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Sample records for learning difficulties predicted

  1. Value of supervised learning events in predicting doctors in difficulty.

    Science.gov (United States)

    Patel, Mumtaz; Agius, Steven; Wilkinson, Jack; Patel, Leena; Baker, Paul

    2016-07-01

    In the UK, supervised learning events (SLE) replaced traditional workplace-based assessments for foundation-year trainees in 2012. A key element of SLEs was to incorporate trainee reflection and assessor feedback in order to drive learning and identify training issues early. Few studies, however, have investigated the value of SLEs in predicting doctors in difficulty. This study aimed to identify principles that would inform understanding about how and why SLEs work or not in identifying doctors in difficulty (DiD). A retrospective case-control study of North West Foundation School trainees' electronic portfolios was conducted. Cases comprised all known DiD. Controls were randomly selected from the same cohort. Free-text supervisor comments from each SLE were assessed for the four domains defined in the General Medical Council's Good Medical Practice Guidelines and each scored blindly for level of concern using a three-point ordinal scale. Cumulative scores for each SLE were then analysed quantitatively for their predictive value of actual DiD. A qualitative thematic analysis was also conducted. The prevalence of DiD in this sample was 6.5%. Receiver operator characteristic curve analysis showed that Team Assessment of Behaviour (TAB) was the only SLE strongly predictive of actual DiD status. The Educational Supervisor Report (ESR) was also strongly predictive of DiD status. Fisher's test showed significant associations of TAB and ESR for both predicted and actual DiD status and also the health and performance subtypes. None of the other SLEs showed significant associations. Qualitative data analysis revealed inadequate completion and lack of constructive, particularly negative, feedback. This indicated that SLEs were not used to their full potential. TAB and the ESR are strongly predictive of DiD. However, SLEs are not being used to their full potential, and the quality of completion of reports on SLEs and feedback needs to be improved in order to better identify

  2. [Difficulties in learning mathematics].

    Science.gov (United States)

    Rebollo, M A; Rodríguez, A L

    2006-02-13

    To discuss our concern for some aspects of mathematics learning disorders related to the nomenclature employed and their diagnosis; these aspects refer to the term 'dyscalculia' and to its diagnosis (especially syndromatic diagnosis). We also intend to propose a classification that could help to define the terminology. Lastly we are going to consider the different aspects of diagnosis and to determine which of them are indispensable in the diagnosis of primary and secondary disorders. As far as the nomenclature is concerned, we refer to the term 'dyscalculia'. The origins of the term are analysed along with the reasons why it should not be used in children with difficulties in learning mathematics. We propose a classification and denominations for the different types that should undoubtedly be discussed. With respect to the diagnosis, several problems related to the syndromatic diagnosis are considered, since in our country there are no standardised tests with which to study performance in arithmetic and geometry. This means that criterion reference tests are conducted to try to establish current and potential performance. At this stage of the diagnosis pedagogical and psychological studies must be conducted. The important factors with regard to the topographical and aetiological diagnoses are prior knowledge, results from the studies that have been carried out and findings from imaging studies. The importance of a genetic study must be defined in the aetiological diagnosis. We propose a nomenclature to replace the term 'dyscalculia'. Standardised tests are needed for the diagnosis. The need to establish current and potential performance is hierarchized. With regard to the topographical diagnosis, we highlight the need for more information about geometry, and in aetiological studies the analyses must be conducted with greater numbers of children.

  3. Desirable difficulties in vocabulary learning

    OpenAIRE

    Bjork, RA; Kroll, JF

    2015-01-01

    © 2015 by the Board of Trustees of the University of Illinois. In this article we discuss the role of desirable difficulties in vocabulary learning from two perspectives, one having to do with identifying conditions of learning that impose initial challenges to the learner but then benefit later retention and transfer, and the other having to do with the role of certain difficulties that are intrinsic to language processes, are engaged during word learning, and reflect how language is underst...

  4. Prediction and Stability of Mathematics Skill and Difficulty

    OpenAIRE

    Martin, Rebecca B.; Cirino, Paul T.; Barnes, Marcia A.; Ewing-Cobbs, Linda; Fuchs, Lynn S.; Stuebing, Karla K.; Fletcher, Jack M.

    2012-01-01

    The present study evaluated the stability of math learning difficulties over a 2-year period and investigated several factors that might influence this stability (categorical vs. continuous change, liberal vs. conservative cut point, broad vs. specific math assessment); the prediction of math performance over time and by performance level was also evaluated. Participants were 144 students initially identified as having a math difficulty (MD) or no learning difficulty according to low achievem...

  5. Researching Learning Difficulties: A Guide for Practitioners

    Science.gov (United States)

    Porter, Jill; Lacey, Penny

    2005-01-01

    The aim of this book is to provide a source for teachers and other professionals working with children and adults with learning difficulties and disabilities that will enable them to: (1) access selected recent and relevant research in the field of learning difficulties, drawn from a range of disciplines and groups of people; (2) reflect on…

  6. LEARNING DIFFICULTIES: AN ANALYSIS BASED ON VIGOTSKY

    Directory of Open Access Journals (Sweden)

    Adriane Cenci

    2010-06-01

    Full Text Available We aimed, along the text, to bring a reflection upon learning difficulties based on Socio-Historical Theory, relating what is observed in schools to what has been discussed about learning difficulties and the theory proposed by Vygotsky in the early XX century. We understand that children enter school carrying experiences and knowledge from their cultural group and that school ignores such knowledge very often. Then, it is in such disengagement that emerges what we started to call learning difficulties. One cannot forget to see a child as a whole – a student is a social being constituted by culture, language and specific values to which one must be attentive.

  7. Creating a supportive learning environment for students with learning difficulties

    OpenAIRE

    Grah, Jana

    2013-01-01

    Co-building of supporting learning environment for the learners with learning difficulties is one of the 21st century inclusive school’s elements. Since the physical presence of learners with learning difficulties in the classroom does not self-evidently lead to an effective co-operation and implementation of 21st century inclusive school, I have dedicated my doctor thesis to the establishment of supporting learning environment for the learners with learning difficulties in primary school wit...

  8. The Role of Metacognitive Reading Strategies, Metacognitive Study and Learning Strategies, and Behavioral Study and Learning Strategies in Predicting Academic Success in Students With and Without a History of Reading Difficulties.

    Science.gov (United States)

    Chevalier, Thérèse M; Parrila, Rauno; Ritchie, Krista C; Deacon, S Hélène

    2017-01-01

    We examined the self-reported use of reading, study, and learning strategies in university students with a history of reading difficulties (HRD; n = 77) and with no history of reading difficulties (NRD; n = 295). We examined both between-groups differences in strategy use and strategy use as a predictive measure of academic success. Participants completed online questionnaires regarding reading history and strategy use. GPA and frequency of use of academic support services were also obtained for all students. University students with HRD reported a different profile of strategy use than their NRD peers, and self-reported strategy use was differentially predictive of GPA for students with HRD and NRD. For students with HRD, the use of metacognitive reading strategies and the use of study aids predicted academic success. Implications for university student services providers are discussed. © Hammill Institute on Disabilities 2015.

  9. Difficulties in Initial Algebra Learning in Indonesia

    Science.gov (United States)

    Jupri, Al; Drijvers, Paul; van den Heuvel-Panhuizen, Marja

    2014-01-01

    Within mathematics curricula, algebra has been widely recognized as one of the most difficult topics, which leads to learning difficulties worldwide. In Indonesia, algebra performance is an important issue. In the Trends in International Mathematics and Science Study (TIMSS) 2007, Indonesian students' achievement in the algebra domain was…

  10. Fractions Learning in Children with Mathematics Difficulties

    Science.gov (United States)

    Tian, Jing; Siegler, Robert S.

    2017-01-01

    Learning fractions is difficult for children in general and especially difficult for children with mathematics difficulties (MD). Recent research on developmental and individual differences in fraction knowledge of children with MD and typically achieving (TA) children has demonstrated that U.S. children with MD start middle school behind their TA…

  11. Difficulties in initial algebra learning in Indonesia

    NARCIS (Netherlands)

    Jupri, Al; Drijvers, Paulus; van den Heuvel - Panhuizen, Marja

    2014-01-01

    Within mathematics curricula, algebra has been widely recognized as one of the most difficult topics, which leads to learning difficulties worldwide. In Indonesia, algebra performance is an important issue. In the Trends in International Mathematics and Science Study (TIMSS) 2007, Indonesian

  12. Pre-Service Science Teachers' PCK: Inconsistency of Pre-Service Teachers' Predictions and Student Learning Difficulties in Newton's Third Law

    Science.gov (United States)

    Zhou, Shaona; Wang, Yanlin; Zhang, Chunbin

    2016-01-01

    There is widespread agreement that science learning always builds upon students' existing ideas and that science teachers should possess knowledge of learners. This study aims at investigating pre-service science teachers' knowledge of student misconceptions and difficulties, a crucial component of PCK, on Newton's Third Law. A questionnaire was…

  13. Predikce poruch učení pomocí testu komplexní imitace pohybu Prediction of learning difficulties with the test of complex imitation of movement

    Directory of Open Access Journals (Sweden)

    Martina Ozbič

    2008-05-01

    can lead to faster intervention resulting in the progress of children with DCD in their movement abilities. This research has shown that on the basis of twenty tasks of the Bergès-Lézine's test of the complex imitation of movement/gestures, we can predict which children have some learning difficulties and which do not. Particularly we wish to emphasize three tasks (12, 17 and 20 where children had to cross the vertical midline of their bodies. These three tasks involve bilateral coordination. Children with DCD signs face problems in spatial orientation and in complex imitation of movement/gestures. On the basis of great differences, found in tasks where pupils had to cross the vertical midline of their bodies and rotate their palms, children can be classified into two groups (with and without motor and learning difficulties.

  14. Status of Muslim Immigrants' Children with Learning Difficulties in Vienna

    Science.gov (United States)

    Mohsin, M. Naeem; Shabbir, Muhammad; Saeed, Wizra; Mohsin, M. Saleem

    2013-01-01

    The study was conducted to know the status of Muslim immigrants' children with learning difficulties and importance of parents' involvement for the education whose children are with learning difficulties, and the factors responsible for the learning difficulties among immigrants' children. There were 81 immigrant children with learning…

  15. Fractions Learning in Children With Mathematics Difficulties.

    Science.gov (United States)

    Tian, Jing; Siegler, Robert S

    Learning fractions is difficult for children in general and especially difficult for children with mathematics difficulties (MD). Recent research on developmental and individual differences in fraction knowledge of children with MD and typically achieving (TA) children has demonstrated that U.S. children with MD start middle school behind their TA peers in fraction understanding and fall further behind during middle school. In contrast, Chinese children, who like the MD children in the United States score in the bottom one third of the distribution in their country, possess reasonably good fraction understanding. We interpret these findings within the framework of the integrated theory of numerical development. By emphasizing the importance of fraction magnitude knowledge for numerical understanding in general, the theory proved useful for understanding differences in fraction knowledge between MD and TA children and for understanding how knowledge can be improved. Several interventions demonstrated the possibility of improving fraction magnitude knowledge and producing benefits that generalize to fraction arithmetic learning among children with MD. The reasonably good fraction understanding of Chinese children with MD and several successful interventions with U.S. students provide hope for the improvement of fraction knowledge among American children with MD.

  16. Predicting Handwriting Difficulties through Spelling Processes

    Science.gov (United States)

    Rodríguez, Cristina; Villarroel, Rebeca

    2017-01-01

    This study examined whether spelling tasks contribute to the prediction of the handwriting status of children with poor and good handwriting skills in a cross-sectional study with 276 Spanish children from Grades 1 and 3. The main hypothesis was that the spelling tasks would predict the handwriting status of the children, although this influence…

  17. Teaching chemistry to students with learning difficulties: exemplary ...

    African Journals Online (AJOL)

    Teaching chemistry to students with learning difficulties: exemplary adaptive instructional practices of experienced teachers. ... Arguably, today's science classrooms are witnessing a situation in which students experience a special learning ...

  18. Learning Difficulty and Learner Identity: A Symbiotic Relationship

    Science.gov (United States)

    Hirano, Eliana

    2009-01-01

    This paper reports on a longitudinal case study of an adult EFL learner who perceived himself as having difficulty learning English. Both learning difficulty and learner identity are viewed as being constructed in discursive interactions throughout one's life and, hence, amenable to reconstruction. Data collected from classroom interactions,…

  19. Difficulties in Learning Inheritance and Polymorphism

    Science.gov (United States)

    Liberman, Neomi; Beeri, Catriel; Kolikant, Yifat Ben-David

    2011-01-01

    This article reports on difficulties related to the concepts of inheritance and polymorphism, expressed by a group of 22 in-service CS teachers with an experience with the procedural paradigm, as they coped with a course on OOP. Our findings are based on the analysis of tests, questionnaires that the teachers completed in the course, as well as on…

  20. Learning Difficulties and Nutrition: Pills or Pedagogy?

    Science.gov (United States)

    Evans, Roy

    1999-01-01

    Examines the efforts to find effective ameliorative measures for literacy difficulties such as dyslexia and dyspraxia, focusing on noneducational techniques found in holistic medicine, complementary therapies, and nutritional supplements. Maintains that dyslexia has become big business for drug companies and that the appropriate research regarding…

  1. Psychological Problems of Children with Learning Difficulties.

    Science.gov (United States)

    Shaughnessy, Michael F.; Scott, Patricia Carol

    The paper presents tips for parents of children with learning problems. It describes the emotional side effects of low achievement which may include low self-esteem, clinical depression, "learned helplessness," suicidal ideation, acting out behavior, low frustration tolerance, guilt feelings, interpersonal problems, withdrawal, running away,…

  2. Learner's Learning Experiences & Difficulties towards (ESL) among UKM Undergraduates

    Science.gov (United States)

    Maarof, Nooreiny; Munusamy, Indira Malani A/P

    2015-01-01

    This paper aims to investigate the learners learning experiences and difficulties of ESL among the UKM undergraduates. This study will be focusing on identifying the factors behind Malaysian undergraduate's experiences and also their difficulties in the English as Second Language (ESL) classroom. This paper discusses some of the issues of English…

  3. Can Excess Bilirubin Levels Cause Learning Difficulties?

    Science.gov (United States)

    Pretorius, E.; Naude, H.; Becker, P. J.

    2002-01-01

    Examined learning problems in South African sample of 7- to 14-year-olds whose mothers reported excessively high infant bilirubin shortly after the child's birth. Found that this sample had lowered verbal ability with the majority also showing impaired short-term and long-term memory. Findings suggested that impaired formation of astrocytes…

  4. Measurement of functional task difficulty during motor learning: What level of difficulty corresponds to the optimal challenge point?

    Science.gov (United States)

    Akizuki, Kazunori; Ohashi, Yukari

    2015-10-01

    The relationship between task difficulty and learning benefit was examined, as was the measurability of task difficulty. Participants were required to learn a postural control task on an unstable surface at one of four different task difficulty levels. Results from the retention test showed an inverted-U relationship between task difficulty during acquisition and motor learning. The second-highest level of task difficulty was the most effective for motor learning, while learning was delayed at the most and least difficult levels. Additionally, the results indicate that salivary α-amylase and the performance dimension of the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) are useful indices of task difficulty. Our findings suggested that instructors may be able to adjust task difficulty based on salivary α-amylase and the performance dimension of the NASA-TLX to enhance learning. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. Preoperative ultrasonography and prediction of technical difficulties during laparoscopic cholecystectomy.

    Science.gov (United States)

    Daradkeh, S S; Suwan, Z; Abu-Khalaf, M

    1998-01-01

    A prospective study was carried out to investigate the value of preoperative ultrasound findings for predicting difficulties encountered during laparoscopic cholecystectomy (LC). Altogether 160 consecutive patients with symptomatic gallbladder (GB) disease (130 females, 30 males) referred to the Jordan University Hospital were recruited for the purpose of this study. All patients underwent detailed ultrasound examination 24 hours prior to LC. The overall difficulty score (ODS), as a dependent variable, was based on the following operative parameters: duration of surgery, bleeding, dissection of Calot's triangle, dissection of gallbladder wall, adhesions, spillage of bile, spillage of stone, and difficulty of gallbladder extraction. Multiple regression analysis was used to assess the significance of the following preoperative ultrasound variables (independent) for predicting the variation in the ODS: size of the GB, number of GB stones, size of stones, location of GB stones, thickness of GB wall, common bile duct (CBD) diameter, and liver size. Only thickness of GB wall and CBD diameter were found to be significant predictors of the variation in the ODS (adjusted R2 = 0.25). We conclude that the preoperative ultrasound examination is of value for predicting difficulties encountered during LC, but it is not the sole predictor.

  6. DIFFICULTIES TO LEARN AND TO TEACH MODERN PHYSICS.

    Directory of Open Access Journals (Sweden)

    R. Antonowiski

    2017-08-01

    Full Text Available Physics is engaged in scientific and technological development in several areas, however, its learning in high school has high failure rates that demonstrate a low level of use. It is a science that allows us to understand the nature of the macroscopic and atomic matter, but it is taught in a disjointed manner, upon presentation of concepts, laws and mathematical sentences, repetitive exercises that have taken the preparatory character for college entrance. Thus, the student gets stuck sentences featuring a partial knowledge and disposable. This study aimed to analyze the main difficulties that undergraduate students in Physics have in Modern Physics learning. Point out the difficulties in teaching and learning Physics is not an easy task and to identify them comes the difficulty of how to solve them. After analysis of several hypotheses we can conclude that there is no single factor responsible for the difficulty of the teaching and learning of Modern Physics. The lack of time to work and developed since middle school, stimulating the curiosity of students, adequately trained teachers, lack of structure offered by the government, parents' responsibilities and students in learning, among others, constitute a major challenge for successful teaching and learning of Modern Physics

  7. Research on Difficulty in Indonesia Students Learning Chinese Language

    Directory of Open Access Journals (Sweden)

    Lydia Anggreani

    2013-05-01

    Full Text Available Chinese has become the world’s second language. Each language has its own law, as is the Chinese. Indonesian students have difficulty in learning Chinese which are are not surprising. Every language has various characteristics, so do Chinese and Bahasa Indonesia. Article analyzes difficulties to learn Chinese, especially for Indonesian students, those are tone, grammar, sounds of “er hua” such as Alice retroflex. The respondents are 100 Indonesian students who are randomly selected for testing samples analyzed. Since there is no tone in Bahasa Indonesia, it makes a lot of Indonesian students in the learning process often appear in Chinese foreign accent phenomenon. This article expects to explore the problem by studying the formation of the causes and solutions. Indonesian students learning Chinese was designed to provide some teaching and learning strategies.

  8. Luck is Hard to Beat: The Difficulty of Sports Prediction

    OpenAIRE

    Aoki, Raquel YS; Assuncao, Renato M; de Melo, Pedro OS Vaz

    2017-01-01

    Predicting the outcome of sports events is a hard task. We quantify this difficulty with a coefficient that measures the distance between the observed final results of sports leagues and idealized perfectly balanced competitions in terms of skill. This indicates the relative presence of luck and skill. We collected and analyzed all games from 198 sports leagues comprising 1503 seasons from 84 countries of 4 different sports: basketball, soccer, volleyball and handball. We measured the competi...

  9. Common difficulties experienced by grade 12 students in learning ...

    African Journals Online (AJOL)

    The objective of this study was to examine the nature and causes of common difficulties experienced by grade twelve students in learning chemistry in Ebinat preparatory school. A qualitative method was employed to investigate the questions, which used interviews and questionnaires with students and teachers. The key ...

  10. Difficulties in Learning and Teaching Statistics: Teacher Views

    Science.gov (United States)

    Koparan, Timur

    2015-01-01

    The purpose of this study is to define teacher views about the difficulties in learning and teaching middle school statistics subjects. To serve this aim, a number of interviews were conducted with 10 middle school maths teachers in 2011-2012 school year in the province of Trabzon. Of the qualitative descriptive research methods, the…

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

    Science.gov (United States)

    Hudson, Diana

    2015-01-01

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

  12. Investigating Difficulties of Learning Computer Programming in Saudi Arabia

    Science.gov (United States)

    Alakeel, Ali M.

    2015-01-01

    Learning computer programming is one of the main requirements of many educational study plans in higher education. Research has shown that many students face difficulties acquiring reasonable programming skills during their first year of college. In Saudi Arabia, there are twenty-three state-owned universities scattered around the country that…

  13. DIFFICULTIES IN TEACHING AND LEARNING GRAMMAR IN AN EFL CONTEXT

    Directory of Open Access Journals (Sweden)

    Abdu Mohammed Al-Mekhlafi

    2011-07-01

    Full Text Available The role of grammar instruction in an ESL/EFL context has been for decades a major issue for students and teachers alike. Researchers have debated whether grammar should be taught in the classroom and students, for their part, have generally looked upon grammar instruction as a necessary evil at best, and an avoidable burden at worst. The paper reports a study undertaken to investigate the difficulties teachers face in teaching grammar to EFL students as well as those faced by students in learning it, in the teachers' perception. The study aimed to find out whether there are significant differences in teachers' perceptions of difficulties in relation to their gender, qualification, teaching experience, and the level they teach in school, thus providing insights into their own and their students' difficulties. Mean scores and t-test were used to interpret the data. The main findings are reported with implications.

  14. An Educational Application of Online Games for Learning Difficulties

    OpenAIRE

    M. Margoudi; Z. Smyrnaiou

    2015-01-01

    The current paper presents the results of a conducted case study. During the past few years the number of children diagnosed with Learning Difficulties has drastically augmented and especially the cases of ADHD (Attention Deficit Hyperactivity Disorder). One of the core characteristics of ADHD is a deficit in working memory functions. The review of the literature indicates a plethora of educational software that aim at training and enhancing the working memory. Neverthele...

  15. Psychological Characteristics of Personality in Students with Learning Difficulties

    Directory of Open Access Journals (Sweden)

    T A Shilova

    2014-12-01

    Full Text Available The article presents the research of the psychological characteristics of the personality of a student with learning difficulties from the position of the mismatch of mental development in operational sphere. The article considers the system of methods of research with the personality-oriented approach. The influence of certain psychological characteristics of a personality on the outcome of the study is revealed. The ways of building correctional-developing programmes for psychological preparation of junior schoolchildren for successful learning are shown.

  16. Multisensory perceptual learning is dependent upon task difficulty.

    Science.gov (United States)

    De Niear, Matthew A; Koo, Bonhwang; Wallace, Mark T

    2016-11-01

    There has been a growing interest in developing behavioral tasks to enhance temporal acuity as recent findings have demonstrated changes in temporal processing in a number of clinical conditions. Prior research has demonstrated that perceptual training can enhance temporal acuity both within and across different sensory modalities. Although certain forms of unisensory perceptual learning have been shown to be dependent upon task difficulty, this relationship has not been explored for multisensory learning. The present study sought to determine the effects of task difficulty on multisensory perceptual learning. Prior to and following a single training session, participants completed a simultaneity judgment (SJ) task, which required them to judge whether a visual stimulus (flash) and auditory stimulus (beep) presented in synchrony or at various stimulus onset asynchronies (SOAs) occurred synchronously or asynchronously. During the training session, participants completed the same SJ task but received feedback regarding the accuracy of their responses. Participants were randomly assigned to one of three levels of difficulty during training: easy, moderate, and hard, which were distinguished based on the SOAs used during training. We report that only the most difficult (i.e., hard) training protocol enhanced temporal acuity. We conclude that perceptual training protocols for enhancing multisensory temporal acuity may be optimized by employing audiovisual stimuli for which it is difficult to discriminate temporal synchrony from asynchrony.

  17. A Case Study on Learning Difficulties and Corresponding Supports for Learning in cMOOCs

    Science.gov (United States)

    Li, Shuang; Tang, Qi; Zhang, Yanxia

    2016-01-01

    cMOOCs, which are based on connectivist learning theory, bring challenges for learners as well as opportunities for self-inquiry. Previous studies have shown that learners in cMOOCs may have difficulties learning, but these studies do not provide any in-depth, empirical explorations of student difficulties or support strategies. This paper…

  18. Learning difficulties or learning English difficulties? Additional language acquisition: an update for paediatricians.

    Science.gov (United States)

    Clifford, Vanessa; Rhodes, Anthea; Paxton, Georgia

    2014-03-01

    Australia is a diverse society: 26% of the population were born overseas, a further 20% have at least one parent born overseas and 19% speak a language other than English at home. Paediatricians are frequently involved in the assessment and management of non-English-speaking-background children with developmental delay, disability or learning issues. Despite the diversity of our patient population, information on how children learn additional or later languages is remarkably absent in paediatric training. An understanding of second language acquisition is essential to provide appropriate advice to this patient group. It takes a long time (5 years or more) for any student to develop academic competency in a second language, even a student who has received adequate prior schooling in their first language. Refugee students are doubly disadvantaged as they frequently have limited or interrupted prior schooling, and many are unable to read and write in their first language. We review the evidence on second language acquisition during childhood, describe support for English language learners within the Australian education system, consider refugee-background students as a special risk group and address common misconceptions about how children learn English as an additional language. © 2013 The Authors. Journal of Paediatrics and Child Health © 2013 Paediatrics and Child Health Division (Royal Australasian College of Physicians).

  19. Assessing College-Level Learning Difficulties and "At Riskness" for Learning Disabilities and ADHD: Development and Validation of the Learning Difficulties Assessment

    Science.gov (United States)

    Kane, Steven T.; Walker, John H.; Schmidt, George R.

    2011-01-01

    This article describes the development and validation of the "Learning Difficulties Assessment" (LDA), a normed and web-based survey that assesses perceived difficulties with reading, writing, spelling, mathematics, listening, concentration, memory, organizational skills, sense of control, and anxiety in college students. The LDA is designed to…

  20. What teacher factors influence their attributions for children's difficulties in learning?

    Science.gov (United States)

    Brady, Katy; Woolfson, Lisa

    2008-12-01

    Identifying the factors that influence teacher beliefs about teaching children with learning difficulties is important for the success of inclusive education. This study explores the relationship between teachers' role, self-efficacy, attitudes towards disabled people, teaching experience and training, on teachers' attributions for children's difficulties in learning. One hundred and eighteen primary school teachers (44 general mainstream, 33 mainstream learning support, and 41 special education teachers) completed the short form of the Teachers' Sense of Efficacy Scale, the Interaction with Disabled Persons Scale (IDP), and a revised version of the Teacher Attribution Scale. Regression analysis found that teachers' role influenced stability and controllability attributions. However, for stability attributions the effect was not sustained when examined in the context of the other factors of teaching efficacy, experience, training, and attitudes towards disability. What emerged as important instead was strong feelings of sympathy towards disabled people which predicted stable attributions about learning difficulties. Experience of teaching children with additional support needs and teaching efficacy positively predicted external locus of causality attributions. Surprisingly, training was not found to have an impact on attributions. A mixed MANOVA found that mainstream teachers' controllability attributions were influenced by whether or not the child had identified learning support needs. Teacher efficacy, experience of teaching students with support needs, attitudes towards disabled people, and teachers' role all impact on teacher attributions, but no relationship with training was found. Implications for teacher training and development, and for student achievement and student self-perception are discussed.

  1. [Detection and specific studies in procedural learning difficulties].

    Science.gov (United States)

    Magallón, S; Narbona, J

    2009-02-27

    The main disabilities in non-verbal learning disorder (NLD) are: the acquisition and automating of motor and cognitive processes, visual spatial integration, motor coordination, executive functions, difficulty in comprehension of the context, and social skills. AIMS. To review the research to date on NLD, and to discuss whether the term 'procedural learning disorder' (PLD) would be more suitable to refer to NLD. A considerable amount of research suggests a neurological correlate of PLD with dysfunctions in the 'posterior' attention system, or the right hemisphere, or the cerebellum. Even if it is said to be difficult the delimitation between NLD and other disorders or syndromes like Asperger syndrome, certain characteristics contribute to differential diagnosis. Intervention strategies for the PLD must lead to the development of motor automatisms and problem solving strategies, including social skills. The basic dysfunction in NLD affects to implicit learning of routines, automating of motor skills and cognitive strategies that spare conscious resources in daily behaviours. These limitations are partly due to a dysfunction in non-declarative procedural memory. Various dimensions of language are also involved: context comprehension, processing of the spatial and emotional indicators of verbal language, language inferences, prosody, organization of the inner speech, use of language and non-verbal communication; this is why the diagnostic label 'PLD' would be more appropriate, avoiding the euphemistic adjective 'non-verbal'.

  2. Processes of Learning with Regard to Students’ Learning Difficulties in Mathematics

    Directory of Open Access Journals (Sweden)

    Amalija Zakelj

    2014-06-01

    Full Text Available In the introduction, we write about the process of learning mathematics: the development of mathematical concepts, numerical and spatial imagery on reading and understanding of texts, etc. The central part of the paper is devoted to the study, in which we find that identifying the learning processes associated with learning difficulties of students in mathematics, is not statistically significantly different between primary school teachers and teachers of mathematics. Both groups expose the development of numerical concepts, logical reasoning, and reading and understanding the text as the ones with which difficulties in learning mathematics appear the most frequently. All the processes of learning that the teachers assessed as the ones that represent the greatest barriers to learning have a fairly uniform average estimates of the degree of complexity, ranging from 2.6 to 2.8, which is very close to the estimate makes learning very difficult.

  3. Student Difficulties in Learning Density: A Distributed Cognition Perspective

    Science.gov (United States)

    Xu, Lihua; Clarke, David

    2012-08-01

    Density has been reported as one of the most difficult concepts for secondary school students (e.g. Smith et al. 1997). Discussion about the difficulties of learning this concept has been largely focused on the complexity of the concept itself or student misconceptions. Few, if any, have investigated how the concept of density was constituted in classroom interactions, and what consequences these interactions have for individual students' conceptual understanding. This paper reports a detailed analysis of two lessons on density in a 7th Grade Australian science classroom, employing the theory of Distributed Cognition (Hollan et al. 1999; Hutchins 1995). The analysis demonstrated that student understanding of density was shaped strongly by the public classroom discussion on the density of two metal blocks. It also revealed the ambiguities associated with the teacher demonstration and the student practical work. These ambiguities contributed to student difficulties with the concept of density identified in this classroom. The results of this study suggest that deliberate effort is needed to establish shared understanding not only about the purpose of the activities, but also about the meaning of scientific language and the utility of tools. It also suggests the importance of appropriate employment of instructional resources in order to facilitate student scientific understanding.

  4. Challenges in Teaching Mathematics: Perspectives From Students’ Learning Difficulties

    Directory of Open Access Journals (Sweden)

    Steve Chinn

    2016-04-01

    Full Text Available Alcock et al (2016, this issue have set out and discussed a potential research agenda for mathematical cognition. It is timely that research topics, along with knowledge uncovered to date, should be incorporated into a coordinated agenda for further research. This commentary focuses on the perspectives that learning difficulties, and dyscalculia, reveal. These perspectives potentially add much to that research agenda. [Commentary on: Alcock, L., Ansari, D., Batchelor, S., Bisson, M.-J., De Smedt, B., Gilmore, C., . . . Weber, K. (2016. Challenges in mathematical cognition: A collaboratively-derived research agenda. Journal of Numerical Cognition, 2, 20-41. doi:10.5964/jnc.v2i1.10

  5. CAN INFOGRAPHICS FACILITATE THE LEARNING OF INDIVIDUALS WITH MATHEMATICAL LEARNING DIFFICULTIES?

    Directory of Open Access Journals (Sweden)

    Basak Baglama

    2017-12-01

    Full Text Available Visualization of data has recently gained great importance in education and use of infographics is regarded as an important tool in teaching mathematics since it presents information in a clear and abstract way. Therefore, use of infographics for helping individuals with mathematical learning difficulties has become an important research question. This study aims to provide an overview on the use of infographics in teaching mathematics to individuals with mathematical learning difficulties. This is a qualitative study in which document analysis was used the collect the data. Results provided information about the definition of infographics, effectiveness of using infographics in education and facilitative role of infographics in enhancing learning of individuals with mathematical learning difficulties, namely dyscalculia. Results were discussed with relevant literature and recommendations for further research and practices were also presented.

  6. A componential view of children's difficulties in learning fractions

    Science.gov (United States)

    Gabriel, Florence; Coché, Frédéric; Szucs, Dénes; Carette, Vincent; Rey, Bernard; Content, Alain

    2013-01-01

    Fractions are well known to be difficult to learn. Various hypotheses have been proposed in order to explain those difficulties: fractions can denote different concepts; their understanding requires a conceptual reorganization with regard to natural numbers; and using fractions involves the articulation of conceptual knowledge with complex manipulation of procedures. In order to encompass the major aspects of knowledge about fractions, we propose to distinguish between conceptual and procedural knowledge. We designed a test aimed at assessing the main components of fraction knowledge. The test was carried out by fourth-, fifth- and sixth-graders from the French Community of Belgium. The results showed large differences between categories. Pupils seemed to master the part-whole concept, whereas numbers and operations posed problems. Moreover, pupils seemed to apply procedures they do not fully understand. Our results offer further directions to explain why fractions are amongst the most difficult mathematical topics in primary education. This study offers a number of recommendations on how to teach fractions. PMID:24133471

  7. A componential view of children's difficulties in learning fractions.

    Science.gov (United States)

    Gabriel, Florence; Coché, Frédéric; Szucs, Dénes; Carette, Vincent; Rey, Bernard; Content, Alain

    2013-01-01

    Fractions are well known to be difficult to learn. Various hypotheses have been proposed in order to explain those difficulties: fractions can denote different concepts; their understanding requires a conceptual reorganization with regard to natural numbers; and using fractions involves the articulation of conceptual knowledge with complex manipulation of procedures. In order to encompass the major aspects of knowledge about fractions, we propose to distinguish between conceptual and procedural knowledge. We designed a test aimed at assessing the main components of fraction knowledge. The test was carried out by fourth-, fifth- and sixth-graders from the French Community of Belgium. The results showed large differences between categories. Pupils seemed to master the part-whole concept, whereas numbers and operations posed problems. Moreover, pupils seemed to apply procedures they do not fully understand. Our results offer further directions to explain why fractions are amongst the most difficult mathematical topics in primary education. This study offers a number of recommendations on how to teach fractions.

  8. Evaluation of maths training programme for children with learning difficulties

    Directory of Open Access Journals (Sweden)

    Antje Ehlert

    2013-06-01

    The study at hand focuses on the question of whether educationally impaired children with large deficits in mathematics can be supported successfully by means of a highly adaptive support measure (MARKO-T, and whether the effects of this support can be maintained over a certain period. For this, 32 educationally impaired third-graders with math deficits were supported individually with MARKO-T twice a week, over a period of ten weeks. As control group, 32 similarly impaired third-graders were paralleled according to the mathematical and cognitive achievements of the training group. Two further control groups, each with 32 unimpaired first-graders, were paralleled according to their mathematical and cognitive achievements, respectively. The results showed that the very poor mathematical performance of the educationally impaired children could be significantly improved with this support programme. Four months after the end of the training, significant support effects could still be established when compared to the educationally impaired control group. The comparison with the two control groups demonstrated that the developmental curve of the children with learning difficulties increased in a way that was comparable to that of the unimpaired first-graders.

  9. Toward Self-Regulated Learning in Vocational Education: Difficulties and Opportunities

    NARCIS (Netherlands)

    Jossberger, Helen

    2011-01-01

    Jossberger, H. (2011). Toward Self-Regulated Learning in Vocational Education: Difficulties and Opportunities. Doctoral Thesis. June, 24, 2011, Heerlen, The Netherlands: Open Universiteit in the Netherlands.

  10. Involving users with learning difficulties in health improvement: lessons from inclusive learning disability research.

    Science.gov (United States)

    Walmsley, Jan

    2004-03-01

    In this paper the author considers the lessons to be drawn from what is termed "inclusive" learning disability research for user involvement around health improvement. Inclusive learning disability research refers to research where people with learning difficulties (intellectual disability) are involved as active participants, as opposed to passive subjects. There is by now a considerable body of such research, developed over the past 25 years. From the review, the author draws attention to areas which can inform practice in involvement of users in a way that adds value.

  11. Learners with learning difficulties in mathematics : attitudes, curriculum and methods of teaching mathematics

    OpenAIRE

    2012-01-01

    D.Ed. The aim of this theses is to find out whether there is any relationship between learners' attitudes and learning difficulties in mathematics: To investigate whether learning difficulties in mathematics are associated with learners' gender. To establish the nature of teachers' perceptions of the learning problem areas in the mathematics curriculum. To find out about the teachers' views on the methods of teaching mathematics, resources, learning of mathematics, extra curricular activit...

  12. Laptops Meet Schools, One-One Draw: M-Learning for Secondary Students with Literacy Difficulties

    Science.gov (United States)

    Conway, Paul F.; Amberson, Jessica

    2011-01-01

    Mobile technology-enhanced literacy initiatives have become a focus of efforts to support learning for students with literacy difficulties. The "Laptops Initiative for Post-Primary Students with Dyslexia or other Reading/Writing Difficulties" offers insights into and addresses questions about ICT policy making regarding m-learning technologies for…

  13. The Curriculum for Children with Severe and Profound Learning Difficulties at Stephen Hawking School

    Science.gov (United States)

    Rayner, Matthew

    2011-01-01

    The increasing number of children with profound and multiple learning difficulties means that many schools for children with severe learning difficulties are having to review the curriculum that they offer. In addition, these schools are continuing to question whether a subject-based approach, in line with the National Curriculum, is the most…

  14. Reconsidering Learning Difficulties and Misconceptions in Chemistry: Emergence in Chemistry and Its Implications for Chemical Education

    Science.gov (United States)

    Tümay, Halil

    2016-01-01

    Identifying students' misconceptions and learning difficulties and finding effective ways of addressing them has been one of the major concerns in chemistry education. However, the chemistry education community has paid little attention to determining discipline-specific aspects of chemistry that can lead to learning difficulties and…

  15. Identifying Students' Difficulties When Learning Technical Skills via a Wireless Sensor Network

    Science.gov (United States)

    Wang, Jingying; Wen, Ming-Lee; Jou, Min

    2016-01-01

    Practical training and actual application of acquired knowledge and techniques are crucial for the learning of technical skills. We established a wireless sensor network system (WSNS) based on the 5E learning cycle in a practical learning environment to improve students' reflective abilities and to reduce difficulties for the learning of technical…

  16. Beyond Stigmatization of Children with Difficulties in Learning

    Science.gov (United States)

    Hido, Margarita; Shehu, Irena

    2010-01-01

    In the Albanian schools settings does not exist religious discrimination, neither gender discrimination, but there exists a discrimination, as unfair against children called "difficulty". The children who drop out of school are by far less numerous compared with those who start school, but who are not properly treated, so that they can…

  17. [The comorbidity of learning difficulties and ADHD symptoms in primary-school-age children].

    Science.gov (United States)

    Schuchardt, Kirsten; Fischbach, Anne; Balke-Melcher, Christina; Mähler, Claudia

    2015-05-01

    Children having difficulties in acquiring early literacy and mathematical skills often show an increased rate of inattention, hyperactivity, and impulsivity. This study provides data on the comorbidity rates of specific learning difficulties and ADHD symptoms. We analyzed the data of 273 children with learning difficulties despite an at least average IQ, 57 children with low IQ, and 270 children without learning difficulties and average IQ (comparison group). We assessed children’s IQ and school achievement using standardized achievement tests. ADHD symptoms were assessed via parents’ ratings. Our results showed that only 5 % of both the control group and the group with solely mathematical difficulties fulfilled the criteria of an ADHD subtype according to the DSM-IV based on parents’ ratings. In contrast, this was the case in even 20 % of the children with difficulties in reading/writing and of those with low IQ. Compared to girls, boys in the control group had a 150% higher risk for matching the criteria of one of the ADHD subtypes in parents’ ratings, whereas boys with learning difficulties and those with low IQ had an even 200% to 600% higher risk for it. The relationship between learning difficulties and ADHD symptoms can be found predominantly in the inattentive type. Possible reasons for the results are discussed.

  18. Delaware Longitudinal Study of Fraction Learning: Implications for Helping Children With Mathematics Difficulties.

    Science.gov (United States)

    Jordan, Nancy C; Resnick, Ilyse; Rodrigues, Jessica; Hansen, Nicole; Dyson, Nancy

    The goal of the present article is to synthesize findings to date from the Delaware Longitudinal Study of Fraction Learning. The study followed a large cohort of children ( N = 536) between Grades 3 and 6. The findings showed that many students, especially those with diagnosed learning disabilities, made minimal growth in fraction knowledge and that some showed only a basic grasp of the meaning of a fraction even after several years of instruction. Children with low growth in fraction knowledge during the intermediate grades were much more likely to fail to meet state standards on a broad mathematics measure at the end of Grade 6. Although a range of general and mathematics-specific competencies predicted fraction outcomes, the ability to estimate numerical magnitudes on a number line was a uniquely important marker of fraction success. Many children with mathematics difficulties have deep-seated problems related to whole number magnitude representations that are complicated by the introduction of fractions into the curriculum. Implications for helping students with mathematics difficulties are discussed.

  19. common difficulties experienced by grade 12 students in learning

    African Journals Online (AJOL)

    Temechegn

    difficult topic student's experiences in learning chemistry is chemical bonding because ... students cooperative enterprise in science, organizing field trips, science ... related to organic, inorganic and physical chemistry that are to be learnt and ...

  20. ADULTS’ LEARNING IN A MULTILEVEL GROUP: DIFFICULTIES AND PROSPECTS

    Directory of Open Access Journals (Sweden)

    Salamatina, I.I.

    2017-12-01

    Full Text Available One of the necessary conditions of modernization of higher education system of the Russian Federation is to increase the level of academic mobility of the teaching community of Russian Universities. To solve this problem in 2014 in the State University of Humanities and Social Studies it was made the decision to organize the biennial English language courses for teachers of non-linguistic specialties, to enhance their level of proficiency. The greatest difficulty in teaching was because of different levels of language proficiency, so the teacher had to develop an effective methodology of teaching English for different levels of students.

  1. Learners' Listening Comprehension Difficulties in English Language Learning: A Literature Review

    Science.gov (United States)

    Gilakjani, Abbas Pourhosein; Sabouri, Narjes Banou

    2016-01-01

    Listening is one of the most important skills in English language learning. When students listen to English language, they face a lot of listening difficulties. Students have critical difficulties in listening comprehension because universities and schools pay more attention to writing, reading, and vocabulary. Listening is not an important part…

  2. A Review of the Approaches Investigating the Post-16 Transition of Young Adults with Learning Difficulties

    Science.gov (United States)

    Carroll, Catherine

    2015-01-01

    Investigations into the lives and transition from compulsory schooling of young adults with a disability, including a learning difficulty (LD), are increasing. The emerging consensus is one which points to this group of young people experiencing greater difficulties and poorer outcomes compared to the general population. How these investigations…

  3. Learning difficulties of senior high school students based on probability understanding levels

    Science.gov (United States)

    Anggara, B.; Priatna, N.; Juandi, D.

    2018-05-01

    Identifying students' difficulties in learning concept of probability is important for teachers to prepare the appropriate learning processes and can overcome obstacles that may arise in the next learning processes. This study revealed the level of students' understanding of the concept of probability and identified their difficulties as a part of the epistemological obstacles identification of the concept of probability. This study employed a qualitative approach that tends to be the character of descriptive research involving 55 students of class XII. In this case, the writer used the diagnostic test of probability concept learning difficulty, observation, and interview as the techniques to collect the data needed. The data was used to determine levels of understanding and the learning difficulties experienced by the students. From the result of students' test result and learning observation, it was found that the mean cognitive level was at level 2. The findings indicated that students had appropriate quantitative information of probability concept but it might be incomplete or incorrectly used. The difficulties found are the ones in arranging sample space, events, and mathematical models related to probability problems. Besides, students had difficulties in understanding the principles of events and prerequisite concept.

  4. Pedagogy framework design in social networked-based learning: Focus on children with learning difficulties

    Directory of Open Access Journals (Sweden)

    Samira Sadat Sajadi

    2014-09-01

    Full Text Available This paper presents an investigation on the theory of constructivism applicable for learners with learning difficulties, specifically learners with Attention Deficit Hyperactivity Disorder (ADHD. The primary objective of this paper is to determine whether a constructivist technology enhanced learning pedagogy could be used to help ADHD learners cope with their educational needs within a social-media learning environment. Preliminary work is stated here, in which we are seeking evidence to determine the viability of a constructivist approach for learners with ADHD. The novelty of this research lies in the proposals to support ADHD learners to overcome their weaknesses with appropriate pedagogically sound interventions. As a result, a framework has been designed to illuminate areas in which constructivist pedagogies require to address the limitations of ADHD learners. An analytical framework addressing the suitability of a constructivist learning for ADHD is developed from a combination of literature and expert advice from those involved in the education of learners with ADHD. This analytical framework is married to a new model of pedagogy, which the authors have derived from literature analysis. Future work will expand this model to develop a constructivist social network-based learning and eventually test it in specialist schools with ADHD learners.

  5. DIFFICULTIES THAT ARAB STUDENTS FACE IN LEARNING ENGLISH

    Directory of Open Access Journals (Sweden)

    Kassem BAHEEJ

    2015-11-01

    Full Text Available Jordan English is not used in everyday situations. Arab students face problems of learning English, both in writing and in speech. They find it hard to learn English in their native country, where language is Arabic. The only way to learn English in Jordan is through formal training, ie inside the classroom foreign language teachers are native speakers of Arabic. There is little opportunity to learn English through natural interaction in the target language. This is possible only when students are faced with native speakers of English who come to the country as tourists, and this happens very rarely.DIFICULTĂŢI CU CARE SE CONFRUNTĂ STUDENŢII ARABI CARE ÎNVAŢĂ LIMBA ENGLEZĂ În Iordania, limba engleză nu este utilizată în situaţii cotidiene. Studenţii arabi se confruntă cu probleme de învăţare a limbii engleze, atât în scris, cât şi în vorbire. Lor le vine greu să înveţe limba engleză în ţara lor natală, dat fiind că limba maternă este araba. Singura modalitate de a învăţa limba engleză în Iordania este prin instruire formală, adică în sala de clasă în care profesorii de limbă străină sunt vorbitori nativi de limbă arabă. Există puţine şanse de a învăţa limba engleză prin interacţiune naturală în limba-ţintă. Acest lucru este posibil numai atunci când elevii conversează cu vorbitori nativi de limbă engleză, care vin în ţară în calitate de turişti, ceea ce se întâmplă foarte rar.

