The Adaptation Study of the Questionnaires of the Attitude towards CALL (A-CALL), the Attitude towards CAL (A-CAL), the Attitude towards Foreign Language Learning (A-FLL) to Turkish Language

Science.gov (United States)

Erdem, Cahit; Saykili, Abdullah; Kocyigit, Mehmet

2018-01-01

This study primarily aims to adapt the Foreign Language Learning (FLL), Computer assisted Learning (CAL) and Computer assisted Language Learning (CALL) scales developed by Vandewaetere and Desmet into Turkish context. The instrument consists of three scales which are "the attitude towards CALL questionnaire" ("A-CALL")…

Learning Based Approach for Optimal Clustering of Distributed Program's Call Flow Graph

Science.gov (United States)

Abofathi, Yousef; Zarei, Bager; Parsa, Saeed

Optimal clustering of call flow graph for reaching maximum concurrency in execution of distributable components is one of the NP-Complete problems. Learning automatas (LAs) are search tools which are used for solving many NP-Complete problems. In this paper a learning based algorithm is proposed to optimal clustering of call flow graph and appropriate distributing of programs in network level. The algorithm uses learning feature of LAs to search in state space. It has been shown that the speed of reaching to solution increases remarkably using LA in search process, and it also prevents algorithm from being trapped in local minimums. Experimental results show the superiority of proposed algorithm over others.

CALL AND COOPERATIVE LEARNING: A SOLUTION TO DEVELOP STUDENTS‟ LISTENING ABILITY

OpenAIRE

Delsa Miranty

2017-01-01

This paper aims to provide some ideas both for English teachers and target learners about how to apply CALL and Cooperative Learning as the solution to develop students‘ listening activities in the classroom. Since teachers need to understand about students‘ needs, background, age and expectations when they learn English as the foreign language in the classroom. Therefore, the English teacher should provide environment which facilitates the children to have fun di the teaching learning proces...

Sustainability in CALL Learning Environments: A Systemic Functional Grammar Approach

Directory of Open Access Journals (Sweden)

Peter McDonald

2014-09-01

Full Text Available This research aims to define a sustainable resource in Computer-Assisted Language Learning (CALL. In order for a CALL resource to be sustainable it must work within existing educational curricula. This feature is a necessary prerequisite of sustainability because, despite the potential for educational change that digitalization has offered since the nineteen nineties, curricula in traditional educational institutions have not fundamentally changed, even as we move from a pre-digital society towards a digital society. Curricula have failed to incorporate CALL resources because no agreed-upon pedagogical language enables teachers to discuss CALL classroom practices. Systemic Functional Grammar (SFG can help to provide this language and bridge the gap between the needs of the curriculum and the potentiality of CALL-based resources. This paper will outline how SFG principles can be used to create a pedagogical language for CALL and it will give practical examples of how this language can be used to create sustainable resources in classroom contexts.

Attitudes of Jordanian Undergraduate Students towards Using Computer Assisted Language Learning (CALL)

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Saeed, Farah Jamal Abed Alrazeq; Al-Zayed, Norma Nawaf

2018-01-01

The study aimed at investigating the attitudes of Jordanian undergraduate students towards using computer assisted-language learning (CALL) and its effectiveness in the process of learning the English language. In order to fulfill the study's objective, the researchers used a questionnaire to collect data, followed-up with semi-structured…

Systems configured to distribute a telephone call, communication systems, communication methods and methods of routing a telephone call to a service representative

Science.gov (United States)

Harris, Scott H.; Johnson, Joel A.; Neiswanger, Jeffery R.; Twitchell, Kevin E.

2004-03-09

The present invention includes systems configured to distribute a telephone call, communication systems, communication methods and methods of routing a telephone call to a customer service representative. In one embodiment of the invention, a system configured to distribute a telephone call within a network includes a distributor adapted to connect with a telephone system, the distributor being configured to connect a telephone call using the telephone system and output the telephone call and associated data of the telephone call; and a plurality of customer service representative terminals connected with the distributor and a selected customer service representative terminal being configured to receive the telephone call and the associated data, the distributor and the selected customer service representative terminal being configured to synchronize, application of the telephone call and associated data from the distributor to the selected customer service representative terminal.

CALL AND COOPERATIVE LEARNING: A SOLUTION TO DEVELOP STUDENTS‟ LISTENING ABILITY

Directory of Open Access Journals (Sweden)

Delsa Miranty

2017-04-01

Full Text Available This paper aims to provide some ideas both for English teachers and target learners about how to apply CALL and Cooperative Learning as the solution to develop students‘ listening activities in the classroom. Since teachers need to understand about students‘ needs, background, age and expectations when they learn English as the foreign language in the classroom. Therefore, the English teacher should provide environment which facilitates the children to have fun di the teaching learning process, nice atmosphere, comfort and enjoyable to learn English and practice it both in the classroom and in the laboratory. Furthermore, this paper will provide what the teachers should do related activities such as: listening to the songs, movies, cartoon by applying STAD (Students Teams – Achievement Divisions in the classroom in order to develop students‘ listening ability both in the classroom and laboratory.

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...

A Call to Action for Research in Digital Learning: Learning without Limits of Time, Place, Path, Pace…or Evidence

Science.gov (United States)

Cavanaugh, Cathy; Sessums, Christopher; Drexler, Wendy

2015-01-01

This essay is a call for rethinking our approach to research in digital learning. It plots a path founded in social trends and advances in education. A brief review of these trends and advances is followed by discussion of what flattened research might look like at scale. Scaling research in digital learning is crucial to advancing understanding…

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...

Reinforcement learning for a biped robot based on a CPG-actor-critic method.

Science.gov (United States)

Nakamura, Yutaka; Mori, Takeshi; Sato, Masa-aki; Ishii, Shin

2007-08-01

Animals' rhythmic movements, such as locomotion, are considered to be controlled by neural circuits called central pattern generators (CPGs), which generate oscillatory signals. Motivated by this biological mechanism, studies have been conducted on the rhythmic movements controlled by CPG. As an autonomous learning framework for a CPG controller, we propose in this article a reinforcement learning method we call the "CPG-actor-critic" method. This method introduces a new architecture to the actor, and its training is roughly based on a stochastic policy gradient algorithm presented recently. We apply this method to an automatic acquisition problem of control for a biped robot. Computer simulations show that training of the CPG can be successfully performed by our method, thus allowing the biped robot to not only walk stably but also adapt to environmental changes.

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.

A Reinforcement Learning Approach to Call Admission Control in HAPS Communication System

Directory of Open Access Journals (Sweden)

Ni Shu Yan

2017-01-01

Full Text Available The large changing of link capacity and number of users caused by the movement of both platform and users in communication system based on high altitude platform station (HAPS will resulting in high dropping rate of handover and reduce resource utilization. In order to solve these problems, this paper proposes an adaptive call admission control strategy based on reinforcement learning approach. The goal of this strategy is to maximize long-term gains of system, with the introduction of cross-layer interaction and the service downgraded. In order to access different traffics adaptively, the access utility of handover traffics and new call traffics is designed in different state of communication system. Numerical simulation result shows that the proposed call admission control strategy can enhance bandwidth resource utilization and the performances of handover traffics.

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.

Neural correlates of threat perception: neural equivalence of conspecific and heterospecific mobbing calls is learned.

Directory of Open Access Journals (Sweden)

Marc T Avey

Full Text Available Songbird auditory areas (i.e., CMM and NCM are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators. Mobbing calls produced in response to smaller, higher-threat predators contain more "D" notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned.

Neural Correlates of Threat Perception: Neural Equivalence of Conspecific and Heterospecific Mobbing Calls Is Learned

Science.gov (United States)

Avey, Marc T.; Hoeschele, Marisa; Moscicki, Michele K.; Bloomfield, Laurie L.; Sturdy, Christopher B.

2011-01-01

Songbird auditory areas (i.e., CMM and NCM) are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise [1]–[2]. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators [3]. Mobbing calls produced in response to smaller, higher-threat predators contain more “D” notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators [4]. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG) expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned. PMID:21909363

Neural correlates of threat perception: neural equivalence of conspecific and heterospecific mobbing calls is learned.

Science.gov (United States)

Avey, Marc T; Hoeschele, Marisa; Moscicki, Michele K; Bloomfield, Laurie L; Sturdy, Christopher B

2011-01-01

Songbird auditory areas (i.e., CMM and NCM) are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators. Mobbing calls produced in response to smaller, higher-threat predators contain more "D" notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG) expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned.

Identification of alternative method of teaching and learning the ...

African Journals Online (AJOL)

This study examines alternative method of teaching and learning of the concept of diffusion. An improvised U-shape glass tube called ionic mobility tube was used to observed and measure the rate of movement of divalent metal ions in an aqueous medium in the absence of an electric current. The study revealed that the ...

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.

From Computer Assisted Language Learning (CALL) to Mobile Assisted Language Use (MALU)

Science.gov (United States)

Jarvis, Huw; Achilleos, Marianna

2013-01-01

This article begins by critiquing the long-established acronym CALL (Computer Assisted Language Learning). We then go on to report on a small-scale study which examines how student non-native speakers of English use a range of digital devices beyond the classroom in both their first (L1) and second (L2) languages. We look also at the extent to…

Exercise, Character Strengths, Well-Being and Learning Climate in the Prediction of Performance over a Six-Month Period at a Call Center

Directory of Open Access Journals (Sweden)

Saleh eMoradi

2014-06-01

Full Text Available Background: Performance monitoring might have an adverse influence on call center agents’ well-being. We investigate how performance, over a six-month period, is related to agents’ perceptions of their learning climate, character strengths, well-being (subjective and psychological, and physical activity.Method: Agents (N = 135 self-reported perception of the learning climate (Learning Climate Questionnaire, character strengths (Values In Action Inventory Short Version, well-being (Positive Affect, Negative Affect Schedule, Satisfaction With Life Scale, Psychological Well-Being Scales Short Version, and how often/intensively they engaged in physical activity. Performance, time on the phone, was monitored for six consecutive months by the same system handling the calls. Results: Performance was positively related to having opportunities to develop, the character strengths clusters of Wisdom and Knowledge (e.g., curiosity for learning, perspective and Temperance (e.g., having self-control, being prudent, humble, and modest, and exercise frequency. Performance was negatively related to the sense of autonomy and responsibility, contentedness, the character strengths clusters of Humanity and Love (e.g., helping others, cooperation and Justice (e.g., affiliation, fairness, leadership, positive affect, life satisfaction and exercise Intensity.Conclusion: Call centers may need to create opportunities to develop to increase agents’ performance and focus on individual differences in the recruitment and selection of agents to prevent future shortcomings or worker dissatisfaction. Nevertheless, performance measurement in call centers may need to include other aspects that are more attuned with different character strengths. After all, allowing individuals to put their strengths at work should empower the individual and at the end the organization itself. Finally, physical activity enhancement programs might offer considerable positive work outcomes.

Call cultures in orang-utans?

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Serge A Wich

Full Text Available BACKGROUND: Several studies suggested great ape cultures, arguing that human cumulative culture presumably evolved from such a foundation. These focused on conspicuous behaviours, and showed rich geographic variation, which could not be attributed to known ecological or genetic differences. Although geographic variation within call types (accents has previously been reported for orang-utans and other primate species, we examine geographic variation in the presence/absence of discrete call types (dialects. Because orang-utans have been shown to have geographic variation that is not completely explicable by genetic or ecological factors we hypothesized that this will be similar in the call domain and predict that discrete call type variation between populations will be found. METHODOLOGY/PRINCIPAL FINDINGS: We examined long-term behavioural data from five orang-utan populations and collected fecal samples for genetic analyses. We show that there is geographic variation in the presence of discrete types of calls. In exactly the same behavioural context (nest building and infant retrieval, individuals in different wild populations customarily emit either qualitatively different calls or calls in some but not in others. By comparing patterns in call-type and genetic similarity, we suggest that the observed variation is not likely to be explained by genetic or ecological differences. CONCLUSION/SIGNIFICANCE: These results are consistent with the potential presence of 'call cultures' and suggest that wild orang-utans possess the ability to invent arbitrary calls, which spread through social learning. These findings differ substantially from those that have been reported for primates before. First, the results reported here are on dialect and not on accent. Second, this study presents cases of production learning whereas most primate studies on vocal learning were cases of contextual learning. We conclude with speculating on how these findings might

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.

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...

LEARNING TO READ SCIENTIFIC RUSSIAN BY THE THREE QUESTION EXPERIMENTAL (3QX) METHOD.

Science.gov (United States)

ALFORD, M.H.T.

A NEW METHOD FOR LEARNING TO READ TECHNICAL LITERATURE IN A FOREIGN LANGUAGE IS BEING DEVELOPED AND TESTED AT THE LANGUAGE CENTRE OF THE UNIVERSITY OF ESSEX, COLCHESTER, ENGLAND. THE METHOD IS CALLED "THREE QUESTION EXPERIMENTAL METHOD (3QX)," AND IT HAS BEEN USED IN THREE COURSES FOR TEACHING SCIENTIFIC RUSSIAN TO PHYSICISTS. THE THREE…

PENGARUH PERMAINAN CALL CARDS TERHADAP HASIL BELAJAR DAN AKTIVITAS PEMBELAJARAN BIOLOGI

Directory of Open Access Journals (Sweden)

A. Machin

2012-10-01

Full Text Available Tujuan penelitian untuk mengetahui pengaruh permainan call cards terhadap hasil belajar dan aktivitas pembelajaran. Aktivitas pembelajaran yang diukur meliputi aktivitas individual siswa dan kinerja guru. Penelitian ini merupakan penelitian eksperimental. Hasil penelitian menunjukkan bahwa media permainan call cards berkontribusi sebesar 46% terhadap hasil belajar siswa. Hasil belajar siswa yang diberi media permainan call cards lebih baik daripada hasil belajar siswa yang tidak diberi mediapermainan call cards. Dengan demikian, media permainan call cards dapat menjadi alternatif dalam pencapaian hasil belajar biologi yang lebih baik.   Research purposes to determine the effect of call cards game against learning outcomes and learning activities. Learning activities that were measured included the activity of individual student and teacher performance. This research was experimental. The results showed that the media play call cards account for 46% of the student learning outcomes. Learning outcomes of students who were given media cards call the game better than the learning outcomes of students who were not given mediapermainan call cards. Thus, the media play call cards can be an alternative in achieving the learning outcomes of biology better.

svclassify: a method to establish benchmark structural variant calls.

Science.gov (United States)

Parikh, Hemang; Mohiyuddin, Marghoob; Lam, Hugo Y K; Iyer, Hariharan; Chen, Desu; Pratt, Mark; Bartha, Gabor; Spies, Noah; Losert, Wolfgang; Zook, Justin M; Salit, Marc

2016-01-16

The human genome contains variants ranging in size from small single nucleotide polymorphisms (SNPs) to large structural variants (SVs). High-quality benchmark small variant calls for the pilot National Institute of Standards and Technology (NIST) Reference Material (NA12878) have been developed by the Genome in a Bottle Consortium, but no similar high-quality benchmark SV calls exist for this genome. Since SV callers output highly discordant results, we developed methods to combine multiple forms of evidence from multiple sequencing technologies to classify candidate SVs into likely true or false positives. Our method (svclassify) calculates annotations from one or more aligned bam files from many high-throughput sequencing technologies, and then builds a one-class model using these annotations to classify candidate SVs as likely true or false positives. We first used pedigree analysis to develop a set of high-confidence breakpoint-resolved large deletions. We then used svclassify to cluster and classify these deletions as well as a set of high-confidence deletions from the 1000 Genomes Project and a set of breakpoint-resolved complex insertions from Spiral Genetics. We find that likely SVs cluster separately from likely non-SVs based on our annotations, and that the SVs cluster into different types of deletions. We then developed a supervised one-class classification method that uses a training set of random non-SV regions to determine whether candidate SVs have abnormal annotations different from most of the genome. To test this classification method, we use our pedigree-based breakpoint-resolved SVs, SVs validated by the 1000 Genomes Project, and assembly-based breakpoint-resolved insertions, along with semi-automated visualization using svviz. We find that candidate SVs with high scores from multiple technologies have high concordance with PCR validation and an orthogonal consensus method MetaSV (99.7 % concordant), and candidate SVs with low scores are

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.

