults…

  3. Machine learning methods for planning

    CERN Document Server

    Minton, Steven

    1993-01-01

    Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning.Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credi

  4. An online supervised learning method based on gradient descent for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Yang, Jing; Zhong, Shuiming

    2017-09-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Effectiveness of Memantine in Improvement of Cognitive Deficits in Specific Learning Disorder

    Directory of Open Access Journals (Sweden)

    Elham Ahmadi Zahrani

    2016-12-01

    Full Text Available Abstract Background: Specific learning disorder is a neurodevelopmental disorder characterized by persistent difficulties in learning academic skills in reading, written expression, or mathematics. This study was performed to investigate the effectiveness of memantine in the relief of cognitive deficits (selective attention, sustained attention, and working memory in specific learning disorder. Materials and Methods: This study is a clinical trial. Of all children 8-12 years referred to Amir Kabir Hospital 94 patients diagnosed with specific learning disorder based on DSMV diagnostic interview referred by specialist and randomly divided by two groups, memantine and placebo. Cognitive deficits before and after treatment were measured with continuous performance test, Stroop test and Wechsler Digit Span forward and reverse and Corsi test. Results: Multivariate analysis of variance showed a significant difference in error when answering, omission answer and corrected answer in continuous performance test, but this difference is not significant in response time. Difference in forward, reverse and collected auditory was significant and not significant in the auditory span. In active visual working memory at corsi cube test, difference was significant (p <0.05. Conclusion: The results showed that memantine in improvement of sustained attention, auditory working memory and visual working memory, is effective, while in selective attention is not effective and according to similarities of learning disorder and Attention deficit / Hyperactivity disorder (ADHD and the effectiveness of memantine in improvement of symptoms of ADHD, we can also use this drug in improvement of cognitive deficits of specific learning disorder.

  6. Cooperative Learning as a Democratic Learning Method

    Science.gov (United States)

    Erbil, Deniz Gökçe; Kocabas, Ayfer

    2018-01-01

    In this study, the effects of applying the cooperative learning method on the students' attitude toward democracy in an elementary 3rd-grade life studies course was examined. Over the course of 8 weeks, the cooperative learning method was applied with an experimental group, and traditional methods of teaching life studies in 2009, which was still…

  7. Qualitative methods in workplace learning

    OpenAIRE

    Fabritius, Hannele

    2015-01-01

    Methods of learning in the workplace will be introduced. The methods are connect to competence development and to the process of conducting development discussions in a dialogical way. The tools developed and applied are a fourfold table, a cycle of work identity, a plan of personal development targets, a learning meeting and a learning map. The methods introduced will aim to better learning at work.

  8. Evaluation Methods on Usability of M-Learning Environments

    Directory of Open Access Journals (Sweden)

    Teresa Magal-Royo

    2007-10-01

    Full Text Available Nowadays there are different evaluation methods focused in the assessment of the usability of telematic methods. The assessment of 3rd generation web environments evaluates the effectiveness and usability of application with regard to the user needs. Wireless usability and, specifically in mobile phones, is concentrated in the validation of the features and tools management using conventional interactive environments. There is not a specific and suitable criterion to evaluate created environments and m-learning platforms, where the restricted and sequential representation is a fundamental aspect to be considered.The present paper exposes the importance of the conventional usability methods to verify both: the employed contents in wireless formats, and the possible interfaces from the conception phases, to the validations of the platform with such characteristics.The development of usability adapted inspection could be complemented with the Remote’s techniques of usability testing, which are being carried out these days in the mobile devices field and which pointed out the need to apply common criteria in the validation of non-located learning scenarios.

  9. One-trial overshadowing: Evidence for fast specific fear learning in humans.

    Science.gov (United States)

    Haesen, Kim; Beckers, Tom; Baeyens, Frank; Vervliet, Bram

    2017-03-01

    Adaptive defensive actions necessitate a fear learning system that is both fast and specific. Fast learning serves to minimize the number of threat confrontations, while specific learning ensures that the acquired fears are tied to threat-relevant cues only. In Pavlovian fear conditioning, fear acquisition is typically studied via repetitive pairings of a single cue with an aversive experience, which is not optimal for the examination of fast specific fear learning. In this study, we adopted the one-trial overshadowing procedure from basic learning research, in which a combination of two visual cues is presented once and paired with an aversive electrical stimulation. Using on-line shock expectancy ratings, skin conductance reactivity and startle reflex modulation as indices of fear learning, we found evidence of strong fear after a single conditioning trial (fast learning) as well as attenuated fear responding when only half of the trained stimulus combination was presented (specific learning). Moreover, specificity of fear responding tended to correlate with levels of state and trait anxiety. These results suggest that one-trial overshadowing can be used as a model to study fast specific fear learning in humans and individual differences therein. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. A Time to Define: Making the Specific Learning Disability Definition Prescribe Specific Learning Disability

    Science.gov (United States)

    Kavale, Kenneth A.; Spaulding, Lucinda S.; Beam, Andrea P.

    2009-01-01

    Unlike other special education categories defined in U.S. law (Individuals with Disabilities Education Act), the definition of specific learning disability (SLD) has not changed since first proposed in 1968. Thus, although the operational definition of SLD has responded to new knowledge and understanding about the construct, the formal definition…

  11. Impact of Computer Aided Learning on Children with Specific Learning Disabilities

    OpenAIRE

    The Spastic Society Of Karnataka , Bangalore

    2004-01-01

    Study conducted by The Spastics Society of Karnataka on behalf of Azim Premji Foundation to assess the effectiveness of computers in enhancing learning for children with specific learning disabilities. Azim Premji Foundation is not liable for any direct or indirect loss or damage whatsoever arising from the use or access of any information, interpretation and conclusions that may be printed in this report.; Study to assess the effectiveness of computers in enhancing learning for children with...

  12. Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

    Science.gov (United States)

    Gueth, P.; Dauvergne, D.; Freud, N.; Létang, J. M.; Ray, C.; Testa, E.; Sarrut, D.

    2013-07-01

    Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations.

  13. Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

    International Nuclear Information System (INIS)

    Gueth, P; Freud, N; Létang, J M; Sarrut, D; Dauvergne, D; Ray, C; Testa, E

    2013-01-01

    Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations. (paper)

  14. Learning LM Specificity for Ganglion Cells

    Science.gov (United States)

    Ahumada, Albert J.

    2015-01-01

    Unsupervised learning models have been proposed based on experience (Ahumada and Mulligan, 1990;Wachtler, Doi, Lee and Sejnowski, 2007) that allow the cortex to develop units with LM specific color opponent receptive fields like the blob cells reported by Hubel and Wiesel on the basis of visual experience. These models used ganglion cells with LM indiscriminate wiring as inputs to the learning mechanism, which was presumed to occur at the cortical level.

  15. The learning continuum based on student's level of competence and specific pedagogical learning material on physiological aspects from teachers's opinions

    Science.gov (United States)

    Hadi, Ria Fitriyani; Subali, Bambang

    2017-08-01

    The scope of learning continuum at the conceptual knowledge is formulated based on the student's level of competence and specific pedagogical learning material. The purpose of this study is to develop a learning continuum of specific pedagogical material aspects of physiology targeted for students in primary and secondary education. This research was conducted in Province of Yogyakarta Special Region from October 2016 to January 2017. The method used in this study was survey method. The data were collected using questionnaire that had been validated from the aspects of construct validity and experts judgements. Respondents in this study consist of 281 Science/Biology teachers at Public Junior and Senior High Schools in the Province of Yogyakarta Special Region which spread in Yogyakarta city and 4 regencies namely Sleman, Bantul, Kulonprogo, and Gunungkidul. The data were taken using a census. Data were analyzed using a descriptive analysis technique. The results show the learning continuum of physiology based on teachers's opinion from grade VII, VIII, and IX are taught in grade VII, VIII, IX and X on level of C2 (understanding) and the learning continuum of physiology based on teachers's opinion from grade X, XI and XII are taught in grade X and XI on level of C2 (understanding), C3 (applying), and C4 (analyzing) based on teachers's opinions. The conclusion is that many teachers refer to the existing curriculum rather than their own original idea for developing learning continuum.

  16. Cognitive Clusters in Specific Learning Disorder.

    Science.gov (United States)

    Poletti, Michele; Carretta, Elisa; Bonvicini, Laura; Giorgi-Rossi, Paolo

    The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. The introduction of the single overarching diagnostic category of specific learning disorder (SLD) could underemphasize interindividual clinical differences regarding intracategory cognitive functioning and learning proficiency, according to current models of multiple cognitive deficits at the basis of neurodevelopmental disorders. The characterization of specific cognitive profiles associated with an already manifest SLD could help identify possible early cognitive markers of SLD risk and distinct trajectories of atypical cognitive development leading to SLD. In this perspective, we applied a cluster analysis to identify groups of children with a Diagnostic and Statistical Manual-based diagnosis of SLD with similar cognitive profiles and to describe the association between clusters and SLD subtypes. A sample of 205 children with a diagnosis of SLD were enrolled. Cluster analyses (agglomerative hierarchical and nonhierarchical iterative clustering technique) were used successively on 10 core subtests of the Wechsler Intelligence Scale for Children-Fourth Edition. The 4-cluster solution was adopted, and external validation found differences in terms of SLD subtype frequencies and learning proficiency among clusters. Clinical implications of these findings are discussed, tracing directions for further studies.

  17. Influence on Learning of a Collaborative Learning Method Comprising the Jigsaw Method and Problem-based Learning (PBL).

    Science.gov (United States)

    Takeda, Kayoko; Takahashi, Kiyoshi; Masukawa, Hiroyuki; Shimamori, Yoshimitsu

    2017-01-01

    Recently, the practice of active learning has spread, increasingly recognized as an essential component of academic studies. Classes incorporating small group discussion (SGD) are conducted at many universities. At present, assessments of the effectiveness of SGD have mostly involved evaluation by questionnaires conducted by teachers, by peer assessment, and by self-evaluation of students. However, qualitative data, such as open-ended descriptions by students, have not been widely evaluated. As a result, we have been unable to analyze the processes and methods involved in how students acquire knowledge in SGD. In recent years, due to advances in information and communication technology (ICT), text mining has enabled the analysis of qualitative data. We therefore investigated whether the introduction of a learning system comprising the jigsaw method and problem-based learning (PBL) would improve student attitudes toward learning; we did this by text mining analysis of the content of student reports. We found that by applying the jigsaw method before PBL, we were able to improve student attitudes toward learning and increase the depth of their understanding of the area of study as a result of working with others. The use of text mining to analyze qualitative data also allowed us to understand the processes and methods by which students acquired knowledge in SGD and also changes in students' understanding and performance based on improvements to the class. This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning.

  18. Exploring Service Learning Outcomes in Students: A Mixed Methods Study for Nursing

    Science.gov (United States)

    Martin, John F.

    2017-01-01

    This mixed methods study exploring student outcomes of service learning experiences is inter-disciplinary, near the intersection of higher education research, moral development, and nursing. The specific problem examined in this study is that service learning among university students is utilized by educators, but largely without a full…

  19. Teaching numerical methods with IPython notebooks and inquiry-based learning

    KAUST Repository

    Ketcheson, David I.

    2014-01-01

    A course in numerical methods should teach both the mathematical theory of numerical analysis and the craft of implementing numerical algorithms. The IPython notebook provides a single medium in which mathematics, explanations, executable code, and visualizations can be combined, and with which the student can interact in order to learn both the theory and the craft of numerical methods. The use of notebooks also lends itself naturally to inquiry-based learning methods. I discuss the motivation and practice of teaching a course based on the use of IPython notebooks and inquiry-based learning, including some specific practical aspects. The discussion is based on my experience teaching a Masters-level course in numerical analysis at King Abdullah University of Science and Technology (KAUST), but is intended to be useful for those who teach at other levels or in industry.

  20. Visual Perceptual Learning and its Specificity and Transfer: A New Perspective

    Directory of Open Access Journals (Sweden)

    Cong Yu

    2011-05-01

    Full Text Available Visual perceptual learning is known to be location and orientation specific, and is thus assumed to reflect the neuronal plasticity in the early visual cortex. However, in recent studies we created “Double training” and “TPE” procedures to demonstrate that these “fundamental” specificities of perceptual learning are in some sense artifacts and that learning can completely transfer to a new location or orientation. We proposed a rule-based learning theory to reinterpret perceptual learning and its specificity and transfer: A high-level decision unit learns the rules of performing a visual task through training. However, the learned rules cannot be applied to a new location or orientation automatically because the decision unit cannot functionally connect to new visual inputs with sufficient strength because these inputs are unattended or even suppressed during training. It is double training and TPE training that reactivate these new inputs, so that the functional connections can be strengthened to enable rule application and learning transfer. Currently we are investigating the properties of perceptual learning free from the bogus specificities, and the results provide some preliminary but very interesting insights into how training reshapes the functional connections between the high-level decision units and sensory inputs in the brain.

  1. Quality specifications in postgraduate medical e-learning: an integrative literature review leading to a postgraduate medical e-learning model.

    Science.gov (United States)

    De Leeuw, R A; Westerman, Michiel; Nelson, E; Ket, J C F; Scheele, F

    2016-07-08

    E-learning is driving major shifts in medical education. Prioritizing learning theories and quality models improves the success of e-learning programs. Although many e-learning quality standards are available, few are focused on postgraduate medical education. We conducted an integrative review of the current postgraduate medical e-learning literature to identify quality specifications. The literature was thematically organized into a working model. Unique quality specifications (n = 72) were consolidated and re-organized into a six-domain model that we called the Postgraduate Medical E-learning Model (Postgraduate ME Model). This model was partially based on the ISO-19796 standard, and drew on cognitive load multimedia principles. The domains of the model are preparation, software design and system specifications, communication, content, assessment, and maintenance. This review clarified the current state of postgraduate medical e-learning standards and specifications. It also synthesized these specifications into a single working model. To validate our findings, the next-steps include testing the Postgraduate ME Model in controlled e-learning settings.

  2. Learning speaker-specific characteristics with a deep neural architecture.

    Science.gov (United States)

    Chen, Ke; Salman, Ahmad

    2011-11-01

    Speech signals convey various yet mixed information ranging from linguistic to speaker-specific information. However, most of acoustic representations characterize all different kinds of information as whole, which could hinder either a speech or a speaker recognition (SR) system from producing a better performance. In this paper, we propose a novel deep neural architecture (DNA) especially for learning speaker-specific characteristics from mel-frequency cepstral coefficients, an acoustic representation commonly used in both speech recognition and SR, which results in a speaker-specific overcomplete representation. In order to learn intrinsic speaker-specific characteristics, we come up with an objective function consisting of contrastive losses in terms of speaker similarity/dissimilarity and data reconstruction losses used as regularization to normalize the interference of non-speaker-related information. Moreover, we employ a hybrid learning strategy for learning parameters of the deep neural networks: i.e., local yet greedy layerwise unsupervised pretraining for initialization and global supervised learning for the ultimate discriminative goal. With four Linguistic Data Consortium (LDC) benchmarks and two non-English corpora, we demonstrate that our overcomplete representation is robust in characterizing various speakers, no matter whether their utterances have been used in training our DNA, and highly insensitive to text and languages spoken. Extensive comparative studies suggest that our approach yields favorite results in speaker verification and segmentation. Finally, we discuss several issues concerning our proposed approach.

  3. An Entry Point for Formal Methods: Specification and Analysis of Event Logs

    Directory of Open Access Journals (Sweden)

    Howard Barringer

    2010-03-01

    Full Text Available Formal specification languages have long languished, due to the grave scalability problems faced by complete verification methods. Runtime verification promises to use formal specifications to automate part of the more scalable art of testing, but has not been widely applied to real systems, and often falters due to the cost and complexity of instrumentation for online monitoring. In this paper we discuss work in progress to apply an event-based specification system to the logging mechanism of the Mars Science Laboratory mission at JPL. By focusing on log analysis, we exploit the "instrumentation" already implemented and required for communicating with the spacecraft. We argue that this work both shows a practical method for using formal specifications in testing and opens interesting research avenues, including a challenging specification learning problem.

  4. Perceptual learning is specific to the trained structure of information.

    Science.gov (United States)

    Cohen, Yamit; Daikhin, Luba; Ahissar, Merav

    2013-12-01

    What do we learn when we practice a simple perceptual task? Many studies have suggested that we learn to refine or better select the sensory representations of the task-relevant dimension. Here we show that learning is specific to the trained structural regularities. Specifically, when this structure is modified after training with a fixed temporal structure, performance regresses to pretraining levels, even when the trained stimuli and task are retained. This specificity raises key questions as to the importance of low-level sensory modifications in the learning process. We trained two groups of participants on a two-tone frequency discrimination task for several days. In one group, a fixed reference tone was consistently presented in the first interval (the second tone was higher or lower), and in the other group the same reference tone was consistently presented in the second interval. When following training, these temporal protocols were switched between groups, performance of both groups regressed to pretraining levels, and further training was needed to attain postlearning performance. ERP measures, taken before and after training, indicated that participants implicitly learned the temporal regularity of the protocol and formed an attentional template that matched the trained structure of information. These results are consistent with Reverse Hierarchy Theory, which posits that even the learning of simple perceptual tasks progresses in a top-down manner, hence can benefit from temporal regularities at the trial level, albeit at the potential cost that learning may be specific to these regularities.

  5. Nurse practitioner preferences for distance education methods related to learning style, course content, and achievement.

    Science.gov (United States)

    Andrusyszyn, M A; Cragg, C E; Humbert, J

    2001-04-01

    The relationships among multiple distance delivery methods, preferred learning style, content, and achievement was sought for primary care nurse practitioner students. A researcher-designed questionnaire was completed by 86 (71%) participants, while 6 engaged in follow-up interviews. The results of the study included: participants preferred learning by "considering the big picture"; "setting own learning plans"; and "focusing on concrete examples." Several positive associations were found: learning on own with learning by reading, and setting own learning plans; small group with learning through discussion; large group with learning new things through hearing and with having learning plans set by others. The most preferred method was print-based material and the least preferred method was audio tape. The most suited method for content included video teleconferencing for counseling, political action, and transcultural issues; and video tape for physical assessment. Convenience, self-direction, and timing of learning were more important than delivery method or learning style. Preferred order of learning was reading, discussing, observing, doing, and reflecting. Recommended considerations when designing distance courses include a mix of delivery methods, specific content, outcomes, learner characteristics, and state of technology.

  6. Computer-enhanced visual learning method: a paradigm to teach and document surgical skills.

    Science.gov (United States)

    Maizels, Max; Mickelson, Jennie; Yerkes, Elizabeth; Maizels, Evelyn; Stork, Rachel; Young, Christine; Corcoran, Julia; Holl, Jane; Kaplan, William E

    2009-09-01

    Changes in health care are stimulating residency training programs to develop new methods for teaching surgical skills. We developed Computer-Enhanced Visual Learning (CEVL) as an innovative Internet-based learning and assessment tool. The CEVL method uses the educational procedures of deliberate practice and performance to teach and learn surgery in a stylized manner. CEVL is a learning and assessment tool that can provide students and educators with quantitative feedback on learning a specific surgical procedure. Methods involved examine quantitative data of improvement in surgical skills. Herein, we qualitatively describe the method and show how program directors (PDs) may implement this technique in their residencies. CEVL allows an operation to be broken down into teachable components. The process relies on feedback and remediation to improve performance, with a focus on learning that is applicable to the next case being performed. CEVL has been shown to be effective for teaching pediatric orchiopexy and is being adapted to additional adult and pediatric procedures and to office examination skills. The CEVL method is available to other residency training programs.

  7. Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

    Science.gov (United States)

    Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S; Prince, Jerry L; Pham, Dzung L

    2015-09-01

    Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

  8. Human reinforcement learning subdivides structured action spaces by learning effector-specific values.

    Science.gov (United States)

    Gershman, Samuel J; Pesaran, Bijan; Daw, Nathaniel D

    2009-10-28

    Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable because of the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning-such as prediction error signals for action valuation associated with dopamine and the striatum-can cope with this "curse of dimensionality." We propose a reinforcement learning framework that allows for learned action valuations to be decomposed into effector-specific components when appropriate to a task, and test it by studying to what extent human behavior and blood oxygen level-dependent (BOLD) activity can exploit such a decomposition in a multieffector choice task. Subjects made simultaneous decisions with their left and right hands and received separate reward feedback for each hand movement. We found that choice behavior was better described by a learning model that decomposed the values of bimanual movements into separate values for each effector, rather than a traditional model that treated the bimanual actions as unitary with a single value. A decomposition of value into effector-specific components was also observed in value-related BOLD signaling, in the form of lateralized biases in striatal correlates of prediction error and anticipatory value correlates in the intraparietal sulcus. These results suggest that the human brain can use decomposed value representations to "divide and conquer" reinforcement learning over high-dimensional action spaces.

  9. Evaluation of a Didactic Method for the Active Learning of Greedy Algorithms

    Science.gov (United States)

    Esteban-Sánchez, Natalia; Pizarro, Celeste; Velázquez-Iturbide, J. Ángel

    2014-01-01

    An evaluation of the educational effectiveness of a didactic method for the active learning of greedy algorithms is presented. The didactic method sets students structured-inquiry challenges to be addressed with a specific experimental method, supported by the interactive system GreedEx. This didactic method has been refined over several years of…

  10. A predictive validity study of the Learning Style Questionnaire (LSQ) using multiple, specific learning criteria

    NARCIS (Netherlands)

    Kappe, F.R.; Boekholt, L.; den Rooyen, C.; van der Flier, H.

    2009-01-01

    Multiple and specific learning criteria were used to examine the predictive validity of the Learning Style Questionnaire (LSQ). Ninety-nine students in a college of higher learning in The Netherlands participated in a naturally occurring field study. The students were categorized into one of four

  11. Effects of Jigsaw Learning Method on Students’ Self-Efficacy and Motivation to Learn

    Directory of Open Access Journals (Sweden)

    Dwi Nur Rachmah

    2017-12-01

    Full Text Available Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas Lambung Mangkurat. The method used in the study is the experimental method using one group pre-test and post-test design. The results of the measurements before and after the use of jigsaw learning method were compared using paired samples t-test. The results showed that there is a difference in students’ self-efficacy and motivation to learn before and after subjected to the treatments; therefore, it can be said that jigsaw learning method had significant effects on self-efficacy and motivation to learn. The application of jigsaw learning model in a classroom with large number of students was the discussion of this study.

  12. An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

    Science.gov (United States)

    Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

    2016-02-01

    Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

  13. Learning Unknown Structure in CRFs via Adaptive Gradient Projection Method

    Directory of Open Access Journals (Sweden)

    Wei Xue

    2016-08-01

    Full Text Available We study the problem of fitting probabilistic graphical models to the given data when the structure is not known. More specifically, we focus on learning unknown structure in conditional random fields, especially learning both the structure and parameters of a conditional random field model simultaneously. To do this, we first formulate the learning problem as a convex minimization problem by adding an l_2-regularization to the node parameters and a group l_1-regularization to the edge parameters, and then a gradient-based projection method is proposed to solve it which combines an adaptive stepsize selection strategy with a nonmonotone line search. Extensive simulation experiments are presented to show the performance of our approach in solving unknown structure learning problems.

  14. Learning Science, Learning about Science, Doing Science: Different Goals Demand Different Learning Methods

    Science.gov (United States)

    Hodson, Derek

    2014-01-01

    This opinion piece paper urges teachers and teacher educators to draw careful distinctions among four basic learning goals: learning science, learning about science, doing science and learning to address socio-scientific issues. In elaboration, the author urges that careful attention is paid to the selection of teaching/learning methods that…

  15. Statistical learning methods: Basics, control and performance

    Energy Technology Data Exchange (ETDEWEB)

    Zimmermann, J. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de

    2006-04-01

    The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms.

  16. Statistical learning methods: Basics, control and performance

    International Nuclear Information System (INIS)

    Zimmermann, J.

    2006-01-01

    The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms

  17. Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins.

    Science.gov (United States)

    Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

    2017-09-05

    In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80.3%, specificity of 94.4%, and accuracy of 92.3%, with MCC of 0.71 for independent dataset. The proposed technique can serve as a powerful tool for identifying electron transport proteins and can help biologists understand the function of the electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  18. Computational learning on specificity-determining residue-nucleotide interactions

    KAUST Repository

    Wong, Ka-Chun; Li, Yue; Peng, Chengbin; Moses, Alan M.; Zhang, Zhaolei

    2015-01-01

    The protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.

  19. Computational learning on specificity-determining residue-nucleotide interactions

    KAUST Repository

    Wong, Ka-Chun

    2015-11-02

    The protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.

  20. Effects of Jigsaw Learning Method on Students’ Self-Efficacy and Motivation to Learn

    OpenAIRE

    Dwi Nur Rachmah

    2017-01-01

    Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas La...

  1. Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

    Science.gov (United States)

    Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

    2009-05-01

    Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.

  2. A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

    Directory of Open Access Journals (Sweden)

    Zekić-Sušac Marijana

    2014-09-01

    Full Text Available Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

  3. The future of the IMS Learning Design specification: a critical look

    NARCIS (Netherlands)

    Sloep, Peter

    2009-01-01

    P. B. Sloep (2009). The future of the IMS Learning Design specification: a critical look. Presentation at the IMS Learning Design seminar 'The future of IMS Learning Design'. December, 10, 2009, Wollongong, Australia: University of Wollongong.

  4. Specific Deficit in Implicit Motor Sequence Learning following Spinal Cord Injury.

    Directory of Open Access Journals (Sweden)

    Ayala Bloch

    Full Text Available Physical and psychosocial rehabilitation following spinal cord injury (SCI leans heavily on learning and practicing new skills. However, despite research relating motor sequence learning to spinal cord activity and clinical observations of impeded skill-learning after SCI, implicit procedural learning following spinal cord damage has not been examined.To test the hypothesis that spinal cord injury (SCI in the absence of concomitant brain injury is associated with a specific implicit motor sequence learning deficit that cannot be explained by depression or impairments in other cognitive measures.Ten participants with SCI in T1-T11, unharmed upper limb motor and sensory functioning, and no concomitant brain injury were compared to ten matched control participants on measures derived from the serial reaction time (SRT task, which was used to assess implicit motor sequence learning. Explicit generation of the SRT sequence, depression, and additional measures of learning, memory, and intelligence were included to explore the source and specificity of potential learning deficits.There was no between-group difference in baseline reaction time, indicating that potential differences between the learning curves of the two groups could not be attributed to an overall reduction in response speed in the SCI group. Unlike controls, the SCI group showed no decline in reaction time over the first six blocks of the SRT task and no advantage for the initially presented sequence over the novel interference sequence. Meanwhile, no group differences were found in explicit learning, depression, or any additional cognitive measures.The dissociation between impaired implicit learning and intact declarative memory represents novel empirical evidence of a specific implicit procedural learning deficit following SCI, with broad implications for rehabilitation and adjustment.

  5. Aligning professional skills and active learning methods: an application for information and communications technology engineering

    Science.gov (United States)

    Llorens, Ariadna; Berbegal-Mirabent, Jasmina; Llinàs-Audet, Xavier

    2017-07-01

    Engineering education is facing new challenges to effectively provide the appropriate skills to future engineering professionals according to market demands. This study proposes a model based on active learning methods, which is expected to facilitate the acquisition of the professional skills most highly valued in the information and communications technology (ICT) market. The theoretical foundations of the study are based on the specific literature on active learning methodologies. The Delphi method is used to establish the fit between learning methods and generic skills required by the ICT sector. An innovative proposition is therefore presented that groups the required skills in relation to the teaching method that best develops them. The qualitative research suggests that a combination of project-based learning and the learning contract is sufficient to ensure a satisfactory skills level for this profile of engineers.

  6. The specificity of learned parallelism in dual-memory retrieval.

    Science.gov (United States)

    Strobach, Tilo; Schubert, Torsten; Pashler, Harold; Rickard, Timothy

    2014-05-01

    Retrieval of two responses from one visually presented cue occurs sequentially at the outset of dual-retrieval practice. Exclusively for subjects who adopt a mode of grouping (i.e., synchronizing) their response execution, however, reaction times after dual-retrieval practice indicate a shift to learned retrieval parallelism (e.g., Nino & Rickard, in Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 373-388, 2003). In the present study, we investigated how this learned parallelism is achieved and why it appears to occur only for subjects who group their responses. Two main accounts were considered: a task-level versus a cue-level account. The task-level account assumes that learned retrieval parallelism occurs at the level of the task as a whole and is not limited to practiced cues. Grouping response execution may thus promote a general shift to parallel retrieval following practice. The cue-level account states that learned retrieval parallelism is specific to practiced cues. This type of parallelism may result from cue-specific response chunking that occurs uniquely as a consequence of grouped response execution. The results of two experiments favored the second account and were best interpreted in terms of a structural bottleneck model.

  7. PYRAMID METHOD OF DISTANCE LEARNING IN HIGER EDUCATION

    Directory of Open Access Journals (Sweden)

    Дмитрий Васильевич Сенашенко

    2017-12-01

    Full Text Available The article deals with modern methods of distance learning in the corporate sector. On the specifics of the application of the described methods is their classification and be subject to review their specific differences based on the features and applications of these techniques given the characteristics of the organization of teaching in higher education, a conclusion about their preferred sides, which can be used in distance education. Later in the article, taking into account the above factors, it is proposed an innovative method of formation of educational programs. In view of the similarity of the rendered appearance of the pyramids, this technique proposed name “pyramid”. Offered by the authors, this technique is best synthesis of the best features of the previously described in the article for the online teaching methods. In the future, we are given a detailed description and conducted a preliminary analysis of the applicability of this technique to the training process in the Russian Federation. The analysis describes the eight alleged authors of distance education problems of high school that this method can help to solve.

  8. Geometrical methods in learning theory

    International Nuclear Information System (INIS)

    Burdet, G.; Combe, Ph.; Nencka, H.

    2001-01-01

    The methods of information theory provide natural approaches to learning algorithms in the case of stochastic formal neural networks. Most of the classical techniques are based on some extremization principle. A geometrical interpretation of the associated algorithms provides a powerful tool for understanding the learning process and its stability and offers a framework for discussing possible new learning rules. An illustration is given using sequential and parallel learning in the Boltzmann machine

  9. Polyunsaturated fatty acids (PUFAs) for children with specific learning disorders.

    Science.gov (United States)

    Tan, May Loong; Ho, Jacqueline J; Teh, Keng Hwang

    2016-09-28

    About 5% of school children have a specific learning disorder, defined as unexpected failure to acquire adequate abilities in reading, writing or mathematics that is not a result of reduced intellectual ability, inadequate teaching or social deprivation. Of these events, 80% are reading disorders. Polyunsaturated fatty acids (PUFAs), in particular, omega-3 and omega-6 fatty acids, which normally are abundant in the brain and in the retina, are important for learning. Some children with specific learning disorders have been found to be deficient in these PUFAs, and it is argued that supplementation of PUFAs may help these children improve their learning abilities. 1. To assess effects on learning outcomes of supplementation of polyunsaturated fatty acids (PUFAs) for children with specific learning disorders.2. To determine whether adverse effects of supplementation of PUFAs are reported in these children. In November 2015, we searched CENTRAL, Ovid MEDLINE, Embase, PsycINFO, 10 other databases and two trials registers. We also searched the reference lists of relevant articles. Randomised controlled trials (RCTs) or quasi-RCTs comparing PUFAs with placebo or no treatment in children younger than 18 years with specific learning disabilities, as diagnosed in accordance with the fifth (or earlier) edition of theDiagnostic and Statistical Manual of Mental Disorders (DSM-5), or the 10th (or earlier) revision of the International Classification of Diseases (ICD-10) or equivalent criteria. We included children with coexisting developmental disorders such as attention deficit hyperactivity disorder (ADHD) or autism. Two review authors (MLT and KHT) independently screened the titles and abstracts of articles identified by the search and eliminated all studies that did not meet the inclusion criteria. We contacted study authors to ask for missing information and clarification, when needed. We used the GRADE approach to assess the quality of evidence. Two small studies

  10. Microgenetic Learning Analytics Methods: Workshop Report

    Science.gov (United States)

    Aghababyan, Ani; Martin, Taylor; Janisiewicz, Philip; Close, Kevin

    2016-01-01

    Learning analytics is an emerging discipline and, as such, benefits from new tools and methodological approaches. This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the second annual Learning Analytics Summer Institute in Cambridge, Massachusetts, on 30 June 2014. Specifically, this paper…

  11. Deep learning versus traditional machine learning methods for aggregated energy demand prediction

    NARCIS (Netherlands)

    Paterakis, N.G.; Mocanu, E.; Gibescu, M.; Stappers, B.; van Alst, W.

    2018-01-01

    In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to

  12. Effect of Methods of Learning and Self Regulated Learning toward Outcomes of Learning Social Studies

    Science.gov (United States)

    Tjalla, Awaluddin; Sofiah, Evi

    2015-01-01

    This research aims to reveal the influence of learning methods and self-regulated learning on students learning scores for Social Studies object. The research was done in Islamic Junior High School (MTs Manba'ul Ulum), Batuceper City Tangerang using quasi-experimental method. The research employed simple random technique to 28 students. Data were…

  13. A Learning Model for L/M Specificity in Ganglion Cells

    Science.gov (United States)

    Ahumada, Albert J.

    2016-01-01

    An unsupervised learning model for developing LM specific wiring at the ganglion cell level would support the research indicating LM specific wiring at the ganglion cell level (Reid and Shapley, 2002). Removing the contributions to the surround from cells of the same cone type improves the signal-to-noise ratio of the chromatic signals. The unsupervised learning model used is Hebbian associative learning, which strengthens the surround input connections according to the correlation of the output with the input. Since the surround units of the same cone type as the center are redundant with the center, their weights end up disappearing. This process can be thought of as a general mechanism for eliminating unnecessary cells in the nervous system.

  14. The method of global learning in teaching foreign languages

    Directory of Open Access Journals (Sweden)

    Tatjana Dragovič

    2001-12-01

    Full Text Available The authors describe the method of global learning of foreign languages, which is based on the principles of neurolinguistic programming (NLP. According to this theory, the educator should use the method of the so-called periphery learning, where students learn relaxation techniques and at the same time they »incidentally « or subconsciously learn a foreign language. The method of global learning imitates successful strategies of learning in early childhood and therefore creates a relaxed attitude towards learning. Global learning is also compared with standard methods.

  15. Do students’ styles of learning affect how they adapt to learning methods and to the learning environment?

    OpenAIRE

    Topal, Kenan; Sarıkaya, Özlem; Basturk, Ramazan; Buke, Akile

    2015-01-01

    Objectives: The process of development and evaluation of undergraduate medical education programs should include analysis of learners’ characteristics, needs, and perceptions about learning methods. This study aims to evaluate medical students’ perceptions about problem-based learning methods and to compare these results with their individual learning styles.Materials and Methods: The survey was conducted at Marmara University Medical School where problem-based learning was implemented in the...

  16. Reflexive Learning through Visual Methods

    DEFF Research Database (Denmark)

    Frølunde, Lisbeth

    2014-01-01

    What. This chapter concerns how visual methods and visual materials can support visually oriented, collaborative, and creative learning processes in education. The focus is on facilitation (guiding, teaching) with visual methods in learning processes that are designerly or involve design. Visual...... methods are exemplified through two university classroom cases about collaborative idea generation processes. The visual methods and materials in the cases are photo elicitation using photo cards, and modeling with LEGO Serious Play sets. Why. The goal is to encourage the reader, whether student...... or professional, to facilitate with visual methods in a critical, reflective, and experimental way. The chapter offers recommendations for facilitating with visual methods to support playful, emergent designerly processes. The chapter also has a critical, situated perspective. Where. This chapter offers case...

  17. A Fast Optimization Method for General Binary Code Learning.

    Science.gov (United States)

    Shen, Fumin; Zhou, Xiang; Yang, Yang; Song, Jingkuan; Shen, Heng; Tao, Dacheng

    2016-09-22

    Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely-used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this work, we propose a novel binary code optimization method, dubbed Discrete Proximal Linearized Minimization (DPLM), which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this work by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised `2 loss encodes the whole NUS-WIDE database into 64-bit binary codes within 10 seconds on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale datasets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.

  18. An Innovative Teaching Method To Promote Active Learning: Team-Based Learning

    Science.gov (United States)

    Balasubramanian, R.

    2007-12-01

    Traditional teaching practice based on the textbook-whiteboard- lecture-homework-test paradigm is not very effective in helping students with diverse academic backgrounds achieve higher-order critical thinking skills such as analysis, synthesis, and evaluation. Consequently, there is a critical need for developing a new pedagogical approach to create a collaborative and interactive learning environment in which students with complementary academic backgrounds and learning skills can work together to enhance their learning outcomes. In this presentation, I will discuss an innovative teaching method ('Team-Based Learning (TBL)") which I recently developed at National University of Singapore to promote active learning among students in the environmental engineering program with learning abilities. I implemented this new educational activity in a graduate course. Student feedback indicates that this pedagogical approach is appealing to most students, and promotes active & interactive learning in class. Data will be presented to show that the innovative teaching method has contributed to improved student learning and achievement.

  19. Interference in Ballistic Motor Learning: Specificity and Role of Sensory Error Signals

    Science.gov (United States)

    Lundbye-Jensen, Jesper; Petersen, Tue Hvass; Rothwell, John C.; Nielsen, Jens Bo

    2011-01-01

    Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic stimulation (rTMS) of corticospinal motor output at intensities below movement threshold did not cause interference, whereas suprathreshold rTMS evoking motor responses and (re)afferent activation did. Finally, the experiments revealed that suprathreshold repetitive electrical stimulation of the agonist (but not antagonist) peripheral nerve caused interference. The present study is, to our knowledge, the first to demonstrate that peripheral nerve stimulation may cause interference. The finding underscores the importance of sensory feedback as error signals in motor learning. We conclude that interference requires competing plasticity in overlapping circuits. Interference is remarkably specific for circuits involved in a specific movement and it may relate to sensory error signals. PMID:21408054

  20. Reduced autobiographical memory specificity is associated with impaired discrimination learning in anxiety disorder patients

    Science.gov (United States)

    Lenaert, Bert; Boddez, Yannick; Vervliet, Bram; Schruers, Koen; Hermans, Dirk

    2015-01-01

    Associative learning plays an important role in the development of anxiety disorders, but a thorough understanding of the variables that impact such learning is still lacking. We investigated whether individual differences in autobiographical memory specificity are related to discrimination learning and generalization. In an associative learning task, participants learned the association between two pictures of female faces and a non-aversive outcome. Subsequently, six morphed pictures functioning as generalization stimuli (GSs) were introduced. In a sample of healthy participants (Study 1), we did not find evidence for differences in discrimination learning as a function of memory specificity. In a sample of anxiety disorder patients (Study 2), individuals who were characterized by low memory specificity showed deficient discrimination learning relative to high specific individuals. In contrast to previous findings, results revealed no effect of memory specificity on generalization. These results indicate that impaired discrimination learning, previously shown in patients suffering from an anxiety disorder, may be—in part—due to limited memory specificity. Together, these studies emphasize the importance of incorporating cognitive variables in associative learning theories and their implications for the development of anxiety disorders. In addition, re-analyses of the data (Study 3) showed that patients suffering from panic disorder showed higher outcome expectancies in the presence of the stimulus that was never followed by an outcome during discrimination training, relative to patients suffering from other anxiety disorders and healthy participants. Because we used a neutral, non-aversive outcome (i.e., drawing of a lightning bolt), these data suggest that learning abnormalities in panic disorder may not be restricted to fear learning, but rather reflect a more general associative learning deficit that also manifests in fear irrelevant contexts. PMID

  1. Learning Method, Facilities And Infrastructure, And Learning Resources In Basic Networking For Vocational School

    OpenAIRE

    Pamungkas, Bian Dwi

    2017-01-01

    This study aims to examine the contribution of learning methods on learning output, the contribution of facilities and infrastructure on output learning, the contribution of learning resources on learning output, and the contribution of learning methods, the facilities and infrastructure, and learning resources on learning output. The research design is descriptive causative, using a goal-oriented assessment approach in which the assessment focuses on assessing the achievement of a goal. The ...

  2. Impact of an education program on parental knowledge of specific learning disability

    OpenAIRE

    Karande Sunil; Mehta Vishal; Kulkarni Madhuri

    2007-01-01

    Background :A supportive home environment is one of the factors that can favorably determine the outcome of specific learning disability (SpLD) in a school-going child. However, there is no reliable information available on parental knowledge about SpLD. Aims :To investigate parental knowledge of SpLD and to evaluate the impact of an educational intervention on it. Settings and Design : Prospective questionnaire-based study conducted in our clinic. Materials and Methods : From April to Novemb...

  3. An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data.

    Science.gov (United States)

    Liu, Yuzhe; Gopalakrishnan, Vanathi

    2017-03-01

    Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

  4. E-Learning Lifecycles:How Communities and Context can affect E-learning Specifications and Tool Design

    Directory of Open Access Journals (Sweden)

    Michael Magee

    2004-10-01

    Full Text Available The development of a large body of e-learning specifications, such as IMS and SCORM, has led to the proposal for a new way to facilitate content workflow. This involves the movement of educational digital content and the knowledge of pedagogical communities into an online space. Several projects have looked at the theoretical structure of these specifications. They implemented a series of tools in order to examine and research the issues around the actual usage of these specifications. The CAREO, ALOHA and ALOHA 2 projects were designed to expose both individual users and whole institutions to these ideas. Initial research into the result of those interactions indicates that there is some utility in the adoption of e-learning specifications. The future success of them will depend on their ability to adapt and meet the needs of the educational community as they begin to adopt, use and evolve the way they use the specifications and the tools created around them.

  5. New e-learning method using databases

    Directory of Open Access Journals (Sweden)

    Andreea IONESCU

    2012-10-01

    Full Text Available The objective of this paper is to present a new e-learning method that use databases. The solution could pe implemented for any typeof e-learning system in any domain. The article will purpose a solution to improve the learning process for virtual classes.