  6. Effects of Semantic Ambiguity Detection Training on Reading Comprehension Achievement of English Learners with Learning Difficulties

    Science.gov (United States)

    Jozwik, Sara L.; Douglas, Karen H.

    2016-01-01

    This study examined how explicit instruction in semantic ambiguity detection affected the reading comprehension and metalinguistic awareness of five English learners (ELs) with learning difficulties (e.g., attention deficit/hyperactivity disorder, specific learning disability). A multiple probe across participants design (Gast & Ledford, 2010)…

  7. Greek Parents' Perceptions and Experiences regarding Their Children's Learning and Social-Emotional Difficulties

    Science.gov (United States)

    Adamopoulou, Eirini

    2010-01-01

    A survey instrument, the Test of Psychosocial Adaptation, originally developed for use with teachers in Greece, was given to 298 Greek parents in Athens and several rural areas. One hundred and five respondents indicated that their children exhibit learning and/or social-emotional learning difficulties. Parents rated higher externalizing behaviors…

  8. Storytelling Supported by Technology: An Alternative for EFL Children with Learning Difficulties

    Science.gov (United States)

    Lee, Sy-ying

    2012-01-01

    This action research aims to investigate how technology improves the conditions of storytelling to help enhance the learning attitude and motivation of EFL children with learning difficulty using power point designs and an online recording system--VoiceThread (http://voicethread.com/). The use of power point designs is to assure children of clear…

  9. EasyLexia 2.0: Redesigning Our Mobile Application for Children with Learning Difficulties

    Science.gov (United States)

    Skiada, Roxani; Soroniati, Eva; Gardeli, Anna; Zissis, Dimitrios

    2014-01-01

    Dyslexia is one of the most common learning difficulties affecting approximately 15 to 20 per cent of the world's population. A large amount of research is currently being conducted in exploring the potential benefits of using Information & Communication Technologies as a learning platform for individuals and especially children with such…

  10. Morphing Images: A Potential Tool for Teaching Word Recognition to Children with Severe Learning Difficulties

    Science.gov (United States)

    Sheehy, Kieron

    2005-01-01

    Children with severe learning difficulties who fail to begin word recognition can learn to recognise pictures and symbols relatively easily. However, finding an effective means of using pictures to teach word recognition has proved problematic. This research explores the use of morphing software to support the transition from picture to word…

  11. Enabling Pupils with Learning Difficulties to Reflect on Their Own Thinking.

    Science.gov (United States)

    Powell, Stuart D.; Makin, Michael

    1994-01-01

    Reports on a study of the impact of metacognition among 10 middle school-aged British students with learning difficulties. Finds that student awareness and subsequent control over thought processes were enhanced through self-reporting and self-appraisal. Examines this kind of reflection on enhanced learning capabilities and self-esteem. (CFR)

  12. Identifying College Students at Risk for Learning Disabilities: Evidence for Use of the Learning Difficulties Assessment in Postsecondary Settings

    Science.gov (United States)

    Kane, Steven T.; Roy, Soma; Medina, Steffanie

    2013-01-01

    This article describes research supporting the use of the Learning Difficulties Assessment (LDA), a normed and no-cost, web-based survey that assesses difficulties with reading, writing, spelling, mathematics, listening, concentration, memory, organizational skills, sense of control, and anxiety in college students. Previous research has supported…

  13. University Students with Reading Difficulties: Do Perceived Supports and Comorbid Difficulties Predict Well-being and GPA?

    Science.gov (United States)

    Stack-Cutler, Holly L.; Parrila, Rauno K.; Torppa, Minna

    2016-01-01

    We examined the impact of the number of comorbid difficulties, social support, and community support on life satisfaction and academic achievement among 120 university students or recent graduates with self-reported reading difficulties. Participants completed a questionnaire assessing perceived social support, perceived community support, the…

  14. Motor performance and learning difficulties in schoolchildren aged 7 to 10 years old

    Directory of Open Access Journals (Sweden)

    J. Silva

    2011-01-01

    Full Text Available The general objective of this study was to evaluate the motor performance of children with and without learning difficulty indicatives. Took part in the study 406 students aged 7 to 10 years old, being 231 girls (56.9% and 175 (43.1% boys enrolled in a municipal public school in São José, Santa Catarina, Brazil. The indicative of learning difficulties was verified through the TDE, while motor performance was evaluated with the MABC. Boys without learning difficulties had better performance in the majority of the abilities evaluated, beyond an association between the indicative of motor problems with learning difficulties towards writing, arithmetic, reading, and in general. On the other hand, female students of the sample with and without any indicative of learning difficulties did not differentiate themselves as to motor abilities evaluated, with an association merely between the indicative of motor problems and reading problems. Based on the differences identified between girls and boys, results call attention to the need for future research in this area, considering gender as a differential variable in this relationship.

  15. Dyslexia and English: Degree of Difficulties Faced by the Students with Dyslexia while Learning English

    Directory of Open Access Journals (Sweden)

    Paraskevi Kaperoni

    2016-12-01

    Full Text Available This research aims to investigate the hypothesis that students diagnosed with dyslexia face a greater amount of difficulty when they attempt to learn a foreign language and especially English. On a survey carried out in the form of a questionnaire, two groups of students completed the same questionnaire regarding their difficulty to learn the basic skills such as reading, writing, listening, and speaking. The questions mostly focused on the difficulty they may face in spelling, reading, and listening which are the main aspects of the language dyslexic students’ score lower than students without dyslexia. The answers were evaluated with the use of the statistical method of t-test. The findings of the survey displayed a great difference on the score chosen by the two teams, which indicates the greater degree of difficulty the dyslexic students face confirming the original hypothesis.

  16. Socioeconomic variation, number competence, and mathematics learning difficulties in young children.

    Science.gov (United States)

    Jordan, Nancy C; Levine, Susan C

    2009-01-01

    As a group, children from disadvantaged, low-income families perform substantially worse in mathematics than their counterparts from higher-income families. Minority children are disproportionately represented in low-income populations, resulting in significant racial and social-class disparities in mathematics learning linked to diminished learning opportunities. The consequences of poor mathematics achievement are serious for daily functioning and for career advancement. This article provides an overview of children's mathematics difficulties in relation to socioeconomic status (SES). We review foundations for early mathematics learning and key characteristics of mathematics learning difficulties. A particular focus is the delays or deficiencies in number competencies exhibited by low-income children entering school. Weaknesses in number competence can be reliably identified in early childhood, and there is good evidence that most children have the capacity to develop number competence that lays the foundation for later learning.

  17. Teachers' Perceptions of the Concomitance of Emotional Behavioural Difficulties and Learning Disabilities in Children Referred for Learning Disabilities in Oman

    Science.gov (United States)

    Emam, Mahmoud Mohamed; Kazem, Ali Mahdi

    2015-01-01

    Research has documented overlapping and coexisting characteristics of learning disabilities (LD) and emotional and behavioural difficulties (EBD). Such concomitance may impact teacher referrals of children at risk for LD which in turn may influence service delivery. Using the Learning Disabilities Diagnostic Inventory (LDDI) and the Strengths and…

  18. Importance and difficulties of cooperative learning application in class teaching from teachers' perspective

    Directory of Open Access Journals (Sweden)

    Ilić Marina Ž.

    2016-01-01

    Full Text Available Based on previous knowledge of cooperative learning two approaches stand out in researching the importance of cooperative learning: a the first approach tries to examine the effects, conditions and mechanisms by which educational outcomes are realized in the application of cooperative learning; and b the second approach moves the focus towards attitudes and perceptions of teachers and students on the relevance of cooperative learning. By applying descriptive-analytical technique we conducted a research aimed at examining the opinions of teachers (N=305 about the importance and difficulties in application of cooperative learning in the context of class teaching. The results show that the teachers had positive attitudes towards the importance of cooperative learning for reaching various educational goals and socio-affective and cognitive development of students. It turned out that the opinions of the teachers were not determined by the level of their education or work experience. Additionally, it turned out that the teachers' opinions about the difficulties of application in class are due more to work organization and were not assessed from the aspect of knowledge, attitudes and convictions of the participants in the teaching process. The obtained results, although generally encouraging for teaching practice indicate a need for further advancement of this segment of the teacher's work in order to understand better the value of cooperative learning and consider more critically the difficulties for its application in classroom.

  19. Workplace-based assessments of junior doctors: do scores predict training difficulties?

    Science.gov (United States)

    Mitchell, Colin; Bhat, Sarita; Herbert, Anne; Baker, Paul

    2011-12-01

    Workplace-based assessment (WPBA) is an increasingly important part of postgraduate medical training and its results may be used as evidence of professional competence. This study evaluates the ability of WPBA to distinguish UK Foundation Programme (FP) doctors with training difficulties and its effectiveness as a surrogate marker for deficiencies in professional competence. We conducted a retrospective observational study using anonymised records for 1646 trainees in a single UK postgraduate deanery. Data for WPBAs conducted from August 2005 to April 2009 were extracted from the e-portfolio database. These data included all scores submitted by trainees in FP years 1 and 2 on mini-clinical evaluation exercise (mini-CEX), case-based discussion (CbD), direct observation of procedural skills (DOPS) and mini-peer assessment tool (mini-PAT) assessments. Records of trainees in difficulty, as identified by their educational supervisors, were tagged as index cases. Main outcome measures were odds ratios (ORs) for associations between mean WPBA scores and training difficulties. Further analyses by the reported aetiology of the training difficulty (health-, conduct- or performance-related) were performed. Of the 1646 trainees, 92 had been identified as being in difficulty. Mean CbD and mini-CEX scores were lower for trainees in difficulty and an association was found between identified training difficulties and average scores on the mini-CEX (OR = 0.54; p = 0.034) and CbD (OR = 0.39; p = 0.002). A receiver operator characteristic curve analysis of mean WPBA scores for diagnosing 'in difficulty' status yielded an area under the curve of 0.64, indicating weak predictive value. There was no statistical evidence that mean scores on DOPS and mini-PAT assessments differed between the two groups. Analysis of a large dataset of WPBA scores revealed significant associations between training difficulties and lower mean scores on both the mini-CEX and CbD. Models show that using WPBA

  20. Learning Constructive Primitives for Real-time Dynamic Difficulty Adjustment in Super Mario Bros

    OpenAIRE

    Shi, Peizhi; Chen, Ke

    2017-01-01

    Among the main challenges in procedural content generation (PCG), content quality assurance and dynamic difficulty adjustment (DDA) of game content in real time are two major issues concerned in adaptive content generation. Motivated by the recent learning-based PCG framework, we propose a novel approach to seamlessly address two issues in Super Mario Bros (SMB). To address the quality assurance issue, we exploit the synergy between rule-based and learning-based methods to produce quality gam...

  1. Language delays, reading delays, and learning difficulties: interactive elements requiring multidimensional programming.

    Science.gov (United States)

    Hay, Ian; Elias, Gordon; Fielding-Barnsley, Ruth; Homel, Ross; Freiberg, Kate

    2007-01-01

    Researchers have hypothesized four levels of instructional dialogue and claimed that teachers can improve children's language development by incorporating these dialogue levels in their classrooms. It has also been hypothesized that enhancing children's early language development enhances children's later reading development. This quasi-experimental research study investigated both of these hypotheses using a collaborative service delivery model for Grade 1 children with language difficulties from a socially and economically disadvantaged urban community in Australia. Comparing the end-of-year reading achievement scores for the 57 children who received the language intervention with those of the 59 children in the comparison group, the findings from this research are supportive of both hypotheses. The interrelationships between learning difficulties, reading difficulties, and language difficulties are discussed along with children's development in vocabulary, use of memory strategies and verbal reasoning, and the need for multidimensional programming.

  2. Empowering Learners to Choose the Difficulty Level of Problems Based on Their Learning Needs

    Directory of Open Access Journals (Sweden)

    Janet Mannheimer Zydney

    2010-08-01

    Full Text Available Research has found that increasing learner control offers several benefits, including increased motivation, attitude, and learning. The goal of the present study was to determine how prior math achievement influences students' selection of the difficulty level of problems within Math Pursuits, a hypermedia learning program. Math Pursuits was designed to help children understand mathematics by discovering how it relates to the world around them. The program presented each learner with an adjustable level of challenge, along with the necessary scaffolding to support success. The researchers hypothesized that students with lower math skills would choose to start with a lower difficultly level; whereas, students with higher math skills would begin the program by choosing a question with a higher level of difficulty. Results supported these hypotheses. This research also examined the motivational framework guiding students' selection of problem difficulty.

  3. Self-Control of Task Difficulty During Early Practice Promotes Motor Skill Learning.

    Science.gov (United States)

    Andrieux, Mathieu; Boutin, Arnaud; Thon, Bernard

    2016-01-01

    This study was designed to determine whether the effect of self-control of task difficulty on motor learning is a function of the period of self-control administration. In a complex anticipation-coincidence task that required participants to intercept 3 targets with a virtual racquet, the task difficulty was either self-controlled or imposed to the participants in the two phases of the acquisition session. First, the results confirmed the beneficial effects of self-control over fully prescribed conditions. Second, the authors also demonstrated that a partial self-control of task difficulty better promotes learning than does a complete self-controlled procedure. Overall, the results revealed that these benefits are increased when this choice is allowed during early practice. The findings are discussed in terms of theoretical and applied perspectives.

  4. Mathematical Interventions for Secondary Students with Learning Disabilities and Mathematics Difficulties: A Meta-Analysis

    Science.gov (United States)

    Jitendra, Asha K.; Lein, Amy E.; Im, Soo-hyun; Alghamdi, Ahmed A.; Hefte, Scott B.; Mouanoutoua, John

    2018-01-01

    This meta-analysis is the first to provide a quantitative synthesis of empirical evaluations of mathematical intervention programs implemented in secondary schools for students with learning disabilities and mathematics difficulties. Included studies used a treatment-control group design. A total of 19 experimental and quasi-experimental studies…

  5. Survey of Complementary and Alternative Therapies Used by Children with Specific Learning Difficulties (Dyslexia)

    Science.gov (United States)

    Bull, Leona

    2009-01-01

    Background: Dyslexia is a common learning difficulty affecting up to 10% of British children that is associated with a wide range of cognitive, emotional and physical symptoms. In the absence of effective conventional treatment, it is likely that parents will seek complementary and alternative medicine (CAM) to try and help their children.…

  6. English Language Learning Difficulty of Korean Students in a Philippine Multidisciplinary University

    Science.gov (United States)

    de Guzman, Allan B.; Albela, Emmanuel Jeric A.; Nieto, Deborah Rosalind D.; Ferrer, John Bernard F.; Santos, Rior N.

    2006-01-01

    This qualitative study analyzed the English language learning difficulties of 13 purposively chosen Korean students relative to their sociolinguistic competence, motivation in using the English language, and cultural factors. Interview responses were transcribed, categorized and thematised according to saliency, meaning and homogeneity. The…

  7. Meta-analysis of fluid intelligence tests of children from the Chinese mainland with learning difficulties.

    Science.gov (United States)

    Tong, Fang; Fu, Tong

    2013-01-01

    To evaluate the differences in fluid intelligence tests between normal children and children with learning difficulties in China. PubMed, MD Consult, and other Chinese Journal Database were searched from their establishment to November 2012. After finding comparative studies of Raven measurements of normal children and children with learning difficulties, full Intelligent Quotation (FIQ) values and the original values of the sub-measurement were extracted. The corresponding effect model was selected based on the results of heterogeneity and parallel sub-group analysis was performed. Twelve documents were included in the meta-analysis, and the studies were all performed in mainland of China. Among these, two studies were performed at child health clinics, the other ten sites were schools and control children were schoolmates or classmates. FIQ was evaluated using a random effects model. WMD was -13.18 (95% CI: -16.50- -9.85). Children with learning difficulties showed significantly lower FIQ scores than controls (Pintelligence of children with learning difficulties was lower than that of normal children. Delayed development in sub-items of C, D, and E was more obvious.

  8. Do Chinese Dyslexic Children Have Difficulties Learning English as a Second Language?

    Science.gov (United States)

    Ho, Connie Suk-Han; Fong, Kin-Man

    2005-01-01

    The aim of the present study was to examine whether Chinese dyslexic children had difficulties learning English as a second language given the distinctive characteristics of the two scripts. Twenty-five Chinese primary school children with developmental dyslexia and 25 normally achieving children were tested on a number of English vocabulary,…

  9. Working Memory Deficits in ADHD: The Contribution of Age, Learning/Language Difficulties, and Task Parameters

    Science.gov (United States)

    Sowerby, Paula; Seal, Simon; Tripp, Gail

    2011-01-01

    Objective: To further define the nature of working memory (WM) impairments in children with combined-type ADHD. Method: A total of 40 Children with ADHD and an age and gender-matched control group (n = 40) completed two measures of visuo-spatial WM and two measures of verbal WM. The effects of age and learning/language difficulties on performance…

  10. Reflections on providing sport science support for athletes with learning difficulties.

    Science.gov (United States)

    Hills, Laura; Utley, Andrea

    2010-01-01

    To highlight the benefits and the need for sport science support for athletes with learning difficulties, and to reflect on our experience of working with the GB squad for athletes with learning difficulties. A review of key and relevant literature is presented, followed by a discussion of the sport science support provision and the issues that emerged in working with athletes with learning difficulties. Pre- and post- physiological tests along with evaluations of athletes' potential to benefit from sport psychology support were conducted. The aim of these tests was to provide information for the athletes and the coaches on fitness levels, to use this information to plan future training, and to identify how well the performance could be enhanced. A case study is presented for one athlete, who had competed in distance events. The focus is the psychological support that was provided. It is clear that athletes with learning difficulties require the same type of sports science support as their mainstream peers. However, sport scientists will need to consider ways to extend their practice in order to provide the appropriate level of support.

  11. A School-Based Movement Programme for Children with Motor Learning Difficulty

    Science.gov (United States)

    Mannisto, Juha-Pekka; Cantell, Marja; Huovinen, Tommi; Kooistra, Libbe; Larkin, Dawne

    2006-01-01

    The study investigated the effectiveness of a school-based movement programme for a population of 5 to 7 year old children. Performance profiles on the Movement ABC were used to classify the children and to assess skill changes over time. Children were assigned to four different groups: motor learning difficulty (n = 10), borderline motor learning…

  12. Forensic comparison and matching of fingerprints: using quantitative image measures for estimating error rates through understanding and predicting difficulty.

    Directory of Open Access Journals (Sweden)

    Philip J Kellman

    Full Text Available Latent fingerprint examination is a complex task that, despite advances in image processing, still fundamentally depends on the visual judgments of highly trained human examiners. Fingerprints collected from crime scenes typically contain less information than fingerprints collected under controlled conditions. Specifically, they are often noisy and distorted and may contain only a portion of the total fingerprint area. Expertise in fingerprint comparison, like other forms of perceptual expertise, such as face recognition or aircraft identification, depends on perceptual learning processes that lead to the discovery of features and relations that matter in comparing prints. Relatively little is known about the perceptual processes involved in making comparisons, and even less is known about what characteristics of fingerprint pairs make particular comparisons easy or difficult. We measured expert examiner performance and judgments of difficulty and confidence on a new fingerprint database. We developed a number of quantitative measures of image characteristics and used multiple regression techniques to discover objective predictors of error as well as perceived difficulty and confidence. A number of useful predictors emerged, and these included variables related to image quality metrics, such as intensity and contrast information, as well as measures of information quantity, such as the total fingerprint area. Also included were configural features that fingerprint experts have noted, such as the presence and clarity of global features and fingerprint ridges. Within the constraints of the overall low error rates of experts, a regression model incorporating the derived predictors demonstrated reasonable success in predicting objective difficulty for print pairs, as shown both in goodness of fit measures to the original data set and in a cross validation test. The results indicate the plausibility of using objective image metrics to predict expert

  13. Forensic comparison and matching of fingerprints: using quantitative image measures for estimating error rates through understanding and predicting difficulty.

    Science.gov (United States)

    Kellman, Philip J; Mnookin, Jennifer L; Erlikhman, Gennady; Garrigan, Patrick; Ghose, Tandra; Mettler, Everett; Charlton, David; Dror, Itiel E

    2014-01-01

    Latent fingerprint examination is a complex task that, despite advances in image processing, still fundamentally depends on the visual judgments of highly trained human examiners. Fingerprints collected from crime scenes typically contain less information than fingerprints collected under controlled conditions. Specifically, they are often noisy and distorted and may contain only a portion of the total fingerprint area. Expertise in fingerprint comparison, like other forms of perceptual expertise, such as face recognition or aircraft identification, depends on perceptual learning processes that lead to the discovery of features and relations that matter in comparing prints. Relatively little is known about the perceptual processes involved in making comparisons, and even less is known about what characteristics of fingerprint pairs make particular comparisons easy or difficult. We measured expert examiner performance and judgments of difficulty and confidence on a new fingerprint database. We developed a number of quantitative measures of image characteristics and used multiple regression techniques to discover objective predictors of error as well as perceived difficulty and confidence. A number of useful predictors emerged, and these included variables related to image quality metrics, such as intensity and contrast information, as well as measures of information quantity, such as the total fingerprint area. Also included were configural features that fingerprint experts have noted, such as the presence and clarity of global features and fingerprint ridges. Within the constraints of the overall low error rates of experts, a regression model incorporating the derived predictors demonstrated reasonable success in predicting objective difficulty for print pairs, as shown both in goodness of fit measures to the original data set and in a cross validation test. The results indicate the plausibility of using objective image metrics to predict expert performance and

  14. Emergence, Learning Difficulties, and Misconceptions in Chemistry Undergraduate Students' Conceptualizations of Acid Strength

    Science.gov (United States)

    Tümay, Halil

    2016-03-01

    Philosophical debates about chemistry have clarified that the issue of emergence plays a critical role in the epistemology and ontology of chemistry. In this article, it is argued that the issue of emergence has also significant implications for understanding learning difficulties and finding ways of addressing them in chemistry. Particularly, it is argued that many misconceptions in chemistry may derive from students' failure to consider emergence in a systemic manner by taking into account all relevant factors in conjunction. Based on this argument, undergraduate students' conceptions of acids, and acid strength (an emergent chemical property) were investigated and it was examined whether or not they conceptualized acid strength as an emergent chemical property. The participants were 41 third- and fourth-year undergraduate students. A concept test and semi-structured interviews were used to probe students' conceptualizations and reasoning about acid strength. Findings of the study revealed that the majority of the undergraduate students did not conceptualize acid strength as an emergent property that arises from interactions among multiple factors. They generally focused on a single factor to predict and explain acid strength, and their faulty responses stemmed from their failure to recognize and consider all factors that affect acid strength. Based on these findings and insights from philosophy of chemistry, promoting system thinking and epistemologically sound argumentative discourses among students is suggested for meaningful chemical education.

  15. Effects of Mathematics Anxiety and Mathematical Metacognition on Word Problem Solving in Children with and without Mathematical Learning Difficulties.

    Directory of Open Access Journals (Sweden)

    Yinghui Lai

    Full Text Available Mathematics is one of the most objective, logical, and practical academic disciplines. Yet, in addition to cognitive skills, mathematical problem solving also involves affective factors. In the current study, we first investigated effects of mathematics anxiety (MA and mathematical metacognition on word problem solving (WPS. We tested 224 children (116 boys, M = 10.15 years old, SD = 0.56 with the Mathematics Anxiety Scale for Children, the Chinese Revised-edition Questionnaire of Pupil's Metacognitive Ability in Mathematics, and WPS tasks. The results indicated that mathematical metacognition mediated the effect of MA on WPS after controlling for IQ. Second, we divided the children into four mathematics achievement groups including high achieving (HA, typical achieving (TA, low achieving (LA, and mathematical learning difficulty (MLD. Because mathematical metacognition and MA predicted mathematics achievement, we compared group differences in metacognition and MA with IQ partialled out. The results showed that children with MLD scored lower in self-image and higher in learning mathematics anxiety (LMA than the TA and HA children, but not in mathematical evaluation anxiety (MEA. MLD children's LMA was also higher than that of their LA counterparts. These results provide insight into factors that may mediate poor WPS performance which emerges under pressure in mathematics. These results also suggest that the anxiety during learning mathematics should be taken into account in mathematical learning difficulty interventions.

  16. Effects of Mathematics Anxiety and Mathematical Metacognition on Word Problem Solving in Children with and without Mathematical Learning Difficulties.

    Science.gov (United States)

    Lai, Yinghui; Zhu, Xiaoshuang; Chen, Yinghe; Li, Yanjun

    2015-01-01

    Mathematics is one of the most objective, logical, and practical academic disciplines. Yet, in addition to cognitive skills, mathematical problem solving also involves affective factors. In the current study, we first investigated effects of mathematics anxiety (MA) and mathematical metacognition on word problem solving (WPS). We tested 224 children (116 boys, M = 10.15 years old, SD = 0.56) with the Mathematics Anxiety Scale for Children, the Chinese Revised-edition Questionnaire of Pupil's Metacognitive Ability in Mathematics, and WPS tasks. The results indicated that mathematical metacognition mediated the effect of MA on WPS after controlling for IQ. Second, we divided the children into four mathematics achievement groups including high achieving (HA), typical achieving (TA), low achieving (LA), and mathematical learning difficulty (MLD). Because mathematical metacognition and MA predicted mathematics achievement, we compared group differences in metacognition and MA with IQ partialled out. The results showed that children with MLD scored lower in self-image and higher in learning mathematics anxiety (LMA) than the TA and HA children, but not in mathematical evaluation anxiety (MEA). MLD children's LMA was also higher than that of their LA counterparts. These results provide insight into factors that may mediate poor WPS performance which emerges under pressure in mathematics. These results also suggest that the anxiety during learning mathematics should be taken into account in mathematical learning difficulty interventions.

  17. Effects of Mathematics Anxiety and Mathematical Metacognition on Word Problem Solving in Children with and without Mathematical Learning Difficulties

    Science.gov (United States)

    Lai, Yinghui; Zhu, Xiaoshuang; Chen, Yinghe; Li, Yanjun

    2015-01-01

    Mathematics is one of the most objective, logical, and practical academic disciplines. Yet, in addition to cognitive skills, mathematical problem solving also involves affective factors. In the current study, we first investigated effects of mathematics anxiety (MA) and mathematical metacognition on word problem solving (WPS). We tested 224 children (116 boys, M = 10.15 years old, SD = 0.56) with the Mathematics Anxiety Scale for Children, the Chinese Revised-edition Questionnaire of Pupil’s Metacognitive Ability in Mathematics, and WPS tasks. The results indicated that mathematical metacognition mediated the effect of MA on WPS after controlling for IQ. Second, we divided the children into four mathematics achievement groups including high achieving (HA), typical achieving (TA), low achieving (LA), and mathematical learning difficulty (MLD). Because mathematical metacognition and MA predicted mathematics achievement, we compared group differences in metacognition and MA with IQ partialled out. The results showed that children with MLD scored lower in self-image and higher in learning mathematics anxiety (LMA) than the TA and HA children, but not in mathematical evaluation anxiety (MEA). MLD children’s LMA was also higher than that of their LA counterparts. These results provide insight into factors that may mediate poor WPS performance which emerges under pressure in mathematics. These results also suggest that the anxiety during learning mathematics should be taken into account in mathematical learning difficulty interventions. PMID:26090806

  18. Self-reported learning difficulties and dietary intake in Norwegian adolescents.

    Science.gov (United States)

    Øverby, Nina Cecilie; Lüdemann, Eva; Høigaard, Rune

    2013-11-01

    The academic performance of children impacts future educational attainment which may increase socioeconomic status which again influences their health. One of several factors that might affect academic performance is the diet. The aim of this study was to investigate the cross sectional relation between diet and self-reported reading-, writing-, and mathematical difficulties in Norwegian adolescents. In total, 475 ninth- and tenth-grade students out of 625 eligible ones from four different secondary schools in three different municipalities in Vest-Agder County, Norway, participated, giving a participation rate of 77%. The students filled in a questionnaire with food frequency questions of selected healthy and unhealthy food items, questions of meal frequency and different learning difficulties. Regular breakfast was significantly associated with decreased odds of both writing and reading difficulties (OR: 0.44 (0.2-0.8), p = 0.01) and mathematical difficulties (OR: 0.33 (0.2-0.6), p ≤ 0.001). In addition, having lunch, dinner and supper regularly were associated with decreased odds of mathematical difficulties. Further, a high intake of foods representing a poor diet (sugar-sweetened soft drinks, sweets, chocolate, savory snacks, pizza and hot dogs) was significantly associated with increased odds of mathematical difficulties. Having a less-frequent intake of unhealthy foods and not skipping meals are associated with decreased odds of self-reported learning difficulties in Norwegian adolescents in this study. The results of this study support the need for a larger study with a more representative sample.

  19. Associations among attitudes, perceived difficulty of learning science, gender, parents' occupation and students' scientific competencies

    Science.gov (United States)

    Chi, ShaoHui; Wang, Zuhao; Liu, Xiufeng; Zhu, Lei

    2017-11-01

    This study investigated the associations among students' attitudes towards science, students' perceived difficulty of learning science, gender, parents' occupations and their scientific competencies. A sample of 1591 (720 males and 871 females) ninth-grade students from 29 junior high schools in Shanghai completed a scientific competency test and a Likert scale questionnaire. Multiple regression analysis revealed that students' general interest of science, their parents' occupations and perceived difficulty of science significantly associated with their scientific competencies. However, there was no gender gap in terms of scientific competencies.

  20. Learner’s Learning Experiences & Difficulties towards (ESL among UKM Undergraduates

    Directory of Open Access Journals (Sweden)

    Nooreiny Maarof

    2015-06-01

    Full Text Available This paper aims to investigate the learners learning experiences and difficulties of ESL among the UKM undergraduates. This study will be focusing on identifying the factors behind Malaysian undergraduate’s experiences and also their difficulties in the English as Second Language (ESL classroom. This paper discusses some of the issues of English language learning experiences at the tertiary level in this country. It reflects on how the teaching of English is variously conceptualized in our classrooms, raising important questions about the positions of English literacy to Malaysian undergraduates. A qualitative research method was employed, whereby a semi-structured interview session was conducted compromising thirty Bachelor of Arts undergraduates (BA ELS. The findings of this study suggests learners at tertiary  level do face challenges in their ESL classroom learning,  in areas such as the learning environment itself needs to be improved, the quality of education, the academics, the role of educators and the teaching approach were among others pointed out by the learners themselves.  Keywords: English language teaching, English as Second language (ESL, learner’s experiences, learner’s difficulties, language learners

  1. Meta-analysis of fluid intelligence tests of children from the Chinese mainland with learning difficulties.

    Directory of Open Access Journals (Sweden)

    Fang Tong

    Full Text Available OBJECTIVE: To evaluate the differences in fluid intelligence tests between normal children and children with learning difficulties in China. METHOD: PubMed, MD Consult, and other Chinese Journal Database were searched from their establishment to November 2012. After finding comparative studies of Raven measurements of normal children and children with learning difficulties, full Intelligent Quotation (FIQ values and the original values of the sub-measurement were extracted. The corresponding effect model was selected based on the results of heterogeneity and parallel sub-group analysis was performed. RESULTS: Twelve documents were included in the meta-analysis, and the studies were all performed in mainland of China. Among these, two studies were performed at child health clinics, the other ten sites were schools and control children were schoolmates or classmates. FIQ was evaluated using a random effects model. WMD was -13.18 (95% CI: -16.50- -9.85. Children with learning difficulties showed significantly lower FIQ scores than controls (P<0.00001; Type of learning difficulty and gender differences were evaluated using a fixed-effects model (I² = 0%. The sites and purposes of the studies evaluated here were taken into account, but the reasons of heterogeneity could not be eliminated; The sum IQ of all the subgroups showed considerable heterogeneity (I² = 76.5%. The sub-measurement score of document A showed moderate heterogeneity among all documents, and AB, B, and E showed considerable heterogeneity, which was used in a random effect model. Individuals with learning difficulties showed heterogeneity as well. There was a moderate delay in the first three items (-0.5 to -0.9, and a much more pronounced delay in the latter three items (-1.4 to -1.6. CONCLUSION: In the Chinese mainland, the level of fluid intelligence of children with learning difficulties was lower than that of normal children. Delayed development in sub-items of C, D

  2. The Creation of Task-Based Differentiated Learning Materials for Students with Learning Difficulties and/or Disabilities.

    Science.gov (United States)

    Barker, Trevor; Jones, Sara; Britton, Carol; Messer, David

    This paper describes Horizon, a European-funded project designed to increase employment opportunities for students with disabilities or learning difficulties. The project established a working cafe/restaurant (Cafe Horizon) in East London staffed by students. Part of the project involved the creation of multimedia units linked directly to Level 1…

  3. "May We Please Have Sex Tonight?"--People with Learning Difficulties Pursuing Privacy in Residential Group Settings

    Science.gov (United States)

    Hollomotz, Andrea

    2009-01-01

    Many residential group settings for people with learning difficulties do not provide individuals with the private space in which they can explore their sexual relationships in a safe and dignified manner. Lack of agreed private spaces seriously infringes the individual's human rights. Many people with learning difficulties who lack privacy have no…

  4. Differential Constraints on the Working Memory and Reading Abilities of Individuals with Learning Difficulties and Typically Developing Children

    Science.gov (United States)

    Bayliss, Donna M.; Jarrold, Christopher; Baddeley, Alan D.; Leigh, Eleanor

    2005-01-01

    This study examined the factors that constrain the working memory span performance and reading ability of individuals with generalized learning difficulties. In the study, 50 individuals with learning difficulties (LD) and 50 typically developing children (TD) matched for reading age completed two working memory span tasks. Participants also…

  5. Perceived Difficulties in e-Learning During the First Term at University

    Directory of Open Access Journals (Sweden)

    Galina Kavaliauskienė

    2013-08-01

    Full Text Available Purpose—the focus of this article is to explore difficulties that are encountered by students during the first term at university. It is well known that students can have various problems in learning English and make mistakes in grammar, vocabulary, and pronunciation. The native language of a learner affects learning and using English. Speaking and e-listening are the skills that are more common on an everyday basis than reading and writing. Moreover, these skills are more difficult to master. English vocabulary presents another problem for language learners. Albeit, at the university level students study English for Specific Purposes (ESP, in other words, the foreign language for their future profession, and they might face particular difficulties in their studies of ESP. Design/methodology/approach—the research paper adopts the qualitative research approach. The questionnaire on learner perceptions of difficulties in e-learning was administered to students of three different specializations. Students’ self-assessments of achievements or failures were analysed. Findings. The results indicated that perceptions of difficulties to adapting to university studies depended on their chosen specialization. The findings show that undergraduates of all three investigated specializations encounter the same difficulties, but to a different degree. In other words, there are no significantly specific difficulties due to the complexity of the professional vocabulary that students must learn. The ratings of Psychology, Social Work and Public Policy and Management students reveal higher mean values and wider range of Standard Deviations than reported by other researchers (Berman, Cheng, 2001. The results obtained imply that Lithuanian learners are more positive than their foreign counterparts. Computations of Pearson’s correlations coefficients demonstrate that there are some good correlational relationships within each specialization. Research

  6. Visuospatial working memory in young adults and in children with learning difficulties

    OpenAIRE

    Ricardo Basso Garcia

    2013-01-01

    Visuospatial working memory (VSWM) comprises specialised subsystems devoted to storage of visual features and spatial locations. Recently, research has been focused on understanding feature binding in memory and how bound objects are temporarily held in working memory. In the current thesis we have addressed two broad questions: What is the nature of bound visual representations in working memory? Is there a specific deficit in binding in individuals with learning difficulties? In Study 1, yo...

  7. Influence of the temperature on materials electric behaviour: Understanding and students’ learning difficulties

    Directory of Open Access Journals (Sweden)

    Antonio García Carmona

    2006-03-01

    Full Text Available In this article, we defend that in the teaching/learning of the electricity, its contents must be associa ted with contents concerning the structure and behaviour of the matter. Thus, it is possible to understand some electricity topics as the influence of the temperature on electric behaviour of materials. In this sense, we propose a conceptual framework for its teaching, coherent with the Spanish Physics and Chemistry curriculum of Secondary Education. Likewise, we show the results of a research carried out with 60 pupils (age 14-15, about theirs understanding levels and theirs learning difficulties regarding considered topic.

  8. Difficulties of learning probability concepts, the reasons why these concepts cannot be learned and suggestions for solution

    Directory of Open Access Journals (Sweden)

    Dilek Sezgin MEMNUN

    2008-06-01

    Full Text Available Probability holds the first place among the subjects that both teachers and students have difficulty in handling. Although probability has an important role in many professions and a great many decisions we make for our daily lives, the understanding of the probability concepts is not an easy ability to gain for many students. Most of the students develop perception about lots of probability concepts and they have difficulty finding a reason for probability events. Thus, in the present study, the difficulties faced while learning probability concepts and the reasons why these concepts cannot be learned well are investigated, these reasons are tried to be put forward, and some suggestions for solutions regarding these concepts are presented. In this study, cross-hatching model was used. National and international studies on the subject of probability are investigated, the reasons why these concepts cannot be learned were categorized in the light of findings obtained, and the reasons why these concepts cannot be learned and taught are tried to be discovered. The categorization was displayed with Ishikawa diagram. In the diagram, the reasons why these concepts cannot be learned were noted as six categories. These categories were age, the insufficiency of advanced information, the deficiency of argumentation ability, teacher, error in concept, and students’ negative attitudes.

  9. Learning to Predict Chemical Reactions

    Science.gov (United States)

    Kayala, Matthew A.; Azencott, Chloé-Agathe; Chen, Jonathan H.

    2011-01-01

    Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles respectively are not high-throughput, are not generalizable or scalable, or lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry dataset consisting of 1630 full multi-step reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval, problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of non-productive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system

  10. Learning to predict chemical reactions.

    Science.gov (United States)

    Kayala, Matthew A; Azencott, Chloé-Agathe; Chen, Jonathan H; Baldi, Pierre

    2011-09-26

    Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top-ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of nonproductive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system

  11. Proband Mental Health Difficulties and Parental Stress Predict Mental Health in Toddlers at High-Risk for Autism Spectrum Disorders.

    Science.gov (United States)

    Crea, Katherine; Dissanayake, Cheryl; Hudry, Kristelle

    2016-10-01

    Family-related predictors of mental health problems were investigated among 30 toddlers at familial high-risk for autism spectrum disorders (ASD) and 28 controls followed from age 2- to 3-years. Parents completed the self-report Depression Anxiety Stress Scales and the parent-report Behavior Assessment System for Children. High-risk toddlers were assessed for ASD at 3-years. Parent stress and proband mental health difficulties predicted concurrent toddler mental health difficulties at 2-years, but only baseline proband internalising problems continued to predict toddler internalising problems at 3-years; high-risk status did not confer additional risk. Baseline toddler mental health difficulties robustly predicted later difficulties, while high-risk status and diagnostic outcome conferred no additional risk. A family systems perspective may be useful for understanding toddler mental health difficulties.

  12. Development of cognitive processes inschoolchildren with learning difficulties inthe light ofanalysis ofWISC-R results

    Directory of Open Access Journals (Sweden)

    Joanna Mazurkiewicz-Gronowska

    2012-09-01

    Full Text Available For several years now, noticeable has been a significant increase in the interest of psychologists – practitioners and scientists, of parents and teachers in the issues of dyslexia, dyscalculia, and other developmental disorders. Specific learning difficulties constitute one of the most prevalent causes of reporting children to psychological and pedagogic outpatient departments. The results of the performed studies enable inter- and intra-group comparisons as well as a global analysis of the structure of intellectual development in children with various learning difficulties. This leads to interesting conclusions and allows for comprehensive scientific discussions. The subject of the article is presentation of the results of studies and conclusions formulated according to them, about the structure of intellectual development of children with learning difficulties diagnosed in two psychological-pedagogic outpatient departments in Lublin region (Psychological-Pedagogic Outpatient Department No 5 in Lublin and PsychologicalPedagogic Outpatient Department No 2 in Zamość. Analysed were the results of the WISC-R scale obtained by schoolchildren from forms IV-VI of elementary schools and junior secondary schools in Lublin and schools of Zamość county. As scholastic difficulties constitute quite a comprehensive term, generally perceived as problems in acquisition of information and mastering school skills, in our study we take into account the following three groups of schoolchildren: with developmental dyslexia, intelligence lower than average, and specific disorders in arithmetic skills. The performed analyses are aimed at familiarization with the developmental level of the schoolchildren’s cognitive functions and their intellectual skills structure based on a three-factor analysis. Our studies continue earlier analyses, including more comprehensive research areas with larger groups.

  13. Mislabeled Reading and Learning Disabilities: Assessment and Treatment for Reading Difficulties in Students with Learning Disabilities

    Science.gov (United States)

    Sze, Susan

    2009-01-01

    Reading affects a plethora of areas in life. Students with learning disabilities often fall into this category due to a lack of practice with reading and less time to focus on building skills. This paper examines the background, the relationship between reading and learning disabilities, the characteristics of students with learning disabilities…

  14. STUDENTS’ DIFFICULTIES IN BIOCHEMISTRY LEARNING ANALYZED THROUGH AN ON LINE ACADEMIC DROP-IN CENTER

    Directory of Open Access Journals (Sweden)

    F. Schoenmaker

    2007-05-01

    Full Text Available The biochemistry discipline integrates the curriculum of all graduation courses onthe Biological Area and is a basis for other disciplines. The students’ difficulties inthis discipline are already widely recognized. To investigate these difficulties, wecreated a drop-in on line service that has two purposes: (1 To give support tostudents’ learning by answering their questions and solving their problemswhenever they appear and (2 to analyze the questions presented, as a strategy todiagnose the most prevalent difficulties. After two semesters of this service on line,217 questions were received and answered. There were few conceptual questionsbeing the majority related to problems and exercises. The most frequent questionsdealt with cell metabolism (54,4%, mainly lipid metabolism and aerobicmetabolism; basic concepts (11,5% such as about amino acids and buffer, proteinstructure (8,3% and enzymes (7,8%. These percentages are correlated to thenumber of hours dedicated to each subjects in the disciplines. The main difficultiesfounded were: integration of metabolic processes in different tissues, induction ofenergy reserve oxidation, reciprocal regulation between glycolysis andgluconeogenesis. We suppose that the lack of laboratory practice difficults thelearning of basic concepts. A more in-deep analysis will be necessary toinvestigate the causes of the pointed difficulties.

  15. Optimization of perceptual learning: effects of task difficulty and external noise in older adults.

    Science.gov (United States)

    DeLoss, Denton J; Watanabe, Takeo; Andersen, George J

    2014-06-01

    Previous research has shown a wide array of age-related declines in vision. The current study examined the effects of perceptual learning (PL), external noise, and task difficulty in fine orientation discrimination with older individuals (mean age 71.73, range 65-91). Thirty-two older subjects participated in seven 1.5-h sessions conducted on separate days over a three-week period. A two-alternative forced choice procedure was used in discriminating the orientation of Gabor patches. Four training groups were examined in which the standard orientations for training were either easy or difficult and included either external noise (additive Gaussian noise) or no external noise. In addition, the transfer to an untrained orientation and noise levels were examined. An analysis of the four groups prior to training indicated no significant differences between the groups. An analysis of the change in performance post-training indicated that the degree of learning was related to task difficulty and the presence of external noise during training. In addition, measurements of pupil diameter indicated that changes in orientation discrimination were not associated with changes in retinal illuminance. These results suggest that task difficulty and training in noise are factors important for optimizing the effects of training among older individuals. Copyright © 2013 Elsevier B.V. All rights reserved.

  16. Study on the Strategy of Transforming Students with Learning Difficulties in Polytechnic Schools

    Directory of Open Access Journals (Sweden)

    Fan Cui

    2014-05-01

    Full Text Available As demand of China’s social construction increasing and as the development of vocational education, polytechnic school plays an increasing important part, which, on the other hand, constitutes unprecedented challenges to the teaching in polytechnic schools. Most students, in the aspect of vocational education, are those from middle schools who have difficulties in their study. These students are entitled “Underachievers”. They are short in intellectual study and poor in curricular foundations. Teaching tasks cannot be satisfactorily accomplished in many of the polytechnic schools for the increasing number of underachievers. What have been harnessing the polytechnic school development is the poor study effect of these students. In this article, the internal reason of character and the external reason of social influence are analyzed as the cause that contributes to learning difficulties. And this article offers a pragmatic set of ideas for underachiever transformation.

  17. Helping students with learning difficulties in medical and health-care education.

    Science.gov (United States)

    Coles, C R

    1990-05-01

    In health profession education many more students than is currently acknowledged experience often extreme difficulties with their studying. This booklet is intended to help them. It outlines an approach being adopted in the Faculty of Medicine at the University of Southampton by which students are encouraged to reflect on and discuss their approaches to studying, identifying their perception of their task and where necessary changing this. It is shown that students need to elaborate their knowledge, that is to structure the factual information they are receiving and to relate it to their practical experiences. A number of suggestions are made to encourage this, and their theoretical underpinnings are discussed. It is concluded that while inappropriate curricula and teaching methods and not some weakness on the part of students are largely the cause of learning difficulties, it will take time to change these. Establishing a kind of 'clinic' for helping students cope can be of value immediately.

  18. On the learning difficulty of visual and auditory modal concepts: Evidence for a single processing system.

    Science.gov (United States)

    Vigo, Ronaldo; Doan, Karina-Mikayla C; Doan, Charles A; Pinegar, Shannon

    2018-02-01

    The logic operators (e.g., "and," "or," "if, then") play a fundamental role in concept formation, syntactic construction, semantic expression, and deductive reasoning. In spite of this very general and basic role, there are relatively few studies in the literature that focus on their conceptual nature. In the current investigation, we examine, for the first time, the learning difficulty experienced by observers in classifying members belonging to these primitive "modal concepts" instantiated with sets of acoustic and visual stimuli. We report results from two categorization experiments that suggest the acquisition of acoustic and visual modal concepts is achieved by the same general cognitive mechanism. Additionally, we attempt to account for these results with two models of concept learning difficulty: the generalized invariance structure theory model (Vigo in Cognition 129(1):138-162, 2013, Mathematical principles of human conceptual behavior, Routledge, New York, 2014) and the generalized context model (Nosofsky in J Exp Psychol Learn Mem Cogn 10(1):104-114, 1984, J Exp Psychol 115(1):39-57, 1986).