Developing an effective corrective action process : lessons learned from operating a confidential close call reporting system

Science.gov (United States)

2013-03-05

In 2007, the Federal Railroad Administration (FRA) launched : C3RS, the Confidential Close Call Reporting System, as a : demonstration project to learn how to facilitate the effective : reporting and implementation of corrective actions, and assess t...

A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing.

Science.gov (United States)

van den Akker, Jeroen; Mishne, Gilad; Zimmer, Anjali D; Zhou, Alicia Y

2018-04-17

Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. With recent advances in NGS technology and software tools, the majority of variants called using NGS alone are in fact accurate and reliable. However, a small subset of difficult-to-call variants that still do require orthogonal confirmation exist. For this reason, many clinical laboratories confirm NGS results using orthogonal technologies such as Sanger sequencing. Here, we report the development of a deterministic machine-learning-based model to differentiate between these two types of variant calls: those that do not require confirmation using an orthogonal technology (high confidence), and those that require additional quality testing (low confidence). This approach allows reliable NGS-based calling in a clinical setting by identifying the few important variant calls that require orthogonal confirmation. We developed and tested the model using a set of 7179 variants identified by a targeted NGS panel and re-tested by Sanger sequencing. The model incorporated several signals of sequence characteristics and call quality to determine if a variant was identified at high or low confidence. The model was tuned to eliminate false positives, defined as variants that were called by NGS but not confirmed by Sanger sequencing. The model achieved very high accuracy: 99.4% (95% confidence interval: +/- 0.03%). It categorized 92.2% (6622/7179) of the variants as high confidence, and 100% of these were confirmed to be present by Sanger sequencing. Among the variants that were categorized as low confidence, defined as NGS calls of low quality that are likely to be artifacts, 92.1% (513/557) were found to be not present by Sanger sequencing. This work shows that NGS data contains sufficient characteristics for a machine-learning-based model to

Study on effective methods to call attention in power plant sites. Towards sharing know-how

International Nuclear Information System (INIS)

Tsukada, Tetsuya; Nakamura, Hajime

1999-01-01

Methods to call attention during field work in nuclear power plants have not obtained the desired results due to redundancy and poor theoretical support. From the points of view of psychology and human engineering, we theoretically examined the validity of each of the following methods for calling attention: methods deployed in power plants, methods obtained through case studies in other industries, and newly developed methods, and then systematized these methods. Using five typical operations with different operating characteristics as models, we also determined methods deployed for each situation in the operation process. Then we determined and categorized the ways of utilizing methods to call attention according to each operating characteristic. With the aim of utilizing these results in many power plants and promoting the sharing of know-how concerning calling attention, we put together an easy-to-understand 'instruction manual', which contains know-how concerning methods to call attention and an introduction to the newly developed methods. Moreover, we established a 'database' (with a registration function) of methods to call attention, which contains organized methods and patterns of utilizing such methods in each operating characteristic. The present study is thus a report that aims at sharing the know-how, centered on this database. (author)

The Differences across Distributed Leadership Practices by School Position According to the Comprehensive Assessment of Leadership for Learning (CALL)

Science.gov (United States)

Blitz, Mark H.; Modeste, Marsha

2015-01-01

The Comprehensive Assessment of Leadership for Learning (CALL) is a multi-source assessment of distributed instructional leadership. As part of the validation of CALL, researchers examined differences between teacher and leader ratings in assessing distributed leadership practices. The authors utilized a t-test for equality of means for the…

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

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

Convergence of calls as animals form social bonds, active compensation for noisy communication channels, and the evolution of vocal learning in mammals.

Science.gov (United States)

Tyack, Peter L

2008-08-01

The classic evidence for vocal production learning involves imitation of novel, often anthropogenic sounds. Among mammals, this has been reported for dolphins, elephants, harbor seals, and humans. A broader taxonomic distribution has been reported for vocal convergence, where the acoustic properties of calls from different individuals converge when they are housed together in captivity or form social bonds in the wild. Vocal convergence has been demonstrated for animals as diverse as songbirds, parakeets, hummingbirds, bats, elephants, cetaceans, and primates. For most species, call convergence is thought to reflect a group-distinctive identifier, with shared calls reflecting and strengthening social bonds. A ubiquitous function for vocal production learning that is starting to receive attention involves modifying signals to improve communication in a noisy channel. Pooling data on vocal imitation, vocal convergence, and compensation for noise suggests a wider taxonomic distribution of vocal production learning among mammals than has been generally appreciated. The wide taxonomic distribution of this evidence for vocal production learning suggests that perhaps more of the neural underpinnings for vocal production learning are in place in mammals than is usually recognized. (c) 2008 APA, all rights reserved

Blended Learning

OpenAIRE

Bauerová, Andrea

2013-01-01

This thesis is focused on a new approach of education called blended learning. The history and developement of Blended Learning is described in the first part. Then the methods and tools of Blended Learning are evaluated and compared to the traditional methods of education. At the final part an efficient developement of the educational programs is emphasized.

Investigating CALL in the Classroom: Situational Variables to Consider

Directory of Open Access Journals (Sweden)

Darlene Liutkus

2012-01-01

Full Text Available A new paradigm in second language pedagogy has Computer Assisted Language Learning (CALL playing a significant role. Much of the literature to-date claims that CALL can have a positive impact on students’ second language acquisition (SLA. Mixed method of research produces data to investigate if CALL positively affects student language proficiency, motivation and autonomy. Classroom observation of participants in their natural environment is a qualitative technique used but has situational variables that could skew results if not structured. A questionnaire is a quantitative tool that can offer insight regarding participants’ perception of performance but can contradict what the researcher has observed. This paper will take an in-depth look at variables such as: instructor’s pedagogical application; blending CALL into the curriculum; types of CALL implemented; feedback received and their implications for design of the data collection tools

The Effects of the CALL Model on College English Reading Teaching

Directory of Open Access Journals (Sweden)

Dan Zhang

2017-12-01

Full Text Available Computer Assisted Language Learning (CALL is an important concept in English teaching method reform. College students’ English reading ability is an important indicator in the evaluation on the college students’ English proficiency. Therefore, this paper applies the CALL model in English reading teaching. Firstly, it introduces the application and development prospect of the CALL model, and analyzes its advantages and disadvantages; secondly, it analyzes the present situation of college English teaching and its influencing factors and then designs an application example to integrate the CALL model with different aspect of English reading. Finally, it analyzes the teaching results of college English reading under the CALL model. Therefore, in both theory and practice, this paper proves the effectiveness and innovativeness of the CALL model.

The Ghost in the Machine: Are "Teacherless" CALL Programs Really Possible?

Science.gov (United States)

Davies, Ted; Williamson, Rodney

1998-01-01

Reflects critically on pedagogical issues in the production of computer-assisted language learning (CALL) courseware and ways CALL has affected the practice of language learning. Concludes that if CALL is to reach full potential, it must be more than a simple medium of information; it should provide a teaching/learning process, with the real…

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…

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.

Aggregative Learning Method and Its Application for Communication Quality Evaluation

Science.gov (United States)

Akhmetov, Dauren F.; Kotaki, Minoru

2007-12-01

In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.

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

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.

Bayesian methods for addressing long-standing problems in associative learning: The case of PREE.

Science.gov (United States)

Blanco, Fernando; Moris, Joaquín

2017-07-20

Most associative models typically assume that learning can be understood as a gradual change in associative strength that captures the situation into one single parameter, or representational state. We will call this view single-state learning. However, there is ample evidence showing that under many circumstances different relationships that share features can be learned independently, and animals can quickly switch between expressing one or another. We will call this multiple-state learning. Theoretically, it is understudied because it needs a different data analysis approach from those usually employed. In this paper, we present a Bayesian model of the Partial Reinforcement Extinction Effect (PREE) that can test the predictions of the multiple-state view. This implies estimating the moment of change in the responses (from the acquisition to the extinction performance), both at the individual and at the group levels. We used this model to analyze data from a PREE experiment with three levels of reinforcement during acquisition (100%, 75% and 50%). We found differences in the estimated moment of switch between states during extinction, so that it was delayed after leaner partial reinforcement schedules. The finding is compatible with the multiple-state view. It is the first time, to our knowledge, that the predictions from the multiple-state view are tested directly. The paper also aims to show the benefits that Bayesian methods can bring to the associative learning field.

Skype™ Conference Calls: A Way to Promote Speaking Skills in the Teaching and Learning of English

Directory of Open Access Journals (Sweden)

Yeferson Romaña Correa

2015-01-01

Full Text Available This article presents the results of a research project on the teaching and learning of English through the use of Skype™ conference calls. The research was carried out with a group of 12 English as a foreign language adult learners in the language institute of Universidad Distrital Francisco José de Caldas, Bogotá, Colombia. The findings of this study suggest that Skype™ conference calls might be considered as an influential computer-mediated communication tool in order to promote English as a foreign language adult A1 learners’ speaking skill, especially for social interaction purposes and oral reinforcement of both language fluency and course contents outside of classroom settings.

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.

Indico CONFERENCE: Define the Call for Abstracts

CERN Multimedia

CERN. Geneva; Ferreira, Pedro

2017-01-01

In this tutorial, you will learn how to define and open a call for abstracts. When defining a call for abstracts, you will be able to define settings related to the type of questions asked during a review of an abstract, select the users who will review the abstracts, decide when to open the call for abstracts, and more.

Learning Performance Enhancement Using Computer-Assisted Language Learning by Collaborative Learning Groups

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Ya-huei Wang

2017-08-01

Full Text Available This study attempted to test whether the use of computer-assisted language learning (CALL and innovative collaborative learning could be more effective than the use of traditional collaborative learning in improving students’ English proficiencies. A true experimental design was used in the study. Four randomly-assigned groups participated in the study: a traditional collaborative learning group (TCLG, 34 students, an innovative collaborative learning group (ICLG, 31 students, a CALL traditional collaborative learning group (CALLTCLG, 32 students, and a CALL innovative collaborative learning group (CALLICLG, 31 students. TOEIC (Test of English for International Communication listening, reading, speaking, and writing pre-test and post-test assessments were given to all students at an interval of sixteen weeks. Multivariate analysis of covariance (MANCOVA, multivariate analysis of variance (MANOVA, and analysis of variance (ANOVA were used to analyze the data. The results revealed that students who used CALL had significantly better learning performance than those who did not. Students in innovative collaborative learning had significantly better learning performances than those in traditional collaborative learning. Additionally, students using CALL innovative collaborative learning had better learning performances than those in CALL collaborative learning, those in innovative collaborative learning, and those in traditional collaborative learning.

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

Editorial (special issue on CALL, e-learning and m-learning

Directory of Open Access Journals (Sweden)

Jo Mynard

2011-09-01

Full Text Available Technology has, in one form of another, been a part of self-access learning since the very first self-access centres (SACs of the 1980s. Some of the better-funded centres featured elaborate listening and recording machinery and (occasionally early personal computers. Early software programmes and language-learning websites available for self-access use tended to be aimed at individual study, initially following the language lab model, and were often designed to teach or test discrete language points. Of course, in 2011 programmes aimed at individual study do still exist and certainly have a place in self-access learning, particularly if a learner has identified a target language area that the software or website covers. However, in this special issue we go beyond language learning software and look at tools and technologies currently available to the learner as self-access resources.

Exercise, character strengths, well-being, and learning climate in the prediction of performance over a 6-month period at a call center.

Science.gov (United States)

Moradi, Saleh; Nima, Ali A; Rapp Ricciardi, Max; Archer, Trevor; Garcia, Danilo

2014-01-01

Performance monitoring might have an adverse influence on call center agents' well-being. We investigate how performance, over a 6-month period, is related to agents' perceptions of their learning climate, character strengths, well-being (subjective and psychological), and physical activity. Agents (N = 135) self-reported perception of the learning climate (Learning Climate Questionnaire), character strengths (Values In Action Inventory Short Version), well-being (Positive Affect, Negative Affect Schedule, Satisfaction With Life Scale, Psychological Well-Being Scales Short Version), and how often/intensively they engaged in physical activity. Performance, "time on the phone," was monitored for 6 consecutive months by the same system handling the calls. Performance was positively related to having opportunities to develop, the character strengths clusters of Wisdom and Knowledge (e.g., curiosity for learning, perspective) and Temperance (e.g., having self-control, being prudent, humble, and modest), and exercise frequency. Performance was negatively related to the sense of autonomy and responsibility, contentedness, the character strengths clusters of Humanity and Love (e.g., helping others, cooperation) and Justice (e.g., affiliation, fairness, leadership), positive affect, life satisfaction and exercise Intensity. Call centers may need to create opportunities to develop to increase agents' performance and focus on individual differences in the recruitment and selection of agents to prevent future shortcomings or worker dissatisfaction. Nevertheless, performance measurement in call centers may need to include other aspects that are more attuned with different character strengths. After all, allowing individuals to put their strengths at work should empower the individual and at the end the organization itself. Finally, physical activity enhancement programs might offer considerable positive work outcomes.

Pembuatan Kakas Pendeteksi Unused Method pada Kode Program PHP dengan Framework CodeIgniter Menggunakan Call Graph

Directory of Open Access Journals (Sweden)

Divi Galih Prasetyo Putri

2014-03-01

Full Text Available Proses evolusi dan perawatan dari sebuah sistem merupakan proses yang sangat penting dalam rekayasa perangkat lunak tidak terkecuali pada aplikasi web. Pada proses ini kebanyakan pengembang tidak lagi berpatokan pada rancangan sistem. Hal ini menyebabkan munculnya unused method. Bagian-bagian program ini tidak lagi terpakai namun masih berada dalam sistem. Keadaan ini meningkatkan kompleksitas dan mengurangi tingkat understandability sistem. Guna mendeteksi adanya unused method pada progam diperlukan teknik untuk melakukan code analysis. Teknik static analysis yang digunakan memanfaatkan call graph yang dibangun dari kode program untuk mengetahui adanya unused method. Call graph dibangun berdasarkan pemanggilan antar method. Aplikasi ini mendeteksi unused method pada kode program PHP yang dibangun menggunakan framework CodeIgniter. Kode program sebagai inputan diurai kedalam bentuk Abstract Syntax Tree (AST yang kemudian dimanfaatkan untuk melakukan analisis terhadap kode program. Proses analisis tersebut kemudian menghasilkan sebuah call graph. Dari call graph yang dihasilkan dapat dideteksi method-method mana saja yang tidak berhasil ditelusuri dan tergolong kedalam unused method. Kakas telah diuji coba pada 5 aplikasi PHP dengan hasil  rata-rata nilai presisi sistem sebesar 0.749 dan recall sebesar 1.

Impact of Using CALL on Iranian EFL Learners' Vocabulary Knowledge

Science.gov (United States)

Yunus, Melor Md; Salehi, Hadi; Amini, Mahdi

2016-01-01

Computer Assisted Language Learning (CALL) integration in EFL contexts has intensified noticeably in recent years. This integration might be in different ways and for different purposes such as vocabulary acquisition, grammar learning, phonology, writing skills, etc. More explicitly, this study is an attempt to explore the effect of using CALL on…

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.

CALL in the Year 2000: A Look Back from 2016

Science.gov (United States)

Chapelle, Carol A.