  6. Agent-specific learning signals for self-other distinction during mentalising.

    Directory of Open Access Journals (Sweden)

    Sam Ereira

    2018-04-01

    Full Text Available Humans have a remarkable ability to simulate the minds of others. How the brain distinguishes between mental states attributed to self and mental states attributed to someone else is unknown. Here, we investigated how fundamental neural learning signals are selectively attributed to different agents. Specifically, we asked whether learning signals are encoded in agent-specific neural patterns or whether a self-other distinction depends on encoding agent identity separately from this learning signal. To examine this, we tasked subjects to learn continuously 2 models of the same environment, such that one was selectively attributed to self and the other was selectively attributed to another agent. Combining computational modelling with magnetoencephalography (MEG enabled us to track neural representations of prediction errors (PEs and beliefs attributed to self, and of simulated PEs and beliefs attributed to another agent. We found that the representational pattern of a PE reliably predicts the identity of the agent to whom the signal is attributed, consistent with a neural self-other distinction implemented via agent-specific learning signals. Strikingly, subjects exhibiting a weaker neural self-other distinction also had a reduced behavioural capacity for self-other distinction and displayed more marked subclinical psychopathological traits. The neural self-other distinction was also modulated by social context, evidenced in a significantly reduced decoding of agent identity in a nonsocial control task. Thus, we show that self-other distinction is realised through an encoding of agent identity intrinsic to fundamental learning signals. The observation that the fidelity of this encoding predicts psychopathological traits is of interest as a potential neurocomputational psychiatric biomarker.

  7. Working memory and novel word learning in children with hearing impairment and children with specific language impairment.

    Science.gov (United States)

    Hansson, K; Forsberg, J; Löfqvist, A; Mäki-Torkko, E; Sahlén, B

    2004-01-01

    Working memory is considered to influence a range of linguistic skills, i.e. vocabulary acquisition, sentence comprehension and reading. Several studies have pointed to limitations of working memory in children with specific language impairment. Few studies, however, have explored the role of working memory for language deficits in children with hearing impairment. The first aim was to compare children with mild-to-moderate bilateral sensorineural hearing impairment, children with a preschool diagnosis of specific language impairment and children with normal language development, aged 9-12 years, for language and working memory. The special focus was on the role of working memory in learning new words for primary school age children. The assessment of working memory included tests of phonological short-term memory and complex working memory. Novel word learning was assessed according to the methods of. In addition, a range of language tests was used to assess language comprehension, output phonology and reading. Children with hearing impairment performed significantly better than children with a preschool diagnosis of specific language impairment on tasks assessing novel word learning, complex working memory, sentence comprehension and reading accuracy. No significant correlation was found between phonological short-term memory and novel word learning in any group. The best predictor of novel word learning in children with specific language impairment and in children with hearing impairment was complex working memory. Furthermore, there was a close relationship between complex working memory and language in children with a preschool diagnosis of specific language impairment but not in children with hearing impairment. Complex working memory seems to play a significant role in vocabulary acquisition in primary school age children. The interpretation is that the results support theories suggesting a weakened influence of phonological short-term memory on novel word

  8. Active learning methods for interactive image retrieval.

    Science.gov (United States)

    Gosselin, Philippe Henri; Cord, Matthieu

    2008-07-01

    Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

  9. Using budget-friendly methods to analyze sport specific movements

    Science.gov (United States)

    Jackson, Lindsay; Williams, Sarah; Ferrara, Davon

    2015-03-01

    When breaking down the physics behind sport specific movements, athletes, usually professional, are often assessed in multimillion-dollar laboratories and facilities. Budget-friendly methods, such as video analysis using low-cost cameras, iPhone sensors, or inexpensive force sensors can make this process more accessible to amateur athletes, which in-turn can give insight into injury mechanisms. Here we present a comparison of two methods of determining the forces experienced by a cheerleader during co-education stunting and soccer goalies while side-diving. For the cheerleader, accelerometer measurements were taken by an iPhone 5 and compared to video analysis. The measurements done on the soccer players were taken using FlexiForce force sensors and again compared to video analysis. While these budget-friendly methods could use some refining, they show promise for producing usable measurements for possibly increasing our understanding of injury in amateur players. Furthermore, low-cost physics experiments with sports can foster an active learning environment for students with minimum physics and mathematical background.

  10. Adaptive e-learning methods and IMS Learning Design. An integrated approach

    NARCIS (Netherlands)

    Burgos, Daniel; Specht, Marcus

    2006-01-01

    Please, cite this publication as: Burgos, D., & Specht, M. (2006). Adaptive e-learning methods and IMS Learning Design. In Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. G. Sampson & W. Didderen (Eds.), Proceedings of the 6th IEEE International Conference on Advanced Learning Technologies (pp.

  11. Vulnerability Assessment by Learning Attack Specifications in Graphs

    NARCIS (Netherlands)

    Nunes Leal Franqueira, V.; Lopes, Raul H.C.

    This paper presents an evolutionary approach for learning attack specifications that describe attack scenarios. The objective is to find vulnerabilities in computer networks which minimise the cost of an attack with maximum impact. Although we focus on Insider Threat, the proposed approach applies

  12. The lasting effects of process-specific versus stimulus-specific learning during infancy.

    Science.gov (United States)

    Hadley, Hillary; Pickron, Charisse B; Scott, Lisa S

    2015-09-01

    The capacity to tell the difference between two faces within an infrequently experienced face group (e.g. other species, other race) declines from 6 to 9 months of age unless infants learn to match these faces with individual-level names. Similarly, the use of individual-level labels can also facilitate differentiation of a group of non-face objects (strollers). This early learning leads to increased neural specialization for previously unfamiliar face or object groups. The current investigation aimed to determine whether early conceptual learning between 6 and 9 months leads to sustained behavioral advantages and neural changes in these same children at 4-6 years of age. Results suggest that relative to a control group of children with no previous training and to children with infant category-level naming experience, children with early individual-level training exhibited faster response times to human faces. Further, individual-level training with a face group - but not an object group - led to more adult-like neural responses for human faces. These results suggest that early individual-level learning results in long-lasting process-specific effects, which benefit categories that continue to be perceived and recognized at the individual level (e.g. human faces). © 2014 John Wiley & Sons Ltd.

  13. Student Achievement in Basic College Mathematics: Its Relationship to Learning Style and Learning Method

    Science.gov (United States)

    Gunthorpe, Sydney

    2006-01-01

    From the assumption that matching a student's learning style with the learning method best suited for the student, it follows that developing courses that correlate learning method with learning style would be more successful for students. Albuquerque Technical Vocational Institute (TVI) in New Mexico has attempted to provide students with more…

  14. Case study teaching method improves student performance and perceptions of learning gains.

    Science.gov (United States)

    Bonney, Kevin M

    2015-05-01

    Following years of widespread use in business and medical education, the case study teaching method is becoming an increasingly common teaching strategy in science education. However, the current body of research provides limited evidence that the use of published case studies effectively promotes the fulfillment of specific learning objectives integral to many biology courses. This study tested the hypothesis that case studies are more effective than classroom discussions and textbook reading at promoting learning of key biological concepts, development of written and oral communication skills, and comprehension of the relevance of biological concepts to everyday life. This study also tested the hypothesis that case studies produced by the instructor of a course are more effective at promoting learning than those produced by unaffiliated instructors. Additionally, performance on quantitative learning assessments and student perceptions of learning gains were analyzed to determine whether reported perceptions of learning gains accurately reflect academic performance. The results reported here suggest that case studies, regardless of the source, are significantly more effective than other methods of content delivery at increasing performance on examination questions related to chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication. This finding was positively correlated to increased student perceptions of learning gains associated with oral and written communication skills and the ability to recognize connections between biological concepts and other aspects of life. Based on these findings, case studies should be considered as a preferred method for teaching about a variety of concepts in science courses.

  15. Case Study Teaching Method Improves Student Performance and Perceptions of Learning Gains

    Directory of Open Access Journals (Sweden)

    Kevin M. Bonney

    2015-02-01

    Full Text Available Following years of widespread use in business and medical education, the case study teaching method is becoming an increasingly common teaching strategy in science education. However, the current body of research provides limited evidence that the use of published case studies effectively promotes the fulfillment of specific learning objectives integral to many biology courses. This study tested the hypothesis that case studies are more effective than classroom discussions and textbook reading at promoting learning of key biological concepts, development of written and oral communication skills, and comprehension of the relevance of biological concepts to everyday life. This study also tested the hypothesis that case studies produced by the instructor of a course are more effective at promoting learning than those produced by unaffiliated instructors. Additionally, performance on quantitative learning assessments and student perceptions of learning gains were analyzed to determine whether reported perceptions of learning gains accurately reflect academic performance. The results reported here suggest that case studies, regardless of the source, are significantly more effective than other methods of content delivery at increasing performance on examination questions related to chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication. This finding was positively correlated to increased student perceptions of learning gains associated with oral and written communication skills and the ability to recognize connections between biological concepts and other aspects of life. Based on these findings, case studies should be considered as a preferred method for teaching about a variety of concepts in science courses.

  16. A machine learning approach for efficient uncertainty quantification using multiscale methods

    Science.gov (United States)

    Chan, Shing; Elsheikh, Ahmed H.

    2018-02-01

    Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these coarse scale basis functions. Specifically, we employ a neural network predictor fitted using a set of solution samples from which it learns to generate subsequent basis functions at a lower computational cost than solving the local problems. The computational advantage of this approach is realized for uncertainty quantification tasks where a large number of realizations has to be evaluated. We attribute the ability to learn these basis functions to the modularity of the local problems and the redundancy of the permeability patches between samples. The proposed method is evaluated on elliptic problems yielding very promising results.

  17. Measuring the surgical 'learning curve': methods, variables and competency.

    Science.gov (United States)

    Khan, Nuzhath; Abboudi, Hamid; Khan, Mohammed Shamim; Dasgupta, Prokar; Ahmed, Kamran

    2014-03-01

    To describe how learning curves are measured and what procedural variables are used to establish a 'learning curve' (LC). To assess whether LCs are a valuable measure of competency. A review of the surgical literature pertaining to LCs was conducted using the Medline and OVID databases. Variables should be fully defined and when possible, patient-specific variables should be used. Trainee's prior experience and level of supervision should be quantified; the case mix and complexity should ideally be constant. Logistic regression may be used to control for confounding variables. Ideally, a learning plateau should reach a predefined/expert-derived competency level, which should be fully defined. When the group splitting method is used, smaller cohorts should be used in order to narrow the range of the LC. Simulation technology and competence-based objective assessments may be used in training and assessment in LC studies. Measuring the surgical LC has potential benefits for patient safety and surgical education. However, standardisation in the methods and variables used to measure LCs is required. Confounding variables, such as participant's prior experience, case mix, difficulty of procedures and level of supervision, should be controlled. Competency and expert performance should be fully defined. © 2013 The Authors. BJU International © 2013 BJU International.

  18. Specific Learning Difficulties--What Teachers Need to Know

    Science.gov (United States)

    Hudson, Diana

    2015-01-01

    This book clearly explains what Specific Learning Difficulties (SpLD) are, and describes the symptoms of conditions most commonly encountered in the mainstream classroom: dyslexia, dyspraxia, dyscalculia, dysgraphia, Autism Spectrum Disorder, ADHD, and OCD. The author provides an overview of the strengths and weaknesses commonly associated with…

  19. A Swarm-Based Learning Method Inspired by Social Insects

    Science.gov (United States)

    He, Xiaoxian; Zhu, Yunlong; Hu, Kunyuan; Niu, Ben

    Inspired by cooperative transport behaviors of ants, on the basis of Q-learning, a new learning method, Neighbor-Information-Reference (NIR) learning method, is present in the paper. This is a swarm-based learning method, in which principles of swarm intelligence are strictly complied with. In NIR learning, the i-interval neighbor's information, namely its discounted reward, is referenced when an individual selects the next state, so that it can make the best decision in a computable local neighborhood. In application, different policies of NIR learning are recommended by controlling the parameters according to time-relativity of concrete tasks. NIR learning can remarkably improve individual efficiency, and make swarm more "intelligent".

  20. Managing specific learning disability in schools in India.

    Science.gov (United States)

    Karande, Sunil; Sholapurwala, Rukhshana; Kulkarni, Madhuri

    2011-07-01

    Specific learning disability (dyslexia, dysgraphia, and dyscalculia) afflicts 5-15% of school-going children. Over the last decade; awareness about this invisible handicap has grown in India. However, much needs to be done to ensure that each afflicted child gets an opportunity to achieve his or her full academic potential in regular mainstream schools. In order to achieve this ideal scenario, all regular classroom teachers should be sensitized to suspect, and trained to screen for this disability when the child is in primary school. School managements should become proactive to set up resource rooms and employ special educators to ensure that these children receive regular and affordable remedial education; and be diligent in ensuring that these children get the mandatory provisions both during school and board examinations. Once specific learning disability is recognized as a disability by the Government of India, these children with the backing of the Right to Education Act, would be able to benefit significantly.

  1. Qualitative and quantitative revaluation of specific learning disabilities: a multicentric study.

    Science.gov (United States)

    Operto, Francesca F; Mazza, Roberta; Buttiglione, Maura; Craig, Francesco; Frolli, Alessandro; Pisano, Simone; Margari, Lucia; Coppola, Giangennaro

    2018-04-12

    Specific learning disabilities are disorders that affect the instrumental skills of academic learning, leaving intact the general intellectual functioning. It is possible to distinguish: dyslexia, dysorthography, dysgraphia, and dyscalculia. The diagnosis is made according to DSMV. The aim of this study is to evaluate the implementation of Law N° 170 following a diagnosis of specific learning disabilities in children and their evolution over time. The sample under examination consists of 75 children, 56 males and 18 females aged 7,8 to 16 years, with a diagnosis of specific learning disabilities; a revaluation was carried outthrough the use of standardized instruments according to age and school attended. A twopart questionnaire was proposed: the first part turned to the parents/carers of the child and the second part turned to the boy himself. The improvement parameter has been linked, through a statistical analysis of univarianza with intelligence quotient, age, application of the law 10 October 2010 n 170, rehabilitative paths and attending afterschool program. Most of the guys are followed at school by the application of the law 170 and, outside school, by attending speech and neuropsychological therapy and after school. Going to investigate the actual use of the measures put in place by the school, it is evident a partial and incomplete application of Law 170. The most suitable measures for these children are pedagogical measures in order to make them integrate with the group class and strengthen their capacities through specific measures provided by a specific legislative decree.

  2. Methods for control over learning individual trajectory

    Science.gov (United States)

    Mitsel, A. A.; Cherniaeva, N. V.

    2015-09-01

    The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects.

  3. Comparison of executive functions in students with and without specific learning disability with the characteristic reading and writing

    Directory of Open Access Journals (Sweden)

    Saba Hasanvandi

    2017-03-01

    Full Text Available Background: The aim of present study was to investigate executive functions included of working memory, organization-planning and reasoning in the children with and without specific learning disability with the characteristic reading and writing. Materials and methods: The design of this research was Ex-Post Facto design. Statistical population was all male students of third grade primary schools in Tehran which were referred to education institution with diagnosis special learning disorders in educational centers. The sample included of 90 students chosen and assigned into 3 groups of 30 students, included of: children who had specific learning disability with characteristic reading, children who had specific learning disability with characteristic writing, normal children were selected by systematic randomized sampling and 3 groups were compared. The data instruments were: Wechsler’ subtests of similarities and digit differences, Andre Ray test, in formal (unofficial reading and dictation test. The obtained data were analyzed with ANOVA. Results: The results showed that there was difference between the group of normal children and other group in executive functions including working memory, organization-planning and reasoning (P<0.05. Also there was difference between two children groups with specific learning disability with  characteristic reading and writing in working memory and reasoning, whereas for organization-planning parameter there were not seen any differences between these two groups (P<0.05. Conclusion: Regarding to obtained results, it is recommended to adoption some ways for improvements of working memory, organization-planning and reasoning

  4. Academic Achievement and Memory Differences among Specific Learning Disabilities Subtypes

    Science.gov (United States)

    Carmichael, Jessica A.; Fraccaro, Rebecca L.; Miller, Daniel C.; Maricle, Denise E.

    2014-01-01

    Reading, writing, and math are academic skills involving a number of different executive functions, particularly working memory. Children with specific learning disabilities (SLD) may present myriad academic difficulties, depending on their specific area(s) of processing weakness. is study examined differences in academic achievement and working…

  5. The Guided Autobiography Method: A Learning Experience

    Science.gov (United States)

    Thornton, James E.

    2008-01-01

    This article discusses the proposition that learning is an unexplored feature of the guided autobiography method and its developmental exchange. Learning, conceptualized and explored as the embedded and embodied processes, is essential in narrative activities of the guided autobiography method leading to psychosocial development and growth in…

  6. Are students' impressions of improved learning through active learning methods reflected by improved test scores?

    Science.gov (United States)

    Everly, Marcee C

    2013-02-01

    To report the transformation from lecture to more active learning methods in a maternity nursing course and to evaluate whether student perception of improved learning through active-learning methods is supported by improved test scores. The process of transforming a course into an active-learning model of teaching is described. A voluntary mid-semester survey for student acceptance of the new teaching method was conducted. Course examination results, from both a standardized exam and a cumulative final exam, among students who received lecture in the classroom and students who had active learning activities in the classroom were compared. Active learning activities were very acceptable to students. The majority of students reported learning more from having active-learning activities in the classroom rather than lecture-only and this belief was supported by improved test scores. Students who had active learning activities in the classroom scored significantly higher on a standardized assessment test than students who received lecture only. The findings support the use of student reflection to evaluate the effectiveness of active-learning methods and help validate the use of student reflection of improved learning in other research projects. Copyright © 2011 Elsevier Ltd. All rights reserved.

  7. [Multilingualism and child psychiatry: on differential diagnoses of language disorder, specific learning disorder, and selective mutism].

    Science.gov (United States)

    Tamiya, Satoshi

    2014-01-01

    Multilingualism poses unique psychiatric problems, especially in the field of child psychiatry. The author discusses several linguistic and transcultural issues in relation to Language Disorder, Specific Learning Disorder and Selective Mutism. Linguistic characteristics of multiple language development, including so-called profile effects and code-switching, need to be understood for differential diagnosis. It is also emphasized that Language Disorder in a bilingual person is not different or worse than that in a monolingual person. Second language proficiency, cultural background and transfer from the first language all need to be considered in an evaluation for Specific Learning Disorder. Selective Mutism has to be differentiated from the silent period observed in the normal successive bilingual development. The author concludes the review by remarking on some caveats around methods of language evaluation in a multilingual person.

  8. A Scale Development for Teacher Competencies on Cooperative Learning Method

    Science.gov (United States)

    Kocabas, Ayfer; Erbil, Deniz Gokce

    2017-01-01

    Cooperative learning method is a learning method studied both in Turkey and in the world for long years as an active learning method. Although cooperative learning method takes place in training programs, it cannot be implemented completely in the direction of its principles. The results of the researches point out that teachers have problems with…

  9. Understanding Impulsivity among Children with Specific Learning Disabilities in Inclusion Schools

    Science.gov (United States)

    Al-Dababneh, Kholoud Adeeb; Al-Zboon, Eman K.

    2018-01-01

    Impulsive behavior is a characteristic of children with specific learning disabilities (SLD), and is related to learning ability. The present study aims to identify impulsivity behavior in children with SLD who attend inclusion schools, from their resource room teachers' perspectives. A 31-item questionnaire that addressed four subscales was…

  10. Introduction of active learning method in learning physiology by MBBS students.

    Science.gov (United States)

    Gilkar, Suhail Ahmad; Lone, Shabiruddin; Lone, Riyaz Ahmad

    2016-01-01

    Active learning has received considerable attention over the past several years, often presented or perceived as a radical change from traditional instruction methods. Current research on learning indicates that using a variety of teaching strategies in the classroom increases student participation and learning. To introduce active learning methodology, i.e., "jigsaw technique" in undergraduate medical education and assess the student and faculty response to it. This study was carried out in the Department of Physiology in a Medical College of North India. A topic was chosen and taught using one of the active learning methods (ALMs), i.e., jigsaw technique. An instrument (questionnaire) was developed in English through an extensive review of literature and was properly validated. The students were asked to give their response on a five-point Likert scale. The feedback was kept anonymous. Faculty also provided their feedback in a separately provided feedback proforma. The data were collected, compiled, and analyzed. Of 150 students of MBBS-first year batch 2014, 142 participated in this study along with 14 faculty members of the Physiology Department. The majority of the students (>90%) did welcome the introduction of ALM and strongly recommended the use of such methods in teaching many more topics in future. 100% faculty members were of the opinion that many more topics shall be taken up using ALMs. This study establishes the fact that both the medical students and faculty want a change from the traditional way of passive, teacher-centric learning, to the more active teaching-learning techniques.

  11. Implementing Collaborative Learning Methods in the Political Science Classroom

    Science.gov (United States)

    Wolfe, Angela

    2012-01-01

    Collaborative learning is one, among other, active learning methods, widely acclaimed in higher education. Consequently, instructors in fields that lack pedagogical training often implement new learning methods such as collaborative learning on the basis of trial and error. Moreover, even though the benefits in academic circles are broadly touted,…

  12. Learning styles: The learning methods of air traffic control students

    Science.gov (United States)

    Jackson, Dontae L.

    In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

  13. [Which learning methods are expected for ultrasound training? Blended learning on trial].

    Science.gov (United States)

    Röhrig, S; Hempel, D; Stenger, T; Armbruster, W; Seibel, A; Walcher, F; Breitkreutz, R

    2014-10-01

    Current teaching methods in graduate and postgraduate training often include frontal presentations. Especially in ultrasound education not only knowledge but also sensomotory and visual skills need to be taught. This requires new learning methods. This study examined which types of teaching methods are preferred by participants in ultrasound training courses before, during and after the course by analyzing a blended learning concept. It also investigated how much time trainees are willing to spend on such activities. A survey was conducted at the end of a certified ultrasound training course. Participants were asked to complete a questionnaire based on a visual analogue scale (VAS) in which three categories were defined: category (1) vote for acceptance with a two thirds majority (VAS 67-100%), category (2) simple acceptance (50-67%) and category (3) rejection (learning program with interactive elements, short presentations (less than 20 min), incorporating interaction with the audience, hands-on sessions in small groups, an alternation between presentations and hands-on-sessions, live demonstrations and quizzes. For post-course learning, interactive and media-assisted approaches were preferred, such as e-learning, films of the presentations and the possibility to stay in contact with instructors in order to discuss the results. Participants also voted for maintaining a logbook for documentation of results. The results of this study indicate the need for interactive learning concepts and blended learning activities. Directors of ultrasound courses may consider these aspects and are encouraged to develop sustainable learning pathways.

  14. Non-adjacent dependency learning in Cantonese-speaking\\ud children with and without a history of specific language\\ud impairment

    OpenAIRE

    Iao, L-S; Ng, LY; Wong, AMY; Lee, OT

    2017-01-01

    Purpose: This study investigated non-adjacent dependency learning in Cantonese-speaking children with and without a history of Specific Language Impairment (SLI) in an artificial linguistic context.\\ud \\ud Method: Sixteen Cantonese-speaking children with SLI history and 16 Cantonese-speaking children with typical language development (TLD) were tested with a non-adjacent dependency learning task using artificial languages that mimic Cantonese.\\ud \\ud Results: Children with TLD performed above...

  15. Decomposition methods for unsupervised learning

    DEFF Research Database (Denmark)

    Mørup, Morten

    2008-01-01

    This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding...... methods and clustering problems is derived both in terms of classical point clustering but also in terms of community detection in complex networks. A guiding principle throughout this thesis is the principle of parsimony. Hence, the goal of Unsupervised Learning is here posed as striving for simplicity...... in the decompositions. Thus, it is demonstrated how a wide range of decomposition methods explicitly or implicitly strive to attain this goal. Applications of the derived decompositions are given ranging from multi-media analysis of image and sound data, analysis of biomedical data such as electroencephalography...

  16. Effect of Chemistry Triangle Oriented Learning Media on Cooperative, Individual and Conventional Method on Chemistry Learning Result

    Science.gov (United States)

    Latisma D, L.; Kurniawan, W.; Seprima, S.; Nirbayani, E. S.; Ellizar, E.; Hardeli, H.

    2018-04-01

    The purpose of this study was to see which method are well used with the Chemistry Triangle-oriented learning media. This quasi experimental research involves first grade of senior high school students in six schools namely each two SMA N in Solok city, in Pasaman and two SMKN in Pariaman. The sampling technique was done by Cluster Random Sampling. Data were collected by test and analyzed by one-way anova and Kruskall Wallish test. The results showed that the high school students in Solok learning taught by cooperative method is better than the results of student learning taught by conventional and Individual methods, both for students who have high initial ability and low-ability. Research in SMK showed that the overall student learning outcomes taught by conventional method is better than the student learning outcomes taught by cooperative and individual methods. Student learning outcomes that have high initial ability taught by individual method is better than student learning outcomes that are taught by cooperative method and for students who have low initial ability, there is no difference in student learning outcomes taught by cooperative, individual and conventional methods. Learning in high school in Pasaman showed no significant difference in learning outcomes of the three methods undertaken.

  17. Efficient model learning methods for actor-critic control.

    Science.gov (United States)

    Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

    2012-06-01

    We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

  18. Identifying Learning Patterns of Children at Risk for Specific Reading Disability

    Science.gov (United States)

    Barbot, Baptiste; Krivulskaya, Suzanna; Hein, Sascha; Reich, Jodi; Thuma, Philip E.; Grigorenko, Elena L.

    2016-01-01

    Differences in learning patterns of vocabulary acquisition in children at risk (+SRD) and not at risk (-SRD) for Specific Reading Disability (SRD) were examined using a microdevelopmental paradigm applied to the multi-trial Foreign Language Learning Task (FLLT; Baddeley et al., 1995). The FLLT was administered to 905 children from rural…

  19. Statistical learning methods in high-energy and astrophysics analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zimmermann, J. [Forschungszentrum Juelich GmbH, Zentrallabor fuer Elektronik, 52425 Juelich (Germany) and Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de; Kiesling, C. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)

    2004-11-21

    We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application.

  20. Statistical learning methods in high-energy and astrophysics analysis

    International Nuclear Information System (INIS)

    Zimmermann, J.; Kiesling, C.

    2004-01-01

    We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application

  1. Active teaching methods, studying responses and learning

    DEFF Research Database (Denmark)

    Christensen, Hans Peter; Vigild, Martin Etchells; Thomsen, Erik Vilain

    2010-01-01

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed.......Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed....

  2. Enriching behavioral ecology with reinforcement learning methods.

    Science.gov (United States)

    Frankenhuis, Willem E; Panchanathan, Karthik; Barto, Andrew G

    2018-02-13

    This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  3. Deep learning methods for protein torsion angle prediction.

    Science.gov (United States)

    Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

    2017-09-18

    Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

  4. How specific is specific self-efficacy?

    DEFF Research Database (Denmark)

    Nielsen, Tine; Makransky, Guido; Vang, Maria Louison

    2017-01-01

    academic learning self-efficacy (SAL-SE) and specific academic exam self-efficacy (SAE-SE), each scale being measurement invariant relative to age, Gender, admission method and specific course targeted. Furthermore, significant and relevant differences between the SAL-SE and SAE-SE scores dependent......Self-efficacy is an important and much used construct in psychology and social science studies. The validity of the measurements used is not always sufficiently evaluated. The aim was to evaluate the psychometric properties of the Danish translation of the self-efficacy subscale of The Motivated...... Strategies for Learning Questionnaire (MSLQ-SE) within a higher education context. Rasch measurement models were employed focusing on measurement invariance and dimensionality. Results with one students sample showed the MSLQ-SE to be not one, but two separate unidimensional subscales, measuring specific...

  5. Assessing the Learning Path Specification: a Pragmatic Quality Approach

    NARCIS (Netherlands)

    Janssen, José; Berlanga, Adriana; Heyenrath, Stef; Martens, Harrie; Vogten, Hubert; Finders, Anton; Herder, Eelco; Hermans, Henry; Melero, Javier; Schaeps, Leon; Koper, Rob

    2010-01-01

    Janssen, J., Berlanga, A. J., Heyenrath, S., Martens, H., Vogten, H., Finders, A., Herder, E., Hermans, H., Melero Gallardo, J., Schaeps, L., & Koper, R. (2010). Assessing the Learning Path Specification: a Pragmatic Quality Approach. Journal of Universal Computer Science, 16(21), 3191-3209.

  6. Self-Esteem among Boys with and without Specific Learning Disabilities.

    Science.gov (United States)

    Bingham, Grace

    1980-01-01

    The self-esteem of 120 males with and without specific learning disabilities, at each of two levels of development (preadolescent and adolescent) was measured using Coopersmith Self-esteem Inventory. (MP)

  7. FLIPPED CLASSROOM LEARNING METHOD TO IMPROVE CARING AND LEARNING OUTCOME IN FIRST YEAR NURSING STUDENT

    Directory of Open Access Journals (Sweden)

    Ni Putu Wulan Purnama Sari

    2017-08-01

    Full Text Available Background and Purpose: Caring is the essence of nursing profession. Stimulation of caring attitude should start early. Effective teaching methods needed to foster caring attitude and improve learning achievement. This study aimed to explain the effect of applying flipped classroom learning method for improving caring attitude and learning achievement of new student nurses at nursing institutions in Surabaya. Method: This is a pre-experimental study using the one group pretest posttest and posttest only design. Population was all new student nurses on nursing institutions in Surabaya. Inclusion criteria: female, 18-21 years old, majoring in nursing on their own volition and being first choice during students selection process, status were active in the even semester of 2015/2016 academic year. Sample size was 67 selected by total sampling. Variables: 1 independent: application of flipped classroom learning method; 2 dependent: caring attitude, learning achievement. Instruments: teaching plan, assignment descriptions, presence list, assignment assessment rubrics, study materials, questionnaires of caring attitude. Data analysis: paired and one sample t test. Ethical clearance was available. Results: Most respondents were 20 years old (44.8%, graduated from high school in Surabaya (38.8%, living with parents (68.7% in their homes (64.2%. All data were normally distributed. Flipped classroom learning method could improve caring attitude by 4.13%. Flipped classroom learning method was proved to be effective for improving caring attitude (p=0.021 and learning achievement (p=0.000. Conclusion and Recommendation: Flipped classroom was effective for improving caring attitude and learning achievement of new student nurse. It is recommended to use mix-method and larger sample for further study.

  8. Category Specificity in Normal Episodic Learning: Applications to Object Recognition and Category-Specific Agnosia

    Science.gov (United States)

    Bukach, Cindy M.; Bub, Daniel N.; Masson, Michael E. J.; Lindsay, D. Stephen

    2004-01-01

    Studies of patients with category-specific agnosia (CSA) have given rise to multiple theories of object recognition, most of which assume the existence of a stable, abstract semantic memory system. We applied an episodic view of memory to questions raised by CSA in a series of studies examining normal observers' recall of newly learned attributes…

  9. Interference in ballistic motor learning: specificity and role of sensory error signals

    DEFF Research Database (Denmark)

    Lundbye-Jensen, Jesper; Petersen, Tue Hvass; Rothwell, John C

    2011-01-01

    Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlap......Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity...... in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non......-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic...

  10. A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

    Science.gov (United States)

    Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

    2017-06-09

    Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. A Comparison between the Effect of Cooperative Learning Teaching Method and Lecture Teaching Method on Students' Learning and Satisfaction Level

    Science.gov (United States)

    Mohammadjani, Farzad; Tonkaboni, Forouzan

    2015-01-01

    The aim of the present research is to investigate a comparison between the effect of cooperative learning teaching method and lecture teaching method on students' learning and satisfaction level. The research population consisted of all the fourth grade elementary school students of educational district 4 in Shiraz. The statistical population…

  12. A cross-benchmark comparison of 87 learning to rank methods

    NARCIS (Netherlands)

    Tax, N.; Bockting, S.; Hiemstra, D.

    2015-01-01

    Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. New learning to rank methods are generally evaluated on benchmark test collections. However, comparison of learning to rank methods based on evaluation results is hindered

  13. Color image definition evaluation method based on deep learning method

    Science.gov (United States)

    Liu, Di; Li, YingChun

    2018-01-01

    In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

  14. Domain-specific and domain-general constraints on word and sequence learning.

    Science.gov (United States)

    Archibald, Lisa M D; Joanisse, Marc F

    2013-02-01

    The relative influences of language-related and memory-related constraints on the learning of novel words and sequences were examined by comparing individual differences in performance of children with and without specific deficits in either language or working memory. Children recalled lists of words in a Hebbian learning protocol in which occasional lists repeated, yielding improved recall over the course of the task on the repeated lists. The task involved presentation of pictures of common nouns followed immediately by equivalent presentations of the spoken names. The same participants also completed a paired-associate learning task involving word-picture and nonword-picture pairs. Hebbian learning was observed for all groups. Domain-general working memory constrained immediate recall, whereas language abilities impacted recall in the auditory modality only. In addition, working memory constrained paired-associate learning generally, whereas language abilities disproportionately impacted novel word learning. Overall, all of the learning tasks were highly correlated with domain-general working memory. The learning of nonwords was additionally related to general intelligence, phonological short-term memory, language abilities, and implicit learning. The results suggest that distinct associations between language- and memory-related mechanisms support learning of familiar and unfamiliar phonological forms and sequences.

  15. A Learning Method for Neural Networks Based on a Pseudoinverse Technique

    Directory of Open Access Journals (Sweden)

    Chinmoy Pal

    1996-01-01

    Full Text Available A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. The efficiency and robustness of the method are verified with the help of an Exclusive OR problem and a dynamic system identification of a linear single degree of freedom mass–spring problem. It is observed that, compared with the conventional backpropagation method, the proposed method has a better convergence rate and a higher degree of learning accuracy with a lower equivalent learning coefficient. It is also found that unlike the steepest descent method, the learning capability of which is dependent on the value of the learning coefficient ν, the proposed pseudoinverse based backpropagation algorithm is comparatively robust with respect to its equivalent variable learning coefficient. A combination of the pseudoinverse method and the steepest descent method is proposed for a faster, more accurate learning capability.

  16. Learning strategies and general cognitive ability as predictors of gender- specific academic achievement.

    Science.gov (United States)

    Ruffing, Stephanie; Wach, F-Sophie; Spinath, Frank M; Brünken, Roland; Karbach, Julia

    2015-01-01

    Recent research has revealed that learning behavior is associated with academic achievement at the college level, but the impact of specific learning strategies on academic success as well as gender differences therein are still not clear. Therefore, the aim of this study was to investigate gender differences in the incremental contribution of learning strategies over general cognitive ability in the prediction of academic achievement. The relationship between these variables was examined by correlation analyses. A set of t-tests was used to test for gender differences in learning strategies, whereas structural equation modeling as well as multi-group analyses were applied to investigate the incremental contribution of learning strategies for male and female students' academic performance. The sample consisted of 461 students (mean age = 21.2 years, SD = 3.2). Correlation analyses revealed that general cognitive ability as well as the learning strategies effort, attention, and learning environment were positively correlated with academic achievement. Gender differences were found in the reported application of many learning strategies. Importantly, the prediction of achievement in structural equation modeling revealed that only effort explained incremental variance (10%) over general cognitive ability. Results of multi-group analyses showed no gender differences in this prediction model. This finding provides further knowledge regarding gender differences in learning research and the specific role of learning strategies for academic achievement. The incremental assessment of learning strategy use as well as gender-differences in their predictive value contributes to the understanding and improvement of successful academic development.

  17. Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.

    Science.gov (United States)

    Kong, Xiangyi; Gong, Shun; Su, Lijuan; Howard, Newton; Kong, Yanguo

    2018-01-01

    Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  18. Placement and Achievement of Urban Hispanic Middle Schoolers with Specific Learning Disabilities

    Science.gov (United States)

    Barrocas, Lisa; Cramer, Elizabeth D.

    2014-01-01

    This study examined achievement gains in reading and math for Hispanic middle school students with specific learning disabilities in inclusive versus segregated settings in a large urban school district. The authors report learning gains for students with and without disabilities in inclusive versus segregated settings. Results indicate no…

  19. Pragmatics of Contemporary Teaching and Learning Methods

    Directory of Open Access Journals (Sweden)

    Ryszard Józef Panfil

    2013-09-01

    Full Text Available The dynamics of the environment in which educational institutions operate have a significant influence on the basic activity of these institutions, i.e. the process of educating, and particularly teaching and learning methods used during that process: traditional teaching, tutoring, mentoring and coaching. The identity of an educational institution and the appeal of its services depend on how flexible, diverse and adaptable is the educational process it offers as a core element of its services. Such a process is determined by how its pragmatism is displayed in the operational relativism of methods, their applicability, as well as practical dimension of achieved results and values. Based on the above premises, this publication offers a pragmatic-systemic identification of contemporary teaching and learning methods, while taking into account the differences between them and the scope of their compatibility. Secondly, using the case of sport coaches’ education, the author exemplifies the pragmatic theory of perception of contemporary teaching and learning methods.

  20. The Method of High School English Word Learning

    Institute of Scientific and Technical Information of China (English)

    吴博涵

    2016-01-01

    Most Chinese students are not interested in English learning, especially English words. In this paper, I focus on English vocabulary learning, for example, the study of high school students English word learning method, and also introduce several ways to make vocabulary memory becomes more effective. The purpose is to make high school students grasp more English word learning skills.

  1. Question presentation methods for paired-associate learning

    NARCIS (Netherlands)

    Engel, F.L.; Geerings, M.P.W.

    1988-01-01

    Four different methods of question presentation, in interactive computeraided learning of Dutch-English word pairs are evaluated experimentally. These methods are: 1) the 'open-question method', 2) the 'multiple-choice method', 3) the 'sequential method' and 4) the 'true/ false method'. When

  2. Think Pair Share (TPS as Method to Improve Student’s Learning Motivation and Learning Achievement

    Directory of Open Access Journals (Sweden)

    Hetika Hetika

    2018-03-01

    Full Text Available This research aims to find out the application of Think Pair Share (TPS learning method in improving learning motivation and learning achievement in the subject of Introduction to Accounting I of the Accounting Study Program students of Politeknik Harapan Bersama. The Method of data collection in this study used observation method, test method, and documentation method. The research instruments used observation sheet, questionnaire and test question. This research used Class Action Research Design which is an action implementation oriented research, with the aim of improving quality or problem solving in a group by carefully and observing the success rate due to the action. The method of analysis used descriptive qualitative and quantitative analysis method. The results showed that the application of Think Pair Share Learning (TPS Method can improve the Learning Motivation and Achievement. Before the implementation of the action, the obtained score is 67% then in the first cycle increases to 72%, and in the second cycle increasws to 80%. In addition, based on questionnaires distributed to students, it also increases the score of Accounting Learning Motivation where the score in the first cycle of 76% increases to 79%. In addition, in the first cycle, the score of pre test and post test of the students has increased from 68.86 to 76.71 while in the second cycle the score of pre test and post test of students has increased from 79.86 to 84.86.

  3. Improving Nursing Students' Learning Outcomes in Fundamentals of Nursing Course through Combination of Traditional and e-Learning Methods.

    Science.gov (United States)

    Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

    2018-01-01

    Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills.

  4. The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods

    NARCIS (Netherlands)

    Verpoorten, Dominique; Poumay, M; Leclercq, D

    2006-01-01

    Please, cite this publication as: Verpoorten, D., Poumay, M., & Leclercq, D. (2006). The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence

  5. Characterizing Reinforcement Learning Methods through Parameterized Learning Problems

    Science.gov (United States)

    2011-06-03

    extraneous. The agent could potentially adapt these representational aspects by applying methods from feature selection ( Kolter and Ng, 2009; Petrik et al...611–616. AAAI Press. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature selection in least-squares temporal difference learning. In A. P

  6. Projection specificity in heterogeneous locus coeruleus cell populations: implications for learning and memory

    Science.gov (United States)

    Uematsu, Akira; Tan, Bao Zhen

    2015-01-01

    Noradrenergic neurons in the locus coeruleus (LC) play a critical role in many functions including learning and memory. This relatively small population of cells sends widespread projections throughout the brain including to a number of regions such as the amygdala which is involved in emotional associative learning and the medial prefrontal cortex which is important for facilitating flexibility when learning rules change. LC noradrenergic cells participate in both of these functions, but it is not clear how this small population of neurons modulates these partially distinct processes. Here we review anatomical, behavioral, and electrophysiological studies to assess how LC noradrenergic neurons regulate these different aspects of learning and memory. Previous work has demonstrated that subpopulations of LC noradrenergic cells innervate specific brain regions suggesting heterogeneity of function in LC neurons. Furthermore, noradrenaline in mPFC and amygdala has distinct effects on emotional learning and cognitive flexibility. Finally, neural recording data show that LC neurons respond during associative learning and when previously learned task contingencies change. Together, these studies suggest a working model in which distinct and potentially opposing subsets of LC neurons modulate particular learning functions through restricted efferent connectivity with amygdala or mPFC. This type of model may provide a general framework for understanding other neuromodulatory systems, which also exhibit cell type heterogeneity and projection specificity. PMID:26330494

  7. Non-Gaussian Methods for Causal Structure Learning.

    Science.gov (United States)

    Shimizu, Shohei

    2018-05-22

    Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

  8. Deep kernel learning method for SAR image target recognition

    Science.gov (United States)

    Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

    2017-10-01

    With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

  9. Is there a need for a specific educational scholarship for using e-learning in medical education?

    Science.gov (United States)

    Sandars, John; Goh, Poh Sun

    2016-10-01

    We propose the need for a specific educational scholarship when using e-learning in medical education. Effective e-learning has additional factors that require specific critical attention, including the design and delivery of e-learning. An important aspect is the recognition that e-learning is a complex intervention, with several interconnecting components that have to be aligned. This alignment requires an essential iterative development process with usability testing. Effectiveness of e-learning in one context may not be fully realized in another context unless there is further consideration of applicability and scalability. We recommend a participatory approach for an educational scholarship for using e-learning in medical education, such as by action research or design-based research.

  10. Choosing Learning Methods Suitable for Teaching and Learning in Computer Science

    Science.gov (United States)

    Taylor, Estelle; Breed, Marnus; Hauman, Ilette; Homann, Armando

    2013-01-01

    Our aim is to determine which teaching methods students in Computer Science and Information Systems prefer. There are in total 5 different paradigms (behaviorism, cognitivism, constructivism, design-based and humanism) with 32 models between them. Each model is unique and states different learning methods. Recommendations are made on methods that…

  11. First-order and higher order sequence learning in specific language impairment.

    Science.gov (United States)

    Clark, Gillian M; Lum, Jarrad A G

    2017-02-01

    A core claim of the procedural deficit hypothesis of specific language impairment (SLI) is that the disorder is associated with poor implicit sequence learning. This study investigated whether implicit sequence learning problems in SLI are present for first-order conditional (FOC) and higher order conditional (HOC) sequences. Twenty-five children with SLI and 27 age-matched, nonlanguage-impaired children completed 2 serial reaction time tasks. On 1 version, the sequence to be implicitly learnt comprised a FOC sequence and on the other a HOC sequence. Results showed that the SLI group learned the HOC sequence (η p ² = .285, p = .005) but not the FOC sequence (η p ² = .099, p = .118). The control group learned both sequences (FOC η p ² = .497, HOC η p 2= .465, ps < .001). The SLI group's difficulty learning the FOC sequence is consistent with the procedural deficit hypothesis. However, the study provides new evidence that multiple mechanisms may underpin the learning of FOC and HOC sequences. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  12. Machine Learning and Data Mining Methods in Diabetes Research.