  19. Difficulty in Learning Similar-Sounding Words: A Developmental Stage or a General Property of Learning?

    Science.gov (United States)

    Pajak, Bozena; Creel, Sarah C.; Levy, Roger

    2016-01-01

    How are languages learned, and to what extent are learning mechanisms similar in infant native-language (L1) and adult second-language (L2) acquisition? In terms of vocabulary acquisition, we know from the infant literature that the ability to discriminate similar-sounding words at a particular age does not guarantee successful word-meaning…

  20. Analysis of difficulties in mathematics learning on students with guardian personality type in problem-solving HOTS geometry test

    Science.gov (United States)

    Karimah, R. K. N.; Kusmayadi, T. A.; Pramudya, I.

    2018-04-01

    Learning in the current 2013 curriculum is based on contextual issues based on questions that can encourage students to think broadly. HOTS is a real-life based assessment of everyday life, but in practice, the students are having trouble completing the HOTS issue. Learning difficulty is also influenced by personality type Based on the fact that the real difference one can see from a person is behavior. Kersey classifies the personality into 4 types, namely Idealist, Rational, Artisan, and Guardian. The researcher focuses on the type of guardian personality that is the type of personality that does not like the picture. This study aims to describe the difficulty of learning mathematics in students with a type of guardian personality in the completion of Geometry materials especially in solving HOTS. This research type is descriptive qualitative research. Instruments used in this study were the researchers themselves, personality class test sheets, learning difficulty test sheets in the form of HOTS Geometry test, and interview guides. The results showed that students with guardian personality it was found that a total of 3.37 % difficulties of number fact skill, 4.49 % difficulties of arithmetics skill, 37.08 % difficulties of information skill, 31.46% difficulties of language skill, 23.60 % difficulties of visual-spatial skill.

  1. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Directory of Open Access Journals (Sweden)

    Saerom Park

    Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  2. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Science.gov (United States)

    Park, Saerom; Lee, Jaewook; Son, Youngdoo

    2016-01-01

    Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  3. [Executive functioning and motivation in preschool children at risk for learning difficulties in mathematics].

    Science.gov (United States)

    Presentación-Herrero, M Jesús; Mercader-Ruiz, Jessica; Siegenthaler-Hierro, Rebeca; Fernández-Andrés, Inmaculada; Miranda-Casas, Ana

    2015-02-25

    Early identification of the factors involved in the development of learning difficulties in mathematics is essential to be able to understand their origin and implement successful interventions. This study analyses the capacity of executive functioning and of variables from the motivational belief system to differentiate and classify preschool children with and without risk of having difficulties in mathematics. A total of 146 subjects from the third year of preschool education took part in the study, divided into risk/no risk according to the score obtained on the operations subtest of the TEDI-MATH test. Working memory (verbal and visuospatial) and inhibition (with auditory and visual stimuli) neuropsychological tasks were applied. Teachers filled in a questionnaire on the children's motivation with regard to learning. Significant differences were found between the two groups on the working memory and inhibition-auditory factors, as well as on all the motivation variables. The results also show a similar power of classification, with percentages above 80%, for both groups of variables. The implications of these findings for educational practice are discussed.

  4. A Meta-Analysis of Working Memory Deficits in Children with Learning Difficulties: Is There a Difference between Verbal Domain and Numerical Domain?

    Science.gov (United States)

    Peng, Peng; Fuchs, Douglas

    2016-01-01

    Children with learning difficulties suffer from working memory (WM) deficits. Yet the specificity of deficits associated with different types of learning difficulties remains unclear. Further research can contribute to our understanding of the nature of WM and the relationship between it and learning difficulties. The current meta-analysis…

  5. Building Knowledge Structures by Testing Helps Children With Mathematical Learning Difficulty.

    Science.gov (United States)

    Zhang, Yiyun; Zhou, Xinlin

    2016-01-01

    Mathematical learning difficulty (MLD) is prevalent in the development of mathematical abilities. Previous interventions for children with MLD have focused on number sense or basic mathematical skills. This study investigated whether mathematical performance of fifth grade children with MLD could be improved by developing knowledge structures by testing using a web-based curriculum learning system. A total of 142 children with MLD were recruited; half of the children were in the experimental group (using the system), and the other half were in the control group (not using the system). The children were encouraged to use the web-based learning system at home for at least a 15-min session, at least once a week, for one and a half months. The mean accumulated time of testing on the system for children in the experimental group was 56.2 min. Children in the experimental group had significantly higher scores on their final mathematical examination compared to the control group. The results suggest that web-based curriculum learning through testing that promotes the building of knowledge structures for a mathematical course was helpful for children with MLD. © Hammill Institute on Disabilities 2014.

  6. Factors Predicting Difficulty of Laparoscopic Low Anterior Resection for Rectal Cancer with Total Mesorectal Excision and Double Stapling Technique.

    Directory of Open Access Journals (Sweden)

    Weiping Chen

    Full Text Available Laparoscopic sphincter-preserving low anterior resection for rectal cancer is a surgery demanding great skill. Immense efforts have been devoted to identifying factors that can predict operative difficulty, but the results are inconsistent.Our study was conducted to screen patients' factors to build models for predicting the operative difficulty using well controlled data.We retrospectively reviewed records of 199 consecutive patients who had rectal cancers 5-8 cm from the anal verge. All underwent laparoscopic sphincter-preserving low anterior resections with total mesorectal excision (TME and double stapling technique (DST. Data of 155 patients from one surgeon were utilized to build models to predict standardized endpoints (operative time, blood loss and postoperative morbidity. Data of 44 patients from other surgeons were used to test the predictability of the built models.Our results showed prior abdominal surgery, preoperative chemoradiotherapy, tumor distance to anal verge, interspinous distance, and BMI were predictors for the standardized operative times. Gender and tumor maximum diameter were related to the standardized blood loss. Temporary diversion and tumor diameter were predictors for postoperative morbidity. The model constructed for the operative time demonstrated excellent predictability for patients from different surgeons.With a well-controlled patient population, we have built a predictable model to estimate operative difficulty. The standardized operative time will make it possible to significantly increase sample size and build more reliable models to predict operative difficulty for clinical use.

  7. Investigating Cognitive Task Difficulties and Expert Skills in E-Learning Storyboards Using a Cognitive Task Analysis Technique

    Science.gov (United States)

    Yusoff, Nor'ain Mohd; Salim, Siti Salwah

    2012-01-01

    E-learning storyboards have been a useful approach in distance learning development to support interaction between instructional designers and subject-matter experts. Current works show that researchers are focusing on different approaches for use in storyboards, and there is less emphasis on the effect of design and process difficulties faced by…

  8. Predicting ADHD in school age when using the Strengths and Difficulties Questionnaire in preschool age

    DEFF Research Database (Denmark)

    Rimvall, Martin K; Elberling, Hanne; Rask, Charlotte Ulrikka

    2014-01-01

    Indicated prevention of ADHD may reduce impairment and need of treatment in youth. The Strengths and Difficulties Questionnaire (SDQ) is a brief questionnaire assessing child mental health, reported to be a valid screening instrument for concurrent ADHD. This study aimed to examine the validity o...... can identify a group of children with highly increased risk of later being diagnosed and/or treated for ADHD in school age....... of using the SDQ in preschool age to predict ADHD in school age in a longitudinal design. The study population included 2,315 children from the Copenhagen child cohort 2000 with no prior history of clinically diagnosed ADHD, who were assessed at age 5-7 years by the SDQ completed by parents and preschool...... regression analyses estimated the risk of later ADHD diagnosis for screen-positive children. A total of 2.94% of the study population were clinically diagnosed and/or were treated with central stimulants for ADHD before age 11-12. Children with possible/probable disorder according to the SDQ hyperactivity...

  9. Mathematical difficulties in nonverbal learning disability or co-morbid dyscalculia and dyslexia.

    Science.gov (United States)

    Mammarella, Irene C; Bomba, Monica; Caviola, Sara; Broggi, Fiorenza; Neri, Francesca; Lucangeli, Daniela; Nacinovich, Renata

    2013-01-01

    The main goal of the present study was to shed further light on the weaknesses of children with different profiles of mathematical difficulties, testing children with nonverbal learning disability (NLD), co-morbid dyscalculia and dyslexia (D&D), or typical development (TD). Sixteen children with NLD, 15 with D&D, and 16 with TD completed tasks derived from Butterworth (2003 ) and divided into: a capacity subscale (i.e., a number-dots comparison task, a number comparison task, and a dots comparison task); and an achievement subscale (i.e., mental calculations and arithmetical fact retrieval). Children with NLD were impaired in the dots comparison task, children with D&D in the mental calculation and arithmetical facts.

  10. How does the legal system respond when children with learning difficulties are victimized?

    Science.gov (United States)

    Cederborg, Ann-Christin; Lamb, Michael E

    2006-05-01

    To understand how the Swedish legal system perceives and handles mentally handicapped children who may have been victimized. Twenty-two judicial districts in Sweden provided complete files on 39 District Court cases (including the Appeals Court files on 17 of these cases) involving children with learning difficulties or other handicaps as alleged victims of abuse, threat and neglect. The children (25 girls and 14 boys) averaged 11.8 years of age when first allegedly victimized. Sexual abuse was the most frequently alleged crime (33 cases). Court transcripts, court files and expert assessments of the alleged victims' handicaps and their possible consequences were examined to elucidate the ways in which courts evaluated the credibility of the alleged victims. The children's reports of their victimization were expected to have the characteristics emphasized by proponents of Statement Reality Analysis (SRA) and Criterion Based Content Analysis (CBCA) in order to be deemed credible. Expert reports were seldom available or adequate. Because many reports were poorly written or prepared by experts who lacked the necessary skills, courts were left to rely on their own assumptions and knowledge when evaluating children's capacities and credibility. Children with learning difficulties or other handicaps were expected to provide the same sort of reports as other children. To minimize the risk that judgments may be based on inaccurate assumptions courts need to require more thorough assessments of children's limitations and their implications. Assessments by competent mental health professionals could inform and strengthen legal decision-making. A standardized procedure that included psycho-diagnostic instruments would allow courts to understand better the abilities, capacities, and behavior of specific handicapped children.

  11. Attitudes and learning difficulties in middle school science in South Korea

    Science.gov (United States)

    Jung, Eun Sook

    The purpose of this study is to investigate the relationship between cognitive and attitudinal aspects of learning science, concentrating mainly on the influence of cognitive understanding and learning difficulty on attitudes to science. This theme is selected, in particular, because it is reported that Korean students at secondary level do not enjoy studying science and have not enough confidence, although their achievements are high. Johnstone's information processing model (1993) is used to account for cognitive aspects of science education. Learning processes are understood in terms of student's own knowledge construction through the operation of perception filters, processing in working memory space and storing in long term memory. In particular, the overload of student's working memory space is considered as the main factor causing learning difficulty and, in consequence, learning failure. The research took place in one middle school located in Seoul, the capital city in South Korea. 364 students aged 13 and 350 aged 15 participated. In order to try to find relationships between cognitive and affective factors of science learning, individual student's working memory space was measured and a questionnaire designed to gather information about students' attitudes was prepared and given to all students. To determine the working memory space capacity of the students, the Figural Intersection Test (F.I.T), designed by Pascual-Leone, was used. Two kinds of analysis, comparison and correlation, were performed with data from the Figural Intersection Test and the questionnaire applied to students. For the comparison of attitudes between age 13 and 15, the distributions of frequencies of responses were analyzed for each particular statement in a question. The Chi-square (?[2]) test was applied to judge the statistically significant differences in responses of the two groups. The levels of significance used were 0.05, 0.01 and 0.001. In order to see whether there is

  12. Difficulties that Students who Learn Turkish as a Foreign Language Encounter During Listening Skills

    Directory of Open Access Journals (Sweden)

    Abdullah KALDIRIM

    2017-04-01

    Full Text Available Listening skills play an important role in an individual’s communication with others and in their understanding of the environment. Since it provides a basis for the acquisition of language skills it is one of the most important learning tools, and because it is frequently used in everyday life and in the learning process, listening skill is the foreground of foreign language teaching. It is important for students to understand what they listen to in order that they do not encounter any difficulties in the language learning process. To ensure success in the environments where the Turkish language is taught as a foreign language, it is necessary to follow the listening processes of the students attentively and to identify the problems they face during this process. This study aims to identify the listening barriers encountered by university students learning Turkish as a foreign language at level B2, and was designed based on a qualitative research approach and a phenomenological design. Within the scope of the study, eight students studying at Dumlupınar University’s TÖMER (Turkish & Foreign Languages Research and Application Center were identified as participants. Data were collected through semi-structured interviews conducted with students included in the sample, and descriptive analysis technique was applied in the analysis of the research data. Participants expressed views that they often encountered problems such as accented speech, frequent use of idioms and proverbs during listening, lack of vocabulary development, and lack of emphasis and voice intonation during speech. Also, factors that make listening easy to understand are identified as the other languages they speak, good vocabulary knowledge, interesting topics, listening to audiovisual elements, and the speaker’s use of gestures and mimics.

  13. Using Machine Learning to Predict Student Performance

    OpenAIRE

    Pojon, Murat

    2017-01-01

    This thesis examines the application of machine learning algorithms to predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in terms of how much they improve the prediction performance. Three different machine learning methods were used in this thesis. They are linear regression, decision trees, and naïve Bayes classification. Feature engineering, the process of modification ...

  14. Difficulties in Learning English Faced By Visually Impaired Students at Center of Language Development (P2B in State Islamic University (UIN Sunan Kalijaga

    Directory of Open Access Journals (Sweden)

    Widya Aryanti

    2014-12-01

    The result shows that there are some difficulties faced by VIS. These difficulties can be put into two different categories: internal and external difficulties. Internal difficulties come from the VIS themselves which relates to VIS’ sight conditions and their learning strategies. External difficulties come from the learning environment including difficulties from the lecturer, friends, materials and the facilities.VIS have different learning strategies. The lecturer should discuss some classroom adaptations such as seating arrangement, friends’ assistance and peer teaching, adapted facilities and exam accommodation, for instance exam assistance, longer exam time, inclusive examination and larger print for low vision students. Finally, the lecturer should choose appropriate teaching strategies, media and teaching aids.

  15. Video Scene Parsing with Predictive Feature Learning

    OpenAIRE

    Jin, Xiaojie; Li, Xin; Xiao, Huaxin; Shen, Xiaohui; Lin, Zhe; Yang, Jimei; Chen, Yunpeng; Dong, Jian; Liu, Luoqi; Jie, Zequn; Feng, Jiashi; Yan, Shuicheng

    2016-01-01

    In this work, we address the challenging video scene parsing problem by developing effective representation learning methods given limited parsing annotations. In particular, we contribute two novel methods that constitute a unified parsing framework. (1) \\textbf{Predictive feature learning}} from nearly unlimited unlabeled video data. Different from existing methods learning features from single frame parsing, we learn spatiotemporal discriminative features by enforcing a parsing network to ...

  16. Predicting Process Behaviour using Deep Learning

    OpenAIRE

    Evermann, Joerg; Rehse, Jana-Rebecca; Fettke, Peter

    2016-01-01

    Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real da...

  17. The Relationships between Indonesian Fourth Graders’ Difficulties in Fractions and the Opportunity to Learn Fractions: A Snapshot of TIMSS Results

    OpenAIRE

    Ariyadi Wijaya

    2017-01-01

    This paper reports an exploration into Indonesian fourth graders’ difficulties in fractions and their relation to the opportunity to learn fractions students got at schools. The concept of ‘opportunity to learn’ is often considered as a framework to investigate possible reasons for students’ difficulties. The data for this study was drawn from TIMSS 2015 that comprised test results and teachers’ responses to TIMSS Teacher Questionnaire. The test and questionnaire data were anal...

  18. Differential constraints on the working memory and reading abilities of individuals with learning difficulties and typically developing children.

    Science.gov (United States)

    Bayliss, Donna M; Jarrold, Christopher; Baddeley, Alan D; Leigh, Eleanor

    2005-09-01

    This study examined the factors that constrain the working memory span performance and reading ability of individuals with generalized learning difficulties. In the study, 50 individuals with learning difficulties (LD) and 50 typically developing children (TD) matched for reading age completed two working memory span tasks. Participants also completed independent measures of the processing and storage operations involved in each working memory span task and Raven's Coloured Progressive Matrices. The results showed that despite an equivalent level of working memory span, the relative importance of the constraints on working memory differed between the groups. In addition, working memory span was not closely related to word recognition or sentence comprehension performance in the LD group. These results suggest that the working memory span performance of LD and TD individuals may reflect different working memory limitations and that individuals with generalized learning difficulties may approach cognitive tasks in a qualitatively different way from that of typically developing individuals.

  19. Difficulties Of Self-Learning To The Open Arab University Students In The Sultanate Of Oman From The Perspective Of The Students

    Directory of Open Access Journals (Sweden)

    Dr. Mahmoud Mohamed Ali

    2015-08-01

    Full Text Available This study aimed to reveal the most important difficulties that hinder the Arab Open University in Oman students for the practice of self-learning method in their studies has been rated difficulties to the three pillars namely difficulties related to students and the skills of self-learning and difficulties related to teachers and methods of teaching and difficulties related to the curriculum and learning resources and after the application of the study of the identification of the difficulties tool 200 of university students 697 study concluded that many of the results that were notably that the difficulties related to students and the skills of self-learning more difficulties impeding the exercise of self-learning compared to the difficulties related to the other two mentioned and the students ability to connect and communicate and to evaluate themselves and correct educational careers and their ability to control their behavior and direct their activities toward self-learning are more difficulties influential and disability for students on their ability to exercise self-learning in their study of the Arab open University and as for the axis of teachers and teaching methods has shown results weakness encourage teachers to students to apply and practice this kind of learning and focus on traditional methods and weak development of skills for self-learning is one of the more difficulties that limit the exercise of the students of this method of learning and for the focus of the curriculum and learning resources has shown results of the study that the lack of educational software miscellaneous non-availability of electronic research base and lack of stimulating courses on the exercise of self-learning method is one of the most difficulties that hinder students ability to exercise self-learning.

  20. Recent Advances in Predictive (Machine) Learning

    Energy Technology Data Exchange (ETDEWEB)

    Friedman, J

    2004-01-24

    Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.

  1. A Componential View of Children’s Difficulties in Learning Fractions

    Directory of Open Access Journals (Sweden)

    Florence Claude Gabriel

    2013-10-01

    Full Text Available Fractions are well known to be difficult to learn. Various hypotheses have been proposed in order to explain those difficulties: fractions can denote different concepts; their understanding requires a conceptual reorganisation with regard to natural numbers; and using fractions involves the articulation of conceptual knowledge with complex manipulation of procedures. In order to encompass the major aspects of knowledge about fractions, we propose to distinguish between conceptual and procedural knowledge. We designed a test aimed at assessing the main components of fraction knowledge. The test was carried out by fourth-, fifth- and sixth-graders from the French Community of Belgium. The results showed large differences between categories. Pupils seemed to master the part-whole concept, whereas numbers and operations posed problems. Moreover, pupils seemed to apply procedures they do not fully understand. Our results offer further directions to explain why fractions are amongst the most difficult mathematical topics in primary education. This study offers a number of recommendations on how to teach fractions.

  2. Signed reward prediction errors drive declarative learning

    NARCIS (Netherlands)

    De Loof, E.; Ergo, K.; Naert, L.; Janssens, C.; Talsma, D.; van Opstal, F.; Verguts, T.

    2018-01-01

    Reward prediction errors (RPEs) are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning). However, empirical evidence on whether RPEs drive declarative learning–a quintessentially human form of learning–remains surprisingly absent. We

  3. Predicting Learned Helplessness Based on Personality

    Science.gov (United States)

    Maadikhah, Elham; Erfani, Nasrollah

    2014-01-01

    Learned helplessness as a negative motivational state can latently underlie repeated failures and create negative feelings toward the education as well as depression in students and other members of a society. The purpose of this paper is to predict learned helplessness based on students' personality traits. The research is a predictive…

  4. Ingestive Skill Difficulties are Frequent Among Acutely-Hospitalized Frail Elderly Patients, and Predict Hospital Outcomes

    DEFF Research Database (Denmark)

    Hansen, Tina; Lambert, Heather; Faber, Jens

    2012-01-01

    Purpose : To examine the relationship between ingestive skill performance while eating and drinking and frailty status in acutely-hospitalized elderly patients and to examine whether there is a relationship between the proportion of ingestive skill difficulties and Length of Hospital Stay (LOS) a...

  5. Enhancing Peer Acceptance of Children with Learning Difficulties: Classroom Goal Orientation and Effects of a Storytelling Programme with Drama Techniques

    Science.gov (United States)

    Law, Yin-kum; Lam, Shui-fong; Law, Wilbert; Tam, Zoe W. Y.

    2017-01-01

    Peer acceptance is an important facilitator for the success of inclusive education. The aim of the current study is twofold: (1) to examine how classroom goal orientation is associated with children's acceptance of peers with learning difficulties; and (2) to evaluate the effectiveness of a storytelling programme with drama techniques on…

  6. Probing High School Students' Cognitive Structures and Key Areas of Learning Difficulties on Ethanoic Acid Using the Flow Map Method

    Science.gov (United States)

    Zhou, Qing; Wang, Tingting; Zheng, Qi

    2015-01-01

    The purpose of this study was primarily to explore high school students' cognitive structures and to identify their learning difficulties on ethanoic acid through the flow map method. The subjects of this study were 30 grade 1 students from Dong Yuan Road Senior High School in Xi'an, China. The interviews were conducted a week after the students…

  7. Unconscious and Unnoticed Professional Practice within an Outstanding School for Children and Young People with Complex Learning Difficulties and Disabilities

    Science.gov (United States)

    Crombie, Richard; Sullivan, Lesley; Walker, Kate; Warnock, Rebecca

    2014-01-01

    This article describes a three-year project undertaken at Pear Tree School for children and young people with severe and multiple and profound learning difficulties. Lesley Sullivan, the school's head teacher, believed that much of the value within the work of this outstanding school went unidentified by existing approaches to planning, monitoring…

  8. Keeping Wartime Memory Alive: An Oral History Project about the Wartime Memories of People with Learning Difficulties in Cumbria

    Science.gov (United States)

    Dias, John; Eardley, Malcolm; Harkness, Elizabeth; Townson, Louise; Brownlee-Chapman, Chloe; Chapman, Rohhss

    2012-01-01

    This article discusses an oral history project funded by the Heritage Lottery. It recorded the memories of eight people with learning difficulties during the Second World War in Cumbria, UK, before their personal histories were lost forever. This qualitative, inclusive research project was supported by various organisations. The process of…

  9. Cognitive Function of Children and Adolescents with Attention Deficit Hyperactivity Disorder and Learning Difficulties: A Developmental Perspective

    Directory of Open Access Journals (Sweden)

    Fang Huang

    2016-01-01

    Conclusions: Children and adolescents with ADHD and learning difficulties have more severe cognitive impairment than pure ADHD patients even after controlling for the effect of ADHD symptoms. However, the differences in impairment in inhibition and shift function are no longer significant when these individuals were 12–14 years old.

  10. To What Extent Does Information and Communication Technology Support Inclusion in Education of Students with Learning Difficulties?

    Science.gov (United States)

    Mølster, Terje; Nes, Kari

    2018-01-01

    The main intention of this study is to explore the relationship between information and communication technology (ICT) and inclusion. The target group is students who are conceived as having learning difficulties or special educational needs. To illuminate this issue, we draw on data collected in a recent national research project about the…

  11. Comparability of Self-Concept among Normal Achievers and Children with Learning Difficulties within a Greek Context.

    Science.gov (United States)

    Leonadari, Angeliki

    1994-01-01

    Assessed Greek third through sixth graders on the Perceived Competence Scale for Children (PCSC). Subjects were normally achieving (NA) and low achieving students and a special class (SC) of students identified as at risk for learning difficulties. The SC students scored lower than the NA students on the PCSC global self-worth, competence affect,…

  12. The Effects of Self-Regulation on Science Vocabulary Acquisition of English Language Learners with Learning Difficulties

    Science.gov (United States)

    Kim, Woori; Linan-Thompson, Sylvia

    2013-01-01

    This multiple-probe study examined the effects of self-regulation on the acquisition of science vocabulary by four third-grade English language learners (ELLs) with learning difficulties. The students were provided only direct vocabulary instruction in a baseline phase, followed by intervention and maintenance phases into which self-regulation…

  13. Associations among Attitudes, Perceived Difficulty of Learning Science, Gender, Parents' Occupation and Students' Scientific Competencies

    Science.gov (United States)

    Chi, ShaoHui; Wang, Zuhao; Liu, Xiufeng; Zhu, Lei

    2017-01-01

    This study investigated the associations among students' attitudes towards science, students' perceived difficulty of learning science, gender, parents' occupations and their scientific competencies. A sample of 1591 (720 males and 871 females) ninth-grade students from 29 junior high schools in Shanghai completed a scientific competency test and…

  14. Psycho-Pedagogical Interventions in the Prevention and the Therapy of Learning Difficulties in the Field of Mathematics

    Science.gov (United States)

    Anca, Maria; Hategan, Carolina

    2009-01-01

    In the given study dyscalculia is approached in the context of learning difficulties, but also in relation with damaged psychic processes and functions. The practical part of the study describes intervention models from the perspective of dyscalculia prevention and therapymaterialized in personalized intervention programs.

  15. Perry's Scheme of Intellectual and Epistemological Development as a Framework for Describing Student Difficulties in Learning Organic Chemistry

    Science.gov (United States)

    Grove, Nathaniel P.; Bretz, Stacey Lowery

    2010-01-01

    We have investigated student difficulties with the learning of organic chemistry. Using Perry's Model of Intellectual Development as a framework revealed that organic chemistry students who function as dualistic thinkers struggle with the complexity of the subject matter. Understanding substitution/elimination reactions and multi-step syntheses is…

  16. Discriminating Children with Autism from Children with Learning Difficulties with an Adaptation of the Short Sensory Profile

    Science.gov (United States)

    O'Brien, Justin; Tsermentseli, Stella; Cummins, Omar; Happe, Francesca; Heaton, Pamela; Spencer, Janine

    2009-01-01

    In this article, we examine the extent to which children with autism and children with learning difficulties can be discriminated from their responses to different patterns of sensory stimuli. Using an adapted version of the Short Sensory Profile (SSP), sensory processing was compared in 34 children with autism to 33 children with typical…

  17. Working Memory Updating Training Improves Mathematics Performance in Middle School Students With Learning Difficulties.

    Science.gov (United States)

    Zhang, Hongxia; Chang, Lei; Chen, Xiaoying; Ma, Liang; Zhou, Renlai

    2018-01-01

    Working memory (WM) deficit is considered the key cause of learning difficulties (LDs). Studies have shown that WM is plastic and thus can be improved through training. This positive effect is transferable to fluid intelligence and academic performance. This study investigated whether WM updating ability and academic performance in children with LDs could be improved through WM updating training and explored the effects of this training on the children's brain activity. We used a running memory task lasting approximately 40 min per day for 28 days to train a group of 23 children with LDs (TLDs group). We also selected two control groups of 22 children with LDs (CLDs group) and 20 children without LDs (normal control [NC] group). The behavioral results of a pretest indicated that WM updating ability and academic performance in the TLDs and CLDs groups were significantly lower than those in the NC group before training. Compared with the CLDs group, the TLDs group exhibited significant performance improvement in a 2-back WM task, as well as in mathematical ability. Event-related potentials (ERPs) results suggested that the amplitudes of N160 (representative of visual recognition) and P300 (representative of updating processing, which is a valid index for updating WM) in the TLDs and CLDs groups were markedly lower than those in the NC group before training. In the TLDs group, these two components increased considerably after training, approaching levels similar to those in the NC group. The results of this study suggest that WM updating training can improve WM updating ability in children with LDs and the training effect can transfer to mathematical performance in such children. Furthermore, the participants' brain activity levels can exhibit positive changes. This article provides experimental evidence that WM updating training could mitigate the symptoms of LDs to a certain degree.

  18. Exploring the disclosure decisions made by physiotherapists with a specific learning difficulty.

    Science.gov (United States)

    Yeowell, G; Rooney, J; Goodwin, P C

    2018-06-01

    To explore the disclosure decisions made in the workplace by physiotherapy staff with a specific learning difficulty (SpLD). An exploratory qualitative design was used, which was informed by the social model of disability. The research was undertaken in North West England. It is presented according to the Consolidated Criteria for Reporting Qualitative Research. A purposive sample of eight physiotherapists recognised as having a SpLD were recruited. All participants had studied on one of two programmes at a university in England between 2004-2012. Their NHS workplace experience was from across the UK. In-depth, semi-structured interviews were undertaken within the university setting or via telephone. Interviews lasted 40 to 70minutes and were digitally recorded. An interview guide was used to direct the interview. Interview data were transcribed verbatim and analysed using thematic analysis. Four participants were female. The mean number of years qualified as a physiotherapist was 4.5years (SD=2.27). Three themes were identified: 'Disclosing during the workplace application'; 'Positive about disabled people scheme'; 'Disclosing in the workplace'. Disclosure of dyslexia is a selective process and is a central dilemma in the lives of individuals who have a concealable stigmatised identity. As a consequence, physiotherapy staff with dyslexia may choose to conceal their disability and not disclose to their employer. In order for staff with dyslexia to get the support they need in the workplace, disclosure is recommended. A number of recommendations have been made to facilitate the disclosure process. Copyright © 2017 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

  19. Pathway evidence of how musical perception predicts word-level reading ability in children with reading difficulties.

    Directory of Open Access Journals (Sweden)

    Hugo Cogo-Moreira

    Full Text Available To investigate whether specific domains of musical perception (temporal and melodic domains predict the word-level reading skills of eight- to ten-year-old children (n = 235 with reading difficulties, normal quotient of intelligence, and no previous exposure to music education classes.A general-specific solution of the Montreal Battery of Evaluation of Amusia (MBEA, which underlies a musical perception construct and is constituted by three latent factors (the general, temporal, and the melodic domain, was regressed on word-level reading skills (rate of correct isolated words/non-words read per minute.General and melodic latent domains predicted word-level reading skills.

  20. The effects of autonomous difficulty selection on engagement, motivation, and learning in a motion-controlled video game task.

    Science.gov (United States)

    Leiker, Amber M; Bruzi, Alessandro T; Miller, Matthew W; Nelson, Monica; Wegman, Rebecca; Lohse, Keith R

    2016-10-01

    This experiment investigated the relationship between motivation, engagement, and learning in a video game task. Previous studies have shown increased autonomy during practice leads to superior retention of motor skills, but it is not clear why this benefit occurs. Some studies suggest this benefit arises from increased motivation during practice; others suggest the benefit arises from better information processing. Sixty novice participants were randomly assigned to a self-controlled group, who chose the progression of difficulty during practice, or to a yoked group, who experienced the same difficulty progression but did not have choice. At the end of practice, participants completed surveys measuring intrinsic motivation and engagement. One week later, participants returned for a series of retention tests at three different difficulty levels. RM-ANCOVA (controlling for pre-test) showed that the self-controlled group had improved retention compared to the yoked group, on average, β=46.78, 95% CI=[2.68, 90.87], p=0.04, but this difference was only statistically significant on the moderate difficulty post-test (p=0.004). The self-controlled group also showed greater intrinsic motivation during practice, t(58)=2.61, p=0.01. However, there was no evidence that individual differences in engagement (p=0.20) or motivation (p=0.87) were associated with learning, which was the relationship this experiment was powered to detect. These data are inconsistent with strictly motivational accounts of how autonomy benefits learning, instead suggesting the benefits of autonomy may be mediated through other mechanisms. For instance, within the information processing framework, the learning benefits may emerge from learners appropriately adjusting difficulty to maintain an appropriate level of challenge (i.e., maintaining the relationship between task demands and cognitive resources). Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Signed reward prediction errors drive declarative learning.

    Directory of Open Access Journals (Sweden)

    Esther De Loof

    Full Text Available Reward prediction errors (RPEs are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning. However, empirical evidence on whether RPEs drive declarative learning-a quintessentially human form of learning-remains surprisingly absent. We therefore coupled RPEs to the acquisition of Dutch-Swahili word pairs in a declarative learning paradigm. Signed RPEs (SRPEs; "better-than-expected" signals during declarative learning improved recognition in a follow-up test, with increasingly positive RPEs leading to better recognition. In addition, classic declarative memory mechanisms such as time-on-task failed to explain recognition performance. The beneficial effect of SRPEs on recognition was subsequently affirmed in a replication study with visual stimuli.

  2. Signed reward prediction errors drive declarative learning.

    Science.gov (United States)

    De Loof, Esther; Ergo, Kate; Naert, Lien; Janssens, Clio; Talsma, Durk; Van Opstal, Filip; Verguts, Tom

    2018-01-01

    Reward prediction errors (RPEs) are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning). However, empirical evidence on whether RPEs drive declarative learning-a quintessentially human form of learning-remains surprisingly absent. We therefore coupled RPEs to the acquisition of Dutch-Swahili word pairs in a declarative learning paradigm. Signed RPEs (SRPEs; "better-than-expected" signals) during declarative learning improved recognition in a follow-up test, with increasingly positive RPEs leading to better recognition. In addition, classic declarative memory mechanisms such as time-on-task failed to explain recognition performance. The beneficial effect of SRPEs on recognition was subsequently affirmed in a replication study with visual stimuli.

  3. What roles do errors serve in motor skill learning? An examination of two theoretical predictions.

    Science.gov (United States)

    Sanli, Elizabeth A; Lee, Timothy D

    2014-01-01

    Easy-to-difficult and difficult-to-easy progressions of task difficulty during skill acquisition were examined in 2 experiments that assessed retention, dual-task, and transfer tests of learning. Findings of the first experiment suggest that an easy-to difficult progression did not consistently induce implicit learning processes and was not consistently beneficial to performance under a secondary-task load. The findings of experiment two did not support the predictions made based on schema theory and only partially supported predictions based on reinvestment theory. The authors interpret these findings to suggest that the timing of error in relation to the difficulty of the task (functional task difficulty) plays a role in the transfer of learning to novel versions of a task.

  4. Does a dynamic test of phonological awareness predict early reading difficulties?

    DEFF Research Database (Denmark)

    Gellert, Anna Steenberg; Elbro, Carsten

    2017-01-01

    A few studies have indicated that dynamic measures of phonological awareness may contribute uniquely to the prediction of early reading development. However, standard control measures have been few and limited by floor effects, thus limiting their predictive value. The purpose of the present stud...

  5. A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

    OpenAIRE

    Li, Guohui; Zhang, Songling; Yang, Hong

    2017-01-01

    Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

  6. Improvement of Word Problem Solving and Basic Mathematics Competencies in Students with Attention Deficit/Hyperactivity Disorder and Mathematical Learning Difficulties

    Science.gov (United States)

    González-Castro, Paloma; Cueli, Marisol; Areces, Débora; Rodríguez, Celestino; Sideridis, Georgios

    2016-01-01

    Problem solving represents a salient deficit in students with mathematical learning difficulties (MLD) primarily caused by difficulties with informal and formal mathematical competencies. This study proposes a computerized intervention tool, the integrated dynamic representation (IDR), for enhancing the early learning of basic mathematical…

  7. The Relationships between Indonesian Fourth Graders' Difficulties in Fractions and the Opportunity to Learn Fractions: A Snapshot of TIMSS Results

    Science.gov (United States)

    Wijaya, Ariyadi

    2017-01-01

    This paper reports an exploration into Indonesian fourth graders' difficulties in fractions and their relation to the opportunity to learn fractions students got at schools. The concept of "opportunity to learn" is often considered as a framework to investigate possible reasons for students' difficulties. The data for this study was…

  8. Predicting Virtual Learning Environment Adoption

    DEFF Research Database (Denmark)

    Penjor, Sonam; Zander, Pär-Ola Mikael

    2016-01-01

    This study investigates the significance of Rogers’ Diffusion of Innovations (DOI) theory with regard to the use of a Virtual Learning Environment (VLE) at the Royal University of Bhutan (RUB). The focus is on different adoption types and characteristics of users. Rogers’ DOI theory is applied...... to investigate the influence of five predictors (relative advantage, complexity, compatibility, trialability and observability) and their significance in the perception of academic staff at the RUB in relation to the probability of VLE adoption. These predictors are attributes of the VLE that determine the rate...... of adoption by various adopter group memberships (Innovators, Early Adopters, Early Majority, Late Majority, Laggards). Descriptive statistics and regression analysis were deployed to analyse adopter group memberships and predictor significance in VLE adoption and use. The results revealed varying attitudes...

  9. Difficulties faced by eighth grade students in the learning of linear equation problems at a high school in Heredia

    Directory of Open Access Journals (Sweden)

    Gilberto Chavarría Arroyo

    2014-06-01

    Full Text Available The current article presents the results of a study that aimed to analyze the difficulties faced by eighth grade students when learning to solve algebraic problems based on linear equations with one unknown variable. The participants were learners with low average performance in mathematics at a high school in Heredia. The research followed a naturalistic paradigm and the case study method with a qualitative approach. Different techniques like class observations, questionnaires to students, non-structured interviews to teachers and interviews to the learners were applied. The research helped to identify the main causes of difficulty when learning to solve algebraic problems. Some of the causes that were identified are affective aspects, lack of previous knowledge, poor relational understanding, fatigue, diversion, reading deficiencies and misunderstanding of terminology.

  10. Cognitive Function of Children and Adolescents with Attention Deficit Hyperactivity Disorder and Learning Difficulties: A Developmental Perspective

    Science.gov (United States)

    Huang, Fang; Sun, Li; Qian, Ying; Liu, Lu; Ma, Quan-Gang; Yang, Li; Cheng, Jia; Cao, Qing-Jiu; Su, Yi; Gao, Qian; Wu, Zhao-Min; Li, Hai-Mei; Qian, Qiu-Jin; Wang, Yu-Feng

    2016-01-01

    Background: The cognitive function of children with either attention deficit hyperactivity disorder (ADHD) or learning disabilities (LDs) is known to be impaired. However, little is known about the cognitive function of children with comorbid ADHD and LD. The present study aimed to explore the cognitive function of children and adolescents with ADHD and learning difficulties in comparison with children with ADHD and healthy controls in different age groups in a large Chinese sample. Methods: Totally, 1043 participants with ADHD and learning difficulties (the ADHD + learning difficulties group), 870 with pure ADHD (the pure ADHD group), and 496 healthy controls were recruited. To investigate the difference in cognitive impairment using a developmental approach, all participants were divided into three age groups (6–8, 9–11, and 12–14 years old). Measurements were the Chinese-Wechsler Intelligence Scale for Children, the Stroop Color-Word Test, the Trail-Making Test, and the Behavior Rating Inventory of Executive Function-Parents (BRIEF). Multivariate analysis of variance was used. Results: The results showed that after controlling for the effect of ADHD symptoms, the ADHD + learning difficulties group was still significantly worse than the pure ADHD group, which was, in turn, worse than the control group on full intelligence quotient (98.66 ± 13.87 vs. 105.17 ± 14.36 vs. 112.93 ± 13.87, P ADHD symptoms, intelligence quotient, age, and gender. As for the age groups, the differences among groups became nonsignificant in the 12–14 years old group for inhibition (meaning interference of the Stroop Color-Word Test, 18.00 [13.00, 25.00] s vs. 17.00 [15.00, 26.00] s vs. 17.00 [10.50, 20.00] s, P = 0.704) and shift function (shifting time of the Trail-Making Test, 62.00 [43.00, 97.00] s vs. 53.00 [38.00, 81.00] s vs. 101.00 [88.00, 114.00] s, P = 0.778). Conclusions: Children and adolescents with ADHD and learning difficulties have more severe cognitive

  11. Russian language for Persian learners A research on the difficulties of learning motion verbs of

    OpenAIRE

    ایزانلو ایزانلو

    2009-01-01

    Since motion verbs of Russian language is one of those complex issues in Russian language syntax, Iranian students who are learning Russian language face problems when learning this grammatical category. These problems in learning appear in two stages. a)The stage of learning and understanding the meaning of these verbs in the Russian language itself; b) The stage of transition of these verbs from Russian language into Persian language when translating texts into Persian. It seems that the di...

  12. [Usefulness and limitations of rapid automatized naming to predict reading difficulties after school entry in preschool children].

    Science.gov (United States)

    Kaneko, Masato; Uno, Akira; Haruhara, Noriko; Awaya, Noriko

    2012-01-01

    We investigated the usability and limitations of Rapid Automatized Naming (RAN) results in 6-year-old Japanese preschool children to estimate whether reading difficulties will be encountered after school entry. We administered a RAN task to 1,001 preschool children. Then after they had entered school, we performed follow-up surveys yearly to assess their reading performance when these children were in the first, second, third and fourth grades. Also, we examined Hiragana non-words and Kanji words at each time point to detect the children who were having difficulty with reading Hiragana and Kanji. Results by Receiver Operating Characteristic analysis showed that the RAN result in 6-year-old preschool children was predictive of Kanji reading difficulty in the lower grades of elementary school, especially in the second grade with a probability of 0.86, and the area under the curve showed a probability of 0.84 in the third grade. These results suggested that the RAN task was useful as a screening tool.

  13. The Pre-Service Science Teachers' Mental Models for Concept of Atoms and Learning Difficulties

    Science.gov (United States)

    Kiray, Seyit Ahmet

    2016-01-01

    The purpose of this study is to reveal the pre-service science teachers' difficulties about the concept of atoms. The data was collected from two different sources: The Draw an Atom Test (DAAT) and face-to-face interviews. Draw an atom test (DAAT) were administered to the 142 science teacher candidates. To elaborate the results, the researcher…

  14. Integration of a framework with a learning management system for detection, assessment and assistance of university students with reading difficulties

    Directory of Open Access Journals (Sweden)

    Carolina Mejía Corredor

    2015-12-01

    Full Text Available Rev.esc.adm.neg Dyslexia is a common learning disability in Spanish-speaking university students, and requires special attention from higher educational institutions in order to support affected individuals during their learning process. In previous studies, a framework to detect, assess and assist university students with reading difficulties related to dyslexia was developed. In this paper, the integration of this framework with a Learning Management System (LMS is presented. Two case studies were performed to test the functionality and the usability of this integration. The first case study was carried out with 20 students, while the second one with four teachers. The results show that both students and teachers were satisfied with the integration performed in Moodle.ce, among others.

  15. Global discriminative learning for higher-accuracy computational gene prediction.

    Directory of Open Access Journals (Sweden)

    Axel Bernal

    2007-03-01

    Full Text Available Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.

  16. Machine Learning Methods to Predict Diabetes Complications.

    Science.gov (United States)

    Dagliati, Arianna; Marini, Simone; Sacchi, Lucia; Cogni, Giulia; Teliti, Marsida; Tibollo, Valentina; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo

    2018-03-01

    One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

  17. Learning Predictive Statistics: Strategies and Brain Mechanisms.

    Science.gov (United States)

    Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

    2017-08-30

    When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory-motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions. SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to

  18. Warping similarity space in category learning by human subjects: the role of task difficulty

    OpenAIRE

    Pevtzow, Rachel; Harnad, Stevan

    1997-01-01

    In innate Categorical Perception (CP) (e.g., colour perception), similarity space is "warped," with regions of increased within-category similarity (compression) and regions of reduced between-category similarity (separation) enh ancing the category boundaries and making categorisation reliable and all-or-none rather than graded. We show that category learning can likewise warp similarity space, resolving uncertainty near category boundaries. Two Hard and two Easy texture learning tasks were ...

  19. Machine learning methods for metabolic pathway prediction

    Directory of Open Access Journals (Sweden)

    Karp Peter D

    2010-01-01

    Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.

  20. Machine learning methods for metabolic pathway prediction

    Science.gov (United States)

    2010-01-01

    Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214

  1. The Impact of Learning Difficulties and Socioemotional and Behavioural Problems on Transition to Postsecondary Education or Work Life in Finland: A Five-Year Follow-Up Study

    Science.gov (United States)

    Hakkarainen, Airi M.; Holopainen, Leena K.; Savolainen, Hannu K.

    2016-01-01

    Learning difficulties have been found to dilute the possibilities that young adults have in their educational careers. However, during the last few decades, education has become increasingly important for employment and overall life satisfaction. In the present study, we were interested in the effects of mathematical and reading difficulties and…

  2. Informal Workplace Learning among Nurses: Organisational Learning Conditions and Personal Characteristics That Predict Learning Outcomes

    Science.gov (United States)

    Kyndt, Eva; Vermeire, Eva; Cabus, Shana

    2016-01-01

    Purpose: This paper aims to examine which organisational learning conditions and individual characteristics predict the learning outcomes nurses achieve through informal learning activities. There is specific relevance for the nursing profession because of the rapidly changing healthcare systems. Design/Methodology/Approach: In total, 203 nurses…

  3. Working memory: its role in dyslexia and other specific learning difficulties.

    Science.gov (United States)

    Jeffries, Sharman; Everatt, John

    2004-08-01

    This paper reports a study contrasting dyslexic children against a control group of children without special educational needs (SEN) and a group with varied SENs. Children's abilities were compared on tasks assessing phonological processing, visuo-spatial/motor coordination and executive/inhibitory functioning; being targeted for assessment based on theoretical proposals related to the working memory model. Primary and secondary school level children were tested: 21 assessed as dyslexic with no comorbid difficulties, 26 children assessed with difficulties including dyspraxia, emotional/behavioural problems and attention deficits, 40 children with no known education-related deficits were controls. Results indicated both SEN groups performed worse than controls on working memory phonological loop measures. However, SEN groups could only be differentiated on phonological awareness measures: the dyslexics showing lower scores. Dyslexics performed as well as controls on working memory visuo-spatial scratch pad measures and one of two additional visual-motor coordination tasks, whereas the performance of the other SEN children was lowest on the majority of these measures. Central executive and interference measures engendered mixed performances, both SEN groups showing evidence of deficits in one or more of these areas of functioning, although, of the two SEN groups, the dyslexics seem to have performed the worse when digit name processing was required.