2016-01-01

This commentary offers a brief reflection on the state of CALL in 1997, when "Language Learning & Technology" was launched with my paper entitled "CALL in the year 2000: Still in search of research paradigms?" The point of my 1997 paper was to suggest the potential value of research on second language learning for the study…

Bat calls while preying: A method for reconstructing the signal emitted by a directional sound source

DEFF Research Database (Denmark)

Guarato, Francesco; Hallam, John

2010-01-01

Understanding and modeling bat biosonar behavior should take into account what the bat actually emitted while exploring the surrounding environment. Recording of the bat calls could be performed by means of a telemetry system small enough to sit on the bat head, though filtering due to bat...... directivity affects recordings and not all bat species are able to carry such a device. Instead, remote microphone recordings of the bat calls could be processed by means of a mathematical method that estimates bat head orientation as a first step before calculating the amplitudes of each call for each...... and discussed. A further improvement of the method is necessary as its performance for call reconstruction strongly depends on correct choice of the sample at which the recorded call is thought to start in each microphone data set....

Women with learning disabilities and access to cervical screening: retrospective cohort study using case control methods

Directory of Open Access Journals (Sweden)

Stanistreet Debbi

2008-01-01

Full Text Available Abstract 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 X2: 24.236; p.value X2: 286.341; p.value 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.

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…

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.

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

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...

Help Options in CALL: A Systematic Review

Science.gov (United States)

Cardenas-Claros, Monica S.; Gruba, Paul A.

2009-01-01

This paper is a systematic review of research investigating help options in the different language skills in computer-assisted language learning (CALL). In this review, emerging themes along with is-sues affecting help option research are identified and discussed. We argue that help options in CALL are application resources that do not only seem…

Factors Affecting the Normalization of CALL in Chinese Senior High Schools

Science.gov (United States)

He, Bi; Puakpong, Nattaya; Lian, Andrew

2015-01-01

With the development of Information Technology, increasing attention has been paid to Computer Assisted Language Learning (CALL). Meanwhile, increasing enthusiasm is seen for English learning and teaching in China. Yet, few research studies have focused on the normalization of CALL in ethnically diverse areas. In response to this research gap,…

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

Science.gov (United States)

Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

2017-05-01

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

Factors Hindering the Integration of CALL in a Tertiary Institution

Directory of Open Access Journals (Sweden)

Izaham Shah Ismail

2008-12-01

Full Text Available The field of Computer Assisted Language Learning (CALL is a field that is constantly evolving as it is very much dependent on the advancement of computer technologies. With new technologies being invented almost every day, experts in the field are looking for ways to apply these new technologies in the language classroom. Despite that, teachers are said to be slow at adopting technology in their classrooms and language teachers, whether at schools or tertiary institutions, are no exception. This study attempts to investigate the factors that hinder ESL instructors at an institution of higher learning from integrating CALL in their lessons. Interviews were conducted with five ESL instructors and results revealed that factors which hinder them from integrating CALL in their teaching are universal factors such as knowledge in technology and pedagogy, computer facilities and resources, absence of exemplary integration of CALL, personal beliefs on language teaching, views on the role of a computers as teacher, and evaluation of learning outcomes.

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.

Relabeling exchange method (REM) for learning in neural networks

Science.gov (United States)

Wu, Wen; Mammone, Richard J.

1994-02-01

The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.

RAPPORT-BUILDING THROUGH CALL IN TEACHING CHINESE AS A FOREIGN LANGUAGE: AN EXPLORATORY STUDY

Directory of Open Access Journals (Sweden)

Wenying Jiang

2005-05-01

Full Text Available Technological advances have brought about the ever-increasing utilisation of computer-assisted language learning (CALL media in the learning of a second language (L2. Computer-mediated communication, for example, provides a practical means for extending the learning of spoken language, a challenging process in tonal languages such as Chinese, beyond the realms of the classroom. In order to effectively improve spoken language competency, however, CALL applications must also reproduce the social interaction that lies at the heart of language learning and language use. This study draws on data obtained from the utilisation of CALL in the learning of L2 Chinese to explore whether this medium can be used to extend opportunities for rapport-building in language teaching beyond the face-to-face interaction of the classroom. Rapport's importance lies in its potential to enhance learning, motivate learners, and reduce learner anxiety. To date, CALL's potential in relation to this facet of social interaction remains a neglected area of research. The results of this exploratory study suggest that CALL may help foster learner-teacher rapport and that scaffolding, such as strategically composing rapport-fostering questions in sound-files, is conducive to this outcome. The study provides an instruction model for this application of CALL.

Zebra finch mates use their forebrain song system in unlearned call communication.

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Ter Maat, Andries; Trost, Lisa; Sagunsky, Hannes; Seltmann, Susanne; Gahr, Manfred

2014-01-01

Unlearned calls are produced by all birds whereas learned songs are only found in three avian taxa, most notably in songbirds. The neural basis for song learning and production is formed by interconnected song nuclei: the song control system. In addition to song, zebra finches produce large numbers of soft, unlearned calls, among which "stack" calls are uttered frequently. To determine unequivocally the calls produced by each member of a group, we mounted miniature wireless microphones on each zebra finch. We find that group living paired males and females communicate using bilateral stack calling. To investigate the role of the song control system in call-based male female communication, we recorded the electrical activity in a premotor nucleus of the song control system in freely behaving male birds. The unique combination of acoustic monitoring together with wireless brain recording of individual zebra finches in groups shows that the neuronal activity of the song system correlates with the production of unlearned stack calls. The results suggest that the song system evolved from a brain circuit controlling simple unlearned calls to a system capable of producing acoustically rich, learned vocalizations.

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

Criteria for Evaluating a Game-Based CALL Platform

Science.gov (United States)

Ní Chiaráin, Neasa; Ní Chasaide, Ailbhe

2017-01-01

Game-based Computer-Assisted Language Learning (CALL) is an area that currently warrants attention, as task-based, interactive, multimodal games increasingly show promise for language learning. This area is inherently multidisciplinary--theories from second language acquisition, games, and psychology must be explored and relevant concepts from…

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

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

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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…

An Evaluation Framework for CALL

Science.gov (United States)

McMurry, Benjamin L.; Williams, David Dwayne; Rich, Peter J.; Hartshorn, K. James

2016-01-01

Searching prestigious Computer-assisted Language Learning (CALL) journals for references to key publications and authors in the field of evaluation yields a short list. The "American Journal of Evaluation"--the flagship journal of the American Evaluation Association--is only cited once in both the "CALICO Journal and Language…

Extremely Randomized Machine Learning Methods for Compound Activity Prediction

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Wojciech M. Czarnecki

2015-11-01

Full Text Available Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

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...

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...

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

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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.

Language teacher education in CALL: history and perspectives

Directory of Open Access Journals (Sweden)

Ana Cristina Biondo Salomão

2013-01-01

Full Text Available Over the last years, the new technologies have changed the way we relate to information and communicate with other people, which has brought on impact to foreign language teaching and learning, and, consequently, to the area of foreign language teacher education. The abbreviation CALL (Computer Assisted Language Learning has been used to designate the processes of language teaching and learning with the use of computers, and language teacher education in CALL to name teacher education for and with the use of new technologies, since a number of authors point to the interdependence of both processes. We intend in this article to present an overview of the literature of the area of language teacher education in CALL nowadays and discuss issues related to the use of new technologies concerning its integration to teacher education and the functional and institutional roles to be taken. We also present two proposals of teacher education with the use of new technologies which are being implemented and at the same time studied in Brazil, which we believe have essential elements for the development of language teachers for and with the use of new technologies currently.

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 ...

Don't Call It School

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Robb, Daniel

2006-01-01

"Homeschooling," "deschooling," and "unschooling" are commonly used terms in the alternative-education world, but each lacks specificity. In this article, the author describes what he discovered during several visits to North Star. Known officially as North Star: Self-Directed Learning for Teens, it is not as structured as a so-called "free"…

Finite Volume Method for Pricing European Call Option with Regime-switching Volatility

Science.gov (United States)

Lista Tauryawati, Mey; Imron, Chairul; Putri, Endah RM

2018-03-01

In this paper, we present a finite volume method for pricing European call option using Black-Scholes equation with regime-switching volatility. In the first step, we formulate the Black-Scholes equations with regime-switching volatility. we use a finite volume method based on fitted finite volume with spatial discretization and an implicit time stepping technique for the case. We show that the regime-switching scheme can revert to the non-switching Black Scholes equation, both in theoretical evidence and numerical simulations.

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.

Student Teachers and CALL: Personal and Pedagogical Uses and Beliefs

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Hlas, Anne Cummings; Conroy, Kelly; Hildebrandt, Susan A.

2017-01-01

The student teaching semester affords teacher candidates the chance to apply what they have learned during their teacher preparation coursework. Therefore, it can be a prime opportunity for student teachers to use technology for their own language learning and to implement computer assisted language learning (CALL) in their instruction. This study…

The Effect of Computer Assisted Language Learning (CALL) on Performance in the Test of English for International Communication (TOEIC) Listening Module

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van Han, Nguyen; van Rensburg, Henriette

2014-01-01

Many companies and organizations have been using the Test of English for International Communication (TOEIC) for business and commercial communication purpose in Vietnam and around the world. The present study investigated the effect of Computer Assisted Language Learning (CALL) on performance in the Test of English for International Communication…

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.

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.

Education and learning: what's on the horizon?

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Pilcher, Jobeth

2014-01-01

Numerous organizations have called for significant changes in education for health care professionals. The call has included the need to incorporate evidence-based as well as innovative strategies. Previous articles in this column have focused primarily on evidence-based teaching strategies, including concept mapping, brain-based learning strategies, methods of competency assessment, and so forth. This article shifts the focus to new ways of thinking about knowledge and education. The article will also introduce evolving, innovative, less commonly used learning strategies and provide a peek into the future of learning.

Self-directed learning can outperform direct instruction in the course of a modern German medical curriculum - results of a mixed methods trial.

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Peine, Arne; Kabino, Klaus; Spreckelsen, Cord

2016-06-03

Modernised medical curricula in Germany (so called "reformed study programs") rely increasingly on alternative self-instructed learning forms such as e-learning and curriculum-guided self-study. However, there is a lack of evidence that these methods can outperform conventional teaching methods such as lectures and seminars. This study was conducted in order to compare extant traditional teaching methods with new instruction forms in terms of learning effect and student satisfaction. In a randomised trial, 244 students of medicine in their third academic year were assigned to one of four study branches representing self-instructed learning forms (e-learning and curriculum-based self-study) and instructed learning forms (lectures and seminars). All groups participated in their respective learning module with standardised materials and instructions. Learning effect was measured with pre-test and post-test multiple-choice questionnaires. Student satisfaction and learning style were examined via self-assessment. Of 244 initial participants, 223 completed the respective module and were included in the study. In the pre-test, the groups showed relatively homogenous scores. All students showed notable improvements compared with the pre-test results. Participants in the non-self-instructed learning groups reached scores of 14.71 (seminar) and 14.37 (lecture), while the groups of self-instructed learners reached higher scores with 17.23 (e-learning) and 15.81 (self-study). All groups improved significantly (p learning group, whose self-assessment improved by 2.36. The study shows that students in modern study curricula learn better through modern self-instructed methods than through conventional methods. These methods should be used more, as they also show good levels of student acceptance and higher scores in personal self-assessment of knowledge.

Investigating the Effectiveness of Computer-Assisted Language Learning (CALL) Using Google Documents in Enhancing Writing--A Study on Senior 1 Students in a Chinese Independent High School

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Ambrose, Regina Maria; Palpanathan, Shanthini

2017-01-01

Computer-assisted language learning (CALL) has evolved through various stages in both technology as well as the pedagogical use of technology (Warschauer & Healey, 1998). Studies show that the CALL trend has facilitated students in their English language writing with useful tools such as computer based activities and word processing. Students…

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…

Combining advanced networked technology and pedagogical methods to improve collaborative distance learning.

Science.gov (United States)

Staccini, Pascal; Dufour, Jean-Charles; Raps, Hervé; Fieschi, Marius

2005-01-01

Making educational material be available on a network cannot be reduced to merely implementing hypermedia and interactive resources on a server. A pedagogical schema has to be defined to guide students for learning and to provide teachers with guidelines to prepare valuable and upgradeable resources. Components of a learning environment, as well as interactions between students and other roles such as author, tutor and manager, can be deduced from cognitive foundations of learning, such as the constructivist approach. Scripting the way a student will to navigate among information nodes and interact with tools to build his/her own knowledge can be a good way of deducing the features of the graphic interface related to the management of the objects. We defined a typology of pedagogical resources, their data model and their logic of use. We implemented a generic and web-based authoring and publishing platform (called J@LON for Join And Learn On the Net) within an object-oriented and open-source programming environment (called Zope) embedding a content management system (called Plone). Workflow features have been used to mark the progress of students and to trace the life cycle of resources shared by the teaching staff. The platform integrated advanced on line authoring features to create interactive exercises and support live courses diffusion. The platform engine has been generalized to the whole curriculum of medical studies in our faculty; it also supports an international master of risk management in health care and will be extent to all other continuous training diploma.

Creating a memory of causal relationships an integration of empirical and explanation-based learning methods

CERN Document Server

Pazzani, Michael J

2014-01-01

This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and expl

76 Computer Assisted Language Learning (CALL) Software ...

African Journals Online (AJOL)

Ike Odimegwu

combination with other factors which may enhance or ameliorate the ... form of computer-based learning which carries two important features: .... To take some commonplace examples, a ... photographs, and even full-motion video clips.

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".

Classification of large acoustic datasets using machine learning and crowdsourcing: Application to whale calls

NARCIS (Netherlands)

Shamir, L.; Carol Yerby, C.; Simpson, R.; Benda-Beckmann, A.M. von; Tyack, P.; Samarra, F.; Miller, P.; Wallin, J.

2014-01-01

Vocal communication is a primary communication method of killer and pilot whales, and is used for transmitting a broad range of messages and information for short and long distance. The large variation in call types of these species makes it challenging to categorize them. In this study, sounds

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.

Bidirectional extreme learning machine for regression problem and its learning effectiveness.

Science.gov (United States)

Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang

2012-09-01

It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.

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.

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…

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...

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.

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…

MEDLINE MeSH Indexing: Lessons Learned from Machine Learning and Future Directions

DEFF Research Database (Denmark)

Jimeno-Yepes, Antonio; Mork, James G.; Wilkowski, Bartlomiej

2012-01-01

and analyzed the issues when using standard machine learning algorithms. We show that in some cases machine learning can improve the annotations already recommended by MTI, that machine learning based on low variance methods achieves better performance and that each MeSH heading presents a different behavior......Map and a k-NN approach called PubMed Related Citations (PRC). Our motivation is to improve the quality of MTI based on machine learning. Typical machine learning approaches fit this indexing task into text categorization. In this work, we have studied some Medical Subject Headings (MeSH) recommended by MTI...

“Computer Assisted Language Learning” (CALL

Directory of Open Access Journals (Sweden)

Nazlı Gündüz

2005-10-01

Full Text Available This article will provide an overview of computers; an overview of the history of CALL, itspros and cons, the internet, World Wide Web, Multimedia, and research related to the uses of computers in the language classroom. Also, it also aims to provide some background for the beginnerson using the Internet in language classes today. It discusses some of the common types of Internetactivities that are being used today, what the minimum requirements are for using the Internet forlanguage learning, and some easy activities you can adapt for your classes. Some special terminology related to computers will also be used in this paper. For example, computer assisted language learning(CALL refers to the sets of instructions which need to be loaded into the computer for it to be able to work in the language classroom. It should be borne in mind that CALL does not refer to the use of acomputer by a teacher to type out a worksheet or a class list or preparing his/her own teaching alone.Hardware refers to any computer equipment used, including the computer itself, the keyboard, screen (or the monitor, the disc-drive, and the printer. Software (computer programs refers to the sets of instructions which need to be loaded into the computer for it to be able to work.

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.

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,…

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.

[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.

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...

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.

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.