    Science.gov (United States)

    Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

    2017-01-01

    The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

  13. Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method

    Directory of Open Access Journals (Sweden)

    Muneki Yasuda

    2018-04-01

    Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.

  14. Studying depression using imaging and machine learning methods.

    Science.gov (United States)

    Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

    2016-01-01

    Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

  15. Characterizing Engineering Learners' Preferences for Active and Passive Learning Methods

    Science.gov (United States)

    Magana, Alejandra J.; Vieira, Camilo; Boutin, Mireille

    2018-01-01

    This paper studies electrical engineering learners' preferences for learning methods with various degrees of activity. Less active learning methods such as homework and peer reviews are investigated, as well as a newly introduced very active (constructive) learning method called "slectures," and some others. The results suggest that…

  16. Approximation methods for efficient learning of Bayesian networks

    CERN Document Server

    Riggelsen, C

    2008-01-01

    This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

  17. Unsupervised process monitoring and fault diagnosis with machine learning methods

    CERN Document Server

    Aldrich, Chris

    2013-01-01

    This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data

  18. Impact of international humanitarian service-learning on emerging adult social competence: A mixed-methods evaluation

    Directory of Open Access Journals (Sweden)

    Paul Schvaneveldt

    2016-09-01

    Full Text Available This article presents the results from a study into international humanitarian service-learning experiences on young adult volunteers. Specifically, the service-learning experiences of emerging adults who had served in orphanages in Latin America were assessed, in a pre- and post-test design, for their development in areas of social competency such as identity, self-efficacy, self-esteem and ethnocentric attitudes. A mixed-methods design using both qualitative and quantitative measures was used. Both qualitative and quantitative results identified significant and important impacts on the development of the social competencies of these emerging adults. In addition, several qualitative themes illustrated that longer term international service-learning experiences have a profound impact on the social competence of emerging adults. Keywords: International humanitarian service, service-learning, emerging adult competency

  19. Assessment of two e-learning methods teaching undergraduate students cephalometry in orthodontics.

    Science.gov (United States)

    Ludwig, B; Bister, D; Schott, T C; Lisson, J A; Hourfar, J

    2016-02-01

    Cephalometry is important for orthodontic diagnosis and treatment planning and is part of the core curriculum for training dentists. Training involves identifying anatomical landmarks. The aim of this investigation was to assess whether e-learning improves learning efficiency; a programme specifically designed for this purpose was compared to commercially available software. Thirty undergraduate students underwent traditional training of cephalometry consisting of lectures and tutorials. Tracing skills were tested immediately afterwards (T0). The students were then randomly allocated to three groups: 10 students served as control (CF); they were asked to improve their skills using the material provided so far. Ten students were given a program specifically designed for this study that was based on a power point presentation (PPT). The last group was given a commercially available program that included teaching elements (SW). The groups were tested at the end the six week training (T1). The test consisted of tracing 30 points on two radiographs and a point score improvement was calculated. The students were interviewed after the second test. Both e-learning groups improved more than the traditional group. Improvement scores were four for CF; 8.6 for PPT and 2.8 for SW. For PPT all participants improved and the student feedback was the best compared to the other groups. For the other groups some candidates worsened. Blended learning produced better learning outcomes compared to using a traditional teaching method alone. The easy to use Power Point based custom software produced better results than the commercially available software. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  20. Code-specific learning rules improve action selection by populations of spiking neurons.

    Science.gov (United States)

    Friedrich, Johannes; Urbanczik, Robert; Senn, Walter

    2014-08-01

    Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

  1. A Preliminary Survey of the Preferred Learning Methods for Interpretation Students

    Science.gov (United States)

    Heinz, Michael

    2013-01-01

    There are many different methods that individuals use to learn languages like reading books or writing essays. Not all methods are equally successful for second language learners but nor do all successful learners of a second language show identical preferences for learning methods. Additionally, at the highest level of language learning various…

  2. The Context-Specific Conceptions of Learning in Case-Based Accounting Assignments, Students' Characteristics and Performance

    Science.gov (United States)

    Moilanen, Sinikka

    2017-01-01

    The present study contributes to accounting education literature by describing context-specific conceptions of learning related to case assignments, and by exploring the associations between the conceptions of learning, students' characteristics and performance. The data analysed consist of 1320 learning diaries of 336 students, connected with…

  3. The «PBL WORKING ENVIRONMENT» as interactive and expert system to learn the problem-based learning method

    Directory of Open Access Journals (Sweden)

    Susana Correnti

    2016-01-01

    Full Text Available The «PBL working environment» is a virtual environment developed in the framework of SCENE project (profeSsional development for an effeCtive PBL approach: a practical experiENce through ICT-enabled lEarning solution, co-funded by the European Lifelong Learning Program. The «PBL working environment» is devoted to prepare headmasters and teachers of secondary and vocational schools to use Problem-Based Learning (PBL pedagogy effectively. It is a student-centered pedagogy where learners are «actively» engaged in real world problems to solve or challenges to meet. Students develop problem-solving, self-directed learning and team skills. The «PBL working environment» is an virtual tool including three main elements: e-learning platform, virtual facilitator and PBL repository. Teachers, trainers and headmasters/school managers learn the PBL pedagogy by attending an on-line course (e-learning platform delivered through the «inductive method». It allows learners to experience PBL approach, by practicing it stage by stage, and then learn to turn practice into theory by abstracting their experience to build a theoretical understanding. Since generating the proper scenario is the most critical aspect of PBL, after benefiting from the on-line course, users can benefit from a further support: the Virtual Facilitator. It provides tips and hints on how correctly design a problem scenario and by asking questions to collect data on user's specific needs. The Virtual Facilitator is able to provide a/or more suitable example(s which match as closest as possible the teacher/trainer need. Finally, users can share problem scenarios and projects of different subjects of studies and with different characteristics uploaded and downloaded in the PBL repository.

  4. A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

    Science.gov (United States)

    Moore, Jason H; Shestov, Maksim; Schmitt, Peter; Olson, Randal S

    2018-01-01

    A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important role in this process by making it easy for computational researchers to easily access real data for this purpose. Genomics has in some examples taken a leading role in the open data effort starting with DNA microarrays. While real data from experimental and observational studies is necessary for developing computational methods it is not sufficient. This is because it is not possible to know what the ground truth is in real data. This must be accompanied by simulated data where that balance between signal and noise is known and can be directly evaluated. Unfortunately, there is a lack of methods and software for simulating data with the kind of complexity found in real biological and biomedical systems. We present here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating complex biological and biomedical data. Further, we introduce new methods for developing simulation models that generate data that specifically allows discrimination between different machine learning methods.

  5. Studying depression using imaging and machine learning methods

    Directory of Open Access Journals (Sweden)

    Meenal J. Patel

    2016-01-01

    Full Text Available Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1 presents a background on depression, imaging, and machine learning methodologies; (2 reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3 suggests directions for future depression-related studies.

  6. d-Cycloserine reduces context specificity of sexual extinction learning.

    Science.gov (United States)

    Brom, Mirte; Laan, Ellen; Everaerd, Walter; Spinhoven, Philip; Trimbos, Baptist; Both, Stephanie

    2015-11-01

    d-Cycloserine (DCS) enhances extinction processes in animals. Although classical conditioning is hypothesized to play a pivotal role in the aetiology of appetitive motivation problems, no research has been conducted on the effect of DCS on the reduction of context specificity of extinction in human appetitive learning, while facilitation hereof is relevant in the context of treatment of problematic reward-seeking behaviors. Female participants were presented with two conditioned stimuli (CSs) that either predicted (CS+) or did not predict (CS-) a potential sexual reward (unconditioned stimulus (US); genital vibrostimulation). Conditioning took place in context A and extinction in context B. Subjects received DCS (125mg) or placebo directly after the experiment on day 1 in a randomized, double-blind, between-subject fashion (Placebo n=31; DCS n=31). Subsequent testing for CS-evoked conditioned responses (CRs) in both the conditioning (A) and the extinction context (B) took place 24h later on day 2. Drug effects on consolidation were then assessed by comparing the recall of sexual extinction memories between the DCS and the placebo groups. Post learning administration of DCS facilitates sexual extinction memory consolidation and affects extinction's fundamental context specificity, evidenced by reduced conditioned genital and subjective sexual responses, relative to placebo, for presentations of the reward predicting cue 24h later outside the extinction context. DCS makes appetitive extinction memories context-independent and prevents the return of conditioned response. NMDA receptor glycine site agonists may be potential pharmacotherapies for the prevention of relapse of appetitive motivation disorders with a learned component. Copyright © 2015 Elsevier Inc. All rights reserved.

  7. SMALL GROUP LEARNING METHODS AND THEIR EFFECT ON LEARNERS’ RELATIONSHIPS

    Directory of Open Access Journals (Sweden)

    Radka Borůvková

    2016-04-01

    Full Text Available Building relationships in the classroom is an essential part of any teacher's career. Having healthy teacher-to-learner and learner-to-learner relationships is an effective way to help prevent pedagogical failure, social conflict and quarrelsome behavior. Many strategies are available that can be used to achieve good long-lasting relationships in the classroom setting. Successful teachers’ pedagogical work in the classroom requires detailed knowledge of learners’ relationships. Good understanding of the relationships is necessary, especially in the case of teenagers’ class. This sensitive period of adolescence demands attention of all teachers who should deal with the problems of their learners. Special care should be focused on children that are out of their classmates’ interest (so called isolated learners or isolates in such class and on possibilities to integrate them into the class. Natural idea how to do it is that of using some modern non-traditional teaching/learning methods, especially the methods based on work in small groups involving learners’ cooperation. Small group education (especially problem-based learning, project-based learning, cooperative learning, collaborative learning or inquire-based learning as one of these methods involves a high degree of interaction. The effectiveness of learning groups is determined by the extent to which the interaction enables members to clarify their own understanding, build upon each other's contributions, sift out meanings, ask and answer questions. An influence of this kind of methods (especially cooperative learning (CL on learners’ relationships was a subject of the further described research. Within the small group education, students work with their classmates to solve complex and authentic problems that help develop content knowledge as well as problem-solving, reasoning, communication, and self-assessment skills. The aim of the research was to answer the question: Can the

  8. The Goal Specificity Effect on Strategy Use and Instructional Efficiency during Computer-Based Scientific Discovery Learning

    Science.gov (United States)

    Kunsting, Josef; Wirth, Joachim; Paas, Fred

    2011-01-01

    Using a computer-based scientific discovery learning environment on buoyancy in fluids we investigated the "effects of goal specificity" (nonspecific goals vs. specific goals) for two goal types (problem solving goals vs. learning goals) on "strategy use" and "instructional efficiency". Our empirical findings close an important research gap,…

  9. Learning trajectories for speech motor performance in children with specific language impairment.

    Science.gov (United States)

    Richtsmeier, Peter T; Goffman, Lisa

    2015-01-01

    Children with specific language impairment (SLI) often perform below expected levels, including on tests of motor skill and in learning tasks, particularly procedural learning. In this experiment we examined the possibility that children with SLI might also have a motor learning deficit. Twelve children with SLI and thirteen children with typical development (TD) produced complex nonwords in an imitation task. Productions were collected across three blocks, with the first and second blocks on the same day and the third block one week later. Children's lip movements while producing the nonwords were recorded using an Optotrak camera system. Movements were then analyzed for production duration and stability. Movement analyses indicated that both groups of children produced shorter productions in later blocks (corroborated by an acoustic analysis), and the rate of change was comparable for the TD and SLI groups. A nonsignificant trend for more stable productions was also observed in both groups. SLI is regularly accompanied by a motor deficit, and this study does not dispute that. However, children with SLI learned to make more efficient productions at a rate similar to their peers with TD, revealing some modification of the motor deficit associated with SLI. The reader will learn about deficits commonly associated with specific language impairment (SLI) that often occur alongside the hallmark language deficit. The authors present an experiment showing that children with SLI improved speech motor performance at a similar rate compared to typically developing children. The implication is that speech motor learning is not impaired in children with SLI. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. Experts in Teams – An experiential learning method

    DEFF Research Database (Denmark)

    Johansen, Steffen Kjær

    2017-01-01

    T becomes a learning method rather than a teaching method. Besides discussing the pedagogical characteristics of EiT, the study also gives a general introduction to EiT as it was taught at SDU fall 2016 as well as a brief review of the basic theory behind experiential learning. As such this study serves...... courses. Most of the practical courses are group work along the lines of project based learning. EiT is in a way both. It is a practical course in as much as our students get hands-on experience with interdisciplinary team work and innovation processes. EiT is a theoretical course in as much as our...... both as an introduction to e.g. new teachers of EiT but also as a starting point for a clarification of the features that makes EiT an experiential learning endeavor....

  11. Developing a Blended Learning-Based Method for Problem-Solving in Capability Learning

    Science.gov (United States)

    Dwiyogo, Wasis D.

    2018-01-01

    The main objectives of the study were to develop and investigate the implementation of blended learning based method for problem-solving. Three experts were involved in the study and all three had stated that the model was ready to be applied in the classroom. The implementation of the blended learning-based design for problem-solving was…

  12. Active Learning Methods

    Science.gov (United States)

    Zayapragassarazan, Z.; Kumar, Santosh

    2012-01-01

    Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…

  13. Learning Method and Its Influence on Nutrition Study Results Throwing the Ball

    Science.gov (United States)

    Samsudin; Nugraha, Bayu

    2015-01-01

    This study aimed to know the difference between playing and learning methods of exploratory learning methods to learning outcomes throwing the ball. In addition, this study also aimed to determine the effect of nutritional status of these two learning methods mentioned above. This research was conducted at SDN Cipinang Besar Selatan 16 Pagi East…

  14. Context Fear Learning Specifically Activates Distinct Populations of Neurons in Amygdala and Hypothalamus

    Science.gov (United States)

    Trogrlic, Lidia; Wilson, Yvette M.; Newman, Andrew G.; Murphy, Mark

    2011-01-01

    The identity and distribution of neurons that are involved in any learning or memory event is not known. In previous studies, we identified a discrete population of neurons in the lateral amygdala that show learning-specific activation of a c-"fos"-regulated transgene following context fear conditioning. Here, we have extended these studies to…

  15. Are Language Learning Websites Special? Towards a Research Agenda for Discipline-Specific Usability

    Science.gov (United States)

    Shield, Lesley; Kukulska-Hulme, Agnes

    2006-01-01

    With the intention of defining an initial research agenda for discipline-specific factors in the usability of e-learning websites, this article focuses on the example of foreign language learning. First, general notions and concepts of usability are analyzed, and the term "pedagogical usability" is proposed as a means of focusing on the close…

  16. The Graduating European Dentist: Contemporaneous Methods of Teaching, Learning and Assessment in Dental Undergraduate Education.

    Science.gov (United States)

    Field, J C; Walmsley, A D; Paganelli, C; McLoughlin, J; Szep, S; Kavadella, A; Manzanares Cespedes, M C; Davies, J R; DeLap, E; Levy, G; Gallagher, J; Roger-Leroi, V; Cowpe, J G

    2017-12-01

    It is often the case that good teachers just "intuitively" know how to teach. Whilst that may be true, there is now a greater need to understand the various processes that underpin both the ways in which a curriculum is delivered, and the way in which the students engage with learning; curricula need to be designed to meet the changing needs of our new graduates, providing new, and robust learning opportunities, and be communicated effectively to both staff and students. The aim of this document is to draw together robust and contemporaneous methods of teaching, learning and assessment that help to overcome some of the more traditional barriers within dental undergraduate programmes. The methods have been chosen to map specifically to The Graduating European Dentist, and should be considered in parallel with the benchmarking process that educators and institutions employ locally. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  17. Issues in Learning About and Teaching Qualitative Research Methods and Methodology in the Social Sciences

    Directory of Open Access Journals (Sweden)

    Franz Breuer

    2007-01-01

    Full Text Available For many qualitative researchers in the social sciences, learning about and teaching qualitative research methods and methodology raises a number of questions. This topic was the focus of a symposium held during the Second Berlin Summer School for Qualitative Research Methods in July 2006. In this contribution, some of the issues discussed during the symposium are taken up and extended, and some basic dimensions underlying these issues are summarized. How qualitative research methods and methodology are taught is closely linked to the ways in which qualitative researchers in the social sciences conceptualize themselves and their discipline. In the following, we distinguish between a paradigmatic and a pragmatic view. From a pragmatic point of view, qualitative research methods are considered research strategies or techniques and can be taught in the sense of recipes with specific steps to be carried out. According to a paradigmatic point of view (strongly inspired by constructivism, qualitative research methods and methodology are conceptualized as a craft to be practiced together by a "master" and an "apprentice." Moreover, the teaching of qualitative research methods also depends heavily on the institutional standing of qualitative compared to quantitative research method. Based on these considerations, five basic dimensions of learning about and teaching qualitative research methods are suggested: ways of teaching (ranging from the presentation of textbook knowledge to cognitive apprenticeship and instructors' experience with these; institutional contexts, including their development and the teaching of qualitative research methods in other than university contexts; the "fit" between personality and method, including relevant personal skills and talents; and, as a special type of instructional context that increasingly has gained importance, distance learning and its implications for learning about and teaching qualitative research methods

  18. The Keyimage Method of Learning Sound-Symbol Correspondences: A Case Study of Learning Written Khmer

    Directory of Open Access Journals (Sweden)

    Elizabeth Lavolette

    2009-01-01

    Full Text Available I documented my strategies for learning sound-symbol correspondences during a Khmer course. I used a mnemonic strategy that I call the keyimage method. In this method, a character evokes an image (the keyimage, which evokes the corresponding sound. For example, the keyimage for the character 2 could be a swan with its head tucked in. This evokes the sound "kaw" that a swan makes, which sounds similar to the Khmer sound corresponding to 2. The method has some similarities to the keyword method. Considering the results of keyword studies, I hypothesize that the keyimage method is more effective than rote learning and that peer-generated keyimages are more effective than researcher- or teacher-generated keyimages, which are more effective than learner-generated ones. In Dr. Andrew Cohen's plenary presentation at the Hawaii TESOL 2007 conference, he mentioned that more case studies are needed on learning strategies (LSs. One reason to study LSs is that what learners do with input to produce output is unclear, and knowing what strategies learners use may help us understand that process (Dornyei, 2005, p. 170. Hopefully, we can use that knowledge to improve language learning, perhaps by teaching learners to use the strategies that we find. With that in mind, I have examined the LSs that I used in studying Khmer as a foreign language, focusing on learning the syllabic alphabet.

  19. Statistical word learning in children with autism spectrum disorder and specific language impairment.

    Science.gov (United States)

    Haebig, Eileen; Saffran, Jenny R; Ellis Weismer, Susan

    2017-11-01

    Word learning is an important component of language development that influences child outcomes across multiple domains. Despite the importance of word knowledge, word-learning mechanisms are poorly understood in children with specific language impairment (SLI) and children with autism spectrum disorder (ASD). This study examined underlying mechanisms of word learning, specifically, statistical learning and fast-mapping, in school-aged children with typical and atypical development. Statistical learning was assessed through a word segmentation task and fast-mapping was examined in an object-label association task. We also examined children's ability to map meaning onto newly segmented words in a third task that combined exposure to an artificial language and a fast-mapping task. Children with SLI had poorer performance on the word segmentation and fast-mapping tasks relative to the typically developing and ASD groups, who did not differ from one another. However, when children with SLI were exposed to an artificial language with phonemes used in the subsequent fast-mapping task, they successfully learned more words than in the isolated fast-mapping task. There was some evidence that word segmentation abilities are associated with word learning in school-aged children with typical development and ASD, but not SLI. Follow-up analyses also examined performance in children with ASD who did and did not have a language impairment. Children with ASD with language impairment evidenced intact statistical learning abilities, but subtle weaknesses in fast-mapping abilities. As the Procedural Deficit Hypothesis (PDH) predicts, children with SLI have impairments in statistical learning. However, children with SLI also have impairments in fast-mapping. Nonetheless, they are able to take advantage of additional phonological exposure to boost subsequent word-learning performance. In contrast to the PDH, children with ASD appear to have intact statistical learning, regardless of

  20. Mapping of olfactory memory circuits: region-specific c-fos activation after odor-reward associative learning or after its retrieval.

    Science.gov (United States)

    Tronel, Sophie; Sara, Susan J

    2002-01-01

    Although there is growing knowledge about intracellular mechanisms underlying neuronal plasticity and memory consolidation and reconsolidation after retrieval, information concerning the interaction among brain areas during formation and retrieval of memory is relatively sparse and fragmented. Addressing this question requires simultaneous monitoring of activity in multiple brain regions during learning, the post-acquisition consolidation period, and retrieval and subsequent reconsolidation. Immunoreaction to the immediate early gene c-fos is a powerful tool to mark neuronal activation of specific populations of neurons. Using this method, we are able to report, for the first time, post-training activation of a network of closely related brain regions, particularly in the frontal cortex and the basolateral amygdala (BLA), that is specific to the learning of an odor-reward association. On the other hand, retrieval of a well-established associative memory trace does not seem to differentially activate the same regions. The amygdala, in particular, is not engaged after retrieval, whereas the lateral habenula (LHab) shows strong activation that is restricted to animals having previously learned the association. Although intracellular mechanisms may be similar during consolidation and reconsolidation, this study indicates that different brain circuits are involved in the two processes, at least with respect to a rapidly learned olfactory task.

  1. Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation

    Science.gov (United States)

    Hindriks, Koen V.; Tykhonov, Dmytro

    In automated negotiation, information gained about an opponent's preference profile by means of learning techniques may significantly improve an agent's negotiation performance. It therefore is useful to gain a better understanding of how various negotiation factors influence the quality of learning. The quality of learning techniques in negotiation are typically assessed indirectly by means of comparing the utility levels of agreed outcomes and other more global negotiation parameters. An evaluation of learning based on such general criteria, however, does not provide any insight into the influence of various aspects of negotiation on the quality of the learned model itself. The quality may depend on such aspects as the domain of negotiation, the structure of the preference profiles, the negotiation strategies used by the parties, and others. To gain a better understanding of the performance of proposed learning techniques in the context of negotiation and to be able to assess the potential to improve the performance of such techniques a more systematic assessment method is needed. In this paper we propose such a systematic method to analyse the quality of the information gained about opponent preferences by learning in single-instance negotiations. The method includes measures to assess the quality of a learned preference profile and proposes an experimental setup to analyse the influence of various negotiation aspects on the quality of learning. We apply the method to a Bayesian learning approach for learning an opponent's preference profile and discuss our findings.

  2. Effects of practice schedule and task specificity on the adaptive process of motor learning.

    Science.gov (United States)

    Barros, João Augusto de Camargo; Tani, Go; Corrêa, Umberto Cesar

    2017-10-01

    This study investigated the effects of practice schedule and task specificity based on the perspective of adaptive process of motor learning. For this purpose, tasks with temporal and force control learning requirements were manipulated in experiments 1 and 2, respectively. Specifically, the task consisted of touching with the dominant hand the three sequential targets with specific movement time or force for each touch. Participants were children (N=120), both boys and girls, with an average age of 11.2years (SD=1.0). The design in both experiments involved four practice groups (constant, random, constant-random, and random-constant) and two phases (stabilisation and adaptation). The dependent variables included measures related to the task goal (accuracy and variability of error of the overall movement and force patterns) and movement pattern (macro- and microstructures). Results revealed a similar error of the overall patterns for all groups in both experiments and that they adapted themselves differently in terms of the macro- and microstructures of movement patterns. The study concludes that the effects of practice schedules on the adaptive process of motor learning were both general and specific to the task. That is, they were general to the task goal performance and specific regarding the movement pattern. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Preparing Students for Flipped or Team-Based Learning Methods

    Science.gov (United States)

    Balan, Peter; Clark, Michele; Restall, Gregory

    2015-01-01

    Purpose: Teaching methods such as Flipped Learning and Team-Based Learning require students to pre-learn course materials before a teaching session, because classroom exercises rely on students using self-gained knowledge. This is the reverse to "traditional" teaching when course materials are presented during a lecture, and students are…

  4. Different protein-protein interface patterns predicted by different machine learning methods.

    Science.gov (United States)

    Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

    2017-11-22

    Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

  5. Perceptual learning of basic visual features remains task specific with Training-Plus-Exposure (TPE) training.

    Science.gov (United States)

    Cong, Lin-Juan; Wang, Ru-Jie; Yu, Cong; Zhang, Jun-Yun

    2016-01-01

    Visual perceptual learning is known to be specific to the trained retinal location, feature, and task. However, location and feature specificity can be eliminated by double-training or TPE training protocols, in which observers receive additional exposure to the transfer location or feature dimension via an irrelevant task besides the primary learning task Here we tested whether these new training protocols could even make learning transfer across different tasks involving discrimination of basic visual features (e.g., orientation and contrast). Observers practiced a near-threshold orientation (or contrast) discrimination task. Following a TPE training protocol, they also received exposure to the transfer task via performing suprathreshold contrast (or orientation) discrimination in alternating blocks of trials in the same sessions. The results showed no evidence for significant learning transfer to the untrained near-threshold contrast (or orientation) discrimination task after discounting the pretest effects and the suprathreshold practice effects. These results thus do not support a hypothetical task-independent component in perceptual learning of basic visual features. They also set the boundary of the new training protocols in their capability to enable learning transfer.

  6. A Review on Different Virtual Learning Methods in Pharmacy Education

    Directory of Open Access Journals (Sweden)

    Amin Noori

    2015-10-01

    Full Text Available Virtual learning is a type of electronic learning system based on the web. It models traditional in- person learning by providing virtual access to classes, tests, homework, feedbacks and etc. Students and teachers can interact through chat rooms or other virtual environments. Web 2.0 services are usually used for this method. Internet audio-visual tools, multimedia systems, a disco CD-ROMs, videotapes, animation, video conferencing, and interactive phones can all be used to deliver data to the students. E-learning can occur in or out of the classroom. It is time saving with lower costs compared to traditional methods. It can be self-paced, it is suitable for distance learning and it is flexible. It is a great learning style for continuing education and students can independently solve their problems but it has its disadvantages too. Thereby, blended learning (combination of conventional and virtual education is being used worldwide and has improved knowledge, skills and confidence of pharmacy students.The aim of this study is to review, discuss and introduce different methods of virtual learning for pharmacy students.Google scholar, Pubmed and Scupus databases were searched for topics related to virtual, electronic and blended learning and different styles like computer simulators, virtual practice environment technology, virtual mentor, virtual patient, 3D simulators, etc. are discussed in this article.Our review on different studies on these areas shows that the students are highly satisfied withvirtual and blended types of learning.

  7. A deep learning-based multi-model ensemble method for cancer prediction.

    Science.gov (United States)

    Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

    2018-01-01

    Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images.

    Science.gov (United States)

    Wang, Hongkai; Zhou, Zongwei; Li, Yingci; Chen, Zhonghua; Lu, Peiou; Wang, Wenzhi; Liu, Wanyu; Yu, Lijuan

    2017-12-01

    This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18 F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images

  9. Computer game-based and traditional learning method: a comparison regarding students' knowledge retention.

    Science.gov (United States)

    Rondon, Silmara; Sassi, Fernanda Chiarion; Furquim de Andrade, Claudia Regina

    2013-02-25

    Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students' prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students' performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students' short and long-term knowledge retention.

  10. Talker-specific learning in amnesia: Insight into mechanisms of adaptive speech perception.

    Science.gov (United States)

    Trude, Alison M; Duff, Melissa C; Brown-Schmidt, Sarah

    2014-05-01

    A hallmark of human speech perception is the ability to comprehend speech quickly and effortlessly despite enormous variability across talkers. However, current theories of speech perception do not make specific claims about the memory mechanisms involved in this process. To examine whether declarative memory is necessary for talker-specific learning, we tested the ability of amnesic patients with severe declarative memory deficits to learn and distinguish the accents of two unfamiliar talkers by monitoring their eye-gaze as they followed spoken instructions. Analyses of the time-course of eye fixations showed that amnesic patients rapidly learned to distinguish these accents and tailored perceptual processes to the voice of each talker. These results demonstrate that declarative memory is not necessary for this ability and points to the involvement of non-declarative memory mechanisms. These results are consistent with findings that other social and accommodative behaviors are preserved in amnesia and contribute to our understanding of the interactions of multiple memory systems in the use and understanding of spoken language. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. Computer game-based and traditional learning method: a comparison regarding students’ knowledge retention

    Directory of Open Access Journals (Sweden)

    Rondon Silmara

    2013-02-01

    Full Text Available Abstract Background Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Methods Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students’ prior knowledge (i.e. before undergoing the learning method, short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method were assessed with a multiple choice questionnaire. Students’ performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Results Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. Conclusions The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students’ short and long-term knowledge retention.

  12. Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

    Science.gov (United States)

    Soroush, Masoud; Weinberger, Charles B.

    2010-01-01

    This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…

  13. On Combining Elements of Different Ways of Learning, Methods and Knowledge

    Directory of Open Access Journals (Sweden)

    Dušana Findeisen

    2013-12-01

    Full Text Available The paper deals with different thinkers' attitude towards methods in adult education. It examines the value of some elements of »trial and error learning« and »non-directive learning«. Like a multifaceted approach based on elements drawn from different methods, the way we learn can also be eclectic.  To illustrate this assertion, the author analyses the »anti method« used by Maurice Pialat, a French film director, contrasting it with methods in which the aim is set in advance and the process leading towards it is organised in sequences. This is most often the case in script-based shooting of films, directing a theatre performance or running adult education. Moreover, the author argues that learning about how to do something is combined with learning about how to be. She further emphasises that methods should not be used to impose one’s knowledge and one’s reality on the learner, thus destroying circumstances necessary for gaining or creating knowledge.

  14. Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides

    Directory of Open Access Journals (Sweden)

    Stanislawski Jerzy

    2013-01-01

    Full Text Available Abstract Background Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. Results We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%. The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile to 0.5 CPU-hours (simplified 3D profile to seconds (machine learning. Conclusions We showed that the simplified profile generation method does not introduce an error with regard to the original method, while

  15. Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides.

    Science.gov (United States)

    Stanislawski, Jerzy; Kotulska, Malgorzata; Unold, Olgierd

    2013-01-17

    Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning). We showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset

  16. Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption

    OpenAIRE

    Bao, Yukun; Xiong, Tao; Hu, Zhongyi; Kibelloh, Mboni

    2014-01-01

    Reasons for contradictory findings regarding the gender moderate effect on computer self-efficacy in the adoption of e-learning/mobile learning are limited. Recognizing the multilevel nature of the computer self-efficacy (CSE), this study attempts to explore gender differences in the adoption of mobile learning, by extending the Technology Acceptance Model (TAM) with general and specific CSE. Data collected from 137 university students were tested against the research model using the structur...

  17. Cognitive profiles in bilingual children born to immigrant parents and Italian monolingual native children with specific learning disorders

    Directory of Open Access Journals (Sweden)

    Riva A

    2016-12-01

    Full Text Available Anna Riva, Renata Nacinovich, Nadia Bertuletti, Valentina Montrasi, Sara Marchetti, Francesca Neri, Monica Bomba Child and Adolescent Mental Health Department, University of Milan Bicocca, San Gerardo Hospital, Monza, Italy Purpose: The aim of this study is to compare the Wechsler Intelligence Scale for Children® – fourth edition IV (WISC IV intellectual profile of two groups of children with specific learning disorder, a group of bilingual children and a group of monolingual Italian children, in order to identify possible significant differences between them. Patients and methods: A group of 48 bilingual children and a group of 48 Italian monolingual children were included in this study. A preliminary comparison showed the homogeneity of the two groups regarding learning disorder typology and sociodemographic characteristics (age at WISC IV assessment, sex and years of education in Italy with the exception of socioeconomic status. Socioeconomic status was then used as a covariate in the analysis. Results: Even if the two groups were comparable in specific learning disorder severity and, in particular, in the text comprehension performance, our findings showed that the WISC IV performances of the bilingual group were significantly worse than the Italian group in Full Scale Intelligence Quotient (P=0.03, in General Ability Index (P=0.03, in Working Memory Index (P=0.009 and in some subtests and clusters requiring advanced linguistic abilities. Conclusion: These results support the hypothesis of a weakness in metalinguistic abilities in bilingual children with specific learning disorders than monolinguals. If confirmed, this result must be considered in the rehabilitation treatment. Keywords: children, bilingualism, WISC IV, SLD

  18. Actively Teaching Research Methods with a Process Oriented Guided Inquiry Learning Approach

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    Mullins, Mary H.

    2017-01-01

    Active learning approaches have shown to improve student learning outcomes and improve the experience of students in the classroom. This article compares a Process Oriented Guided Inquiry Learning style approach to a more traditional teaching method in an undergraduate research methods course. Moving from a more traditional learning environment to…

  19. The Effect of Using Cooperative Learning Method on Tenth Grade Students' Learning Achievement and Attitude towards Biology

    Science.gov (United States)

    Rabgay, Tshewang

    2018-01-01

    The study investigated the effect of using cooperative learning method on tenth grade students' learning achievement in biology and their attitude towards the subject in a Higher Secondary School in Bhutan. The study used a mixed method approach. The quantitative component included an experimental design where cooperative learning was the…

  20. Are Students' Learning Styles Discipline Specific?

    Science.gov (United States)

    Jones, Cheryl; Reichard, Carla; Mokhtari, Kouider

    2003-01-01

    This study examines the extent to which community college students' learning style preferences vary as a function of discipline. Reports significant differences in students' learning style preferences across disciplines, but not by gender. Adds that student learning style preferences varied by academic performance as measured by gender. Discusses…

  1. Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis

    Directory of Open Access Journals (Sweden)

    Rita Obeid

    2016-08-01

    Full Text Available Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI and Autism Spectrum Disorder (ASD. Using meta-analysis, we examined statistical learning in SLI (14 studies, 15 comparisons and ASD (13 studies, 20 comparisons to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, Probabilistic Classification. Individuals with SLI showed deficits in statistical learning relative to age-matched controls g = .47, 95% CI [.28, .66], p < .001. In contrast, statistical learning was intact in individuals with ASD relative to controls, g = –.13, 95% CI [–.34, .08], p = .22. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping social-communicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman & Pierpont, 2005, impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD.

  2. Classification of older adults with/without a fall history using machine learning methods.

    Science.gov (United States)

    Lin Zhang; Ou Ma; Fabre, Jennifer M; Wood, Robert H; Garcia, Stephanie U; Ivey, Kayla M; McCann, Evan D

    2015-01-01

    Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for training a classifier which is capable of classifying individual older adults into a high risk group and a low risk group (distinguished by whether or not the members of the group have a recent history of falls). Using a 3D motion capture system, significant gait features related to falls risk are extracted. By training these features, classification hypotheses are obtained based on machine learning techniques (K Nearest-neighbour, Naive Bayes, Logistic Regression, Neural Network, and Support Vector Machine). Training and test accuracies with sensitivity and specificity of each of these techniques are assessed. The feature adjustment and tuning of the machine learning algorithms are discussed. The outcome of the study will benefit the prediction and prevention of falls.

  3. Learning in Non-Stationary Environments Methods and Applications

    CERN Document Server

    Lughofer, Edwin

    2012-01-01

    Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences.   Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dyna...

  4. Using principles of learning to inform language therapy design for children with specific language impairment.

    Science.gov (United States)

    Alt, Mary; Meyers, Christina; Ancharski, Alexandra

    2012-01-01

    Language treatment for children with specific language impairment (SLI) often takes months to achieve moderate results. Interventions often do not incorporate the principles that are known to affect learning in unimpaired learners. To outline some key findings about learning in typical populations and to suggest a model of how they might be applied to language treatment design as a catalyst for further research and discussion. Three main principles of implicit learning are reviewed: variability, complexity and sleep-dependent consolidation. After explaining these principles, evidence is provided as to how they influence learning tasks in unimpaired learners. Information is reviewed on principles of learning as they apply to impaired populations, current treatment designs are also reviewed that conform to the principles, and ways in which principles of learning might be incorporated into language treatment design are demonstrated. This paper provides an outline for how theoretical knowledge might be applied to clinical practice in an effort to promote discussion. Although the authors look forward to more specific details on how the principles of learning relate to impaired populations, there is ample evidence to suggest that these principles should be considered during treatment design. © 2012 Royal College of Speech and Language Therapists.

  5. Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

    Science.gov (United States)

    Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

    2017-12-04

    Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further

  6. Specification, authoring and prototyping of personalised workplace learning solutions

    DEFF Research Database (Denmark)

    Dolog, Peter; Kravcik, Milos; Cristea, Alexandra

    2007-01-01

    The main goal of this document is to survey the existing approaches for the authoring and engineering of personalisation and adaptation in e-learning systems. This document enables the comparison of various methods and techniques, and facilitates their integration or reuse. It offers a cohesive r...

  7. Advanced Steel Microstructural Classification by Deep Learning Methods.

    Science.gov (United States)

    Azimi, Seyed Majid; Britz, Dominik; Engstler, Michael; Fritz, Mario; Mücklich, Frank

    2018-02-01

    The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

  8. The simulation method in learning interpersonal communication competence--experiences of masters' degree students of health sciences.

    Science.gov (United States)

    Saaranen, Terhi; Vaajoki, Anne; Kellomäki, Marjaana; Hyvärinen, Marja-Leena

    2015-02-01

    This article describes the experiences of master students of nursing science in learning interpersonal communication competence through the simulation method. The exercises reflected challenging interactive situations in the field of health care. Few studies have been published on using the simulation method in the communication education of teachers, managers, and experts in this field. The aim of this study is to produce information which can be utilised in developing the simulation method to promote the interpersonal communication competence of master-level students of health sciences. This study used the qualitative, descriptive research method. At the Department of Nursing Science, the University of Eastern Finland, students major in nursing science specialise in nursing leadership and management, preventive nursing science, or nurse teacher education. Students from all three specialties taking the Challenging Situations in Speech Communication course participated (n=47). Essays on meaningful learning experiences collected using the critical incident technique, underwent content analysis. Planning of teaching, carrying out different stages of the simulation exercise, participant roles, and students' personal factors were central to learning interpersonal communication competence. Simulation is a valuable method in developing the interpersonal communication competence of students of health sciences at the masters' level. The methods used in the simulation teaching of emergency care are not necessarily applicable as such to communication education. The role of teacher is essential to supervising students' learning in simulation exercises. In the future, it is important to construct questions that help students to reflect specifically on communication. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Implementation of Active Learning Method in Unit Operations II Subject

    OpenAIRE

    Ma'mun, Sholeh

    2018-01-01

    ABSTRACT: Active Learning Method which requires students to take an active role in the process of learning in the classroom has been applied in Department of Chemical Engineering, Faculty of Industrial Technology, Islamic University of Indonesia for Unit Operations II subject in the Even Semester of Academic Year 2015/2016. The purpose of implementation of the learning method is to assist students in achieving competencies associated with the Unit Operations II subject and to help in creating...

  10. Cognitive deficits are a matter of emotional context: inflexible strategy use mediates context-specific learning impairments in OCD.

    Science.gov (United States)

    Zetsche, Ulrike; Rief, Winfried; Westermann, Stefan; Exner, Cornelia

    2015-01-01

    The present study examines the interplay between cognitive deficits and emotional context in obsessive-compulsive disorder (OCD) and social phobia (SP). Specifically, this study examines whether the inflexible use of efficient learning strategies in an emotional context underlies impairments in probabilistic classification learning (PCL) in OCD, and whether PCL impairments are specific to OCD. Twenty-three participants with OCD, 30 participants with SP and 30 healthy controls completed a neutral and an OCD-specific PCL task. OCD participants failed to adopt efficient learning strategies and showed fewer beneficial strategy switches than controls only in an OCD-specific context, but not in a neutral context. Additionally, OCD participants did not show any explicit memory impairments. Number of beneficial strategy switches in the OCD-specific task mediated the difference in PCL performance between OCD and control participants. Individuals with SP were impaired in both PCL tasks. In contrast to neuropsychological models postulating general cognitive impairments in OCD, the present findings suggest that it is the interaction between cognition and emotion that is impaired in OCD. Specifically, activated disorder-specific fears may impair the flexible adoption of efficient learning strategies and compromise otherwise unimpaired PCL. Impairments in PCL are not specific to OCD.

  11. Digital game based learning: A new method in teaching and learning mathematics

    Science.gov (United States)

    Hussain, Sayed Yusoff bin Syed; Hoe, Tan Wee; Idris, Muhammad Zaffwan bin

    2017-05-01

    Digital game-based learning (DGBL) had been regarded as a sound learning strategy in raising pupils' willingness and interest in many disciplines. Normally, video and digital games are used in the teaching and learning mathematics. based on literature, digital games have proven its capability in making pupils motivated and are more likely to contribute to effective learning mathematics. Hence this research aims to construct a DGBL in the teaching of Mathematics for Year 1 pupils. Then, a quasi-experimental study was carried out in a school located in Gua Musang, Kelantan, involving 39 pupils. Specifically, this article tests the effectiveness of the use of DGBL in the teaching of the topic Addition of Less than 100 on pupil's achievement. This research employed a quasi-experiment, Pre and Post Test of Non-equivalent Control Group design. The data were analysed using the Nonparametric test namely the Mann-Whitney U. The research finding shows the use of the DGBL could increase the pupils' achievement in the topic of Addition of Less than 100. In practice, this research indicates that the DBGL can utilized as an alternative reference strategy for Mathematics teacher.