  4. Learning receptive fields using predictive feedback.

    Science.gov (United States)

    Jehee, Janneke F M; Rothkopf, Constantin; Beck, Jeffrey M; Ballard, Dana H

    2006-01-01

    Previously, it was suggested that feedback connections from higher- to lower-level areas carry predictions of lower-level neural activities, whereas feedforward connections carry the residual error between the predictions and the actual lower-level activities [Rao, R.P.N., Ballard, D.H., 1999. Nature Neuroscience 2, 79-87.]. A computational model implementing the hypothesis learned simple cell receptive fields when exposed to natural images. Here, we use predictive feedback to explain tuning properties in medial superior temporal area (MST). We implement the hypothesis using a new, biologically plausible, algorithm based on matching pursuit, which retains all the features of the previous implementation, including its ability to efficiently encode input. When presented with natural images, the model developed receptive field properties as found in primary visual cortex. In addition, when exposed to visual motion input resulting from movements through space, the model learned receptive field properties resembling those in MST. These results corroborate the idea that predictive feedback is a general principle used by the visual system to efficiently encode natural input.

  5. An analysis of how electromagnetic induction and Faraday's law are presented in general physics textbooks, focusing on learning difficulties

    International Nuclear Information System (INIS)

    Guisasola, Jenaro; Zuza, Kristina; Almudi, José-Manuel

    2013-01-01

    Textbooks are a very important tool in the teaching–learning process and influence important aspects of the process. This paper presents an analysis of the chapter on electromagnetic induction and Faraday's law in 19 textbooks on general physics for first-year university courses for scientists and engineers. This analysis was based on criteria formulated from the theoretical framework of electromagnetic induction in classical physics and students' learning difficulties concerning these concepts. The aim of the work presented here is not to compare a textbook against the ideal book, but rather to try and find a series of explanations, examples, questions, etc that provide evidence on how the topic is presented in relation to the criteria above. It concludes that despite many aspects being covered properly, there are others that deserve greater attention. (paper)

  6. Managing eating and drinking difficulties (dysphagia) with children who have learning disabilities: What is effective?

    Science.gov (United States)

    Harding, Celia; Cockerill, Helen

    2015-07-01

    People who work with children who have neurological and learning disabilities frequently need to manage the health and emotional risks associated with eating, drinking and swallowing (dysphagia). Some approaches can support children to develop oral feeding competence or to maximise their ability to maintain some oral intake supplemented with tube feeding. However, some clinicians feel that oral-motor exercises can support eating and drinking skills as well as speech and language development, whereas there is little evidence to support this.The implied "beneficial" association between oral-motor exercises, speech and swallowing skills gives a false impression in terms of future outcomes for parents and carers of children with learning disabilities. This paper considers oral-motor approaches in the remediation of dysphagia and the need for a cultural shift away from this view. Realistic and useful outcomes for people with learning disabilities need to be an essential part of therapeutic intervention. © The Author(s) 2014.

  7. How Role Play Addresses the Difficulties Students Perceive when Writing Reflectively about the Concepts They are Learning in Science

    Science.gov (United States)

    Millar, Susan

    A fundamental problem which confronts Science teachers is the difficulty many students experience in the construction, understanding and remembering of concepts. This is more likely to occur when teachers adhere to a Transmission model of teaching and learning, and fail to provide students with opportunities to construct their own learning. Social construction, followed by individual reflective writing, enables students to construct their own understanding of concepts and effectively promotes deep learning. This method of constructing knowledge in the classroom is often overlooked by teachers as they either have no knowledge of it, or do not know how to appropriate it for successful teaching in Science. This study identifies the difficulties which students often experience when writing reflectively and offers solutions which are likely to reduce these difficulties. These solutions, and the use of reflective writing itself, challenge the ideology of the Sydney Genre School, which forms the basis of the attempt to deal with literacy in the NSW Science Syllabus. The findings of this investigation support the concept of literacy as the ability to use oral and written language, reading and listening to construct meaning. The investigation demonstrates how structured discussion, role play and reflective writing can be used to this end. While the Sydney Genre School methodology focuses on the structure of genre as a prerequisite for understanding concepts in Science, the findings of this study demonstrate that students can use their own words to discuss and write reflectively as they construct scientific concepts for themselves. Social construction and reflective writing can contribute to the construction of concepts and the development of metacognition in Science. However, students often experience difficulties when writing reflectively about scientific concepts they are learning. In this investigation, students identified these difficulties as an inability to understand

  8. Workshop on Friction: Understanding and Addressing Students' Difficulties in Learning Science through a Hermeneutical Perspective

    Science.gov (United States)

    Ha, Sangwoo; Lee, Gyoungho; Kalman, Calvin S.

    2013-01-01

    Hermeneutics is useful in science and science education by emphasizing the process of understanding. The purpose of this study was to construct a workshop based upon hermeneutical principles and to interpret students' learning in the workshop through a hermeneutical perspective. When considering the history of Newtonian mechanics, it could be…

  9. Intrinsic Difficulties in Learning Common Greek-Originated English Words: The Case of Pluralization

    Science.gov (United States)

    Kavakli, Nurdan

    2016-01-01

    Knowing the origin of a language helps us to determine the historical background of that language. As language itself is such a system of a society that is continuously evolving as that aforementioned society learns and technologically develops along with its roots or origins. Like many other languages, English is also a language that has roots or…

  10. Difficulties Encountered by Both Teachers and Students in Teaching and Learning Turkish as a Second Language

    Science.gov (United States)

    Canbulat, Mehmet; Dilekçi, Atilla

    2015-01-01

    The objective of this research is to identify and suggest solutions to the problems experienced by students learning Turkish as a second language according to the opinions of both teachers and students evaluated. The research has been conducted among the classroom teachers, Turkish language teachers and the students attending the schools in…

  11. Inculcating Positive Thinking in the Self-Concept of Children with Learning Difficulties

    Science.gov (United States)

    Abed, Mohaned Ghazi

    2017-01-01

    Inculcating positive thinking can act as a valuable tool in enhancing the overall self-concept of children with learning disabilities. The value of positive psychology is recognized as the basis for recent research conducted in the field of strength development. Positive psychology is centered on the view that individual lives can be improved by…

  12. A Pilot Memory Café for People with Learning Disabilities and Memory Difficulties

    Science.gov (United States)

    Kiddle, Hannah; Drew, Neil; Crabbe, Paul; Wigmore, Jonathan

    2016-01-01

    Memory cafés have been found to normalise experiences of dementia and provide access to an accepting social network. People with learning disabilities are at increased risk of developing dementia, but the possible benefits of attending a memory café are not known. This study evaluates a 12-week pilot memory café for people with learning…

  13. The Relationships between Indonesian Fourth Graders’ Difficulties in Fractions and the Opportunity to Learn Fractions: A Snapshot of TIMSS Results

    Directory of Open Access Journals (Sweden)

    Ariyadi Wijaya

    2017-11-01

    Full Text Available This paper reports an exploration into Indonesian fourth graders’ difficulties in fractions and their relation to the opportunity to learn fractions students got at schools. The concept of ‘opportunity to learn’ is often considered as a framework to investigate possible reasons for students’ difficulties. The data for this study was drawn from TIMSS 2015 that comprised test results and teachers’ responses to TIMSS Teacher Questionnaire. The test and questionnaire data were analysed by using descriptive statistics. In addition to test and questionnaire, this study also included an analysis of Indonesian textbooks in order to get a broader scope of the opportunity to learn. Qualitative approach was used to analyse the textbooks. The analysis of the TIMSS results shows Indonesian students’ low conceptual understanding of fractions. Three possible reasons for students’ low conceptual understanding were revealed. First, the content of Indonesian curriculum that gave low emphasis on basic concepts of fractions and introduced operations of fractions too early. Second, the Indonesian mathematics textbooks only presented one definition of fractions, i.e. fractions as parts of wholes. Third, there is a limited use of models or representations of fractions in the classroom practices.

  14. Potential or problem? An investigation of secondary school teachers' attributions of the educational outcomes of students with specific learning difficulties.

    Science.gov (United States)

    Woodcock, Stuart; Hitches, Elizabeth

    2017-10-01

    Despite strong support for inclusive education in principle, many teachers and administrators still demonstrate mixed responses to the inclusion of certain students in their classrooms. Students with specific learning difficulties (SpLD) form a large group of students in inclusive classrooms yet some provincial, state and national jurisdictions fail to acknowledge the existence of these students. Not acknowledging and understanding these students can deny them the recognition and resources necessary for their genuine participation in education and, in turn, society. The aim of this study was to examine British in-service secondary teachers' attributional responses to students with and without specific learning difficulties. The participants included 122 British secondary school teachers who were surveyed in response to vignettes of hypothetical male students who had failed a class test. The study found that while teachers attributed more positive causes towards students without SpLD, they exhibited more negative causes towards students with SpLD. Teachers' causal attributional outcomes of students' level of achievement can impact upon the students' own attributions, with teachers' responses for students with SpLD having the potential to, unintentionally, influence students' own sense of self-efficacy and motivation. The paper concludes with a consideration of the implications of the research and recommendations for practice.

  15. Reading Stories to Learn Math: Mathematics Vocabulary Instruction for Children with Early Numeracy Difficulties.

    Science.gov (United States)

    Hassinger-Das, Brenna; Jordan, Nancy C; Dyson, Nancy

    2015-12-01

    The present study involved examining whether a storybook reading intervention targeting mathematics vocabulary, such as "equal," "more," and "less," and associated number concepts would increase at-risk children's vocabulary knowledge and number competencies. Children with early numeracy difficulties (N = 124) were recruited from kindergarten classes in four schools. Participants were randomly assigned to one of three groups: a storybook number competencies (SNC) intervention, a number sense intervention, or a business-as-usual control. Interventions were carried out in groups of four children over 8 weeks (24 thirty-minute sessions). Findings demonstrated that the SNC intervention group outperformed the other groups on measures of mathematics vocabulary, both in terms of words that were closely aligned to the intervention and those that were not. There was no effect of the SNC intervention, however, on general mathematics measures, suggesting a need to provide the mathematics vocabulary work along with more intensive instruction in number concepts.

  16. Modern Languages and Specific Learning Difficulties (SpLD): Implications of Teaching Adult Learners with Dyslexia in Distance Learning

    Science.gov (United States)

    Gallardo, Matilde; Heiser, Sarah; Arias McLaughlin, Ximena

    2015-01-01

    In modern language (ML) distance learning programmes, teachers and students use online tools to facilitate, reinforce and support independent learning. This makes it essential for teachers to develop pedagogical expertise in using online communication tools to perform their role. Teachers frequently raise questions of how best to support the needs…

  17. Physiognomy: Personality Traits Prediction by Learning

    Institute of Scientific and Technical Information of China (English)

    Ting Zhang; Ri-Zhen Qin; Qiu-Lei Dong; Wei Gao; Hua-Rong Xu; Zhan-Yi Hu

    2017-01-01

    Evaluating individuals' personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences.To assess the possible correlations between personality traits (also measured intelligence) and face images,we first construct a dataset consisting of face photographs,personality measurements,and intelligence measurements.Then,we build an end-to-end convolutional neural network for prediction of personality traits and intelligence to investigate whether self-reported personality traits and intelligence can be predicted reliably from a face image.To our knowledge,it is the first work where deep learning is applied to this problem.Experimental results show the following three points:1)"Rule-consciousness" and "Tension" can be reliably predicted from face images.2) It is difficult,if not impossible,to predict intelligence from face images,a finding in accord with previous studies.3) Convolutional neural network (CNN) features outperform traditional handcrafted features in predicting traits.

  18. Intelligence test at preschool-age predicts reading difficulty among school-aged very low birth weight infants in Japan.

    Science.gov (United States)

    Takeuchi, Akihito; Ogino, Tatsuya; Koeda, Tatsuya; Oka, Makio; Yorifuji, Takashi; Takayanagi, Toshimitsu; Sato, Kazuo; Sugino, Noriko; Bonno, Motoki; Nakamura, Makoto; Kageyama, Misao

    2018-05-21

    To elucidate whether the results of an intelligence test at preschool age are predictive of reading difficulty (RD) at school age among very low birth weight infants (VLBWI). Subjects were 48 Japanese children whose birth weight was Intelligence Scale for Children-III (WISC-III) during the last grade of kindergarten, and four reading tasks during the second to fourth grade of elementary school. All participants had a full-scale intelligence quotient score of 85 or higher. Subjects with a standard deviation reading time score greater than 2.0 in two or more tasks were considered to have RD. We evaluated the associations between each WISC-III score and RD using logistic regression analyses. Furthermore, we performed receiver operating characteristic (ROC) analysis to determine a cutoff WISC-III score predictive of RD. In the mutually-adjusted model, the adjusted odds ratio per 1 score increase of freedom from distractibility (FD) was 0.832 (95% confidence interval: 0.720-0.962). In the ROC analysis, an FD score of memory and attention, is a risk factor for RD at school age among Japanese VLBWI. Further investigation is desired to clarify the cognitive deficits underlying RD in Japanese-speaking preterm children, and to establish appropriate interventions for these children. Copyright © 2018 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

  19. Virtual Classrooms in Brazil: teachers' difficulties and anxieties towards technology in language learning

    Directory of Open Access Journals (Sweden)

    Andréa Machado de Almeida Mattos

    2003-01-01

    Full Text Available Many researchers, nowadays, have been enthusiastic in promoting the advantages of introducing technology in the language classroom, but few have been worried with the problems and anxieties that result from changes in a long-lasting culture such as the culture of language learning. This paper aims at discussing the problems faced by teachers who have been working with technology in their language classrooms. The research design was based on theoretical and empirical studies both in the areas of Computer Assisted Language Learning and Teacher Development. The main objective of this paper is, thus, to achieve a global understanding of the teachers' anxieties in relation to the virtual environment of language learning. Data was gathered through interviews with the teachers, leading to a qualitative analysis of the findings.Atualmente, muitos pesquisadores têm promovido entusiasticamente as vantagens de se introduzir tecnologia na sala de aula de língua estrangeira (LE, mas poucos têm-se preocupado com os problemas e as ansiedades que resultam de mudanças numa cultura tão antiga quanto a da sala de aula de LE. Este trabalho visa a discutir os problemas enfrentados por professores que trabalham com tecnologia em suas salas de aula de língua. A pesquisa foi baseada em estudos teóricos e empíricos tanto na área de ensino mediado por computador quanto no campo de desenvolvimento de professores. O objetivo principal deste trabalho é, assim, obter um entendimento global das ansiedades do professor em relação ao ambiente virtual de aprendizagem de língua. Os dados foram coletados mediante entrevistas com os professores informantes, levando a uma análise qualitativa dos resultados.

  20. TEACHING GEOGRAPHY TO CHILDREN WITH LEARNING DIFFICULTIES, IN THE EUROPEAN SCHOOLS

    Directory of Open Access Journals (Sweden)

    IULIA ŞCHIOPU

    2016-01-01

    Full Text Available European Schools offer the opportunity to observe the differences between students and how attention towards the individual is needed. Backed up by a set of frameworks, such as Gestion Mentale, Neuro-linguistic Programming and thinkers such as Paulo Freire, the work of empowering the individual when a challenge is present is exemplified in the context of Geography. What makes a student succeed when the default method is proven challenging. There is no limit as to what methods could be applied as long as they are all guided by the lines of placing the student in a powerful spot: the spot where a challenge becomes a learning opportunity.

  1. Using Machine Learning to Predict MCNP Bias

    Energy Technology Data Exchange (ETDEWEB)

    Grechanuk, Pavel Aleksandrovi [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2018-01-09

    For many real-world applications in radiation transport where simulations are compared to experimental measurements, like in nuclear criticality safety, the bias (simulated - experimental keff) in the calculation is an extremely important quantity used for code validation. The objective of this project is to accurately predict the bias of MCNP6 [1] criticality calculations using machine learning (ML) algorithms, with the intention of creating a tool that can complement the current nuclear criticality safety methods. In the latest release of MCNP6, the Whisper tool is available for criticality safety analysts and includes a large catalogue of experimental benchmarks, sensitivity profiles, and nuclear data covariance matrices. This data, coming from 1100+ benchmark cases, is used in this study of ML algorithms for criticality safety bias predictions.

  2. Predicting radiotherapy outcomes using statistical learning techniques

    International Nuclear Information System (INIS)

    El Naqa, Issam; Bradley, Jeffrey D; Deasy, Joseph O; Lindsay, Patricia E; Hope, Andrew J

    2009-01-01

    Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model

  3. Chess activities system to improve psychomotor functions in children with learning difficulties

    Directory of Open Access Journals (Sweden)

    Zuleida Rodríguez Díaz

    2016-12-01

    Full Text Available Cuban society and the education system in particular is the most prepared to achieve the formation of the personality of new generations, including particularly children with special educational needs. This work arises from the need to provide a response in the development of motor skills and shortcomings that have children between 7 and 10 years old with moderate mental retardation, special school "28 de Enero" the municipality Pinar del Río. For the development of scientific research, empirical, theoretical and statistical methods were used. a system of activities aimed at helping to improve motor skills in these children, based from the historical-cultural approach and a process of teaching and learning to develop their full potential is proposed. This system is in line with the activities proposed in the teaching programs, and responds to the current requirements of the comprehensive education of children with moderate mental retardation; It is a resource to develop an efficient intervention in the process of learning of motor skills.

  4. Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.

    Directory of Open Access Journals (Sweden)

    Xia Jiang

    Full Text Available The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS datasets, which involve millions of single nucleotide polymorphism (SNPs, where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects.We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer's dataset, we investigated the performance of MBS-IGain.When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer's dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is significant because we have increasingly

  5. Predicting Increased Blood Pressure Using Machine Learning

    Science.gov (United States)

    Golino, Hudson Fernandes; Amaral, Liliany Souza de Brito; Duarte, Stenio Fernando Pimentel; Soares, Telma de Jesus; dos Reis, Luciana Araujo

    2014-01-01

    The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R 2 (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R 2 (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power. PMID:24669313

  6. Predicting increased blood pressure using machine learning.

    Science.gov (United States)

    Golino, Hudson Fernandes; Amaral, Liliany Souza de Brito; Duarte, Stenio Fernando Pimentel; Gomes, Cristiano Mauro Assis; Soares, Telma de Jesus; Dos Reis, Luciana Araujo; Santos, Joselito

    2014-01-01

    The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R (2) (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R (2) (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

  7. Predicting Increased Blood Pressure Using Machine Learning

    Directory of Open Access Journals (Sweden)

    Hudson Fernandes Golino

    2014-01-01

    Full Text Available The present study investigates the prediction of increased blood pressure by body mass index (BMI, waist (WC and hip circumference (HC, and waist hip ratio (WHR using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42, misclassification (.19, and the higher pseudo R2 (.43. This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25, misclassification (.16, and the higher pseudo R2 (.46. This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

  8. Assessing self-regulated learning in early childhood education: Difficulties, needs, and prospects.

    Science.gov (United States)

    de la Fuente Arias, Jesús; Lozano Díaz, Antonia

    2010-05-01

    Self-regulated learning is one of the main processes being investigated today within developmental and educational psychology; however, the research has come up against a number of challenges for which no satisfactory response has been found, and which are impeding progress in the field. These challenges are two-fold: one part is methodological, as the process of self-regulation must be evaluated at the very moment in which it occurs, and the other part is developmental, as these processes have not been fully assessed in children under the age of 6 years. This article gives a broad overview of these challenges, as well as prospects for future solutions which are beginning to take shape.

  9. Prediction of preterm deliveries from EHG signals using machine learning.

    Directory of Open Access Journals (Sweden)

    Paul Fergus

    Full Text Available There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography, could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term. The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial

  10. A longitudinal study on predictors of early calculation development among young children at risk for learning difficulties.

    Science.gov (United States)

    Peng, Peng; Namkung, Jessica M; Fuchs, Douglas; Fuchs, Lynn S; Patton, Samuel; Yen, Loulee; Compton, Donald L; Zhang, Wenjuan; Miller, Amanda; Hamlett, Carol

    2016-12-01

    The purpose of this study was to explore domain-general cognitive skills, domain-specific academic skills, and demographic characteristics that are associated with calculation development from first grade to third grade among young children with learning difficulties. Participants were 176 children identified with reading and mathematics difficulties at the beginning of first grade. Data were collected on working memory, language, nonverbal reasoning, processing speed, decoding, numerical competence, incoming calculations, socioeconomic status, and gender at the beginning of first grade and on calculation performance at four time points: the beginning of first grade, the end of first grade, the end of second grade, and the end of third grade. Latent growth modeling analysis showed that numerical competence, incoming calculation, processing speed, and decoding skills significantly explained the variance in calculation performance at the beginning of first grade. Numerical competence and processing speed significantly explained the variance in calculation performance at the end of third grade. However, numerical competence was the only significant predictor of calculation development from the beginning of first grade to the end of third grade. Implications of these findings for early calculation instructions among young at-risk children are discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. Pragmatics of language and theory of mind in children with dyslexia with associated language difficulties or nonverbal learning disabilities.

    Science.gov (United States)

    Cardillo, Ramona; Garcia, Ricardo Basso; Mammarella, Irene C; Cornoldi, Cesare

    2017-03-15

    The present study aims to find empirical evidence of deficits in linguistic pragmatic skills and theory of mind (ToM) in children with dyslexia with associated language difficulties or nonverbal learning disabilities (NLD), when compared with a group of typically developing (TD) children matched for age and gender. Our results indicate that children with dyslexia perform less well than TD children in most of the tasks measuring pragmatics of language, and in one of the tasks measuring ToM. In contrast, children with NLD generally performed better than the dyslexia group, and performed significantly worse than the TD children only in a metaphors task based on visual stimuli. A discriminant function analysis confirmed the crucial role of the metaphors subtest and the verbal ToM task in distinguishing between the groups. We concluded that, contrary to a generally-held assumption, children with dyslexia and associated language difficulties may be weaker than children with NLD in linguistic pragmatics and ToM, especially when language is crucially involved. The educational and clinical implications of these findings are discussed.

  12. Predictive Variable Gain Iterative Learning Control for PMSM

    Directory of Open Access Journals (Sweden)

    Huimin Xu

    2015-01-01

    Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.

  13. Learning and Retention through Predictive Inference and Classification

    Science.gov (United States)

    Sakamoto, Yasuaki; Love, Bradley C.

    2010-01-01

    Work in category learning addresses how humans acquire knowledge and, thus, should inform classroom practices. In two experiments, we apply and evaluate intuitions garnered from laboratory-based research in category learning to learning tasks situated in an educational context. In Experiment 1, learning through predictive inference and…

  14. Fuzzy-logic based learning style prediction in e-learning using web ...

    Indian Academy of Sciences (India)

    tion, especially in web environments and proposes to use Fuzzy rules to handle the uncertainty in .... learning in safe and supportive environment ... working of the proposed Fuzzy-logic based learning style prediction in e-learning. Section 4.

  15. Randomized Prediction Games for Adversarial Machine Learning.

    Science.gov (United States)

    Rota Bulo, Samuel; Biggio, Battista; Pillai, Ignazio; Pelillo, Marcello; Roli, Fabio

    In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the classification function accordingly. However, both the classification function and the simulated data manipulations have been modeled in a deterministic manner, without accounting for any form of randomization. In this paper, we overcome this limitation by proposing a randomized prediction game, namely, a noncooperative game-theoretic formulation in which the classifier and the attacker make randomized strategy selections according to some probability distribution defined over the respective strategy set. We show that our approach allows one to improve the tradeoff between attack detection and false alarms with respect to the state-of-the-art secure classifiers, even against attacks that are different from those hypothesized during design, on application examples including handwritten digit recognition, spam, and malware detection.In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different

  16. Recursos no ambiente familiar e dificuldades de aprendizagem na escola Home resources and school learning difficulties

    Directory of Open Access Journals (Sweden)

    Edna Maria Marturano

    1999-08-01

    Full Text Available Recursos do ambiente familiar, que favorecem o desempenho escolar, incluem materiais educacionais e envolvimento parental. Realizou-se um estudo com o objetivo de investigar esses recursos em uma amostra clínica de 100 crianças, encaminhadas por dificuldades na aprendizagem escolar. Os dados foram coletados durante entrevista com a mãe, por meio de um roteiro para sondagem de recursos e circunstâncias adversas. À criança era solicitado um texto a partir de um desenho. A análise estatística incluiu análise de regressão, correlação e comparação entre médias de subgrupos constituídos segundo idade, atraso escolar e desempenho na escrita. Os resultados indicaram que o nível de elaboração da escrita está positivamente associado à disponibilidade de livros e brinquedos, enquanto o atraso escolar está negativamente associado à organização das rotinas e à diversidade de atividades compartilhadas com os pais. Circunstâncias adversas têm associação positiva com atraso. Os resultados são discutidos quanto à sua aplicabilidade em programas de orientação psicopedagógica às famílias.Home resources which promote school achievement include educational materials and parent involvement with children's schooling. A study was performed with the aim of investigating these resources in a clinical sample of 100 children, who were referred for school learning problems. The data were collected with the mother, by means of a interview designed for the assessment of home resources and adversities. The child was asked to write a text from a drawing. Statistical analysis included regression analysis, correlation and comparisons between groups formed by age, school backwardness, and writing performance. Results indicated that writing level is positively related with the provision of books and toys, while school backwardness is negatively related with regularity in the use of time and with diversity of activities shared with parents

  17. Psychomotor development and learning difficulties in preschool children with probable attention deficit hyperactivity disorder: An epidemiological study in Navarre and La Rioja.

    Science.gov (United States)

    Marín-Méndez, J J; Borra-Ruiz, M C; Álvarez-Gómez, M J; Soutullo Esperón, C

    2017-10-01

    ADHD symptoms begin to appear at preschool age. ADHD may have a significant negative impact on academic performance. In Spain, there are no standardized tools for detecting ADHD at preschool age, nor is there data about the incidence of this disorder. To evaluate developmental factors and learning difficulties associated with probable ADHD and to assess the impact of ADHD in school performance. We conducted a population-based study with a stratified multistage proportional cluster sample design. We found significant differences between probable ADHD and parents' perception of difficulties in expressive language, comprehension, and fine motor skills, as well as in emotions, concentration, behaviour, and relationships. Around 34% of preschool children with probable ADHD showed global learning difficulties, mainly in patients with the inattentive type. According to the multivariate analysis, learning difficulties were significantly associated with both delayed psychomotor development during the first 3 years of life (OR: 5.57) as assessed by parents, and probable ADHD (OR: 2.34) CONCLUSIONS: There is a connection between probable ADHD in preschool children and parents' perception of difficulties in several dimensions of development and learning. Early detection of ADHD at preschool ages is necessary to start prompt and effective clinical and educational interventions. Copyright © 2016 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

  18. Exploration of Machine Learning Approaches to Predict Pavement Performance

    Science.gov (United States)

    2018-03-23

    Machine learning (ML) techniques were used to model and predict pavement condition index (PCI) for various pavement types using a variety of input variables. The primary objective of this research was to develop and assess PCI predictive models for t...

  19. A causal link between prediction errors, dopamine neurons and learning.

    Science.gov (United States)

    Steinberg, Elizabeth E; Keiflin, Ronald; Boivin, Josiah R; Witten, Ilana B; Deisseroth, Karl; Janak, Patricia H

    2013-07-01

    Situations in which rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.

  20. Critical evidence for the prediction error theory in associative learning.

    Science.gov (United States)

    Terao, Kanta; Matsumoto, Yukihisa; Mizunami, Makoto

    2015-03-10

    In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. Complete evidence for the prediction error theory, however, has not been obtained in any learning systems: Prediction error theory stems from the finding of a blocking phenomenon, but blocking can also be accounted for by other theories, such as the attentional theory. We demonstrated blocking in classical conditioning in crickets and obtained evidence to reject the attentional theory. To obtain further evidence supporting the prediction error theory and rejecting alternative theories, we constructed a neural model to match the prediction error theory, by modifying our previous model of learning in crickets, and we tested a prediction from the model: the model predicts that pharmacological intervention of octopaminergic transmission during appetitive conditioning impairs learning but not formation of reward prediction itself, and it thus predicts no learning in subsequent training. We observed such an "auto-blocking", which could be accounted for by the prediction error theory but not by other competitive theories to account for blocking. This study unambiguously demonstrates validity of the prediction error theory in associative learning.

  1. Implicit Learning Abilities Predict Treatment Response in Autism Spectrum Disorders

    Science.gov (United States)

    2015-09-01

    early behavioral interventions are the most effective treatment for Autism Spectrum Disorder (ASD), but almost half of the children do not make...behavioral intervention . 2. KEYWORDS Autism Spectrum Disorder , implicit learning, associative learning, individual differences, functional Magnetic...2 AWARD NUMBER: W81XWH-14-1-0261 TITLE: Implicit Learning Abilities Predict Treatment Response in Autism Spectrum Disorders PRINCIPAL

  2. Predicting Student Performance in a Collaborative Learning Environment

    Science.gov (United States)

    Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol

    2015-01-01

    Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

  3. Teacher Effectiveness in Adapting Instruction to the Needs of Pupils With Learning Difficulties in Regular Primary Schools in Ghana

    Directory of Open Access Journals (Sweden)

    Abdul-Razak Kuyini Alhassan

    2014-01-01

    Full Text Available Ghana education system has failed to effectively address the needs of pupils with learning difficulties (LDs in regular classrooms. Underachievement, school dropout, streetism, and antisocial behaviors are the consequences. Teachers’ lack of adequate competence in adaptive instruction is one of the fundamental reasons responsible for this anomaly. This study aims to examine teachers’ competence in adapting instructions to teach pupils with LDs in the regular classroom in Ghana. The data were gathered from 387 sampled teachers in a cross-sectional survey using questionnaires and structured observation methods. We analyzed the data using descriptive statistic, chi-square test, correlation, t test, and ANOVA. The results show that (a teachers have limited to moderate competence in adaptive instruction, (b adaptive teaching is strongly associated with teachers’ competence in teaching pupils with LDs in the regular classroom, and (c apart from gender and class size, teachers’ background variables such as school location and teaching experience differ significantly. The study has serious implications for Ghana’s inclusive education policy and teaching practice.

  4. A novel time series link prediction method: Learning automata approach

    Science.gov (United States)

    Moradabadi, Behnaz; Meybodi, Mohammad Reza

    2017-09-01

    Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

  5. Conformal prediction for reliable machine learning theory, adaptations and applications

    CERN Document Server

    Balasubramanian, Vineeth; Vovk, Vladimir

    2014-01-01

    The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detecti

  6. Predicting and Manipulating the Difficulty of Text-Completion Exercises for Language Learning

    OpenAIRE

    Beinborn, Lisa Marina

    2016-01-01

    The increasing levels of international communication in all aspects of life lead to a growing demand of language skills. Traditional language courses compete nowadays with a wide range of online offerings that promise higher flexibility. However, most platforms provide rather static educational content and do not yet incorporate the recent progress in educational natural language processing. In the last years, many researchers developed new methods for automatic exercise generation, but ...

  7. The Effective Use of Symbols in Teaching Word Recognition to Children with Severe Learning Difficulties: A Comparison of Word Alone, Integrated Picture Cueing and the Handle Technique.

    Science.gov (United States)

    Sheehy, Kieron

    2002-01-01

    A comparison is made between a new technique (the Handle Technique), Integrated Picture Cueing, and a Word Alone Method. Results show using a new combination of teaching strategies enabled logographic symbols to be used effectively in teaching word recognition to 12 children with severe learning difficulties. (Contains references.) (Author/CR)

  8. Teachers' Perceptions of the Availability and Need of a Support Program for Students with Learning Difficulties Attending Elementary Schools in the Atlantic Union Conference

    Science.gov (United States)

    Coke, Lileth Althea

    2013-01-01

    Purpose of the Study. Support programs have been known to be very effective in helping students succeed academically, socially, behaviorally, and emotionally. The purpose of this study was to investigate teachers' perceptions of the availability and need of a support program for students with learning difficulties who attend elementary schools…

  9. Using Tic-Tac Software to Reduce Anxiety-Related Behaviour in Adults with Autism and Learning Difficulties during Waiting Periods: A Pilot Study

    Science.gov (United States)

    Campillo, Cristina; Herrera, Gerardo; Remírez de Ganuza, Conchi; Cuesta, José L.; Abellán, Raquel; Campos, Arturo; Navarro, Ignacio; Sevilla, Javier; Pardo, Carlos; Amati, Fabián

    2014-01-01

    Deficits in the perception of time and processing of changes across time are commonly observed in individuals with autism. This pilot study evaluated the efficacy of the use of the software tool Tic-Tac, designed to make time visual, in three adults with autism and learning difficulties. This research focused on applying the tool in waiting…

  10. Inclusive Education for Children with Specific Learning Difficulties: Analysis of Opportunities and Barriers in Inclusive Education in Slovenia

    Directory of Open Access Journals (Sweden)

    Marija Kavkler

    2015-03-01

    Full Text Available Inclusive education allows for universal inclusion, participation and achievement of all children, including children with specific learning difficulties (SpLD. Children with SpLD form a heterogeneous group with diverse cognitive deficits, special educational needs (SEN and strengths, and have a legislated right to the continuum of both assistance and support programmes. Although their intellectual capacity is average or above average, their learning achievements in some learning domains are modest, and they are poorly integrated into their social environment, which often results in their discrimination. Barriers and opportunities in the area of SpLD were analysed with the aid of Ball’s model (1994, with factors and conditions being analysed within the contexts of policy influence, text production and practice. The contexts of policy influence and text production provide the basic conditions for the in clusive education of children with SpLD. The context of influence on in clusive policy for children with SpLD represents a systematic approach to policy initiation and to the prerequisites for its implementation in practice. The context of policy text production focuses on professionals and their impact on the enactment of the rights of children with severe SpLD. The context of practice concerns barriers and opportunities for implementing inclusion in practice. Early identification and diagnosis of pupils’ strengths, deficits and SEN, together with intensified treatment corresponding to the SEN of children with SpLD, could significantly influence the efficiency of the educational process. Barriers, primarily of an immaterial nature, are mainly encountered in those schools that do not implement the five-tier Response to Intervention (RTI approach. This approach enables children with SpLD a continuum of team-based diagnostic evaluation, effective adaptations and assistance. The main reasons for the unfavourable situation concern education

  11. Player Modeling for Intelligent Difficulty Adjustment

    Science.gov (United States)

    Missura, Olana; Gärtner, Thomas

    In this paper we aim at automatically adjusting the difficulty of computer games by clustering players into different types and supervised prediction of the type from short traces of gameplay. An important ingredient of video games is to challenge players by providing them with tasks of appropriate and increasing difficulty. How this difficulty should be chosen and increase over time strongly depends on the ability, experience, perception and learning curve of each individual player. It is a subjective parameter that is very difficult to set. Wrong choices can easily lead to players stopping to play the game as they get bored (if underburdened) or frustrated (if overburdened). An ideal game should be able to adjust its difficulty dynamically governed by the player’s performance. Modern video games utilise a game-testing process to investigate among other factors the perceived difficulty for a multitude of players. In this paper, we investigate how machine learning techniques can be used for automatic difficulty adjustment. Our experiments confirm the potential of machine learning in this application.

  12. Statistical and Machine Learning Models to Predict Programming Performance

    OpenAIRE

    Bergin, Susan

    2006-01-01

    This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant...

  13. The Predictive Role of Difficulties in Emotion Regulation and Self-Control with Susceptibility to Addiction in Drug-Dependent Individuals

    Directory of Open Access Journals (Sweden)

    Mahmoud Shirazi

    2015-06-01

    Full Text Available Objective: The present study aimed to examine the predictive role of difficulties in emotion regulation and self-control in potential for addiction among drug-dependent individuals. Method: This was a correlational study which falls within the category of descriptive studies. The statistical population of the current study included all patients under treatment in outpatient health centers in Bam, among whom 315 individuals were selected through cluster sampling method as the participants of the study. Difficulties in Emotion Regulation Scale, Self-Control Scale, and Addiction Susceptibility Questionnaire were used for data collection purposes. Results: The results indicated that difficulties engaging in goal directed behavior, impulse control difficulties, lack of emotional awareness, and lack of emotional clarity (dimensions of difficulties in emotion regulation had a significant positive correlation with potential for addiction. In addition, there was a negative significant relationship between self-control and potential for addiction among drug-dependent individuals. Conclusion: In addition to common methods of abstinence from drug dependence, teaching self-control and emotional control techniques to addicted patients can help them reduce their dependence.

  14. Asperger syndrome and nonverbal learning difficulties in adult males: self- and parent-reported autism, attention and executive problems.

    Science.gov (United States)

    Hagberg, Bibbi; Billstedt, Eva; Nydén, Agneta; Gillberg, Christopher

    2015-08-01

    A specific overlap between Asperger syndrome (AS) and nonverbal learning difficulties (NLD) has been proposed, based on the observation that, as a group, people with AS tend to have significantly higher verbal IQ (VIQ) than performance IQ (PIQ), one of the core features of NLD. The primary aim was to assess the longer term outcome of NLD--broken down into persistent and transient forms. The present study of 68 individuals was performed in the context of a larger prospective longitudinal study to late adolescence/early adult life of 100 boys with AS. Using self- and parent-report measures, we studied the longer term outcome of the NLD (defined as VIQ > PIQ by 15 points) as regards social communication, repetitive behaviour, attention, and executive function (EF) was studied. Three subgroups were identified: (1) Persistent NLD (P-NLD), (2) Childhood "only" NLD (CO-NLD) and (3) Never NLD (NO-NLD). The P-NLD group had the worst outcome overall. The CO-NLD group had better reported EF scores than the two other AS subgroups. There were no differences between the subgroups regarding social communication, repetitive behaviour, or attentional skills. Low PIQ increased the risk of ADHD symptoms. In the context of AS in males, P-NLD carries a relatively poor outcome, particularly with regard to self-reported EF. However, CO-NLD appears to entail a significantly better outcome. The results underscore the importance of analysing the cognitive profile both at diagnosis and after several years, so as to be able to formulate a realistic prognosis.

  15. The Predictive Role of Difficulties in Emotion Regulation and Self-Control with Susceptibility to Addiction in Drug-Dependent Individuals

    OpenAIRE

    Mahmoud Shirazi; Monavar Janfaza

    2015-01-01

    Objective: The present study aimed to examine the predictive role of difficulties in emotion regulation and self-control in potential for addiction among drug-dependent individuals. Method: This was a correlational study which falls within the category of descriptive studies. The statistical population of the current study included all patients under treatment in outpatient health centers in Bam, among whom 315 individuals were selected through cluster sampling method as the participants of t...

  16. Perceived difficulty quitting predicts enrollment in a smoking-cessation program for patients with head and neck cancer.

    Science.gov (United States)

    Duffy, Sonia A; Scheumann, Angela L; Fowler, Karen E; Darling-Fisher, Cynthia; Terrell, Jeffrey E

    2010-05-01

    To determine the predictors of participation in a smoking-cessation program among patients with head and neck cancer. This cross-sectional study is a substudy of a larger, randomized trial of patients with head and neck cancer that determined the predictors of smokers' participation in a cessation intervention. Otolaryngology clinics at three Veterans Affairs medical centers (Ann Arbor, MI, Gainesville, FL, and Dallas, TX), and the University of Michigan Hospital in Ann Arbor. 286 patients who had smoked within six months of the screening survey were eligible for a smoking-cessation intervention. Descriptive statistics and bivariate and multivariate logistic regression were used to determine the independent predictors of smokers' participation in an intervention study. Perceived difficulty quitting (as a construct of self-efficacy), health behaviors (i.e., smoking and problem drinking), clinical characteristics (i.e., depression and cancer site and stage), and demographic variables. Forty-eight percent of those eligible participated. High perceived difficulty quitting was the only statistically significant predictor of participation, whereas problem drinking, lower depressive symptoms, and laryngeal cancer site approached significance. Special outreach may be needed to reach patients with head and neck cancer who are overly confident in quitting, problem drinkers, and patients with laryngeal cancer. Oncology nurses are in an opportune position to assess patients' perceived difficulty quitting smoking and motivate them to enroll in cessation programs, ultimately improving quality of life, reducing risk of recurrence, and increasing survival for this population.

  17. Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons

    NARCIS (Netherlands)

    Keysers, C.; Perrett, David I; Gazzola, Valeria

    Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and

  18. 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.

  19. Predictable Locations Aid Early Object Name Learning

    Science.gov (United States)

    Benitez, Viridiana L.; Smith, Linda B.

    2012-01-01

    Expectancy-based localized attention has been shown to promote the formation and retrieval of multisensory memories in adults. Three experiments show that these processes also characterize attention and learning in 16- to 18-month old infants and, moreover, that these processes may play a critical role in supporting early object name learning. The…

  20. Prostate Cancer Probability Prediction By Machine Learning Technique.

    Science.gov (United States)

    Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

    2017-11-26

    The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

  1. Learned predictiveness and outcome predictability effects are not simply two sides of the same coin.

    Science.gov (United States)

    Thorwart, Anna; Livesey, Evan J; Wilhelm, Francisco; Liu, Wei; Lachnit, Harald

    2017-10-01

    The Learned Predictiveness effect refers to the observation that learning about the relationship between a cue and an outcome is influenced by the predictive relevance of the cue for other outcomes. Similarly, the Outcome Predictability effect refers to a recent observation that the previous predictability of an outcome affects learning about this outcome in new situations, too. We hypothesize that both effects may be two manifestations of the same phenomenon and stimuli that have been involved in highly predictive relationships may be learned about faster when they are involved in new relationships regardless of their functional role in predictive learning as cues and outcomes. Four experiments manipulated both the relationships and the function of the stimuli. While we were able to replicate the standard effects, they did not survive a transfer to situations where the functional role of the stimuli changed, that is the outcome of the first phase becomes a cue in the second learning phase or the cue of the first phase becomes the outcome of the second phase. Furthermore, unlike learned predictiveness, there was little indication that the distribution of overt attention in the second phase was influenced by previous predictability. The results suggest that these 2 very similar effects are not manifestations of a more general phenomenon but rather independent from each other. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  2. Analysis of students' difficulties on the material elasticity and harmonic oscillation in the inquiry-based physics learning in senior high school

    Directory of Open Access Journals (Sweden)

    Halimatus Sa’diyah

    2017-12-01

    Full Text Available The purpose of this research is to analyze of students' difficulties on the material elasticity and harmonic oscillation in the inquiry-based physics learning. It has eight stages. They are the orientation, the problem formulation, the formulation of hypotheses, the data obtaining, the testing hypotheses, conclusions, the implementation of the conclusions and generalizations, and the reflection stage. This research determines the student's learning difficulties on the each stage. The subject of this research is all of the students in X IPA 4 SMA N Sambungmacan Sragen. The amount of this research subject is thirty students. The method used in this research is descriptive qualitative. The data acquired with the learning process observation, the student's response questionnaire, and the student's cognitive tests. The results show that the student has difficulty in analyzing the elasticity and the force of deviation, speed, and acceleration concept, illustrates hooke law, and the matter's modulus elasticity. The difficult stages of the inquiry-based physics learning are the problem formulation, the formulation of hypotheses, the data obtaining, the testing hypotheses, conclusions, the implementation of the conclusions and generalizations, and the reflection stage.

  3. Using machine learning, neural networks and statistics to predict bankruptcy

    NARCIS (Netherlands)

    Pompe, P.P.M.; Feelders, A.J.; Feelders, A.J.

    1997-01-01

    Recent literature strongly suggests that machine learning approaches to classification outperform "classical" statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees, and neural networks in predicting corporate bankruptcy. Linear

  4. [Neurological and neuropsychological comparison between subjects with learning disorder and those suffering from learning difficulties when eeg abnormalities are detected at pediatric age].

    Science.gov (United States)

    Borsetti, L; Viberti, B; Ariano, C; Isocrono, A

    2015-12-01

    The objective of the study is to compare data and investigate the points of overlap between the two clinical conditions. The hypothesis is to observe a similar cognitive and neuropsychological profile in LD children and subjects with electroencephalogram (EEG) abnormalities. The present study consists of a descriptive analysis of 35 children who have been tested for suspected learning disorder (LD). The diagnostic protocol includes a detailed cognitive and neuropsychological evaluation, as well as logopedic and neuropsychomotor assessment. Children carried neurological visit, EEG in waking and encephalic nuclear magnetic resonance (NMR). In this study, anamnestic data and the results of some of the neuropsychological tests were administrated to children and subsequently were analyzed. Depending on EEG report (positive or negative), subjects were split in two subsample: subjects with "pure" LD and subjects who showed significant paroxysmal abnormalities at the EEG. This comparison shows that the profile of the two subsamples matches for many aspects. The only statistically significant differences are the increased impairment of meta-phonological skills and reading speed in children with EEG abnormalities. On the other hand, children with "pure" LD are inclined to manifest more frequently difficulties in highly-modularized processes, such as counting. In conclusion, the substantial overlap of the two profiles causes a reflection about the difficulty in making differential diagnosis in children who show a suspected LD, in absence of an accurate neurophysiological and neuroradiological investigation. The study did not find out useful markers to select subjects who should carry EEG and encephalic NMR. Our team established to keep EEG in waking in the diagnostic protocol, for all children with LD diagnosis. Only in case of abnormalities at the track, we prescribed EEG in sleeping and encephalic NMR.

  5. Resting alpha activity predicts learning ability in alpha neurofeedback

    Directory of Open Access Journals (Sweden)

    Wenya eNan

    2014-07-01

    Full Text Available Individuals differ in their ability to learn how to regulate the alpha activity by neurofeedback. This study aimed to investigate whether the resting alpha activity is related to the learning ability of alpha enhancement in neurofeedback and could be used as a predictor. A total of 25 subjects performed 20 sessions of individualized alpha neurofeedback in order to learn how to enhance activity in the alpha frequency band. The learning ability was assessed by three indices respectively: the training parameter changes between two periods, within a short period and across the whole training time. It was found that the resting alpha amplitude measured before training had significant positive correlations with all learning indices and could be used as a predictor for the learning ability prediction. This finding would help the researchers in not only predicting the training efficacy in individuals but also gaining further insight into the mechanisms of alpha neurofeedback.