Methodological Challenges in Sustainability Science: A Call for Method Plurality, Procedural Rigor and Longitudinal Research

Directory of Open Access Journals (Sweden)

Henrik von Wehrden

2017-02-01

Full Text Available Sustainability science encompasses a unique field that is defined through its purpose, the problem it addresses, and its solution-oriented agenda. However, this orientation creates significant methodological challenges. In this discussion paper, we conceptualize sustainability problems as wicked problems to tease out the key challenges that sustainability science is facing if scientists intend to deliver on its solution-oriented agenda. Building on the available literature, we discuss three aspects that demand increased attention for advancing sustainability science: 1 methods with higher diversity and complementarity are needed to increase the chance of deriving solutions to the unique aspects of wicked problems; for instance, mixed methods approaches are potentially better suited to allow for an approximation of solutions, since they cover wider arrays of knowledge; 2 methodologies capable of dealing with wicked problems demand strict procedural and ethical guidelines, in order to ensure their integration potential; for example, learning from solution implementation in different contexts requires increased comparability between research approaches while carefully addressing issues of legitimacy and credibility; and 3 approaches are needed that allow for longitudinal research, since wicked problems are continuous and solutions can only be diagnosed in retrospect; for example, complex dynamics of wicked problems play out across temporal patterns that are not necessarily aligned with the common timeframe of participatory sustainability research. Taken together, we call for plurality in methodologies, emphasizing procedural rigor and the necessity of continuous research to effectively addressing wicked problems as well as methodological challenges in sustainability science.

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.

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

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....

Virtual Learning Environments on the Go: CALL Meets MALL

Science.gov (United States)

Arús Hita, Jorge

2016-01-01

This paper presents "Eating out," a Moodle-based digital learning resource for English as a Foreign Language (EFL) teaching that can be run both on computers and mobile devices. It is argued that Mobile Assisted Language Learning (MALL) resources do not necessarily need to be specifically designed for such platforms. Rather, a carefully…

IMPACT OF THE PROBLEM-BASED LEARNING CURRICULUM ON ...

African Journals Online (AJOL)

(:entred, and to a large extent self-directed. ... 5. Operation learning scale: this scale relates to the reliance on step-by-step logical approach to learning and to the emphasis on factual .... called progressive disclosure method) in order to arrive at a diagnosis ... the students' personality traits more than the mode of learning.

Failure of operant control of vocal learning in budgerigars

Directory of Open Access Journals (Sweden)

Yoshimasa Seki

2018-02-01

Full Text Available Budgerigars were trained by operant conditioning to produce contact calls immediately after hearing a stimulus contact call. In Experiments 1 and 2, playback stimuli were chosen from two different contact call classes from the bird’s repertoire. Once this task was learned, the birds were then tested with other probe stimulus calls from its repertoire, which differed from the original calls drawn from the two classes. Birds failed to mimic the probe stimuli but instead produced one of the two call classes as in the training sessions, showing that birds learned that each stimulus call served as a discriminative stimulus but not as a vocal template for imitation. In Experiment 3, birds were then trained with stimulus calls falling along a 24-step acoustic gradient which varied between the two sounds representing the two contact call categories. As before, birds obtained a reward when the bird’s vocalization matched that of the stimulus above a criterion level. Since the first step and the last step in the gradient were the birds’ original contact calls,　these two patterns were easily matched. Intermediate contact calls in the gradient were much harder for the birds to match. After extensive training, one bird learned to produce contact calls that had only a modest similarity to the intermediate contact calls along the gradient. In spite of remarkable vocal plasticity under natural conditions, operant conditioning methods with budgerigars, even after extensive training and rigorous control of vocal discriminative stimuli, failed to show vocal learning.

Integrating CALL into an Iranian EAP Course: Constraints and Affordances

Science.gov (United States)

Mehran, Parisa; Alizadeh, Mehrasa

2015-01-01

Iranian universities have recently displayed a growing interest in integrating Computer-Assisted Language Learning (CALL) into teaching/learning English. The English for Academic Purposes (EAP) context, however, is not keeping pace with the current changes since EAP courses are strictly text-based and exam-oriented, and little research has thus…

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.

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.

[Validation of the telephone call as a method for measuring compliance to arterial hypertension treatment in Extremadura].

Science.gov (United States)

Espinosa-García, J; Cobaleda-Polo, J; González-Velasco, M; Fernández-Bergés, D

2014-10-01

Pharmacological non-compliance is a significant problem that can affect patient health. The main aim of this investigation is to validate the telephone call to the patient' home as a self-report method of counting the amount of tablets taken by the patient, as an alternative method to a simple tablet count in the clinic (gold standard). An observational, multicentre, prospective, and longitudinal study was conducted by 25 researchers in different health centres in Extremadura, and which included 125 consecutively enrolled patients with uncontrolled arterial hypertension, 121 ended the study. Three visits were made, including enrollment visit, follow-up visit at 4 weeks, and final visit at 8 weeks. A telephone call was made prior to the enrollment and final visit to remind the patients of the next visit, and to ask at the same time about the number of tablets remaining. A total of 121 patients completed the study. In the final visit, the phone-call method of compliance showed: 100% sensitivity, 86% specificity, 86.8% of overall accuracy, 30.4% PPV, 100% NPV, CP+ 7.13, CP- 0.0, and a kappa index of 0.415 (Pphone call, as a therapeutic compliance method, can be a good alternative due to being almost universal, easy to use, its reduced cost, and without the need of patients to go to the medical centres. Copyright © 2013 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España. All rights reserved.

Nonpasserine bird produces soft calls and pays retaliation cost

OpenAIRE

Paweł Ręk; Tomasz S. Osiejuk

2011-01-01

Low-amplitude vocalizations produced during aggressive encounters, courtship, or both (quiet/soft songs) have been described for many species of song-learning passerines; however, such signals have not been studied among nonlearning birds. During aggressive interactions, apart from using the broadcast call, male corncrakes (Crex crex) produce a low-amplitude, gurgling--mewing call, which appears to be equivalent to soft songs of songbirds. Previous studies have shown that low-amplitude vocali...

An Integrated Mixed Methods Research Design: Example of the Project Foreign Language Learning Strategies and Achievement: Analysis of Strategy Clusters and Sequences

OpenAIRE

Vlčková Kateřina

2014-01-01

The presentation focused on an so called integrated mixed method research design example on a basis of a Czech Science Foundation Project Nr. GAP407/12/0432 "Foreign Language Learning Strategies and Achievement: Analysis of Strategy Clusters and Sequences". All main integrated parts of the mixed methods research design were discussed: the aim, theoretical framework, research question, methods and validity threats. Prezentace se zaměřovala na tzv. integrovaný vícemetodový výzkumný design na...

COMPUTER ASSISTED LANGUAGE LEARNING (CALL: ITS PROSPECTS AND CONSEQUENCES FOR NIGERIAN LANGUAGES / L'APPRENTISSAGE DE LA LANGUE À L'AIDE DES TICE: PERSPECTIVES ET CONSÉQUENCES POUR LES LANGUES NIGÉRIENNES / ÎNVĂŢAREA LIMBII CU TIC: PERSPECTIVE ŞI CONSECINŢE PENTRU LIMBILE NIGERIENE

Directory of Open Access Journals (Sweden)

Oyè Táíwò

2014-11-01

Full Text Available The shift in language learning today is from “classical teaching environment” to “self-learning environment”. In Nigeria today, although CALL efforts are made by schools and individuals, these effort are geared towards the English language learning other than Nigerian languages. This paper seeks to explore the development of CALL for Nigerian Languages and the challenges of running CALL in Nigeria. The results indicate that CALL for Nigerian languages is needed and should be promoted. CALL in Nigerian can only be successful if the shortcomings of CALL are recognized and the mitigating circumstances tackled. Adequate arrangements must be made to manage CALL and Teacher-Assisted language learning (TALL in consideration of the socioeconomic impact of CALL on the teachers Nigerian languages. The attitude of Nigerians towards Nigerian languages should be positive. The government, corporate bodies and individuals must intervene in CALL programs in schools so as to control the resulting high tuition fee.

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…

Call Forecasting for Inbound Call Center

Directory of Open Access Journals (Sweden)

Peter Vinje

2009-01-01

Full Text Available In a scenario of inbound call center customer service, the ability to forecast calls is a key element and advantage. By forecasting the correct number of calls a company can predict staffing needs, meet service level requirements, improve customer satisfaction, and benefit from many other optimizations. This project will show how elementary statistics can be used to predict calls for a specific company, forecast the rate at which calls are increasing/decreasing, and determine if the calls may stop at some point.

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

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.

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.

New Learning Methods for Marine Oil Spill Response Training

Directory of Open Access Journals (Sweden)

Justiina Halonen

2017-06-01

Full Text Available In Finland the Regional Fire and Rescue Services (RFRS are responsible for near shore oil spill response and shoreline cleanup operations. In addition, they assist in other types of maritime incidents, such as search and rescue operations and fire-fighting on board. These statutory assignments require the RFRS to have capability to act both on land and at sea. As maritime incidents occur infrequently, little routine has been established. In order to improve their performance in maritime operations, the RFRS are participating in a new oil spill training programme to be launched by South-Eastern Finland University of Applied Sciences. This training programme aims to utilize new educational methods; e-learning and simulator based training. In addition to fully exploiting the existing navigational bridge simulator, radio communication simulator and crisis management simulator, an entirely new simulator is developed. This simulator is designed to model the oil recovery process; recovery method, rate and volume in various conditions with different oil types. New simulator enables creation of a comprehensive training programme covering training tasks from a distress call to the completion of an oil spill response operation. Structure of the training programme, as well as the training objectives, are based on the findings from competence and education surveys conducted in spring 2016. In these results, a need for vessel maneuvering and navigation exercises together with actual response measures training were emphasized. Also additional training for maritime radio communication, GMDSS-emergency protocols and collaboration with maritime authorities were seemed important. This paper describes new approach to the maritime operations training designed for rescue authorities, a way of learning by doing, without mobilising the vessels at sea.

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.

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.

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.

Identifying Students learning Styles as a Way to Promote Learning Quality

Directory of Open Access Journals (Sweden)

Jafar Sadegh Tabrizi

2013-05-01

Full Text Available Introduction: The major part of peoples knowledge, skills and abilities are achieved during the complex process called learning. Learning is not simply the product of mere intelligence and capabilities of individual; it also depends on other factors such as personality traits, personal interests, and t ype of duty and di fferent methods and st yles. The understanding of each individual fits with his/her learning style. The aim of this study was to determine the learning st yles of Health Care Management students in Tabriz University of Medical Sciences. Methods: Learning styles of 55 Health Services Management students in Tabriz Health and Nutrition Faculty were evaluated in 2009 using a twelve-question Kolb questionnaire in a descriptive study. The data was anal yzed using SPSS. And the frequency of students learning styles was identified by their ages and averages. Results: In this study, 69% of the students were female and the dominant learning method was Assimilator (42%. Other styles with a regard to their frequency were Diverge (24%, Coverage (22%and Accommodator (12%. In the present study,no statistically significant relationship was found in learning styles between the gender (p= 0.644and average (p = 0.676of the students. Conclusion: Assimilator and Diverge methods were the most common ones among the management students. Hence, to improve the quality of learning in this group of students, it is proposed that the teachers use interactive and creative teaching methods such as small and la rge group discussion,brain storming, problem solving, debate-based learning, self-learning and lecturing.

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.…

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

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.

Sharing Histories-a transformative learning/teaching method to empower community health workers to support health behavior change of mothers.

Science.gov (United States)

Altobelli, Laura C

2017-08-23

One of the keys to improving health globally is promoting mothers' adoption of healthy home practices for improved nutrition and illness prevention in the first 1000 days of life from conception. Customarily, mothers are taught health messages which, even if simplified, are hard to remember. The challenge is how to promote learning and behavior change of mothers more effectively in low-resource settings where access to health information is poor, educational levels are low, and traditional beliefs are strong. In addressing that challenge, a new learning/teaching method called "Sharing Histories" is in development to improve the performance of female community health workers (CHWs) in promoting mothers' behaviors for maternal, neonatal and child health (MNCH). This method builds self-confidence and empowerment of CHWs in learning sessions that are built on guided sharing of their own memories of childbearing and child care. CHWs can later share histories with the mother, building her trust and empowerment to change. For professional primary health care staff who are not educators, Sharing Histories is simple to learn and use so that the method can be easily incorporated into government health systems and ongoing CHW programs. I present here the Sharing Histories method, describe how it differs from other social and behavior change methods, and discuss selected literature from psychology, communications, and neuroscience that helps to explain how and why this method works as a transformative tool to engage, teach, transform, and empower CHWs to be more effective change agents with other mothers in their communities, thereby contributing to the attainment of the Sustainable Development Goals.

Comparing the Effectiveness of Self-Learning Java Workshops with Traditional Classrooms

Science.gov (United States)

Eranki, Kiran L. N.; Moudgalya, Kannan M.

2016-01-01

In this work, we study the effectiveness of a method called Spoken Tutorial, which is a candidate technique for self-learning. The performance of college students who self-learned Java through the Spoken Tutorial method is found to be better than that of conventional learners. Although the method evaluated in this work helps both genders, females…

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.

Tourism websites in English as a source for the autonomous learning of specialized terminology: A CALL application

Directory of Open Access Journals (Sweden)

Ángel Felices Lago

2016-04-01

Full Text Available For years now, it has been an unquestioned fact that a large majority of textbooks available in English for Tourism, either in book format, CD-Rom or web site access are based on situations and professional contexts connected with the Anglo-Saxon environment, even though the vast majority of graduates in Tourism in Spain (and other countries end up working in the region (autonomous community of origin or in the province of reference for studies. There is, therefore, a clear dysfunction between the textbooks available in the market and the materials and situations that the students will face in their professional future. However, the Internet now allows us to exploit the availability of vast quantities of local resources (websites, blogs, etc. with their corresponding versions in English, which include tourist information referring to, for example, hotels, restaurants, historical and artistic heritage sites, tour operators, travel agencies, trade fairs or specialized services at the national, regional or communal levels. All these sites offer a special showcase of all the linguistic resources available (be they lexical, syntactic or terminological that the learners must acquire for their professional development. Consequently, the purpose of this study is to offer the results of the computer-assisted language learning (CALL project entitled Autonomous Learning of Specialized Vocabulary in English for Tourism (http://wdb.ugr.es/~afelices/, which takes into consideration the previous premises in order to promote, as its title indicates, autonomous learning in a more realistic professional context and to serve as a model for the development of similar e-learning platforms in other regions or countries.

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

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

Sex differences in the representation of call stimuli in a songbird secondary auditory area.