  12. TEACHING METHODS IN MBA AND LIFELONG LEARNING PROGRAMMES FOR MANAGERS

    Directory of Open Access Journals (Sweden)

    Jarošová, Eva

    2017-09-01

    Full Text Available Teaching methods in MBA and Lifelong Learning Programmes (LLP for managers should be topically relevant in terms of content as well as the teaching methods used. In terms of the content, the integral part of MBA and Lifelong Learning Programmes for managers should be the development of participants’ leadership competencies and their understanding of current leadership concepts. The teaching methods in educational programmes for managers as adult learners should correspond to the strategy of learner-centred teaching that focuses on the participants’ learning process and their active involvement in class. The focus on the participants’ learning process also raises questions about whether the programme’s participants perceive the teaching methods used as useful and relevant for their development as leaders. The paper presents the results of the analysis of the responses to these questions in a sample of 54 Czech participants in the MBA programme and of lifelong learning programmes at the University of Economics, Prague. The data was acquired based on written or electronically submitted questionnaires. The data was analysed in relation to the usefulness of the teaching methods for understanding the concepts of leadership, leadership skills development as well as respondents’ personal growth. The results show that the respondents most valued the methods that enabled them to get feedback, activated them throughout the programme and got them involved in discussions with others in class. Implications for managerial education practices are discussed.

  13. Investigating Learning with an Interactive Tutorial: A Mixed-Methods Strategy

    Science.gov (United States)

    de Villiers, M. R.; Becker, Daphne

    2017-01-01

    From the perspective of parallel mixed-methods research, this paper describes interactivity research that employed usability-testing technology to analyse cognitive learning processes; personal learning styles and times; and errors-and-recovery of learners using an interactive e-learning tutorial called "Relations." "Relations"…

  14. Specific learning disability in mathematics: a comprehensive review.

    Science.gov (United States)

    Soares, Neelkamal; Evans, Teresa; Patel, Dilip R

    2018-01-01

    Math skills are necessary for success in the childhood educational and future adult work environment. This article reviews the changing terminology for specific learning disabilities (SLD) in math and describes the emerging genetics and neuroimaging studies that relate to individuals with math disability (MD). It is important to maintain a developmental perspective on MD, as presentation changes with age, instruction, and the different models (educational and medical) of identification. Intervention requires a systematic approach to screening and remediation that has evolved with more evidence-based literature. Newer directions in behavioral, educational and novel interventions are described.

  15. Considerations for Task Analysis Methods and Rapid E-Learning Development Techniques

    Directory of Open Access Journals (Sweden)

    Dr. Ismail Ipek

    2014-02-01

    Full Text Available The purpose of this paper is to provide basic dimensions for rapid training development in e-learning courses in education and business. Principally, it starts with defining task analysis and how to select tasks for analysis and task analysis methods for instructional design. To do this, first, learning and instructional technologies as visions of the future were discussed. Second, the importance of task analysis methods in rapid e-learning was considered, with learning technologies as asynchronous and synchronous e-learning development. Finally, rapid instructional design concepts and e-learning design strategies were defined and clarified with examples, that is, all steps for effective task analysis and rapid training development techniques based on learning and instructional design approaches were discussed, such as m-learning and other delivery systems. As a result, the concept of task analysis, rapid e-learning development strategies and the essentials of online course design were discussed, alongside learner interface design features for learners and designers.

  16. Principals' Perceptions of Instructional Leadership for Middle School Students of Color with Specific Learning Disabilities

    Science.gov (United States)

    Shannon-Luster, Beverly

    2013-01-01

    Instructional leadership is the most important responsibility for principals and the most vulnerable students in need of productive instructional leadership are students of color with specific learning disabilities. Instructional leaders are challenged with creating supportive learning environments and school cultures that promotes the education…

  17. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

    Science.gov (United States)

    Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

    2017-03-01

    Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Grammar predicts procedural learning and consolidation deficits in children with Specific Language Impairment.

    Science.gov (United States)

    Hedenius, Martina; Persson, Jonas; Tremblay, Antoine; Adi-Japha, Esther; Veríssimo, João; Dye, Cristina D; Alm, Per; Jennische, Margareta; Bruce Tomblin, J; Ullman, Michael T

    2011-01-01

    The Procedural Deficit Hypothesis (PDH) posits that Specific Language Impairment (SLI) can be largely explained by abnormalities of brain structures that subserve procedural memory. The PDH predicts impairments of procedural memory itself, and that such impairments underlie the grammatical deficits observed in the disorder. Previous studies have indeed reported procedural learning impairments in SLI, and have found that these are associated with grammatical difficulties. The present study extends this research by examining consolidation and longer-term procedural sequence learning in children with SLI. The Alternating Serial Reaction Time (ASRT) task was given to children with SLI and typically developing (TD) children in an initial learning session and an average of three days later to test for consolidation and longer-term learning. Although both groups showed evidence of initial sequence learning, only the TD children showed clear signs of consolidation, even though the two groups did not differ in longer-term learning. When the children were re-categorized on the basis of grammar deficits rather than broader language deficits, a clearer pattern emerged. Whereas both the grammar impaired and normal grammar groups showed evidence of initial sequence learning, only those with normal grammar showed consolidation and longer-term learning. Indeed, the grammar-impaired group appeared to lose any sequence knowledge gained during the initial testing session. These findings held even when controlling for vocabulary or a broad non-grammatical language measure, neither of which were associated with procedural memory. When grammar was examined as a continuous variable over all children, the same relationships between procedural memory and grammar, but not vocabulary or the broader language measure, were observed. Overall, the findings support and further specify the PDH. They suggest that consolidation and longer-term procedural learning are impaired in SLI, but that these

  19. Grammar Predicts Procedural Learning and Consolidation Deficits in Children with Specific Language Impairment

    Science.gov (United States)

    Hedenius, Martina; Persson, Jonas; Tremblay, Antoine; Adi-Japha, Esther; Veríssimo, João; Dye, Cristina D.; Alm, Per; Jennische, Margareta; Tomblin, J. Bruce; Ullman, Michael T.

    2011-01-01

    The Procedural Deficit Hypothesis (PDH) posits that Specific Language Impairment (SLI) can be largely explained by abnormalities of brain structures that subserve procedural memory. The PDH predicts impairments of procedural memory itself, and that such impairments underlie the grammatical deficits observed in the disorder. Previous studies have indeed reported procedural learning impairments in SLI, and have found that these are associated with grammatical difficulties. The present study extends this research by examining the consolidation and longer-term procedural sequence learning in children with SLI. The Alternating Serial Reaction Time (ASRT) task was given to children with SLI and typically-developing (TD) children in an initial learning session and an average of three days later to test for consolidation and longer-term learning. Although both groups showed evidence of initial sequence learning, only the TD children showed clear signs of consolidation, even though the two groups did not differ in longer-term learning. When the children were re-categorized on the basis of grammar deficits rather than broader language deficits, a clearer pattern emerged. Whereas both the grammar impaired and normal grammar groups showed evidence of initial sequence learning, only those with normal grammar showed consolidation and longer-term learning. Indeed, the grammar-impaired group appeared to lose any sequence knowledge gained during the initial testing session. These findings held even when controlling for vocabulary or a broad non-grammatical language measure, neither of which were associated with procedural memory. When grammar was examined as a continuous variable over all children, the same relationships between procedural memory and grammar, but not vocabulary or the broader language measure, were observed. Overall, the findings support and further specify the PDH. They suggest that consolidation and longer-term procedural learning are impaired in SLI, but that

  20. Suicide Attempts among Individuals with Specific Learning Disorders: An Underrecognized Issue

    Science.gov (United States)

    Fuller-Thomson, Esme; Carroll, Samara Z.; Yang, Wook

    2018-01-01

    Several studies have linked specific learning disorders (SLDs) with suicidal ideation, but less is known about the disorders' association with suicide attempts. This gap in the literature is addressed via the 2012 nationally representative Canadian Community Health Survey (n = 21,744). The prevalence of lifetime suicide attempts among those with…

  1. Learning to Act: Qualitative Learning of Deterministic Action Models

    DEFF Research Database (Denmark)

    Bolander, Thomas; Gierasimczuk, Nina

    2017-01-01

    In this article we study learnability of fully observable, universally applicable action models of dynamic epistemic logic. We introduce a framework for actions seen as sets of transitions between propositional states and we relate them to their dynamic epistemic logic representations as action...... in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while arbitrary (non-deterministic) actions require more learning power—they are identifiable in the limit. We then move on to a particular learning method, i.e. learning via update......, which proceeds via restriction of a space of events within a learning-specific action model. We show how this method can be adapted to learn conditional and unconditional deterministic action models. We propose update learning mechanisms for the afore mentioned classes of actions and analyse...

  2. Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server.

    KAUST Repository

    Olimpieri, Pier Paolo

    2013-06-26

    MOTIVATION: Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode. RESULTS: In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen. AVAILABILITY: http://www.biocomputing.it/proABC. CONTACT: anna.tramontano@uniroma1.it or paolo.marcatili@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

  3. Machine Learning Methods for Attack Detection in the Smart Grid.

    Science.gov (United States)

    Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

    2016-08-01

    Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

  4. Towards sophisticated learning from EHRs: increasing prediction specificity and accuracy using clinically meaningful risk criteria.

    Science.gov (United States)

    Vasiljeva, Ieva; Arandjelovic, Ognjen

    2016-08-01

    Computer based analysis of Electronic Health Records (EHRs) has the potential to provide major novel insights of benefit both to specific individuals in the context of personalized medicine, as well as on the level of population-wide health care and policy. The present paper introduces a novel algorithm that uses machine learning for the discovery of longitudinal patterns in the diagnoses of diseases. Two key technical novelties are introduced: one in the form of a novel learning paradigm which enables greater learning specificity, and another in the form of a risk driven identification of confounding diagnoses. We present a series of experiments which demonstrate the effectiveness of the proposed techniques, and which reveal novel insights regarding the most promising future research directions.

  5. A reward optimization method based on action subrewards in hierarchical reinforcement learning.

    Science.gov (United States)

    Fu, Yuchen; Liu, Quan; Ling, Xionghong; Cui, Zhiming

    2014-01-01

    Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.

  6. Research on demand-oriented Business English learning method

    Directory of Open Access Journals (Sweden)

    Zhou Yuan

    2016-01-01

    Full Text Available Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher vocational students to learn Business English Visual-audio-oral Course, master Business English knowledge, and improve communicative competence of Business English.

  7. Activating teaching methods, studying responses and learning

    OpenAIRE

    Christensen, Hans Peter; Vigild, Martin E.; Thomsen, Erik; Szabo, Peter; Horsewell, Andy

    2009-01-01

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed. Peer Reviewed

  8. Fostering Self-Concept and Interest for Statistics through Specific Learning Environments

    Science.gov (United States)

    Sproesser, Ute; Engel, Joachim; Kuntze, Sebastian

    2016-01-01

    Supporting motivational variables such as self-concept or interest is an important goal of schooling as they relate to learning and achievement. In this study, we investigated whether specific interest and self-concept related to the domains of statistics and mathematics can be fostered through a four-lesson intervention focusing on statistics.…

  9. Comparative Evaluations of Four Specification Methods for Real-Time Systems

    Science.gov (United States)

    1989-12-01

    December 1989 Comparative Evaluations of Four Specification Methods for Real - Time Systems David P. Wood William G. Wood Specification and Design Methods...Methods for Real - Time Systems Abstract: A number of methods have been proposed in the last decade for the specification of system and software requirements...and software specification for real - time systems . Our process for the identification of methods that meet the above criteria is described in greater

  10. The Effect of Task-based Teaching on Incidental Vocabulary Learning in English for Specific Purposes

    OpenAIRE

    FALLAHRAFIE, Zahra; RAHMANY, Ramin; SADEGHI, Bahador

    2015-01-01

    Abstract. Learning vocabulary is an essential part of language learning linking the four skills of speaking, listening, reading and writing together. This paper considers the incidental vocabulary teaching and learning within the framework of task-based activities in the hope of improving learners’ vocabulary acquiring in English for Specific Purposes courses (ESP), concentrating on Mechanical Engineering students at Islamic Azad University of Hashtgerd, Iran. A total number of 55 male and fe...

  11. rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning

    Directory of Open Access Journals (Sweden)

    Miron B. Kursa

    2014-11-01

    Full Text Available Random ferns is a very simple yet powerful classification method originally introduced for specific computer vision tasks. In this paper, I show that this algorithm may be considered as a constrained decision tree ensemble and use this interpretation to introduce a series of modifications which enable the use of random ferns in general machine learning problems. Moreover, I extend the method with an internal error approximation and an attribute importance measure based on corresponding features of the random forest algorithm. I also present the R package rFerns containing an efficient implementation of this modified version of random ferns.

  12. Incorporating Meaningful Gamification in a Blended Learning Research Methods Class: Examining Student Learning, Engagement, and Affective Outcomes

    Science.gov (United States)

    Tan, Meng; Hew, Khe Foon

    2016-01-01

    In this study, we investigated how the use of meaningful gamification affects student learning, engagement, and affective outcomes in a short, 3-day blended learning research methods class using a combination of experimental and qualitative research methods. Twenty-two postgraduates were randomly split into two groups taught by the same…

  13. Simultaneous anatomical sketching as learning by doing method of teaching human anatomy.

    Science.gov (United States)

    Noorafshan, Ali; Hoseini, Leila; Amini, Mitra; Dehghani, Mohammad-Reza; Kojuri, Javad; Bazrafkan, Leila

    2014-01-01

    Learning by lecture is a passive experience. Many innovative techniques have been presented to stimulate students to assume a more active attitude toward learning. In this study, simultaneous sketch drawing, as an interactive learning technique was applied to teach anatomy to the medical students. We reconstructed a fun interactive model of teaching anatomy as simultaneous anatomic sketching. To test the model's instruction effectiveness, we conducted a quasi- experimental study and then the students were asked to write their learning experiences in their portfolio, also their view was evaluated by a questionnaire. The results of portfolio evaluation revealed that students believed that this method leads to deep learning and understanding anatomical subjects better. Evaluation of the students' views on this teaching approach was showed that, more than 80% of the students were agreed or completely agreed with this statement that leaning anatomy concepts are easier and the class is less boring with this method. More than 60% of the students were agreed or completely agreed to sketch anatomical figures with professor simultaneously. They also found the sketching make anatomy more attractive and it reduced the time for learning anatomy. These number of students were agree or completely agree that the method help them learning anatomical concept in anatomy laboratory. More than 80% of the students found the simultaneous sketching is a good method for learning anatomy overall. Sketch drawing, as an interactive learning technique, is an attractive for students to learn anatomy.

  14. Decentralized indirect methods for learning automata games.

    Science.gov (United States)

    Tilak, Omkar; Martin, Ryan; Mukhopadhyay, Snehasis

    2011-10-01

    We discuss the application of indirect learning methods in zero-sum and identical payoff learning automata games. We propose a novel decentralized version of the well-known pursuit learning algorithm. Such a decentralized algorithm has significant computational advantages over its centralized counterpart. The theoretical study of such a decentralized algorithm requires the analysis to be carried out in a nonstationary environment. We use a novel bootstrapping argument to prove the convergence of the algorithm. To our knowledge, this is the first time that such analysis has been carried out for zero-sum and identical payoff games. Extensive simulation studies are reported, which demonstrate the proposed algorithm's fast and accurate convergence in a variety of game scenarios. We also introduce the framework of partial communication in the context of identical payoff games of learning automata. In such games, the automata may not communicate with each other or may communicate selectively. This comprehensive framework has the capability to model both centralized and decentralized games discussed in this paper.

  15. Active semi-supervised learning method with hybrid deep belief networks.

    Science.gov (United States)

    Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

    2014-01-01

    In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

  16. Method to predict process signals to learn for SVM

    International Nuclear Information System (INIS)

    Minowa, Hirotsugu; Gofuku, Akio

    2013-01-01

    Study of diagnostic system using machine learning to reduce the incidents of the plant is in advance because an accident causes large damage about human, economic and social loss. There is a problem that 2 performances between a classification performance and generalization performance on the machine diagnostic machine is exclusive. However, multi agent diagnostic system makes it possible to use a diagnostic machine specialized either performance by multi diagnostic machines can be used. We propose method to select optimized variables to improve classification performance. The method can also be used for other supervised learning machine but Support Vector Machine. This paper reports that our method and result of evaluation experiment applied our method to output 40% of Monju. (author)

  17. Post-Learning Sleep Transiently Boosts Context Specific Operant Extinction Memory

    Directory of Open Access Journals (Sweden)

    Marion Inostroza

    2017-04-01

    Full Text Available Operant extinction is learning to supress a previously rewarded behavior. It is known to be strongly associated with the specific context in which it was acquired, which limits the therapeutic use of operant extinction in behavioral treatments, e.g., of addiction. We examined whether sleep influences contextual memory of operant extinction over time, using two different recall tests (Recent and Remote. Rats were trained in an operant conditioning task (lever press in context A, then underwent extinction training in context B, followed by a 3-h retention period that contained either spontaneous morning sleep, morning sleep deprivation, or spontaneous evening wakefulness. A recall test was performed either immediately after the 3-h experimental retention period (Recent recall or after 48 h (Remote, in the extinction context B and in a novel context C. The two main findings were: (i at the Recent recall test, sleep in comparison with sleep deprivation and spontaneous wakefulness enhanced extinction memory but, only in the extinction context B; (ii at the Remote recall, extinction performance after sleep was enhanced in both contexts B and C to an extent comparable to levels at Recent recall in context B. Interestingly, extinction performance at Remote recall was also improved in the sleep deprivation groups in both contexts, with no difference to performance in the sleep group. Our results suggest that 3 h of post-learning sleep transiently facilitate the context specificity of operant extinction at a Recent recall. However, the improvement and contextual generalization of operant extinction memory observed in the long-term, i.e., after 48 h, does not require immediate post-learning sleep.

  18. Construction of the Questionnaire on Foreign Language Learning Strategies in Specific Croatian Context.

    Science.gov (United States)

    Božinović, Nikolina; Sindik, Joško

    2017-03-01

    Learning strategies are special thoughts or behaviours that individuals use to understand, learn or retain new information, according to the point of view of O’Malley & Chamot. The other view, promoted by Oxford, believes learning strategies are specific actions taken by the learner to make learning easier, faster, more enjoyable, and more transferrable to new situations of language learning and use. The use of appropriate strategies ensures greater success in language learning. The aim of the research was to establish metric characteristics of the Questionnaire on learning strategies created by the author, in line with the template of the original SILL questionnaire (Strategy Inventory for Language Learning). The research was conducted at the Rochester Institute of Technology Croatia on a sample of 201 participants who learned German, Spanish, French and Italian as a foreign language. The results have shown that one-component latent dimensions which describe the space of foreign language learning strategies according to Oxford’s classification, have metric characteristics which are low, but still satisfactory (reliability and validity). All dimensions of learning strategies appeared not to be adequately defined. Therefore, we excluded compensation strategies and merged social and affective strategies into social-affective strategies into the unique dimension. Overall, this version of Oxford’s original questionnaire, based on Oxford’s theoretical construct, applied on Croatian students, clearly shows that current version of the questionnaire has poor metric characteristics. One of the explanations of the results obtained could be positioned in multicultural context and intercultural dialogue. Namely, particular social, political and economic context in Croatia could shape even foreign language learning strategies.

  19. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

    Science.gov (United States)

    Choi, Ickwon; Chung, Amy W; Suscovich, Todd J; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J; Francis, Donald; Robb, Merlin L; Michael, Nelson L; Kim, Jerome H; Alter, Galit; Ackerman, Margaret E; Bailey-Kellogg, Chris

    2015-04-01

    The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

  20. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

    Directory of Open Access Journals (Sweden)

    Ickwon Choi

    2015-04-01

    Full Text Available The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release. We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

  1. Parallelization of the ROOT Machine Learning Methods

    CERN Document Server

    Vakilipourtakalou, Pourya

    2016-01-01

    Today computation is an inseparable part of scientific research. Specially in Particle Physics when there is a classification problem like discrimination of Signals from Backgrounds originating from the collisions of particles. On the other hand, Monte Carlo simulations can be used in order to generate a known data set of Signals and Backgrounds based on theoretical physics. The aim of Machine Learning is to train some algorithms on known data set and then apply these trained algorithms to the unknown data sets. However, the most common framework for data analysis in Particle Physics is ROOT. In order to use Machine Learning methods, a Toolkit for Multivariate Data Analysis (TMVA) has been added to ROOT. The major consideration in this report is the parallelization of some TMVA methods, specially Cross-Validation and BDT.

  2. Students' Ideas on Cooperative Learning Method

    Science.gov (United States)

    Yoruk, Abdulkadir

    2016-01-01

    Aim of this study is to investigate students' ideas on cooperative learning method. For that purpose students who are studying at elementary science education program are distributed into two groups through an experimental design. Factors threaten the internal validity are either eliminated or reduced to minimum value. Data analysis is done…

  3. Specific learning disorder: prevalence and gender differences.

    Directory of Open Access Journals (Sweden)

    Kristina Moll

    Full Text Available Comprehensive models of learning disorders have to consider both isolated learning disorders that affect one learning domain only, as well as comorbidity between learning disorders. However, empirical evidence on comorbidity rates including all three learning disorders as defined by DSM-5 (deficits in reading, writing, and mathematics is scarce. The current study assessed prevalence rates and gender ratios for isolated as well as comorbid learning disorders in a representative sample of 1633 German speaking children in 3rd and 4th Grade. Prevalence rates were analysed for isolated as well as combined learning disorders and for different deficit criteria, including a criterion for normal performance. Comorbid learning disorders occurred as frequently as isolated learning disorders, even when stricter cutoff criteria were applied. The relative proportion of isolated and combined disorders did not change when including a criterion for normal performance. Reading and spelling deficits differed with respect to their association with arithmetic problems: Deficits in arithmetic co-occurred more often with deficits in spelling than with deficits in reading. In addition, comorbidity rates for arithmetic and reading decreased when applying stricter deficit criteria, but stayed high for arithmetic and spelling irrespective of the chosen deficit criterion. These findings suggest that the processes underlying the relationship between arithmetic and reading might differ from those underlying the relationship between arithmetic and spelling. With respect to gender ratios, more boys than girls showed spelling deficits, while more girls were impaired in arithmetic. No gender differences were observed for isolated reading problems and for the combination of all three learning disorders. Implications of these findings for assessment and intervention of learning disorders are discussed.

  4. Methodes for identification of specific language impairment

    Directory of Open Access Journals (Sweden)

    Toktam Maleki Shahmahmood

    2014-06-01

    Full Text Available Background and Aim: Specific language impiarment (SLI is one of the most prevalent developmental language disorders its diagnosis is a problematic issue among researchers and clinicians because of the heterogeneity of language profiles in the affected population and overlapping with other developmental language disorders. The aim of this study was to review the suggested diagnostic criteria for this disorder, controversies about these criteria and identify the most accurate diagnostic methods.Methods: Published article from 1980 to 2012 in bibliographic and publisher databases including Pubmed, Google scholar, Cochran library, Web of Science, ProQuest, Springer, Oxford, Science direct, Ovid, Iran Medex and Magiran about the diagnostic methods for discriminating preschoool children with specific language impiarment from normal developing children were reviewd in this article. These keywords were used for research: “specific language impairment”, “SLI”, “diagnosis or identification”, “standardized tests”, and “tests for language development”.Conclusion: The results of this study show inspite of agreement of researchers and clinicians about exclusionary criteria as one basic part of the diagnosis of specific language impiarment , there is no consensus about the other part, inclusionary criteria. Different studies used different inclusionary criteria which can be divided to categories of clincal judgment, discrepancy-based criteria, standardized testing, clinical markers and markers from spontaneous speech samples. Advantages, disadvantages, and clinical applicability of each diagnostic method are discussed in this article.

  5. Targeting Academic Programs to Student Diversity Utilizing Learning Styles and Learning-Study Strategies.

    Science.gov (United States)

    Grimes, Sue K.

    1995-01-01

    A diagnostic, prescriptive model was utilized (n=394) in identification of learning styles and learning-study strategies of diverse student groups and in the analysis of prescriptive methods to address their specific needs. High-risk groups demonstrated auditory, tactile concrete, and group learning style preferences and were weaker on cognitive,…

  6. E-learning as new method of medical education.

    Science.gov (United States)

    Masic, Izet

    2008-01-01

    NONE DECLARED Distance learning refers to use of technologies based on health care delivered on distance and covers areas such as electronic health, tele-health (e-health), telematics, telemedicine, tele-education, etc. For the need of e-health, telemedicine, tele-education and distance learning there are various technologies and communication systems from standard telephone lines to the system of transmission digitalized signals with modem, optical fiber, satellite links, wireless technologies, etc. Tele-education represents health education on distance, using Information Communication Technologies (ICT), as well as continuous education of a health system beneficiaries and use of electronic libraries, data bases or electronic data with data bases of knowledge. Distance learning (E-learning) as a part of tele-education has gained popularity in the past decade; however, its use is highly variable among medical schools and appears to be more common in basic medical science courses than in clinical education. Distance learning does not preclude traditional learning processes; frequently it is used in conjunction with in-person classroom or professional training procedures and practices. Tele-education has mostly been used in biomedical education as a blended learning method, which combines tele-education technology with traditional instructor-led training, where, for example, a lecture or demonstration is supplemented by an online tutorial. Distance learning is used for self-education, tests, services and for examinations in medicine i.e. in terms of self-education and individual examination services. The possibility of working in the exercise mode with image files and questions is an attractive way of self education. Automated tracking and reporting of learners' activities lessen faculty administrative burden. Moreover, e-learning can be designed to include outcomes assessment to determine whether learning has occurred. This review article evaluates the current

  7. A Doctoral Seminar in Qualitative Research Methods: Lessons Learned

    Directory of Open Access Journals (Sweden)

    Suzanne Franco

    2016-09-01

    Full Text Available New qualitative research methods continue to emerge in response to factors such as renewed interest in mixed methods, better understanding of the importance of a researcher’s philosophical stance, as well as the increased use of technology in data collection and analysis, to name a few. As a result, those facilitating research methods courses must revisit content and instructional strategies in order to prepare well-informed researchers. Approaches range from paradigm to pragmatic emphasis. This descriptive case study of a doctoral seminar for novice qualitative researchers describes the intricacies of the syllabus of a pragmatic approach in a constructivist/social constructionist learning environment. The purpose was to document the delivery and faculty/student interactions and reactions. Noteworthy were the contradictions and frustrations in the delivery as well as in student experiences. In the end, student input led to seminal learning experiences. The confirmation of the effectiveness of a constructivist/social constructivist learning environment is applicable to higher education pedagogy in general.

  8. A diagram retrieval method with multi-label learning

    Science.gov (United States)

    Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi

    2015-01-01

    In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

  9. Addiction memory as a specific, individually learned memory imprint.

    Science.gov (United States)

    Böning, J

    2009-05-01

    The construct of "addiction memory" (AM) and its importance for relapse occurrence has been the subject of discussion for the past 30 years. Neurobiological findings from "social neuroscience" and biopsychological learning theory, in conjunction with construct-valid behavioral pharmacological animal models, can now also provide general confirmation of addiction memory as a pathomorphological correlate of addiction disorders. Under multifactorial influences, experience-driven neuronal learning and memory processes of emotional and cognitive processing patterns in the specific individual "set" and "setting" play an especially pivotal role in this connection. From a neuropsychological perspective, the episodic (biographical) memory, located at the highest hierarchical level, is of central importance for the formation of the AM in certain structural and functional areas of the brain and neuronal networks. Within this context, neuronal learning and conditioning processes take place more or less unconsciously and automatically in the preceding long-term-memory systems (in particular priming and perceptual memory). They then regulate the individually programmed addiction behavior implicitly and thus subsequently stand for facilitated recollection of corresponding, previously stored cues or context situations. This explains why it is so difficult to treat an addiction memory, which is embedded above all in the episodic memory, from the molecular carrier level via the neuronal pattern level through to the psychological meaning level, and has thus meanwhile become a component of personality.

  10. Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

    Science.gov (United States)

    Zeng, Irene Sui Lan; Lumley, Thomas

    2018-01-01

    Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

  11. Exploring Non-Traditional Learning Methods in Virtual and Real-World Environments

    Science.gov (United States)

    Lukman, Rebeka; Krajnc, Majda

    2012-01-01

    This paper identifies the commonalities and differences within non-traditional learning methods regarding virtual and real-world environments. The non-traditional learning methods in real-world have been introduced within the following courses: Process Balances, Process Calculation, and Process Synthesis, and within the virtual environment through…

  12. Unsupervised behaviour-specific dictionary learning for abnormal event detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

    2015-01-01

    the training data is only a small proportion of the surveillance data. Therefore, we propose behavior-specific dictionaries (BSD) through unsupervised learning, pursuing atoms from the same type of behavior to represent one behavior dictionary. To further improve the dictionary by introducing information from...... potential infrequent normal patterns, we refine the dictionary by searching ‘missed atoms’ that have compact coefficients. Experimental results show that our BSD algorithm outperforms state-of-the-art dictionaries in abnormal event detection on the public UCSD dataset. Moreover, BSD has less false alarms...

  13. An Activity-based Approach to the Learning and Teaching of Research Methods: Measuring Student Engagement and Learning

    Directory of Open Access Journals (Sweden)

    Eimear Fallon

    2013-05-01

    Full Text Available This paper discusses a research project carried out with 82 final and third year undergraduate students, learning Research Methods prior to undertaking an undergraduate thesis during the academic years 2010 and 2011. The research had two separate, linked objectives, (a to develop a Research Methods module that embraces an activity-based approach to learning in a group environment, (b to improve engagement by all students. The Research Methods module was previously taught through a traditional lecture-based format. Anecdotally, it was felt that student engagement was poor and learning was limited. It was believed that successful completion of the development of this Module would equip students with a deeply-learned battery of research skills to take into their further academic and professional careers. Student learning was achieved through completion of a series of activities based on different research methods. In order to encourage student engagement, a wide variety of activities were used. These activities included workshops, brainstorming, mind-mapping, presentations, written submissions, peer critiquing, lecture/seminar, and ‘speed dating’ with more senior students and self reflection. Student engagement was measured through a survey based on a U.S. National Survey of Student Engagement (2000. A questionnaire was devised to establish whether, and to what degree, students were engaged in the material that they were learning, while they were learning it. The results of the questionnaire were very encouraging with between 63% and 96% of students answering positively to a range of questions concerning engagement. In terms of the two objectives set, these were satisfactorily met. The module was successfully developed and continues to be delivered, based upon this new and significant level of student engagement.

  14. Executive Functioning and Psychopathology in Psychotherapy for Adolescents with Specific Learning Disorders

    Science.gov (United States)

    Kopelman-Rubin, Daphne; Klomek, Anat Brunstein; Al-Yagon, Michal; Mufson, Laura; Apter, Alan; Mikulincer, Mario

    2017-01-01

    This study examined the contribution of executive functioning (EF) to improvements in psychiatric symptomatology following I Can Succeed (ICS; Kopelman-Rubin, 2012) psychotherapy, a skill-enhancement intervention designed to target EF and socio-emotional aspects of specific learning disabilities (SLD). Forty adolescents with SLD underwent ICS in…

  15. Research on demand-oriented Business English learning method

    OpenAIRE

    Zhou Yuan

    2016-01-01

    Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher voca...

  16. Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011.

    Science.gov (United States)

    Meyers, Alysha R; Al-Tarawneh, Ibraheem S; Wurzelbacher, Steven J; Bushnell, P Timothy; Lampl, Michael P; Bell, Jennifer L; Bertke, Stephen J; Robins, David C; Tseng, Chih-Yu; Wei, Chia; Raudabaugh, Jill A; Schnorr, Teresa M

    2018-01-01

    This study leveraged a state workers' compensation claims database and machine learning techniques to target prevention efforts by injury causation and industry. Injury causation auto-coding methods were developed to code more than 1.2 million Ohio Bureau of Workers' Compensation claims for this study. Industry groups were ranked for soft-tissue musculoskeletal claims that may have been preventable with biomechanical ergonomic (ERGO) or slip/trip/fall (STF) interventions. On the basis of the average of claim count and rate ranks for more than 200 industry groups, Skilled Nursing Facilities (ERGO) and General Freight Trucking (STF) were the highest risk for lost-time claims (>7 days). This study created a third, major causation-specific U.S. occupational injury surveillance system. These findings are being used to focus prevention resources on specific occupational injury types in specific industry groups, especially in Ohio. Other state bureaus or insurers may use similar methods.

  17. IMPROVEMENT OF RECOGNITION QUALITY IN DEEP LEARNING NETWORKS BY SIMULATED ANNEALING METHOD

    Directory of Open Access Journals (Sweden)

    A. S. Potapov

    2014-09-01

    Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.

  18. Specific learning disabilities in children: deficits and neuropsychological profile.

    Science.gov (United States)

    Kohli, Adarsh; Malhotra, Savita; Mohanty, Manju; Khehra, Nitasha; Kaur, Manreet

    2005-06-01

    The public is gradually becoming aware of specific learning disabilities (SLDs), which are very often the cause of academic difficulties. The aim of the study was to assess the SLDs in the clinic population at the Child and Adolescent Psychiatry Clinic at the Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh using the National Institute of Mental Health and Neurosciences SLD index and subsequently to assess the children's neuropsychological functions using a battery of tests. Thirty-five children in the age range of 7-14 years (both boys and girls) were recruited as the cohort, diagnosed clinically and assessed using the battery of tests for SLDs and neuropsychological tests consisting of the PGIMER memory scale for children, the Wisconsin card sorting test, the Bender visuo-motor gestalt test and Malin's intelligence scale for Indian children. The study revealed deficits in language and writing skills and impairments in specific areas of memory, executive functions and perceptuo-motor tasks. Identification of SLDs is useful in drawing up a treatment plan specific for a particular child.

  19. A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

    Science.gov (United States)

    Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

    2018-06-12

    Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

  20. Studying learning in the healthcare setting: the potential of quantitative diary methods.

    Science.gov (United States)

    Ciere, Yvette; Jaarsma, Debbie; Visser, Annemieke; Sanderman, Robbert; Snippe, Evelien; Fleer, Joke

    2015-08-01

    Quantitative diary methods are longitudinal approaches that involve the repeated measurement of aspects of peoples' experience of daily life. In this article, we outline the main characteristics and applications of quantitative diary methods and discuss how their use may further research in the field of medical education. Quantitative diary methods offer several methodological advantages, such as measuring aspects of learning with great detail, accuracy and authenticity. Moreover, they enable researchers to study how and under which conditions learning in the health care setting occurs and in which way learning can be promoted. Hence, quantitative diary methods may contribute to theory development and the optimization of teaching methods in medical education.

  1. Implicit sequence-specific motor learning after sub-cortical stroke is associated with increased prefrontal brain activations: An fMRI study

    Science.gov (United States)

    Meehan, Sean K.; Randhawa, Bubblepreet; Wessel, Brenda; Boyd, Lara A.

    2010-01-01

    Implicit motor learning is preserved after stroke, but how the brain compensates for damage to facilitate learning is unclear. We used a random effects analysis to determine how stroke alters patterns of brain activity during implicit sequence-specific motor learning as compared to general improvements in motor control. Nine healthy participants and 9 individuals with chronic, right focal sub-cortical stroke performed a continuous joystick-based tracking task during an initial fMRI session, over 5 days of practice, and a retention test during a separate fMRI session. Sequence-specific implicit motor learning was differentiated from general improvements in motor control by comparing tracking performance on a novel, repeated tracking sequences during early practice and again at the retention test. Both groups demonstrated implicit sequence-specific motor learning at the retention test, yet substantial differences were apparent. At retention, healthy control participants demonstrated increased BOLD response in left dorsal premotor cortex (BA 6) but decreased BOLD response left dorsolateral prefrontal cortex (DLPFC; BA 9) during repeated sequence tracking. In contrast, at retention individuals with stroke did not show this reduction in DLPFC during repeated tracking. Instead implicit sequence-specific motor learning and general improvements in motor control were associated with increased BOLD response in the left middle frontal gyrus BA 8, regardless of sequence type after stroke. These data emphasize the potential importance of a prefrontal-based attentional network for implicit motor learning after stroke. The present study is the first to highlight the importance of the prefrontal cortex for implicit sequence-specific motor learning after stroke. PMID:20725908

  2. Comparing three experiential learning methods and their effect on medical students' attitudes to learning communication skills.

    Science.gov (United States)

    Koponen, Jonna; Pyörälä, Eeva; Isotalus, Pekka

    2012-01-01

    Despite numerous studies exploring medical students' attitudes to communication skills learning (CSL), there are apparently no studies comparing different experiential learning methods and their influence on students' attitudes. We compared medical students' attitudes to learning communication skills before and after a communication course in the data as a whole, by gender and when divided into three groups using different methods. Second-year medical students (n = 129) were randomly assigned to three groups. In group A (n = 42) the theatre in education method, in group B (n = 44) simulated patients and in group C (n = 43) role-play were used. The data were gathered before and after the course using Communication Skills Attitude Scale. Students' positive attitudes to learning communication skills (PAS; positive attitude scale) increased significantly and their negative attitudes (NAS; negative attitude scale) decreased significantly between the beginning and end of the course. Female students had more positive attitudes than the male students. There were no significant differences in the three groups in the mean scores for PAS or NAS measured before or after the course. The use of experiential methods and integrating communication skills training with visits to health centres may help medical students to appreciate the importance of CSL.

  3. "Debate" Learning Method and Its Implications for the Formal Education System

    Science.gov (United States)

    Najafi, Mohammad; Motaghi, Zohre; Nasrabadi, Hassanali Bakhtiyar; Heshi, Kamal Nosrati

    2016-01-01

    Regarding the importance of enhancement in learner's social skills, especially in learning process, this study tries to introduce one of the group learning programs entitled "debate" as a teaching method in Iran religious universities. It also considers the concept and the history of this method by qualitative and descriptive-analytical…

  4. Adult Learners' Preferred Methods of Learning Preventative Heart Disease Care

    Science.gov (United States)

    Alavi, Nasim

    2016-01-01

    The purpose of this study was to investigate the preferred method of learning about heart disease by adult learners. This research study also investigated if there was a statistically significant difference between race/ethnicity, age, and gender of adult learners and their preferred method of learning preventative heart disease care. This…

  5. Multi-Role Project (MRP): A New Project-Based Learning Method for STEM

    Science.gov (United States)

    Warin, Bruno; Talbi, Omar; Kolski, Christophe; Hoogstoel, Frédéric

    2016-01-01

    This paper presents the "Multi-Role Project" method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity…

  6. Predicting Solar Activity Using Machine-Learning Methods

    Science.gov (United States)

    Bobra, M.

    2017-12-01

    Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

  7. An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation

    Science.gov (United States)

    Ma, Sisi; Kemmeren, Patrick; Aliferis, Constantin F.; Statnikov, Alexander

    2016-01-01

    Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods’ performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost. PMID:26939894

  8. Pitch discrimination learning: specificity for pitch and harmonic resolvability, and electrophysiological correlates.

    Science.gov (United States)

    Carcagno, Samuele; Plack, Christopher J

    2011-08-01

    Multiple-hour training on a pitch discrimination task dramatically decreases the threshold for detecting a pitch difference between two harmonic complexes. Here, we investigated the specificity of this perceptual learning with respect to the pitch and the resolvability of the trained harmonic complex, as well as its cortical electrophysiological correlates. We trained 24 participants for 12 h on a pitch discrimination task using one of four different harmonic complexes. The complexes differed in pitch and/or spectral resolvability of their components by the cochlea, but were filtered into the same spectral region. Cortical-evoked potentials and a behavioral measure of pitch discrimination were assessed before and after training for all the four complexes. The change in these measures was compared to that of two control groups: one trained on a level discrimination task and one without any training. The behavioral results showed that learning was partly specific to both pitch and resolvability. Training with a resolved-harmonic complex improved pitch discrimination for resolved complexes more than training with an unresolved complex. However, we did not find evidence that training with an unresolved complex leads to specific learning for unresolved complexes. Training affected the P2 component of the cortical-evoked potentials, as well as a later component (250-400 ms). No significant changes were found on the mismatch negativity (MMN) component, although a separate experiment showed that this measure was sensitive to pitch changes equivalent to the pitch discriminability changes induced by training. This result suggests that pitch discrimination training affects processes not measured by the MMN, for example, processes higher in level or parallel to those involved in MMN generation.

  9. Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence.

    Science.gov (United States)

    Ahn, Woo-Young; Vassileva, Jasmin

    2016-04-01

    Recent animal and human studies reveal distinct cognitive and neurobiological differences between opiate and stimulant addictions; however, our understanding of the common and specific effects of these two classes of drugs remains limited due to the high rates of polysubstance-dependence among drug users. The goal of the current study was to identify multivariate substance-specific markers classifying heroin dependence (HD) and amphetamine dependence (AD), by using machine-learning approaches. Participants included 39 amphetamine mono-dependent, 44 heroin mono-dependent, 58 polysubstance dependent, and 81 non-substance dependent individuals. The majority of substance dependent participants were in protracted abstinence. We used demographic, personality (trait impulsivity, trait psychopathy, aggression, sensation seeking), psychiatric (attention deficit hyperactivity disorder, conduct disorder, antisocial personality disorder, psychopathy, anxiety, depression), and neurocognitive impulsivity measures (Delay Discounting, Go/No-Go, Stop Signal, Immediate Memory, Balloon Analogue Risk, Cambridge Gambling, and Iowa Gambling tasks) as predictors in a machine-learning algorithm. The machine-learning approach revealed substance-specific multivariate profiles that classified HD and AD in new samples with high degree of accuracy. Out of 54 predictors, psychopathy was the only classifier common to both types of addiction. Important dissociations emerged between factors classifying HD and AD, which often showed opposite patterns among individuals with HD and AD. These results suggest that different mechanisms may underlie HD and AD, challenging the unitary account of drug addiction. This line of work may shed light on the development of standardized and cost-efficient clinical diagnostic tests and facilitate the development of individualized prevention and intervention programs for HD and AD. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  10. 19 CFR 134.43 - Methods of marking specific articles.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 1 2010-04-01 2010-04-01 false Methods of marking specific articles. 134.43...; DEPARTMENT OF THE TREASURY COUNTRY OF ORIGIN MARKING Method and Location of Marking Imported Articles § 134.43 Methods of marking specific articles. (a) Marking previously required by certain provisions of the...