  6. Machine learning applied to the prediction of citrus production

    OpenAIRE

    Díaz Rodríguez, Susana Irene; Mazza, Silvia M.; Fernández-Combarro Álvarez, Elías; Giménez, Laura I.; Gaiad, José E.

    2017-01-01

    An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analyse...

  7. Machine learning applied to crime prediction

    OpenAIRE

    Vaquero Barnadas, Miquel

    2016-01-01

    Machine Learning is a cornerstone when it comes to artificial intelligence and big data analysis. It provides powerful algorithms that are capable of recognizing patterns, classifying data, and, basically, learn by themselves to perform a specific task. This field has incredibly grown in popularity these days, however, it still remains unknown for the majority of people, and even for most professionals. This project intends to provide an understandable explanation of what is it, what types ar...

  8. Learning Behavior Models for Interpreting and Predicting Traffic Situations

    OpenAIRE

    Gindele, Tobias

    2014-01-01

    In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees.

  9. Predicting Student Success from the "LASSI for Learning Online" (LLO)

    Science.gov (United States)

    Carson, Andrew D.

    2011-01-01

    This study tested the degree to which subscales of the "LASSI for Learning Online" (LLO) (Weinstein & Palmer, 2006), a measure of learning strategies and study skills, predict student success in the form of passing grades, using a combination of large training (N = 4,409) and cross-validation (N = 3,203) samples. Discriminant function analysis…

  10. Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction.

    Science.gov (United States)

    Luo, Gang

    2016-01-01

    Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. For the champion machine learning model of the competition, our method explained prediction results for 87.4 % of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. Our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.

  11. WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.

    Directory of Open Access Journals (Sweden)

    George L Sutphin

    2016-11-01

    Full Text Available The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

  12. WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning

    Science.gov (United States)

    Sutphin, George L.; Mahoney, J. Matthew; Sheppard, Keith; Walton, David O.; Korstanje, Ron

    2016-01-01

    The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/. PMID:27812085

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

    NARCIS (Netherlands)

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

    2009-01-01

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

  14. Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

    Science.gov (United States)

    Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

    2009-01-01

    In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

  15. 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

  16. Difficulty with learning of exercise instructions associated with 'working memory' dysfunction and frontal glucose hypometabolism in a patient with very mild subcortical vascular dementia with knee osteoarthritis.

    Science.gov (United States)

    Takeda, Kenji; Meguro, Kenichi; Tanaka, Naofumi; Nakatsuka, Masahiro

    2013-07-25

    We present a patient with no dementia, depression or apathy, who had difficulty in learning self-exercise instructions. The patient was an 80-year-old right-handed woman who was admitted to a rehabilitation unit to receive postoperative rehabilitation after a femoral neck fracture. She was instructed quadriceps isometric exercises to perform 10 repetitions and to hold each stretch for 10 s. She performed the exercise correctly with motivation, but she had difficulty in learning the number of repetitions and the duration of each stretch. She had no history of cerebrovascular accident and the neurological examination was normal. Neuropsychological testing, MRI and (18)F-fluoro- D-glucose-positron emission tomography (FDG-PET) were performed to examine the neural mechanisms associated with this difficulty in learning instructions. Neuropsychological tests revealed dysfunction of working memory while other cognitive domains were relatively preserved. Her neuropsychological tests scores were (1) Mini-Mental State Examination: 24 (mild cognitive impairment), (2) Geriatric Depression Scale-15: 2 (no depression), (3) Apathy Scale: 2 (no apathy), (4) digit span forward: 5 (normal), (5) digit span backward: 2 (impaired), (6) visuospatial span forward: 4 (normal), (7) visuospatial span backward: 2 (impaired), (8) frontal assessment battery: 11 (normal), (9) Weigl test: 0 (impaired), (10) trail making test A: 52 s (normal), (11) train making test B: failed (impaired). T2-weighted and fluid-attenuated inversion recovery MRI showed high signal-intensity lesions in the cerebral deep white matter. FDG-PET revealed hypometabolic areas in the bilateral frontal lobes, particularly in the bilateral dorsolateral frontal area, anterior cingulate cortex and orbitofrontal cortex. One of the possible neural mechanisms underlying the learning difficulties in this patient may have been partial blockage of the cingulofrontal network by deep white matter lesions.

  17. Universal LD50 predictions using deep learning

    Science.gov (United States)

    NICEATM Predictive Models for Acute Oral Systemic Toxicity LD50 entry Risa R. Sayre (sayre.risa@epa.gov) & Christopher M. Grulke Our approach uses an ensemble of multilayer perceptron regressions to predict rat acute oral LD50 values from chemical features. Features were genera...

  18. Machine learning in Python essential techniques for predictive analysis

    CERN Document Server

    Bowles, Michael

    2015-01-01

    Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, d

  19. Predictive value of health-related fitness tests for self-reported mobility difficulties among high-functioning elderly men and women.

    Science.gov (United States)

    Hämäläinen, H Pauliina; Suni, Jaana H; Pasanen, Matti E; Malmberg, Jarmo J; Miilunpalo, Seppo I

    2006-06-01

    The functional independence of elderly populations deteriorates with age. Several tests of physical performance have been developed for screening elderly persons who are at risk of losing their functional independence. The purpose of the present study was to investigate whether several components of health-related fitness (HRF) are valid in predicting the occurrence of self-reported mobility difficulties (MD) among high-functioning older adults. Subjects were community-dwelling men and women, born 1917-1941, who participated in the assessment of HRF [6.1-m (20-ft) walk, one-leg stand, backwards walk, trunk side-bending, dynamic back extension, one-leg squat, 1-km walk] and who were free of MD in 1996 (no difficulties in walking 2- km, n=788; no difficulties in climbing stairs, n=647). Postal questionnaires were used to assess the prevalence of MD in 1996 and the occurrence of new MD in 2002. Logistic regression analysis was used as the statistical method. Both inability to perform the backwards walk and a poorer result in it were associated with risk of walking difficulties in the logistic model, with all the statistically significant single test items included. Results of 1-km walk time and one-leg squat strength test were also associated with risk, although the squat was statistically significant only in two older birth cohorts. Regarding stair-climbing difficulties, poorer results in the 1-km walk, dynamic back extension and one-leg squat tests were associated with increased risk of MD. The backwards walk, one-leg squat, dynamic back extension and 1-km walk tests were the best predictors of MD. These tests are recommended for use in screening high-functioning older people at risk of MD, as well as to target physical activity counseling to those components of HRF that are important for functional independence.

  20. 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.

  1. Robust portfolio choice with ambiguity and learning about return predictability

    DEFF Research Database (Denmark)

    Larsen, Linda Sandris; Branger, Nicole; Munk, Claus

    2013-01-01

    We analyze the optimal stock-bond portfolio under both learning and ambiguity aversion. Stock returns are predictable by an observable and an unobservable predictor, and the investor has to learn about the latter. Furthermore, the investor is ambiguity-averse and has a preference for investment...... strategies that are robust to model misspecifications. We derive a closed-form solution for the optimal robust investment strategy. We find that both learning and ambiguity aversion impact the level and structure of the optimal stock investment. Suboptimal strategies resulting either from not learning...... or from not considering ambiguity can lead to economically significant losses....

  2. Predicting Contextual Informativeness for Vocabulary Learning

    Science.gov (United States)

    Kapelner, Adam; Soterwood, Jeanine; Nessaiver, Shalev; Adlof, Suzanne

    2018-01-01

    Vocabulary knowledge is essential to educational progress. High quality vocabulary instruction requires supportive contextual examples to teach word meaning and proper usage. Identifying such contexts by hand for a large number of words can be difficult. In this work, we take a statistical learning approach to engineer a system that predicts…

  3. Improving sequence segmentation learning by predicting trigrams

    NARCIS (Netherlands)

    van den Bosch, A.; Daelemans, W.; Dagan, I.; Gildea, D.

    2005-01-01

    Symbolic machine-learning classifiers are known to suffer from near-sightedness when performing sequence segmentation (chunking) tasks in natural language processing: without special architectural additions they are oblivious of the decisions they made earlier when making new ones. We introduce a

  4. A data-driven predictive approach for drug delivery using machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Li

    Full Text Available In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.

  5. The strengths and weaknesses in verbal short-term memory and visual working memory in children with hearing impairment and additional language learning difficulties.

    Science.gov (United States)

    Willis, Suzi; Goldbart, Juliet; Stansfield, Jois

    2014-07-01

    To compare verbal short-term memory and visual working memory abilities of six children with congenital hearing-impairment identified as having significant language learning difficulties with normative data from typically hearing children using standardized memory assessments. Six children with hearing loss aged 8-15 years were assessed on measures of verbal short-term memory (Non-word and word recall) and visual working memory annually over a two year period. All children had cognitive abilities within normal limits and used spoken language as the primary mode of communication. The language assessment scores at the beginning of the study revealed that all six participants exhibited delays of two years or more on standardized assessments of receptive and expressive vocabulary and spoken language. The children with hearing-impairment scores were significantly higher on the non-word recall task than the "real" word recall task. They also exhibited significantly higher scores on visual working memory than those of the age-matched sample from the standardized memory assessment. Each of the six participants in this study displayed the same pattern of strengths and weaknesses in verbal short-term memory and visual working memory despite their very different chronological ages. The children's poor ability to recall single syllable words in relation to non-words is a clinical indicator of their difficulties in verbal short-term memory. However, the children with hearing-impairment do not display generalized processing difficulties and indeed demonstrate strengths in visual working memory. The poor ability to recall words, in combination with difficulties with early word learning may be indicators of children with hearing-impairment who will struggle to develop spoken language equal to that of their normally hearing peers. This early identification has the potential to allow for target specific intervention that may remediate their difficulties. Copyright © 2014. Published

  6. Learning to forget: continual prediction with LSTM.

    Science.gov (United States)

    Gers, F A; Schmidhuber, J; Cummins, F

    2000-10-01

    Long short-term memory (LSTM; Hochreiter & Schmidhuber, 1997) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing continual input streams that are not a priori segmented into subsequences with explicitly marked ends at which the network's internal state could be reset. Without resets, the state may grow indefinitely and eventually cause the network to break down. Our remedy is a novel, adaptive "forget gate" that enables an LSTM cell to learn to reset itself at appropriate times, thus releasing internal resources. We review illustrative benchmark problems on which standard LSTM outperforms other RNN algorithms. All algorithms (including LSTM) fail to solve continual versions of these problems. LSTM with forget gates, however, easily solves them, and in an elegant way.

  7. Dificuldades de aprendizagem: reflexões a partir da teoria histórico-cultural / Learning difficulties: reflections based on culturalhistorical theory

    Directory of Open Access Journals (Sweden)

    Fabiane Adela Tonetto Costas

    2010-01-01

    Full Text Available Ao estudar as dificuldades de aprendizagem não se pode esquecer que o aluno é um sujeito sociocultural com uma história e valores específicos aos quais se deve estar atento e que a escola também é atravessada por uma história e uma cultura específica. A reflexão proposta parte da Teoria Histórico-Cultural, tendo em Vygotsky a principal referência, além de outros autores como Luria, Leontiev e Duarte. Ressaltamos que nossa análise das dificuldades de aprendizagem pressupõe a presença de fatores sociais e culturais, enfocando as dificuldades que são produzidas no processo de escolarização e não os problemas/dificuldades de aprendizagem em si. Ao final, não chegamos a uma conclusão, restam-nos questões que mantêm a reflexão aberta.Abstract When studying learning difficulties one must not forget that the pupil is a sociocultural subject with a history and specific values to which one must be attentive to and that school is also per passed by a history and a specific culture. The reflection proposed here is based on Cultural-Historical Theory, which has in Vygotsky its main reference, besides other authors such as Luria, Leontiev and Duarte. We highlight that our analysis of learning difficulties assume in advance the presence of social and cultural factors, focusing on the difficulties which are produced in the educational process, and not on learning problems/difficulties themselves. Finally, we could not reach a conclusion, but we left many questions to be answered that keep the reflection open.

  8. Proactive Interference in Human Predictive Learning

    OpenAIRE

    Castro, Leyre; Ortega, Nuria; Matute, Helena

    2002-01-01

    The impairment in responding to a secondly trained association because of the prior training of another (i.e., proactive interference) is a well-established effect in human and animal research, and it has been demonstrated in many paradigms. However, learning theories have been concerned with proactive interference only when the competing stimuli have been presented in compound at some moment of the training phase. In this experiment we investigated the possibility of proactive interference b...

  9. 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…

  10. Visual Descriptor Learning for Predicting Grasping Affordances

    DEFF Research Database (Denmark)

    Thomsen, Mikkel Tang

    2016-01-01

    by the task of grasping unknown objects given visual sensor information. The contributions from this thesis stem from three works that all relate to the task of grasping unknown objects but with particular focus on the visual representation part of the problem. First an investigation of a visual feature space...... consisting of surface features was performed. Dimensions in the visual space were varied and the effects were evaluated with the task of grasping unknown object. The evaluation was performed using a novel probabilistic grasp prediction approach based on neighbourhood analysis. The resulting success......-rates for predicting grasps were between 75% and 90% depending on the object class. The investigations also provided insights into the importance of selecting a proper visual feature space when utilising it for predicting affordances. As a consequence of the gained insights, a semi-local surface feature, the Sliced...

  11. Strength of Temporal White Matter Pathways Predicts Semantic Learning.

    Science.gov (United States)

    Ripollés, Pablo; Biel, Davina; Peñaloza, Claudia; Kaufmann, Jörn; Marco-Pallarés, Josep; Noesselt, Toemme; Rodríguez-Fornells, Antoni

    2017-11-15

    Learning the associations between words and meanings is a fundamental human ability. Although the language network is cortically well defined, the role of the white matter pathways supporting novel word-to-meaning mappings remains unclear. Here, by using contextual and cross-situational word learning, we tested whether learning the meaning of a new word is related to the integrity of the language-related white matter pathways in 40 adults (18 women). The arcuate, uncinate, inferior-fronto-occipital and inferior-longitudinal fasciculi were virtually dissected using manual and automatic deterministic fiber tracking. Critically, the automatic method allowed assessing the white matter microstructure along the tract. Results demonstrate that the microstructural properties of the left inferior-longitudinal fasciculus predict contextual learning, whereas the left uncinate was associated with cross-situational learning. In addition, we identified regions of special importance within these pathways: the posterior middle temporal gyrus, thought to serve as a lexical interface and specifically related to contextual learning; the anterior temporal lobe, known to be an amodal hub for semantic processing and related to cross-situational learning; and the white matter near the hippocampus, a structure fundamental for the initial stages of new-word learning and, remarkably, related to both types of word learning. No significant associations were found for the inferior-fronto-occipital fasciculus or the arcuate. While previous results suggest that learning new phonological word forms is mediated by the arcuate fasciculus, these findings show that the temporal pathways are the crucial neural substrate supporting one of the most striking human abilities: our capacity to identify correct associations between words and meanings under referential indeterminacy. SIGNIFICANCE STATEMENT The language-processing network is cortically (i.e., gray matter) well defined. However, the role of the

  12. Learning from sensory and reward prediction errors during motor adaptation.

    Science.gov (United States)

    Izawa, Jun; Shadmehr, Reza

    2011-03-01

    Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.

  13. Spontaneous brain activity predicts learning ability of foreign sounds.

    Science.gov (United States)

    Ventura-Campos, Noelia; Sanjuán, Ana; González, Julio; Palomar-García, María-Ángeles; Rodríguez-Pujadas, Aina; Sebastián-Gallés, Núria; Deco, Gustavo; Ávila, César

    2013-05-29

    Can learning capacity of the human brain be predicted from initial spontaneous functional connectivity (FC) between brain areas involved in a task? We combined task-related functional magnetic resonance imaging (fMRI) and resting-state fMRI (rs-fMRI) before and after training with a Hindi dental-retroflex nonnative contrast. Previous fMRI results were replicated, demonstrating that this learning recruited the left insula/frontal operculum and the left superior parietal lobe, among other areas of the brain. Crucially, resting-state FC (rs-FC) between these two areas at pretraining predicted individual differences in learning outcomes after distributed (Experiment 1) and intensive training (Experiment 2). Furthermore, this rs-FC was reduced at posttraining, a change that may also account for learning. Finally, resting-state network analyses showed that the mechanism underlying this reduction of rs-FC was mainly a transfer in intrinsic activity of the left frontal operculum/anterior insula from the left frontoparietal network to the salience network. Thus, rs-FC may contribute to predict learning ability and to understand how learning modifies the functioning of the brain. The discovery of this correspondence between initial spontaneous brain activity in task-related areas and posttraining performance opens new avenues to find predictors of learning capacities in the brain using task-related fMRI and rs-fMRI combined.

  14. A Phenomenological Study Highlighting the Voices of Students with Mental Health Difficulties Concerning Barriers to Classroom Learning

    Science.gov (United States)

    Buchanan, Denise

    2014-01-01

    Despite the Mental Health Foundation (2012) stating that each year, one in four people in the United Kingdom (UK) will have some kind of mental health difficulty, students from this group are underrepresented in further education (FE) colleges. In light of this, the purpose of this exploratory research, which was rooted in the phenomenological…

  15. Learner Outcomes in Science in South Africa: Role of the Nature of Learner Difficulties with the Language for Learning and Teaching Science

    Science.gov (United States)

    Oyoo, Samuel Ouma

    2017-08-01

    Paul Leslie Gardner pioneered the study of student difficulties with everyday words presented in the science context (Gardner 1971); several similarly designed studies (e.g. Cassels and Johnstone 1985; Tao in Research in Science Education, 24, 322-330, 1994; Farell and Ventura in Language and Education, 12(4), 243-254, 1998; Childs and O'Farell in Chemistry Education: Research and Practice, 4(3), 233-247, 2003) have since been reported in literature. This article draws from an exploratory study of the difficulties South African High School physical science learners encounter with everyday English words when presented in the science context. The participants (1107 learners and 35 respective physical science teachers) were drawn from 35 public secondary schools in Johannesburg area of South Africa. Data were obtained through a word test to participant learners followed by group interviews but face-to-face interviews with each physical science teacher. This study has revealed that in similar ways as have been reported in each of the studies so far, South African learners also face difficulties with meanings of everyday words presented in a science context. The main source of difficulties encountered was learner inability to distinguish between the meanings of familiar everyday words as used in everyday parlance from the `new' meanings of the same everyday words when used in the science context. Interpretations of learner interview responses revealed that fewer difficulties would have been experienced by learners if science teachers generally explained the context meanings of the words as used during science teaching. The findings suggest that focusing on contextual proficiency more than on general proficiency in the language of learning and teaching (LOLT) during teaching perhaps holds more promise for enhanced learning and achievement in science. Steps necessary to raise teacher awareness of the potential impact of context on meanings of everyday words of the LOLT

  16. Predicting breast screening attendance using machine learning techniques.

    Science.gov (United States)

    Baskaran, Vikraman; Guergachi, Aziz; Bali, Rajeev K; Naguib, Raouf N G

    2011-03-01

    Machine learning-based prediction has been effectively applied for many healthcare applications. Predicting breast screening attendance using machine learning (prior to the actual mammogram) is a new field. This paper presents new predictor attributes for such an algorithm. It describes a new hybrid algorithm that relies on back-propagation and radial basis function-based neural networks for prediction. The algorithm has been developed in an open source-based environment. The algorithm was tested on a 13-year dataset (1995-2008). This paper compares the algorithm and validates its accuracy and efficiency with different platforms. Nearly 80% accuracy and 88% positive predictive value and sensitivity were recorded for the algorithm. The results were encouraging; 40-50% of negative predictive value and specificity warrant further work. Preliminary results were promising and provided ample amount of reasons for testing the algorithm on a larger scale.

  17. Deep-Learning-Based Approach for Prediction of Algal Blooms

    Directory of Open Access Journals (Sweden)

    Feng Zhang

    2016-10-01

    Full Text Available Algal blooms have recently become a critical global environmental concern which might put economic development and sustainability at risk. However, the accurate prediction of algal blooms remains a challenging scientific problem. In this study, a novel prediction approach for algal blooms based on deep learning is presented—a powerful tool to represent and predict highly dynamic and complex phenomena. The proposed approach constructs a five-layered model to extract detailed relationships between the density of phytoplankton cells and various environmental parameters. The algal blooms can be predicted by the phytoplankton density obtained from the output layer. A case study is conducted in coastal waters of East China using both our model and a traditional back-propagation neural network for comparison. The results show that the deep-learning-based model yields better generalization and greater accuracy in predicting algal blooms than a traditional shallow neural network does.

  18. Improving orbit prediction accuracy through supervised machine learning

    Science.gov (United States)

    Peng, Hao; Bai, Xiaoli

    2018-05-01

    Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

  19. Mathematics difficulties & classroom leadership

    DEFF Research Database (Denmark)

    Schmidt, Maria Christina Secher

    2016-01-01

    This article investigates possible links between inclusion, students, for whom mathematics is extensively difficult, and classroom leadership through a case study on teaching strategies and student participation in four classrooms at two different primary schools in Denmark. Three sets of results...... are presented: 1) descriptions of the teachers’ classroom leadership to include all their students in the learning community, 2) the learning community produced by stated and practiced rules for teaching and learning behavior, 3) the classroom behavior of students who experience difficulties with mathematics....... The findings suggest that the teachers’ pedagogical choices and actions support an active learning environment for students in diverse learning needs, and that the teachers practise dimensions of inclusive classroom leadership that are known to be successful for teaching mathematics to all students. Despite...

  20. Learning biases predict a word order universal.

    Science.gov (United States)

    Culbertson, Jennifer; Smolensky, Paul; Legendre, Géraldine

    2012-03-01

    How recurrent typological patterns, or universals, emerge from the extensive diversity found across the world's languages constitutes a central question for linguistics and cognitive science. Recent challenges to a fundamental assumption of generative linguistics-that universal properties of the human language acquisition faculty constrain the types of grammatical systems which can occur-suggest the need for new types of empirical evidence connecting typology to biases of learners. Using an artificial language learning paradigm in which adult subjects are exposed to a mix of grammatical systems (similar to a period of linguistic change), we show that learners' biases mirror a word-order universal, first proposed by Joseph Greenberg, which constrains typological patterns of adjective, numeral, and noun ordering. We briefly summarize the results of a probabilistic model of the hypothesized biases and their effect on learning, and discuss the broader implications of the results for current theories of the origins of cross-linguistic word-order preferences. Copyright © 2011 Elsevier B.V. All rights reserved.

  1. Predicting the dissolution kinetics of silicate glasses using machine learning

    Science.gov (United States)

    Anoop Krishnan, N. M.; Mangalathu, Sujith; Smedskjaer, Morten M.; Tandia, Adama; Burton, Henry; Bauchy, Mathieu

    2018-05-01

    Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties.

  2. Machine learning algorithms for datasets popularity prediction

    CERN Document Server

    Kancys, Kipras

    2016-01-01

    This report represents continued study where ML algorithms were used to predict databases popularity. Three topics were covered. First of all, there was a discrepancy between old and new meta-data collection procedures, so a reason for that had to be found. Secondly, different parameters were analysed and dropped to make algorithms perform better. And third, it was decided to move modelling part on Spark.

  3. The Difficulties of Online Learning for Indigenous Australian Students Living in Remote Communities--It's an Issue of Access

    Science.gov (United States)

    Anthony, Sarah G.; Keating, Michael S.

    2013-01-01

    Online learning and new technologies are driving a trend in worldwide education that is not only gaining momentum, it is becoming a juggernaut. While the positives for online learning are clear and are often being touted by Universities and Vocational Education and Training providers as a panacea for educational access, what is not clear is the…

  4. Auditory working memory predicts individual differences in absolute pitch learning.

    Science.gov (United States)

    Van Hedger, Stephen C; Heald, Shannon L M; Koch, Rachelle; Nusbaum, Howard C

    2015-07-01

    Absolute pitch (AP) is typically defined as the ability to label an isolated tone as a musical note in the absence of a reference tone. At first glance the acquisition of AP note categories seems like a perceptual learning task, since individuals must assign a category label to a stimulus based on a single perceptual dimension (pitch) while ignoring other perceptual dimensions (e.g., loudness, octave, instrument). AP, however, is rarely discussed in terms of domain-general perceptual learning mechanisms. This is because AP is typically assumed to depend on a critical period of development, in which early exposure to pitches and musical labels is thought to be necessary for the development of AP precluding the possibility of adult acquisition of AP. Despite this view of AP, several previous studies have found evidence that absolute pitch category learning is, to an extent, trainable in a post-critical period adult population, even if the performance typically achieved by this population is below the performance of a "true" AP possessor. The current studies attempt to understand the individual differences in learning to categorize notes using absolute pitch cues by testing a specific prediction regarding cognitive capacity related to categorization - to what extent does an individual's general auditory working memory capacity (WMC) predict the success of absolute pitch category acquisition. Since WMC has been shown to predict performance on a wide variety of other perceptual and category learning tasks, we predict that individuals with higher WMC should be better at learning absolute pitch note categories than individuals with lower WMC. Across two studies, we demonstrate that auditory WMC predicts the efficacy of learning absolute pitch note categories. These results suggest that a higher general auditory WMC might underlie the formation of absolute pitch categories for post-critical period adults. Implications for understanding the mechanisms that underlie the

  5. Applications of machine learning in cancer prediction and prognosis.

    Science.gov (United States)

    Cruz, Joseph A; Wishart, David S

    2007-02-11

    Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on "older" technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

  6. KPG Index versus OPG Measurements: A Comparison between 3D and 2D Methods in Predicting Treatment Duration and Difficulty Level for Patients with Impacted Maxillary Canines

    Directory of Open Access Journals (Sweden)

    Domenico Dalessandri

    2014-01-01

    Full Text Available Aim. The aim of this study was to test the agreement between orthopantomography (OPG based 2D measurements and the KPG index, a new index based on 3D Cone Beam Computed Tomography (CBCT images, in predicting orthodontic treatment duration and difficulty level of impacted maxillary canines. Materials and Methods. OPG and CBCT images of 105 impacted canines were independently scored by three orthodontists at t0 and after 1 month (t1, using the KPG index and the following 2D methods: distance from cusp tip and occlusal plane, cusp tip position in relation to the lateral incisor, and canine inclination. Pearson’s coefficients were used to evaluate the degree of agreement and the χ2 with Yates correction test was used to assess the independence between them. Results. Inter- and intrarater reliability were higher with KPG compared to 2D methods. Pearson’s coefficients showed a statistically significant association between all the indexes, while the χ2 with Yates correction test resulted in a statistically significant rejection of independency only for one 2D index. Conclusions. 2D indexes for predicting impacted maxillary canines treatment duration and difficulty sometimes are discordant; a 3D index like the KPG index could be useful in solving these conflicts.

  7. Motor proficiency in normal children and with learning difficulty: a comparative and correlational study based on the motor proficiency test of Bruininks-Oseretsky

    Directory of Open Access Journals (Sweden)

    Nilson Roberto Moreira

    2008-06-01

    Full Text Available The aim of this investigation is to verify the difference between children with learning disabilities and children without learning disabilities through motor proficiency test of Bruininks and Ozeretsky (1978. The sample was constituted by 30 children, with 8-year average age, 15 males and 15 females, subdivided into two groups of 15 children from both sexes: children without learning disabilities attending 3rd grade and children with learning disabilities attending 2nd grade having failed a term once. All of them came from a middle class background, according to Grafar scale (adapted by Fonseca, 1991. All children presenting any other disabilities were excluded from the sample. Intelligence factor “G” was controlled by using a percentile, higher or equal to 50 (middle and high level, measured by Raven’s (1974 progressive combinations test. In motor proficiency, children with learning disabilities showed significant differences when compared with normal children of the same age, in all components of global, composed and fine motricity. The tests administered showed a strong correlation between the variables of the motor proficiency components. The results lead to the conclusion that there were significant differences in motor proficiency between normal children and children with learning disabilities, who showed specific motor difficulties evincing a more vulnerable motor profile and not the presence of neurological dysfunction signs.

  8. Learning predictive statistics from temporal sequences: Dynamics and strategies.

    Science.gov (United States)

    Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

    2017-10-01

    Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

  9. The cerebellum does more than sensory prediction error-based learning in sensorimotor adaptation tasks.

    Science.gov (United States)

    Butcher, Peter A; Ivry, Richard B; Kuo, Sheng-Han; Rydz, David; Krakauer, John W; Taylor, Jordan A

    2017-09-01

    Individuals with damage to the cerebellum perform poorly in sensorimotor adaptation paradigms. This deficit has been attributed to impairment in sensory prediction error-based updating of an internal forward model, a form of implicit learning. These individuals can, however, successfully counter a perturbation when instructed with an explicit aiming strategy. This successful use of an instructed aiming strategy presents a paradox: In adaptation tasks, why do individuals with cerebellar damage not come up with an aiming solution on their own to compensate for their implicit learning deficit? To explore this question, we employed a variant of a visuomotor rotation task in which, before executing a movement on each trial, the participants verbally reported their intended aiming location. Compared with healthy control participants, participants with spinocerebellar ataxia displayed impairments in both implicit learning and aiming. This was observed when the visuomotor rotation was introduced abruptly ( experiment 1 ) or gradually ( experiment 2 ). This dual deficit does not appear to be related to the increased movement variance associated with ataxia: Healthy undergraduates showed little change in implicit learning or aiming when their movement feedback was artificially manipulated to produce similar levels of variability ( experiment 3 ). Taken together the results indicate that a consequence of cerebellar dysfunction is not only impaired sensory prediction error-based learning but also a difficulty in developing and/or maintaining an aiming solution in response to a visuomotor perturbation. We suggest that this dual deficit can be explained by the cerebellum forming part of a network that learns and maintains action-outcome associations across trials. NEW & NOTEWORTHY Individuals with cerebellar pathology are impaired in sensorimotor adaptation. This deficit has been attributed to an impairment in error-based learning, specifically, from a deficit in using sensory

  10. Just do it: action-dependent learning allows sensory prediction.

    Directory of Open Access Journals (Sweden)

    Itai Novick

    Full Text Available Sensory-motor learning is commonly considered as a mapping process, whereby sensory information is transformed into the motor commands that drive actions. However, this directional mapping, from inputs to outputs, is part of a loop; sensory stimuli cause actions and vice versa. Here, we explore whether actions affect the understanding of the sensory input that they cause. Using a visuo-motor task in humans, we demonstrate two types of learning-related behavioral effects. Stimulus-dependent effects reflect stimulus-response learning, while action-dependent effects reflect a distinct learning component, allowing the brain to predict the forthcoming sensory outcome of actions. Together, the stimulus-dependent and the action-dependent learning components allow the brain to construct a complete internal representation of the sensory-motor loop.

  11. Social learning through prediction error in the brain

    Science.gov (United States)

    Joiner, Jessica; Piva, Matthew; Turrin, Courtney; Chang, Steve W. C.

    2017-06-01

    Learning about the world is critical to survival and success. In social animals, learning about others is a necessary component of navigating the social world, ultimately contributing to increasing evolutionary fitness. How humans and nonhuman animals represent the internal states and experiences of others has long been a subject of intense interest in the developmental psychology tradition, and, more recently, in studies of learning and decision making involving self and other. In this review, we explore how psychology conceptualizes the process of representing others, and how neuroscience has uncovered correlates of reinforcement learning signals to explore the neural mechanisms underlying social learning from the perspective of representing reward-related information about self and other. In particular, we discuss self-referenced and other-referenced types of reward prediction errors across multiple brain structures that effectively allow reinforcement learning algorithms to mediate social learning. Prediction-based computational principles in the brain may be strikingly conserved between self-referenced and other-referenced information.

  12. Predicting Solar Activity Using Machine-Learning Methods

    Science.gov (United States)

    Bobra, M.

    2017-12-01

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

  13. [Nineteen cases of school-aged children with degenerative or metabolic neurological disorders initially presenting with learning difficulty and/or behavior disturbance].

    Science.gov (United States)

    Honzawa, Shiho; Sugai, Kenji; Akaike, Hiroto; Nakayama, Tojo; Fujikawa, Yoshinao; Komaki, Hirofumi; Nakagawa, Eiji; Sasaki, Masayuki

    2012-07-01

    We reported 19 cases of school-aged children. They were initially judged to have learning difficulty or school maladaptation because of attention deficits, hyperactive behaviors or poor school performance, followed by the diagnosis such as degenerative or metabolic neurological diseases. The patients consisted of 4 cases of adrenoleukodystrophy, 5 cases of dentatorubral-pallidoluysian atrophy, 3 cases of Sanfilippo syndrome, 3 cases of subacute sclerosing panencephalitis, and each one case of juvenile Gaucher disease, juvenile Huntington disease, juvenile metachromatic leukodystrophy and Leigh disease. They had markedly poor school performance, and/or abnormal behaviors, followed by seizures, character disorders or psychomotor regression. The diagnostic clues included brain CT scan and/or MRI, peculiar facial appearance and notable family histories. When the children were indicated to have learning difficulty or maladjustment to school life, we should make deliberate differential diagnoses before concluding that they have a learning disorder and/or attention-deficit/hyperactivity disorder. Instead they should be recommended to visit child neurologists, when they present with any problems as aforesaid.

  14. Dificuldade de aprendizagem em escolares de muito baixo peso ao nascer Learning difficulties in schoolchildren born with very low birth weight

    Directory of Open Access Journals (Sweden)

    Maura C. C. de Rodrigues

    2006-02-01

    Full Text Available OBJETIVOS: Investigar a associação entre muito baixo peso ao nascer e dificuldade de aprendizagem à idade escolar, através de revisão sistemática da literatura, identificando quais os padrões de dificuldade de aprendizagem nesses escolares, possíveis correlações cognitivas, singularidades nos extratos ponderais de muito baixo peso ao nascer e interferência de fatores socioeconômicos e clínicos nos resultados. FONTES DOS DADOS:Busca bibliográfica (MEDLINE, LILACS, Excerpta Medica, listas de referências de artigos originais, periódicos ligados ao tema, informações de experts da área e bancos de teses e dissertações, utilizando as palavras-chave: prematuridade/muito baixo peso ao nascer, dificuldade de aprendizagem/realização acadêmica/performance escolar, seguimento/resultados/coorte. SÍNTESE DOS DADOS: Com a busca, 114 artigos foram captados, e os 18 com adequação metodológica foram selecionados, abordando dificuldade de aprendizagem em escolares de muito baixo peso ao nascer. Observou-se pior desempenho acadêmico destes, quando toda a população de estudo era comparada aos nascidos a termo. A área mais acometida foi a matemática. O risco de evoluir com dificuldades de aprendizagem mostrou-se maior conforme diminuiu o peso ao nascer. Constatou-se associação entre muito baixo peso ao nascer e comprometimentos cognitivos. CONCLUSÕES:A abordagem sistemática corroborou os resultados obtidos de estudos da literatura: os escolares de muito baixo peso ao nascer apresentaram maior risco de dificuldades de aprendizagem quando comparados aos a termo. Predominou o acometimento de múltiplos domínios acadêmicos, sendo a matemática a área mais acometida. Observou-se um gradiente crescente de risco à medida que o peso ao nascer diminuía. Houve associação entre muito baixo peso ao nascer e comprometimento cognitivo.OBJECTIVES: To investigate the relationship between very low birth weight and learning difficulties at

  15. Effectiveness Of Education To Improve Ability Game For Children Learning Difficulties Additive (Study Case at SDN 1 Lamongan

    Directory of Open Access Journals (Sweden)

    Nisaul Barokati

    2015-03-01

    Full Text Available This research is motivated by the child's learning disabilities who cannot do the sums 1-20. From roommates Researchers Gave test, the child cannot do the sums 1-20. This study aims to Prove the effectiveness of educational games for kids summation improves learning disabilities. Type of research is the Single Subject Research, with ABA design and the data analysis techniques using visual analysis chart. Measurement variables using a percentage of the number of questions answered the Correctly. From the results of this study indicate that effective educational games to Enhance the abilities of the sum of numbers for child learning disabilities in SDN 1 Lamongan So that educational games can be used as one solution to help improve the child's ability to sum numbers learning disabilities

  16. Time-sensitive Customer Churn Prediction based on PU Learning

    OpenAIRE

    Wang, Li; Chen, Chaochao; Zhou, Jun; Li, Xiaolong

    2018-01-01

    With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the...

  17. Prediction of Employee Turnover in Organizations using Machine Learning Algorithms

    OpenAIRE

    Rohit Punnoose; Pankaj Ajit

    2016-01-01

    Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared t...

  18. Applications of Machine Learning in Cancer Prediction and Prognosis

    Directory of Open Access Journals (Sweden)

    Joseph A. Cruz

    2006-01-01

    Full Text Available Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25% improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

  19. Predicting Knowledge Workers' Participation in Voluntary Learning with Employee Characteristics and Online Learning Tools

    Science.gov (United States)

    Hicks, Catherine

    2018-01-01

    Purpose: This paper aims to explore predicting employee learning activity via employee characteristics and usage for two online learning tools. Design/methodology/approach: Statistical analysis focused on observational data collected from user logs. Data are analyzed via regression models. Findings: Findings are presented for over 40,000…

  20. Construção das dificuldades de aprendizagem em crianças adotadas Construction of learning difficulties by adopted children

    Directory of Open Access Journals (Sweden)

    Sueli Cristina De Pauli

    2009-12-01

    Full Text Available Breve revisão bibliográfica acerca da usual concepção de que crianças adotivas comumente possuem dificuldades de aprendizagem escolar. Investigações sobre o processo de construção de dificuldades de aprendizagem em crianças adotivas são praticamente inexistentes na produção científica brasileira e internacional. Algumas poucas pesquisas fazem referência direta aos problemas de aprendizagem desses sujeitos, relacionando o sintoma com o curto ou longo tempo de institucionalização por eles vivenciado. Já a literatura psicopedagógica aponta alguns sintomas apresentados por essas crianças, os quais teriam influência sobre a sua não aprendizagem, tais como: dificuldades na estruturação egóica, baixa autoestima, rebaixamento intelectual associado a problemas de comportamento, hiperatividade, desatenção. Todavia, esses problemas não são descritos em quantidade e/ou profundidade, de sorte que sobre a questão há uma escassez, um vácuo, o que indica a necessidade de realização de estudos específicos que tornem visíveis tanto o número de ocorrências das dificuldades de aprendizagem entre adotados quanto os aspectos que contribuem para a constituição (ou não de tais sintomas.The objective of this work is to present the results of a brief bibliographic review concerning the common idea that adopted children often experience difficulties in school learning. Studies of how difficulties in learning arise in adopted children are quite scarce both in Brazil and internationally. Some of the few researches found make direct reference to the problems in learning for these children - usually relating these difficulties to the length of time children are in institutions. The literature in educational psychology has previously identified some symptoms associated with adoption - for example, problems in ego strength, low self-esteem, reduced intellectual functioning associated with behavioral problems, hyperactivity, and deficits

  1. Regulating sadness and fear from outside and within: mothers' emotion socialization and adolescents' parasympathetic regulation predict the development of internalizing difficulties.

    Science.gov (United States)

    Hastings, Paul D; Klimes-Dougan, Bonnie; Kendziora, Kimberly T; Brand, Ann; Zahn-Waxler, Carolyn

    2014-11-01

    Multilevel models of developmental psychopathology implicate both characteristics of the individual and their rearing environment in the etiology of internalizing problems and disorders. Maladaptive regulation of fear and sadness, the core of anxiety and depression, arises from the conjoint influences of ineffective parasympathetic regulation of emotion and ineffective emotion socialization experiences. In 171 youths (84 female, M = 13.69 years, SD = 1.84), we measured changes of respiratory sinus arrhythmia (RSA) in response to sadness- and fear-inducing film clips and maternal supportive and punitive responses to youths' internalizing emotions. Youths and mothers reported on youths' internalizing problems and anxiety and depression symptoms concurrently and 2 years later at Time 2. Maternal supportive emotion socialization predicted fewer, and punitive socialization predicted more, mother-reported internalizing problems at Time 2 only for youths who showed RSA suppression to fear-inducing films. More RSA suppression to sadness-inducing films predicted more youth-reported internalizing problems at Time 2 in girls only. In addition, less supportive emotion socialization predicted more youth-reported depression symptoms at Time 2 only for girls who showed more RSA suppression to sadness. RSA suppression to sadness versus fear might reflect different patterns of atypical parasympathetic regulation of emotional arousal, both of which increase the risk for internalizing difficulties in youths, and especially girls, who lack maternal support for regulating emotions.

  2. A semi-supervised learning approach for RNA secondary structure prediction.

    Science.gov (United States)

    Yonemoto, Haruka; Asai, Kiyoshi; Hamada, Michiaki

    2015-08-01

    RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Quicksilver: Fast predictive image registration - A deep learning approach.

    Science.gov (United States)

    Yang, Xiao; Kwitt, Roland; Styner, Martin; Niethammer, Marc

    2017-09-01

    This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. Learning Political Science with Prediction Markets: An Experimental Study

    Science.gov (United States)

    Ellis, Cali Mortenson; Sami, Rahul

    2012-01-01

    Prediction markets are designed to aggregate the information of many individuals to forecast future events. These markets provide participants with an incentive to seek information and a forum for interaction, making markets a promising tool to motivate student learning. We carried out a quasi-experiment in an introductory political science class…

  5. Do Judgments of Learning Predict Automatic Influences of Memory?

    Science.gov (United States)

    Undorf, Monika; Böhm, Simon; Cüpper, Lutz

    2016-01-01

    Current memory theories generally assume that memory performance reflects both recollection and automatic influences of memory. Research on people's predictions about the likelihood of remembering recently studied information on a memory test, that is, on judgments of learning (JOLs), suggests that both magnitude and resolution of JOLs are linked…

  6. Social inequalities and cancer: can the European deprivation index predict patients' difficulties in health care access? a pilot study

    Science.gov (United States)

    Moriceau, Guillaume; Bourmaud, Aurélie; Tinquaut, Fabien; Oriol, Mathieu; Jacquin, Jean-Philippe; Fournel, Pierre; Magné, Nicolas; Chauvin, Franck

    2016-01-01

    Context The European Deprivation Index (EDI), is a new ecological estimate for Socio-Economic Status (SES). This study postulates that Time-To-Treatment could be used as a cancer quality-of -care surrogate in order to identify the association between cancer patient's SES and quality of care in a French comprehensive cancer center. Methods retrospective mono-centered cohort study. All consecutive incoming adult patients diagnosed for breast cancer(BC), prostate cancer(PC), colorectal cancer (CRC), lung cancer(LC) or sarcoma(S) were included between January 2013 and December 2013. The association of EDI and Time-To-Diagnosis(TTD), as well as Time-To-Treatment(TTT) was analyzed using a cox regression, and a strata analysis per tumor site was performed. Results 969 patients were included. Primitive tumor site was 505 BC(52%), 169 PC(17%), 145 LC(15%), 116 CRC(12%), and 34 S(4%). Median TTD was 1.41 months (Q1-Q3 0.5 to 3.5 months). Median TTT was 0.9 months (0.4 - 1.4). In a multivariate analysis, we identified the tumor site as a predictive factor to influence TTD, shorter for BC (0.75months, [0.30- 1.9]) than PC (4.69 months [1.6-29.7]), HR 0.27 95%CI= [0.22-0.34], p < 0.001. TTT was also shorter for BC (0.75months [0.4-1.1]) than PC (2.02 [0.9-3.2]), HR 0.32 95%CI= [0.27-0.39], p < 0.001. EDI quintiles were not found associated with either TTT or TTD. Conclusions Deprivation estimated by the EDI does not appear to be related to an extension of the Time-to-Diagnosis or Time-to-Treatment in our real-life population. Further research should be done to identify other frailty-sensitive factors that could be responsible for delays in care. PMID:26540571

  7. Machine Learning Principles Can Improve Hip Fracture Prediction

    DEFF Research Database (Denmark)

    Kruse, Christian; Eiken, Pia; Vestergaard, Peter

    2017-01-01

    Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data.......89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an “xgbTree” model. Machine learning can improve hip fracture...... prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration....

  8. Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons.

    Science.gov (United States)

    Keysers, Christian; Perrett, David I; Gazzola, Valeria

    2014-04-01

    Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization.

  9. Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons

    OpenAIRE

    Keysers, C.; Perrett, D.I.; Gazzola, V.

    2014-01-01

    Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization. Publisher PDF Peer reviewed

  10. Ensemble learned vaccination uptake prediction using web search queries

    DEFF Research Database (Denmark)

    Hansen, Niels Dalum; Lioma, Christina; Mølbak, Kåre

    2016-01-01

    We present a method that uses ensemble learning to combine clinical and web-mined time-series data in order to predict future vaccination uptake. The clinical data is official vaccination registries, and the web data is query frequencies collected from Google Trends. Experiments with official...... vaccine records show that our method predicts vaccination uptake eff?ectively (4.7 Root Mean Squared Error). Whereas performance is best when combining clinical and web data, using solely web data yields comparative performance. To our knowledge, this is the ?first study to predict vaccination uptake...

  11. Prediction of length-of-day using extreme learning machine

    Directory of Open Access Journals (Sweden)

    Yu Lei

    2015-03-01

    Full Text Available Traditional artificial neural networks (ANN such as back-propagation neural networks (BPNN provide good predictions of length-of-day (LOD. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine (ELM, to improve the efficiency of LOD predictions. Earth orientation parameters (EOP C04 time-series provides daily values from International Earth Rotation and Reference Systems Service (IERS, which serves as our database. First, the known predictable effects that can be described by functional models—such as the effects of solid earth, ocean tides, or seasonal atmospheric variations—are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data (i.e., the irregular and quasi-periodic variations are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks (GRNN, and adaptive network-based fuzzy inference systems (ANFIS. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction techniques, the mean-absolute-error (MAE from the proposed method is compared with that from the EOP prediction comparison campaign (EOP PCC. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.