Science.gov (United States)

Giret, Nicolas; Menardy, Fabien; Del Negro, Catherine

2015-01-01

Understanding how communication sounds are encoded in the central auditory system is critical to deciphering the neural bases of acoustic communication. Songbirds use learned or unlearned vocalizations in a variety of social interactions. They have telencephalic auditory areas specialized for processing natural sounds and considered as playing a critical role in the discrimination of behaviorally relevant vocal sounds. The zebra finch, a highly social songbird species, forms lifelong pair bonds. Only male zebra finches sing. However, both sexes produce the distance call when placed in visual isolation. This call is sexually dimorphic, is learned only in males and provides support for individual recognition in both sexes. Here, we assessed whether auditory processing of distance calls differs between paired males and females by recording spiking activity in a secondary auditory area, the caudolateral mesopallium (CLM), while presenting the distance calls of a variety of individuals, including the bird itself, the mate, familiar and unfamiliar males and females. In males, the CLM is potentially involved in auditory feedback processing important for vocal learning. Based on both the analyses of spike rates and temporal aspects of discharges, our results clearly indicate that call-evoked responses of CLM neurons are sexually dimorphic, being stronger, lasting longer, and conveying more information about calls in males than in females. In addition, how auditory responses vary among call types differ between sexes. In females, response strength differs between familiar male and female calls. In males, temporal features of responses reveal a sensitivity to the bird's own call. These findings provide evidence that sexual dimorphism occurs in higher-order processing areas within the auditory system. They suggest a sexual dimorphism in the function of the CLM, contributing to transmit information about the self-generated calls in males and to storage of information about the

Sex differences in the representation of call stimuli in a songbird secondary auditory area

Directory of Open Access Journals (Sweden)

Nicolas eGiret

2015-10-01

Full Text Available Understanding how communication sounds are encoded in the central auditory system is critical to deciphering the neural bases of acoustic communication. Songbirds use learned or unlearned vocalizations in a variety of social interactions. They have telencephalic auditory areas specialized for processing natural sounds and considered as playing a critical role in the discrimination of behaviorally relevant vocal sounds. The zebra finch, a highly social songbird species, forms lifelong pair bonds. Only male zebra finches sing. However, both sexes produce the distance call when placed in visual isolation. This call is sexually dimorphic, is learned only in males and provides support for individual recognition in both sexes. Here, we assessed whether auditory processing of distance calls differs between paired males and females by recording spiking activity in a secondary auditory area, the caudolateral mesopallium (CLM, while presenting the distance calls of a variety of individuals, including the bird itself, the mate, familiar and unfamiliar males and females. In males, the CLM is potentially involved in auditory feedback processing important for vocal learning. Based on both the analyses of spike rates and temporal aspects of discharges, our results clearly indicate that call-evoked responses of CLM neurons are sexually dimorphic, being stronger, lasting longer and conveying more information about calls in males than in females. In addition, how auditory responses vary among call types differ between sexes. In females, response strength differs between familiar male and female calls. In males, temporal features of responses reveal a sensitivity to the bird’s own call. These findings provide evidence that sexual dimorphism occurs in higher-order processing areas within the auditory system. They suggest a sexual dimorphism in the function of the CLM, contributing to transmit information about the self-generated calls in males and to storage of

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.

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.

Interdisciplinary Research at the Intersection of CALL, NLP, and SLA: Methodological Implications from an Input Enhancement Project

Science.gov (United States)

Ziegler, Nicole; Meurers, Detmar; Rebuschat, Patrick; Ruiz, Simón; Moreno-Vega, José L.; Chinkina, Maria; Li, Wenjing; Grey, Sarah

2017-01-01

Despite the promise of research conducted at the intersection of computer-assisted language learning (CALL), natural language processing, and second language acquisition, few studies have explored the potential benefits of using intelligent CALL systems to deepen our understanding of the process and products of second language (L2) learning. The…

Evaluating the Motivational Impact of CALL Systems: Current Practices and Future Directions

Science.gov (United States)

Bodnar, Stephen; Cucchiarini, Catia; Strik, Helmer; van Hout, Roeland

2016-01-01

A major aim of computer-assisted language learning (CALL) is to create computer environments that facilitate students' second language (L2) acquisition. To achieve this aim, CALL employs technological innovations to create novel types of language practice. Evaluations of the new practice types serve the important role of distinguishing effective…

The difficult medical emergency call

DEFF Research Database (Denmark)

Møller, Thea Palsgaard; Kjærulff, Thora Majlund; Viereck, Søren

2017-01-01

BACKGROUND: Pre-hospital emergency care requires proper categorization of emergency calls and assessment of emergency priority levels by the medical dispatchers. We investigated predictors for emergency call categorization as "unclear problem" in contrast to "symptom-specific" categories and the ......BACKGROUND: Pre-hospital emergency care requires proper categorization of emergency calls and assessment of emergency priority levels by the medical dispatchers. We investigated predictors for emergency call categorization as "unclear problem" in contrast to "symptom-specific" categories...... and the effect of categorization on mortality. METHODS: Register-based study in a 2-year period based on emergency call data from the emergency medical dispatch center in Copenhagen combined with nationwide register data. Logistic regression analysis (N = 78,040 individuals) was used for identification...

Call mimicry by eastern towhees and its significance in relation to auditory learning

Science.gov (United States)

Jon S. Greenlaw; Clifford E. Shackelford; Raymond E. Brown

1998-01-01

The authors document cases of eastern towhees (Pipilo erythrophthalmus) using mimicked alarm calls from three presumptive models (blue jay (Cyanocitta cristata), brown thrasher (Toxostoma rufum), and American robin (Turdus migratorius)). In four instances, male towhees employed heterospecific calls without substitution in their own call repertoires. Three birds (New...

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…

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.

Learning design: reflections upon the current landscape

Directory of Open Access Journals (Sweden)

Brock Craft

2012-08-01

Full Text Available The mounting wealth of open and readily available information and the accelerated evolution of social, mobile and creative technologies call for a re-conceptualisation of the role of educators: from providers of knowledge to designers of learning. This call is reverberated by the rising trend of research in learning design (LD. Addressing this, the Art and Science of Learning Design workshop brought together leading voices in the field, and provided a forum for discussing its key issues. It focused on three major themes: (1 practices, methods and methodologies, (2 tools and resources and (3 theoretical frameworks. This paper proposes a definition of LD, reviews the main contributions from the workshop, and suggests some challenges for future research.

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.

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.

The Game Embedded CALL System to Facilitate English Vocabulary Acquisition and Pronunciation

Science.gov (United States)

Young, Shelley Shwu-Ching; Wang, Yi-Hsuan

2014-01-01

The aim of this study is to make a new attempt to explore the potential of integrating game strategies with automatic speech recognition technologies to provide learners with individual opportunities for English pronunciation learning. The study developed the Game Embedded CALL (GeCALL) system with two activities for on-line speaking practice. For…

Sparse alignment for robust tensor learning.

Science.gov (United States)

Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming

2014-10-01

Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.

Neutrality as Obstructionist in Academic Activism: Calling Bullshit

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Rose, Barbara J.

2018-01-01

In this essay, the author uses experiences as a teacher educator and learning from a historically significant activism movement to (a) compare values in activism-centered and education-centered organizations, (b) posit ways that the concept of neutrality weakens academic activism, and (c) call for teacher education curricula and practices that…

Young doctors' problem solving strategies on call may be improved.

Science.gov (United States)

Michelsen, Jens; Malchow-Møller, Axel; Charles, Peder; Eika, Berit

2013-03-01

The first year following graduation from medical school is challenging as learning from books changes to workplace-based learning. Analysis and reflection on experience may ease this transition. We used Significant Event Analysis (SEA) as a tool to explore what pre-registration house officers (PRHOs) consider successful and problematic events, and to identify what problem-solving strategies they employ. A senior house officer systematically led the PRHO through the SEA of one successful and one problematic event following a night call. The PRHO wrote answers to questions about diagnosis, what happened, how he or she contributed and what knowledge-gaining activities the PRHO would prioritise before the next call. By using an inductive, thematic data analysis, we identified five problem-solving strategies: non-analytical reasoning, analytical reasoning, communication with patients, communication with colleagues and professional behaviour. On average, 1.5 strategies were used in the successful events and 1.2 strategies in the problematic events. Most PRHOs were unable to suggest activities other than reading textbooks. SEA was valuable for the identification of PRHOs' problem-solving strategies in a natural setting. PRHOs should be assisted in increasing their repertoire of strategies, and they should also be helped to "learn to learn" as they were largely unable to point to new learning strategies. not relevant. not relevant.

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.

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

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.

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…

INTEGRATING CORPUS-BASED RESOURCES AND NATURAL LANGUAGE PROCESSING TOOLS INTO CALL

Directory of Open Access Journals (Sweden)

Pascual Cantos Gomez

2002-06-01

Full Text Available This paper ainis at presenting a survey of computational linguistic tools presently available but whose potential has been neither fully considered not exploited to its full in modern CALL. It starts with a discussion on the rationale of DDL to language learning, presenting typical DDL-activities. DDL-software and potential extensions of non-typical DDL-software (electronic dictionaries and electronic dictionary facilities to DDL . An extended section is devoted to describe NLP-technology and how it can be integrated into CALL, within already existing software or as stand alone resources. A range of NLP-tools is presentcd (MT programs, taggers, lemn~atizersp, arsers and speech technologies with special emphasis on tagged concordancing. The paper finishes with a number of reflections and ideas on how language technologies can be used efficiently within the language learning context and how extensive exploration and integration of these technologies might change and extend both modern CAI,I, and the present language learning paradigiii..

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.

"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…

THE BLENDED LEARNING OF ELECTRICITY USING LEARNING OBJECTS IN ENGINEERING

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Lilia Maria Siqueira

2010-09-01

Full Text Available This work presents a proposal for the blended learning of Electricity education in Engineering, using resources called learning objects. The experience occurred with students enrolled on the Electrical Engineering and Computer Engineering courses at PUCPR University. It made possible the contact with interdisciplinary themes related to the study of electricity and the professional curriculum contents. The learning objects, offered during the semester, were anchored on PUCPR’s proprietary virtual educational environment, called Eureka. The students’ evaluation results showed that the study through learning objects in a virtual environment is significant for learning.

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....

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…

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…

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…

I WHATSAPP AN IGUANA: AN ATTEMPT TO APPLY UBIQUITOUS LEARNING

OpenAIRE

Dwi Haryanti

2017-01-01

This paper aims at presenting how Whatsapp provides alternative method to enhance writing skills on personal letter for senior high school student. This method Whatsapps inspired by the concept called ubiquitous learning (U-Learning), which means ―learning supported by ICT resources held anytime, anywhere and fits the context of the learner‖ (de Sousa Monteiro et al., 2014). Participants were 40 11-th grade students in one class in Cirebon High School. A short story in the form of personal le...

New CALL-SLA Research Interfaces for the 21st Century: Towards Equitable Multilingualism

Science.gov (United States)

Ortega, Lourdes

2017-01-01

The majority of the world is multilingual, but inequitably multilingual, and much of the world is also technologized, but inequitably so. Thus, researchers in the fields of computer-assisted language learning (CALL) and second language acquisition (SLA) would profit from considering multilingualism and social justice when envisioning new CALL-SLA…

Boosting compound-protein interaction prediction by deep learning.

Science.gov (United States)

Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

2016-11-01

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

Learning Progressions as Tools for Assessment and Learning

Science.gov (United States)

Shepard, Lorrie A.

2018-01-01

This article addresses the teaching and learning side of the learning progressions literature, calling out for measurement specialists the knowledge most needed when collaborating with subject-matter experts in the development of learning progressions. Learning progressions are one of the strongest instantiations of principles from "Knowing…

On the Edge: Intelligent CALL in the 1990s.

Science.gov (United States)

Underwood, John

1989-01-01

Examines the possibilities of developing computer-assisted language learning (CALL) based on the best of modern technology, arguing that artificial intelligence (AI) strategies will radically improve the kinds of exercises that can be performed. Recommends combining AI technology with other tools for delivering instruction, such as simulation and…

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.

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…

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.

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.

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.

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.

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.

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.

A strategy for quantum algorithm design assisted by machine learning

International Nuclear Information System (INIS)

Bang, Jeongho; Lee, Jinhyoung; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin

2014-01-01

We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum–classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch–Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method. (paper)

A strategy for quantum algorithm design assisted by machine learning

Science.gov (United States)

Bang, Jeongho; Ryu, Junghee; Yoo, Seokwon; Pawłowski, Marcin; Lee, Jinhyoung

2014-07-01

We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a ‘quantum student’ is being taught by a ‘classical teacher’. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.

English Lecturer’s Attitude Toward English Variety In English Learning

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Rahma Yanti

2018-04-01

Full Text Available This research investigates English lecturers’ attitude toward the variation of English in the process of English learning. The purpose of this study is to discuss the types of English variations uttered by English lecturers who are considered as respondents in this study and their attitudes toward the variations of language that appear to their own speech during the teaching-learning process. This research is descriptive by using qualitative approach. The data to be presented in this research is taken from several words of Indonesian lecturers at several universities in Padang West Sumatra by distributing some questionnaires and also interviews. The method of data analysis is divided into two types, first, methods which related to the correlation of external language objects with a non-linguistic element called correlation method or method of matching, and secondly, methods which related to surgical, processing or internal verbal tampering called the method of operation or distribution method.

Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

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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…

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

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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.

Bioinspired Architecture Selection for Multitask Learning

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Andrés Bueno-Crespo

2017-06-01

Full Text Available Faced with a new concept to learn, our brain does not work in isolation. It uses all previously learned knowledge. In addition, the brain is able to isolate the knowledge that does not benefit us, and to use what is actually useful. In machine learning, we do not usually benefit from the knowledge of other learned tasks. However, there is a methodology called Multitask Learning (MTL, which is based on the idea that learning a task along with other related tasks produces a transfer of information between them, what can be advantageous for learning the first one. This paper presents a new method to completely design MTL architectures, by including the selection of the most helpful subtasks for the learning of the main task, and the optimal network connections. In this sense, the proposed method realizes a complete design of the MTL schemes. The method is simple and uses the advantages of the Extreme Learning Machine to automatically design a MTL machine, eliminating those factors that hinder, or do not benefit, the learning process of the main task. This architecture is unique and it is obtained without testing/error methodologies that increase the computational complexity. The results obtained over several real problems show the good performances of the designed networks with this method.

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

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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

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

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…

Self-enhancement learning: target-creating learning and its application to self-organizing maps.

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Kamimura, Ryotaro

2011-05-01

In this article, we propose a new learning method called "self-enhancement learning." In this method, targets for learning are not given from the outside, but they can be spontaneously created within a neural network. To realize the method, we consider a neural network with two different states, namely, an enhanced and a relaxed state. The enhanced state is one in which the network responds very selectively to input patterns, while in the relaxed state, the network responds almost equally to input patterns. The gap between the two states can be reduced by minimizing the Kullback-Leibler divergence between the two states with free energy. To demonstrate the effectiveness of this method, we applied self-enhancement learning to the self-organizing maps, or SOM, in which lateral interactions were added to an enhanced state. We applied the method to the well-known Iris, wine, housing and cancer machine learning database problems. In addition, we applied the method to real-life data, a student survey. Experimental results showed that the U-matrices obtained were similar to those produced by the conventional SOM. Class boundaries were made clearer in the housing and cancer data. For all the data, except for the cancer data, better performance could be obtained in terms of quantitative and topological errors. In addition, we could see that the trustworthiness and continuity, referring to the quality of neighborhood preservation, could be improved by the self-enhancement learning. Finally, we used modern dimensionality reduction methods and compared their results with those obtained by the self-enhancement learning. The results obtained by the self-enhancement were not superior to but comparable with those obtained by the modern dimensionality reduction methods.

There's a Call for You. Whenever the Phone Rings. The Helping Hand Series.

Science.gov (United States)

Nash, Claire

This booklet is intended to assist employees in the hotel and catering industry in learning to make effective use of the telephone in their jobs. The first two sections review some unpleasant experiences that a person can have when calling another organization or when receiving a business call from someone. The importance of the impression created…

Quality in e-learning

DEFF Research Database (Denmark)

Masoumi, Davoud; Lindstrom, Berner

2012-01-01

With the growing demand for e-learning along with striving for excellence associated with globalization, there are worldwide calls for enhancing and assuring quality in e-learning, specifically in the context of the developing countries. Such calls for quality enhancement, accountability, added v...

Professionals calling in lifelong learning centers

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Victor Manuel Monteiro Seco

2013-06-01

Full Text Available Purpose: This study aims to understand how the way people see their work and the authentizotic character of their organizational climate contribute to the building of a Great Place to Work. Design/methodology/approach: This paper presents the results of a quantitative investigation that correlate the perceptions of organizational climate and the work orientations of professionals with different occupations on Portuguese lifelong education centers. Findings: The study indicates that all the core elements of an authentizotic organization contribute to explain what people potentially expect from their companies:  adequate  material  conditions  plus  a  meaningful contribution. Practical implications: The study has implications in the future for National Qualification Agency directors, education politicians and human resource managers who are responsible for providing good expectations within a healthy context of talent retention. Originality/value: The novel contribution of this paper is the finding that employee’s work orientations and authentizotic climate are related to each other in a Lifelong learning Center in the public education sector.