  11. The Effect of WhatsApp Messenger As Mobile Learning Integrated with Group Investigation Method of Learning Achievement

    Directory of Open Access Journals (Sweden)

    Hendrik Pratama

    2017-12-01

    Full Text Available The purpose of this research was determined the effect of application WhatsApp Messenger in the Group Investigation (GI method on learning achievement. The methods used experimental research with control group pretest-postest design. The sampling procedure used the purposive sampling technique that consists of 17 students as a control group and 17 students as an experimental group. The sample in this research is students in Electrical Engineering Education Study Program. The experimental group used the GI method that integrated with WhatsApp Messenger. The control group used lecture method without social media integration. The collecting data used observation, documentation, interview, questionnaire, and test. The researcher used a t-test for compared the control group and the experimental group’s learning outcomes at an alpha level of 0,05. The results showed differences between the experiment group and the control group. The study result of the experimental higher than the control groups. This learning was designed with start, grouping, planning, presenting, organizing, investigating, evaluating, ending’s stage. Integration of WhatsApp with group investigation method could cause the positive communication between student and lecturer. Discussion in this learning was well done, the student’s knowledge could appear in a group and the information could spread evenly and quickly.

  12. Hybrid Method for Mobile learning Cooperative: Study of Timor Leste

    Science.gov (United States)

    da Costa Tavares, Ofelia Cizela; Suyoto; Pranowo

    2018-02-01

    In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process) and SAW (simple additive Weighting) method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.

  13. Hybrid Method for Mobile learning Cooperative: Study of Timor Leste

    Directory of Open Access Journals (Sweden)

    da Costa Tavares Ofelia Cizela

    2018-01-01

    Full Text Available In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process and SAW (simple additive Weighting method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.

  14. Later learning stages in procedural memory are impaired in children with Specific Language Impairment.

    Science.gov (United States)

    Desmottes, Lise; Meulemans, Thierry; Maillart, Christelle

    2016-01-01

    According to the Procedural Deficit Hypothesis (PDH), difficulties in the procedural memory system may contribute to the language difficulties encountered by children with Specific Language Impairment (SLI). Most studies investigating the PDH have used the sequence learning paradigm; however these studies have principally focused on initial sequence learning in a single practice session. The present study sought to extend these investigations by assessing the consolidation stage and longer-term retention of implicit sequence-specific knowledge in 42 children with or without SLI. Both groups of children completed a serial reaction time task and were tested 24h and one week after practice. Results showed that children with SLI succeeded as well as children with typical development (TD) in the early acquisition stage of the sequence learning task. However, as training blocks progressed, only TD children improved their sequence knowledge while children with SLI did not appear to evolve any more. Moreover, children with SLI showed a lack of the consolidation gains in sequence knowledge displayed by the TD children. Overall, these results were in line with the predictions of the PDH and suggest that later learning stages in procedural memory are impaired in SLI. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Learning Practice-Based Research Methods: Capturing the Experiences of MSW Students

    Science.gov (United States)

    Natland, Sidsel; Weissinger, Erika; Graaf, Genevieve; Carnochan, Sarah

    2016-01-01

    The literature on teaching research methods to social work students identifies many challenges, such as dealing with the tensions related to producing research relevant to practice, access to data to teach practice-based research, and limited student interest in learning research methods. This is an exploratory study of the learning experiences of…

  16. Machine Learning Method Applied in Readout System of Superheated Droplet Detector

    Science.gov (United States)

    Liu, Yi; Sullivan, Clair Julia; d'Errico, Francesco

    2017-07-01

    Direct readability is one advantage of superheated droplet detectors in neutron dosimetry. Utilizing such a distinct characteristic, an imaging readout system analyzes image of the detector for neutron dose readout. To improve the accuracy and precision of algorithms in the imaging readout system, machine learning algorithms were developed. Deep learning neural network and support vector machine algorithms are applied and compared with generally used Hough transform and curvature analysis methods. The machine learning methods showed a much higher accuracy and better precision in recognizing circular gas bubbles.

  17. Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

    Directory of Open Access Journals (Sweden)

    Kuchin Yan

    2017-12-01

    Full Text Available The paper explores geophysical methods of wells survey, as well as their role in the development of Kazakhstan’s uranium deposit mining efforts. An analysis of the existing methods for solving the problem of interpreting geophysical data using machine learning in petroleum geophysics is made. The requirements and possible applications of machine learning methods in regard to uranium deposits of Kazakhstan are formulated in the paper.

  18. A Collaborative Learning Network Approach to Improvement: The CUSP Learning Network.

    Science.gov (United States)

    Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J

    2015-04-01

    Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.

  19. Heuristic Evaluation of E-Learning Courses: A Comparative Analysis of Two E-Learning Heuristic Sets

    Science.gov (United States)

    Zaharias, Panagiotis; Koutsabasis, Panayiotis

    2012-01-01

    Purpose: The purpose of this paper is to discuss heuristic evaluation as a method for evaluating e-learning courses and applications and more specifically to investigate the applicability and empirical use of two customized e-learning heuristic protocols. Design/methodology/approach: Two representative e-learning heuristic protocols were chosen…

  20. Project Oriented Immersion Learning: Method and Results

    DEFF Research Database (Denmark)

    Icaza, José I.; Heredia, Yolanda; Borch, Ole M.

    2005-01-01

    A pedagogical approach called “project oriented immersion learning” is presented and tested on a graduate online course. The approach combines the Project Oriented Learning method with immersion learning in a virtual enterprise. Students assumed the role of authors hired by a fictitious publishing...... house that develops digital products including e-books, tutorials, web sites and so on. The students defined the problem that their product was to solve; choose the type of product and the content; and built the product following a strict project methodology. A wiki server was used as a platform to hold...

  1. Nitric oxide regulates input specificity of long-term depression and context dependence of cerebellar learning.

    Directory of Open Access Journals (Sweden)

    Hideaki Ogasawara

    2007-01-01

    Full Text Available Recent studies have shown that multiple internal models are acquired in the cerebellum and that these can be switched under a given context of behavior. It has been proposed that long-term depression (LTD of parallel fiber (PF-Purkinje cell (PC synapses forms the cellular basis of cerebellar learning, and that the presynaptically synthesized messenger nitric oxide (NO is a crucial "gatekeeper" for LTD. Because NO diffuses freely to neighboring synapses, this volume learning is not input-specific and brings into question the biological significance of LTD as the basic mechanism for efficient supervised learning. To better characterize the role of NO in cerebellar learning, we simulated the sequence of electrophysiological and biochemical events in PF-PC LTD by combining established simulation models of the electrophysiology, calcium dynamics, and signaling pathways of the PC. The results demonstrate that the local NO concentration is critical for induction of LTD and for its input specificity. Pre- and postsynaptic coincident firing is not sufficient for a PF-PC synapse to undergo LTD, and LTD is induced only when a sufficient amount of NO is provided by activation of the surrounding PFs. On the other hand, above-adequate levels of activity in nearby PFs cause accumulation of NO, which also allows LTD in neighboring synapses that were not directly stimulated, ruining input specificity. These findings lead us to propose the hypothesis that NO represents the relevance of a given context and enables context-dependent selection of internal models to be updated. We also predict sparse PF activity in vivo because, otherwise, input specificity would be lost.

  2. Empowering and Engaging Students in Learning Research Methods

    Science.gov (United States)

    Liu, Shuang; Breit, Rhonda

    2013-01-01

    The capacity to conduct research is essential for university graduates to survive and thrive in their future career. However, research methods courses have often been considered by students as "abstract", "uninteresting", and "hard". Thus, motivating students to engage in the process of learning research methods has become a crucial challenge for…

  3. A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction.

    Science.gov (United States)

    Lu, Hongyang; Wei, Jingbo; Liu, Qiegen; Wang, Yuhao; Deng, Xiaohua

    2016-01-01

    Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

  4. The learning continuum of ecology based on teachers' opinion about student's level of competence and specific pedagogical learning material

    Science.gov (United States)

    Pramesti, Indah Cahaya; Subali, Bambang

    2017-08-01

    This study aims at designing learning continuum for developing a curriculum based on teachers' opinion about student's level of competence and specific pedagogical learning material on ecological aspect targeted for students of Primary and Secondary Education. This research is a descriptive research using survey methods. The researchers conducted a census by distributing questionnaires that had been validated from the aspects of construct validity and experts judgements to 147 natural science teachers at junior high school and 134 Biology teachers at senior high school as a population throughout 4 regencies and 1 city in Yogyakarta Special Region.. Data analysis techniques used descriptive analysis. In conclusion, teacher's opinion is influenced by curriculum that exist today. According to the opinions of Natural Science teachers at Junior High School, most of the ecological aspects such as characteristics of biomes, characteristics of ecosystems, characteristics of communities, characteristics of populations, etc. should be taught in grade VII with the level of competence: to understand (C2), while Biology teachers at Senior High School state that the ecological aspect should be taught in class X with the level of competence: to understand (C2), apply (C3) and analyze (C4). Teachers should be a privy in the formulation of the curriculum, so they're not only accept and apply the existing curriculum but also give opinions to improve the curriculum, especially in terms of ecology.

  5. Integration of Traditional and E-Learning Methods to Improve Learning Outcomes for Dental Students in Histopathology.

    Science.gov (United States)

    Ariana, Armin; Amin, Moein; Pakneshan, Sahar; Dolan-Evans, Elliot; Lam, Alfred K

    2016-09-01

    Dental students require a basic ability to explain and apply general principles of pathology to systemic, dental, and oral pathology. Although there have been recent advances in electronic and online resources, the academic effectiveness of using self-directed e-learning tools in pathology courses for dental students is unclear. The aim of this study was to determine if blended learning combining e-learning with traditional learning methods of lectures and tutorials would improve students' scores and satisfaction over those who experienced traditional learning alone. Two consecutive cohorts of Bachelor of Dentistry and Oral Health students taking the general pathology course at Griffith University in Australia were compared. The control cohort experienced traditional methods only, while members of the study cohort were also offered self-directed learning materials including online resources and online microscopy classes. Final assessments for the course were used to compare the differences in effectiveness of the intervention, and students' satisfaction with the teaching format was evaluated using questionnaires. On the final course assessments, students in the study cohort had significantly higher scores than students in the control cohort (plearning tools such as virtual microscopy and interactive online resources for delivering pathology instruction can be an effective supplement for developing dental students' competence, confidence, and satisfaction.

  6. The Learners’ Attitudes towards Using Different Learning Methods in E-Learning Portal Environment

    Directory of Open Access Journals (Sweden)

    Issham Ismail

    2011-09-01

    Full Text Available This study investigates the learners’ preference of academic, collaborative and social interaction towards interaction methods in e-learning portal. Academic interaction consists of interaction between learners and online learning resources such as online reading, online explanation, online examination and also online question answering. Collaborative interaction occurs when learners interact among themselves using online group discussion. Social interaction happens when learners and instructors participate in the session either via online text chatting or voice chatting. The study employed qualitative methodology where data were collected through questionnaire that was administered to 933 distance education students from Bachelor of Management, Bachelor of Science, Bachelor of Social Science and Bachelor of Art. The survey responses were tabulated in a 5-point Likert scale and analyzed using the Statistical Package for Social Science (SPSS Version 12.0 based on frequency and percentage distribution. The result of the study suggest that among three types of interaction, most of the student prefer academic interaction for their learning supports in e-learning portal compared to collaborative and social interaction. They wish to interact with learning content rather than interact with people. They prefer to read and learn from the resources rather than sharing knowledge among themselves and instructors via collaborative and social interaction.

  7. An Analytical framework of social learning facilitated by participatory methods

    NARCIS (Netherlands)

    Scholz, G.; Dewulf, A.; Pahl-Wostl, C.

    2014-01-01

    Social learning among different stakeholders is often a goal in problem solving contexts such as environmental management. Participatory methods (e.g., group model-building and role playing games) are frequently assumed to stimulate social learning. Yet understanding if and why this assumption is

  8. Affordances of telecollaboration tools for English for Specific Purposes online learning

    Directory of Open Access Journals (Sweden)

    Ana Sevilla-Pavón

    2016-11-01

    Full Text Available This paper explores students’ perceptions of the affordances of different telecollaboration tools used in an innovation project for English for Specific Purposes online learning carried out between the University of Valencia (Spain and Wofford College (South Carolina, United States during the school year 2015-2016. Different tools for synchronous and asynchronous communication were used. The asynchronous tools included a discussion forum, a wiki, social networking websites and Google forms; while the tools used for synchronous communication were text, voice and video chat, videoconferencing tools and Google Drive. All the tools were accessible through the online platform used in the project, Google+. By using these tools, students from both sides of the Atlantic Ocean carried out a number of activities and tasks through online telecollaborative methods, involving both synchronous and asynchronous communication. The tasks completed by students through the use of the different tools were aimed at fostering distance online collaboration among American and Spanish students for the development of their linguistic, intercultural and digital literacies.

  9. Perception and coping with the specific learning disabilities impacts on everyday life of children with this diagnosis

    OpenAIRE

    Vilímová, Zuzana

    2015-01-01

    TITLE: Perception and coping with the specific learning disabilities impacts on everyday life of children with this diagnosis. ABSTRACT This text is focused on recognition of impacts of the specific learning disabilities on everyday life as the children with this diagnosis themselves see it and the strategies used by these children in order to cope with these disabilities. The theoretical part summarizes the necessary knowledge of the early school age developmental stage, the interaction of a...

  10. Model-based machine learning.

    Science.gov (United States)

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

  11. Improving the quality of learning in science through optimization of lesson study for learning community

    Science.gov (United States)

    Setyaningsih, S.

    2018-03-01

    Lesson Study for Learning Community is one of lecturer profession building system through collaborative and continuous learning study based on the principles of openness, collegiality, and mutual learning to build learning community in order to form professional learning community. To achieve the above, we need a strategy and learning method with specific subscription technique. This paper provides a description of how the quality of learning in the field of science can be improved by implementing strategies and methods accordingly, namely by applying lesson study for learning community optimally. Initially this research was focused on the study of instructional techniques. Learning method used is learning model Contextual teaching and Learning (CTL) and model of Problem Based Learning (PBL). The results showed that there was a significant increase in competence, attitudes, and psychomotor in the four study programs that were modelled. Therefore, it can be concluded that the implementation of learning strategies in Lesson study for Learning Community is needed to be used to improve the competence, attitude and psychomotor of science students.

  12. A study of active learning methods for named entity recognition in clinical text.

    Science.gov (United States)

    Chen, Yukun; Lasko, Thomas A; Mei, Qiaozhu; Denny, Joshua C; Xu, Hua

    2015-12-01

    Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they often require large amounts of annotated samples, which are expensive to build due to the requirement of domain experts in annotation. Active learning (AL), a sample selection approach integrated with supervised ML, aims to minimize the annotation cost while maximizing the performance of ML-based models. In this study, our goal was to develop and evaluate both existing and new AL methods for a clinical NER task to identify concepts of medical problems, treatments, and lab tests from the clinical notes. Using the annotated NER corpus from the 2010 i2b2/VA NLP challenge that contained 349 clinical documents with 20,423 unique sentences, we simulated AL experiments using a number of existing and novel algorithms in three different categories including uncertainty-based, diversity-based, and baseline sampling strategies. They were compared with the passive learning that uses random sampling. Learning curves that plot performance of the NER model against the estimated annotation cost (based on number of sentences or words in the training set) were generated to evaluate different active learning and the passive learning methods and the area under the learning curve (ALC) score was computed. Based on the learning curves of F-measure vs. number of sentences, uncertainty sampling algorithms outperformed all other methods in ALC. Most diversity-based methods also performed better than random sampling in ALC. To achieve an F-measure of 0.80, the best method based on uncertainty sampling could save 66% annotations in sentences, as compared to random sampling. For the learning curves of F-measure vs. number of words, uncertainty sampling methods again outperformed all other methods in ALC. To achieve 0.80 in F-measure, in comparison to random

  13. Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures.

    Science.gov (United States)

    Rock, Adam J; Coventry, William L; Morgan, Methuen I; Loi, Natasha M

    2016-01-01

    Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.

  14. Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures

    Science.gov (United States)

    Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.

    2016-01-01

    Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology. PMID:27014147

  15. Teaching Research Methods and Statistics in eLearning Environments:Pedagogy, Practical Examples and Possible Futures

    Directory of Open Access Journals (Sweden)

    Adam John Rock

    2016-03-01

    Full Text Available Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal, Ginsburg, & Schau, 1997. Given the ubiquitous and distributed nature of eLearning systems (Nof, Ceroni, Jeong, & Moghaddam, 2015, teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.

  16. Strategic Management: An Evaluation of the Use of Three Learning Methods.

    Science.gov (United States)

    Jennings, David

    2002-01-01

    A study of 46 management students compared three methods for learning strategic management: cases, simulation, and action learning through consulting projects. Simulation was superior to action learning on all outcomes and equal or superior to cases on two. Simulation gave students a central role in management and greater control of the learning…

  17. Frank Gilbreth and health care delivery method study driven learning.

    Science.gov (United States)

    Towill, Denis R

    2009-01-01

    The purpose of this article is to look at method study, as devised by the Gilbreths at the beginning of the twentieth century, which found early application in hospital quality assurance and surgical "best practice". It has since become a core activity in all modern methods, as applied to healthcare delivery improvement programmes. The article traces the origin of what is now currently and variously called "business process re-engineering", "business process improvement" and "lean healthcare" etc., by different management gurus back to the century-old pioneering work of Frank Gilbreth. The outcome is a consistent framework involving "width", "length" and "depth" dimensions within which healthcare delivery systems can be analysed, designed and successfully implemented to achieve better and more consistent performance. Healthcare method (saving time plus saving motion) study is best practised as co-joint action learning activity "owned" by all "players" involved in the re-engineering process. However, although process mapping is a key step forward, in itself it is no guarantee of effective re-engineering. It is not even the beginning of the end of the change challenge, although it should be the end of the beginning. What is needed is innovative exploitation of method study within a healthcare organisational learning culture accelerated via the Gilbreth Knowledge Flywheel. It is shown that effective healthcare delivery pipeline improvement is anchored into a team approach involving all "players" in the system especially physicians. A comprehensive process study, constructive dialogue, proper and highly professional re-engineering plus managed implementation are essential components. Experience suggests "learning" is thereby achieved via "natural groups" actively involved in healthcare processes. The article provides a proven method for exploiting Gilbreths' outputs and their many successors in enabling more productive evidence-based healthcare delivery as summarised

  18. M-Learning: Implications in Learning Domain Specificities, Adaptive Learning, Feedback, Augmented Reality, and the Future of Online Learning

    Science.gov (United States)

    Squires, David R.

    2014-01-01

    The aim of this paper is to examine the potential and effectiveness of m-learning in the field of Education and Learning domains. The purpose of this research is to illustrate how mobile technology can and is affecting novel change in instruction, from m-learning and the link to adaptive learning, to the uninitiated learner and capacities of…

  19. A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

    Directory of Open Access Journals (Sweden)

    Hongyang Lu

    2016-01-01

    Full Text Available Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV approach and adaptive dictionary learning (DL. In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

  20. Future Competencies and Learning Methods in Engineering Education

    DEFF Research Database (Denmark)

    Kolmos, Anette

    2002-01-01

    What are the competencies for tommorow´s enginnering education and the implications of these regarding the choice of teaching content and learning methods? The paper analyses two trends: the traditional and the techo-science approach. These two trends are based on technological innovation...... and change processes and impact on educational content and methods....

  1. Teacher and learner: Supervised and unsupervised learning in communities.

    Science.gov (United States)

    Shafto, Michael G; Seifert, Colleen M

    2015-01-01

    How far can teaching methods go to enhance learning? Optimal methods of teaching have been considered in research on supervised and unsupervised learning. Locally optimal methods are usually hybrids of teaching and self-directed approaches. The costs and benefits of specific methods have been shown to depend on the structure of the learning task, the learners, the teachers, and the environment.

  2. A specific implicit sequence learning deficit as an underlying cause of dyslexia? Investigating the role of attention in implicit learning tasks.

    Science.gov (United States)

    Staels, Eva; Van den Broeck, Wim

    2017-05-01

    Recently, a general implicit sequence learning deficit was proposed as an underlying cause of dyslexia. This new hypothesis was investigated in the present study by including a number of methodological improvements, for example, the inclusion of appropriate control conditions. The second goal of the study was to explore the role of attentional functioning in implicit and explicit learning tasks. In a 2 × 2 within-subjects design 4 tasks were administered in 30 dyslexic and 38 control children: an implicit and explicit serial reaction time (RT) task and an implicit and explicit contextual cueing task. Attentional functioning was also administered. The entire learning curves of all tasks were analyzed using latent growth curve modeling in order to compare performances between groups and to examine the role of attentional functioning on the learning curves. The amount of implicit learning was similar for both groups. However, the dyslexic group showed slower RTs throughout the entire task. This group difference reduced and became nonsignificant after controlling for attentional functioning. Both implicit learning tasks, but none of the explicit learning tasks, were significantly affected by attentional functioning. Dyslexic children do not suffer from a specific implicit sequence learning deficit. The slower RTs of the dyslexic children throughout the entire implicit sequence learning process are caused by their comorbid attention problems and overall slowness. A key finding of the present study is that, in contrast to what was assumed for a long time, implicit learning relies on attentional resources, perhaps even more than explicit learning does. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  3. Affordances of Telecollaboration tools for English for Specific Purposes online learning

    OpenAIRE

    Sevilla Pavón, Ana

    2016-01-01

    This paper explores students’ perceptions of the affordances of different telecollaboration tools used in an innovation project for English for Specific Purposes online learning carried out between the University of Valencia (Spain) and Wofford College (South Carolina, United States) during the school year 2015-2016. Different tools for synchronous and asynchronous communication were used. The asynchronous tools included a discussion forum, a wiki, social networking websites and Google forms;...

  4. Personalization for Specific Users : Designing Decision Support Systems to Support Stimulating Learning Environments

    NARCIS (Netherlands)

    Maruster, Laura; Faber, Niels R.; van Haren, Rob J.; Salvendy, G; Smith, MJ

    2009-01-01

    Creating adaptive systems becomes increasingly attractive in the context of specific groups of users, such as agricultural users. This group of users seems to differ with respect to information processing, knowledge management and learning styles. In this work we aim to offer directions toward

  5. Ants use partner specific odors to learn to recognize a mutualistic partner.

    Directory of Open Access Journals (Sweden)

    Masaru K Hojo

    Full Text Available Regulation via interspecific communication is an important for the maintenance of many mutualisms. However, mechanisms underlying the evolution of partner communication are poorly understood for many mutualisms. Here we show, in an ant-lycaenid butterfly mutualism, that attendant ants selectively learn to recognize and interact cooperatively with a partner. Workers of the ant Pristomyrmex punctatus learn to associate cuticular hydrocarbons of mutualistic Narathura japonica caterpillars with food rewards and, as a result, are more likely to tend the caterpillars. However, the workers do not learn to associate the cuticular hydrocarbons of caterpillars of a non-ant-associated lycaenid, Lycaena phlaeas, with artificial food rewards. Chemical analysis revealed cuticular hydrocarbon profiles of the mutualistic caterpillars were complex compared with those of non-ant-associated caterpillars. Our results suggest that partner-recognition based on partner-specific chemical signals and cognitive abilities of workers are important mechanisms underlying the evolution and maintenance of mutualism with ants.

  6. Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods.

    Science.gov (United States)

    Serag, Ahmed; Blesa, Manuel; Moore, Emma J; Pataky, Rozalia; Sparrow, Sarah A; Wilkinson, A G; Macnaught, Gillian; Semple, Scott I; Boardman, James P

    2016-03-24

    Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases 'uniformly' distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multi-modal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course.

  7. A new learning paradigm: learning using privileged information.

    Science.gov (United States)

    Vapnik, Vladimir; Vashist, Akshay

    2009-01-01

    In the Afterword to the second edition of the book "Estimation of Dependences Based on Empirical Data" by V. Vapnik, an advanced learning paradigm called Learning Using Hidden Information (LUHI) was introduced. This Afterword also suggested an extension of the SVM method (the so called SVM(gamma)+ method) to implement algorithms which address the LUHI paradigm (Vapnik, 1982-2006, Sections 2.4.2 and 2.5.3 of the Afterword). See also (Vapnik, Vashist, & Pavlovitch, 2008, 2009) for further development of the algorithms. In contrast to the existing machine learning paradigm where a teacher does not play an important role, the advanced learning paradigm considers some elements of human teaching. In the new paradigm along with examples, a teacher can provide students with hidden information that exists in explanations, comments, comparisons, and so on. This paper discusses details of the new paradigm and corresponding algorithms, introduces some new algorithms, considers several specific forms of privileged information, demonstrates superiority of the new learning paradigm over the classical learning paradigm when solving practical problems, and discusses general questions related to the new ideas.

  8. Preferred Methods of Learning for Nursing Students in an On-Line Degree Program.

    Science.gov (United States)

    Hampton, Debra; Pearce, Patricia F; Moser, Debra K

    Investigators have demonstrated that on-line courses result in effective learning outcomes, but limited information has been published related to preferred teaching strategies. Delivery of on-line courses requires various teaching methods to facilitate interaction between students, content, and technology. The purposes of this study were to understand student teaching/learning preferences in on-line courses to include (a) differences in preferred teaching/learning methods for on-line nursing students across generations and (b) which teaching strategies students found to be most engaging and effective. Participants were recruited from 2 accredited, private school nursing programs (N=944) that admit students from across the United States and deliver courses on-line. Participants provided implied consent, and 217 (23%) students completed the on-line survey. Thirty-two percent of the students were from the Baby Boomer generation (1946-1964), 48% from Generation X (1965-1980), and 20% from the Millennial Generation (born after 1980). The preferred teaching/learning methods for students were videos or narrated PowerPoint presentations, followed by synchronous Adobe Connect educations sessions, assigned journal article reading, and e-mail dialog with the instructor. The top 2 methods identified by participants as the most energizing/engaging and most effective for learning were videos or narrated PowerPoint presentations and case studies. The teaching/learning method least preferred by participants and that was the least energizing/engaging was group collaborative projects with other students; the method that was the least effective for learning was wikis. Baby Boomers and Generation X participants had a significantly greater preference for discussion board (PBaby Boomer and Generation X students and rated on-line games as significantly more energizing/engaging and more effective for learning (PBaby Boomer and Generation X students. In conclusion, the results of this

  9. Emotional and Meta-Emotional Intelligence as Predictors of Adjustment Problems in Students with Specific Learning Disorders

    Science.gov (United States)

    D'Amico, Antonella; Guastaferro, Teresa

    2017-01-01

    The purpose of this study was to analyse adjustment problems in a group of adolescents with a Specific Learning Disorder (SLD), examining to what extent they depend on the severity level of the learning disorder and/or on the individual's level of emotional intelligence. Adjustment problems,, perceived severity levels of SLD, and emotional and…

  10. Extracurricular activities and the development of social skills in children with intellectual and specific learning disabilities.

    Science.gov (United States)

    Brooks, B A; Floyd, F; Robins, D L; Chan, W Y

    2015-07-01

    Children with intellectual disability and specific learning disabilities often lack age-appropriate social skills, which disrupts their social functioning. Because of the limited effectiveness of classroom mainstreaming and social skills training for these children, it is important to explore alternative opportunities for social skill acquisition. Participation in social activities is positively related to children's social adjustment, but little is known about the benefits of activity participation for children with intellectual and specific learning disabilities. This study investigated the association between frequency and type of social activity participation and the social competence of 8-11-year-old children with intellectual disability (n = 40) and specific learning disabilities (n = 53), in comparison with typically developing peers (n = 24). More time involved in unstructured activities, but not structured activities, was associated with higher levels of social competence for all children. This association was strongest for children with intellectual disability, suggesting that participation in unstructured social activities was most beneficial for these children. Future research on the quality of involvement is necessary to further understand specific aspects of unstructured activities that might facilitate social development. © 2014 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

  11. Task-specificity of unilateral anodal and dual-M1 tDCS effects on motor learning.

    Science.gov (United States)

    Karok, Sophia; Fletcher, David; Witney, Alice G

    2017-01-08

    Task-specific effects of transcranial direct current stimulation (tDCS) on motor learning were investigated in 30 healthy participants. In a sham-controlled, mixed design, participants trained on 3 different motor tasks (Purdue Pegboard Test, Visuomotor Grip Force Tracking Task and Visuomotor Wrist Rotation Speed Control Task) over 3 consecutive days while receiving either unilateral anodal over the right primary motor cortex (M1), dual-M1 or sham stimulation. Retention sessions were administered 7 and 28 days after the end of training. In the Purdue Pegboard Test, both anodal and dual-M1 stimulation reduced average completion time approximately equally, an improvement driven by online learning effects and maintained for about 1 week. The Visuomotor Grip Force Tracking Task and the Visuomotor Wrist Rotation Speed Control Task were associated with an advantage of dual-M1 tDCS in consolidation processes both between training sessions and when testing at long-term retention; both were maintained for at least 1 month. This study demonstrates that M1-tDCS enhances and sustains motor learning with different electrode montages. Stimulation-induced effects emerged at different learning phases across the tasks, which strongly suggests that the influence of tDCS on motor learning is dynamic with respect to the functional recruitment of the distributed motor system at the time of stimulation. Divergent findings regarding M1-tDCS effects on motor learning may partially be ascribed to task-specific consequences and the effects of offline consolidation. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Measuring the learning effectiveness of Web-based teacher professional development in the hypothesis based learning method of teaching science

    Science.gov (United States)

    Wilson, Penne L.

    2007-12-01

    This study was conducted as part of the five year evaluation of the Star Schools grant awarded to Oklahoma State University for the development on online teacher professional development in the Hypothesis Based Learning (HbL) method of science instruction. Participants in this research were five teachers who had completed the online workshop, submitted a lesson plan, and who allowed this researcher and other members of the University of New Mexico Evaluation Team into their classrooms to observe and to determine if the learning of the method from the online HbL workshop had translated into practice. These teachers worked in inner city, suburban, metropolitan, and rural communities in the U.S. Southwest. This study was conducted to determine if teachers learned the HbL method from the online HbL workshop, to examine the relationship of satisfaction to learning, and to determine the elements of the online workshop that led to teacher learning. To measure learning of HbL, three different assessment instruments were used: embedded assessments within the online HbL workshop that gave teachers a scenario and asked them to generate questions to facilitate the HbL process; the analysis of a lesson plan provided by teachers using a science concept that they wished to incorporate in their curriculum using an HbL lesson template provided at the HbL website; and, observations of teachers facilitating the HbL process conducted at three different times during the year that they began the HbL online workshop. To determine if teachers were satisfied with the learning environment, the online HbL workshop, and the product, HbL Method for Teaching Science, and to determine if teachers could identify the elements of the online workshop that led to learning, interviews with the participants were conducted. The research findings were presented in two parts: Part I is an analysis of data provided by the assessment instruments and a content analysis of the transcripts of the teacher

  13. Suggestology as an Effective Language Learning Method.

    Science.gov (United States)

    MaCoy, Katherine W.

    The methods used and the results obtained by means of the accelerated language learning techniques developed by Georgi Lozanov, Director of the Institute of Suggestology in Bulgaria, are discussed. The following topics are included: (1) discussion of hypermnesia, "super memory," and the reasons foreign languages were chosen for purposes…

  14. A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

    Science.gov (United States)

    Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed

    2013-01-01

    Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

  15. A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

    Directory of Open Access Journals (Sweden)

    Ching Lee Koo

    2013-01-01

    Full Text Available Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs, support vector machine (SVM, and random forests (RFs in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

  16. A fast learning method for large scale and multi-class samples of SVM

    Science.gov (United States)

    Fan, Yu; Guo, Huiming

    2017-06-01

    A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

  17. The Role of Interactive E-Learning in Problem-Solving Skill and Independent Learning of Medical Students in Psychology Courses

    OpenAIRE

    Sahar Mohamadi; Sayedeh Maryam Hosseiny; Sayedeh Sana Hosseiny

    2016-01-01

    Introduction: Today, due to the speed and amount of science production in the world, knowledge processing and learning is changing. In this regard, independent learning using new instructional methods interactively with a specific goal in order to dominate the content and assignments with self-regulation strategies is of great importance. This study is aimed to evaluate interactive e-learning role on medical students’ learning in psychology courses. Methods: This study is a experimental r...

  18. Psychological co-morbidity in children with specific learning disorders

    Directory of Open Access Journals (Sweden)

    Manoj K Sahoo

    2015-01-01

    Full Text Available Children under 19 years of age constitute over 40% of India′s population and information about their mental health needs is a national imperative. Children with specific learning disorders (SLDs exhibit academic difficulties disproportionate to their intellectual capacities. Prevalence of SLD ranges from 2% to 10%. Dyslexia (developmental reading disorder is the most common type, affecting 80% of all SLD. About 30% of learning disabled children have behavioral and emotional problems, which range from attention deficit hyperactivity disorder (most common to depression, anxiety, suicide etc., to substance abuse (least common. Co-occurrence of such problems with SLD further adds to the academic difficulty. In such instances, diagnosis is difficult and tricky; improvement in academics demands comprehensive holistic treatment approach. SLD remains a large public health problem because of under-recognition, inadequate treatment and therefore merits greater effort to understand the co-morbidities, especially in the Indian population. As the literature is scarce regarding co-morbid conditions in learning disability in Indian scenario, the present study has tried to focus on Indian population. The educational concessions (recent most given to such children by Central Board of Secondary Education, New Delhi are referred to. The issues to be addressed by the family physicians are: Low level of awareness among families and teachers, improper dissemination of accurate information about psychological problems, available help seeking avenues, need to develop service delivery models in rural and urban areas and focus on the integration of mental health and primary care keeping such co-morbidity in mind.

  19. Psychological Co-morbidity in Children with Specific Learning Disorders.

    Science.gov (United States)

    Sahoo, Manoj K; Biswas, Haritha; Padhy, Susanta Kumar

    2015-01-01

    Children under 19 years of age constitute over 40% of India's population and information about their mental health needs is a national imperative. Children with specific learning disorders (SLDs) exhibit academic difficulties disproportionate to their intellectual capacities. Prevalence of SLD ranges from 2% to 10%. Dyslexia (developmental reading disorder) is the most common type, affecting 80% of all SLD. About 30% of learning disabled children have behavioral and emotional problems, which range from attention deficit hyperactivity disorder (most common) to depression, anxiety, suicide etc., to substance abuse (least common). Co-occurrence of such problems with SLD further adds to the academic difficulty. In such instances, diagnosis is difficult and tricky; improvement in academics demands comprehensive holistic treatment approach. SLD remains a large public health problem because of under-recognition, inadequate treatment and therefore merits greater effort to understand the co-morbidities, especially in the Indian population. As the literature is scarce regarding co-morbid conditions in learning disability in Indian scenario, the present study has tried to focus on Indian population. The educational concessions (recent most) given to such children by Central Board of Secondary Education, New Delhi are referred to. The issues to be addressed by the family physicians are: Low level of awareness among families and teachers, improper dissemination of accurate information about psychological problems, available help seeking avenues, need to develop service delivery models in rural and urban areas and focus on the integration of mental health and primary care keeping such co-morbidity in mind.

  20. Training School Psychologists to Identify Specific Learning Disabilities: A Content Analysis of Syllabi

    Science.gov (United States)

    Barrett, Courtenay A.; Cottrell, Joseph M.; Newman, Daniel S.; Pierce, Benjamin G.; Anderson, Alisha

    2015-01-01

    Approximately 2.4 million children receive special education services for specific learning disabilities (SLDs), and school psychologists are key contributors to the SLD eligibility decision-making process. The Individuals with Disabilities Education Act (2004) enabled local education agencies to use response to intervention (RTI) instead of the…

  1. Implementing Adaptive Educational Methods with IMS Learning Design

    NARCIS (Netherlands)

    Specht, Marcus; Burgos, Daniel

    2006-01-01

    Please, cite this publication as: Specht, M. & Burgos, D. (2006). Implementing Adaptive Educational Methods with IMS Learning Design. Proceedings of Adaptive Hypermedia. June, Dublin, Ireland. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

  2. Linked-Class Problem-Based Learning in Engineering: Method and Evaluation

    Science.gov (United States)

    Hunt, Emily M.; Lockwood-Cooke, Pamela; Kelley, Judy

    2010-01-01

    Problem-Based Learning (PBL) is a problem-centered teaching method with exciting potential in engineering education for motivating and enhancing student learning. Implementation of PBL in engineering education has the potential to bridge the gap between theory and practice. Two common problems are encountered when attempting to integrate PBL into…

  3. Early Language Learning: Complexity and Mixed Methods

    Science.gov (United States)

    Enever, Janet, Ed.; Lindgren, Eva, Ed.

    2017-01-01

    This is the first collection of research studies to explore the potential for mixed methods to shed light on foreign or second language learning by young learners in instructed contexts. It brings together recent studies undertaken in Cameroon, China, Croatia, Ethiopia, France, Germany, Italy, Kenya, Mexico, Slovenia, Spain, Sweden, Tanzania and…

  4. Survey compare team based learning and lecture teaching method, on learning-teaching process nursing student\\'s, in Surgical and Internal Diseases course

    Directory of Open Access Journals (Sweden)

    AA Vaezi

    2015-12-01

    Full Text Available Introduction: The effect of teaching methods on learning process of students will help teachers to improve the quality of teaching by selecting an appropriate method. This study aimed to compare the team- based learning and lecture teaching method on learning-teaching process of nursing students in surgical and internal diseases courses. Method: This quasi-experimental study was carried on the nursing students in the School of Nursing and Midwifery in Yazd and Meybod cities. Studied sample was all of the students in the sixth term in the Faculty of Nursing in Yazd (48 persons and the Faculty of Nursing in Meybod (28 persons. The rate of students' learning through lecture was measured using MCQ tests and teaching based on team-based learning (TBL method was run using MCQ tests (IRAT, GRAT, Appeals and Task group. Therefore, in order to examine the students' satisfaction about the TBL method, a 5-point Likert scale (translated questionnaire (1=completely disagree, 2= disagree, 3=not effective, 4=agree, and 5=completely agree consisted of 22 items was utilized. The reliability and validity of this translated questionnaire was measured. The collected data were analyzed through SPSS 17.0 using descriptive and analytical statistic. Result: The results showed that the mean scores in team-based learning were meaningful in individual assessment (17±84 and assessment group (17.2±1.17. The mean of overall scores in TBL method (17.84±0.98% was higher compared with the lecture teaching method (16±2.31. Most of the students believed that TBL method has improved their interpersonal and group interaction skills (100%. Among them, 97.7% of students mentioned that this method (TBL helped them to understand the course content better. The lowest levels of the satisfaction have related to the continuous learning during lifelong (51.2%. Conclusion: The results of the present study showed that the TBL method led to improving the communication skills, understanding

  5. COOPERATIVE LEARNING IN DISTANCE LEARNING: A MIXED METHODS STUDY

    Directory of Open Access Journals (Sweden)

    Lori Kupczynski

    2012-07-01

    Full Text Available Distance learning has facilitated innovative means to include Cooperative Learning (CL in virtual settings. This study, conducted at a Hispanic-Serving Institution, compared the effectiveness of online CL strategies in discussion forums with traditional online forums. Quantitative and qualitative data were collected from 56 graduate student participants. Quantitative results revealed no significant difference on student success between CL and Traditional formats. The qualitative data revealed that students in the cooperative learning groups found more learning benefits than the Traditional group. The study will benefit instructors and students in distance learning to improve teaching and learning practices in a virtual classroom.

  6. LEARNING MATERIALS SELECTION FOR DIFFERENTIATED INSTRUCTION OF ENGLISH FOR SPECIFIC PURPOSES OF FUTURE PROFESSIONALS IN THE FIELD OF INFORMATION TECHNOLOGY

    Directory of Open Access Journals (Sweden)

    Oksana Synekop

    2017-09-01

    Full Text Available In conditions of differentiation the learning materials selection will optimize the training English for Specific Purposes of the future professionals in the field of information technology at university level. The purpose of the article is to define the basic unit of learning material, the factors of influence on the learning material selection, principles, criteria and the procedure of learning material selection in this paper. Reviewing the scientific achievements in the learning material selection in teaching English has become a basis for defining the factors of influence, principles and criteria in the research. The basic unit of learning material (learning English text for professional purposes is outlined. The factors of influence and principles (correspondence of learning materials to professional interests and needs of information technology students; necessary ability and accessibility; regarding the linguistic and stylistic necessity and sufficiency; availability of Internet sources information of the learning material selection are defined. Also, the qualitative criteria (authenticity; professional significance, relevance and informativeness; conformity of foreign language level and intellectual development of students; variety of genres and forms of speech, their sufficient filling by linguistic material; coherence, integrity, consistency, semantic completeness; topic conformity; situation conformity; unlimited access, reliability and exemplarity of Internet sources and the quantitative criteria (the amount of material of the learning material selection are highlighted. The process of English for Specific Purposes material selection (defining the disciplines of different cycles; defining spheres and related topics; outlining situations, communicative roles and intentions of professional communication; specifying the sources of selection; evaluating the texts; analysis of the knowledge, skills and sub-skills required for the

  7. Indirect learning control for nonlinear dynamical systems

    Science.gov (United States)

    Ryu, Yeong Soon; Longman, Richard W.

    1993-01-01

    In a previous paper, learning control algorithms were developed based on adaptive control ideas for linear time variant systems. The learning control methods were shown to have certain advantages over their adaptive control counterparts, such as the ability to produce zero tracking error in time varying systems, and the ability to eliminate repetitive disturbances. In recent years, certain adaptive control algorithms have been developed for multi-body dynamic systems such as robots, with global guaranteed convergence to zero tracking error for the nonlinear system euations. In this paper we study the relationship between such adaptive control methods designed for this specific class of nonlinear systems, and the learning control problem for such systems, seeking to converge to zero tracking error in following a specific command repeatedly, starting from the same initial conditions each time. The extension of these methods from the adaptive control problem to the learning control problem is seen to be trivial. The advantages and disadvantages of using learning control based on such adaptive control concepts for nonlinear systems, and the use of other currently available learning control algorithms are discussed.

  8. Teaching Power Electronics with a Design-Oriented and Project-Based Learning Method at the Technical University of Denmark

    DEFF Research Database (Denmark)

    Zhang, Zhe; Hansen, Claus Thorp; Andersen, Michael A. E.

    2016-01-01

    Power electronics is a fast developing technology within the electrical engineering field. This paper presents the results and experiences gained from DesignOriented Project Based Learning of switch-mode power supply design within a power electronics course at the Technical University of Denmark...... (DTU). Project-based learning (PBL) is known to be a motivating and problem-centered teaching method that not only places students at the core of the teaching and learning activities but also gives students the ability to transfer their acquired scientific knowledge into industrial practices. Students...... are asked to choose a specification from different power converter applications such as a fuel cell power conditioning converter, a light-emitting diode (LED) driver or a battery charger. Based upon their choice, the students select topology, design magnetic components, calculate input/output filters...