  12. Predicting genome-wide redundancy using machine learning

    Directory of Open Access Journals (Sweden)

    Shasha Dennis E

    2010-11-01

    Full Text Available Abstract Background Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here. Results Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1, suggesting that redundancy is stable over long evolutionary periods. Conclusions Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.

  13. Attentional performance and executive functions in children with learning difficulties Desempenho atencional e funções executivas em crianças com dificuldades de aprendizagem

    Directory of Open Access Journals (Sweden)

    Ricardo Franco de Lima

    2011-01-01

    Full Text Available Studies have described changes in visual attention and executive function in children with developmental dyslexia. This study intended to compare the performance of children with and without learning difficulties on tasks of visual attention and executive functions. The participants were 23 students, aged between 9 and 14 years old, with a mean age of 10.8 years. They were divided into three groups: (a with learning difficulties; (b with dyslexia; and (c control (without any difficulty. For the evaluation, Tests of Cancellation, Trail Making Test, Stroop Color-Word Test and Tower of London Test were used. The results indicated that children with dyslexia had the worst performance on different measures of attention and executive functions, indicating that such changes may be characteristic of the disorder and keep the deficit in the phonological component of language.Estudos têm descrito alterações na atenção visual e nas funções executivas em crianças com Dislexia do Desenvolvimento. O presente trabalho pretendeu comparar o desempenho de crianças com e sem dificuldades de aprendizagem em tarefas de atenção visual e funções executivas. Participaram 23 estudantes, com idade entre 9 e 14 anos e idade média de 10,8 anos, divididos em três grupos: com dificuldades escolares, com dislexia e controle sem dificuldades. Para a avaliação foram usados os Testes de Cancelamento, Trail Making Test, Stroop Color Word Test e Tower of London. Os resultados indicaram que as crianças com dislexia apresentaram piores desempenhos em diferentes medidas atencionais e das funções executivas, indicando que tais alterações podem ser características do quadro e acompanhar o déficit no componente fonológico da linguagem.

  14. Machine learning modelling for predicting soil liquefaction susceptibility

    Directory of Open Access Journals (Sweden)

    P. Samui

    2011-01-01

    Full Text Available This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN based on multi-layer perceptions (MLP that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N160] and cyclic stress ratio (CSR. Further, an attempt has been made to simplify the models, requiring only the two parameters [(N160 and peck ground acceleration (amax/g], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

  15. Machine Learning and Conflict Prediction: A Use Case

    Directory of Open Access Journals (Sweden)

    Chris Perry

    2013-10-01

    Full Text Available For at least the last two decades, the international community in general and the United Nations specifically have attempted to develop robust, accurate and effective conflict early warning system for conflict prevention. One potential and promising component of integrated early warning systems lies in the field of machine learning. This paper aims at giving conflict analysis a basic understanding of machine learning methodology as well as to test the feasibility and added value of such an approach. The paper finds that the selection of appropriate machine learning methodologies can offer substantial improvements in accuracy and performance. It also finds that even at this early stage in testing machine learning on conflict prediction, full models offer more predictive power than simply using a prior outbreak of violence as the leading indicator of current violence. This suggests that a refined data selection methodology combined with strategic use of machine learning algorithms could indeed offer a significant addition to the early warning toolkit. Finally, the paper suggests a number of steps moving forward to improve upon this initial test methodology.

  16. Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow Misconceptions

    Directory of Open Access Journals (Sweden)

    Medha Kumar

    2018-03-01

    Full Text Available Research shows that people’s wait-and-see preferences for actions against climate change are a result of several factors, including cognitive misconceptions. The use of simulation tools could help reduce these misconceptions concerning Earth’s climate. However, it is still unclear whether the learning in these tools is of the problem’s surface features (dimensions of emissions and absorptions and cover-story used or of the problem’s structural features (how emissions and absorptions cause a change in CO2 concentration under different CO2 concentration scenarios. Also, little is known on how problem’s difficulty in these tools (the shape of CO2 concentration trajectory, as well as the use of these tools as a decision aid influences performance. The primary objective of this paper was to investigate how learning about Earth’s climate via simulation tools is influenced by problem’s surface and structural features, problem’s difficulty, and decision aids. In experiment 1, we tested the influence of problem’s surface and structural features in a simulation called Dynamic Climate Change Simulator (DCCS on subsequent performance in a paper-and-pencil Climate Stabilization (CS task (N = 100 across four between-subject conditions. In experiment 2, we tested the effects of problem’s difficulty in DCCS on subsequent performance in the CS task (N = 90 across three between-subject conditions. In experiment 3, we tested the influence of DCCS as a decision aid on subsequent performance in the CS task (N = 60 across two between-subject conditions. Results revealed a significant reduction in people’s misconceptions in the CS task after performing in DCCS compared to when performing in CS task in the absence of DCCS. The decrease in misconceptions in the CS task was similar for both problems’ surface and structural features, showing both structure and surface learning in DCCS. However, the proportion of misconceptions was similar across

  17. PREDICTING ACADEMIC ACHIEVEMENT: THE ROLE OF MOTIVATION AND LEARNING STRATEGIES

    Directory of Open Access Journals (Sweden)

    Juliana Beatriz Stover

    2014-04-01

    Full Text Available The aim of this study consists in testing a predictive model of academic achievement including motivation and learning strategies as predictors. Motivation is defined as the energy and the direction of behaviors; it is categorized in three types of motivation –intrinsic, extrinsic and amotivation (Deci & Ryan, 1985. Learning strategies are deliberate operations oriented towards information processing in academic activities (Valle, Barca, González & Núñez, 1999. Several studies analysed the relationship between motivation and learning strategies in high school and college environments. Students with higher academic achievement were intrinsically motivated and used a wider variety of learning strategies more frequently. A non-experimental predictive design was developed. The sample was composed by 459 students (55.2% high-schoolers; 44.8% college students. Data were gathered by means of sociodemographic and academic surveys, and also by the local versions of the Academic Motivation Scale –EMA, Echelle de Motivation en Éducation (Stover, de la Iglesia, Rial Boubeta & Fernández Liporace, 2012; Vallerand, Blais, Briere & Pelletier, 1989 and the Learning and Study Strategies Inventory –LASSI (Stover, Uriel & Fernández Liporace, 2012; Weinstein, Schulte & Palmer, 1987. Several path analyses were carried out to test a hypothetical model to predict academic achievement (Kline, 1998. Results indicated that self-determined motivation explained academic achievement through the use of learning strategies. The final model obtained an excellent fit (χ2=16.523, df= 6, p=0.011; GFI=0.987; AGFI=0.955; SRMR=0.0320; NFI=0.913; IFI=0.943; CFI=0.940. Results are discussed considering Self Determination Theory and previous research.

  18. How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

    Science.gov (United States)

    Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C

    2014-03-01

    Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward

  19. The assessment of math learning difficulties in a primary grade-4 child with high support needs: Mixed methods approach

    Directory of Open Access Journals (Sweden)

    Lawrence Mundia

    2012-03-01

    Full Text Available This mixed-methods study incorporated elements of survey, case study and action research approaches in investigating an at-risk child. Using an in-take interview, a diagnostic test, an error analysis, and a think-aloud clinical interview, the study identified the child’s major presenting difficulties. These included: inability to use the four arithmetic operations (addition, subtraction, multiplication, division efficiently; not understanding the relationship between units, tens and hundreds; using any two of the four arithmetic processes (+, - , x, ÷ in combination within one operation; treating each column as a separate problem; place value problems / wrong alignment of numbers; poor eye-hand coordination leading to dysgraphia; and memory lapses. The other problems that became apparent through this investigation and implied in the findings include possible causal factors such as dyscalculia, dyslexia, low self-esteem, low self-efficacy, and math anxiety. Further assessment, intervention and research are recommended to address problems of this vulnerable child.

  20. The Assessment of Math Learning Difficulties in a Primary Grade-4 Child with High Support Needs: Mixed Methods Approach

    Directory of Open Access Journals (Sweden)

    Lawrence Mundia

    2012-03-01

    Full Text Available This mixed-methods study incorporated elements of survey, case study and action research approaches in investigating an at-risk child. Using an in-take interview, a diagnostic test, an error analysis, and a think-aloud clinical interview, the study identified the child’s major presenting difficulties. These included: inability to use the four arithmetic operations (addition, subtraction, multiplication, division efficiently; not understanding the relationship between units, tens and hundreds; using any two of the four arithmetic processes (+, - , x, ÷ in combination within one operation; treating each column as a separate problem; place value problems / wrong alignment of numbers; poor eye-hand coordination leading to dysgraphia; and memory lapses. The other problems that became apparent through this investigation and implied in the findings include possible causal factors such as dyscalculia, dyslexia, low self-esteem, low self-efficacy, and math anxiety. Further assessment, intervention and research are recommended to address problems of this vulnerable child.

  1. ICT as a tool in English teaching : A literature review on the use of ICT for Swedish students with learning difficulties and their literacy learning in grades 7-9

    OpenAIRE

    Kjellin Ifverson, Ebba

    2015-01-01

    Information and communication technology (ICT) is a subject that is being discussed as a tool that is used within education around the world. Furthermore it can be seen as a tool for teachers to individualize students´ education. Students with literacy difficulties, such as dyslexia, are in constant need of new ways to learn, and new ways to be motivated to learn. The aim of this study is to see what research says in regard to how ICT can be used as a tool to help students with literacy diffi...

  2. ERPs recorded during early second language exposure predict syntactic learning.

    Science.gov (United States)

    Batterink, Laura; Neville, Helen J

    2014-09-01

    Millions of adults worldwide are faced with the task of learning a second language (L2). Understanding the neural mechanisms that support this learning process is an important area of scientific inquiry. However, most previous studies on the neural mechanisms underlying L2 acquisition have focused on characterizing the results of learning, relying upon end-state outcome measures in which learning is assessed after it has occurred, rather than on the learning process itself. In this study, we adopted a novel and more direct approach to investigate neural mechanisms engaged during L2 learning, in which we recorded ERPs from beginning adult learners as they were exposed to an unfamiliar L2 for the first time. Learners' proficiency in the L2 was then assessed behaviorally using a grammaticality judgment task, and ERP data acquired during initial L2 exposure were sorted as a function of performance on this task. High-proficiency learners showed a larger N100 effect to open-class content words compared with closed-class function words, whereas low-proficiency learners did not show a significant N100 difference between open- and closed-class words. In contrast, amplitude of the N400 word category effect correlated with learners' L2 comprehension, rather than predicting syntactic learning. Taken together, these results indicate that learners who spontaneously direct greater attention to open- rather than closed-class words when processing L2 input show better syntactic learning, suggesting a link between selective attention to open-class content words and acquisition of basic morphosyntactic rules. These findings highlight the importance of selective attention mechanisms for L2 acquisition.

  3. Machine learning methods in predicting the student academic motivation

    Directory of Open Access Journals (Sweden)

    Ivana Đurđević Babić

    2017-01-01

    Full Text Available Academic motivation is closely related to academic performance. For educators, it is equally important to detect early students with a lack of academic motivation as it is to detect those with a high level of academic motivation. In endeavouring to develop a classification model for predicting student academic motivation based on their behaviour in learning management system (LMS courses, this paper intends to establish links between the predicted student academic motivation and their behaviour in the LMS course. Students from all years at the Faculty of Education in Osijek participated in this research. Three machine learning classifiers (neural networks, decision trees, and support vector machines were used. To establish whether a significant difference in the performance of models exists, a t-test of the difference in proportions was used. Although, all classifiers were successful, the neural network model was shown to be the most successful in detecting the student academic motivation based on their behaviour in LMS course.

  4. Machine learning application in online lending risk prediction

    OpenAIRE

    Yu, Xiaojiao

    2017-01-01

    Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with various format and size were collected from public website, third-parties and assembled with client's loan application information data. Ensemble machine learning models, random fo...

  5. Pileup Subtraction and Jet Energy Prediction Using Machine Learning

    OpenAIRE

    Kong, Vein S; Li, Jiakun; Zhang, Yujia

    2015-01-01

    In the Large Hardron Collider (LHC), multiple proton-proton collisions cause pileup in reconstructing energy information for a single primary collision (jet). This project aims to select the most important features and create a model to accurately estimate jet energy. Different machine learning methods were explored, including linear regression, support vector regression and decision tree. The best result is obtained by linear regression with predictive features and the performance is improve...

  6. Machine learning derived risk prediction of anorexia nervosa.

    Science.gov (United States)

    Guo, Yiran; Wei, Zhi; Keating, Brendan J; Hakonarson, Hakon

    2016-01-20

    Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

  7. Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

    Science.gov (United States)

    Su, X.

    2017-12-01

    A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

  8. Human medial frontal cortex activity predicts learning from errors.

    Science.gov (United States)

    Hester, Robert; Barre, Natalie; Murphy, Kevin; Silk, Tim J; Mattingley, Jason B

    2008-08-01

    Learning from errors is a critical feature of human cognition. It underlies our ability to adapt to changing environmental demands and to tune behavior for optimal performance. The posterior medial frontal cortex (pMFC) has been implicated in the evaluation of errors to control behavior, although it has not previously been shown that activity in this region predicts learning from errors. Using functional magnetic resonance imaging, we examined activity in the pMFC during an associative learning task in which participants had to recall the spatial locations of 2-digit targets and were provided with immediate feedback regarding accuracy. Activity within the pMFC was significantly greater for errors that were subsequently corrected than for errors that were repeated. Moreover, pMFC activity during recall errors predicted future responses (correct vs. incorrect), despite a sizeable interval (on average 70 s) between an error and the next presentation of the same recall probe. Activity within the hippocampus also predicted future performance and correlated with error-feedback-related pMFC activity. A relationship between performance expectations and pMFC activity, in the absence of differing reinforcement value for errors, is consistent with the idea that error-related pMFC activity reflects the extent to which an outcome is "worse than expected."

  9. The role of Postural instability/gait difficulty and Fear of falling in predicting falls in non-demented older adults

    Science.gov (United States)

    Allali, Gilles; Ayers, Emmeline I.; Holtzer, Roee; Verghese, Joe

    2016-01-01

    Introduction Postural instability/gait difficulty (PIGD) and fear of falling (FoF) frequently co-exist, but their individual predictive values for falls have not been compared in aging. This study aims to determine both independent and combined effect of PIGD and FoF to falls in older adults without dementia. Methods PIGD and other extrapyramidal signs were systematically assessed in 449 community-dwelling participants without Parkinson’s disease (76.48 ± 6.61 ys; 56.8% female) enrolled in this longitudinal cohort study. Presence of FoF was measured by a single-item question (Do you have a FoF?) and self-confidence by the Activities-specific Balance Confidence scale (ABC scale). Results One hundred sixty-nine participants (38 %) had an incident fall over a mean follow-up of 20.1 ± 12.2 months. PIGD was present in 32% and FoF in 23% of the participants. Both PIGD (adjusted hazard ratio (aHR): 2.28; p = 0.016) and self-confidence (aHR: 0.99; p = 0.040) predicted falls when entered simultaneously in the Cox model. However, presence of FoF (aHR: 1.99; p = 0.021) and self-confidence (aHR: 0.98; p = 0.006) predicted falls only in individuals with PIGD. Conclusions PIGD and FoF were associated with future falls in older adults without dementia but FoF was a fall’s predictor only in individuals with PIGD. PMID:27866086

  10. Sleep Quality Prediction From Wearable Data Using Deep Learning.

    Science.gov (United States)

    Sathyanarayana, Aarti; Joty, Shafiq; Fernandez-Luque, Luis; Ofli, Ferda; Srivastava, Jaideep; Elmagarmid, Ahmed; Arora, Teresa; Taheri, Shahrad

    2016-11-04

    The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and

  11. Machine learning models in breast cancer survival prediction.

    Science.gov (United States)

    Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

    2016-01-01

    Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

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

    Science.gov (United States)

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

    2011-01-01

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

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

    Science.gov (United States)

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

    2011-01-01

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

  14. Intrapartum translabial ultrasound with pushing used to predict the difficulty in vacuum-assisted delivery of fetuses in non-occiput posterior position.

    Science.gov (United States)

    Antonio Sainz, José; Borrero, Carlota; Aquise, Adriana; García-Mejido, José Antonio; Gutierrez, Laura; Fernández-Palacín, Ana

    2016-10-01

    Our aim is to evaluate the capacity of intrapartum translabial ultrasound (ITU) with pushing in the prediction of difficulty of fetal extraction in vacuum assisted deliveries. Prospective, observational study performed (2/2015-8/2015) on 75 nulliparous women, ≥37 weeks with singleton pregnancies at full dilatation who had ITU-with-pushing performed, previous to vacuum-placement for fetal extraction. Working on the translabial sagittal-plane, we assessed: Angle-Progression (AoP), Progression-Distance (PD) and Head-Direction (HD); in the axial plane we evaluated: Midline-Angle (MLA) and Head-Perineum-Distance (HPD). Vacuum extractions were classified as easy-difficulty (ED) (≤3 vacuum-pulls), difficult-unsuccessful (DD) (>3 vacuum-pulls). We did not assess occipito-posterior-presentations. Seventy nulliparous were studied (44-ED,26-DD). We observed no differences in obstetric, neonatal or intrapartum characteristics between the two study groups, with the following exceptions: newborn weight (3272 ± 438 g versus 3540 ± 372 g; p = 0.011) and number of vacuum-pulls (1.4-ED-vs-4.4-DD; p pushing was 143.9° ± 14.6° in ED and 115.1°± 12.9° in DD (p Pushing was 42.7 ± 11.3 mm versus 30.4 ± 9.8 mm (p Pushing was 27.6°± 26.6° versus 57.5°±26.5°(p=0.025); HPD-Pushing was 40.8 ± 10.0 mm versus 47.4 ± 10.9 mm (p = 0.039). We identified that the presence of an AoP-Pushing > 128° predicts an Easy-Vacuum-Delivery (≤3 Vacuum-Pulls) in  >85% of cases (Sen 80%-FPR 9.3%).

  15. Machine Learning Techniques for Prediction of Early Childhood Obesity.

    Science.gov (United States)

    Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

    2015-01-01

    This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

  16. Machine learning applied to the prediction of citrus production

    Energy Technology Data Exchange (ETDEWEB)

    Díaz, I.; Mazza, S.M.; Combarro, E.F.; Giménez, L.I.; Gaiad, J.E.

    2017-07-01

    An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees' age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

  17. Machine learning applied to the prediction of citrus production

    International Nuclear Information System (INIS)

    Díaz, I.; Mazza, S.M.; Combarro, E.F.; Giménez, L.I.; Gaiad, J.E.

    2017-01-01

    An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees' age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

  18. Machine learning applied to the prediction of citrus production

    Directory of Open Access Journals (Sweden)

    Irene Díaz

    2017-07-01

    Full Text Available An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i studies the effectiveness of machine learning techniques for predicting orchards production; and (ii variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees’ age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8 and relative mean absolute error (~0.1. These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

  19. Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

    OpenAIRE

    Mingjie Tan; Peiji Shao

    2015-01-01

    The high rate of dropout is a serious problem in E-learning program. Thus it has received extensive concern from the education administrators and researchers. Predicting the potential dropout students is a workable solution to prevent dropout. Based on the analysis of related literature, this study selected student’s personal characteristic and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN), Decision Tree (DT) and Bayesian Ne...

  20. Learning and Prediction of Slip from Visual Information

    Science.gov (United States)

    Angelova, Anelia; Matthies, Larry; Helmick, Daniel; Perona, Pietro

    2007-01-01

    This paper presents an approach for slip prediction from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering such terrain can be very useful for better planning and avoiding these areas. To address this problem, terrain appearance and geometry information about map cells are correlated to the slip measured by the rover while traversing each cell. This relationship is learned from previous experience, so slip can be predicted remotely from visual information only. The proposed method consists of terrain type recognition and nonlinear regression modeling. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The final slip prediction error is about 20%. The system is intended for improved navigation on steep slopes and rough terrain for Mars rovers.

  1. Prediction of skin sensitization potency using machine learning approaches.

    Science.gov (United States)

    Zang, Qingda; Paris, Michael; Lehmann, David M; Bell, Shannon; Kleinstreuer, Nicole; Allen, David; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Strickland, Judy

    2017-07-01

    The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave-one-out cross-validation. A one-tiered strategy modeled all three categories of response together while a two-tiered strategy modeled sensitizer/non-sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two-tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one-tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non-animal methods may provide valuable information for assessing skin sensitization potency. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  2. Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.

    Science.gov (United States)

    Senders, Joeky T; Staples, Patrick C; Karhade, Aditya V; Zaki, Mark M; Gormley, William B; Broekman, Marike L D; Smith, Timothy R; Arnaout, Omar

    2018-01-01

    Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction. A systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1, 2017. Thirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts. In the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Reminder cues modulate the renewal effect in human predictive learning

    Directory of Open Access Journals (Sweden)

    Javier Bustamante

    2016-12-01

    Full Text Available Associative learning refers to our ability to learn about regularities in our environment. When a stimulus is repeatedly followed by a specific outcome, we learn to expect the outcome in the presence of the stimulus. We are also able to modify established expectations in the face of disconfirming information (the stimulus is no longer followed by the outcome. Both the change of environmental regularities and the related processes of adaptation are referred to as extinction. However, extinction does not erase the initially acquired expectations. For instance, following successful extinction, the initially learned expectations can recover when there is a context change – a phenomenon called the renewal effect, which is considered as a model for relapse after exposure therapy. Renewal was found to be modulated by reminder cues of acquisition and extinction. However, the mechanisms underlying the effectiveness of reminder cues are not well understood. The aim of the present study was to investigate the impact of reminder cues on renewal in the field of human predictive learning. Experiment I demonstrated that renewal in human predictive learning is modulated by cues related to acquisition or extinction. Initially, participants received pairings of a stimulus and an outcome in one context. These stimulus-outcome pairings were preceded by presentations of a reminder cue (acquisition cue. Then, participants received extinction in a different context in which presentations of the stimulus were no longer followed by the outcome. These extinction trials were preceded by a second reminder cue (extinction cue. During a final phase conducted in a third context, participants showed stronger expectations of the outcome in the presence of the stimulus when testing was accompanied by the acquisition cue compared to the extinction cue. Experiment II tested an explanation of the reminder cue effect in terms of simple cue-outcome associations. Therefore

  4. Evaluation of Deep Learning Models for Predicting CO2 Flux

    Science.gov (United States)

    Halem, M.; Nguyen, P.; Frankel, D.

    2017-12-01

    Artificial neural networks have been employed to calculate surface flux measurements from station data because they are able to fit highly nonlinear relations between input and output variables without knowing the detail relationships between the variables. However, the accuracy in performing neural net estimates of CO2 flux from observations of CO2 and other atmospheric variables is influenced by the architecture of the neural model, the availability, and complexity of interactions between physical variables such as wind, temperature, and indirect variables like latent heat, and sensible heat, etc. We evaluate two deep learning models, feed forward and recurrent neural network models to learn how they each respond to the physical measurements, time dependency of the measurements of CO2 concentration, humidity, pressure, temperature, wind speed etc. for predicting the CO2 flux. In this paper, we focus on a) building neural network models for estimating CO2 flux based on DOE data from tower Atmospheric Radiation Measurement data; b) evaluating the impact of choosing the surface variables and model hyper-parameters on the accuracy and predictions of surface flux; c) assessing the applicability of the neural network models on estimate CO2 flux by using OCO-2 satellite data; d) studying the efficiency of using GPU-acceleration for neural network performance using IBM Power AI deep learning software and packages on IBM Minsky system.

  5. Reinforcement Learning Based Novel Adaptive Learning Framework for Smart Grid Prediction

    Directory of Open Access Journals (Sweden)

    Tian Li

    2017-01-01

    Full Text Available Smart grid is a potential infrastructure to supply electricity demand for end users in a safe and reliable manner. With the rapid increase of the share of renewable energy and controllable loads in smart grid, the operation uncertainty of smart grid has increased briskly during recent years. The forecast is responsible for the safety and economic operation of the smart grid. However, most existing forecast methods cannot account for the smart grid due to the disabilities to adapt to the varying operational conditions. In this paper, reinforcement learning is firstly exploited to develop an online learning framework for the smart grid. With the capability of multitime scale resolution, wavelet neural network has been adopted in the online learning framework to yield reinforcement learning and wavelet neural network (RLWNN based adaptive learning scheme. The simulations on two typical prediction problems in smart grid, including wind power prediction and load forecast, validate the effectiveness and the scalability of the proposed RLWNN based learning framework and algorithm.

  6. Intrinsic Motivation of Chinese Learning in Predicting Online Learning Self-Efficacy and Flow Experience Relevant to Students' Learning Progress

    Science.gov (United States)

    Hong, Jon-Chao; Hwang, Ming-Yueh; Tai, Kai-Hsin; Lin, Pei-Hsin

    2017-01-01

    Students of Southeast Asian Heritage Learning Chinese (SSAHLC) in Taiwan have frequently demonstrated difficulty with traditional Chinese (a graphical character) radical recognition due to their limited exposure to the written language form since childhood. In this study, we designed a Chinese radical learning game (CRLG), which adopted a drill…

  7. Glutamatergic model psychoses: prediction error, learning, and inference.

    Science.gov (United States)

    Corlett, Philip R; Honey, Garry D; Krystal, John H; Fletcher, Paul C

    2011-01-01

    Modulating glutamatergic neurotransmission induces alterations in conscious experience that mimic the symptoms of early psychotic illness. We review studies that use intravenous administration of ketamine, focusing on interindividual variability in the profundity of the ketamine experience. We will consider this individual variability within a hypothetical model of brain and cognitive function centered upon learning and inference. Within this model, the brains, neural systems, and even single neurons specify expectations about their inputs and responding to violations of those expectations with new learning that renders future inputs more predictable. We argue that ketamine temporarily deranges this ability by perturbing both the ways in which prior expectations are specified and the ways in which expectancy violations are signaled. We suggest that the former effect is predominantly mediated by NMDA blockade and the latter by augmented and inappropriate feedforward glutamatergic signaling. We suggest that the observed interindividual variability emerges from individual differences in neural circuits that normally underpin the learning and inference processes described. The exact source for that variability is uncertain, although it is likely to arise not only from genetic variation but also from subjects' previous experiences and prior learning. Furthermore, we argue that chronic, unlike acute, NMDA blockade alters the specification of expectancies more profoundly and permanently. Scrutinizing individual differences in the effects of acute and chronic ketamine administration in the context of the Bayesian brain model may generate new insights about the symptoms of psychosis; their underlying cognitive processes and neurocircuitry.

  8. Support vector machine incremental learning triggered by wrongly predicted samples

    Science.gov (United States)

    Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

    2018-05-01

    According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

  9. Predicting DPP-IV inhibitors with machine learning approaches

    Science.gov (United States)

    Cai, Jie; Li, Chanjuan; Liu, Zhihong; Du, Jiewen; Ye, Jiming; Gu, Qiong; Xu, Jun

    2017-04-01

    Dipeptidyl peptidase IV (DPP-IV) is a promising Type 2 diabetes mellitus (T2DM) drug target. DPP-IV inhibitors prolong the action of glucagon-like peptide-1 (GLP-1) and gastric inhibitory peptide (GIP), improve glucose homeostasis without weight gain, edema, and hypoglycemia. However, the marketed DPP-IV inhibitors have adverse effects such as nasopharyngitis, headache, nausea, hypersensitivity, skin reactions and pancreatitis. Therefore, it is still expected for novel DPP-IV inhibitors with minimal adverse effects. The scaffolds of existing DPP-IV inhibitors are structurally diversified. This makes it difficult to build virtual screening models based upon the known DPP-IV inhibitor libraries using conventional QSAR approaches. In this paper, we report a new strategy to predict DPP-IV inhibitors with machine learning approaches involving naïve Bayesian (NB) and recursive partitioning (RP) methods. We built 247 machine learning models based on 1307 known DPP-IV inhibitors with optimized molecular properties and topological fingerprints as descriptors. The overall predictive accuracies of the optimized models were greater than 80%. An external test set, composed of 65 recently reported compounds, was employed to validate the optimized models. The results demonstrated that both NB and RP models have a good predictive ability based on different combinations of descriptors. Twenty "good" and twenty "bad" structural fragments for DPP-IV inhibitors can also be derived from these models for inspiring the new DPP-IV inhibitor scaffold design.

  10. Machine learning landscapes and predictions for patient outcomes

    Science.gov (United States)

    Das, Ritankar; Wales, David J.

    2017-07-01

    The theory and computational tools developed to interpret and explore energy landscapes in molecular science are applied to the landscapes defined by local minima for neural networks. These machine learning landscapes correspond to fits of training data, where the inputs are vital signs and laboratory measurements for a database of patients, and the objective is to predict a clinical outcome. In this contribution, we test the predictions obtained by fitting to single measurements, and then to combinations of between 2 and 10 different patient medical data items. The effect of including measurements over different time intervals from the 48 h period in question is analysed, and the most recent values are found to be the most important. We also compare results obtained for neural networks as a function of the number of hidden nodes, and for different values of a regularization parameter. The predictions are compared with an alternative convex fitting function, and a strong correlation is observed. The dependence of these results on the patients randomly selected for training and testing decreases systematically with the size of the database available. The machine learning landscapes defined by neural network fits in this investigation have single-funnel character, which probably explains why it is relatively straightforward to obtain the global minimum solution, or a fit that behaves similarly to this optimal parameterization.

  11. A Symbiotic Framework for coupling Machine Learning and Geosciences in Prediction and Predictability

    Science.gov (United States)

    Ravela, S.

    2017-12-01

    In this presentation we review the two directions of a symbiotic relationship between machine learning and the geosciences in relation to prediction and predictability. In the first direction, we develop ensemble, information theoretic and manifold learning framework to adaptively improve state and parameter estimates in nonlinear high-dimensional non-Gaussian problems, showing in particular that tractable variational approaches can be produced. We demonstrate these applications in the context of autonomous mapping of environmental coherent structures and other idealized problems. In the reverse direction, we show that data assimilation, particularly probabilistic approaches for filtering and smoothing offer a novel and useful way to train neural networks, and serve as a better basis than gradient based approaches when we must quantify uncertainty in association with nonlinear, chaotic processes. In many inference problems in geosciences we seek to build reduced models to characterize local sensitivies, adjoints or other mechanisms that propagate innovations and errors. Here, the particular use of neural approaches for such propagation trained using ensemble data assimilation provides a novel framework. Through these two examples of inference problems in the earth sciences, we show that not only is learning useful to broaden existing methodology, but in reverse, geophysical methodology can be used to influence paradigms in learning.

  12. Deep-Learning-Based Drug-Target Interaction Prediction.

    Science.gov (United States)

    Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

    2017-04-07

    Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

  13. Research on cardiovascular disease prediction based on distance metric learning

    Science.gov (United States)

    Ni, Zhuang; Liu, Kui; Kang, Guixia

    2018-04-01

    Distance metric learning algorithm has been widely applied to medical diagnosis and exhibited its strengths in classification problems. The k-nearest neighbour (KNN) is an efficient method which treats each feature equally. The large margin nearest neighbour classification (LMNN) improves the accuracy of KNN by learning a global distance metric, which did not consider the locality of data distributions. In this paper, we propose a new distance metric algorithm adopting cosine metric and LMNN named COS-SUBLMNN which takes more care about local feature of data to overcome the shortage of LMNN and improve the classification accuracy. The proposed methodology is verified on CVDs patient vector derived from real-world medical data. The Experimental results show that our method provides higher accuracy than KNN and LMNN did, which demonstrates the effectiveness of the Risk predictive model of CVDs based on COS-SUBLMNN.

  14. Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

    Directory of Open Access Journals (Sweden)

    Mingjie Tan

    2015-02-01

    Full Text Available The high rate of dropout is a serious problem in E-learning program. Thus it has received extensive concern from the education administrators and researchers. Predicting the potential dropout students is a workable solution to prevent dropout. Based on the analysis of related literature, this study selected student’s personal characteristic and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN, Decision Tree (DT and Bayesian Networks (BNs. A large sample of 62375 students was utilized in the procedures of model training and testing. The results of each model were presented in confusion matrix, and analyzed by calculating the rates of accuracy, precision, recall, and F-measure. The results suggested all of the three machine learning methods were effective in student dropout prediction, and DT presented a better performance. Finally, some suggestions were made for considerable future research.

  15. Stress before extinction learning enhances and generalizes extinction memory in a predictive learning task.

    Science.gov (United States)

    Meir Drexler, Shira; Hamacher-Dang, Tanja C; Wolf, Oliver T

    2017-05-01

    In extinction learning, the individual learns that a previously acquired association (e.g. between a threat and its predictor) is no longer valid. This learning is the principle underlying many cognitive-behavioral psychotherapeutic treatments, e.g. 'exposure therapy'. However, extinction is often highly-context dependent, leading to renewal (relapse of extinguished conditioned response following context change). We have previously shown that post-extinction stress leads to a more context-dependent extinction memory in a predictive learning task. Yet as stress prior to learning can impair the integration of contextual cues, here we aim to create a more generalized extinction memory by inducing stress prior to extinction. Forty-nine men and women learned the associations between stimuli and outcomes in a predictive learning task (day 1), extinguished them shortly after an exposure to a stress/control condition (day 2), and were tested for renewal (day 3). No group differences were seen in acquisition and extinction learning, and a renewal effect was present in both groups. However, the groups differed in the strength and context-dependency of the extinction memory. Compared to the control group, the stress group showed an overall reduced recovery of responding to the extinguished stimuli, in particular in the acquisition context. These results, together with our previous findings, demonstrate that the effects of stress exposure on extinction memory depend on its timing. While post-extinction stress makes the memory more context-bound, pre-extinction stress strengthens its consolidation for the acquisition context as well, making it potentially more resistant to relapse. These results have implications for the use of glucocorticoids as extinction-enhancers in exposure therapy. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Informative sensor selection and learning for prediction of lower limb kinematics using generative stochastic neural networks.

    Science.gov (United States)

    Eunsuk Chong; Taejin Choi; Hyungmin Kim; Seung-Jong Kim; Yoha Hwang; Jong Min Lee

    2017-07-01

    We propose a novel approach of selecting useful input sensors as well as learning a mathematical model for predicting lower limb joint kinematics. We applied a feature selection method based on the mutual information called the variational information maximization, which has been reported as the state-of-the-art work among information based feature selection methods. The main difficulty in applying the method is estimating reliable probability density of input and output data, especially when the data are high dimensional and real-valued. We addressed this problem by applying a generative stochastic neural network called the restricted Boltzmann machine, through which we could perform sampling based probability estimation. The mutual informations between inputs and outputs are evaluated in each backward sensor elimination step, and the least informative sensor is removed with its network connections. The entire network is fine-tuned by maximizing conditional likelihood in each step. Experimental results are shown for 4 healthy subjects walking with various speeds, recording 64 sensor measurements including electromyogram, acceleration, and foot-pressure sensors attached on both lower limbs for predicting hip and knee joint angles. For test set of walking with arbitrary speed, our results show that our suggested method can select informative sensors while maintaining a good prediction accuracy.

  17. Gene Prediction in Metagenomic Fragments with Deep Learning

    Directory of Open Access Journals (Sweden)

    Shao-Wu Zhang

    2017-01-01

    Full Text Available Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features and using deep stacking networks learning model, we present a novel method (called Meta-MFDL to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.

  18. Older age, higher perceived disability and depressive symptoms predict the amount and severity of work-related difficulties in persons with multiple sclerosis.

    Science.gov (United States)

    Raggi, Alberto; Giovannetti, Ambra Mara; Schiavolin, Silvia; Brambilla, Laura; Brenna, Greta; Confalonieri, Paolo Agostino; Cortese, Francesca; Frangiamore, Rita; Leonardi, Matilde; Mantegazza, Renato Emilio; Moscatelli, Marco; Ponzio, Michela; Torri Clerici, Valentina; Zaratin, Paola; De Torres, Laura

    2018-04-16

    This cross-sectional study aims to identify the predictors of work-related difficulties in a sample of employed persons with multiple sclerosis as addressed with the Multiple Sclerosis Questionnaire for Job Difficulties. Hierarchical linear regression analysis was conducted to identify predictors of work difficulties: predictors included demographic variables (age, formal education), disease duration and severity, perceived disability and psychological variables (cognitive dysfunction, depression and anxiety). The targets were the questionnaire's overall score and its six subscales. A total of 177 participants (108 females, aged 21-63) were recruited. Age, perceived disability and depression were direct and significant predictors of the questionnaire total score, and the final model explained 43.7% of its variation. The models built on the questionnaire's subscales show that perceived disability and depression were direct and significant predictors of most of its subscales. Our results show that, among patients with multiple sclerosis, those who were older, with higher perceived disability and higher depression symptoms have more and more severe work-related difficulties. The Multiple Sclerosis Questionnaire for Job Difficulties can be fruitfully exploited to plan tailored actions to limit the likelihood of near-future job loss in persons of working age with multiple sclerosis. Implications for rehabilitation Difficulties with work are common among people with multiple sclerosis and are usually addressed in terms of unemployment or job loss. The Multiple Sclerosis Questionnaire for Job Difficulties is a disease-specific questionnaire developed to address the amount and severity of work-related difficulties. We found that work-related difficulties were associated to older age, higher perceived disability and depressive symptoms. Mental health issues and perceived disability should be consistently included in future research targeting work-related difficulties.

  19. Developing Professional Learning for Staff Working with Children with Speech, Language and Communication Needs Combined with Moderate-Severe Learning Difficulties

    Science.gov (United States)

    Anderson, Carolyn

    2011-01-01

    This article presents research undertaken as part of a PhD by Carolyn Anderson who is a senior lecturer on the BSc (Hons) in Speech and Language Pathology at the University of Strathclyde. The study explores the professional learning experiences of 49 teachers working in eight schools and units for children with additional support needs in…

  20. Spam comments prediction using stacking with ensemble learning

    Science.gov (United States)

    Mehmood, Arif; On, Byung-Won; Lee, Ingyu; Ashraf, Imran; Choi, Gyu Sang

    2018-01-01

    Illusive comments of product or services are misleading for people in decision making. The current methodologies to predict deceptive comments are concerned for feature designing with single training model. Indigenous features have ability to show some linguistic phenomena but are hard to reveal the latent semantic meaning of the comments. We propose a prediction model on general features of documents using stacking with ensemble learning. Term Frequency/Inverse Document Frequency (TF/IDF) features are inputs to stacking of Random Forest and Gradient Boosted Trees and the outputs of the base learners are encapsulated with decision tree to make final training of the model. The results exhibits that our approach gives the accuracy of 92.19% which outperform the state-of-the-art method.

  1. Prediction of beta-turns with learning machines.

    Science.gov (United States)

    Cai, Yu-Dong; Liu, Xiao-Jun; Li, Yi-Xue; Xu, Xue-biao; Chou, Kuo-Chen

    2003-05-01

    The support vector machine approach was introduced to predict the beta-turns in proteins. The overall self-consistency rate by the re-substitution test for the training or learning dataset reached 100%. Both the training dataset and independent testing dataset were taken from Chou [J. Pept. Res. 49 (1997) 120]. The success prediction rates by the jackknife test for the beta-turn subset of 455 tetrapeptides and non-beta-turn subset of 3807 tetrapeptides in the training dataset were 58.1 and 98.4%, respectively. The success rates with the independent dataset test for the beta-turn subset of 110 tetrapeptides and non-beta-turn subset of 30,231 tetrapeptides were 69.1 and 97.3%, respectively. The results obtained from this study support the conclusion that the residue-coupled effect along a tetrapeptide is important for the formation of a beta-turn.

  2. Machine-Learning-Based No Show Prediction in Outpatient Visits

    Directory of Open Access Journals (Sweden)

    Carlos Elvira

    2018-03-01

    Full Text Available A recurring problem in healthcare is the high percentage of patients who miss their appointment, be it a consultation or a hospital test. The present study seeks patient’s behavioural patterns that allow predicting the probability of no- shows. We explore the convenience of using Big Data Machine Learning models to accomplish this task. To begin with, a predictive model based only on variables associated with the target appointment is built. Then the model is improved by considering the patient’s history of appointments. In both cases, the Gradient Boosting algorithm was the predictor of choice. Our numerical results are considered promising given the small amount of information available. However, there seems to be plenty of room to improve the model if we manage to collect additional data for both patients and appointments.

  3. Negative correlation learning for customer churn prediction: a comparison study.

    Science.gov (United States)

    Rodan, Ali; Fayyoumi, Ayham; Faris, Hossam; Alsakran, Jamal; Al-Kadi, Omar

    2015-01-01

    Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.

  4. Prediction of stroke thrombolysis outcome using CT brain machine learning

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    Paul Bentley

    2014-01-01

    Full Text Available A critical decision-step in the emergency treatment of ischemic stroke is whether or not to administer thrombolysis — a treatment that can result in good recovery, or deterioration due to symptomatic intracranial haemorrhage (SICH. Certain imaging features based upon early computerized tomography (CT, in combination with clinical variables, have been found to predict SICH, albeit with modest accuracy. In this proof-of-concept study, we determine whether machine learning of CT images can predict which patients receiving tPA will develop SICH as opposed to showing clinical improvement with no haemorrhage. Clinical records and CT brains of 116 acute ischemic stroke patients treated with intravenous thrombolysis were collected retrospectively (including 16 who developed SICH. The sample was split into training (n = 106 and test sets (n = 10, repeatedly for 1760 different combinations. CT brain images acted as inputs into a support vector machine (SVM, along with clinical severity. Performance of the SVM was compared with established prognostication tools (SEDAN and HAT scores; original, or after adaptation to our cohort. Predictive performance, assessed as area under receiver-operating-characteristic curve (AUC, of the SVM (0.744 compared favourably with that of prognostic scores (original and adapted versions: 0.626–0.720; p < 0.01. The SVM also identified 9 out of 16 SICHs, as opposed to 1–5 using prognostic scores, assuming a 10% SICH frequency (p < 0.001. In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods.

  5. Test Framing Generates a Stability Bias for Predictions of Learning by Causing People to Discount their Learning Beliefs

    Science.gov (United States)

    Ariel, Robert; Hines, Jarrod C.; Hertzog, Christopher

    2014-01-01

    People estimate minimal changes in learning when making predictions of learning (POLs) for future study opportunities despite later showing increased performance and an awareness of that increase (Kornell & Bjork, 2009). This phenomenon is conceptualized as a stability bias in judgments about learning. We investigated the malleability of this effect, and whether it reflected people’s underlying beliefs about learning. We manipulated prediction framing to emphasize the role of testing vs. studying on memory and directly measured beliefs about multi-trial study effects on learning by having participants construct predicted learning curves before and after the experiment. Mean POLs were more sensitive to the number of study-test opportunities when performance was framed in terms of study benefits rather than testing benefits and POLs reflected pre-existing beliefs about learning. The stability bias is partially due to framing and reflects discounted beliefs about learning benefits rather than inherent belief in the stability of performance. PMID:25067885

  6. Positive-Unlabeled Learning for Pupylation Sites Prediction

    Directory of Open Access Journals (Sweden)

    Ming Jiang

    2016-01-01

    Full Text Available Pupylation plays a key role in regulating various protein functions as a crucial posttranslational modification of prokaryotes. In order to understand the molecular mechanism of pupylation, it is important to identify pupylation substrates and sites accurately. Several computational methods have been developed to identify pupylation sites because the traditional experimental methods are time-consuming and labor-sensitive. With the existing computational methods, the experimentally annotated pupylation sites are used as the positive training set and the remaining nonannotated lysine residues as the negative training set to build classifiers to predict new pupylation sites from the unknown proteins. However, the remaining nonannotated lysine residues may contain pupylation sites which have not been experimentally validated yet. Unlike previous methods, in this study, the experimentally annotated pupylation sites were used as the positive training set whereas the remaining nonannotated lysine residues were used as the unlabeled training set. A novel method named PUL-PUP was proposed to predict pupylation sites by using positive-unlabeled learning technique. Our experimental results indicated that PUL-PUP outperforms the other methods significantly for the prediction of pupylation sites. As an application, PUL-PUP was also used to predict the most likely pupylation sites in nonannotated lysine sites.

  7. Application of Machine Learning for Dragline Failure Prediction

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    Taghizadeh Amir

    2017-01-01

    Full Text Available Overburden stripping in open cast coal mines is extensively carried out by walking draglines. Draglines’ unavailability and unexpected failures result in delayed productions and increased maintenance and operating costs. Therefore, achieving high availability of draglines plays a crucial role for increasing economic feasibility of mining projects. Applications of methodologies which can forecast the failure type of dragline based on the available failure data not only help to reduce the maintenance and operating costs but also increase the availability and the production rate. In this study, Machine Learning approaches have been applied for data which has been gathered from an operating coal mine in Turkey. The study methodology consists of three algorithms as: i implementation of K-Nearest Neighbors, ii implementation of Multi-Layer Perceptron, and iii implementation of Radial Basis Function. The algorithms have been utilized for predicting the draglines’ failure types. In this sense, the input data, which are mean time-to-failure, and the output data, failure types, have been fed to the algorithms. The regression analysis of methodologies have been compared and showed the K- Nearest Neighbors has a higher rate of regression which is around 70 percent. Thus, the K-Nearest Neighbor algorithm can be applied in order to preventive components replacement which causes to minimized preventive and corrective cost parameters. The accurate prediction of failure type, indeed, causes to optimized number of inspections. The novelty of this study is application of machine learning approaches in draglines’ reliability subject for first time.

  8. Predicting Smoking Status Using Machine Learning Algorithms and Statistical Analysis

    Directory of Open Access Journals (Sweden)

    Charles Frank

    2018-03-01

    Full Text Available Smoking has been proven to negatively affect health in a multitude of ways. As of 2009, smoking has been considered the leading cause of preventable morbidity and mortality in the United States, continuing to plague the country’s overall health. This study aims to investigate the viability and effectiveness of some machine learning algorithms for predicting the smoking status of patients based on their blood tests and vital readings results. The analysis of this study is divided into two parts: In part 1, we use One-way ANOVA analysis with SAS tool to show the statistically significant difference in blood test readings between smokers and non-smokers. The results show that the difference in INR, which measures the effectiveness of anticoagulants, was significant in favor of non-smokers which further confirms the health risks associated with smoking. In part 2, we use five machine learning algorithms: Naïve Bayes, MLP, Logistic regression classifier, J48 and Decision Table to predict the smoking status of patients. To compare the effectiveness of these algorithms we use: Precision, Recall, F-measure and Accuracy measures. The results show that the Logistic algorithm outperformed the four other algorithms with Precision, Recall, F-Measure, and Accuracy of 83%, 83.4%, 83.2%, 83.44%, respectively.