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

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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…

The Importance of a Teacher in a Distance Education and the Progressive Methods of Teaching in a Virtual Learning Environment

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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

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

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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…

Acquiring organizational learning norms: a contingency approach for understanding deutero learning

NARCIS (Netherlands)

Wijnhoven, Alphonsus B.J.M.

2001-01-01

'The Learning Organization' is a configuration of learning norms (called a learning prototype here), which is seldom related to varying levels of learning needs. This article assumes that organizational environmental complexity and dynamics define four learning needs levels. Consequently, four

Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

OpenAIRE

Rosenberg, Ishai; Shabtai, Asaf; Rokach, Lior; Elovici, Yuval

2017-01-01

In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. We show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such...

Parallel strategy for optimal learning in perceptrons

International Nuclear Information System (INIS)

Neirotti, J P

2010-01-01

We developed a parallel strategy for learning optimally specific realizable rules by perceptrons, in an online learning scenario. Our result is a generalization of the Caticha-Kinouchi (CK) algorithm developed for learning a perceptron with a synaptic vector drawn from a uniform distribution over the N-dimensional sphere, so called the typical case. Our method outperforms the CK algorithm in almost all possible situations, failing only in a denumerable set of cases. The algorithm is optimal in the sense that it saturates Bayesian bounds when it succeeds.

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...

Motivation in service-learning: an improvement over traditional instructional methods

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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.  

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

Pedagogical Scholarship in Public Health: A Call for Cultivating Learning Communities to Support Evidence-Based Education.

Science.gov (United States)

Merzel, Cheryl; Halkitis, Perry; Healton, Cheryl

Public health education is experiencing record growth and transformation. The current emphasis on learning outcomes necessitates attention to creating and evaluating the best curricula and learning methods for helping public health students develop public health competencies. Schools and programs of public health would benefit from active engagement in pedagogical research and additional platforms to support dissemination and implementation of educational research findings. We reviewed current avenues for sharing public health educational research, curricula, and best teaching practices; we identified useful models from other health professions; and we offered suggestions for how the field of public health education can develop communities of learning devoted to supporting pedagogy. Our goal was to help advance an agenda of innovative evidence-based public health education, enabling schools and programs of public health to evaluate and measure success in meeting the current and future needs of the public health profession.

CLIL and CALL for a teacher’s expertise: an international training experience

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Letizia Cinganotto

2016-07-01

Full Text Available The paper deals with the link between Content and Language Integrated Learning (CLIL and Computer Assisted Language Learning (CALL, that is the use of ICT to enhance language teaching-learning and the teaching of subject content in a foreign language. Starting from this background, the paper describes an online training initiative promoted by the authors within an international community of peers, made up of teachers, trainers and educators from all over the world, named “Techno-CLIL for EVO 2016”. The initiative was aimed at supporting and guiding participants to discover and experiment digital tools for CLIL lessons, offering the opportunity to share ideas, materials, good practices in an international perspective. Particular attention is devoted to the personal and professional enrichment and growth this training pathway may have helped the 5.000 participants to achieve.CLIL e CALL nell’expertise del docente: un’esperienza di formazione internazionaleIl contributo focalizza l’attenzione sulla correlazione tra Content and Language Integrated Learning (CLIL e Computer Assisted Language Learning (CALL, cioè l’uso delle tecnologie per una maggiore efficacia dell’insegnamento-apprendimento delle lingue o di contenuti disciplinari veicolati in lingua straniera. Partendo da questo background, il contributo descrive una iniziativa di formazione online in lingua inglese promossa dalle autrici all’interno di un contesto internazionale, costituito da una comunità di pratica di docenti, formatori, educatori di tutto il mondo, denominata “Techno-CLIL for EVO 2016”. L’iniziativa mirava a sensibilizzare e guidare i partecipanti nella scoperta e sperimentazione della didattica CLIL in modalità digitale, offrendo l’opportunità di un confronto ed uno scambio di idee, materiali, buone pratiche in una prospettiva internazionale. Particolarmente significative le ricadute che questo percorso ha comportato per i circa 5.000 partecipanti

Translating and transforming (a) CALL for leadership for learning

DEFF Research Database (Denmark)

Weinreich, Elvi; Bjerg, Helle

2015-01-01

"The paper pursues the argument that the process of translation is not solely a linguistic exercise. It also implies methodological and conceptual questions related to the translation and as such transformation of general and theoretical research based models of leadership for learning...

Model-based and model-free Pavlovian reward learning: revaluation, revision, and revelation.

Science.gov (United States)

Dayan, Peter; Berridge, Kent C

2014-06-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.

An ELT's Solution to Combat Plagiarism: "Birth" of CALL.

Science.gov (United States)

Sabieh, Christine

One English-as-a Second-Language professor fought plagiarism using computer assisted language learning (CALL). She succeeded in getting half of her class to write documented research papers free of plagiarism. Although all of the students claimed to know how to avoid plagiarizing, 35 percent presented the work with minor traces of plagiarism. The…

Machine learning a probabilistic perspective

CERN Document Server

Murphy, Kevin P

2012-01-01

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic method...

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...

The Significant Incidents and Close Calls in Human Space Flight Chart: Lessons Learned Gone Viral

Science.gov (United States)

Wood, Bill; Pate, Dennis; Thelen, David

2010-01-01

This presentation will explore the surprising history and events that transformed a mundane spreadsheet of historical spaceflight incidents into a popular and widely distributed visual compendium of lessons learned. The Significant Incidents and Close Calls in Human Space Flight Chart (a.k.a. The Significant Incidents Chart) is a popular and visually captivating reference product that has arisen from the work of the Johnson Space Center (JSC) Safety and Mission Assurance (S&MA) Flight Safety Office (FSO). It began as an internal tool intended to increase our team s awareness of historical and modern space flight incidents. Today, the chart is widely recognized across the agency as a reference tool. It appears in several training and education programs. It is used in familiarization training in the JSC Building 9 Mockup Facility and is seen by hundreds of center visitors each week. The chart visually summarizes injuries, fatalities, and close calls sustained during the continuing development of human space flight. The poster-sized chart displays over 100 total events that have direct connections to human space flight endeavors. The chart is updated periodically. The update process itself has become a collaborative effort. Many people, spanning multiple NASA organizations, have provided suggestions for additional entries. The FSO maintains a growing list of subscribers who have requested to receive updates. The presenters will discuss the origins and motivations behind the significant incidents chart. A review of the inclusion criteria used to select events will be offered. We will address how the chart is used today by S&MA and offer a vision of how it might be used by other organizations now and in the future. Particular emphasis will be placed on features of the chart that have met with broad acceptance and have helped spread awareness of the most important lessons in human spaceflight.

Transcriptomic SNP discovery for custom genotyping arrays: impacts of sequence data, SNP calling method and genotyping technology on the probability of validation success.

Science.gov (United States)

Humble, Emily; Thorne, Michael A S; Forcada, Jaume; Hoffman, Joseph I

2016-08-26

Single nucleotide polymorphism (SNP) discovery is an important goal of many studies. However, the number of 'putative' SNPs discovered from a sequence resource may not provide a reliable indication of the number that will successfully validate with a given genotyping technology. For this it may be necessary to account for factors such as the method used for SNP discovery and the type of sequence data from which it originates, suitability of the SNP flanking sequences for probe design, and genomic context. To explore the relative importance of these and other factors, we used Illumina sequencing to augment an existing Roche 454 transcriptome assembly for the Antarctic fur seal (Arctocephalus gazella). We then mapped the raw Illumina reads to the new hybrid transcriptome using BWA and BOWTIE2 before calling SNPs with GATK. The resulting markers were pooled with two existing sets of SNPs called from the original 454 assembly using NEWBLER and SWAP454. Finally, we explored the extent to which SNPs discovered using these four methods overlapped and predicted the corresponding validation outcomes for both Illumina Infinium iSelect HD and Affymetrix Axiom arrays. Collating markers across all discovery methods resulted in a global list of 34,718 SNPs. However, concordance between the methods was surprisingly poor, with only 51.0 % of SNPs being discovered by more than one method and 13.5 % being called from both the 454 and Illumina datasets. Using a predictive modeling approach, we could also show that SNPs called from the Illumina data were on average more likely to successfully validate, as were SNPs called by more than one method. Above and beyond this pattern, predicted validation outcomes were also consistently better for Affymetrix Axiom arrays. Our results suggest that focusing on SNPs called by more than one method could potentially improve validation outcomes. They also highlight possible differences between alternative genotyping technologies that could be

Conversational Analysis as a Method for Research on Intercultural Learning: A Report on a Project with the Aim of "Learning by Undertaking Research"

Directory of Open Access Journals (Sweden)

Gabriele Berkenbusch

2009-01-01

Full Text Available Conversational analysis—situated between pragmatic linguistics and qualitative empirical research—is a complex method, which needs a lot of time and dedication. It is necessary to develop a so-called “analytical mentality”. The aim of the project presented in this paper was to develop the theoretical insights and the practical skills of a group of students for this kind of research. They worked together throughout the duration of the project, especially in the collec¬tion of empiric material: i.e. the recording of conversations between foreign and German stu¬dents, the transcription of the material, a group discussion on the data and finally its analysis. This articles aims at showing what students can learn by doing this kind of work, based on examples of the collected empirical material: (1 they will be introduced to the different levels and stages of the research process and have the chance to develop a methodical and methodological competence; (2 their general communicative competences and their special competences of the foreign language will increase, as well as (3 their knowledge of intercultural learning by working with authentic data of intercultural communication. So, for instance, stereotypes and how they have been constructed during the interaction may be analysed and precisely described on a micro-analytical level. URN: urn:nbn:de:0114-fqs0901335

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.

Learning theories and skills in online second language teaching and learning

DEFF Research Database (Denmark)

Petersen, Karen Bjerg

2014-01-01

For decades foreign and second language teachers have taken advantage of the technology development and ensuing possibilities to use e-learning facilities for language training. Since the 1980s, the use of computer assisted language learning (CALL), Internet, web 2.0, and various kinds of e-learning...... in Denmark with special attention towards the development of web-based materials for Danish pronunciation. This paper sets out to introduce differences between the international and Danish use of web-based language learning and teaching. Finally, dilemmas and challenges for the use of CALL, IT, and web 2.0 in...

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

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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.

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.

Implementing a Project-Based Learning Model in a Pre-Service Leadership Program

Science.gov (United States)

Albritton, Shelly; Stacks, Jamie

2016-01-01

This paper describes two instructors' efforts to more authentically engage students in a preservice leadership program's course called Program Planning and Evaluation by using a project-based learning approach. Markham, Larmer, and Ravitz (2003) describe project-based learning (PjBL) as "a systematic teaching method that engages students in…

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.

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.

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.

Camp as a Teaching Method in Health Education

DEFF Research Database (Denmark)

Ringby, Betina

Background Camp as a learning activity was introduced in entrepreneurship teaching. Students were engaged to get experiences on how to cope with uncertainty, complexity and to take action in collaboration with external partners. Relevance Society calls for creative and innovative health professio......Background Camp as a learning activity was introduced in entrepreneurship teaching. Students were engaged to get experiences on how to cope with uncertainty, complexity and to take action in collaboration with external partners. Relevance Society calls for creative and innovative health...... to the future didactic development in health education. Camp as a learning process based on participation, creativity and an innovative approach combined with a professional focus seems relevant when trying to engage students to take action. Keywords Innovation, method, camp...

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

SYSTEM APPROACH TO THE BLENDED LEARNING

Directory of Open Access Journals (Sweden)

Vladimir Kukharenko

2015-10-01

Full Text Available Currently, much attention is paid to the development of learning sour cream – a combination of traditional and distance (30-70% of training. Such training is sometimes called hybrid and referred to disruptive technologies. Purpose – to show that the use of systemic campaign in blended learning provides a high quality of education, and the technology can be devastating. The subject of the study – blended learning, object of study – Mixed learning process. The analysis results show that the combined training increases the motivation of students, qualification of teachers, personalized learning process. At the same time there are no reliable methods of assessing the quality of education and training standards. It is important that blended learning strategy to support the institutional goals and had an effective organizational model for support.

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…

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.

Frogs Call at a Higher Pitch in Traffic Noise

Directory of Open Access Journals (Sweden)

Kirsten M. Parris

2009-06-01

Full Text Available Male frogs call to attract females for mating and to defend territories from rival males. Female frogs of some species prefer lower-pitched calls, which indicate larger, more experienced males. Acoustic interference occurs when background noise reduces the active distance or the distance over which an acoustic signal can be detected. Birds are known to call at a higher pitch or frequency in urban noise, decreasing acoustic interference from low-frequency noise. Using Bayesian linear regression, we investigated the effect of traffic noise on the pitch of advertisement calls in two species of frogs, the southern brown tree frog (Litoria ewingii and the common eastern froglet (Crinia signifera. We found evidence that L. ewingii calls at a higher pitch in traffic noise, with an average increase in dominant frequency of 4.1 Hz/dB of traffic noise, and a total effect size of 123 Hz. This frequency shift is smaller than that observed in birds, but is still large enough to be detected by conspecific frogs and confer a significant benefit to the caller. Mathematical modelling predicted a 24% increase in the active distance of a L. ewingii call in traffic noise with a frequency shift of this size. Crinia signifera may also call at a higher pitch in traffic noise, but more data are required to be confident of this effect. Because frog calls are innate rather than learned, the frequency shift demonstrated by L. ewingii may represent an evolutionary adaptation to noisy conditions. The phenomenon of frogs calling at a higher pitch in traffic noise could therefore constitute an intriguing trade-off between audibility and attractiveness to potential mates.

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.

Manifold Regularized Experimental Design for Active Learning.

Science.gov (United States)

Zhang, Lining; Shum, Hubert P H; Shao, Ling

2016-12-02

Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized. The problem of insufficient training data in real-world systems limits the potential applications of these approaches. This paper presents a novel method of active learning called manifold regularized experimental design (MRED), which can label multiple informative samples at one time for training. In addition, MRED gives an explicit geometric explanation for the selected samples to be labeled by the user. Different from existing active learning methods, our method avoids the intrinsic problems caused by insufficiently labeled samples in real-world applications. Various experiments on synthetic datasets, the Yale face database and the Corel image database have been carried out to show how MRED outperforms existing methods.

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...

The Internet, Language Learning, and International Dialogue: Constructing Online Foreign Language Learning Websites

Science.gov (United States)

Kartal, Erdogan; Uzun, Levent

2010-01-01

In the present study we call attention to the close connection between languages and globalization, and we also emphasize the importance of the Internet and online websites in foreign language teaching and learning as unavoidable elements of computer assisted language learning (CALL). We prepared a checklist by which we investigated 28 foreign…

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)

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.

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…

Development of Speaking Skills through Activity Based Learning at the Elementary Level

Science.gov (United States)

Ul-Haq, Zahoor; Khurram, Bushra Ahmed; Bangash, Arshad Khan

2017-01-01

Purpose: This paper discusses an effective instructional method called "activity based learning" that can be used to develop the speaking skills of students in the elementary school level. The present study was conducted to determine the effect of activity based learning on the development of the speaking skills of low and high achievers…

Learning Analytics: The next frontier for computer assisted language learning in big data age

Directory of Open Access Journals (Sweden)

Yu Qinglan

2015-01-01

Full Text Available Learning analytics (LA has been applied to various learning environments, though it is quite new in the field of computer assisted language learning (CALL. This article attempts to examine the application of learning analytics in the upcoming big data age. It starts with an introduction and application of learning analytics in other fields, followed by a retrospective review of historical interaction between learning and media in CALL, and a penetrating analysis on why people would go to learning analytics to increase the efficiency of foreign language education. As approved in previous research, new technology, including big data mining and analysis, would inevitably enhance the learning of foreign languages. Potential changes that learning analytics would bring to Chinese foreign language education and researches are also presented in the article.

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

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.