  9. Using Problem Based Learning Methods from Engineering Education in Company Based Development

    DEFF Research Database (Denmark)

    Kofoed, Lise B.; Jørgensen, Frances

    2007-01-01

    This paper discusses how Problem-Based Learning (PBL) methods were used to support a Danish company in its efforts to become more of a 'learning organisation', characterized by sharing of knowledge and experiences. One of the central barriers to organisational learning in this company involved...

  10. Non-linguistic learning in aphasia: Effects of training method and stimulus characteristics

    Science.gov (United States)

    Vallila-Rohter, Sofia; Kiran, Swathi

    2013-01-01

    Purpose The purpose of the current study was to explore non-linguistic learning ability in patients with aphasia, examining the impact of stimulus typicality and feedback on success with learning. Method Eighteen patients with aphasia and eight healthy controls participated in this study. All participants completed four computerized, non-linguistic category-learning tasks. We probed learning ability under two methods of instruction: feedback-based (FB) and paired-associate (PA). We also examined the impact of task complexity on learning ability, comparing two stimulus conditions: typical (Typ) and atypical (Atyp). Performance was compared between groups and across conditions. Results Results demonstrated that healthy controls were able to successfully learn categories under all conditions. For our patients with aphasia, two patterns of performance arose. One subgroup of patients was able to maintain learning across task manipulations and conditions. The other subgroup of patients demonstrated a sensitivity to task complexity, learning successfully only in the typical training conditions. Conclusions Results support the hypothesis that impairments of general learning are present in aphasia. Some patients demonstrated the ability to extract category information under complex training conditions, while others learned only under conditions that were simplified and emphasized salient category features. Overall, the typical training condition facilitated learning for all participants. Findings have implications for therapy, which are discussed. PMID:23695914

  11. Parents' Perspectives on Coping with Duchenne Muscular Dystrophy and Concomitant Specific Learning Disabilities

    Science.gov (United States)

    Webb, Carol L.

    2005-01-01

    This study addresses parental perspectives and coping strategies related to Duchenne muscular dystrophy and specific learning disabilities. Data were collected through individual semi-structured in-depth interviews with fifteen sets of parents. Participants were selected based on variables such as age of children, number of children with both…

  12. Internet-Specific Epistemic Beliefs and Self-Regulated Learning in Online Academic Information Searching

    Science.gov (United States)

    Chiu, Yen-Lin; Liang, Jyh-Chong; Tsai, Chin-Chung

    2013-01-01

    Epistemic beliefs have been considered as important components of the self-regulatory model; however, their relationships with self-regulated learning processes in the Internet context need further research. The main purpose of this study was to examine the relationships between Internet-specific epistemic belief dimensions and self-regulated…

  13. A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

    Science.gov (United States)

    Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

    This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

  14. Influence Cooperative Learning Method and Personality Type to Ability to Write The Scientific Article (Experiment Study on SMAN 2 Students Ciamis Learning Indonesian Subject

    Directory of Open Access Journals (Sweden)

    Supriatna Supriatna

    2017-10-01

    Full Text Available The purpose of this research was to know the influence of cooperative learning method (Jigsaw and TPS and personality type (extrovert and introvert toward students’ ability in scientific writing at the SMA Negeri 2 Ciamis class XII. The research used experimental method with 2 x 2 factorial design. The population was the students of class XII which consisted of 150. The sample was 57 students. The results showed that: (1 The ability to write scientific articles of students learning by cooperative learning method jigsaw model (= 65,88 is higher than students who learn by cooperative technique method of TPS (= 59,88, (2 Ability writing scientific articles of students whose extroverted personality (= 65.69 is higher than introverted students (= 60.06; (3 there is interaction between cooperative learning method and personality type to score of writing ability of scientific article (4 ability to write scientific article of extrovert student and studying with technique of Jigsaw (= 77,75 higher than extrovert student learning with cooperative learning method model of TPS (= 53,63 to score of writing ability of scientific article, (5 ability to write introverted student's scientific article and get treatment of cooperative learning method of jigsaw model (= 54,00 lower than introverted student learning TPS technique = 66,13, (6 the ability to write extroverted students' scientific articles studied with jigsaw techniques, and introverted students who studied Jigsaw techniques (= 77.75 were higher than those with introverted personality types studied by the Jigsaw technique (= 54.00 , (7 Ability to write scientific articles of students learning by cooperative techniques of TPS technique and have extrovert personality type ( = 53.63 lower than introverted students learning TPS techniques (= 66.13.

  15. Listening to Our Students: Understanding How They Learn Research Methods in Geography

    Science.gov (United States)

    Keenan, Kevin; Fontaine, Danielle

    2012-01-01

    How undergraduate students learn research methods in geography has been understudied. Existing work has focused on course description from the instructor's perspective. This study, however, uses a grounded theory approach to allow students' voices to shape a new theory of how they themselves say that they learn research methods. Data from two…

  16. Keystone Method: A Learning Paradigm in Mathematics

    Science.gov (United States)

    Siadat, M. Vali; Musial, Paul M.; Sagher, Yoram

    2008-01-01

    This study reports the effects of an integrated instructional program (the Keystone Method) on the students' performance in mathematics and reading, and tracks students' persistence and retention. The subject of the study was a large group of students in remedial mathematics classes at the college, willing to learn but lacking basic educational…

  17. Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods

    OpenAIRE

    Shan, Min

    2017-01-01

    With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...

  18. A Weighted Block Dictionary Learning Algorithm for Classification

    OpenAIRE

    Shi, Zhongrong

    2016-01-01

    Discriminative dictionary learning, playing a critical role in sparse representation based classification, has led to state-of-the-art classification results. Among the existing discriminative dictionary learning methods, two different approaches, shared dictionary and class-specific dictionary, which associate each dictionary atom to all classes or a single class, have been studied. The shared dictionary is a compact method but with lack of discriminative information; the class-specific dict...

  19. Comparing two methods of education (virtual versus traditional) on learning of Iranian dental students: a post-test only design study.

    Science.gov (United States)

    Moazami, Fariborz; Bahrampour, Ehsan; Azar, Mohammad Reza; Jahedi, Farzad; Moattari, Marzieh

    2014-03-05

    The importance of using technologies such as e-learning in different disciplines is discussed in the literature. Researchers have measured the effectiveness of e-learning in a number of fields.Considering the lack of research on the effectiveness of online learning in dental education particularly in Iran, the advantages of these learning methods and the positive university atmosphere regarding the use of online learning. This study, therefore, aims to compare the effects of two methods of teaching (virtual versus traditional) on student learning. This post-test only design study approached 40, fifth year dental students of Shiraz University of Medical Sciences. From this group, 35 students agreed to participate. These students were randomly allocated into two groups, experimental (virtual learning) and comparison (traditional learning). To ensure similarity between groups, we compared GPAs of all participants by the Mann-Whitney U test (P > 0.05). The experimental group received a virtual learning environment courseware package specifically designed for this study, whereas the control group received the same module structured in a traditional lecture form. The virtual learning environment consisted of online and offline materials. Two identical valid, reliable post-tests that consisted of 40 multiple choice questions (MCQs) and 4 essay questions were administered immediately (15 min) after the last session and two months later to assess for knowledge retention. Data were analyzed by SPSS version 20. A comparison of the mean knowledge score of both groups showed that virtual learning was more effective than traditional learning (effect size = 0.69). The newly designed virtual learning package is feasible and will result in more effective learning in comparison with lecture-based training. However further studies are needed to generalize the findings of this study.

  20. Linking actions and objects: Context-specific learning of novel weight priors.

    Science.gov (United States)

    Trewartha, Kevin M; Flanagan, J Randall

    2017-06-01

    Distinct explicit and implicit memory processes support weight predictions used when lifting objects and making perceptual judgments about weight, respectively. The first time that an object is encountered weight is predicted on the basis of learned associations, or priors, linking size and material to weight. A fundamental question is whether the brain maintains a single, global representation of priors, or multiple representations that can be updated in a context specific way. A second key question is whether the updating of priors, or the ability to scale lifting forces when repeatedly lifting unusually weighted objects requires focused attention. To investigate these questions we compared the adaptability of weight predictions used when lifting objects and judging their weights in different groups of participants who experienced size-weight inverted objects passively (with the objects placed on the hands) or actively (where participants lift the objects) under full or divided attention. To assess weight judgments we measured the size-weight illusion after every 20 trials of experience with the inverted objects both passively and actively. The attenuation of the illusion that arises when lifting inverted object was found to be context-specific such that the attenuation was larger when the mode of interaction with the inverted objects matched the method of assessment of the illusion. Dividing attention during interaction with the inverted objects had no effect on attenuation of the illusion, but did slow the rate at which lifting forces were scaled to the weight inverted objects. These findings suggest that the brain stores multiple representations of priors that are context specific, and that focused attention is important for scaling lifting forces, but not for updating weight predictions used when judging object weight. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning.

    Science.gov (United States)

    Stark-Inbar, Alit; Raza, Meher; Taylor, Jordan A; Ivry, Richard B

    2017-01-01

    In standard taxonomies, motor skills are typically treated as representative of implicit or procedural memory. We examined two emblematic tasks of implicit motor learning, sensorimotor adaptation and sequence learning, asking whether individual differences in learning are correlated between these tasks, as well as how individual differences within each task are related to different performance variables. As a prerequisite, it was essential to establish the reliability of learning measures for each task. Participants were tested twice on a visuomotor adaptation task and on a sequence learning task, either the serial reaction time task or the alternating reaction time task. Learning was evident in all tasks at the group level and reliable at the individual level in visuomotor adaptation and the alternating reaction time task but not in the serial reaction time task. Performance variability was predictive of learning in both domains, yet the relationship was in the opposite direction for adaptation and sequence learning. For the former, faster learning was associated with lower variability, consistent with models of sensorimotor adaptation in which learning rates are sensitive to noise. For the latter, greater learning was associated with higher variability and slower reaction times, factors that may facilitate the spread of activation required to form predictive, sequential associations. Interestingly, learning measures of the different tasks were not correlated. Together, these results oppose a shared process for implicit learning in sensorimotor adaptation and sequence learning and provide insight into the factors that account for individual differences in learning within each task domain. We investigated individual differences in the ability to implicitly learn motor skills. As a prerequisite, we assessed whether individual differences were reliable across test sessions. We found that two commonly used tasks of implicit learning, visuomotor adaptation and the

  2. Perception of mathematics teachers on cooperative learning method in the 21st century

    Science.gov (United States)

    Taufik, Nurshahira Alwani Mohd; Maat, Siti Mistima

    2017-05-01

    Mathematics education is one of the branches to be mastered by students to help them compete with the upcoming challenges that are very challenging. As such, all parties should work together to help increase student achievement in Mathematics education in line with the Malaysian Education Blueprint (MEB) 2010-2025. Teaching methods play a very important role in attracting and fostering student understanding and interested in learning Mathematics. Therefore, this study was conducted to identify the perceptions of teachers in carrying out cooperative methods in the teaching and learning of mathematics. Participants of this study involving 4 teachers who teach Mathematics in primary schools around the state of Negeri Sembilan. Interviews are used as a method for gathering data. The findings indicate that cooperative methods help increasing interest and understanding in the teaching and learning of mathematics. In conclusion, the teaching methods affect the interest and understanding of students in the learning of Mathematics in the classroom.

  3. Implementation of Simulation Based-Concept Attainment Method to Increase Interest Learning of Engineering Mechanics Topic

    Science.gov (United States)

    Sultan, A. Z.; Hamzah, N.; Rusdi, M.

    2018-01-01

    The implementation of concept attainment method based on simulation was used to increase student’s interest in the subjects Engineering of Mechanics in second semester of academic year 2016/2017 in Manufacturing Engineering Program, Department of Mechanical PNUP. The result of the implementation of this learning method shows that there is an increase in the students’ learning interest towards the lecture material which is summarized in the form of interactive simulation CDs and teaching materials in the form of printed books and electronic books. From the implementation of achievement method of this simulation based concept, it is noted that the increase of student participation in the presentation and discussion as well as the deposit of individual assignment of significant student. With the implementation of this method of learning the average student participation reached 89%, which before the application of this learning method only reaches an average of 76%. And also with previous learning method, for exam achievement of A-grade under 5% and D-grade above 8%. After the implementation of the new learning method (simulation based-concept attainment method) the achievement of Agrade has reached more than 30% and D-grade below 1%.

  4. Specific Learning Disabilities in DSM-5: Are the Changes for Better or Worse?

    Science.gov (United States)

    Tannock, Rosemary

    2013-01-01

    DSM-5, the fifth edition of the American Psychiatric Association's "Diagnostic and Statistical Manual of Mental Disorders," was published in May 2013, amidst a storm of controversy. This article focuses on changes made to the diagnostic criteria for Specific Learning Disorders (SLD). Primary criticisms of the changes in the SLD concern…

  5. A Pharmacy Preregistration Course Using Online Teaching and Learning Methods

    Science.gov (United States)

    McDowell, Jenny; Marriott, Jennifer L.; Calandra, Angela; Duncan, Gregory

    2009-01-01

    Objective To design and evaluate a preregistration course utilizing asynchronous online learning as the primary distance education delivery method. Design Online course components including tutorials, quizzes, and moderated small-group asynchronous case-based discussions were implemented. Online delivery was supplemented with self-directed and face-to-face learning. Assessment Pharmacy graduates who had completed the course in 2004 and 2005 were surveyed. The majority felt they had benefited from all components of the course, and that online delivery provided benefits including increased peer support, shared learning, and immediate feedback on performance. A majority of the first cohort reported that the workload associated with asynchronous online discussions was too great. The course was altered in 2005 to reduce the online component. Participant satisfaction improved, and most felt that the balance of online to face-to-face delivery was appropriate. Conclusion A new pharmacy preregistration course was successfully implemented. Online teaching and learning was well accepted and appeared to deliver benefits over traditional distance education methods once workload issues were addressed. PMID:19777092

  6. How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach.

    Science.gov (United States)

    Ichikawa, Daisuke; Saito, Toki; Ujita, Waka; Oyama, Hiroshi

    2016-12-01

    Our purpose was to develop a new machine-learning approach (a virtual health check-up) toward identification of those at high risk of hyperuricemia. Applying the system to general health check-ups is expected to reduce medical costs compared with administering an additional test. Data were collected during annual health check-ups performed in Japan between 2011 and 2013 (inclusive). We prepared training and test datasets from the health check-up data to build prediction models; these were composed of 43,524 and 17,789 persons, respectively. Gradient-boosting decision tree (GBDT), random forest (RF), and logistic regression (LR) approaches were trained using the training dataset and were then used to predict hyperuricemia in the test dataset. Undersampling was applied to build the prediction models to deal with the imbalanced class dataset. The results showed that the RF and GBDT approaches afforded the best performances in terms of sensitivity and specificity, respectively. The area under the curve (AUC) values of the models, which reflected the total discriminative ability of the classification, were 0.796 [95% confidence interval (CI): 0.766-0.825] for the GBDT, 0.784 [95% CI: 0.752-0.815] for the RF, and 0.785 [95% CI: 0.752-0.819] for the LR approaches. No significant differences were observed between pairs of each approach. Small changes occurred in the AUCs after applying undersampling to build the models. We developed a virtual health check-up that predicted the development of hyperuricemia using machine-learning methods. The GBDT, RF, and LR methods had similar predictive capability. Undersampling did not remarkably improve predictive power. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Fast Low-Rank Shared Dictionary Learning for Image Classification.

    Science.gov (United States)

    Tiep Huu Vu; Monga, Vishal

    2017-11-01

    Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e., claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Furthermore, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image data sets establish the advantages of our method over the state-of-the-art dictionary learning methods.

  8. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  9. DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

    Science.gov (United States)

    Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

    2015-12-01

    Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

  10. Arabic Supervised Learning Method Using N-Gram

    Science.gov (United States)

    Sanan, Majed; Rammal, Mahmoud; Zreik, Khaldoun

    2008-01-01

    Purpose: Recently, classification of Arabic documents is a real problem for juridical centers. In this case, some of the Lebanese official journal documents are classified, and the center has to classify new documents based on these documents. This paper aims to study and explain the useful application of supervised learning method on Arabic texts…

  11. Critical thinking instruction and technology enhanced learning from the student perspective: A mixed methods research study.

    Science.gov (United States)

    Swart, Ruth

    2017-03-01

    Critical thinking is acclaimed as a valuable asset for graduates from higher education programs. Technology has advanced in quantity and quality; recognized as a requirement of 21st century learners. A mixed methods research study was undertaken, examining undergraduate nursing student engagement with critical thinking instruction, platformed on two technology-enhanced learning environments: a classroom response system face-to-face in-class and an online discussion forum out-of-class. The Community of Inquiry framed the study capturing constructivist collaborative inquiry to support learning, and facilitate critical thinking capability. Inclusion of quantitative and qualitative data sources aimed to gather a comprehensive understanding of students' development of critical thinking and engagement with technology-enhanced learning. The findings from the students' perspectives were positive toward the inclusion of technology-enhanced learning, and use in supporting their development of critical thinking. Students considered the use of two forms of technology beneficial in meeting different needs and preferences, offering varied means to actively participate in learning. They valued critical thinking instruction being intentionally aligned with subject-specific content facilitating understanding, application, and relevance of course material. While the findings are limited to student participants, the instructional strategies and technology-enhanced learning identified as beneficial can inform course design for the development of critical thinking. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. A Case Study to Evaluate Balance Training with Movement Test Items and through Teaching Observation: Beyond Specificity and Transfer of Learning

    Science.gov (United States)

    Kluwe, Margret; Miyahara, Motohide; Heveldt, Kate

    2012-01-01

    Background: Specificity and transfer of learning have been examined in experimental studies. However, their findings may not be relevant to practitioners because of the difference between the experiment conditions and teaching situations. This case study investigates the theoretical issue of specificity vs. transfer of learning by conducting…

  13. A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses

    Science.gov (United States)

    Montes, Jesus; Gomez, Elena; Merchán-Pérez, Angel; DeFelipe, Javier; Peña, Jose-Maria

    2013-01-01

    Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is

  14. A machine learning method for the prediction of receptor activation in the simulation of synapses.

    Directory of Open Access Journals (Sweden)

    Jesus Montes

    Full Text Available Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of

  15. WebMail versus WebApp: Comparing Problem-Based Learning Methods in a Business Research Methods Course

    Science.gov (United States)

    Williams van Rooij, Shahron

    2007-01-01

    This study examined the impact of two Problem-Based Learning (PBL) approaches on knowledge transfer, problem-solving self-efficacy, and perceived learning gains among four intact classes of adult learners engaged in a group project in an online undergraduate business research methods course. With two of the classes using a text-only PBL workbook…

  16. Ensemble Methods

    Science.gov (United States)

    Re, Matteo; Valentini, Giorgio

    2012-03-01

    Ensemble methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. Ensembles are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote ensemble, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall ensemble [158]. In the literature, a plethora of terms other than ensembles has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term ensemble in its widest meaning, in order to include the whole range of combination methods. Nowadays, ensemble methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on ensemble methods is witnessed by conferences and workshops specifically devoted to ensembles, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been

  17. Collaborative distance learning: Developing an online learning community

    Science.gov (United States)

    Stoytcheva, Maria

    2017-12-01

    The method of collaborative distance learning has been applied for years in a number of distance learning courses, but they are relatively few in foreign language learning. The context of this research is a hybrid distance learning of French for specific purposes, delivered through the platform UNIV-RcT (Strasbourg University), which combines collaborative activities for the realization of a common problem-solving task online. The study focuses on a couple of aspects: on-line interactions carried out in small, tutored groups and the process of community building online. By analyzing the learner's perceptions of community and collaborative learning, we have tried to understand the process of building and maintenance of online learning community and to see to what extent the collaborative distance learning contribute to the development of the competence expectations at the end of the course. The analysis of the results allows us to distinguish the advantages and limitations of this type of e-learning and thus evaluate their pertinence.

  18. Research progress in machine learning methods for gene-gene interaction detection.

    Science.gov (United States)

    Peng, Zhe-Ye; Tang, Zi-Jun; Xie, Min-Zhu

    2018-03-20

    Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.

  19. Understanding the Effects of Time on Collaborative Learning Processes in Problem Based Learning: A Mixed Methods Study

    Science.gov (United States)

    Hommes, J.; Van den Bossche, P.; de Grave, W.; Bos, G.; Schuwirth, L.; Scherpbier, A.

    2014-01-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…

  20. Interest in Currency Trading Learning – Preferred Methods and Motivational Factors

    Directory of Open Access Journals (Sweden)

    Pintar Rok

    2016-02-01

    Full Text Available Background and purpose: This paper analyzes the interest of potential users for learning in the field of currency trading or foreign exchange (forex, FX. The purpose of our article is a to present currency trading, b to present different options, methods and learning approaches to educating in forex, c to present the research results discovering the interest of potential users for learning in the field of currency trading.

  1. [Study on commercial specification of atractylodes based on Delphi method].

    Science.gov (United States)

    Wang, Hao; Chen, Li-Xiao; Huang, Lu-Qi; Zhang, Tian-Tian; Li, Ying; Zheng, Yu-Guang

    2016-03-01

    This research adopts "Delphi method" to evaluate atractylodes traditional traits and rank correlation. By using methods of mathematical statistics the relationship of the traditional identification indicators and atractylodes goods rank correlation was analyzed, It is found that the main characteristics affectingatractylodes commodity specifications and grades of main characters wereoil points of transaction,color of transaction,color of surface,grain of transaction,texture of transaction andspoilage. The study points out that the original "seventy-six kinds of medicinal materials commodity specification standards of atractylodes differentiate commodity specification" is not in conformity with the actual market situation, we need to formulate corresponding atractylodes medicinal products specifications and grades.This study combined with experimental results "Delphi method" and the market actual situation, proposed the new draft atractylodes commodity specifications and grades, as the new atractylodes commodity specifications and grades standards. It provides a reference and theoretical basis. Copyright© by the Chinese Pharmaceutical Association.

  2. An analysis of learning in an online biology course for teachers and teacher candidates: A mixed methods approach

    Science.gov (United States)

    Lebec, Michael Thomas

    Due to discipline specific shortages, web-based learning has been proposed as a convenient way to upgrade the content knowledge of instructors interested in learning to teach science. Despite quantitative evidence that web-based instruction is equivalent to traditional methods, questions remain regarding its use. The efficiency and practicality of this approach with teachers in particular has not been extensively studied. This investigation examines learning in an online biology course designed to help teachers prepare for science certification exams. Research questions concern flow teachers learn biology in the online environment and how this setting influences the learning process. Quantitative and qualitative methodologies are employed in an attempt to provide a more complete perspective than typical studies of online learning. Concept maps, tests, and online discussion transcripts are compared as measures of assimilated knowledge, while interviews reflect participants' views on the course. Findings indicate that participants experienced gains in declarative knowledge, but little improvement with respect to conditional knowledge. Qualitative examination of concept maps demonstrates gaps in participants' understandings of key course ideas. Engagement in the use of online resources varied according to participants' attitudes towards online learning. Subjects also reported a lack of motivation to fully engage in the course due to busy teaching schedules and the absence of accountability.

  3. Spatial Visualization Learning in Engineering: Traditional Methods vs. a Web-Based Tool

    Science.gov (United States)

    Pedrosa, Carlos Melgosa; Barbero, Basilio Ramos; Miguel, Arturo Román

    2014-01-01

    This study compares an interactive learning manager for graphic engineering to develop spatial vision (ILMAGE_SV) to traditional methods. ILMAGE_SV is an asynchronous web-based learning tool that allows the manipulation of objects with a 3D viewer, self-evaluation, and continuous assessment. In addition, student learning may be monitored, which…

  4. DNA Methylation Adjusts the Specificity of Memories Depending on the Learning Context and Promotes Relearning in Honeybees.

    Science.gov (United States)

    Biergans, Stephanie D; Claudianos, Charles; Reinhard, Judith; Galizia, C G

    2016-01-01

    The activity of the epigenetic writers DNA methyltransferases (Dnmts) after olfactory reward conditioning is important for both stimulus-specific long-term memory (LTM) formation and extinction. It, however, remains unknown which components of memory formation Dnmts regulate (e.g., associative vs. non-associative) and in what context (e.g., varying training conditions). Here, we address these aspects in order to clarify the role of Dnmt-mediated DNA methylation in memory formation. We used a pharmacological Dnmt inhibitor and classical appetitive conditioning in the honeybee Apis mellifera, a well characterized model for classical conditioning. We quantified the effect of DNA methylation on naïve odor and sugar responses, and on responses following olfactory reward conditioning. We show that (1) Dnmts do not influence naïve odor or sugar responses, (2) Dnmts do not affect the learning of new stimuli, but (3) Dnmts influence odor-coding, i.e., 'correct' (stimulus-specific) LTM formation. Particularly, Dnmts reduce memory specificity when experience is low (one-trial training), and increase memory specificity when experience is high (multiple-trial training), generating an ecologically more useful response to learning. (4) In reversal learning conditions, Dnmts are involved in regulating both excitatory (re-acquisition) and inhibitory (forgetting) processes.

  5. Learning by Designing Interview Methods in Special Education

    DEFF Research Database (Denmark)

    Jönsson, Lise Høgh

    2017-01-01

    , and people with learning disabilities worked together to develop five new visual and digital methods for interviewing in special education. Thereby not only enhancing the students’ competences, knowledge and proficiency in innovation and research, but also proposing a new teaching paradigm for university...

  6. Studying depression using imaging and machine learning methods

    OpenAIRE

    Patel, Meenal J.; Khalaf, Alexander; Aizenstein, Howard J.

    2015-01-01

    Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presen...

  7. Shape-specific perceptual learning in a figure-ground segregation task.

    Science.gov (United States)

    Yi, Do-Joon; Olson, Ingrid R; Chun, Marvin M

    2006-03-01

    What does perceptual experience contribute to figure-ground segregation? To study this question, we trained observers to search for symmetric dot patterns embedded in random dot backgrounds. Training improved shape segmentation, but learning did not completely transfer either to untrained locations or to untrained shapes. Such partial specificity persisted for a month after training. Interestingly, training on shapes in empty backgrounds did not help segmentation of the trained shapes in noisy backgrounds. Our results suggest that perceptual training increases the involvement of early sensory neurons in the segmentation of trained shapes, and that successful segmentation requires perceptual skills beyond shape recognition alone.

  8. Comparative Analysis of Kernel Methods for Statistical Shape Learning

    National Research Council Canada - National Science Library

    Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen

    2006-01-01

    .... In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding...

  9. Automated effect-specific mammographic pattern measures

    DEFF Research Database (Denmark)

    Raundahl, Jakob; Loog, Marco; Pettersen, Paola

    2008-01-01

    We investigate the possibility to develop methodologies for assessing effect specific structural changes of the breast tissue using a general statistical machine learning framework. We present an approach of obtaining objective mammographic pattern measures quantifying a specific biological effect......, such as hormone replacement therapy (HRT). We compare results using this approach to using standard density measures. We show that the proposed method can quantify both age related effects and effects caused by HRT. Age effects are significantly detected by our method where standard methodologies fail...

  10. Deep learning for image classification

    Science.gov (United States)

    McCoppin, Ryan; Rizki, Mateen

    2014-06-01

    This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

  11. Sunspot drawings handwritten character recognition method based on deep learning

    Science.gov (United States)

    Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

    2016-05-01

    High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

  12. APA's Learning Objectives for Research Methods and Statistics in Practice: A Multimethod Analysis

    Science.gov (United States)

    Tomcho, Thomas J.; Rice, Diana; Foels, Rob; Folmsbee, Leah; Vladescu, Jason; Lissman, Rachel; Matulewicz, Ryan; Bopp, Kara

    2009-01-01

    Research methods and statistics courses constitute a core undergraduate psychology requirement. We analyzed course syllabi and faculty self-reported coverage of both research methods and statistics course learning objectives to assess the concordance with APA's learning objectives (American Psychological Association, 2007). We obtained a sample of…

  13. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  14. A comparative study on effect of e-learning and instructor-led methods on nurses' documentation competency.

    Science.gov (United States)

    Abbaszadeh, Abbas; Sabeghi, Hakimeh; Borhani, Fariba; Heydari, Abbas

    2011-01-01

    Accurate recording of the nursing care indicates the care performance and its quality, so that, any failure in documentation can be a reason for inadequate patient care. Therefore, improving nurses' skills in this field using effective educational methods is of high importance. Since traditional teaching methods are not suitable for communities with rapid knowledge expansion and constant changes, e-learning methods can be a viable alternative. To show the importance of e-learning methods on nurses' care reporting skills, this study was performed to compare the e-learning methods with the traditional instructor-led methods. This was a quasi-experimental study aimed to compare the effect of two teaching methods (e-learning and lecture) on nursing documentation and examine the differences in acquiring competency on documentation between nurses who participated in the e-learning (n = 30) and nurses in a lecture group (n = 31). The results of the present study indicated that statistically there was no significant difference between the two groups. The findings also revealed that statistically there was no significant correlation between the two groups toward demographic variables. However, we believe that due to benefits of e-learning against traditional instructor-led method, and according to their equal effect on nurses' documentation competency, it can be a qualified substitute for traditional instructor-led method. E-learning as a student-centered method as well as lecture method equally promote competency of the nurses on documentation. Therefore, e-learning can be used to facilitate the implementation of nursing educational programs.

  15. Teaching-learning: stereoscopic 3D versus Traditional methods in Mexico City.

    Science.gov (United States)

    Mendoza Oropeza, Laura; Ortiz Sánchez, Ricardo; Ojeda Villagómez, Raúl

    2015-01-01

    In the UNAM Faculty of Odontology, we use a stereoscopic 3D teaching method that has grown more common in the last year, which makes it important to know whether students can learn better with this strategy. The objective of the study is to know, if the 4th year students of the bachelor's degree in dentistry learn more effectively with the use of stereoscopic 3D than the traditional method in Orthodontics. first, we selected the course topics, to be used for both methods; the traditional method using projection of slides and for the stereoscopic third dimension, with the use of videos in digital stereo projection (seen through "passive" polarized 3D glasses). The main topic was supernumerary teeth, including and diverted from their guide eruption. Afterwards we performed an exam on students, containing 24 items, validated by expert judgment in Orthodontics teaching. The results of the data were compared between the two educational methods for determined effectiveness using the model before and after measurement with the statistical package SPSS 20 version. The results presented for the 9 groups of undergraduates in dentistry, were collected with a total of 218 students for 3D and traditional methods, we found in a traditional method a mean 4.91, SD 1.4752 in the pretest and X=6.96, SD 1.26622, St Error 0.12318 for the posttest. The 3D method had a mean 5.21, SD 1.996779 St Error 0.193036 for the pretest X= 7.82, SD =0.963963, St Error 0.09319 posttest; the analysis of Variance between groups F= 5.60 Prob > 0.0000 and Bartlett's test for equal variances 21.0640 Prob > chi2 = 0.007. These results show that the student's learning in 3D means a significant improvement as compared to the traditional teaching method and having a strong association between the two methods. The findings suggest that the stereoscopic 3D method lead to improved student learning compared to traditional teaching.

  16. Teamwork: improved eQTL mapping using combinations of machine learning methods.

    Directory of Open Access Journals (Sweden)

    Marit Ackermann

    Full Text Available Expression quantitative trait loci (eQTL mapping is a widely used technique to uncover regulatory relationships between genes. A range of methodologies have been developed to map links between expression traits and genotypes. The DREAM (Dialogue on Reverse Engineering Assessments and Methods initiative is a community project to objectively assess the relative performance of different computational approaches for solving specific systems biology problems. The goal of one of the DREAM5 challenges was to reverse-engineer genetic interaction networks from synthetic genetic variation and gene expression data, which simulates the problem of eQTL mapping. In this framework, we proposed an approach whose originality resides in the use of a combination of existing machine learning algorithms (committee. Although it was not the best performer, this method was by far the most precise on average. After the competition, we continued in this direction by evaluating other committees using the DREAM5 data and developed a method that relies on Random Forests and LASSO. It achieved a much higher average precision than the DREAM best performer at the cost of slightly lower average sensitivity.

  17. Metric Learning Method Aided Data-Driven Design of Fault Detection Systems

    Directory of Open Access Journals (Sweden)

    Guoyang Yan

    2014-01-01

    Full Text Available Fault detection is fundamental to many industrial applications. With the development of system complexity, the number of sensors is increasing, which makes traditional fault detection methods lose efficiency. Metric learning is an efficient way to build the relationship between feature vectors with the categories of instances. In this paper, we firstly propose a metric learning-based fault detection framework in fault detection. Meanwhile, a novel feature extraction method based on wavelet transform is used to obtain the feature vector from detection signals. Experiments on Tennessee Eastman (TE chemical process datasets demonstrate that the proposed method has a better performance when comparing with existing methods, for example, principal component analysis (PCA and fisher discriminate analysis (FDA.

  18. Machine Learning Identification of Protein Properties Useful for Specific Applications

    KAUST Repository

    Khamis, Abdullah

    2016-03-31

    Proteins play critical roles in cellular processes of living organisms. It is therefore important to identify and characterize their key properties associated with their functions. Correlating protein’s structural, sequence and physicochemical properties of its amino acids (aa) with protein functions could identify some of the critical factors governing the specific functionality. We point out that not all functions of even well studied proteins are known. This, complemented by the huge increase in the number of newly discovered and predicted proteins, makes challenging the experimental characterization of the whole spectrum of possible protein functions for all proteins of interest. Consequently, the use of computational methods has become more attractive. Here we address two questions. The first one is how to use protein aa sequence and physicochemical properties to characterize a family of proteins. The second one focuses on how to use transcription factor (TF) protein’s domains to enhance accuracy of predicting TF DNA binding sites (TFBSs). To address the first question, we developed a novel method using computational representation of proteins based on characteristics of different protein regions (N-terminal, M-region and C-terminal) and combined these with the properties of protein aa sequences. We show that this description provides important biological insight about characterization of the protein functional groups. Using feature selection techniques, we identified key properties of proteins that allow for very accurate characterization of different protein families. We demonstrated efficiency of our method in application to a number of antimicrobial peptide families. To address the second question we developed another novel method that uses a combination of aa properties of DNA binding domains of TFs and their TFBS properties to develop machine learning models for predicting TFBSs. Feature selection is used to identify the most relevant characteristics

  19. SPECIFIC METHOD OF RISK ASSESSMENT IN TOURISM ENTERPRISES

    Directory of Open Access Journals (Sweden)

    Andreea ARMEAN

    2014-12-01

    Full Text Available The objective of this paper is to present an innovative method of risk assessment for tourism businesses. The contribution to literature is the novelty of this method of following paths: is an ante-factum assessment not post-factum; risk assessment is based on perception rather than results; is based on specific risks tourism enterprises not on the overall risks. Is an asset-research methodology and consists in generating its own method of risk assessment based on the ideas summarized from the literature studied. The aim established is tourism enterprises from Romania. The data necessary for the application of this method will result from applying to top level management of tourism enterprises, a questionnaire about risk perception. The results from this study will help identify and measure the risks specific to tourism enterprises. The applicability of the results is to improve risk management in these enterprises.

  20. Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

    Directory of Open Access Journals (Sweden)

    Philippe Burlina

    Full Text Available To evaluate the use of ultrasound coupled with machine learning (ML and deep learning (DL techniques for automated or semi-automated classification of myositis.Eighty subjects comprised of 19 with inclusion body myositis (IBM, 14 with polymyositis (PM, 14 with dermatomyositis (DM, and 33 normal (N subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally were acquired. We considered three problems of classification including (A normal vs. affected (DM, PM, IBM; (B normal vs. IBM patients; and (C IBM vs. other types of myositis (DM or PM. We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification.The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A, 86.6% ± 2.4% for (B and 74.8% ± 3.9% for (C, while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A, 84.3% ± 2.3% for (B and 68.9% ± 2.5% for (C.This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

  1. Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

    Science.gov (United States)

    Burlina, Philippe; Billings, Seth; Joshi, Neil; Albayda, Jemima

    2017-01-01

    To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

  2. Safety based on organisational learning (SOL) - Conceptual approach and verification of a method for event analysis

    International Nuclear Information System (INIS)

    Miller, R.; Wilpert, B.; Fahlbruch, B.

    1999-01-01

    This paper discusses a method for analysing safety-relevant events in NPP which is known as 'SOL', safety based on organisational learning. After discussion of the specific organisational and psychological problems examined in the event analysis, the analytic process using the SOL approach is explained as well as the required general setting. The SOL approach has been tested both with scientific experiments and from the practical perspective, by operators of NPPs and experts from other branches of industry. (orig./CB) [de

  3. Effects of Cooperative Learning Method Type Stad, Language Aptitude, and Intelligence on the Achievement English Hotel at Medan Tourism Academy

    Directory of Open Access Journals (Sweden)

    Abdul Kadir Ritonga

    2017-01-01

    Full Text Available STAD cooperative learning method which is considered effective in achieving the goal of learning the English language, especially for students majoring in Tourism Academy who are required to master English for Specific Purposes (ESP in accordance with their needs. This study uses factorial design 2x3x3 version of the non-equivalent control group design with ANOVA 3 Ways. The subjects were students MDK III / 5 A and B courses MDK III.5 Rooms Division department Hospitality Academy Year 2015/2016. The samples are saturated samples. Data were collected through a pretest, posttest, and instrument of Language Aptitude and Intelligence parametric statistics analyzed by parametric statistics with significance level of 0.05%. The results showed that: (1 there are differences between method STAD cooperative learning and expository on Hospitality English achievement, (2 there are differences between the students who have high language aptitude and low language aptitude on English achievement, (3 there are differences between students who have high language aptitude and medium on Hospitality English achievement, (4 there are differences between students who have the medium language aptitude and low language aptitude on Hospitality English achievement, (5 there are differences between students who have high intelligence and low intelligence\\ on Hospitality English achievement, (6 there are no differences between who have high intelligence and medium intelligence on Hospitality English achievement, (7 there are differences between students who have the medium intelligence and low intelligence on Hospitality English achievement, (8 there is no interaction between the learning method and language aptitude on Hospitality English achievement, (9 there is an interaction between the learning method and the intelligence on Hospitality English achievement, (10 there is no interaction between intelligence and language aptitude on Hospitality English achievement. (11

  4. Machine learning methods to predict child posttraumatic stress: a proof of concept study.

    Science.gov (United States)

    Saxe, Glenn N; Ma, Sisi; Ren, Jiwen; Aliferis, Constantin

    2017-07-10

    The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD. ML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains. Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable. In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to

  5. Peak detection method evaluation for ion mobility spectrometry by using machine learning approaches.

    Science.gov (United States)

    Hauschild, Anne-Christin; Kopczynski, Dominik; D'Addario, Marianna; Baumbach, Jörg Ingo; Rahmann, Sven; Baumbach, Jan

    2013-04-16

    Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors' results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications.

  6. Using Machine Learning to Predict Student Performance

    OpenAIRE

    Pojon, Murat

    2017-01-01

    This thesis examines the application of machine learning algorithms to predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in terms of how much they improve the prediction performance. Three different machine learning methods were used in this thesis. They are linear regression, decision trees, and naïve Bayes classification. Feature engineering, the process of modification ...

  7. A deep learning method for lincRNA detection using auto-encoder algorithm.

    Science.gov (United States)

    Yu, Ning; Yu, Zeng; Pan, Yi

    2017-12-06

    RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly

  8. Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

    Science.gov (United States)

    Eskofier, Bjoern M; Lee, Sunghoon I; Daneault, Jean-Francois; Golabchi, Fatemeh N; Ferreira-Carvalho, Gabriela; Vergara-Diaz, Gloria; Sapienza, Stefano; Costante, Gianluca; Klucken, Jochen; Kautz, Thomas; Bonato, Paolo

    2016-08-01

    The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

  9. How Learning Designs, Teaching Methods and Activities Differ by Discipline in Australian Universities

    Science.gov (United States)

    Cameron, Leanne

    2017-01-01

    This paper reports on the learning designs, teaching methods and activities most commonly employed within the disciplines in six universities in Australia. The study sought to establish if there were significant differences between the disciplines in learning designs, teaching methods and teaching activities in the current Australian context, as…

  10. Investigation of distinctive characteristics of children with specific learning disorder and borderline intellectual functioning

    Directory of Open Access Journals (Sweden)

    Selcuk Ozkan

    Full Text Available Abstract Background Borderline intelligence function (BIF and specific learning disorder (SLD are common diagnoses in children who are brought up for learning problems and school failure. Objective The aim of our study was to determine whether there were distinctive aspects of cognitive testing routinely used in evaluating SLD and BIF and investigate emotion regulation skills and minor neurologic symptoms. Method Sixty children (30 SLD and 30 BIF who are currently attending primary school are selected for study. Visual Aural Digit Span Test – Form B, Gessel Figure Drawing Test, Bender Gestalt Visual Motor Perception Test, WISC-R, Emotion Regulation Scale (ERS and Neurological Evaluation Scale (NES was administered. Results There was no statistically significant difference between groups in cognitive tests. The emotional regulation ability measured by the emotional regulation subscale was better in the SLD group than the BIF group (p = 0.014. In the NES, sensory integration (p = 0.008, motor coordination (p = 0.047 and other (p < 0.001 subscales showed higher scores in the BIF group. Discussion It has been shown that cognitive tests don’t have distinguishing features in the evaluation of SLD and BIF. Emotion regulation subscale score of ERS and sensory integration, motor coordination, and total scores of NES can be used in both discrimination of groups.

  11. Results of a study assessing teaching methods of faculty after measuring student learning style preference.

    Science.gov (United States)

    Stirling, Bridget V

    2017-08-01

    Learning style preference impacts how well groups of students respond to their curricula. Faculty have many choices in the methods for delivering nursing content, as well as assessing students. The purpose was to develop knowledge around how faculty delivered curricula content, and then considering these findings in the context of the students learning style preference. Following an in-service on teaching and learning styles, faculty completed surveys on their methods of teaching and the proportion of time teaching, using each learning style (visual, aural, read/write and kinesthetic). This study took place at the College of Nursing a large all-female university in Saudi Arabia. 24 female nursing faculty volunteered to participate in the project. A cross-sectional design was used. Faculty reported teaching using mostly methods that were kinesthetic and visual, although lecture was also popular (aural). Students preferred kinesthetic and aural learning methods. Read/write was the least preferred by students and the least used method of teaching by faculty. Faculty used visual methods about one third of the time, although they were not preferred by the students. Students' preferred learning style (kinesthetic) was the method most used by faculty. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Characteristics and Consequences of Adult Learning Methods and Strategies. Practical Evaluation Reports, Volume 2, Number 1

    Science.gov (United States)

    Trivette, Carol M.; Dunst, Carl J.; Hamby, Deborah W.; O'Herin, Chainey E.