  9. Application of Machine Learning Algorithms for the Query Performance Prediction

    Directory of Open Access Journals (Sweden)

    MILICEVIC, M.

    2015-08-01

    Full Text Available This paper analyzes the relationship between the system load/throughput and the query response time in a real Online transaction processing (OLTP system environment. Although OLTP systems are characterized by short transactions, which normally entail high availability and consistent short response times, the need for operational reporting may jeopardize these objectives. We suggest a new approach to performance prediction for concurrent database workloads, based on the system state vector which consists of 36 attributes. There is no bias to the importance of certain attributes, but the machine learning methods are used to determine which attributes better describe the behavior of the particular database server and how to model that system. During the learning phase, the system's profile is created using multiple reference queries, which are selected to represent frequent business processes. The possibility of the accurate response time prediction may be a foundation for automated decision-making for database (DB query scheduling. Possible applications of the proposed method include adaptive resource allocation, quality of service (QoS management or real-time dynamic query scheduling (e.g. estimation of the optimal moment for a complex query execution.

  10. Computational neurorehabilitation: modeling plasticity and learning to predict recovery.

    Science.gov (United States)

    Reinkensmeyer, David J; Burdet, Etienne; Casadio, Maura; Krakauer, John W; Kwakkel, Gert; Lang, Catherine E; Swinnen, Stephan P; Ward, Nick S; Schweighofer, Nicolas

    2016-04-30

    Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

  11. Hierarchical prediction errors in midbrain and septum during social learning.

    Science.gov (United States)

    Diaconescu, Andreea O; Mathys, Christoph; Weber, Lilian A E; Kasper, Lars; Mauer, Jan; Stephan, Klaas E

    2017-04-01

    Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling and genetics to address this question in two separate samples (N = 35, N = 47). Participants played a game requiring inference on an adviser's intentions whose motivation to help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates about current advice validity and the adviser's trustworthiness, respectively, depend on different neuromodulatory systems. Low-level prediction errors (PEs) about advice accuracy not only activated regions known to support 'theory of mind', but also the dopaminergic midbrain. Furthermore, PE responses in ventral striatum were influenced by the Met/Val polymorphism of the Catechol-O-Methyltransferase (COMT) gene. By contrast, high-level PEs ('expected uncertainty') about the adviser's fidelity activated the cholinergic septum. These findings, replicated in both samples, have important implications: They suggest that social learning rests on hierarchically related PEs encoded by midbrain and septum activity, respectively, in the same manner as other forms of learning under volatility. Furthermore, these hierarchical PEs may be broadcast by dopaminergic and cholinergic projections to induce plasticity specifically in cortical areas known to represent beliefs about others. © The Author (2017). Published by Oxford University Press.

  12. A deep learning approach for predicting the quality of online health expert question-answering services.

    Science.gov (United States)

    Hu, Ze; Zhang, Zhan; Yang, Haiqin; Chen, Qing; Zuo, Decheng

    2017-07-01

    Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers in existing HQA systems varies in different situations. It is significant to provide effective tools to automatically determine the quality of the answers. Two main characteristics in HQA systems raise the difficulties of classification: (1) physicians' answers in an HQA system are usually written in short text, which yields the data sparsity issue; (2) HQA systems apply the quality control mechanism, which refrains the wisdom of crowd. The important information, such as the best answer and the number of users' votes, is missing. To tackle these issues, we prepare the first HQA research data set labeled by three medical experts in 90days and formulate the problem of predicting the quality of answers in the system as a classification task. We not only incorporate the standard textual feature of answers, but also introduce a set of unique non-textual features, i.e., the popular used surface linguistic features and the novel social features, from other modalities. A multimodal deep belief network (DBN)-based learning framework is then proposed to learn the high-level hidden semantic representations of answers from both textual features and non-textual features while the learned joint representation is fed into popular classifiers to determine the quality of answers. Finally, we conduct extensive experiments to demonstrate the effectiveness of including the non-textual features and the proposed multimodal deep learning framework. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. Curiosity and reward: Valence predicts choice and information prediction errors enhance learning.

    Science.gov (United States)

    Marvin, Caroline B; Shohamy, Daphna

    2016-03-01

    Curiosity drives many of our daily pursuits and interactions; yet, we know surprisingly little about how it works. Here, we harness an idea implied in many conceptualizations of curiosity: that information has value in and of itself. Reframing curiosity as the motivation to obtain reward-where the reward is information-allows one to leverage major advances in theoretical and computational mechanisms of reward-motivated learning. We provide new evidence supporting 2 predictions that emerge from this framework. First, we find an asymmetric effect of positive versus negative information, with positive information enhancing both curiosity and long-term memory for information. Second, we find that it is not the absolute value of information that drives learning but, rather, the gap between the reward expected and reward received, an "information prediction error." These results support the idea that information functions as a reward, much like money or food, guiding choices and driving learning in systematic ways. (c) 2016 APA, all rights reserved).

  14. Learning Pulse: Using Wearable Biosensors and Learning Analytics to Investigate and Predict Learning Success in Self-regulated Learning

    NARCIS (Netherlands)

    Di Mitri, Daniele; Scheffel, Maren; Drachsler, Hendrik; Börner, Dirk; Ternier, Stefaan; Specht, Marcus

    2017-01-01

    The Learning Pulse study aims to explore whether physiological data such as heart rate and step count correlate with learning activity data and whether they are good predictors for learning success during self-regulated learning. To verify this hypothesis an experiment was set up involving eight

  15. Federated learning of predictive models from federated Electronic Health Records.

    Science.gov (United States)

    Brisimi, Theodora S; Chen, Ruidi; Mela, Theofanie; Olshevsky, Alex; Paschalidis, Ioannis Ch; Shi, Wei

    2018-04-01

    In an era of "big data," computationally efficient and privacy-aware solutions for large-scale machine learning problems become crucial, especially in the healthcare domain, where large amounts of data are stored in different locations and owned by different entities. Past research has been focused on centralized algorithms, which assume the existence of a central data repository (database) which stores and can process the data from all participants. Such an architecture, however, can be impractical when data are not centrally located, it does not scale well to very large datasets, and introduces single-point of failure risks which could compromise the integrity and privacy of the data. Given scores of data widely spread across hospitals/individuals, a decentralized computationally scalable methodology is very much in need. We aim at solving a binary supervised classification problem to predict hospitalizations for cardiac events using a distributed algorithm. We seek to develop a general decentralized optimization framework enabling multiple data holders to collaborate and converge to a common predictive model, without explicitly exchanging raw data. We focus on the soft-margin l 1 -regularized sparse Support Vector Machine (sSVM) classifier. We develop an iterative cluster Primal Dual Splitting (cPDS) algorithm for solving the large-scale sSVM problem in a decentralized fashion. Such a distributed learning scheme is relevant for multi-institutional collaborations or peer-to-peer applications, allowing the data holders to collaborate, while keeping every participant's data private. We test cPDS on the problem of predicting hospitalizations due to heart diseases within a calendar year based on information in the patients Electronic Health Records prior to that year. cPDS converges faster than centralized methods at the cost of some communication between agents. It also converges faster and with less communication overhead compared to an alternative distributed

  16. Assisted assessment of cognitive abilities in children with visual impairment and learning difficulties / Avaliação assistida de habilidades cognitivas em crianças com deficiência visual e com dificuldades de aprendizagem

    Directory of Open Access Journals (Sweden)

    Cecilia Guarnieri Batista

    2004-01-01

    Full Text Available This investigation aims at discussing a procedure of assessment of the "potential developmental level", according to the Vygostky's conception, in children with visual impairment (low vision or blindness and with learning difficulties. In the 2 studies that are reported, the assessment procedure consisted of Verbal WISC administration, group assessment of school abilities and individual assisted assessment. The analysis was focused on the children with the lower IQ values. In the second study, the procedure also comprised the search for episodes of "smartness", indicating cognitive abilities, out of the formal assessment procedure. The discussion about modalities of assessment indicated possible sources of difficulty for the search of a reliable "potential developmental level" in children with learning difficulties.

  17. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models

    NARCIS (Netherlands)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.

    2012-01-01

    PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator

  18. Prediction of Baseflow Index of Catchments using Machine Learning Algorithms

    Science.gov (United States)

    Yadav, B.; Hatfield, K.

    2017-12-01

    We present the results of eight machine learning techniques for predicting the baseflow index (BFI) of ungauged basins using a surrogate of catchment scale climate and physiographic data. The tested algorithms include ordinary least squares, ridge regression, least absolute shrinkage and selection operator (lasso), elasticnet, support vector machine, gradient boosted regression trees, random forests, and extremely randomized trees. Our work seeks to identify the dominant controls of BFI that can be readily obtained from ancillary geospatial databases and remote sensing measurements, such that the developed techniques can be extended to ungauged catchments. More than 800 gauged catchments spanning the continental United States were selected to develop the general methodology. The BFI calculation was based on the baseflow separated from daily streamflow hydrograph using HYSEP filter. The surrogate catchment attributes were compiled from multiple sources including digital elevation model, soil, landuse, climate data, other publicly available ancillary and geospatial data. 80% catchments were used to train the ML algorithms, and the remaining 20% of the catchments were used as an independent test set to measure the generalization performance of fitted models. A k-fold cross-validation using exhaustive grid search was used to fit the hyperparameters of each model. Initial model development was based on 19 independent variables, but after variable selection and feature ranking, we generated revised sparse models of BFI prediction that are based on only six catchment attributes. These key predictive variables selected after the careful evaluation of bias-variance tradeoff include average catchment elevation, slope, fraction of sand, permeability, temperature, and precipitation. The most promising algorithms exceeding an accuracy score (r-square) of 0.7 on test data include support vector machine, gradient boosted regression trees, random forests, and extremely randomized

  19. Therapeutic effect assessment of integrated intervention for learning difficulties in children%综合干预学习困难儿童的疗效评估

    Institute of Scientific and Technical Information of China (English)

    林桂秀; 陈珊; 陈达光; 胡君; 殷晓荣

    2005-01-01

    BACKGROUND:Children with learning difficulty often present with cognitive impairments and imbalanced development of intelligence accompanied by emotional and behavioral problems.OBJECTIVE:To evaluate the therapeutic effects of integrated interventions in multiple aspects for children with learning difficulties.DESIGN:A case-control study of children with learning difficulties.SETTING:Department of Pediatric Internal Medicine, Union Hospital Affiliated to Fujian Medical University.PARTICIPANTS:This study involved school-age children of 6 to 12years old with learning difficulties who came to the department specialized in pediatric psychology for consultation from July 1998 to July 2001. The diagnosis of learning difficulties conformed to the diagnostic criteria for special learning disability in ICD-10 with IQ > 70 as an essential condition. Children with emotional disorders, mental deficiency, hyperactivity,visual and auditory dysfunctions and organic cerebral diseases were excluded. The diagnosis was further verified by a professor specialized in neuropsychology. The children voluntarily participated in the cognitive training, behavioral intervention and comprehensive trainings of senses and those who fulfilled the one-year treatment were included in this study. The ratio of therapists to the children was 1:2, and each session of treatment lasted for 90-120 minutes given 2-3 times a week, and a treatment course consisted of 24 sessions. The children were supposed to complete 4 consecutive treatment courses. Totally 31 children completed the treatment courses, including 24 boys and 7 girls with the mean age of(7.7±1.2)years who had an average IQ of 87.5±8.6.grated interventions for one year consisting of cognitive training, behavioral intervention and comprehensive training of the senses. Raven's Deductive Reasoning test and Beery Visual-Motor Integration Test were use to examine the deductive reasoning ability and visual-motor integration of the children, and

  20. The machine learning in the prediction of elections

    Directory of Open Access Journals (Sweden)

    M.S.C. José A. León-Borges

    2015-05-01

    Full Text Available This research article, presents an analysis and a comparison of three different algorithms: A.- Grouping method K-means, B.-Expectation a convergence criteria, EM and C.- Methodology for classification LAMDA, using two software of classification Weka and SALSA, as an aid for the prediction of future elections in the state of Quintana Roo. When working with electoral data, these are classified in a qualitative and quantitative way, by such virtue at the end of this article you will have the elements necessary to decide, which software, has better performance for such learning of classification. The main reason for the development of this work, is to demonstrate the efficiency of algorithms, with different data types. At the end, it may be decided, the algorithm with the better performance in data management.

  1. Regional hippocampal volumes and development predict learning and memory.

    Science.gov (United States)

    Tamnes, Christian K; Walhovd, Kristine B; Engvig, Andreas; Grydeland, Håkon; Krogsrud, Stine K; Østby, Ylva; Holland, Dominic; Dale, Anders M; Fjell, Anders M

    2014-01-01

    The hippocampus is an anatomically and functionally heterogeneous structure, but longitudinal studies of its regional development are scarce and it is not known whether protracted maturation of the hippocampus in adolescence is related to memory development. First, we investigated hippocampal subfield development using 170 longitudinally acquired brain magnetic resonance imaging scans from 85 participants aged 8-21 years. Hippocampal subfield volumes were estimated by the use of automated segmentation of 7 subfields, including the cornu ammonis (CA) sectors and the dentate gyrus (DG), while longitudinal subfield volumetric change was quantified using a nonlinear registration procedure. Second, associations between subfield volumes and change and verbal learning/memory across multiple retention intervals (5 min, 30 min and 1 week) were tested. It was hypothesized that short and intermediate memory would be more closely related to CA2-3/CA4-DG and extended, remote memory to CA1. Change rates were significantly different across hippocampal subfields, but nearly all subfields showed significant volume decreases over time throughout adolescence. Several subfield volumes were larger in the right hemisphere and in males, while for change rates there were no hemisphere or sex differences. Partly in support of the hypotheses, greater volume of CA1 and CA2-3 was related to recall and retention after an extended delay, while longitudinal reduction of CA2-3 and CA4-DG was related to learning. This suggests continued regional development of the hippocampus across adolescence and that volume and volume change in specific subfields differentially predict verbal learning and memory over different retention intervals, but future high-resolution studies are called for. © 2014 S. Karger AG, Basel.

  2. Investigating Mesoscale Convective Systems and their Predictability Using Machine Learning

    Science.gov (United States)

    Daher, H.; Duffy, D.; Bowen, M. K.

    2016-12-01

    A mesoscale convective system (MCS) is a thunderstorm region that lasts several hours long and forms near weather fronts and can often develop into tornadoes. Here we seek to answer the question of whether these tornadoes are "predictable" by looking for a defining characteristic(s) separating MCSs that evolve into tornadoes versus those that do not. Using NASA's Modern Era Retrospective-analysis for Research and Applications 2 reanalysis data (M2R12K), we apply several state of the art machine learning techniques to investigate this question. The spatial region examined in this experiment is Tornado Alley in the United States over the peak tornado months. A database containing select variables from M2R12K is created using PostgreSQL. This database is then analyzed using machine learning methods such as Symbolic Aggregate approXimation (SAX) and DBSCAN (an unsupervised density-based data clustering algorithm). The incentive behind using these methods is to mathematically define a MCS so that association rule mining techniques can be used to uncover some sort of signal or teleconnection that will help us forecast which MCSs will result in tornadoes and therefore give society more time to prepare and in turn reduce casualties and destruction.

  3. Mini-review: Prediction errors, attention and associative learning.

    Science.gov (United States)

    Holland, Peter C; Schiffino, Felipe L

    2016-05-01

    Most modern theories of associative learning emphasize a critical role for prediction error (PE, the difference between received and expected events). One class of theories, exemplified by the Rescorla-Wagner (1972) model, asserts that PE determines the effectiveness of the reinforcer or unconditioned stimulus (US): surprising reinforcers are more effective than expected ones. A second class, represented by the Pearce-Hall (1980) model, argues that PE determines the associability of conditioned stimuli (CSs), the rate at which they may enter into new learning: the surprising delivery or omission of a reinforcer enhances subsequent processing of the CSs that were present when PE was induced. In this mini-review we describe evidence, mostly from our laboratory, for PE-induced changes in the associability of both CSs and USs, and the brain systems involved in the coding, storage and retrieval of these altered associability values. This evidence favors a number of modifications to behavioral models of how PE influences event processing, and suggests the involvement of widespread brain systems in animals' responses to PE. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS) Severity.

    Science.gov (United States)

    Bosch-Bayard, Jorge; Galán-García, Lídice; Fernandez, Thalia; Lirio, Rolando B; Bringas-Vega, Maria L; Roca-Stappung, Milene; Ricardo-Garcell, Josefina; Harmony, Thalía; Valdes-Sosa, Pedro A

    2017-01-01

    In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven) regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to) different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS) disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia), Mathematics (Dyscalculia), or Writing (Dysgraphia). By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

  5. Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS Severity

    Directory of Open Access Journals (Sweden)

    Jorge Bosch-Bayard

    2018-01-01

    Full Text Available In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia, Mathematics (Dyscalculia, or Writing (Dysgraphia. By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

  6. INSIGHTS FROM MACHINE-LEARNED DIET SUCCESS PREDICTION.

    Science.gov (United States)

    Weber, Ingmar; Achananuparp, Palakorn

    2016-01-01

    To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider "quantified self" movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the characteristics of a (un-)successful diet. Concretely, we train a machine learning model to predict repeatedly being over or under self-set daily calories goals and then look at which features contribute to the model's prediction. Our findings include both expected results, such as the token "mcdonalds" or the category "dessert" being indicative for being over the calories goal, but also less obvious ones such as the difference between pork and poultry concerning dieting success, or the use of the "quick added calories" functionality being indicative of over-shooting calorie-wise. This study also hints at the feasibility of using such data for more in-depth data mining, e.g., looking at the interaction between consumed foods such as mixing protein- and carbohydrate-rich foods. To the best of our knowledge, this is the first systematic study of public food diaries.

  7. Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems

    OpenAIRE

    Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

    2016-01-01

    It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed...

  8. Machine learning in updating predictive models of planning and scheduling transportation projects

    Science.gov (United States)

    1997-01-01

    A method combining machine learning and regression analysis to automatically and intelligently update predictive models used in the Kansas Department of Transportations (KDOTs) internal management system is presented. The predictive models used...

  9. Quality prediction modeling for sintered ores based on mechanism models of sintering and extreme learning machine based error compensation

    Science.gov (United States)

    Tiebin, Wu; Yunlian, Liu; Xinjun, Li; Yi, Yu; Bin, Zhang

    2018-06-01

    Aiming at the difficulty in quality prediction of sintered ores, a hybrid prediction model is established based on mechanism models of sintering and time-weighted error compensation on the basis of the extreme learning machine (ELM). At first, mechanism models of drum index, total iron, and alkalinity are constructed according to the chemical reaction mechanism and conservation of matter in the sintering process. As the process is simplified in the mechanism models, these models are not able to describe high nonlinearity. Therefore, errors are inevitable. For this reason, the time-weighted ELM based error compensation model is established. Simulation results verify that the hybrid model has a high accuracy and can meet the requirement for industrial applications.

  10. The conditions that promote fear learning: prediction error and Pavlovian fear conditioning.

    Science.gov (United States)

    Li, Susan Shi Yuan; McNally, Gavan P

    2014-02-01

    A key insight of associative learning theory is that learning depends on the actions of prediction error: a discrepancy between the actual and expected outcomes of a conditioning trial. When positive, such error causes increments in associative strength and, when negative, such error causes decrements in associative strength. Prediction error can act directly on fear learning by determining the effectiveness of the aversive unconditioned stimulus or indirectly by determining the effectiveness, or associability, of the conditioned stimulus. Evidence from a variety of experimental preparations in human and non-human animals suggest that discrete neural circuits code for these actions of prediction error during fear learning. Here we review the circuits and brain regions contributing to the neural coding of prediction error during fear learning and highlight areas of research (safety learning, extinction, and reconsolidation) that may profit from this approach to understanding learning. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

  11. Prediction of adaptation difficulties by country of origin, cumulate psychosocial stressors and attitude toward integrating: a Swedish study of first-generation immigrants from Somalia, Vietnam and China.

    Science.gov (United States)

    Johnsson, Ewa; Zolkowska, Krystyna; McNeil, Thomas F

    2015-03-01

    Different types of accumulated stress have been found to have negative consequences for immigrants' capacity to adapt to the new environment. It remains unclear which factors have the greatest influence. The study investigated whether immigrants' experience of great difficulty in adapting to a new country could best be explained by (1) country of origin, (2) exposure to accumulated stressors before arrival or (3) after arrival in the new country and/or (4) reserved attitude toward integrating into the new society. The 119 first-generation immigrants from Somalia, Vietnam and China, living in Malmö, Sweden, were interviewed in a standardized manner. Experiencing great difficulty in adapting to Sweden was independent of length of residence, but significantly related to all four influences, studied one at a time. Country of origin was also related to stressors and attitude. When the effects of the other influences were mutually controlled for, only exposure to accumulated stressors in Sweden (and especially experiencing discrimination/xenophobia/racism) accounted for great adaptation difficulty. Stressors in Sweden had a greater effect if the immigrant had been exposed to stressors earlier. Immigrants' long-term experiences of great difficulty in adapting to a new country were explained primarily by exposure to accumulated stressors while moving to and living in the new country, rather than by their backgrounds or attitudes toward integrating. This suggests promoting strategies to avoid discrimination and other stressors in the host country. © The Author(s) 2014.

  12. Prediction of adaptation difficulties by country of origin, cumulate psychosocial stressors and attitude toward integrating: A Swedish study of first-generation immigrants from Somalia, Vietnam and China

    Science.gov (United States)

    Zolkowska, Krystyna; McNeil, Thomas F

    2015-01-01

    Background: Different types of accumulated stress have been found to have negative consequences for immigrants’ capacity to adapt to the new environment. It remains unclear which factors have the greatest influence. Aims: The study investigated whether immigrants’ experience of great difficulty in adapting to a new country could best be explained by (1) country of origin, (2) exposure to accumulated stressors before arrival or (3) after arrival in the new country and/or (4) reserved attitude toward integrating into the new society. Methods: The 119 first-generation immigrants from Somalia, Vietnam and China, living in Malmö, Sweden, were interviewed in a standardized manner. Results: Experiencing great difficulty in adapting to Sweden was independent of length of residence, but significantly related to all four influences, studied one at a time. Country of origin was also related to stressors and attitude. When the effects of the other influences were mutually controlled for, only exposure to accumulated stressors in Sweden (and especially experiencing discrimination/xenophobia/racism) accounted for great adaptation difficulty. Stressors in Sweden had a greater effect if the immigrant had been exposed to stressors earlier. Conclusions: Immigrants’ long-term experiences of great difficulty in adapting to a new country were explained primarily by exposure to accumulated stressors while moving to and living in the new country, rather than by their backgrounds or attitudes toward integrating. This suggests promoting strategies to avoid discrimination and other stressors in the host country. PMID:24927925

  13. Examination of the Relationship of Difficulties in Emotion Regulation, Behavioral Activation and Behavioral Inhibition System in the Prediction of Social Anxiety

    Directory of Open Access Journals (Sweden)

    Sohrab Amiri

    2017-07-01

    Full Text Available Background and Objectives: Anxiety has a significant impact on academic and social performance as well as quality of life. The present study was conducted to investigate the relationship between brain/behavioral systems and difficulties in emotion regulation with cognitive and physical aspects of social anxiety. Methods: In this descriptive-correlational study, 306 students were selected from the student population of the Urmia University using multistage cluster sampling. Data collection was performed using measuring scales of social anxiety dimensions, behavioral activation and inhibition system, and difficulties in emotion regulation. Data were analyzed using descriptive indicators, correlation, simultaneous multiple regression analysis, and t-test analysis. Results: In this study, there was a significant positive correlation between behavioral inhibition system and social anxiety dimensions (p<0.001, Also, examination of the relationships of difficulties in emotion regulation and social anxiety indicated a significant positive correlation between difficulties in emotion regulation and social anxiety (p<0.001. In the comparison between women and men in terms of social anxiety components, both groups were different in cognitive dimension of social anxiety, so that the women obtained higher scores than men in the cognitive dimensions. Conclusion: According to the results of this study, individual differences in using negative emotion regulation strategies and personality traits play an important role in the onset and maintenance of anxiety.

  14. Predicting the Failure of Dental Implants Using Supervised Learning Techniques

    Directory of Open Access Journals (Sweden)

    Chia-Hui Liu

    2018-05-01

    Full Text Available Prosthodontic treatment has been a crucial part of dental treatment for patients with full mouth rehabilitation. Dental implant surgeries that replace conventional dentures using titanium fixtures have become the top choice. However, because of the wide-ranging scope of implant surgeries, patients’ body conditions, surgeons’ experience, and the choice of implant system should be considered during treatment. The higher price charged by dental implant treatments compared to conventional dentures has led to a rush among medical staff; therefore, the future impact of surgeries has not been analyzed in detail, resulting in medial disputes. Previous literature on the success factors of dental implants is mainly focused on single factors such as patients’ systemic diseases, operation methods, or prosthesis types for statistical correlation significance analysis. This study developed a prediction model for providing an early warning mechanism to reduce the chances of dental implant failure. We collected the clinical data of patients who received artificial dental implants at the case hospital for a total of 8 categories and 20 variables. Supervised learning techniques such as decision tree (DT, support vector machines, logistic regressions, and classifier ensembles (i.e., Bagging and AdaBoost were used to analyze the prediction of the failure of dental implants. The results show that DT with both Bagging and Adaboost techniques possesses the highest prediction performance for the failure of dental implant (area under the receiver operating characteristic curve, AUC: 0.741; the analysis also revealed that the implant systems affect dental implant failure. The model can help clinical surgeons to reduce medical failures by choosing the optimal implant system and prosthodontics treatments for their patients.

  15. Multimodal manifold-regularized transfer learning for MCI conversion prediction.

    Science.gov (United States)

    Cheng, Bo; Liu, Mingxia; Suk, Heung-Il; Shen, Dinggang; Zhang, Daoqiang

    2015-12-01

    As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.

  16. Big brother is watching you--the ethical implications of electronic surveillance measures in the elderly with dementia and in adults with learning difficulties.

    Science.gov (United States)

    Welsh, S; Hassiotis, A; O'Mahoney, G; Deahl, M

    2003-09-01

    Electronic surveillance has insidiously seeped into the fabric of society with little public debate about its moral implications. Perceived by some as a sinister Orwellian tool of repression and social control, the new technologies offer comfort and security to others; a benevolent parental watchful eye. Nervousness at being watched has been replaced increasingly by nervousness if we're not. These technologies are now becoming widely available to health care professionals who have had little opportunity to consider their ethical and moral ramifications. Electronic tagging and tracking devices may be seen as away of creating a more secure environment for vulnerable individuals such as the elderly with dementia or people with learning disabilities. However, the proponents of surveillance devices have met with considerable resistance and opposition,from those who perceive it as contrary to human dignity and freedom, with its connotations of criminal surveillance. In addition, they cite increased opportunity for abuse through, for example, the withdrawal of staff and financial resources from the care of people with complex needs. Implementing these technologies, therefore, has ethical implications for human rights and civil liberties. Optional alternatives to long-term and/or restrictive care, in the context of the practical difficulties involved in caring for those who represent a risk to themselves from wandering, demands rigorous exploration of pragmatic questions of morality, with reference to risk versus benefit strategies. Like reproductive cloning techniques, the mere existence of surveillance technologies is morally neutral. Rather it is the use (in this instance that of health and social care settings) to which it is put which has the potential for good or bad.

  17. Identifying patterns of anxiety and depression in children with chromosome 22q11.2 deletion syndrome: comorbidity predicts behavioral difficulties and impaired functional communications.

    Science.gov (United States)

    Stephenson, David D; Beaton, Elliott A; Weems, Carl F; Angkustsiri, Kathleen; Simon, Tony J

    2015-01-01

    Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a complex genetic disorder with a variable clinical presentation that can include cardiac, neural, immunological, and psychological issues. Previous studies have measured elevated anxiety and depression in children with 22q11.2DS. Comorbity of anxiety and depression is well established in the pediatric literature but the nature of comorbidity patterns has not been empirically established in children with 22q11.2DS. Comorbidity of anxiety and depression has important implications for treatment and prognosis, and may be a marker of risk in this population of children at high-risk for developing schizophrenia. Participants were 131 boys and girls ages 8-14 with (n=76) and without (n=55) 22q11.2DS and their mothers. Children and mothers independently completed self- and parent-report measures of anxiety and depression. Mothers also completed measures of behavioral functioning including the Behavioral Assessment for Children, 2nd ed. (BASC-2). Cluster analyses were conducted to test if theoretically based groupings of anxiety and depression could be identified. We hypothesized four psychological profiles based on child- and mother-reports: low/no anxiety and low/no depression, higher depression and low/no anxiety, higher anxiety and no/low depression, and a comorbid profile of higher anxiety and higher depression. BASC-2 subscale scores were then compared across subgroups of children to determine if a comorbid profile would predict greater behavioral difficulties. In the full sample of children both with and without 22q11.2DS, cluster analyses of self and maternal reported anxiety and depression revealed the expected subgroups: (1) a group of children with higher anxiety/lower depression (anxious); (2) a group with primary depression (lower anxiety/higher depression (depressed)); (3) a comorbid group with higher anxiety/higher depression (comorbid); and, (4) a lowest anxiety/lowest depression group (NP). Mothers

  18. Developing robust arsenic awareness prediction models using machine learning algorithms.

    Science.gov (United States)

    Singh, Sushant K; Taylor, Robert W; Rahman, Mohammad Mahmudur; Pradhan, Biswajeet

    2018-04-01

    Arsenic awareness plays a vital role in ensuring the sustainability of arsenic mitigation technologies. Thus far, however, few studies have dealt with the sustainability of such technologies and its associated socioeconomic dimensions. As a result, arsenic awareness prediction has not yet been fully conceptualized. Accordingly, this study evaluated arsenic awareness among arsenic-affected communities in rural India, using a structured questionnaire to record socioeconomic, demographic, and other sociobehavioral factors with an eye to assessing their association with and influence on arsenic awareness. First a logistic regression model was applied and its results compared with those produced by six state-of-the-art machine-learning algorithms (Support Vector Machine [SVM], Kernel-SVM, Decision Tree [DT], k-Nearest Neighbor [k-NN], Naïve Bayes [NB], and Random Forests [RF]) as measured by their accuracy at predicting arsenic awareness. Most (63%) of the surveyed population was found to be arsenic-aware. Significant arsenic awareness predictors were divided into three types: (1) socioeconomic factors: caste, education level, and occupation; (2) water and sanitation behavior factors: number of family members involved in water collection, distance traveled and time spent for water collection, places for defecation, and materials used for handwashing after defecation; and (3) social capital and trust factors: presence of anganwadi and people's trust in other community members, NGOs, and private agencies. Moreover, individuals' having higher social network positively contributed to arsenic awareness in the communities. Results indicated that both the SVM and the RF algorithms outperformed at overall prediction of arsenic awareness-a nonlinear classification problem. Lower-caste, less educated, and unemployed members of the population were found to be the most vulnerable, requiring immediate arsenic mitigation. To this end, local social institutions and NGOs could play a

  19. Predictive ability of machine learning methods for massive crop yield prediction

    Directory of Open Access Journals (Sweden)

    Alberto Gonzalez-Sanchez

    2014-04-01

    Full Text Available An important issue for agricultural planning purposes is the accurate yield estimation for the numerous crops involved in the planning. Machine learning (ML is an essential approach for achieving practical and effective solutions for this problem. Many comparisons of ML methods for yield prediction have been made, seeking for the most accurate technique. Generally, the number of evaluated crops and techniques is too low and does not provide enough information for agricultural planning purposes. This paper compares the predictive accuracy of ML and linear regression techniques for crop yield prediction in ten crop datasets. Multiple linear regression, M5-Prime regression trees, perceptron multilayer neural networks, support vector regression and k-nearest neighbor methods were ranked. Four accuracy metrics were used to validate the models: the root mean square error (RMS, root relative square error (RRSE, normalized mean absolute error (MAE, and correlation factor (R. Real data of an irrigation zone of Mexico were used for building the models. Models were tested with samples of two consecutive years. The results show that M5-Prime and k-nearest neighbor techniques obtain the lowest average RMSE errors (5.14 and 4.91, the lowest RRSE errors (79.46% and 79.78%, the lowest average MAE errors (18.12% and 19.42%, and the highest average correlation factors (0.41 and 0.42. Since M5-Prime achieves the largest number of crop yield models with the lowest errors, it is a very suitable tool for massive crop yield prediction in agricultural planning.

  20. Predictive Coding Accelerates Word Recognition and Learning in the Early Stages of Language Development

    Science.gov (United States)

    Ylinen, Sari; Bosseler, Alexis; Junttila, Katja; Huotilainen, Minna

    2017-01-01

    The ability to predict future events in the environment and learn from them is a fundamental component of adaptive behavior across species. Here we propose that inferring predictions facilitates speech processing and word learning in the early stages of language development. Twelve- and 24-month olds' electrophysiological brain responses to heard…

  1. Learned Predictiveness Influences Rapid Attentional Capture: Evidence from the Dot Probe Task

    Science.gov (United States)

    Le Pelley, Mike E.; Vadillo, Miguel; Luque, David

    2013-01-01

    Attentional theories of associative learning and categorization propose that learning about the predictiveness of a stimulus influences the amount of attention that is paid to that stimulus. Three experiments tested this idea by looking at the extent to which stimuli that had previously been experienced as predictive or nonpredictive in a…

  2. Scaling prediction errors to reward variability benefits error-driven learning in humans.

    Science.gov (United States)

    Diederen, Kelly M J; Schultz, Wolfram

    2015-09-01

    Effective error-driven learning requires individuals to adapt learning to environmental reward variability. The adaptive mechanism may involve decays in learning rate across subsequent trials, as shown previously, and rescaling of reward prediction errors. The present study investigated the influence of prediction error scaling and, in particular, the consequences for learning performance. Participants explicitly predicted reward magnitudes that were drawn from different probability distributions with specific standard deviations. By fitting the data with reinforcement learning models, we found scaling of prediction errors, in addition to the learning rate decay shown previously. Importantly, the prediction error scaling was closely related to learning performance, defined as accuracy in predicting the mean of reward distributions, across individual participants. In addition, participants who scaled prediction errors relative to standard deviation also presented with more similar performance for different standard deviations, indicating that increases in standard deviation did not substantially decrease "adapters'" accuracy in predicting the means of reward distributions. However, exaggerated scaling beyond the standard deviation resulted in impaired performance. Thus efficient adaptation makes learning more robust to changing variability. Copyright © 2015 the American Physiological Society.

  3. A "Uses and Gratification Expectancy Model" to Predict Students' "Perceived e-Learning Experience"

    Science.gov (United States)

    Mondi, Makingu; Woods, Peter; Rafi, Ahmad

    2008-01-01

    This study investigates "how and why" students' "Uses and Gratification Expectancy" (UGE) for e-learning resources influences their "Perceived e-Learning Experience." A "Uses and Gratification Expectancy Model" (UGEM) framework is proposed to predict students' "Perceived e-Learning Experience," and…

  4. Predicting Academic Success and Technological Literacy in Secondary Education: A Learning Styles Perspective

    Science.gov (United States)

    Avsec, Stanislav; Szewczyk-Zakrzewska, Agnieszka

    2017-01-01

    This paper aims to investigate the predictive validity of learning styles on academic achievement and technological literacy (TL). For this purpose, secondary school students were recruited (n = 150). An empirical research design was followed where the TL test was used with a learning style inventory measuring learning orientation, processing…

  5. Predicting Refractive Surgery Outcome: Machine Learning Approach With Big Data.

    Science.gov (United States)

    Achiron, Asaf; Gur, Zvi; Aviv, Uri; Hilely, Assaf; Mimouni, Michael; Karmona, Lily; Rokach, Lior; Kaiserman, Igor

    2017-09-01

    To develop a decision forest for prediction of laser refractive surgery outcome. Data from consecutive cases of patients who underwent LASIK or photorefractive surgeries during a 12-year period in a single center were assembled into a single dataset. Training of machine-learning classifiers and testing were performed with a statistical classifier algorithm. The decision forest was created by feature vectors extracted from 17,592 cases and 38 clinical parameters for each patient. A 10-fold cross-validation procedure was applied to estimate the predictive value of the decision forest when applied to new patients. Analysis included patients younger than 40 years who were not treated for monovision. Efficacy of 0.7 or greater and 0.8 or greater was achieved in 16,198 (92.0%) and 14,945 (84.9%) eyes, respectively. Efficacy of less than 0.4 and less than 0.5 was achieved in 322 (1.8%) and 506 (2.9%) eyes, respectively. Patients in the low efficacy group (differences compared with the high efficacy group (≥ 0.8), yet were clinically similar (mean differences between groups of 0.7 years, of 0.43 mm in pupil size, of 0.11 D in cylinder, of 0.22 logMAR in preoperative CDVA, of 0.11 mm in optical zone size, of 1.03 D in actual sphere treatment, and of 0.64 D in actual cylinder treatment). The preoperative subjective CDVA had the highest gain (most important to the model). Correlations analysis revealed significantly decreased efficacy with increased age (r = -0.67, P big data from refractive surgeries may be of interest. [J Refract Surg. 2017;33(9):592-597.]. Copyright 2017, SLACK Incorporated.

  6. Machine learning applications in cancer prognosis and prediction.

    Science.gov (United States)

    Kourou, Konstantina; Exarchos, Themis P; Exarchos, Konstantinos P; Karamouzis, Michalis V; Fotiadis, Dimitrios I

    2015-01-01

    Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

  7. Surprised at all the entropy: hippocampal, caudate and midbrain contributions to learning from prediction errors.

    Directory of Open Access Journals (Sweden)

    Anne-Marike Schiffer

    Full Text Available Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts.

  8. Surprised at all the entropy: hippocampal, caudate and midbrain contributions to learning from prediction errors.

    Science.gov (United States)

    Schiffer, Anne-Marike; Ahlheim, Christiane; Wurm, Moritz F; Schubotz, Ricarda I

    2012-01-01

    Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts.

  9. DeepRT: deep learning for peptide retention time prediction in proteomics

    OpenAIRE

    Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

    2017-01-01

    Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

  10. Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

    Science.gov (United States)

    Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

    2017-12-01

    Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

  11. Extremely Randomized Machine Learning Methods for Compound Activity Prediction

    Directory of Open Access Journals (Sweden)

    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.

  12. Learning new gait patterns: Exploratory muscle activity during motor learning is not predicted by motor modules

    Science.gov (United States)

    Ranganathan, Rajiv; Krishnan, Chandramouli; Dhaher, Yasin Y.; Rymer, William Z.

    2018-01-01

    The motor module hypothesis in motor control proposes that the nervous system can simplify the problem of controlling a large number of muscles in human movement by grouping muscles into a smaller number of modules. Here, we tested one prediction of the modular organization hypothesis by examining whether there is preferential exploration along these motor modules during the learning of a new gait pattern. Healthy college-aged participants learned a new gait pattern which required increased hip and knee flexion during the swing phase while walking in a lower-extremity robot (Lokomat). The new gait pattern was displayed as a foot trajectory in the sagittal plane and participants attempted to match their foot trajectory to this template. We recorded EMG from 8 lower-extremity muscles and we extracted motor modules during both baseline walking and target-tracking using non-negative matrix factorization (NMF). Results showed increased trajectory variability in the first block of learning, indicating that participants were engaged in exploratory behavior. Critically, when we examined the muscle activity during this exploratory phase, we found that the composition of motor modules changed significantly within the first few strides of attempting the new gait pattern. The lack of persistence of the motor modules under even short time scales suggests that motor modules extracted during locomotion may be more indicative of correlated muscle activity induced by the task constraints of walking, rather than reflecting a modular control strategy. PMID:26916510

  13. Combining University Student Self-Regulated Learning Indicators and Engagement with Online Learning Events to Predict Academic Performance

    Science.gov (United States)

    Pardo, Abelardo; Han, Feifei; Ellis, Robert A.

    2017-01-01

    Self-regulated learning theories are used to understand the reasons for different levels of university student academic performance. Similarly, learning analytics research proposes the combination of detailed data traces derived from technology-mediated tasks with a variety of algorithms to predict student academic performance. The former approach…

  14. Predicting High School Student Use of Learning Strategies: The Role of Preferred Learning Styles and Classroom Climate

    Science.gov (United States)

    Cheema, Jehanzeb; Kitsantas, Anastasia

    2016-01-01

    This study investigated the predictiveness of preferred learning styles (competitive and cooperative) and classroom climate (teacher support and disciplinary climate) on learning strategy use in mathematics. The student survey part of the Programme for International Student Assessment 2003 comprising of 4633 US observations was used in a weighted…

  15. Social Emotional Learning and Educational Stress: A Predictive Model

    Science.gov (United States)

    Arslan, Serhat

    2015-01-01

    The purpose of this study is to examine the relationship between social emotional learning and educational stress. Participants were 321 elementary students. Social emotional learning and educational stress scale were used as measures. The relationships between social emotional learning and educational stress were examined using correlation…

  16. 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.

  17. The Enhancement of Communication Skill and Prediction Skill in Colloidal Concept by Problem Solving Learning

    OpenAIRE

    Anggraini, Agita Dzulhajh; Fadiawati, Noor; Diawati, Chansyanah

    2012-01-01

    Accuracy educators in selecting and implementing learning models influence students' science process skills. Models of learning that can be applied to improve science process skills and tend constructivist among athers learning model of problem solving. This research was conducted to describe the effectiveness of the learning model of problem solving in improving communication skills and prediction skills. Subjects in this research were students of high school YP Unila Bandar Lampung Even ...

  18. Predicting Motor Skills from Strengths and Difficulties Questionnaire Scores, Language Ability, and Other Features of New Zealand Children Entering Primary School

    Science.gov (United States)

    Sargisson, Rebecca J.; Powell, Cheniel; Stanley, Peter; de Candole, Rosalind

    2014-01-01

    The motor and language skills, emotional and behavioural problems of 245 children were measured at school entry. Fine motor scores were significantly predicted by hyperactivity, phonetic awareness, prosocial behaviour, and the presence of medical problems. Gross motor scores were significantly predicted by the presence of medical problems. The…

  19. Chiropractic management using a brain-based model of care for a 15-year-old adolescent boy with migraine headaches and behavioral and learning difficulties: a case report

    Science.gov (United States)

    Kuhn, Kurt W.; Cambron, Jerrilyn

    2013-01-01

    Objective The purpose of this report is to describe chiropractic management, using a brain-based model of care, of a teen who had migraine headaches and several social and learning difficulties. Clinical features A 15-year-old adolescent boy with a chronic history of migraines and more than 10 years of learning and behavioral difficulties, including attention-deficit/hyperactivity disorder, obsessive compulsive disorder, and Tourette syndrome, presented for chiropractic care. Intervention and outcome The patient received spinal manipulation and was given home physical coordination activities that were contralateral to the side of the involved basal ganglia and ipsilateral to the involved cerebellum, along with interactive metronome training. Quantitative changes were noted in neurological soft signs, tests of variables of attention Conners’ Parent Rating Scale, the California Achievement Test, grade point, and reduction of medications. The patient reported qualitative improvements in tics, attention, reading, vision, health, relationships with his peers and his family, and self-esteem. Conclusion The patient with migraine headaches and learning difficulties responded well to the course of chiropractic care. This study suggests that there may be value in a brain-based model of care in the chiropractic management of conditions that are beyond musculoskeletal in nature. PMID:24396330

  20. Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.

    Science.gov (United States)

    Nath, Abhigyan; Kumari, Priyanka; Chaube, Radha

    2018-01-01

    Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.

  1. Applying machine learning to predict patient-specific current CD 4 ...

    African Journals Online (AJOL)

    This work shows the application of machine learning to predict current CD4 cell count of an HIV-positive patient using genome sequences, viral load and time. A regression model predicting actual CD4 cell counts and a classification model predicting if a patient's CD4 cell count is less than 200 was built using a support ...

  2. Predictive coding accelerates word recognition and learning in the early stages of language development.

    Science.gov (United States)

    Ylinen, Sari; Bosseler, Alexis; Junttila, Katja; Huotilainen, Minna

    2017-11-01

    The ability to predict future events in the environment and learn from them is a fundamental component of adaptive behavior across species. Here we propose that inferring predictions facilitates speech processing and word learning in the early stages of language development. Twelve- and 24-month olds' electrophysiological brain responses to heard syllables are faster and more robust when the preceding word context predicts the ending of a familiar word. For unfamiliar, novel word forms, however, word-expectancy violation generates a prediction error response, the strength of which significantly correlates with children's vocabulary scores at 12 months. These results suggest that predictive coding may accelerate word recognition and support early learning of novel words, including not only the learning of heard word forms but also their mapping to meanings. Prediction error may mediate learning via attention, since infants' attention allocation to the entire learning situation in natural environments could account for the link between prediction error and the understanding of word meanings. On the whole, the present results on predictive coding support the view that principles of brain function reported across domains in humans and non-human animals apply to language and its development in the infant brain. A video abstract of this article can be viewed at: http://hy.fi/unitube/video/e1cbb495-41d8-462e-8660-0864a1abd02c. [Correction added on 27 January 2017, after first online publication: The video abstract link was added.]. © 2016 John Wiley & Sons Ltd.

  3. Do difficulties in accessing in-hours primary care predict higher use of out-of-hours GP services? Evidence from an English National Patient Survey.