Application of Cognitive and Socio-Cultural Theories in CALL

Directory of Open Access Journals (Sweden)

Mustafa Akın Güngör

2011-06-01

Full Text Available Since it is quite difficult in traditional learning atmospheres for the learners to be exposed to the target language adequately in foreign language acquisition, CALL in which virtual environment is designed in more appropriate way has given rise. Two main paradigms, cognitive model and socio-cultural theory, have also been adopted in CALL. Moreover, rather than applying one theory, combination of these paradigms is unavoidable. However, application of this combination is challenging in practice, as these two theories have different principles. Furthermore, when it comes to online education, it turns into more challenging process. In this poster this combination is presented with the help of sample applications from Gazi University.

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.

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.

The Convergent Learning Space:

DEFF Research Database (Denmark)

Kjærgaard, Hanne Wacher; Kjeldsen, Lars Peter; Asmussen, Jørgen Bering

is described as well as the theoretical construct and hypotheses surrounding the emergence of the concept in technology-rich classrooms, where students bring their own devices and involve their personal learning spaces and networks. The need for new ways of approaching concepts like choice, learning resources......This paper describes the concept of “The Convergent Learning Space” as it is being explored in an ongoing action research project carried out at undergraduate level in select bachelor programs at a Danish University College. The background nature, design, and beginning of this work in progress......, trajectories of participation etc. calls for new action and new pedagogies by teachers in order to secure alignment between students’ worlds and expectations and aims and plans of the teacher. Action research methods are being used to define and test the constituents and variables of the convergent learning...

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.

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.

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…

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.

An Island Called Cuba

Directory of Open Access Journals (Sweden)

Jean Stubbs

2011-06-01

Full Text Available Review of: An Island Called Home: Returning to Jewish Cuba. Ruth Behar, photographs by Humberto Mayol. New Brunswick NJ: Rutgers University Press, 2007. xiii + 297 pp. (Cloth US$ 29.95 Fidel Castro: My Life: A Spoken Autobiography. Fidel Castro & Ignacio Ramonet. New York: Scribner/Simon & Schuster, 2008. vii + 724 pp. (Paper US$ 22.00, e-book US$ 14.99 Cuba: What Everyone Needs to Know. Julia E. Sweig. New York: Oxford University Press, 2009. xiv + 279 pp. (Paper US$ 16.95 [First paragraph] These three ostensibly very different books tell a compelling story of each author’s approach, as much as the subject matter itself. Fidel Castro: My Life: A Spoken Autobiography is based on a series of long interviews granted by the then-president of Cuba, Fidel Castro, to Spanish-Franco journalist Ignacio Ramonet. Cuba: What Everyone Needs to Know, by U.S. political analyst Julia Sweig, is one of a set country series, and, like Ramonet’s, presented in question/answer format. An Island Called Home: Returning to Jewish Cuba, with a narrative by Cuban-American anthropologist Ruth Behar and photographs by Cuban photographer Humberto Mayol, is a retrospective/introspective account of the Jewish presence in Cuba. While from Ramonet and Sweig we learn much about the revolutionary project, Behar and Mayol convey the lived experience of the small Jewish community against that backdrop.

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...

Active Learning in the Era of Big Data

Energy Technology Data Exchange (ETDEWEB)

Jamieson, Kevin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Davis, IV, Warren L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-10-01

Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for realworld, reproducible active learning research. This paper details the challenges of building the system and demonstrates its capabilities with several experiments. The results show how experimentation can help expose strengths and weaknesses of active learning algorithms, in sometimes unexpected and enlightening ways.

EFL Teachers' Knowledge of the Use and Development of Computer-Assisted Language Learning (CALL) Materials

Science.gov (United States)

Dashtestani, Reza

2014-01-01

Even though there are a plethora of CALL materials available to EFL teachers nowadays, very limited attention has been directed toward the issue that most EFL teachers are merely the consumers of CALL materials. The main challenge is to equip EFL teachers with the required CALL materials development skills to enable them to be contributors to CALL…

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

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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.

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.

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.

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

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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.

"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…

Adult Learners' Preferred Methods of Learning Preventative Heart Disease Care

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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…

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…

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.

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

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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.

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.

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.

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…

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.

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

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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.

Blended call center with idling times during the call service

NARCIS (Netherlands)

Legros, Benjamin; Jouini, Oualid; Koole, Ger

We consider a blended call center with calls arriving over time and an infinitely backlogged amount of outbound jobs. Inbound calls have a non-preemptive priority over outbound jobs. The inbound call service is characterized by three successive stages where the second one is a break; i.e., there is

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.

Qualitative Insights from a Canadian Multiinstitutional Research Study: In Search of Meaningful E-learning

OpenAIRE

Lorraine M. Carter; Vince Salyers; Sue Myers; Carol Hipfner; Caroline Hoffart; Christa MacLean; Kathy White; Theresa Matus; Vivian Forssman; Penelope Barrett

2014-01-01

This paper reports the qualitative findings of a mixed methods research study conducted at three Canadian post-secondary institutions. Called the Meaningful E-learning or MEL project, the study was an exploration of the teaching and learning experiences of faculty and students as well as their perceptions of the benefits and challenges of e-learning. Importantly, e-learning was conceptualized as the integration of pedagogy, instructional technology, and the Internet into teaching ...

Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

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C. V. Subbulakshmi

2015-01-01

Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

Investigating Teachers’ and Students’ Beliefs and Assumptions about CALL Programme at Caledonian College of Engineering

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Holi Ibrahim Holi Ali

2012-01-01

Full Text Available This study is set to investigate students’ and teachers’ perceptions and assumptions about newly implemented CALL Programme at the School of Foundation Studies, Caledonian College of Engineering, Oman. Two versions of questionnaire were administered to 24 teachers and 90 students to collect their beliefs and assumption about CALL programame. The results shows that the great majority of the students report that CALL is very interesting, motivating and useful to them and they learn a lot form it. However, the number of CALL hours should be increased, lab should be equipped and arranged in user friendly way, assessment should be integrated into CALL, and smart boards, black boards should be incorporated into the programme.

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…

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.

Can Learning Motivation Predict Learning Achievement? A Case Study of a Mobile Game-Based English Learning Approach

Science.gov (United States)

Tsai, Chia-Hui; Cheng, Ching-Hsue; Yeh, Duen-Yian; Lin, Shih-Yun

2017-01-01

This study applied a quasi-experimental design to investigate the influence and predictive power of learner motivation for achievement, employing a mobile game-based English learning approach. A system called the Happy English Learning System, integrating learning material into a game-based context, was constructed and installed on mobile devices…

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.

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

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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.

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

Steepest descent with momentum for quadratic functions is a version of the conjugate gradient method.

Science.gov (United States)

Bhaya, Amit; Kaszkurewicz, Eugenius

2004-01-01

It is pointed out that the so called momentum method, much used in the neural network literature as an acceleration of the backpropagation method, is a stationary version of the conjugate gradient method. Connections with the continuous optimization method known as heavy ball with friction are also made. In both cases, adaptive (dynamic) choices of the so called learning rate and momentum parameters are obtained using a control Liapunov function analysis of the system.

Wavefront cellular learning automata.

Science.gov (United States)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2018-02-01

This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them. Each neighbor receiving the wave is activated and must choose a new action. This structure for the WCLA is necessary in many dynamic areas such as social networks, computer networks, grid computing, and web mining. In this paper, we introduce the WCLA framework as an optimization tool with diffusion capability, study its behavior over time using ordinary differential equation solutions, and present its accuracy using expediency analysis. To show the superiority of the proposed WCLA, we compare the proposed method with some other types of cellular learning automata using two benchmark problems.

Wavefront cellular learning automata

Science.gov (United States)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2018-02-01

This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them. Each neighbor receiving the wave is activated and must choose a new action. This structure for the WCLA is necessary in many dynamic areas such as social networks, computer networks, grid computing, and web mining. In this paper, we introduce the WCLA framework as an optimization tool with diffusion capability, study its behavior over time using ordinary differential equation solutions, and present its accuracy using expediency analysis. To show the superiority of the proposed WCLA, we compare the proposed method with some other types of cellular learning automata using two benchmark problems.

Bare-Bones Teaching-Learning-Based Optimization

Directory of Open Access Journals (Sweden)

Feng Zou

2014-01-01

Full Text Available Teaching-learning-based optimization (TLBO algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

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

A Review of Multimedia Glosses and Their Effects on L2 Vocabulary Acquisition in CALL Literature

Science.gov (United States)

Mohsen, Mohammed Ali; Balakumar, M.

2011-01-01

This article reviews the literature of multimedia glosses in computer assisted language learning (CALL) and their effects on L2 vocabulary acquisition during the past seventeen years. Several studies have touched on this area to examine the potential of multimedia in a CALL environment in aiding L2 vocabulary acquisition. In this review, the…

Leaders Learning Orientation and the HCM-turn in call centres

DEFF Research Database (Denmark)

Gnaur, Dorina

2013-01-01

as a significant leadership quality that promotes reflexivity in the ongoing processes of interpretation and meaning creation enhancing the human dimension in the production of service. Learning orientation will be related to high-commitment management (HCM) as a way to reconcile the logics of efficiency...

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.

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

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.

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

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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.

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

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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…

Reflexive Photography, Attitudes, Behavior, and CALL: ITAs Improving Spoken English Intelligibility

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Wallace, Lara

2015-01-01

Research in the field of Computer-Assisted Language Learning (CALL) has frequently taken a top-down approach when investigating learners' attitudes and behavior, both in the course as well as for their personal use. Suggestions are given for use of technology, and future research (Beatty, 2010; Levy & Stockwell, 2006). One perspective that has…

The Short Readings Project: A CALL Reading Activity Utilizing Vocabulary Recycling

Science.gov (United States)

Johnson, Andrew; Heffernan, Neil

2006-01-01

In 2003 multimedia-based English Trailers (www.english-trailers.com) joined the vast array of web sites dedicated to language learning enabling students, either autonomously or in a CALL classroom, to study English via movie commercials. To assist students in comprehending 10 trailers found on the site, the authors created the Short Readings…

Diverse Expected Gradient Active Learning for Relative Attributes.

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You, Xinge; Wang, Ruxin; Tao, Dacheng

2014-06-02

The use of relative attributes for semantic understanding of images and videos is a promising way to improve communication between humans and machines. However, it is extremely labor- and time-consuming to define multiple attributes for each instance in large amount of data. One option is to incorporate active learning, so that the informative samples can be actively discovered and then labeled. However, most existing active-learning methods select samples one at a time (serial mode), and may therefore lose efficiency when learning multiple attributes. In this paper, we propose a batch-mode active-learning method, called Diverse Expected Gradient Active Learning (DEGAL). This method integrates an informativeness analysis and a diversity analysis to form a diverse batch of queries. Specifically, the informativeness analysis employs the expected pairwise gradient length as a measure of informativeness, while the diversity analysis forces a constraint on the proposed diverse gradient angle. Since simultaneous optimization of these two parts is intractable, we utilize a two-step procedure to obtain the diverse batch of queries. A heuristic method is also introduced to suppress imbalanced multi-class distributions. Empirical evaluations of three different databases demonstrate the effectiveness and efficiency of the proposed approach.

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

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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.

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....

Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods.

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Honnorat, Nicolas; Dong, Aoyan; Meisenzahl-Lechner, Eva; Koutsouleris, Nikolaos; Davatzikos, Christos

2017-12-20

Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a semi-supervised clustering method. We apply this strategy to a cohort of patients with schizophrenia of varying extends of disease duration, and we describe the neuroanatomical, demographic and clinical characteristics of the subtypes discovered. We analyze the neuroanatomical heterogeneity of 157 patients diagnosed with Schizophrenia, relative to a control population of 169 subjects, using a machine learning method called CHIMERA. CHIMERA clusters the differences between patients and a demographically-matched population of healthy subjects, rather than clustering patients themselves, thereby specifically assessing disease-related neuroanatomical alterations. Voxel-Based Morphometry was conducted to visualize the neuroanatomical patterns associated with each group. The clinical presentation and the demographics of the groups were then investigated. Three subgroups were identified. The first two differed substantially, in that one involved predominantly temporal-thalamic-peri-Sylvian regions, whereas the other involved predominantly frontal regions and the thalamus. Both subtypes included primarily male patients. The third pattern was a mix of these two and presented milder neuroanatomic alterations and comprised a comparable number of men and women. VBM and statistical analyses suggest that these groups could correspond to different neuroanatomical dimensions of schizophrenia. Our analysis suggests that schizophrenia presents distinct neuroanatomical variants. This variability points to the need for a dimensional neuroanatomical approach using data-driven, mathematically principled multivariate pattern analysis methods, and should be taken into account in clinical studies. Copyright © 2017 Elsevier B.V. All rights reserved.

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

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

Learning to learn: self-managed learning

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Jesús Miranda Izquierdo

2006-09-01

Full Text Available Thi is article analyzes the potentialities and weaknesses that non directive Pedagogy presents, an example of the so called self managed pedagogy, whose postulates are good to analyze for the contributions that this position can make to the search of new ways of learning.

Storyboarding: A Method for Bootstrapping the Design of Computer-Based Educational Tasks

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Jones, Ian

2008-01-01

There has been a recent call for the use of more systematic thought experiments when investigating learning. This paper presents a storyboarding method for capturing and sharing initial ideas and their evolution in the design of a mathematics learning task. The storyboards produced can be considered as "virtual data" created by thought experiments…

Optimality of Poisson Processes Intensity Learning with Gaussian Processes

NARCIS (Netherlands)

Kirichenko, A.; van Zanten, H.

2015-01-01

In this paper we provide theoretical support for the so-called "Sigmoidal Gaussian Cox Process" approach to learning the intensity of an inhomogeneous Poisson process on a d-dimensional domain. This method was proposed by Adams, Murray and MacKay (ICML, 2009), who developed a tractable computational

Help Options for L2 Listening in CALL: A Research Agenda

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Cross, Jeremy

2017-01-01

In this article, I present an agenda for researching help options for second language (L2) listening in computer-assisted language learning (CALL) environments. I outline several theories which researchers in the area draw on, then present common points of concern identified from a review of related literature. This serves as a means to…

Campbell's monkeys concatenate vocalizations into context-specific call sequences

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Ouattara, Karim; Lemasson, Alban; Zuberbühler, Klaus

2009-01-01

Primate vocal behavior is often considered irrelevant in modeling human language evolution, mainly because of the caller's limited vocal control and apparent lack of intentional signaling. Here, we present the results of a long-term study on Campbell's monkeys, which has revealed an unrivaled degree of vocal complexity. Adult males produced six different loud call types, which they combined into various sequences in highly context-specific ways. We found stereotyped sequences that were strongly associated with cohesion and travel, falling trees, neighboring groups, nonpredatory animals, unspecific predatory threat, and specific predator classes. Within the responses to predators, we found that crowned eagles triggered four and leopards three different sequences, depending on how the caller learned about their presence. Callers followed a number of principles when concatenating sequences, such as nonrandom transition probabilities of call types, addition of specific calls into an existing sequence to form a different one, or recombination of two sequences to form a third one. We conclude that these primates have overcome some of the constraints of limited vocal control by combinatorial organization. As the different sequences were so tightly linked to specific external events, the Campbell's monkey call system may be the most complex example of ‘proto-syntax’ in animal communication known to date. PMID:20007377

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…

Analisis Intensi Berprestasi Mahasiswa Binus Online Learning

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Agus Putranto

2013-12-01

Full Text Available The utilization of e-learning can improve learning effectiveness and flexibility. Universitas Bina Nusantara (BINUS provides an online learning program called BINUS Online learning. It offers the student to do a study through online activities, where the students are couraged to learn individually. This research was conducted to obtain a perspective of student intention from BINUS Online learning to mark good achievement. Non experimental method with quatitative approached is implemented where the respondents are students of Information System department and Marketing Management Department. The sampling techniques used is non probability purposive sampling. All variables are measured using questionnaire based on Fishbein and Ajzen model. The data of the research are analyzed using descriptive statistics and multiple linier regression analysis. The results show that the intention to mark an achievement of students of BINUS Online learning is very high. It means that BINUS Online learning Program students have strong intention to get good marks and study achievements.