    2009-01-01

    The effectiveness of four adult learning methods (accelerated learning, coaching, guided design, and just-in-time training) constituted the focus of this research synthesis. Findings reported in "How People Learn" (Bransford et al., 2000) were used to operationally define six adult learning method characteristics, and to code and analyze…

  13. Methods of checking general safety criteria in UML statechart specifications

    International Nuclear Information System (INIS)

    Pap, Zsigmond; Majzik, Istvan; Pataricza, Andras; Szegi, Andras

    2005-01-01

    This paper describes methods and tools for safety analysis of UML statechart specifications. A comprehensive set of general safety criteria including completeness and consistency is applied in automated analysis. Analysis techniques are based on OCL expressions, graph transformations and reachability analysis. Two canonical intermediate representations of the statechart specification are introduced. They are suitable for straightforward implementation of checker methods and for the support of the proof of the correctness and soundness of the applied analysis. One of them also serves as a basis of the metamodel of a variant of UML statecharts proposed for the specification of safety-critical control systems. The analysis is extended to object-oriented specifications. Examples illustrate the application of the checker methods implemented by an automated tool-set

  14. Cooperative Learning in Virtual Environments: The Jigsaw Method in Statistical Courses

    Science.gov (United States)

    Vargas-Vargas, Manuel; Mondejar-Jimenez, Jose; Santamaria, Maria-Letica Meseguer; Alfaro-Navarro, Jose-Luis; Fernandez-Aviles, Gema

    2011-01-01

    This document sets out a novel teaching methodology as used in subjects with statistical content, traditionally regarded by students as "difficult". In a virtual learning environment, instructional techniques little used in mathematical courses were employed, such as the Jigsaw cooperative learning method, which had to be adapted to the…

  15. Enhancing the Pronunciation of English Suprasegmental Features through Reflective Learning Method

    Science.gov (United States)

    Suwartono

    2014-01-01

    Suprasegmental features are of paramount importance in spoken English. Yet, these pronunciation features are marginalised in EFL/ESL teaching-learning. This article reported a study that was aimed at improving the students' mastery of English suprasegmental features through the use of reflective learning method. The study adopted Kemmis and…

  16. Distinguishing butchery cut marks from crocodile bite marks through machine learning methods.

    Science.gov (United States)

    Domínguez-Rodrigo, Manuel; Baquedano, Enrique

    2018-04-10

    All models of evolution of human behaviour depend on the correct identification and interpretation of bone surface modifications (BSM) on archaeofaunal assemblages. Crucial evolutionary features, such as the origin of stone tool use, meat-eating, food-sharing, cooperation and sociality can only be addressed through confident identification and interpretation of BSM, and more specifically, cut marks. Recently, it has been argued that linear marks with the same properties as cut marks can be created by crocodiles, thereby questioning whether secure cut mark identifications can be made in the Early Pleistocene fossil record. Powerful classification methods based on multivariate statistics and machine learning (ML) algorithms have previously successfully discriminated cut marks from most other potentially confounding BSM. However, crocodile-made marks were marginal to or played no role in these comparative analyses. Here, for the first time, we apply state-of-the-art ML methods on crocodile linear BSM and experimental butchery cut marks, showing that the combination of multivariate taphonomy and ML methods provides accurate identification of BSM, including cut and crocodile bite marks. This enables empirically-supported hominin behavioural modelling, provided that these methods are applied to fossil assemblages.

  17. The role of problem solving method on the improvement of mathematical learning

    Directory of Open Access Journals (Sweden)

    Saeed Mokhtari-Hassanabad

    2012-10-01

    Full Text Available In history of education, problem solving is one of the important educational goals and teachers or parents have intended that their students have capacity of problem solving. In present research, it is tried that study the problem solving method for mathematical learning. This research is implemented via quasi-experimental method on 49 boy students at high school. The results of Leven test and T-test indicated that problem solving method has more effective on the improvement of mathematical learning than traditional instruction method. Therefore it seems that teachers of mathematics must apply the problem solving method in educational systems till students became self-efficiency in mathematical problem solving.

  18. Advanced methods in NDE using machine learning approaches

    Science.gov (United States)

    Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

    2018-04-01

    Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability

  19. An exploration of learning to link with Wikipedia: features, methods and training collection

    NARCIS (Netherlands)

    He, J.; de Rijke, M.

    2010-01-01

    We describe our participation in the Link-the-Wiki track at INEX 2009. We apply machine learning methods to the anchor-to-best-entry-point task and explore the impact of the following aspects of our approaches: features, learning methods as well as the collection used for training the models. We

  20. Kernel methods for interpretable machine learning of order parameters

    Science.gov (United States)

    Ponte, Pedro; Melko, Roger G.

    2017-11-01

    Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of supervised learning has come from employing neural networks as classifiers. Although very powerful, such algorithms suffer from a lack of interpretability, which is usually desired in scientific applications in order to associate learned features with physical phenomena. In this paper, we explore support vector machines (SVMs), which are a class of supervised kernel methods that provide interpretable decision functions. We find that SVMs can learn the mathematical form of physical discriminators, such as order parameters and Hamiltonian constraints, for a set of two-dimensional spin models: the ferromagnetic Ising model, a conserved-order-parameter Ising model, and the Ising gauge theory. The ability of SVMs to provide interpretable classification highlights their potential for automating feature detection in both synthetic and experimental data sets for condensed matter and other many-body systems.

  1. Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation.

    Science.gov (United States)

    Mikhchi, Abbas; Honarvar, Mahmood; Kashan, Nasser Emam Jomeh; Aminafshar, Mehdi

    2016-06-21

    Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other methods.The running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation time.The tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Attentional Focus in Motor Learning, the Feldenkrais Method, and Mindful Movement.

    Science.gov (United States)

    Mattes, Josef

    2016-08-01

    The present paper discusses attentional focus in motor learning and performance from the point of view of mindful movement practices, taking as a starting point the Feldenkrais method. It is argued that earlier criticism of the Feldenkrais method (and thereby implicitly of mindful movement practices more generally) because of allegedly inappropriate attentional focus turns out to be unfounded in light of recent developments in the study of motor learning and performance. Conversely, the examples of the Feldenkrais method and Ki-Aikido are used to illustrate how both Western and Eastern (martial arts derived) mindful movement practices might benefit sports psychology. © The Author(s) 2016.

  3. Maximum entropy methods for extracting the learned features of deep neural networks.

    Science.gov (United States)

    Finnegan, Alex; Song, Jun S

    2017-10-01

    New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

  4. Inter-Labeler and Intra-Labeler Variability of Condition Severity Classification Models Using Active and Passive Learning Methods

    Science.gov (United States)

    Nissim, Nir; Shahar, Yuval; Boland, Mary Regina; Tatonetti, Nicholas P; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

    2018-01-01

    Background and Objectives Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers’ learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. Methods We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by

  5. Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

    Science.gov (United States)

    Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

    2017-12-01

    Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

  6. An improved segmentation-based HMM learning method for Condition-based Maintenance

    International Nuclear Information System (INIS)

    Liu, T; Lemeire, J; Cartella, F; Meganck, S

    2012-01-01

    In the domain of condition-based maintenance (CBM), persistence of machine states is a valid assumption. Based on this assumption, we present an improved Hidden Markov Model (HMM) learning algorithm for the assessment of equipment states. By a good estimation of initial parameters, more accurate learning can be achieved than by regular HMM learning methods which start with randomly chosen initial parameters. It is also better in avoiding getting trapped in local maxima. The data is segmented with a change-point analysis method which uses a combination of cumulative sum charts (CUSUM) and bootstrapping techniques. The method determines a confidence level that a state change happens. After the data is segmented, in order to label and combine the segments corresponding to the same states, a clustering technique is used based on a low-pass filter or root mean square (RMS) values of the features. The segments with their labelled hidden state are taken as 'evidence' to estimate the parameters of an HMM. Then, the estimated parameters are served as initial parameters for the traditional Baum-Welch (BW) learning algorithms, which are used to improve the parameters and train the model. Experiments on simulated and real data demonstrate that both performance and convergence speed is improved.

  7. Application of a Novel Collaboration Engineering Method for Learning Design: A Case Study

    Science.gov (United States)

    Cheng, Xusen; Li, Yuanyuan; Sun, Jianshan; Huang, Jianqing

    2016-01-01

    Collaborative case studies and computer-supported collaborative learning (CSCL) play an important role in the modern education environment. A number of researchers have given significant attention to learning design in order to improve the satisfaction of collaborative learning. Although collaboration engineering (CE) is a mature method widely…

  8. CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.

    Science.gov (United States)

    Zhang, Li; Ai, Haixin; Chen, Wen; Yin, Zimo; Hu, Huan; Zhu, Junfeng; Zhao, Jian; Zhao, Qi; Liu, Hongsheng

    2017-05-18

    Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models ( http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/ ).

  9. Automata learning algorithms and processes for providing more complete systems requirements specification by scenario generation, CSP-based syntax-oriented model construction, and R2D2C system requirements transformation

    Science.gov (United States)

    Hinchey, Michael G. (Inventor); Margaria, Tiziana (Inventor); Rash, James L. (Inventor); Rouff, Christopher A. (Inventor); Steffen, Bernard (Inventor)

    2010-01-01

    Systems, methods and apparatus are provided through which in some embodiments, automata learning algorithms and techniques are implemented to generate a more complete set of scenarios for requirements based programming. More specifically, a CSP-based, syntax-oriented model construction, which requires the support of a theorem prover, is complemented by model extrapolation, via automata learning. This may support the systematic completion of the requirements, the nature of the requirement being partial, which provides focus on the most prominent scenarios. This may generalize requirement skeletons by extrapolation and may indicate by way of automatically generated traces where the requirement specification is too loose and additional information is required.

  10. Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.

    Science.gov (United States)

    Nissim, Nir; Shahar, Yuval; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

    2017-09-01

    Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers' learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by using the labels provided by seven

  11. The effects of inspecting and constructing part-task-specific visualizations on team and individual learning

    NARCIS (Netherlands)

    Slof, Bert; Erkens, Gijsbert; Kirschner, Paul A.; Helms-Lorenz, Michelle

    This study examined whether inspecting and constructing different part-task-specific visualizations differentially affects learning. To this end, a complex business-economics problem was structured into three phase-related part-tasks: (1) determining core concepts, (2) proposing multiple solutions,

  12. Pitch Discrimination Learning: Specificity for Pitch and Harmonic Resolvability, and Electrophysiological Correlates

    OpenAIRE

    Carcagno, Samuele; Plack, Christopher J.

    2011-01-01

    Multiple-hour training on a pitch discrimination task dramatically decreases the threshold for detecting a pitch difference between two harmonic complexes. Here, we investigated the specificity of this perceptual learning with respect to the pitch and the resolvability of the trained harmonic complex, as well as its cortical electrophysiological correlates. We trained 24 participants for 12 h on a pitch discrimination task using one of four different harmonic complexes. The complexes differed...

  13. Specific surface area evaluation method by using scanning electron microscopy

    International Nuclear Information System (INIS)

    Petrescu, Camelia; Petrescu, Cristian; Axinte, Adrian

    2000-01-01

    Ceramics are among the most interesting materials for a large category of applications, including both industry and health. Among the characteristic of the ceramic materials, the specific surface area is often difficult to evaluate.The paper presents a method of evaluation for the specific surface area of two ceramic powders by means of scanning electron microscopy measurements and an original method of computing the specific surface area.Cumulative curves are used to calculate the specific surface area under assumption that the values of particles diameters follow a normal logarithmic distribution. For two powder types, X7R and NPO the results are the following: - for the density ρ (g/cm 2 ), 5.5 and 6.0, respectively; - for the average diameter D bar (μm), 0.51 and 0.53, respectively; - for σ, 1.465 and 1.385, respectively; - for specific surface area (m 2 /g), 1.248 and 1.330, respectively. The obtained results are in good agreement with the values measured by conventional methods. (authors)

  14. RULE-BASE METHOD FOR ANALYSIS OF QUALITY E-LEARNING IN HIGHER EDUCATION

    Directory of Open Access Journals (Sweden)

    darsih darsih darsih

    2016-04-01

    Full Text Available ABSTRACT Assessing the quality of e-learning courses to measure the success of e-learning systems in online learning is essential. The system can be used to improve education. The study analyzes the quality of e-learning course on the web site www.kulon.undip.ac.id used a questionnaire with questions based on the variables of ISO 9126. Penilaiann Likert scale was used with a web app. Rule-base reasoning method is used to subject the quality of e-learningyang assessed. A case study conducted in four e-learning courses with 133 sample / respondents as users of the e-learning course. From the obtained results of research conducted both for the value of e-learning from each subject tested. In addition, each e-learning courses have different advantages depending on certain variables. Keywords : E-Learning, Rule-Base, Questionnaire, Likert, Measuring.

  15. Intelligent Detection of Structure from Remote Sensing Images Based on Deep Learning Method

    Science.gov (United States)

    Xin, L.

    2018-04-01

    Utilizing high-resolution remote sensing images for earth observation has become the common method of land use monitoring. It requires great human participation when dealing with traditional image interpretation, which is inefficient and difficult to guarantee the accuracy. At present, the artificial intelligent method such as deep learning has a large number of advantages in the aspect of image recognition. By means of a large amount of remote sensing image samples and deep neural network models, we can rapidly decipher the objects of interest such as buildings, etc. Whether in terms of efficiency or accuracy, deep learning method is more preponderant. This paper explains the research of deep learning method by a great mount of remote sensing image samples and verifies the feasibility of building extraction via experiments.

  16. Change Of Learning Environment Using Game Production ­Theory, Methods And Practice

    DEFF Research Database (Denmark)

    Reng, Lars; Kofoed, Lise; Schoenau-Fog, Henrik

    2018-01-01

    will focus on cases in which development of games did change the learning environments into production units where students or employees were producing games as part of the learning process. The cases indicate that the motivation as well as the learning curve became very high. The pedagogical theories......Game Based Learning has proven to have many possibilities for supporting better learning outcomes, when using educational or commercial games in the classroom. However, there is also a great potential in using game development as a motivator in other kinds of learning scenarios. This study...... and methods are based on Problem Based Learning (PBL), but are developed further by combining PBL with a production-oriented/design based approach. We illustrate the potential of using game production as a learning environment with investigation of three game productions. We can conclude that using game...

  17. Toward an Instructionally Oriented Theory of Example-Based Learning

    Science.gov (United States)

    Renkl, Alexander

    2014-01-01

    Learning from examples is a very effective means of initial cognitive skill acquisition. There is an enormous body of research on the specifics of this learning method. This article presents an instructionally oriented theory of example-based learning that integrates theoretical assumptions and findings from three research areas: learning from…

  18. The Legal Meaning of Specific Learning Disability for IDEA Eligibility: The Latest Case Law

    Science.gov (United States)

    Zirkel, Perry A.

    2013-01-01

    Specific learning disability (SLD), although moderately declining in recent years, continues to be the largest of the eligibility classifications under the Individuals with Disabilities Education Act (IDEA; NCES, 2012). The recognition of response to intervention (RTI) in the 2004 amendments of the IDEA as an approach for identifying students with…

  19. A mixed-methods exploration of an environment for learning computer programming

    Directory of Open Access Journals (Sweden)

    Richard Mather

    2015-08-01

    Full Text Available A mixed-methods approach is evaluated for exploring collaborative behaviour, acceptance and progress surrounding an interactive technology for learning computer programming. A review of literature reveals a compelling case for using mixed-methods approaches when evaluating technology-enhanced-learning environments. Here, ethnographic approaches used for the requirements engineering of computing systems are combined with questionnaire-based feedback and skill tests. These are applied to the ‘Ceebot’ animated 3D learning environment. Video analysis with workplace observation allowed detailed inspection of problem solving and tacit behaviours. Questionnaires and knowledge tests provided broad sample coverage with insights into subject understanding and overall response to the learning environment. Although relatively low scores in programming tests seemingly contradicted the perception that Ceebot had enhanced understanding of programming, this perception was nevertheless found to be correlated with greater test performance. Video analysis corroborated findings that the learning environment and Ceebot animations were engaging and encouraged constructive collaborative behaviours. Ethnographic observations clearly captured Ceebot's value in providing visual cues for problem-solving discussions and for progress through sharing discoveries. Notably, performance in tests was most highly correlated with greater programming practice (p≤0.01. It was apparent that although students had appropriated technology for collaborative working and benefitted from visual and tacit cues provided by Ceebot, they had not necessarily deeply learned the lessons intended. The key value of the ‘mixed-methods’ approach was that ethnographic observations captured the authenticity of learning behaviours, and thereby strengthened confidence in the interpretation of questionnaire and test findings.

  20. Application of learning techniques based on kernel methods for the fault diagnosis in industrial processes

    Directory of Open Access Journals (Sweden)

    Jose M. Bernal-de-Lázaro

    2016-05-01

    Full Text Available This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis systems (DDF based on historical data. Specifically this thesis provides: (1 new criteria for adjustment of the kernel methods used to select features with a high discriminative capacity for the fault diagnosis tasks, (2 a proposed approach process monitoring using statistical techniques multivariate that incorporates a reinforced information concerning to the dynamics of the Hotelling's T2 and SPE statistics, whose combination with kernel methods improves the detection of small-magnitude faults; (3 an robustness index to compare the diagnosis classifiers performance taking into account their insensitivity to possible noise and disturbance on historical data.

  1. Using c-Jun to identify fear extinction learning-specific patterns of neural activity that are affected by single prolonged stress.

    Science.gov (United States)

    Knox, Dayan; Stanfield, Briana R; Staib, Jennifer M; David, Nina P; DePietro, Thomas; Chamness, Marisa; Schneider, Elizabeth K; Keller, Samantha M; Lawless, Caroline

    2018-04-02

    Neural circuits via which stress leads to disruptions in fear extinction is often explored in animal stress models. Using the single prolonged stress (SPS) model of post traumatic stress disorder and the immediate early gene (IEG) c-Fos as a measure of neural activity, we previously identified patterns of neural activity through which SPS disrupts extinction retention. However, none of these stress effects were specific to fear or extinction learning and memory. C-Jun is another IEG that is sometimes regulated in a different manner to c-Fos and could be used to identify emotional learning/memory specific patterns of neural activity that are sensitive to SPS. Animals were either fear conditioned (CS-fear) or presented with CSs only (CS-only) then subjected to extinction training and testing. C-Jun was then assayed within neural substrates critical for extinction memory. Inhibited c-Jun levels in the hippocampus (Hipp) and enhanced functional connectivity between the ventromedial prefrontal cortex (vmPFC) and basolateral amygdala (BLA) during extinction training was disrupted by SPS in the CS-fear group only. As a result, these effects were specific to emotional learning/memory. SPS also disrupted inhibited Hipp c-Jun levels, enhanced BLA c-Jun levels, and altered functional connectivity among the vmPFC, BLA, and Hipp during extinction testing in SPS rats in the CS-fear and CS-only groups. As a result, these effects were not specific to emotional learning/memory. Our findings suggest that SPS disrupts neural activity specific to extinction memory, but may also disrupt the retention of fear extinction by mechanisms that do not involve emotional learning/memory. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Learning to spell from reading: general knowledge about spelling patterns influences memory for specific words.

    Science.gov (United States)

    Pacton, Sébastien; Borchardt, Gaëlle; Treiman, Rebecca; Lété, Bernard; Fayol, Michel

    2014-05-01

    Adults often learn to spell words during the course of reading for meaning, without intending to do so. We used an incidental learning task in order to study this process. Spellings that contained double n, r and t which are common doublets in French, were learned more readily by French university students than spellings that contained less common but still legal doublets. When recalling or recognizing the latter, the students sometimes made transposition errors, doubling a consonant that often doubles in French rather than the consonant that was originally doubled (e.g., tiddunar recalled as tidunnar). The results, found in three experiments using different nonwords and different types of instructions, show that people use general knowledge about the graphotactic patterns of their writing system together with word-specific knowledge to reconstruct spellings that they learn from reading. These processes contribute to failures and successes in memory for spellings, as in other domains.

  3. Enhanced Assessment Technology and Neurocognitive Aspects of Specific Learning Disorder with Impairment in Mathematics.

    Directory of Open Access Journals (Sweden)

    Marios A. Pappas

    2018-02-01

    Full Text Available Specific Learning Disorder with impairment in Mathematics (Developmental Dyscalculia is a complex learning disorder which affects arithmetic skills, symbolic magnitude processing, alertness, flexibility in problem solving and maintained attention. Neuro-cognitive studies revealed that such difficulties in children with DD could be related to poor Working Memory and attention deficits. Furthermore, neuroimaging studies indicate that brain structure differences in children with DD compared to typically developing children could affect mathematical performance. In this study we present the cognitive profile of Dyscalculia, as well as the neuropsychological aspects of the deficit, with special reference to the utilization of enhanced assessment technology such as computerized neuropsychological tools and neuroimaging techniques.

  4. The Importance of a Teacher in a Distance Education and the Progressive Methods of Teaching in a Virtual Learning Environment

    Directory of Open Access Journals (Sweden)

    Olga Miščenko

    2014-12-01

    Full Text Available The purpose of the article is to analyze the experience of the first work years of teaching the students, who study by distance, to compare other authors’ experience and to examine the advantages of Moodle virtual learning environment (VLE, searching for new applications of it. The relevance of e-learning is noted. It is affirmed that metacognitive learning strategies are typical for learning foreign languages in virtual environment. It is said that the Internet is a tool that ensures studies by distance. It is said that raising the qualification and learning by distance allows a responsible employee to improve foreign language skills while lifelong learning. VLE adaptability for teaching and studying English is being discussed. It is stated that the Internet conditions all types of methods in the virtual environment, application, and its existence expands and deepens the learning approach. In the paper it is claimed that the Moodle VLE function is to improve the learning process to ensure a high level of expertise and the objectivity of assessment. Studying in conventional way and in the virtual environment are briefly compared. Moodle virtual learning environment application objectives to learning outcomes, emphasizing the importance of the traditional teaching methods, the student’s responsibility to call attention to the learning process and system characteristics are defined. It is noted that learning in the virtual environment is based on the principles of epistemology, therefore the Moodle system meets the didactic tasks. The virtual learning environment possibilities ensure a very good feedback and increase students’ motivation, and, consequently, that provides better knowledge. It is emphasized that while teaching by distance, the teacher’s responsibility, his role in the development of educational material and the course tasks have increased. Some specific cases for various forms of studies and exercises to perform in the

  5. Role of Class III phosphoinositide 3-kinase in the brain development: possible involvement in specific learning disorders.

    Science.gov (United States)

    Inaguma, Yutaka; Matsumoto, Ayumi; Noda, Mariko; Tabata, Hidenori; Maeda, Akihiko; Goto, Masahide; Usui, Daisuke; Jimbo, Eriko F; Kikkawa, Kiyoshi; Ohtsuki, Mamitaro; Momoi, Mariko Y; Osaka, Hitoshi; Yamagata, Takanori; Nagata, Koh-Ichi

    2016-10-01

    Class III phosphoinositide 3-kinase (PIK3C3 or mammalian vacuolar protein sorting 34 homolog, Vps34) regulates vesicular trafficking, autophagy, and nutrient sensing. Recently, we reported that PIK3C3 is expressed in mouse cerebral cortex throughout the developmental process, especially at early embryonic stage. We thus examined the role of PIK3C3 in the development of the mouse cerebral cortex. Acute silencing of PIK3C3 with in utero electroporation method caused positional defects of excitatory neurons during corticogenesis. Time-lapse imaging revealed that the abnormal positioning was at least partially because of the reduced migration velocity. When PIK3C3 was silenced in cortical neurons in one hemisphere, axon extension to the contralateral hemisphere was also delayed. These aberrant phenotypes were rescued by RNAi-resistant PIK3C3. Notably, knockdown of PIK3C3 did not affect the cell cycle of neuronal progenitors and stem cells at the ventricular zone. Taken together, PIK3C3 was thought to play a crucial role in corticogenesis through the regulation of excitatory neuron migration and axon extension. Meanwhile, when we performed comparative genomic hybridization on a patient with specific learning disorders, a 107 Kb-deletion was identified on 18q12.3 (nt. 39554147-39661206) that encompasses exons 5-23 of PIK3C3. Notably, the above aberrant migration and axon growth phenotypes were not rescued by the disease-related truncation mutant (172 amino acids) lacking the C-terminal kinase domain. Thus, functional defects of PIK3C3 might impair corticogenesis and relate to the pathophysiology of specific learning disorders and other neurodevelopmental disorders. Acute knockdown of Class III phosphoinositide 3-kinase (PIK3C3) evokes migration defects of excitatory neurons during corticogenesis. PIK3C3-knockdown also disrupts axon outgrowth, but not progenitor proliferation in vivo. Involvement of PIK3C3 in neurodevelopmental disorders might be an interesting future

  6. Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

    Directory of Open Access Journals (Sweden)

    Pontil Massimiliano

    2009-10-01

    Full Text Available Abstract Background Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots" at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition. Results We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which ΔΔG ≥ 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%. Conclusion We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been

  7. Instructional methods and cognitive and learning styles in web-based learning: report of two randomised trials.

    Science.gov (United States)

    Cook, David A; Gelula, Mark H; Dupras, Denise M; Schwartz, Alan

    2007-09-01

    Adapting web-based (WB) instruction to learners' individual differences may enhance learning. Objectives This study aimed to investigate aptitude-treatment interactions between learning and cognitive styles and WB instructional methods. We carried out a factorial, randomised, controlled, crossover, post-test-only trial involving 89 internal medicine residents, family practice residents and medical students at 2 US medical schools. Parallel versions of a WB course in complementary medicine used either active or reflective questions and different end-of-module review activities ('create and study a summary table' or 'study an instructor-created table'). Participants were matched or mismatched to question type based on active or reflective learning style. Participants used each review activity for 1 course module (crossover design). Outcome measurements included the Index of Learning Styles, the Cognitive Styles Analysis test, knowledge post-test, course rating and preference. Post-test scores were similar for matched (mean +/- standard error of the mean 77.4 +/- 1.7) and mismatched (76.9 +/- 1.7) learners (95% confidence interval [CI] for difference - 4.3 to 5.2l, P = 0.84), as were course ratings (P = 0.16). Post-test scores did not differ between active-type questions (77.1 +/- 2.1) and reflective-type questions (77.2 +/- 1.4; P = 0.97). Post-test scores correlated with course ratings (r = 0.45). There was no difference in post-test subscores for modules completed using the 'construct table' format (78.1 +/- 1.4) or the 'table provided' format (76.1 +/- 1.4; CI - 1.1 to 5.0, P = 0.21), and wholist and analytic styles had no interaction (P = 0.75) or main effect (P = 0.18). There was no association between activity preference and wholist or analytic scores (P = 0.37). Cognitive and learning styles had no apparent influence on learning outcomes. There were no differences in outcome between these instructional methods.

  8. Quality of life of parents of children with newly diagnosed specific learning disability

    Directory of Open Access Journals (Sweden)

    Karande S

    2009-01-01

    Full Text Available Background: Poor school performance in children causes significant stress to parents. Aims: To analyze the quality of life (QOL of parents having a child with newly diagnosed specific learning disability (SpLD and to evaluate the impact of clinical and socio-demographic characteristics on their QOL. Design: Cross-sectional questionnaire-based study. Setting: Learning disability clinic in tertiary care hospital. Materials and Methods: From June 2006 to February 2007, 150 parents (either mother or father of children consecutively diagnosed as having SpLD were enrolled. Parent′s QOL was measured by the WHOQOL-100 instrument which is a generic instrument containing 25 facets of QOL organized in six domains. Statistical Analysis Used: Independent samples t-test, one-way analysis of variance, and multiple regression analysis were carried out for statistical significance. Results: Mean age of parents was 42.6 years (SD 5.5; mothers to fathers ratio 1.3:1; and 19 (12.7% were currently ill. Only four WHOQOL-100 domains (psychological > social relationships > environment > spiritual and five WHOQOL-100 facets (leisur > pfeel > energy > esteem > sex contributed significantly to their "overall" QOL. Female gender, being currently ill, being in paid work, and having a male child were characteristics that independently predicted a poor domain/facet QOL score. Conclusions: The present study has identified domains and facets that need to be addressed by counselors for improving overall QOL of these parents. Initiating these measures would also improve the home environment and help in the rehabilitation of children with SpLD.

  9. Joint Feature Selection and Classification for Multilabel Learning.

    Science.gov (United States)

    Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

    2018-03-01

    Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

  10. Working Memory and Learning in Children with Developmental Coordination Disorder and Specific Language Impairment

    Science.gov (United States)

    Alloway, Tracy Packiam; Archibald, Lisa

    2008-01-01

    The authors compared 6- to 11-year-olds with developmental coordination disorder (DCD) and those with specific language impairment (SLI) on measures of memory (verbal and visuospatial short-term and working memory) and learning (reading and mathematics). Children with DCD with typical language skills were impaired in all four areas of memory…

  11. A Survey on Domain-Specific Languages for Machine Learning in Big Data

    OpenAIRE

    Portugal, Ivens; Alencar, Paulo; Cowan, Donald

    2016-01-01

    The amount of data generated in the modern society is increasing rapidly. New problems and novel approaches of data capture, storage, analysis and visualization are responsible for the emergence of the Big Data research field. Machine Learning algorithms can be used in Big Data to make better and more accurate inferences. However, because of the challenges Big Data imposes, these algorithms need to be adapted and optimized to specific applications. One important decision made by software engi...

  12. Teaching learning methods of an entrepreneurship curriculum

    Directory of Open Access Journals (Sweden)

    KERAMAT ESMI

    2015-10-01

    Full Text Available Introduction: One of the most significant elements of entrepreneurship curriculum design is teaching-learning methods, which plays a key role in studies and researches related to such a curriculum. It is the teaching method, and systematic, organized and logical ways of providing lessons that should be consistent with entrepreneurship goals and contents, and should also be developed according to the learners’ needs. Therefore, the current study aimed to introduce appropriate, modern, and effective methods of teaching entrepreneurship and their validation Methods: This is a mixed method research of a sequential exploratory kind conducted through two stages: a developing teaching methods of entrepreneurship curriculum, and b validating developed framework. Data were collected through “triangulation” (study of documents, investigating theoretical basics and the literature, and semi-structured interviews with key experts. Since the literature on this topic is very rich, and views of the key experts are vast, directed and summative content analysis was used. In the second stage, qualitative credibility of research findings was obtained using qualitative validation criteria (credibility, confirmability, and transferability, and applying various techniques. Moreover, in order to make sure that the qualitative part is reliable, reliability test was used. Moreover, quantitative validation of the developed framework was conducted utilizing exploratory and confirmatory factor analysis methods and Cronbach’s alpha. The data were gathered through distributing a three-aspect questionnaire (direct presentation teaching methods, interactive, and practical-operational aspects with 29 items among 90 curriculum scholars. Target population was selected by means of purposive sampling and representative sample. Results: Results obtained from exploratory factor analysis showed that a three factor structure is an appropriate method for describing elements of

  13. Music Learning Based on Computer Software

    OpenAIRE

    Baihui Yan; Qiao Zhou

    2017-01-01

    In order to better develop and improve students’ music learning, the authors proposed the method of music learning based on computer software. It is still a new field to use computer music software to assist teaching. Hereby, we conducted an in-depth analysis on the computer-enabled music learning and the music learning status in secondary schools, obtaining the specific analytical data. Survey data shows that students have many cognitive problems in the current music classroom, and yet teach...

  14. Deep Learning Methods for Underwater Target Feature Extraction and Recognition

    Directory of Open Access Journals (Sweden)

    Gang Hu

    2018-01-01

    Full Text Available The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

  15. Understanding the effects of time on collaborative learning processes in problem based learning: a mixed methods study.

    Science.gov (United States)

    Hommes, J; Van den Bossche, P; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A

    2014-10-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning processes developed within and over three periods in the first 1,5 study years of an undergraduate curriculum. Next, a qualitative study using semi-structured individual interviews focused on detailed development of group processes driving collaborative learning during one period in seven tutorial groups. The hierarchic multilevel analyses of the quantitative data showed that a varying combination of group processes developed within and over the three observed periods. The qualitative study illustrated development in psychological safety, interdependence, potency, group learning behaviour, social and task cohesion. Two new processes emerged: 'transactive memory' and 'convergence in mental models'. The results indicate that groups are dynamic social systems with numerous contextual influences. Future research should thus include time as an important influence on collaborative learning. Practical implications are discussed.

  16. Geometrical Modification of Learning Vector Quantization Method for Solving Classification Problems

    Directory of Open Access Journals (Sweden)

    Korhan GÜNEL

    2016-09-01

    Full Text Available In this paper, a geometrical scheme is presented to show how to overcome an encountered problem arising from the use of generalized delta learning rule within competitive learning model. It is introduced a theoretical methodology for describing the quantization of data via rotating prototype vectors on hyper-spheres.The proposed learning algorithm is tested and verified on different multidimensional datasets including a binary class dataset and two multiclass datasets from the UCI repository, and a multiclass dataset constructed by us. The proposed method is compared with some baseline learning vector quantization variants in literature for all domains. Large number of experiments verify the performance of our proposed algorithm with acceptable accuracy and macro f1 scores.

  17. Project-Based Learning Using Discussion and Lesson-Learned Methods via Social Media Model for Enhancing Problem Solving Skills

    Science.gov (United States)

    Jewpanich, Chaiwat; Piriyasurawong, Pallop

    2015-01-01

    This research aims to 1) develop the project-based learning using discussion and lesson-learned methods via social media model (PBL-DLL SoMe Model) used for enhancing problem solving skills of undergraduate in education student, and 2) evaluate the PBL-DLL SoMe Model used for enhancing problem solving skills of undergraduate in education student.…

  18. Subsampled Hessian Newton Methods for Supervised Learning.

    Science.gov (United States)

    Wang, Chien-Chih; Huang, Chun-Heng; Lin, Chih-Jen

    2015-08-01

    Newton methods can be applied in many supervised learning approaches. However, for large-scale data, the use of the whole Hessian matrix can be time-consuming. Recently, subsampled Newton methods have been proposed to reduce the computational time by using only a subset of data for calculating an approximation of the Hessian matrix. Unfortunately, we find that in some situations, the running speed is worse than the standard Newton method because cheaper but less accurate search directions are used. In this work, we propose some novel techniques to improve the existing subsampled Hessian Newton method. The main idea is to solve a two-dimensional subproblem per iteration to adjust the search direction to better minimize the second-order approximation of the function value. We prove the theoretical convergence of the proposed method. Experiments on logistic regression, linear SVM, maximum entropy, and deep networks indicate that our techniques significantly reduce the running time of the subsampled Hessian Newton method. The resulting algorithm becomes a compelling alternative to the standard Newton method for large-scale data classification.

  19. Learning methods and strategies of anatomy among medical students in two different Institutions in Riyadh, Saudi Arabia.

    Science.gov (United States)

    Al-Mohrej, Omar A; Al-Ayedh, Noura K; Masuadi, Emad M; Al-Kenani, Nader S

    2017-04-01

    Anatomy instructors adopt individual teaching methods and strategies to convey anatomical information to medical students for learning. Students also exhibit their own individual learning preferences. Instructional methods preferences vary between both instructors and students across different institutions. In attempt to bridge the gap between teaching methods and the students' learning preferences, this study aimed to identify students' learning methods and different strategies of studying anatomy in two different Saudi medical schools in Riyadh. A cross-sectional study, conducted in Saudi Arabia in April 2015, utilized a three-section questionnaire, which was distributed to a consecutive sample of 883 medical students to explore their methods and strategies in learning and teaching anatomy in two separate institutions in Riyadh, Saudi Arabia. Medical students' learning styles and preferences were found to be predominantly affected by different cultural backgrounds, gender, and level of study. Many students found it easier to understand and remember anatomy components using study aids. In addition, almost half of the students felt confident to ask their teachers questions after class. The study also showed that more than half of the students found it easier to study by concentrating on a particular part of the body rather than systems. Students' methods of learning were distributed equally between memorizing facts and learning by hands-on dissection. In addition, the study showed that two thirds of the students felt satisfied with their learning method and believed it was well suited for anatomy. There is no single teaching method which proves beneficial; instructors should be flexible in their teaching in order to optimize students' academic achievements.

  20. Visual paired-associate learning: in search of material-specific effects in adult patients who have undergone temporal lobectomy.

    Science.gov (United States)

    Smith, Mary Lou; Bigel, Marla; Miller, Laurie A

    2011-02-01

    The mesial temporal lobes are important for learning arbitrary associations. It has previously been demonstrated that left mesial temporal structures are involved in learning word pairs, but it is not yet known whether comparable lesions in the right temporal lobe impair visually mediated associative learning. Patients who had undergone left (n=16) or right (n=18) temporal lobectomy for relief of intractable epilepsy and healthy controls (n=13) were administered two paired-associate learning tasks assessing their learning and memory of pairs of abstract designs or pairs of symbols in unique locations. Both patient groups had deficits in learning the designs, but only the right temporal group was impaired in recognition. For the symbol location task, differences were not found in learning, but again a recognition deficit was found for the right temporal group. The findings implicate the mesial temporal structures in relational learning. They support a material-specific effect for recognition but not for learning and recall of arbitrary visual and visual-spatial associative information. Copyright © 2010 Elsevier Inc. All rights reserved.

  1. Comparison of teaching about breast cancer via mobile or traditional learning methods in gynecology residents.

    Science.gov (United States)

    Alipour, Sadaf; Moini, Ashraf; Jafari-Adli, Shahrzad; Gharaie, Nooshin; Mansouri, Khorshid

    2012-01-01

    Mobile learning enables users to interact with educational resources while in variable locations. Medical students in residency positions need to assimilate considerable knowledge besides their practical training and we therefore aimed to evaluate the impact of using short message service via cell phone as a learning tool in residents of Obstetrics and Gynecology in our hospital. We sent short messages including data about breast cancer to the cell phones of 25 residents of gynecology and obstetrics and asked them to study a well-designed booklet containing another set of information about the disease in the same period. The rate of learning derived from the two methods was compared by pre- and post-tests and self-satisfaction assessed by a relevant questionnaire at the end of the program. The mobile learning method had a significantly better effect on learning and created more interest in the subject. Learning via receiving SMS can be an effective and appealing method of knowledge acquisition in higher levels of education.

  2. Learners with learning difficulties in mathematics : attitudes, curriculum and methods of teaching mathematics

    OpenAIRE

    2012-01-01

    D.Ed. The aim of this theses is to find out whether there is any relationship between learners' attitudes and learning difficulties in mathematics: To investigate whether learning difficulties in mathematics are associated with learners' gender. To establish the nature of teachers' perceptions of the learning problem areas in the mathematics curriculum. To find out about the teachers' views on the methods of teaching mathematics, resources, learning of mathematics, extra curricular activit...

  3. Optimisation of technical specifications using probabilistic methods

    International Nuclear Information System (INIS)

    Ericsson, G.; Knochenhauer, M.; Hultqvist, G.

    1986-01-01

    During the last few years the development of methods for modifying and optimising nuclear power plant Technical Specifications (TS) for plant operations has received increased attention. Probalistic methods in general, and the plant and system models of probabilistic safety assessment (PSA) in particular, seem to provide the most forceful tools for optimisation. This paper first gives some general comments on optimisation, identifying important parameters and then gives a description of recent Swedish experiences from the use of nuclear power plant PSA models and results for TS optimisation

  4. Teaching learning methods of an entrepreneurship curriculum.

    Science.gov (United States)

    Esmi, Keramat; Marzoughi, Rahmatallah; Torkzadeh, Jafar

    2015-10-01

    One of the most significant elements of entrepreneurship curriculum design is teaching-learning methods, which plays a key role in studies and researches related to such a curriculum. It is the teaching method, and systematic, organized and logical ways of providing lessons that should be consistent with entrepreneurship goals and contents, and should also be developed according to the learners' needs. Therefore, the current study aimed to introduce appropriate, modern, and effective methods of teaching entrepreneurship and their validation. This is a mixed method research of a sequential exploratory kind conducted through two stages: a) developing teaching methods of entrepreneurship curriculum, and b) validating developed framework. Data were collected through "triangulation" (study of documents, investigating theoretical basics and the literature, and semi-structured interviews with key experts). Since the literature on this topic is very rich, and views of the key experts are vast, directed and summative content analysis was used. In the second stage, qualitative credibility of research findings was obtained using qualitative validation criteria (credibility, confirmability, and transferability), and applying various techniques. Moreover, in order to make sure that the qualitative part is reliable, reliability test was used. Moreover, quantitative validation of the developed framework was conducted utilizing exploratory and confirmatory factor analysis methods and Cronbach's alpha. The data were gathered through distributing a three-aspect questionnaire (direct presentation teaching methods, interactive, and practical-operational aspects) with 29 items among 90 curriculum scholars. Target population was selected by means of purposive sampling and representative sample. Results obtained from exploratory factor analysis showed that a three factor structure is an appropriate method for describing elements of teaching-learning methods of entrepreneurship curriculum

  5. Sparse feature learning for instrument identification: Effects of sampling and pooling methods.

    Science.gov (United States)

    Han, Yoonchang; Lee, Subin; Nam, Juhan; Lee, Kyogu

    2016-05-01

    Feature learning for music applications has recently received considerable attention from many researchers. This paper reports on the sparse feature learning algorithm for musical instrument identification, and in particular, focuses on the effects of the frame sampling techniques for dictionary learning and the pooling methods for feature aggregation. To this end, two frame sampling techniques are examined that are fixed and proportional random sampling. Furthermore, the effect of using onset frame was analyzed for both of proposed sampling methods. Regarding summarization of the feature activation, a standard deviation pooling method is used and compared with the commonly used max- and average-pooling techniques. Using more than 47 000 recordings of 24 instruments from various performers, playing styles, and dynamics, a number of tuning parameters are experimented including the analysis frame size, the dictionary size, and the type of frequency scaling as well as the different sampling and pooling methods. The results show that the combination of proportional sampling and standard deviation pooling achieve the best overall performance of 95.62% while the optimal parameter set varies among the instrument classes.