    Science.gov (United States)

    Zhou, Yin; Abel, Gary; Warren, Fiona; Roland, Martin; Campbell, John; Lyratzopoulos, Georgios

    2015-05-01

    It is believed that some patients are more likely to use out-of-hours primary care services because of difficulties in accessing in-hours care, but substantial evidence about any such association is missing. We analysed data from 567,049 respondents to the 2011/2012 English General Practice Patient Survey who reported at least one in-hours primary care consultation in the preceding 6 months. Of those respondents, 7% also reported using out-of-hours primary care. We used logistic regression to explore associations between use of out-of-hours primary care and five measures of in-hours access (ease of getting through on the telephone, ability to see a preferred general practitioner, ability to get an urgent or routine appointment and convenience of opening hours). We illustrated the potential for reduction in use of out-of-hours primary care in a model where access to in-hours care was made optimal. Worse in-hours access was associated with greater use of out-of-hours primary care for each access factor. In multivariable analysis adjusting for access and patient characteristic variables, worse access was independently associated with increased out-of-hours use for all measures except ease of telephone access. Assuming these associations were causal, we estimated that an 11% relative reduction in use of out-of-hours primary care services in England could be achievable if access to in-hours care were optimal. This secondary quantitative analysis provides evidence for an association between difficulty in accessing in-hours care and use of out-of-hours primary care services. The findings can motivate the development of interventions to improve in-hour access. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  4. An ensemble machine learning approach to predict survival in breast cancer.

    Science.gov (United States)

    Djebbari, Amira; Liu, Ziying; Phan, Sieu; Famili, Fazel

    2008-01-01

    Current breast cancer predictive signatures are not unique. Can we use this fact to our advantage to improve prediction? From the machine learning perspective, it is well known that combining multiple classifiers can improve classification performance. We propose an ensemble machine learning approach which consists of choosing feature subsets and learning predictive models from them. We then combine models based on certain model fusion criteria and we also introduce a tuning parameter to control sensitivity. Our method significantly improves classification performance with a particular emphasis on sensitivity which is critical to avoid misclassifying poor prognosis patients as good prognosis.

  5. Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

    Science.gov (United States)

    Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

    2015-08-01

    Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

  6. The Future of Learning Technology: Some Tentative Predictions

    Science.gov (United States)

    Rushby, Nick

    2013-01-01

    This paper is a snapshot of an evolving vision of what the future may hold for learning technology. It offers three personal visions of the future and raises many questions that need to be explored if learning technology is to realise its full potential.

  7. Predictive codes of familiarity and context during the perceptual learning of facial identities

    Science.gov (United States)

    Apps, Matthew A. J.; Tsakiris, Manos

    2013-11-01

    Face recognition is a key component of successful social behaviour. However, the computational processes that underpin perceptual learning and recognition as faces transition from unfamiliar to familiar are poorly understood. In predictive coding, learning occurs through prediction errors that update stimulus familiarity, but recognition is a function of both stimulus and contextual familiarity. Here we show that behavioural responses on a two-option face recognition task can be predicted by the level of contextual and facial familiarity in a computational model derived from predictive-coding principles. Using fMRI, we show that activity in the superior temporal sulcus varies with the contextual familiarity in the model, whereas activity in the fusiform face area covaries with the prediction error parameter that updated facial familiarity. Our results characterize the key computations underpinning the perceptual learning of faces, highlighting that the functional properties of face-processing areas conform to the principles of predictive coding.

  8. Early identification of children at-risk for academic difficulties, using standardized assessment: stability and predictive validity of preschool math and language scores

    NARCIS (Netherlands)

    Frans, Niek; Post, Wendy J.; Huisman, Mark; Oenema-Mostert, Ineke C.E.; Keegstra, Anne L.; Minnaert, Alexander E.M.G.

    2017-01-01

    Despite the claim by several researchers that variability in performance may complicate the identification of “at-risk” children, variability in the academic performance of young children remains an undervalued area of research. The goal of this study is to examine the predictive validity for future

  9. Improving students' meaningful learning on the predictive nature of quantum mechanics

    Directory of Open Access Journals (Sweden)

    Rodolfo Alves de Carvalho Neto

    2009-03-01

    Full Text Available This paper deals with research about teaching quantum mechanics to 3rd year high school students and their meaningful learning of its predictive aspect; it is based on the Master’s dissertation of one of the authors (CARVALHO NETO, 2006. While teaching quantum mechanics, we emphasized its predictive and essentially probabilistic nature, based on Niels Bohr’s complementarity interpretation (BOHR, 1958. In this context, we have discussed the possibility of predicting measurement results in well-defined experimental contexts, even for individual events. Interviews with students reveal that they have used quantum mechanical ideas, suggesting their meaningful learning of the essentially probabilistic predictions of quantum mechanics.

  10. Modelling Question Difficulty in an A Level Physics Examination

    Science.gov (United States)

    Crisp, Victoria; Grayson, Rebecca

    2013-01-01

    "Item difficulty modelling" is a technique used for a number of purposes such as to support future item development, to explore validity in relation to the constructs that influence difficulty and to predict the difficulty of items. This research attempted to explore the factors influencing question difficulty in a general qualification…

  11. A comparison of machine learning techniques for predicting downstream acid mine drainage

    CSIR Research Space (South Africa)

    van Zyl, TL

    2014-07-01

    Full Text Available windowing approach over historical values to generate a prediction for the current value. We evaluate a number of Machine Learning techniques as regressors including Support Vector Regression, Random Forests, Stochastic Gradient Decent Regression, Linear...

  12. Applying machine learning to predict patient-specific current CD4 ...

    African Journals Online (AJOL)

    Apple apple

    This work shows the application of machine learning to predict current CD4 cell count of an HIV- .... Pre-processing ... remaining data elements of the PR and RT datasets. ... technique based on the structure of the human brain's neuron.

  13. Biological lifestyle factors in adult distance education: predicting cognitive and learning performance

    NARCIS (Netherlands)

    Gijselaers, Jérôme

    2015-01-01

    Gijselaers, H. J. M. (2015, 20 October). Biological lifestyle factors in adult distance education: predicting cognitive and learning performance. Presentation given for the inter-faculty Data Science group at the Open University of the Netherlands, Heerlen, The Netherlands.

  14. Multi-fidelity machine learning models for accurate bandgap predictions of solids

    International Nuclear Information System (INIS)

    Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

    2016-01-01

    Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelity quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.

  15. Early Identification of Reading Difficulties

    DEFF Research Database (Denmark)

    Poulsen, Mads; Nielsen, Anne-Mette Veber; Juul, Holger

    2017-01-01

    Early screening for reading difficulties before the onset of instruction is desirable because it allows intervention that is targeted at prevention rather than remediation of reading difficulties. However, early screening may be too inaccurate to effectively allocate resources to those who need...... them. The present study compared the accuracy of early screening before the onset of formal reading instruction with late screening six months into the first year of instruction. The study followed 164 Danish students from the end of Grade 0 to the end of Grade 2. Early screening included measures...... of phonemic awareness, rapid naming, letter knowledge, paired associate learning, and reading. Late screening included only reading. Results indicated that reading measures improved substantially as predictors over the first six months of Grade 1, to the point where late reading measures alone provided...

  16. Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning

    Science.gov (United States)

    Lu, Owen H. T.; Huang, Anna Y. Q.; Huang, Jeff C. H.; Lin, Albert J. Q.; Ogata, Hiroaki; Yang, Stephen J. H.

    2018-01-01

    Blended learning combines online digital resources with traditional classroom activities and enables students to attain higher learning performance through well-defined interactive strategies involving online and traditional learning activities. Learning analytics is a conceptual framework and is a part of our Precision education used to analyze…

  17. Individual differences in learning predict the return of fear.

    Science.gov (United States)

    Gershman, Samuel J; Hartley, Catherine A

    2015-09-01

    Using a laboratory analogue of learned fear (Pavlovian fear conditioning), we show that there is substantial heterogeneity across individuals in spontaneous recovery of fear following extinction training. We propose that this heterogeneity might stem from qualitative individual differences in the nature of extinction learning. Whereas some individuals tend to form a new memory during extinction, leaving their fear memory intact, others update the original threat association with new safety information, effectively unlearning the fear memory. We formalize this account in a computational model of fear learning and show that individuals who, according to the model, are more likely to form new extinction memories tend to show greater spontaneous recovery compared to individuals who appear to only update a single memory. This qualitative variation in fear and extinction learning may have important implications for understanding vulnerability and resilience to fear-related psychiatric disorders.

  18. COMPARISON OF UPPER LIP BITE TEST WITH MODIFIED MALLAMPATI TEST AND THYROMENTAL DISTANCE FOR PREDICTING DIFFICULTY IN ENDOTRACHEAL INTUBATION: A PROSPECTIVE STUDY

    Directory of Open Access Journals (Sweden)

    Prakash T. S. N

    2016-09-01

    Full Text Available BACKGROUND The significance of difficult or failed tracheal intubation was well recognised as a major cause of morbidity and mortality in anaesthetic practice as per ASA closed claim study. The need to predict potentially difficult tracheal intubation has received more importance, but with limited success. Unanticipated difficult intubation is a risk to patient’s life and a challenge to the skill of the anaesthesiologist. Many anatomical and pathological variables have been identified and have been suggested to be useful in anticipating a difficult airway. These factors have limitations because of wide variations in the incidence of difficult intubation, interobserver variability and inadequate statistical power of the currently measured variables. METHODS After obtaining institutional ethical committee clearance and written informed consent, the present study was conducted in 200 patients aged between 16 yrs. and 65 yrs. at King George Hospital, Andhra Medical College, Visakhapatnam, in the Department of Anaesthesiology. All the 200 patients undergoing elective surgical procedures under general anaesthesia were enrolled in the study. A thorough preanaesthetic evaluation was carried out in all the patients and the procedure was explained in detail to the patients. RESULTS Of the entire two hundred patients, a total of ten patients had difficult intubation, all of them had Cormack-Lehane class III on laryngoscopy. None of them had Cormack-Lehane class IV on laryngoscopy. The incidence of difficult intubation was 5% in the present study. There were no cases of failed intubation. One hundred and eighty seven patients predicted to be easy for intubation by ULBT (i.e. patients who had ULBT class I and II out of whom, however, we encountered difficult intubation in 5 patients. Out of the eight patients predicted to have difficult airway by ULBT III, only one patient had CL III difficult airway and subsequently difficult intubation. CONCLUSIONS MMT

  19. 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 ...

  20. Hebbian learning and predictive mirror neurons for actions, sensations and emotions

    OpenAIRE

    Keysers, C.; Gazzola, Valeria

    2014-01-01

    Spike-timing-dependent plasticity is considered the neurophysiological basis of Hebbian learning and has been shown to be sensitive to both contingency and contiguity between pre- and postsynaptic activity. Here, we will examine how applying this Hebbian learning rule to a system of interconnected neurons in the presence of direct or indirect re-afference (e.g. seeing/hearing one's own actions) predicts the emergence of mirror neurons with predictive properties. In this framework, we analyse ...

  1. Monitoring and regulation of learning in medical education: the need for predictive cues.

    Science.gov (United States)

    de Bruin, Anique B H; Dunlosky, John; Cavalcanti, Rodrigo B

    2017-06-01

    Being able to accurately monitor learning activities is a key element in self-regulated learning in all settings, including medical schools. Yet students' ability to monitor their progress is often limited, leading to inefficient use of study time. Interventions that improve the accuracy of students' monitoring can optimise self-regulated learning, leading to higher achievement. This paper reviews findings from cognitive psychology and explores potential applications in medical education, as well as areas for future research. Effective monitoring depends on students' ability to generate information ('cues') that accurately reflects their knowledge and skills. The ability of these 'cues' to predict achievement is referred to as 'cue diagnosticity'. Interventions that improve the ability of students to elicit predictive cues typically fall into two categories: (i) self-generation of cues and (ii) generation of cues that is delayed after self-study. Providing feedback and support is useful when cues are predictive but may be too complex to be readily used. Limited evidence exists about interventions to improve the accuracy of self-monitoring among medical students or trainees. Developing interventions that foster use of predictive cues can enhance the accuracy of self-monitoring, thereby improving self-study and clinical reasoning. First, insight should be gained into the characteristics of predictive cues used by medical students and trainees. Next, predictive cue prompts should be designed and tested to improve monitoring and regulation of learning. Finally, the use of predictive cues should be explored in relation to teaching and learning clinical reasoning. Improving self-regulated learning is important to help medical students and trainees efficiently acquire knowledge and skills necessary for clinical practice. Interventions that help students generate and use predictive cues hold the promise of improved self-regulated learning and achievement. This framework is

  2. Learning Type Extension Trees for Metal Bonding State Prediction

    DEFF Research Database (Denmark)

    Frasconi, Paolo; Jaeger, Manfred; Passerini, Andrea

    2008-01-01

    Type Extension Trees (TET) have been recently introduced as an expressive representation language allowing to encode complex combinatorial features of relational entities. They can be efficiently learned with a greedy search strategy driven by a generalized relational information gain and a discr......Type Extension Trees (TET) have been recently introduced as an expressive representation language allowing to encode complex combinatorial features of relational entities. They can be efficiently learned with a greedy search strategy driven by a generalized relational information gain...

  3. Predicting the concentration of residual methanol in industrial formalin using machine learning

    OpenAIRE

    Heidkamp, William

    2016-01-01

    In this thesis, a machine learning approach was used to develop a predictive model for residual methanol concentration in industrial formalin produced at the Akzo Nobel factory in Kristinehamn, Sweden. The MATLABTM computational environment supplemented with the Statistics and Machine LearningTM toolbox from the MathWorks were used to test various machine learning algorithms on the formalin production data from Akzo Nobel. As a result, the Gaussian Process Regression algorithm was found to pr...

  4. Beyond Engagement Analytics: Which Online Mixed-Data Factors Predict Student Learning Outcomes?

    Science.gov (United States)

    Strang, Kenneth David

    2017-01-01

    This mixed-method study focuses on online learning analytics, a research area of importance. Several important student attributes and their online activities are examined to identify what seems to work best to predict higher grades. The purpose is to explore the relationships between student grade and key learning engagement factors using a large…

  5. Translating visual information into action predictions: Statistical learning in action and nonaction contexts.

    Science.gov (United States)

    Monroy, Claire D; Gerson, Sarah A; Hunnius, Sabine

    2018-05-01

    Humans are sensitive to the statistical regularities in action sequences carried out by others. In the present eyetracking study, we investigated whether this sensitivity can support the prediction of upcoming actions when observing unfamiliar action sequences. In two between-subjects conditions, we examined whether observers would be more sensitive to statistical regularities in sequences performed by a human agent versus self-propelled 'ghost' events. Secondly, we investigated whether regularities are learned better when they are associated with contingent effects. Both implicit and explicit measures of learning were compared between agent and ghost conditions. Implicit learning was measured via predictive eye movements to upcoming actions or events, and explicit learning was measured via both uninstructed reproduction of the action sequences and verbal reports of the regularities. The findings revealed that participants, regardless of condition, readily learned the regularities and made correct predictive eye movements to upcoming events during online observation. However, different patterns of explicit-learning outcomes emerged following observation: Participants were most likely to re-create the sequence regularities and to verbally report them when they had observed an actor create a contingent effect. These results suggest that the shift from implicit predictions to explicit knowledge of what has been learned is facilitated when observers perceive another agent's actions and when these actions cause effects. These findings are discussed with respect to the potential role of the motor system in modulating how statistical regularities are learned and used to modify behavior.

  6. Participatory cues and program familiarity predict young children’s learning from educational television

    NARCIS (Netherlands)

    Piotrowski, J.

    2014-01-01

    The capacity model is designed to predict young children's learning from educational television. It posits that select program features and individual child characteristics can support this learning either by increasing total working memory allocated to the program or altering the allocation of

  7. Classroom Organization and Teacher Stress Predict Learning Motivation in Kindergarten Children

    Science.gov (United States)

    Pakarinen, Eija; Kiuru, Noona; Lerkkanen, Marja-Kristiina; Poikkeus, Anna-Maija; Siekkinen, Martti; Nurmi, Jari-Erik

    2010-01-01

    This study examined the extent to which observed teaching practices and self-reported teacher stress predict children's learning motivation and phonological awareness in kindergarten. The pre-reading skills of 1,268 children were measured at the beginning of their kindergarten year. Their learning motivation and phonological awareness were…

  8. Dynamic Models of Learning That Characterize Parent-Child Exchanges Predict Vocabulary Growth

    Science.gov (United States)

    Ober, David R.; Beekman, John A.

    2016-01-01

    Cumulative vocabulary models for infants and toddlers were developed from models of learning that predict trajectories associated with low, average, and high vocabulary growth rates (14 to 46 months). It was hypothesized that models derived from rates of learning mirror the type of exchanges provided to infants and toddlers by parents and…

  9. Native-language N400 and P600 predict dissociable language-learning abilities in adults.

    Science.gov (United States)

    Qi, Zhenghan; Beach, Sara D; Finn, Amy S; Minas, Jennifer; Goetz, Calvin; Chan, Brian; Gabrieli, John D E

    2017-04-01

    Language learning aptitude during adulthood varies markedly across individuals. An individual's native-language ability has been associated with success in learning a new language as an adult. However, little is known about how native-language processing affects learning success and what neural markers of native-language processing, if any, are related to success in learning. We therefore related variation in electrophysiology during native-language processing to success in learning a novel artificial language. Event-related potentials (ERPs) were recorded while native English speakers judged the acceptability of English sentences prior to learning an artificial language. There was a trend towards a double dissociation between native-language ERPs and their relationships to novel syntax and vocabulary learning. Individuals who exhibited a greater N400 effect when processing English semantics showed better future learning of the artificial language overall. The N400 effect was related to syntax learning via its specific relationship to vocabulary learning. In contrast, the P600 effect size when processing English syntax predicted future syntax learning but not vocabulary learning. These findings show that distinct neural signatures of native-language processing relate to dissociable abilities for learning novel semantic and syntactic information. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Native-language N400 and P600 predict dissociable language-learning abilities in adults

    Science.gov (United States)

    Qi, Zhenghan; Beach, Sara D.; Finn, Amy S.; Minas, Jennifer; Goetz, Calvin; Chan, Brian; Gabrieli, John D.E.

    2018-01-01

    Language learning aptitude during adulthood varies markedly across individuals. An individual’s native-language ability has been associated with success in learning a new language as an adult. However, little is known about how native-language processing affects learning success and what neural markers of native-language processing, if any, are related to success in learning. We therefore related variation in electrophysiology during native-language processing to success in learning a novel artificial language. Event-related potentials (ERPs) were recorded while native English speakers judged the acceptability of English sentences prior to learning an artificial language. There was a trend towards a double dissociation between native-language ERPs and their relationships to novel syntax and vocabulary learning. Individuals who exhibited a greater N400 effect when processing English semantics showed better future learning of the artificial language overall. The N400 effect was related to syntax learning via its specific relationship to vocabulary learning. In contrast, the P600 effect size when processing English syntax predicted future syntax learning but not vocabulary learning. These findings show that distinct neural signatures of native-language processing relate to dissociable abilities for learning novel semantic and syntactic information. PMID:27737775

  11. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

    Science.gov (United States)

    Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C.

    2017-01-01

    Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs). Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages. PMID:28883801

  12. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle.

    Science.gov (United States)

    Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C

    2017-01-01

    Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs) . Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

  13. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

    Directory of Open Access Journals (Sweden)

    Rebeca Cerezo

    2017-08-01

    Full Text Available Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs. Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques.Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples.Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance.Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

  14. Roles of dopamine neurons in mediating the prediction error in aversive learning in insects.

    Science.gov (United States)

    Terao, Kanta; Mizunami, Makoto

    2017-10-31

    In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. The prediction error theory has been proposed to account for the finding of a blocking phenomenon, in which pairing of a stimulus X with an unconditioned stimulus (US) could block subsequent association of a second stimulus Y to the US when the two stimuli were paired in compound with the same US. Evidence for this theory, however, has been imperfect since blocking can also be accounted for by competitive theories. We recently reported blocking in classical conditioning of an odor with water reward in crickets. We also reported an "auto-blocking" phenomenon in appetitive learning, which supported the prediction error theory and rejected alternative theories. The presence of auto-blocking also suggested that octopamine neurons mediate reward prediction error signals. Here we show that blocking and auto-blocking occur in aversive learning to associate an odor with salt water (US) in crickets, and our results suggest that dopamine neurons mediate aversive prediction error signals. We conclude that the prediction error theory is applicable to both appetitive learning and aversive learning in insects.

  15. Probabilistic Category Learning in Developmental Dyslexia: Evidence from Feedback and Paired-Associate Weather Prediction Tasks

    Science.gov (United States)

    Gabay, Yafit; Vakil, Eli; Schiff, Rachel; Holt, Lori L.

    2015-01-01

    Objective Developmental dyslexia is presumed to arise from specific phonological impairments. However, an emerging theoretical framework suggests that phonological impairments may be symptoms stemming from an underlying dysfunction of procedural learning. Method We tested procedural learning in adults with dyslexia (n=15) and matched-controls (n=15) using two versions of the Weather Prediction Task: Feedback (FB) and Paired-associate (PA). In the FB-based task, participants learned associations between cues and outcomes initially by guessing and subsequently through feedback indicating the correctness of response. In the PA-based learning task, participants viewed the cue and its associated outcome simultaneously without overt response or feedback. In both versions, participants trained across 150 trials. Learning was assessed in a subsequent test without presentation of the outcome, or corrective feedback. Results The Dyslexia group exhibited impaired learning compared with the Control group on both the FB and PA versions of the weather prediction task. Conclusions The results indicate that the ability to learn by feedback is not selectively impaired in dyslexia. Rather it seems that the probabilistic nature of the task, shared by the FB and PA versions of the weather prediction task, hampers learning in those with dyslexia. Results are discussed in light of procedural learning impairments among participants with dyslexia. PMID:25730732

  16. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

    Science.gov (United States)

    Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

    2017-01-01

    Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  17. Life Prediction for FRP composites with Data Fusion & Machine Learning

    Data.gov (United States)

    National Aeronautics and Space Administration — High-fidelity, probabilistic predictions of damage evolution in fiber-reinforced polymer (FRP) composite structures could accelerate development and certification of...

  18. Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis.

    Science.gov (United States)

    van der Burgh, Hannelore K; Schmidt, Ruben; Westeneng, Henk-Jan; de Reus, Marcel A; van den Berg, Leonard H; van den Heuvel, Martijn P

    2017-01-01

    Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.

  19. Predicting perceptual learning from higher-order cortical processing.

    Science.gov (United States)

    Wang, Fang; Huang, Jing; Lv, Yaping; Ma, Xiaoli; Yang, Bin; Wang, Encong; Du, Boqi; Li, Wu; Song, Yan

    2016-01-01

    Visual perceptual learning has been shown to be highly specific to the retinotopic location and attributes of the trained stimulus. Recent psychophysical studies suggest that these specificities, which have been associated with early retinotopic visual cortex, may in fact not be inherent in perceptual learning and could be related to higher-order brain functions. Here we provide direct electrophysiological evidence in support of this proposition. In a series of event-related potential (ERP) experiments, we recorded high-density electroencephalography (EEG) from human adults over the course of learning in a texture discrimination task (TDT). The results consistently showed that the earliest C1 component (68-84ms), known to reflect V1 activity driven by feedforward inputs, was not modulated by learning regardless of whether the behavioral improvement is location specific or not. In contrast, two later posterior ERP components (posterior P1 and P160-350) over the occipital cortex and one anterior ERP component (anterior P160-350) over the prefrontal cortex were progressively modified day by day. Moreover, the change of the anterior component was closely correlated with improved behavioral performance on a daily basis. Consistent with recent psychophysical and imaging observations, our results indicate that perceptual learning can mainly involve changes in higher-level visual cortex as well as in the neural networks responsible for cognitive functions such as attention and decision making. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Spatial extreme learning machines: An application on prediction of disease counts.

    Science.gov (United States)

    Prates, Marcos O

    2018-01-01

    Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.

  1. Residents in difficulty

    DEFF Research Database (Denmark)

    Christensen, Mette Krogh; O'Neill, Lotte; Hansen, Dorthe Høgh

    2016-01-01

    Background The majority of studies on prevalence and characteristics of residents in difficulty have been conducted in English-speaking countries and the existing literature may not reflect the prevalence and characteristics of residents in difficulty in other parts of the world such as the Scand...... in a healthcare system. From our perspective, further sociological and pedagogical investigations in educational cultures across settings and specialties could inform our understanding of and knowledge about pitfalls in residents’ and doctors’ socialization into the healthcare system....

  2. Predicting and preventing organizational failure: learning, stability and safety culture

    International Nuclear Information System (INIS)

    Duffey, R.B.

    2009-01-01

    The physical definition of 'safety culture' is the creation of an organizational and operational structure that places unending emphasis on safety at every level. We propose and prefer the use of the term and the objective of sustaining a 'Learning Environment', where mistakes, outcomes and errors are used as learning vehicles to improve, and we can now define why that is true. Therefore we can manage and quantify safety effectively tracking and analyzing outcomes, using the trends to guide our needed organizational behaviors. (author)

  3. Machine learning and predictive data analytics enabling metrology and process control in IC fabrication

    Science.gov (United States)

    Rana, Narender; Zhang, Yunlin; Wall, Donald; Dirahoui, Bachir; Bailey, Todd C.

    2015-03-01

    Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.

  4. Automatic stimulation of experiments and learning based on prediction failure recognition

    NARCIS (Netherlands)

    Juarez Cordova, A.G.; Kahl, B.; Henne, T.; Prassler, E.

    2009-01-01

    In this paper we focus on the task of automatically and autonomously initiating experimentation and learning based on the recognition of prediction failure. We present a mechanism that utilizes conceptual knowledge to predict the outcome of robot actions, observes their execution and indicates when

  5. Predicting Academic Success from Academic Motivation and Learning Approaches in Classroom Teaching Students

    Science.gov (United States)

    Çetin, Baris

    2015-01-01

    Our aim was to determine whether learning approaches and academic motivation together predict academic success of classroom teaching students. The sample of the study included 536 students (386 female, 150 male) studying at the Classroom Teaching Division of Canakkale 18 Mart University. Our research was designed as a prediction study. Data was…

  6. Decoding the future from past experience: learning shapes predictions in early visual cortex.

    Science.gov (United States)

    Luft, Caroline D B; Meeson, Alan; Welchman, Andrew E; Kourtzi, Zoe

    2015-05-01

    Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex. Copyright © 2015 the American Physiological Society.

  7. Learning to predict is spared in mild cognitive impairment due to Alzheimer's disease.

    Science.gov (United States)

    Baker, Rosalind; Bentham, Peter; Kourtzi, Zoe

    2015-10-01

    Learning the statistics of the environment is critical for predicting upcoming events. However, little is known about how we translate previous knowledge about scene regularities to sensory predictions. Here, we ask whether patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) that are known to have spared implicit but impaired explicit recognition memory are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards oriented gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. Further, we show that executive cognitive control may account for individual variability in predictive learning. That is, we observed significant positive correlations of performance in attentional and working memory tasks with post-training performance in the prediction task. Taken together, these results suggest a mediating role of circuits involved in cognitive control (i.e. frontal circuits) that may support the ability for predictive learning in MCI-AD.

  8. Spontaneous eye movements and trait empathy predict vicarious learning of fear.

    Science.gov (United States)

    Kleberg, Johan L; Selbing, Ida; Lundqvist, Daniel; Hofvander, Björn; Olsson, Andreas

    2015-12-01

    Learning to predict dangerous outcomes is important to survival. In humans, this kind of learning is often transmitted through the observation of others' emotional responses. We analyzed eye movements during an observational/vicarious fear learning procedure, in which healthy participants (N=33) watched another individual ('learning model') receiving aversive treatment (shocks) paired with a predictive conditioned stimulus (CS+), but not a control stimulus (CS-). Participants' gaze pattern towards the model differentiated as a function of whether the CS was predictive or not of a shock to the model. Consistent with our hypothesis that the face of a conspecific in distress can act as an unconditioned stimulus (US), we found that the total fixation time at a learning model's face increased when the CS+ was shown. Furthermore, we found that the total fixation time at the CS+ during learning predicted participants' conditioned responses (CRs) at a later test in the absence of the model. We also demonstrated that trait empathy was associated with stronger CRs, and that autistic traits were positively related to autonomic reactions to watching the model receiving the aversive treatment. Our results have implications for both healthy and dysfunctional socio-emotional learning. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. 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.

  10. Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method

    Directory of Open Access Journals (Sweden)

    Yuhan Jia

    2017-01-01

    Full Text Available Accurate traffic flow prediction is increasingly essential for successful traffic modeling, operation, and management. Traditional data driven traffic flow prediction approaches have largely assumed restrictive (shallow model architectures and do not leverage the large amount of environmental data available. Inspired by deep learning methods with more complex model architectures and effective data mining capabilities, this paper introduces the deep belief network (DBN and long short-term memory (LSTM to predict urban traffic flow considering the impact of rainfall. The rainfall-integrated DBN and LSTM can learn the features of traffic flow under various rainfall scenarios. Experimental results indicate that, with the consideration of additional rainfall factor, the deep learning predictors have better accuracy than existing predictors and also yield improvements over the original deep learning models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time series characteristics of traffic flow data.

  11. Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm

    Directory of Open Access Journals (Sweden)

    Hongye Zhong

    2017-01-01

    Full Text Available With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big health data. Based on the proposed framework, an experimental system is developed as an illustration for the framework implementation.

  12. Sensor Data Air Pollution Prediction by Machine Learning Methods

    Czech Academy of Sciences Publication Activity Database

    Vidnerová, Petra; Neruda, Roman

    submitted 25. 1. (2018) ISSN 1530-437X R&D Projects: GA ČR GA15-18108S Grant - others:GA MŠk(CZ) LM2015042 Institutional support: RVO:67985807 Keywords : machine learning * sensors * air pollution * deep neural networks * regularization networks Subject RIV: IN - Informatics, Computer Science Impact factor: 2.512, year: 2016

  13. Deep learning for predicting the monsoon over the homogeneous ...

    Indian Academy of Sciences (India)

    Moumita Saha

    2017-06-12

    Jun 12, 2017 ... governs the pulse of life for its mankind along with the flora-fauna ..... work. The architecture has the ability to learn the properties of input data space, and it is utilized for feature .... motivation of minimizing the reconstruction error of the input (the .... balance between the accuracy and speed of the method.

  14. Learning in manufacturing organizations : what factors predict effectiveness?

    NARCIS (Netherlands)

    Shipton, H.; Dawson, J.; West, M.A.; Patterson, M.G.

    2002-01-01

    This paper argues that it is possible to identify factors which pre-dispose organizations to adopt effective learning strategies and processes. It is hypothesized that effective OL is associated with: profitability, environmental uncertainty, structure, approach to HRM and quality orientation. The

  15. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

    Science.gov (United States)

    Ye, Qiang

    2010-01-01

    Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

  16. A Model for Predicting Learning Flow and Achievement in Corporate e-Learning

    Science.gov (United States)

    Joo, Young Ju; Lim, Kyu Yon; Kim, Su Mi

    2012-01-01

    The primary objective of this study was to investigate the determinants of learning flow and achievement in corporate online training. Self-efficacy, intrinsic value, and test anxiety were selected as learners' motivational factors, while perceived usefulness and ease of use were also selected as learning environmental factors. Learning flow was…

  17. Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments

    Science.gov (United States)

    Sabourin, Jennifer L.; Shores, Lucy R.; Mott, Bradford W.; Lester, James C.

    2013-01-01

    Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students' success in computer-based learning environments. Consequently, understanding students' self-regulated learning behavior has been the subject of increasing attention. Unfortunately, monitoring these behaviors in real-time has…

  18. Identification of children with reading difficulties: Cheap can be adequate

    DEFF Research Database (Denmark)

    Poulsen, Mads; Nielsen, Anne-Mette Veber

    Classification of reading difficulties: Cheap screening can be accurate Purpose: Three factors are important for identification of students in need of remedial instruction: accuracy, timeliness, and cost. The identification has to be accurate to be of any use, the identification has to be timely......, inexpensive testing. The present study investigated the classification accuracy of three screening models varying in timeliness and cost. Method: We compared the ROC statistics of three logistic models for predicting end of Grade 2 reading difficulties in a sample of 164 students: 1) an early, comprehensive...... model using a battery of Grade 0 tests, including phoneme awareness, rapid naming, and paired associate learning, 2) a late, comprehensive model adding reading measures from January of Grade 1, and 3) a late, inexpensive model using only group-administered reading measures from January of Grade 1...

  19. Interaction of Instrumental and Goal-Directed Learning Modulates Prediction Error Representations in the Ventral Striatum.

    Science.gov (United States)

    Guo, Rong; Böhmer, Wendelin; Hebart, Martin; Chien, Samson; Sommer, Tobias; Obermayer, Klaus; Gläscher, Jan

    2016-12-14

    Goal-directed and instrumental learning are both important controllers of human behavior. Learning about which stimulus event occurs in the environment and the reward associated with them allows humans to seek out the most valuable stimulus and move through the environment in a goal-directed manner. Stimulus-response associations are characteristic of instrumental learning, whereas response-outcome associations are the hallmark of goal-directed learning. Here we provide behavioral, computational, and neuroimaging results from a novel task in which stimulus-response and response-outcome associations are learned simultaneously but dominate behavior at different stages of the experiment. We found that prediction error representations in the ventral striatum depend on which type of learning dominates. Furthermore, the amygdala tracks the time-dependent weighting of stimulus-response versus response-outcome learning. Our findings suggest that the goal-directed and instrumental controllers dynamically engage the ventral striatum in representing prediction errors whenever one of them is dominating choice behavior. Converging evidence in human neuroimaging studies has shown that the reward prediction errors are correlated with activity in the ventral striatum. Our results demonstrate that this region is simultaneously correlated with a stimulus prediction error. Furthermore, the learning system that is currently dominating behavioral choice dynamically engages the ventral striatum for computing its prediction errors. This demonstrates that the prediction error representations are highly dynamic and influenced by various experimental context. This finding points to a general role of the ventral striatum in detecting expectancy violations and encoding error signals regardless of the specific nature of the reinforcer itself. Copyright © 2016 the authors 0270-6474/16/3612650-11$15.00/0.

  20. Predicting High Frequency Exchange Rates using Machine Learning

    OpenAIRE

    Palikuca, Aleksandar; Seidl,, Timo

    2016-01-01

    This thesis applies a committee of Artificial Neural Networks and Support Vector Machines on high-dimensional, high-frequency EUR/USD exchange rate data in an effort to predict directional market movements on up to a 60 second prediction horizon. The study shows that combining multiple classifiers into a committee produces improved precision relative to the best individual committee members and outperforms previously reported results. A trading simulation implementing the committee classifier...

  1. Learning to REDUCE: A Reduced Electricity Consumption Prediction Ensemble

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Chelmis, Charalampos; Prasanna, Viktor

    2016-02-12

    Utilities use Demand Response (DR) to balance supply and demand in the electric grid by involving customers in efforts to reduce electricity consumption during peak periods. To implement and adapt DR under dynamically changing conditions of the grid, reliable prediction of reduced consumption is critical. However, despite the wealth of research on electricity consumption prediction and DR being long in practice, the problem of reduced consumption prediction remains largely un-addressed. In this paper, we identify unique computational challenges associated with the prediction of reduced consumption and contrast this to that of normal consumption and DR baseline prediction.We propose a novel ensemble model that leverages different sequences of daily electricity consumption on DR event days as well as contextual attributes for reduced consumption prediction. We demonstrate the success of our model on a large, real-world, high resolution dataset from a university microgrid comprising of over 950 DR events across a diverse set of 32 buildings. Our model achieves an average error of 13.5%, an 8.8% improvement over the baseline. Our work is particularly relevant for buildings where electricity consumption is not tied to strict schedules. Our results and insights should prove useful to the researchers and practitioners working in the sustainable energy domain.

  2. CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms

    Science.gov (United States)

    Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

    2018-04-01

    CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.

  3. A Model of the Smooth Pursuit Eye Movement with Prediction and Learning

    Directory of Open Access Journals (Sweden)

    Davide Zambrano

    2010-01-01

    Full Text Available Smooth pursuit is one of the five main eye movements in humans, consisting of tracking a steadily moving visual target. Smooth pursuit is a good example of a sensory-motor task that is deeply based on prediction: tracking a visual target is not possible by correcting the error between the eye and the target position or velocity with a feedback loop, but it is only possible by predicting the trajectory of the target. This paper presents a model of smooth pursuit based on prediction and learning. It starts from amodel of the neuro-physiological system proposed by Shibata and Schaal (Shibata et al., Neural Networks, vol. 18, pp. 213-224, 2005. The learning component added here decreases the prediction time in the case of target dynamics already experienced by the system. In the implementation described here, the convergence time is, after the learning phase, 0.8 s.

  4. Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning

    Science.gov (United States)

    Kastner, Lucas; Kube, Jana; Villringer, Arno; Neumann, Jane

    2017-01-01

    Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning. PMID:29163004

  5. Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning

    Directory of Open Access Journals (Sweden)

    Lucas Kastner

    2017-10-01

    Full Text Available Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1 Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2 Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3 Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning.

  6. From "five" to 5 for 5 minutes: Arabic number transcoding as a short, specific, and sensitive screening tool for mathematics learning difficulties.

    Science.gov (United States)

    Moura, Ricardo; Lopes-Silva, Júlia Beatriz; Vieira, Laura Rodrigues; Paiva, Giulia Moreira; Prado, Ana Carolina de Almeida; Wood, Guilherme; Haase, Vitor Geraldi

    2015-02-01

    Number transcoding (e.g., writing 29 when hearing "twenty-nine") is one of the most basic numerical abilities required in daily life and is paramount for mathematics achievement. The aim of this study is to investigate psychometric properties of an Arabic number-writing task and its capacity to identify children with mathematics difficulties. We assessed 786 children (55% girls) from first to fourth grades, who were classified as children with mathematics difficulties (n = 103) or controls (n = 683). Although error rates were low, the task presented adequate internal consistency (0.91). Analyses revealed effective diagnostic accuracy in first and second school grades (specificity equals to 0.67 and 0.76 respectively, and sensitivity equals to 0.70 and 0.88 respectively). Moreover, items tapping the understanding of place-value syntax were the most sensitive to mathematics achievement. Overall, we propose that number transcoding is a useful tool for the assessment of mathematics abilities in early elementary school. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  7. Action-outcome learning and prediction shape the window of simultaneity of audiovisual outcomes.

    Science.gov (United States)

    Desantis, Andrea; Haggard, Patrick

    2016-08-01

    To form a coherent representation of the objects around us, the brain must group the different sensory features composing these objects. Here, we investigated whether actions contribute in this grouping process. In particular, we assessed whether action-outcome learning and prediction contribute to audiovisual temporal binding. Participants were presented with two audiovisual pairs: one pair was triggered by a left action, and the other by a right action. In a later test phase, the audio and visual components of these pairs were presented at different onset times. Participants judged whether they were simultaneous or not. To assess the role of action-outcome prediction on audiovisual simultaneity, each action triggered either the same audiovisual pair as in the learning phase ('predicted' pair), or the pair that had previously been associated with the other action ('unpredicted' pair). We found the time window within which auditory and visual events appeared simultaneous increased for predicted compared to unpredicted pairs. However, no change in audiovisual simultaneity was observed when audiovisual pairs followed visual cues, rather than voluntary actions. This suggests that only action-outcome learning promotes temporal grouping of audio and visual effects. In a second experiment we observed that changes in audiovisual simultaneity do not only depend on our ability to predict what outcomes our actions generate, but also on learning the delay between the action and the multisensory outcome. When participants learned that the delay between action and audiovisual pair was variable, the window of audiovisual simultaneity for predicted pairs increased, relative to a fixed action-outcome pair delay. This suggests that participants learn action-based predictions of audiovisual outcome, and adapt their temporal perception of outcome events based on such predictions. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  8. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

    Science.gov (United States)

    Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

    2015-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

  9. Statistical learning for predictive targeting in online advertising

    DEFF Research Database (Denmark)

    Fruergaard, Bjarne Ørum

    The focus in this thesis is investigation of machine learning methods with applications in computational advertising. Computational advertising is the broad discipline of building systems which can reach audiences browsing the Internet with targeted advertisements. At the core of such systems......, an international online advertising technology partner. This also means that the analyses and methods in this work are developed with particular use-cases within Adform in mind and thus need also to be applicable in Adform’s technology stack. This implies extra thought on scalability and performance...... application in real-time bidding ad exchanges, where each advertiser is given a chance to place bids for showing their ad while the page loads, and the winning bid gets to display their banner. The contributions of this thesis entail application of a hybrid model of explicit and latent features for learning...

  10. 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)

  11. A Machine Learning Perspective on Predictive Coding with PAQ

    OpenAIRE

    Knoll, Byron; de Freitas, Nando

    2011-01-01

    PAQ8 is an open source lossless data compression algorithm that currently achieves the best compression rates on many benchmarks. This report presents a detailed description of PAQ8 from a statistical machine learning perspective. It shows that it is possible to understand some of the modules of PAQ8 and use this understanding to improve the method. However, intuitive statistical explanations of the behavior of other modules remain elusive. We hope the description in this report will be a sta...

  12. Learning Latent Variable and Predictive Models of Dynamical Systems

    Science.gov (United States)

    2009-10-01

    Huijbregts. The ICSI RT07s Speaker Diarization System. Springer-Verlag, 2008. 4.5 [57] Gal Elidan and Nir Friedman. Learning the dimensionality of hidden...13, 435 and a test set of size 1, 771. VOWEL: This data set consists of multiple utterances of a particular Japanese vowel by nine male speakers . We...classification based on cultural style [51]; audio diarization , i.e., extraction of speech segments in long audio signals from background sounds [52]; audio

  13. Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

    Science.gov (United States)

    Gilra, Aditya; Gerstner, Wulfram

    2017-11-27

    The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

  14. The efficiency of the RULES-4 classification learning algorithm in predicting the density of agents

    Directory of Open Access Journals (Sweden)

    Ziad Salem

    2014-12-01

    Full Text Available Learning is the act of obtaining new or modifying existing knowledge, behaviours, skills or preferences. The ability to learn is found in humans, other organisms and some machines. Learning is always based on some sort of observations or data such as examples, direct experience or instruction. This paper presents a classification algorithm to learn the density of agents in an arena based on the measurements of six proximity sensors of a combined actuator sensor units (CASUs. Rules are presented that were induced by the learning algorithm that was trained with data-sets based on the CASU’s sensor data streams collected during a number of experiments with “Bristlebots (agents in the arena (environment”. It was found that a set of rules generated by the learning algorithm is able to predict the number of bristlebots in the arena based on the CASU’s sensor readings with satisfying accuracy.

  15. Human dorsal striatum encodes prediction errors during observational learning of instrumental actions.

    Science.gov (United States)

    Cooper, Jeffrey C; Dunne, Simon; Furey, Teresa; O'Doherty, John P

    2012-01-01

    The dorsal striatum plays a key role in the learning and expression of instrumental reward associations that are acquired through direct experience. However, not all learning about instrumental actions require direct experience. Instead, humans and other animals are also capable of acquiring instrumental actions by observing the experiences of others. In this study, we investigated the extent to which human dorsal striatum is involved in observational as well as experiential instrumental reward learning. Human participants were scanned with fMRI while they observed a confederate over a live video performing an instrumental conditioning task to obtain liquid juice rewards. Participants also performed a similar instrumental task for their own rewards. Using a computational model-based analysis, we found reward prediction errors in the dorsal striatum not only during the experiential learning condition but also during observational learning. These results suggest a key role for the dorsal striatum in learning instrumental associations, even when those associations are acquired purely by observing others.

  16. Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning.

    Science.gov (United States)

    Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming

    2012-01-31

    Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

  17. Energy taxation difficulties

    International Nuclear Information System (INIS)

    Landsberg, H.H.

    1993-01-01

    This paper assesses what may be the underlying reasons for the Clinton administration's recent failure to pass the Btu Tax on energy sources and the current difficulties that this Administration is experiencing in acquiring nation wide consensus on a gasoline tax proposal. Two difficulties stand out - regional differences in climate and thus winter heating requirements, and the differences from state to state in transportation system preferences. The paper cites the positive aspects of energy taxation by noting the petroleum industry's efforts to develop a new less polluting reformulated gasoline

  18. A new approach for crude oil price prediction based on stream learning

    Directory of Open Access Journals (Sweden)

    Shuang Gao

    2017-01-01

    Full Text Available Crude oil is the world's leading fuel, and its prices have a big impact on the global environment, economy as well as oil exploration and exploitation activities. Oil price forecasts are very useful to industries, governments and individuals. Although many methods have been developed for predicting oil prices, it remains one of the most challenging forecasting problems due to the high volatility of oil prices. In this paper, we propose a novel approach for crude oil price prediction based on a new machine learning paradigm called stream learning. The main advantage of our stream learning approach is that the prediction model can capture the changing pattern of oil prices since the model is continuously updated whenever new oil price data are available, with very small constant overhead. To evaluate the forecasting ability of our stream learning model, we compare it with three other popular oil price prediction models. The experiment results show that our stream learning model achieves the highest accuracy in terms of both mean squared prediction error and directional accuracy ratio over a variety of forecast time horizons.

  19. Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

    Science.gov (United States)

    Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

    2017-12-01

    Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

  20. Students’ difficulties in probabilistic problem-solving

    Science.gov (United States)

    Arum, D. P.; Kusmayadi, T. A.; Pramudya, I.

    2018-03-01

    There are many errors can be identified when students solving mathematics problems, particularly in solving the probabilistic problem. This present study aims to investigate students’ difficulties in solving the probabilistic problem. It focuses on analyzing and describing students errors during solving the problem. This research used the qualitative method with case study strategy. The subjects in this research involve ten students of 9th grade that were selected by purposive sampling. Data in this research involve students’ probabilistic problem-solving result and recorded interview regarding students’ difficulties in solving the problem. Those data were analyzed descriptively using Miles and Huberman steps. The results show that students have difficulties in solving the probabilistic problem and can be divided into three categories. First difficulties relate to students’ difficulties in understanding the probabilistic problem. Second, students’ difficulties in choosing and using appropriate strategies for solving the problem. Third, students’ difficulties with the computational process in solving the problem. Based on the result seems that students still have difficulties in solving the probabilistic problem. It means that students have not able to use their knowledge and ability for responding probabilistic problem yet. Therefore, it is important for mathematics teachers to plan probabilistic learning which could optimize students probabilistic thinking ability.