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.

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

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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.

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

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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.

Computer Assisted Language Learning. Routledge Studies in Computer Assisted Language Learning

Science.gov (United States)

Pennington, Martha

2011-01-01

Computer-assisted language learning (CALL) is an approach to language teaching and learning in which computer technology is used as an aid to the presentation, reinforcement and assessment of material to be learned, usually including a substantial interactive element. This books provides an up-to date and comprehensive overview of…

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.

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

Conduits to care: call lights and patients’ perceptions of communication

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Montie, Mary; Shuman, Clayton; Galinato, Jose; Patak, Lance; Anderson, Christine A; Titler, Marita G

2017-01-01

Background Call light systems remain the primary means of hospitalized patients to initiate communication with their health care providers. Although there is vast amounts of literature discussing patient communication with their health care providers, few studies have explored patients’ perceptions concerning call light use and communication. The specific aim of this study was to solicit patients’ perceptions regarding their call light use and communication with nursing staff. Methods Patients invited to this study met the following inclusion criteria: proficient in English, been hospitalized for at least 24 hours, aged ≥21 years, and able to communicate verbally (eg, not intubated). Thirty participants provided written informed consent, were enrolled in the study, and completed interviews. Results Using qualitative descriptive methods, five major themes emerged from patients’ perceptions (namely; establishing connectivity, participant safety concerns, no separation: health care and the call light device, issues with the current call light, and participants’ perceptions of “nurse work”). Multiple minor themes supported these major themes. Data analysis utilized the constant comparative methods of Glaser and Strauss. Discussion Findings from this study extend the knowledge of patients’ understanding of not only why inconsistencies occur between the call light and their nurses, but also why the call light is more than merely a device to initiate communication; rather, it is a direct conduit to their health care and its delivery. PMID:29075125

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

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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…

Active Learning Using Hint Information.

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Li, Chun-Liang; Ferng, Chun-Sung; Lin, Hsuan-Tien

2015-08-01

The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.

Early Language Learning: Complexity and Mixed Methods

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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…

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

COOPERATIVE LEARNING IN DISTANCE LEARNING: A MIXED METHODS STUDY

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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.

Qualitative Insights from a Canadian Multi-Institutional Research Study: In Search of Meaningful E-Learning

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Carter, Lorraine M.; Salyers, Vince; Myers, Sue; Hipfner, Carol; Hoffart, Caroline; MacLean, Christa; White, Kathy; Matus, Theresa; Forssman, Vivian; Barrett, Penelope

2014-01-01

This paper reports the qualitative findings of a mixed methods research study conducted at three Canadian post-secondary institutions. Called the Meaningful E-learning or MEL project, the study was an exploration of the teaching and learning experiences of faculty and students as well as their perceptions of the benefits and challenges of…

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...

Teaching and learning methodologies in engineering education in ...

African Journals Online (AJOL)

The students' outcome in terms of quality of graduates as regard teaching and learning determines whether the existing methods should be reviewed or not. In the recent world ranking of Universities, only University of Ibadan came among the first hundred despite the number of Universities in Nigeria. This calls for general ...

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

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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

Study Circles in Online Learning Environment in the Spirit of Learning-Centered Approach

Directory of Open Access Journals (Sweden)

Simándi Szilvia

2017-08-01

Full Text Available Introduction: In the era of information society and knowledge economy, learning in non-formal environments gets a highlighted role: it can supplement, replace or raise the knowledge and skills gained in the school system to a higher level (Forray & Juhász, 2008, as the so-called “valid” knowledge significantly changes due to the acceleration of development. With the appearance of information technology means and their booming development, the possibilities of gaining information have widened and, according to the forecasts, the role of learning communities will grow. Purpose: Our starting point is that today, with the involvement of community sites (e.g. Google+, Facebook etc. there is a new possibility for inspiring learning communities: by utilizing the power of community and the possibilities of network-based learning (Ollé & Lévai, 2013. Methods: We intend to make a synthesis based on former research and literature focusing on the learning-centered approach, online learning environment, learning communities and study circles (Noesgaard & Ørngreen, 2015; Biggs & Tang, 2007; Kindström, 2010 Conclusions: The online learning environment can be well utilized for community learning. In the online learning environment, the process of learning is built on activity-oriented work for which active participation, and an intensive, initiative communication are necessary and cooperative and collaborative learning get an important role.

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.

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.

Enhancing the Design and Analysis of Flipped Learning Strategies

Science.gov (United States)

Jenkins, Martin; Bokosmaty, Rena; Brown, Melanie; Browne, Chris; Gao, Qi; Hanson, Julie; Kupatadze, Ketevan

2017-01-01

There are numerous calls in the literature for research into the flipped learning approach to match the flood of popular media articles praising its impact on student learning and educational outcomes. This paper addresses those calls by proposing pedagogical strategies that promote active learning in "flipped" approaches and improved…

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.

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…

Dynamic call center routing policies using call waiting and agent idle times

NARCIS (Netherlands)

Chan, W.; Koole, G.M.; L'Ecuyer, P.

2014-01-01

We study call routing policies for call centers with multiple call types and multiple agent groups. We introduce new weight-based routing policies where each pair (call type, agent group) is given a matching priority defined as an affine combination of the longest waiting time for that call type and

Study on modeling of operator's learning mechanism

International Nuclear Information System (INIS)

Yoshimura, Seichi; Hasegawa, Naoko

1998-01-01

One effective method to analyze the causes of human errors is to model the behavior of human and to simulate it. The Central Research Institute of Electric Power Industry (CRIEPI) has developed an operator team behavior simulation system called SYBORG (Simulation System for the Behavior of an Operating Group) to analyze the human errors and to establish the countermeasures for them. As an operator behavior model which composes SYBORG has no learning mechanism and the knowledge of a plant is fixed, it cannot take suitable actions when unknown situations occur nor learn anything from the experience. However, considering actual operators, learning is an essential human factor to enhance their abilities to diagnose plant anomalies. In this paper, Q learning with 1/f fluctuation was proposed as a learning mechanism of an operator and simulation using the mechanism was conducted. The results showed the effectiveness of the learning mechanism. (author)

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…

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 ...

Usability testing of e-learning: an approach incorporating co-discovery and think-aloud

CSIR Research Space (South Africa)

Adebesin, TF

2009-06-01

Full Text Available Computer applications developed to support learning in the cognitive domains are quite different from commercial transaction processing applications. The unique nature of such applications calls for different methods for evaluating their usability...

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.

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%.

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

Online transfer learning with extreme learning machine

Science.gov (United States)

Yin, Haibo; Yang, Yun-an

2017-05-01

In this paper, we propose a new transfer learning algorithm for online training. The proposed algorithm, which is called Online Transfer Extreme Learning Machine (OTELM), is based on Online Sequential Extreme Learning Machine (OSELM) while it introduces Semi-Supervised Extreme Learning Machine (SSELM) to transfer knowledge from the source to the target domain. With the manifold regularization, SSELM picks out instances from the source domain that are less relevant to those in the target domain to initialize the online training, so as to improve the classification performance. Experimental results demonstrate that the proposed OTELM can effectively use instances in the source domain to enhance the learning performance.

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..

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…

The forgotten Elements of Action Learning

DEFF Research Database (Denmark)

Nunez, Heilyn Camacho; Aguirre, María

reflections. Through his work Revans was claiming several times that action learning goes further than other training methods and that its central ideas had been misunderstood. (Revans, 1982, p.531) He was claiming the need to develop the whole person in all its dimensions. From this perspective the Alpha...... system of action learning, where you explore your values, should not be taken as a light activity. It is the essence of action learning, where the person can explore all the significant aspects of his complete development which includes these moral, ethical and spiritual aspects.......The aim of this article is to provide a discussion and description of some topics of action learning that are not so commonly discussed in the literature and that I have called ‘the forgotten elements of action learning’. Those elements are dealing with Revans’ moral, spiritual and ethical...

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.

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…

Acceleration of saddle-point searches with machine learning

Energy Technology Data Exchange (ETDEWEB)

Peterson, Andrew A., E-mail: andrew-peterson@brown.edu [School of Engineering, Brown University, Providence, Rhode Island 02912 (United States)

2016-08-21

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

Acceleration of saddle-point searches with machine learning

International Nuclear Information System (INIS)

Peterson, Andrew A.

2016-01-01

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

Acceleration of saddle-point searches with machine learning.

Science.gov (United States)

Peterson, Andrew A

2016-08-21

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

Critically Evaluating Prensky in a Language Learning Context: The "Digital Natives/Immigrants Debate" and Its Implications for CALL

Science.gov (United States)

Benini, Silvia; Murray, Liam

2013-01-01

More than 10 years have passed since the first introduction of the term "digital natives" in Prensky's (2001a, 2001b) two seminal articles. Prensky argues that students today, having grown up in the Digital Age, learn differently from their predecessors, or "digital immigrants". As such, the pedagogical tools and methods used…

Algorithm Building and Learning Programming Languages Using a New Educational Paradigm

Science.gov (United States)

Jain, Anshul K.; Singhal, Manik; Gupta, Manu Sheel

2011-08-01

This research paper presents a new concept of using a single tool to associate syntax of various programming languages, algorithms and basic coding techniques. A simple framework has been programmed in Python that helps students learn skills to develop algorithms, and implement them in various programming languages. The tool provides an innovative and a unified graphical user interface for development of multimedia objects, educational games and applications. It also aids collaborative learning amongst students and teachers through an integrated mechanism based on Remote Procedure Calls. The paper also elucidates an innovative method for code generation to enable students to learn the basics of programming languages using drag-n-drop methods for image objects.

The Case Method as a Form of Communication.

Science.gov (United States)

Kingsley, Lawrence

1982-01-01

Questions the wisdom of obscurantism as a basis for case writing. Contends that in its present state the case method, for most students, is an inefficient way of learning. Calls for a consensus that cases should be as well-written as other forms of scholarship. (PD)

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.

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…

SNP calling using genotype model selection on high-throughput sequencing data

KAUST Repository

You, Na; Murillo, Gabriel; Su, Xiaoquan; Zeng, Xiaowei; Xu, Jian; Ning, Kang; Zhang, ShouDong; Zhu, Jian-Kang; Cui, Xinping

2012-01-01

calling SNPs. Thus, errors not involved in base-calling or alignment, such as those in genomic sample preparation, are not accounted for.Results: A novel method of consensus and SNP calling, Genotype Model Selection (GeMS), is given which accounts

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.

Learning about “wicked” problems in the Global South. Creating a film-based learning environment with “Visual Problem Appraisal”

NARCIS (Netherlands)

Witteveen, L.M.; Lie, R.

2012-01-01

The current complexity of sustainable development in the Global South calls for the design of learning strategies that can deal with this complexity. One such innovative learning strategy, called Visual Problem Appraisal (VPA), is highlighted in this article. The strategy is termed visual as it

How to develop students’ approaches to learning: Experiences from a programme based on co-regulated learning

Directory of Open Access Journals (Sweden)

Stančić Milan

2017-01-01

Full Text Available Starting from the insight that during their education students do not manage to learn how to learn, we created the programme called Blooming with the intention of enabling the students to reconsider their own approaches to learning by developing collaborative activities and relations in the classroom. The programme was realised in a secondary school class, and research goals were to explore the contribution of the programme to the change in students’ approach to learning - regarding the learning motivation and strategies - and to obtain an insight into students’ perspective of the benefits of the programme. The changes in learning strategies and students’ motivation were investigated using the MSLQ before and after programme attendance. The data on the programme benefits were obtained via focus groups with students and analysed by the thematic content analysis. It has been established that the students achieved a significant improvement when it comes to the mastering of the learning strategies that refer to self-regulation, critical thinking, peer learning and help seeking. In addition, the students pointed out as benefits a different method of work and pleasant atmosphere, the feeling of autonomy in classes, as well as the development of a different understanding of the nature of knowledge, the learning process and instruction. The results indicate that the use of Bloom’s taxonomy as the tool for co-regulated learning and self-evaluation of students can contribute to the change in students’ learning approaches. This finding is relevant for further considering of the possibility for this method to grow from a special programme into everyday teaching practice.

DLTAP: A Network-efficient Scheduling Method for Distributed Deep Learning Workload in Containerized Cluster Environment

Directory of Open Access Journals (Sweden)

Qiao Wei

2017-01-01

Full Text Available Deep neural networks (DNNs have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling data-parallel computation jobs like DNN over containerized clusters is critical for job performance, system throughput, and resource utilization. It becomes even more challenging with the complex workloads. We propose a scheduling method called Deep Learning Task Allocation Priority (DLTAP which performs scheduling decisions in a distributed manner, and each of scheduling decisions takes aggregation degree of parameter sever task and worker task into account, in particularly, to reduce cross-node network transmission traffic and, correspondingly, decrease the DNN training time. We evaluate the DLTAP scheduling method using a state-of-the-art distributed DNN training framework on 3 benchmarks. The results show that the proposed method can averagely reduce 12% cross-node network traffic, and decrease the DNN training time even with the cluster of low-end servers.

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…

Learning to Support Learning Together: An Experience with the Soft Systems Methodology

Science.gov (United States)

Sanchez, Adolfo; Mejia, Andres

2008-01-01

An action research approach called soft systems methodology (SSM) was used to foster organisational learning in a school regarding the role of the learning support department within the school and its relation with the normal teaching-learning activities. From an initial situation of lack of coordination as well as mutual misunderstanding and…

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...

The Internet, Language Learning, And International Dialogue: 
Constructing Online Foreign Language Learning Websites

OpenAIRE

KARTAL, Erdogan; UZUN, Levent

2015-01-01

In the present study we call attention to the close connection between languages and globalization, and we also emphasize the importance of the Internet and online websites in foreign language teaching and learning as unavoidable elements of computer assisted language learning (CALL). We prepared a checklist by which we investigated 28 foreign language teaching websites (4 from each of seven languages including English, French, German, Italian, Russian, Spanish and Turkish). The participants ...

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...

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...

Evolution of learning strategies in temporally and spatially variable environments: a review of theory.

Science.gov (United States)

Aoki, Kenichi; Feldman, Marcus W

2014-02-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change--coevolutionary, two-timescale, and information decay--are compared and shown to sometimes yield contradictory results. The so-called Rogers' paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers' paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. Copyright © 2013 Elsevier Inc. All rights reserved.

Evolution of learning strategies in temporally and spatially variable environments: A review of theory

Science.gov (United States)

Aoki, Kenichi; Feldman, Marcus W.

2013-01-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change – coevolutionary, two-timescale, and information decay – are compared and shown to sometimes yield contradictory results. The so-called Rogers’ paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers’ paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. PMID:24211681

Facilitation of social learning in teacher education: the ‘Dimensions of Social Learning Framework’

NARCIS (Netherlands)

de Laat, M.M.; Vrieling, E.; van den Beemt, A.A.J.; McDonald, J.; Cater-Steel, A.

2017-01-01

To understand the organization of social learning by groups in practice, this chapter elaborates on the use of a framework of dimensions and indicators to explore social learning within (prospective) teacher groups. The applied framework that we call the ‘Dimensions of Social Learning (DSL)

Sunspot drawings handwritten character recognition method based on deep learning

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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.

APA's Learning Objectives for Research Methods and Statistics in Practice: A Multimethod Analysis

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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…

A Photometric Machine-Learning Method to Infer Stellar Metallicity

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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..

A comparative study on effect of e-learning and instructor-led methods on nurses' documentation competency.

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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.

Teaching-learning: stereoscopic 3D versus Traditional methods in Mexico City.

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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.

Metric Learning Method Aided Data-Driven Design of Fault Detection Systems

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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.

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

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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.