  6. Comparison effectiveness of cooperative learning type STAD with cooperative learning type TPS in terms of mathematical method of Junior High School students

    Science.gov (United States)

    Wahyuni, A.

    2018-05-01

    This research is aimed to find out whether the model of cooperative learning type Student Team Achievement Division (STAD) is more effective than cooperative learning type Think-Pair-Share in SMP Negeri 7 Yogyakarta. This research was a quasi-experimental research, using two experimental groups. The population of research was all students of 7thclass in SMP Negeri 7 Yogyakarta that consists of 5 Classes. From the population were taken 2 classes randomly which used as sample. The instrument to collect data was a description test. Measurement of instrument validity use content validity and construct validity, while measuring instrument reliability use Cronbach Alpha formula. To investigate the effectiveness of cooperative learning type STAD and cooperative learning type TPS on the aspect of student’s mathematical method, the datas were analyzed by one sample test. Comparing the effectiveness of cooperative learning type STAD and TPS in terms of mathematical communication skills by using t-test. Normality test was not conducted because the sample of research more than 30 students, while homogeneity tested by using Kolmogorov Smirnov test. The analysis was performed at 5% confidence level.The results show as follows : 1) The model of cooperative learning type STAD and TPS are effective in terms of mathematical method of junior high school students. 2). STAD type cooperative learning model is more effective than TPS type cooperative learning model in terms of mathematical methods of junior high school students.

  7. L2 Vocabulary Acquisition in Children: Effects of Learning Method and Cognate Status

    Science.gov (United States)

    Tonzar, Claudio; Lotto, Lorella; Job, Remo

    2009-01-01

    In this study we investigated the effects of two learning methods (picture- or word-mediated learning) and of word status (cognates vs. noncognates) on the vocabulary acquisition of two foreign languages: English and German. We examined children from fourth and eighth grades in a school setting. After a learning phase during which L2 words were…

  8. Cultivating Collaborations: Site Specific Design for Embodied Science Learning.

    Science.gov (United States)

    Gill, Katherine; Glazier, Jocelyn; Towns, Betsy

    2018-05-21

    Immersion in well-designed outdoor environments can foster the habits of mind that enable critical and authentic scientific questions to take root in students' minds. Here we share two design cases in which careful, collaborative, and intentional design of outdoor learning environments for informal inquiry provide people of all ages with embodied opportunities to learn about the natural world, developing the capacity for understanding ecology and the ability to empathize, problem-solve and reflect. Embodied learning, as facilitated by and in well-designed outdoor learning environments, leads students to develop new ways of seeing, new scientific questions, new ways to connect with ideas, with others and new ways of thinking about the natural world. Using examples from our collaborative practices as experiential learning designers, we illustrate how creating the habits of mind critical to creating scientists, science-interested, and science-aware individuals benefits from providing students spaces to engage in embodied learning in nature. We show how public landscapes designed in creative partnerships between educators, scientists, designers and the public have potential to amplify science learning for all.

  9. Ensemble Learning or Deep Learning? Application to Default Risk Analysis

    Directory of Open Access Journals (Sweden)

    Shigeyuki Hamori

    2018-03-01

    Full Text Available Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data and compares the prediction accuracy and classification ability of three ensemble-learning methods—specifically, bagging, random forest, and boosting—with those of various neural-network methods, each of which has a different activation function. The results obtained indicate that the classification ability of boosting is superior to other machine-learning methods including neural networks. It is also found that the performance of neural-network models depends on the choice of activation function, the number of middle layers, and the inclusion of dropout.

  10. The Implementation of Discovery Learning Method to Increase Learning Outcomes and Motivation of Student in Senior High School

    Directory of Open Access Journals (Sweden)

    Nanda Saridewi

    2017-11-01

    Full Text Available Based on data from the observation of high school students grade XI that daily low student test scores due to a lack of role of students in the learning process. This classroom action research aims to improve learning outcomes and student motivation through discovery learning method in colloidal material. This study uses the approach developed by Lewin consisting of planning, action, observation, and reflection. Data collection techniques used the questionnaires and ability tests end. Based on the research that results for students received a positive influence on learning by discovery learning model by increasing the average value of 74 students from the first cycle to 90.3 in the second cycle and increased student motivation in the form of two statements based competence (KD categories (sometimes on the first cycle and the first statement KD category in the second cycle. Thus the results of this study can be used to improve learning outcomes and student motivation

  11. Methods for Optimizing CRISPR-Cas9 Genome Editing Specificity

    Science.gov (United States)

    Tycko, Josh; Myer, Vic E.; Hsu, Patrick D.

    2016-01-01

    Summary Advances in the development of delivery, repair, and specificity strategies for the CRISPR-Cas9 genome engineering toolbox are helping researchers understand gene function with unprecedented precision and sensitivity. CRISPR-Cas9 also holds enormous therapeutic potential for the treatment of genetic disorders by directly correcting disease-causing mutations. Although the Cas9 protein has been shown to bind and cleave DNA at off-target sites, the field of Cas9 specificity is rapidly progressing with marked improvements in guide RNA selection, protein and guide engineering, novel enzymes, and off-target detection methods. We review important challenges and breakthroughs in the field as a comprehensive practical guide to interested users of genome editing technologies, highlighting key tools and strategies for optimizing specificity. The genome editing community should now strive to standardize such methods for measuring and reporting off-target activity, while keeping in mind that the goal for specificity should be continued improvement and vigilance. PMID:27494557

  12. Second-Order Learning Methods for a Multilayer Perceptron

    International Nuclear Information System (INIS)

    Ivanov, V.V.; Purehvdorzh, B.; Puzynin, I.V.

    1994-01-01

    First- and second-order learning methods for feed-forward multilayer neural networks are studied. Newton-type and quasi-Newton algorithms are considered and compared with commonly used back-propagation algorithm. It is shown that, although second-order algorithms require enhanced computer facilities, they provide better convergence and simplicity in usage. 13 refs., 2 figs., 2 tabs

  13. Educational integrating projects as a method of interactive learning

    Directory of Open Access Journals (Sweden)

    Иван Николаевич Куринин

    2013-12-01

    Full Text Available The article describes a method of interactive learning based on educational integrating projects. Some examples of content of such projects for the disciplines related to the study of information and Internet technologies and their application in management are presented.

  14. Implementation of K-Means Clustering Method for Electronic Learning Model

    Science.gov (United States)

    Latipa Sari, Herlina; Suranti Mrs., Dewi; Natalia Zulita, Leni

    2017-12-01

    Teaching and Learning process at SMK Negeri 2 Bengkulu Tengah has applied e-learning system for teachers and students. The e-learning was based on the classification of normative, productive, and adaptive subjects. SMK Negeri 2 Bengkulu Tengah consisted of 394 students and 60 teachers with 16 subjects. The record of e-learning database was used in this research to observe students’ activity pattern in attending class. K-Means algorithm in this research was used to classify students’ learning activities using e-learning, so that it was obtained cluster of students’ activity and improvement of student’s ability. Implementation of K-Means Clustering method for electronic learning model at SMK Negeri 2 Bengkulu Tengah was conducted by observing 10 students’ activities, namely participation of students in the classroom, submit assignment, view assignment, add discussion, view discussion, add comment, download course materials, view article, view test, and submit test. In the e-learning model, the testing was conducted toward 10 students that yielded 2 clusters of membership data (C1 and C2). Cluster 1: with membership percentage of 70% and it consisted of 6 members, namely 1112438 Anggi Julian, 1112439 Anis Maulita, 1112441 Ardi Febriansyah, 1112452 Berlian Sinurat, 1112460 Dewi Anugrah Anwar and 1112467 Eka Tri Oktavia Sari. Cluster 2:with membership percentage of 30% and it consisted of 4 members, namely 1112463 Dosita Afriyani, 1112471 Erda Novita, 1112474 Eskardi and 1112477 Fachrur Rozi.

  15. Machine learning plus optical flow: a simple and sensitive method to detect cardioactive drugs

    Science.gov (United States)

    Lee, Eugene K.; Kurokawa, Yosuke K.; Tu, Robin; George, Steven C.; Khine, Michelle

    2015-07-01

    Current preclinical screening methods do not adequately detect cardiotoxicity. Using human induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs), more physiologically relevant preclinical or patient-specific screening to detect potential cardiotoxic effects of drug candidates may be possible. However, one of the persistent challenges for developing a high-throughput drug screening platform using iPS-CMs is the need to develop a simple and reliable method to measure key electrophysiological and contractile parameters. To address this need, we have developed a platform that combines machine learning paired with brightfield optical flow as a simple and robust tool that can automate the detection of cardiomyocyte drug effects. Using three cardioactive drugs of different mechanisms, including those with primarily electrophysiological effects, we demonstrate the general applicability of this screening method to detect subtle changes in cardiomyocyte contraction. Requiring only brightfield images of cardiomyocyte contractions, we detect changes in cardiomyocyte contraction comparable to - and even superior to - fluorescence readouts. This automated method serves as a widely applicable screening tool to characterize the effects of drugs on cardiomyocyte function.

  16. Region and task-specific activation of Arc in primary motor cortex of rats following motor skill learning.

    Science.gov (United States)

    Hosp, J A; Mann, S; Wegenast-Braun, B M; Calhoun, M E; Luft, A R

    2013-10-10

    Motor learning requires protein synthesis within the primary motor cortex (M1). Here, we show that the immediate early gene Arc/Arg3.1 is specifically induced in M1 by learning a motor skill. Arc mRNA was quantified using a fluorescent in situ hybridization assay in adult Long-Evans rats learning a skilled reaching task (SRT), in rats performing reaching-like forelimb movement without learning (ACT) and in rats that were trained in the operant but not the motor elements of the task (controls). Apart from M1, Arc expression was assessed within the rostral motor area (RMA), primary somatosensory cortex (S1), striatum (ST) and cerebellum. In SRT animals, Arc mRNA levels in M1 contralateral to the trained limb were 31% higher than ipsilateral (pmotor skill learning in rats. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  17. Comparison of the effect of lecture and blended teaching methods on students’ learning and satisfaction

    Science.gov (United States)

    SADEGHI, ROYA; SEDAGHAT, MOHAMMAD MEHDI; SHA AHMADI, FARAMARZ

    2014-01-01

    Introduction: Blended learning, a new approach in educational planning, is defined as an applying more than one method, strategy, technique or media in education. Todays, due to the development of infrastructure of Internet networks and the access of most of the students, the Internet can be utilized along with traditional and conventional methods of training. The aim of this study was to compare the students’ learning and satisfaction in combination of lecture and e-learning with conventional lecture methods. Methods: This quasi-experimental study is conducted among the sophomore students of Public Health School, Tehran University of Medical Science in 2012-2013. Four classes of the school are randomly selected and are divided into two groups. Education in two classes (45 students) was in the form of lecture method and in the other two classes (48 students) was blended method with e-Learning and lecture methods. The students’ knowledge about tuberculosis in two groups was collected and measured by using pre and post-test. This step has been done by sending self-reported electronic questionnaires to the students' email addresses through Google Document software. At the end of educational programs, students' satisfaction and comments about two methods were also collected by questionnaires. Statistical tests such as descriptive methods, paired t-test, independent t-test and ANOVA were done through the SPSS 14 software, and p≤0.05 was considered as significant difference. Results: The mean scores of the lecture and blended groups were 13.18±1.37 and 13.35±1.36, respectively; the difference between the pre-test scores of the two groups was not statistically significant (p=0.535). Knowledge scores increased in both groups after training, and the mean and standard deviation of knowledge scores of the lectures and combined groups were 16.51±0.69 and 16.18±1.06, respectively. The difference between the post-test scores of the two groups was not statistically

  18. Black Ink and Red Ink (BIRI) Testing: A Testing Method to Evaluate Both Recall and Recognition Learning in Accelerated Adult-Learning Courses

    Science.gov (United States)

    Rodgers, Joseph Lee; Rodgers, Jacci L.

    2011-01-01

    We propose, develop, and evaluate the black ink-red ink (BIRI) method of testing. This approach uses two different methods within the same test administration setting, one that matches recognition learning and the other that matches recall learning. Students purposively define their own tradeoff between the two approaches. Evaluation of the method…

  19. The role of context in preschool learning: a multilevel examination of the contribution of context-specific problem behaviors and classroom process quality to low-income children's approaches to learning.

    Science.gov (United States)

    Domínguez, Ximena; Vitiello, Virginia E; Fuccillo, Janna M; Greenfield, Daryl B; Bulotsky-Shearer, Rebecca J

    2011-04-01

    Research suggests that promoting adaptive approaches to learning early in childhood may help close the gap between advantaged and disadvantaged children. Recent research has identified specific child-level and classroom-level variables that are significantly associated with preschoolers' approaches to learning. However, further research is needed to understand the interactive effects of these variables and determine whether classroom-level variables buffer the detrimental effects of child-level risk variables. Using a largely urban and minority sample (N=275) of preschool children, the present study examined the additive and interactive effects of children's context-specific problem behaviors and classroom process quality dimensions on children's approaches to learning. Teachers rated children's problem behavior and approaches to learning and independent assessors conducted classroom observations to assess process quality. Problem behaviors in structured learning situations and in peer and teacher interactions were found to negatively predict variance in approaches to learning. Classroom process quality domains did not independently predict variance in approaches to learning. Nonetheless, classroom process quality played an important role in these associations; high emotional support buffered the detrimental effects of problem behavior, whereas high instructional support exacerbated them. The findings of this study have important implications for classroom practices aimed at helping children who exhibit problem behaviors. Copyright © 2010 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

  20. System and method for deriving a process-based specification

    Science.gov (United States)

    Hinchey, Michael Gerard (Inventor); Rash, James Larry (Inventor); Rouff, Christopher A. (Inventor)

    2009-01-01

    A system and method for deriving a process-based specification for a system is disclosed. The process-based specification is mathematically inferred from a trace-based specification. The trace-based specification is derived from a non-empty set of traces or natural language scenarios. The process-based specification is mathematically equivalent to the trace-based specification. Code is generated, if applicable, from the process-based specification. A process, or phases of a process, using the features disclosed can be reversed and repeated to allow for an interactive development and modification of legacy systems. The process is applicable to any class of system, including, but not limited to, biological and physical systems, electrical and electro-mechanical systems in addition to software, hardware and hybrid hardware-software systems.

  1. A ranking method for the concurrent learning of compounds with various activity profiles.

    Science.gov (United States)

    Dörr, Alexander; Rosenbaum, Lars; Zell, Andreas

    2015-01-01

    In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each. The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods. SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.

  2. Nonadjacent Dependency Learning in Cantonese-Speaking Children With and Without a History of Specific Language Impairment.

    Science.gov (United States)

    Iao, Lai-Sang; Ng, Lai Yan; Wong, Anita Mei Yin; Lee, Oi Ting

    2017-03-01

    This study investigated nonadjacent dependency learning in Cantonese-speaking children with and without a history of specific language impairment (SLI) in an artificial linguistic context. Sixteen Cantonese-speaking children with a history of SLI and 16 Cantonese-speaking children with typical language development (TLD) were tested with a nonadjacent dependency learning task using artificial languages that mimic Cantonese. Children with TLD performed above chance and were able to discriminate between trained and untrained nonadjacent dependencies. However, children with a history of SLI performed at chance and were not able to differentiate trained versus untrained nonadjacent dependencies. These findings, together with previous findings from English-speaking adults and adolescents with language impairments, suggest that individuals with atypical language development, regardless of age, diagnostic status, language, and culture, show difficulties in learning nonadjacent dependencies. This study provides evidence for early impairments to statistical learning in individuals with atypical language development.

  3. Procedural learning in Parkinson's disease, specific language impairment, dyslexia, schizophrenia, developmental coordination disorder, and autism spectrum disorders: A second-order meta-analysis.

    Science.gov (United States)

    Clark, Gillian M; Lum, Jarrad A G

    2017-10-01

    The serial reaction time task (SRTT) has been used to study procedural learning in clinical populations. In this report, second-order meta-analysis was used to investigate whether disorder type moderates performance on the SRTT. Using this approach to quantitatively summarise past research, it was tested whether autism spectrum disorder, developmental coordination disorder, dyslexia, Parkinson's disease, schizophrenia, and specific language impairment differentially affect procedural learning on the SRTT. The main analysis revealed disorder type moderated SRTT performance (p=0.010). This report demonstrates comparable levels of procedural learning impairment in developmental coordination disorder, dyslexia, Parkinson's disease, schizophrenia, and specific language impairment. However, in autism, procedural learning is spared. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. Facilitation of receptive and productive foreign vocabulary learning using the keyword method: the role of image quality.

    Science.gov (United States)

    Beaton, Alan A; Gruneberg, Michael M; Hyde, Christopher; Shufflebottom, Alex; Sykes, Robert N

    2005-07-01

    Ellis and Beaton (1993a) reported that the keyword method of learning enhanced memory of foreign vocabulary items when receptive learning was measured. However, for productive learning, rote repetition was superior to the keyword method. The first two experiments reported here show that, in comparison with rote repetition, both receptive and productive learning can be enhanced by the keyword method, provided that the quality of the keyword images is adequate. In a third experiment using a subset of words from Ellis and Beaton (1993a), the finding they reported, that for productive learning rote repetition was superior to the keyword method, was reversed. The quality of keyword images will vary from study to study and any generalisation regarding the efficacy of the keyword method must take this into account.

  5. Methods and Case Studies for Teaching and Learning about Failure and Safety.

    Science.gov (United States)

    Bignell, Victor

    1999-01-01

    Discusses methods for analyzing case studies of failures of technological systems. Describes two distance learning courses that compare standard models of failure and success with the actuality of given scenarios. Provides teaching and learning materials and information sources for application to aspects of design, manufacture, inspection, use,…

  6. A Plant Control Technology Using Reinforcement Learning Method with Automatic Reward Adjustment

    Science.gov (United States)

    Eguchi, Toru; Sekiai, Takaaki; Yamada, Akihiro; Shimizu, Satoru; Fukai, Masayuki

    A control technology using Reinforcement Learning (RL) and Radial Basis Function (RBF) Network has been developed to reduce environmental load substances exhausted from power and industrial plants. This technology consists of the statistic model using RBF Network, which estimates characteristics of plants with respect to environmental load substances, and RL agent, which learns the control logic for the plants using the statistic model. In this technology, it is necessary to design an appropriate reward function given to the agent immediately according to operation conditions and control goals to control plants flexibly. Therefore, we propose an automatic reward adjusting method of RL for plant control. This method adjusts the reward function automatically using information of the statistic model obtained in its learning process. In the simulations, it is confirmed that the proposed method can adjust the reward function adaptively for several test functions, and executes robust control toward the thermal power plant considering the change of operation conditions and control goals.

  7. e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods.

    Science.gov (United States)

    Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

    2018-01-01

    In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program "e-Bitter" is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

  8. e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-learning Methods

    Science.gov (United States)

    Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

    2018-03-01

    In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

  9. An Improved Ensemble Learning Method for Classifying High-Dimensional and Imbalanced Biomedicine Data.

    Science.gov (United States)

    Yu, Hualong; Ni, Jun

    2014-01-01

    Training classifiers on skewed data can be technically challenging tasks, especially if the data is high-dimensional simultaneously, the tasks can become more difficult. In biomedicine field, skewed data type often appears. In this study, we try to deal with this problem by combining asymmetric bagging ensemble classifier (asBagging) that has been presented in previous work and an improved random subspace (RS) generation strategy that is called feature subspace (FSS). Specifically, FSS is a novel method to promote the balance level between accuracy and diversity of base classifiers in asBagging. In view of the strong generalization capability of support vector machine (SVM), we adopt it to be base classifier. Extensive experiments on four benchmark biomedicine data sets indicate that the proposed ensemble learning method outperforms many baseline approaches in terms of Accuracy, F-measure, G-mean and AUC evaluation criterions, thus it can be regarded as an effective and efficient tool to deal with high-dimensional and imbalanced biomedical data.

  10. A comparison of the cooperative learning and traditional learning methods in theory classes on nursing students' communication skill with patients at clinical settings.

    Science.gov (United States)

    Baghcheghi, Nayereh; Koohestani, Hamid Reza; Rezaei, Koresh

    2011-11-01

    The purpose of this study was to compare the effect of traditional learning and cooperative learning methods on nursing students' communication skill with patients. This was an experimental study in which 34 nursing students in their 2nd semester of program participated. They were divided randomly into two groups, a control group who were taught their medical/surgical nursing course by traditional learning method and an experimental group, who were taught the same material using cooperative learning method. Before and after the teaching intervention, the students' communication skills with patients at clinical settings were examined. The results showed that no significant difference between the two groups in students' communication skills scores before the teaching intervention, but did show a significant difference between the two groups in the interaction skills and problem follow up sub-scales scores after the teaching intervention. This study provides evidence that cooperative learning is an effective method for improving and increasing communication skills of nursing students especially in interactive skills and follow up the problems sub-scale, thereby it is recommended to increase nursing students' participation in arguments by applying active teaching methods which can provide the opportunity for increased communication skills. Copyright © 2011 Elsevier Ltd. All rights reserved.

  11. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models

    NARCIS (Netherlands)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.

    2012-01-01

    PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator

  12. A study on the engineering education methods with a learning management system

    OpenAIRE

    海老澤, 賢史; Ebisawa, Satoshi

    2017-01-01

    The educational methods with Learning Management System (LMS) are described, which are applied to two specialized courses for engineering education and a research guidance for graduation work at Niigata Institute of Technology.According to the analysis of LMS usage situation for graduation work, the LMS has provided an effect that learning time outside class hour is held and the convenience of students in learning is enhanced.In the specializedcourses, the rate of utilization of LMS has depen...

  13. Comparison of the effect of lecture and blended teaching methods on students' learning and satisfaction.

    Science.gov (United States)

    Sadeghi, Roya; Sedaghat, Mohammad Mehdi; Sha Ahmadi, Faramarz

    2014-10-01

    Blended learning, a new approach in educational planning, is defined as an applying more than one method, strategy, technique or media in education. Todays, due to the development of infrastructure of Internet networks and the access of most of the students, the Internet can be utilized along with traditional and conventional methods of training. The aim of this study was to compare the students' learning and satisfaction in combination of lecture and e-learning with conventional lecture methods. This quasi-experimental study is conducted among the sophomore students of Public Health School, Tehran University of Medical Science in 2012-2013. Four classes of the school are randomly selected and are divided into two groups. Education in two classes (45 students) was in the form of lecture method and in the other two classes (48 students) was blended method with e-Learning and lecture methods. The students' knowledge about tuberculosis in two groups was collected and measured by using pre and post-test. This step has been done by sending self-reported electronic questionnaires to the students' email addresses through Google Document software. At the end of educational programs, students' satisfaction and comments about two methods were also collected by questionnaires. Statistical tests such as descriptive methods, paired t-test, independent t-test and ANOVA were done through the SPSS 14 software, and p≤0.05 was considered as significant difference. The mean scores of the lecture and blended groups were 13.18±1.37 and 13.35±1.36, respectively; the difference between the pre-test scores of the two groups was not statistically significant (p=0.535). Knowledge scores increased in both groups after training, and the mean and standard deviation of knowledge scores of the lectures and combined groups were 16.51±0.69 and 16.18±1.06, respectively. The difference between the post-test scores of the two groups was not statistically significant (p=0.112). Students

  14. Predicting protein complexes using a supervised learning method combined with local structural information.

    Science.gov (United States)

    Dong, Yadong; Sun, Yongqi; Qin, Chao

    2018-01-01

    The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

  15. The Keyword Method of Foreign Vocabulary Learning: An Investigation of Its Generalizability. Working Paper No. 270.

    Science.gov (United States)

    Pressley, Michael; And Others

    In five experiments, college-age students of differing foreign language-learning abilities were asked to learn Latin word translations to determine the effectiveness of the keyword method of foreign language vocabulary learning. The Latin words were the types for which it has been argued that the keyword method effects would be maximized (the…

  16. Teachers' opinion about learning continuum based on student's level of competence and specific pedagogical material in classification topics

    Science.gov (United States)

    Andriani, Aldina Eka; Subali, Bambang

    2017-08-01

    This research discusses learning continuum development for designing a curriculum. The objective of this study is to gather the opinion of public junior and senior high school teachers about learning continuum based on student's level of competence and specific pedagogical material in classification topics. This research was conducted in Yogyakarta province from October 2016 to January 2017. This research utilizes a descriptive survey method. Respondents in this study consist of 281 science teachers at junior and senior high school in Yogyakarta city and 4 regencies namely Sleman, Bantul, Kulonprogo, and Gunung Kidul. The sample were taken using a census. The collection of data used questionnaire that had been validated from the aspects of construct validity and experts judgements. Data were analyzed using a descriptive analysis technique. The results of the analysis show that the opinions of teachers regarding specific pedagogical material in classification topics of living things at the junior high school taught in grade VII to the ability level of C2 (Understanding). At senior high school level, it is taught in grade X with the ability level C2 (Understanding). Based on these results, it can be concluded that the opinions of teachers still refer to the current syllabus and curriculum so that the teachers do not have pure opinions about the student's competence level in classification topics that should be taught at the level of the grade in accordance with the level of corresponding competency.

  17. Sequence-specific bias correction for RNA-seq data using recurrent neural networks.

    Science.gov (United States)

    Zhang, Yao-Zhong; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru

    2017-01-25

    The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance. We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.

  18. Kernel Methods for Machine Learning with Life Science Applications

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie

    Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear a...

  19. Learning Analytics Architecture to Scaffold Learning Experience through Technology-based Methods

    Directory of Open Access Journals (Sweden)

    Jannicke Madeleine Baalsrud Hauge

    2015-02-01

    Full Text Available The challenge of delivering personalized learning experiences is often increased by the size of classrooms and online learning communities. Serious Games (SGs are increasingly recognized for their potential to improve education. However, the issues related to their development and their level of effectiveness can be seriously affected when brought too rapidly into growing online learning communities. Deeper insights into how the students are playing is needed to deliver a comprehensive and intelligent learning framework that facilitates better understanding of learners' knowledge, effective assessment of their progress and continuous evaluation and optimization of the environments in which they learn. This paper discusses current SOTA and aims to explore the potential in the use of games and learning analytics towards scaffolding and supporting teaching and learning experience. The conceptual model (ecosystem and architecture discussed in this paper aims to highlight the key considerations that may advance the current state of learning analytics, adaptive learning and SGs, by leveraging SGs as an suitable medium for gathering data and performing adaptations.

  20. An Efficient Ensemble Learning Method for Gene Microarray Classification

    Directory of Open Access Journals (Sweden)

    Alireza Osareh

    2013-01-01

    Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

  1. Prevalence of specific learning disabilities among primary school children in a South Indian city.

    Science.gov (United States)

    Mogasale, Vijayalaxmi V; Patil, Vishwanath D; Patil, Nanasaheb M; Mogasale, Vittal

    2012-03-01

    To measure the prevalence of specific learning disabilities (SpLDs) such as dyslexia, dysgraphia and dyscalculia among primary school children in a South Indian city. A cross-sectional multi-staged stratified randomized cluster sampling study was conducted among children aged 8-11 years from third and fourth standard. A six level screening approach that commenced with identification of scholastic backwardness followed by stepwise exclusion of impaired vision and hearing, chronic medical conditions and subnormal intelligence was carried out among these children. In the final step, the remaining children were subjected to specific tests for reading, comprehension, writing and mathematical calculation. The prevalence of specific learning disabilities was 15.17% in sampled children, whereas 12.5%, 11.2% and 10.5% had dysgraphia, dyslexia and dyscalculia respectively. This study suggests that the prevalence of SpLDs is at the higher side of previous estimations in India. The study is unique due to its large geographically representative design and identification of the problem using simplified screening approach and tools, which minimizes the number and time of specialist requirement and spares the expensive investigation. This approach and tools are suitable for field situations and resource scarce settings. Based on the authors' experience, they express the need for more prevalence studies, remedial education and policy interventions to manage SpLDs at main stream educational system to improve the school performance in Indian children.

  2. Status of knowledge on student-learning environments in nursing homes: A mixed-method systematic review.

    Science.gov (United States)

    Husebø, Anne Marie Lunde; Storm, Marianne; Våga, Bodil Bø; Rosenberg, Adriana; Akerjordet, Kristin

    2018-04-01

    To give an overview of empirical studies investigating nursing homes as a learning environment during nursing students' clinical practice. A supportive clinical learning environment is crucial to students' learning and for their development into reflective and capable practitioners. Nursing students' experience with clinical practice can be decisive in future workplace choices. A competent workforce is needed for the future care of older people. Opportunities for maximum learning among nursing students during clinical practice studies in nursing homes should therefore be explored. Mixed-method systematic review using PRISMA guidelines, on learning environments in nursing homes, published in English between 2005-2015. Search of CINAHL with Full Text, Academic Search Premier, MEDLINE and SocINDEX with Full Text, in combination with journal hand searches. Three hundred and thirty-six titles were identified. Twenty studies met the review inclusion criteria. Assessment of methodological quality was based on the Mixed Methods Appraisal Tool. Data were extracted and synthesised using a data analysis method for integrative reviews. Twenty articles were included. The majority of the studies showed moderately high methodological quality. Four main themes emerged from data synthesis: "Student characteristic and earlier experience"; "Nursing home ward environment"; "Quality of mentoring relationship and learning methods"; and "Students' achieved nursing competencies." Nursing home learning environments may be optimised by a well-prepared academic-clinical partnership, supervision by encouraging mentors and high-quality nursing care of older people. Positive learning experiences may increase students' professional development through achievement of basic nursing skills and competencies and motivate them to choose the nursing home as their future workplace. An optimal learning environment can be ensured by thorough preplacement preparations in academia and in nursing home wards

  3. Learning to see the difference specifically alters the most informative V4 neurons.

    Science.gov (United States)

    Raiguel, Steven; Vogels, Rufin; Mysore, Santosh G; Orban, Guy A

    2006-06-14

    Perceptual learning is an instance of adult plasticity whereby training in a sensory (e.g., a visual task) results in neuronal changes leading to an improved ability to perform the task. Yet studies in primary visual cortex have found that changes in neuronal response properties were relatively modest. The present study examines the effects of training in an orientation discrimination task on the response properties of V4 neurons in awake rhesus monkeys. Results indicate that the changes induced in V4 are indeed larger than those in V1. Nonspecific effects of training included a decrease in response variance, and an increase in overall orientation selectivity in V4. The orientation-specific changes involved a local steepening in the orientation tuning curve around the trained orientation that selectively improved orientation discriminability at the trained orientation. Moreover, these changes were largely confined to the population of neurons whose orientation tuning was optimal for signaling small differences in orientation at the trained orientation. Finally, the modifications were restricted to the part of the tuning curve close to the trained orientation. Thus, we conclude that it is the most informative V4 neurons, those most directly involved in the discrimination, that are specifically modified by perceptual learning.

  4. Women with learning disabilities and access to cervical screening: retrospective cohort study using case control methods

    Science.gov (United States)

    Reynolds, Fiona; Stanistreet, Debbi; Elton, Peter

    2008-01-01

    Background Several studies in the UK have suggested that women with learning disabilities may be less likely to receive cervical screening tests and a previous local study in had found that GPs considered screening unnecessary for women with learning disabilities. This study set out to ascertain whether women with learning disabilities are more likely to be ceased from a cervical screening programme than women without; and to examine the reasons given for ceasing women with learning disabilities. It was carried out in Bury, Heywood-and-Middleton and Rochdale. Methods Carried out using retrospective cohort study methods, women with learning disabilities were identified by Read code; and their cervical screening records were compared with the Call-and-Recall records of women without learning disabilities in order to examine their screening histories. Analysis was carried out using case-control methods – 1:2 (women with learning disabilities: women without learning disabilities), calculating odds ratios. Results 267 women's records were compared with the records of 534 women without learning disabilities. Women with learning disabilities had an odds ratio (OR) of 0.48 (Confidence Interval (CI) 0.38 – 0.58; X2: 72.227; p.value learning disabilities. Conclusion The reasons given for ceasing and/or not screening suggest that merely being coded as having a learning disability is not the sole reason for these actions. There are training needs among smear takers regarding appropriate reasons not to screen and providing screening for women with learning disabilities. PMID:18218106

  5. Blended learning – integrating E-learning with traditional learning methods in teaching basic medical science

    OpenAIRE

    J.G. Bagi; N.K. Hashilkar

    2014-01-01

    Background: Blended learning includes an integration of face to face classroom learning with technology enhanced online material. It provides the convenience, speed and cost effectiveness of e-learning with the personal touch of traditional learning. Objective: The objective of the present study was to assess the effectiveness of a combination of e-learning module and traditional teaching (Blended learning) as compared to traditional teaching alone to teach acid base homeostasis to Phase I MB...

  6. E-learning support for Economic-mathematical methods

    Directory of Open Access Journals (Sweden)

    Pavel Kolman

    2009-01-01

    Full Text Available Article is describing process of creating and using of e-learning program for graphical solution of li­near programming problems that is used in the Economic mathematical methods course on Faculty of Business and Economics, MZLU. The program was created within FRVŠ 788/2008 grant and is intended for practicing of graphical solution of LP problems and allows better understanding of the li­near programming problems. In the article is on one hand described the way, how does the program work, it means how were the algorithms implemented, and on the other hand there is described way of use of that program. The program is constructed for working with integer and rational numbers. At the end of the article are shown basic statistics of programs use of students in the present form and the part-time form of study. It is mainly the number of programs downloads and comparison to another programs and students opinion on the e-learning support.

  7. Contingency learning is not affected by conflict experience: Evidence from a task conflict-free, item-specific Stroop paradigm.

    Science.gov (United States)

    Levin, Yulia; Tzelgov, Joseph

    2016-02-01

    A contingency learning account of the item-specific proportion congruent effect has been described as an associative stimulus-response learning process that has nothing to do with controlling the Stroop conflict. As supportive evidence, contingency learning has been demonstrated with response conflict-free stimuli, such as neutral words. However, what gives rise to response conflict and to Stroop interference in general is task conflict. The present study investigated whether task conflict can constitute a trigger or, alternatively, a booster to the contingency learning process. This was done by employing a "task conflict-free" condition (i.e., geometric shapes) and comparing it with a "task conflict" condition (i.e., neutral words). The results showed a significant contingency learning effect in both conditions, refuting the possibility that contingency learning is triggered by the presence of a task conflict. Contingency learning was also not enhanced by the task conflict experience, indicating its complete insensitivity to Stroop conflict(s). Thus, the results showed no evidence that performance optimization as a result of contingency learning is greater under conflict, implying that contingency learning is not recruited to assist the control system to overcome conflict. Copyright © 2015 Elsevier B.V. All rights reserved.

  8. Time-trends in method-specific suicide rates compared with the availability of specific compounds

    DEFF Research Database (Denmark)

    Nordentoft, Merete; Qin, Ping; Helweg-Larsen, Karin

    2006-01-01

    Restriction of means for suicide is an important part of suicide preventive strategies in different countries. All suicides in Denmark between 1970 and 2000 were examined with regard to method used for suicide. Overall suicide mortality and method-specific suicide mortality was compared...... in the number of suicides by self-poisoning with these compounds. Restricted access occurred concomittantly with a 55% decrease in suicide rate....

  9. A new supervised learning algorithm for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

  10. Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

    Science.gov (United States)

    Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

    2016-06-14

    In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

  11. Motivation in service-learning: an improvement over traditional instructional methods

    Directory of Open Access Journals (Sweden)

    Monika Ciesielkiewicz

    2018-05-01

    Full Text Available This paper aims at exploring the motivation of university students who participated in service-learning projects as part of their coursework, and to determine whether their level of motivation is higher for the service-learning project as compared to performing more traditional academic tasks and assignments. The Service-Learning project carried out during the ICT in Education course intended to support the development of digital literacy in a Maasai school in Kenya. The instrument used to evaluate motivation of the university students is the motivation scale called Motivated Strategies for Learning Questionnaire (MSLQ proposed by Pintrich and his collaborators (1991 adapted to the Spanish population by Roces Montero (1996. The results of the research indicate that there are significant differences in favor of service-learning in relation to motivation in general for the completion of the activities and specifically in relation to the utility of the activity as seen at the present moment and in the future, as well as promoting creativity, the interest in the task which includes the perception of the importance of the project, the need to work hard and thoroughly and willingness to face challenges and difficulties in order to achieve the set objective. No significant differences have been observed in relation to the desire to obtain a better grade for completing the activity or need to prove personal value to others, as well as to broaden the information to complete the activity.  

  12. Different levels of food restriction reveal genotype-specific differences in learning a visual discrimination task.

    Directory of Open Access Journals (Sweden)

    Kalina Makowiecki

    Full Text Available In behavioural experiments, motivation to learn can be achieved using food rewards as positive reinforcement in food-restricted animals. Previous studies reduce animal weights to 80-90% of free-feeding body weight as the criterion for food restriction. However, effects of different degrees of food restriction on task performance have not been assessed. We compared learning task performance in mice food-restricted to 80 or 90% body weight (BW. We used adult wildtype (WT; C57Bl/6j and knockout (ephrin-A2⁻/⁻ mice, previously shown to have a reverse learning deficit. Mice were trained in a two-choice visual discrimination task with food reward as positive reinforcement. When mice reached criterion for one visual stimulus (80% correct in three consecutive 10 trial sets they began the reverse learning phase, where the rewarded stimulus was switched to the previously incorrect stimulus. For the initial learning and reverse phase of the task, mice at 90%BW took almost twice as many trials to reach criterion as mice at 80%BW. Furthermore, WT 80 and 90%BW groups significantly differed in percentage correct responses and learning strategy in the reverse learning phase, whereas no differences between weight restriction groups were observed in ephrin-A2⁻/⁻ mice. Most importantly, genotype-specific differences in reverse learning strategy were only detected in the 80%BW groups. Our results indicate that increased food restriction not only results in better performance and a shorter training period, but may also be necessary for revealing behavioural differences between experimental groups. This has important ethical and animal welfare implications when deciding extent of diet restriction in behavioural studies.

  13. Specific methods on in-pile gammametry

    International Nuclear Information System (INIS)

    Pointud, M.-L.; Michel, Francois.

    1979-01-01

    The gammametry technique in nuclear research reactors has evolved by the adequation of its means and the quality of its results since its beginnings in 1972. We do not propose here to make a detailed presentation, nor describe the kinds of well known results that can henceforth be attained with it in a conventional manner; our intention, on the other hand, is to describe a few specific methods developed for using it in the SILOE reactor of the CEN/G [fr

  14. Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches

    DEFF Research Database (Denmark)

    Hauschild, Anne-Christin; Kopczynski, Dominik; D'Addario, Marianna

    2013-01-01

    machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region......-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods...

  15. Label Information Guided Graph Construction for Semi-Supervised Learning.

    Science.gov (United States)

    Zhuang, Liansheng; Zhou, Zihan; Gao, Shenghua; Yin, Jingwen; Lin, Zhouchen; Ma, Yi

    2017-09-01

    In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.

  16. EFFECTS OF COOPERATIVE LEARNING METHOD ON THE DEVELOPMENT OF LISTENING COMPREHENSION AND LISTENING SKILLS

    Directory of Open Access Journals (Sweden)

    Abdülkadir

    2017-04-01

    Full Text Available In this study, the effect of the learning together technique, which is one of the cooperative learning methods, on the development of the listening comprehension and listening skills of the secondary school eighth grade students was investigated. Regarding the purpose of the research, experimental and control groups consisting of 75 students from, Yakutiye district Şair Nef'i Secondary School and Palandöken District, Alparslan Secondary School of Erzurum province were selected. Socio-economic statuses and success rates were taken into consideration when selecting the experimental and control groups. 'Listening-Comprehension Achievement Test' was applied to measure the listening skills of the experimental and control groups. In terms of pre-test scores, it was determined that the listening skills of the experiment and control group were similar. The selected experimental groups were taught by the learning together technique of cooperative learning method for seven weeks and the control group was taught in the traditional way. As a result of the research, the 'Listening-Comprehension Achievement Test', which was applied as the pre-test to the experimental and control groups, was applied again as the final test. When the findings obtained from the research were examined, it was determined that the students in the experimental group were more successful than the students in the control group in terms of post - test achievement scores. When the results of the study are examined, it can be said that the learning together technique, which is one of the cooperative learning methods, is more effective than the traditional learning method in improving the listening comprehension and the listening skills of the eighth grade students in Turkish class.

  17. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' < or = 18 mag), with 4500 K < or = Teff < or = 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of approx.0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  18. Reframing Photovoice to Boost Its Potential for Learning Research

    Directory of Open Access Journals (Sweden)

    Lucian Ciolan

    2017-04-01

    Full Text Available Visual methods are not new within education research field, but they are certainly an innovative approach, especially in higher education where students’ voice is understood as a central need. In this positional article, the authors intend to accomplish two key objectives. First, the article aims to emphasize that visual method, especially photovoice, can be enriching for studying the ways students engage in learning activities and support authentic conversations about how learning takes place and what students are thinking about this process (metacognition. The second objective is to set theoretical and methodological grounds to apply visually based methods such as photovoice and bubble dialogue in education research, particularly in learning research area. The considerations regarding specific methodological aspects are based on the discussion of a study conducted by using photovoice methodology. The authors suggest that participatory analysis and particularly interpretative phenomenological analysis are appropriate to complete the process of data analysis. The article, therefore, contributes to expanding knowledge about specific visual methods and set the ground for methodological innovation in learning research.

  19. Perspective for applying traditional and inovative teaching and learning methods to nurses continuing education

    OpenAIRE

    Bendinskaitė, Irmina

    2015-01-01

    Bendinskaitė I. Perspective for applying traditional and innovative teaching and learning methods to nurse’s continuing education, magister thesis / supervisor Assoc. Prof. O. Riklikienė; Departament of Nursing and Care, Faculty of Nursing, Lithuanian University of Health Sciences. – Kaunas, 2015, – p. 92 The purpose of this study was to investigate traditional and innovative teaching and learning methods perspective to nurse’s continuing education. Material and methods. In a period fro...

  20. Computer-Assisted Mathematics Instruction for Students with Specific Learning Disability: A Review of the Literature

    Science.gov (United States)

    Stultz, Sherry L.

    2017-01-01

    This review was conducted to evaluate the current body of scholarly research regarding the use of computer-assisted instruction (CAI) to teach mathematics to students with specific learning disability (SLD). For many years, computers are utilized for educational purposes. However, the effectiveness of CAI for teaching mathematics to this specific…