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.

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.

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

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.

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…

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.

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.

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.

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…

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

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…

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...

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.

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.

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.

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.

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…

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…

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.

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.

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…

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…

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…

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…

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…

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.

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)

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.

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.

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.

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 ...

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.

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…

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.

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.

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…

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

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…

"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…

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

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.

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…

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.

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.

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…

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…

[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.

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

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.

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.

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...

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…

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.

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 ...

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.

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…

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…

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…

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…

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…

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.

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

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…

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.

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.

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.

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…

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.

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

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

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…

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,…

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.

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...

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)…

[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'.

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.

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...

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

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

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…

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.

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

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.

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.

Improving readability through extractive summarization for learners with reading difficulties

Directory of Open Access Journals (Sweden)

K. Nandhini

2013-11-01

Full Text Available In this paper, we describe the design and evaluation of extractive summarization approach to assist the learners with reading difficulties. As existing summarization approaches inherently assign more weights to the important sentences, our approach predicts the summary sentences that are important as well as readable to the target audience with good accuracy. We used supervised machine learning technique for summary extraction of science and social subjects in the educational text. Various independent features from the existing literature for predicting important sentences and proposed learner dependent features for predicting readable sentences are extracted from texts and are used for automatic classification. We performed both extrinsic and intrinsic evaluation on this approach and the intrinsic evaluation is carried out using F-measure and readability analysis. The extrinsic evaluation comprises of learner feedback using likert scale and the effect of assistive summary on improving readability for learners’ with reading difficulty using ANOVA. The results show significant improvement in readability for the target audience using assistive summary.

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.

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.

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…

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.

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…

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.

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.

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.

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.

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...

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.…

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).

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.

[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.

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

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.

Review of student difficulties in upper-level quantum mechanics

Directory of Open Access Journals (Sweden)

Chandralekha Singh

2015-09-01

Full Text Available [This paper is part of the Focused Collection on Upper Division Physics Courses.] Learning advanced physics, in general, is challenging not only due to the increased mathematical sophistication but also because one must continue to build on all of the prior knowledge acquired at the introductory and intermediate levels. In addition, learning quantum mechanics can be especially challenging because the paradigms of classical mechanics and quantum mechanics are very different. Here, we review research on student reasoning difficulties in learning upper-level quantum mechanics and research on students’ problem-solving and metacognitive skills in these courses. Some of these studies were multiuniversity investigations. The investigations suggest that there is large diversity in student performance in upper-level quantum mechanics regardless of the university, textbook, or instructor, and many students in these courses have not acquired a functional understanding of the fundamental concepts. The nature of reasoning difficulties in learning quantum mechanics is analogous to reasoning difficulties found via research in introductory physics courses. The reasoning difficulties were often due to overgeneralizations of concepts learned in one context to another context where they are not directly applicable. Reasoning difficulties in distinguishing between closely related concepts and in making sense of the formalism of quantum mechanics were common. We conclude with a brief summary of the research-based approaches that take advantage of research on student difficulties in order to improve teaching and learning of quantum mechanics.

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...

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.

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.

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…

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.

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.

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.

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…

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…

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.

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.

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.

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

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

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.

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 ...

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.

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

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.

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…

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).

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.

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,…

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…

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.

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.

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.

Introduction to machine learning

OpenAIRE

Baştanlar, Yalın; Özuysal, Mustafa

2014-01-01

The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning app...

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.

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

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...

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.

The Answering Process for Multiple-Choice Questions in Collaborative Learning: A Mathematical Learning Model Analysis

Science.gov (United States)

Nakamura, Yasuyuki; Nishi, Shinnosuke; Muramatsu, Yuta; Yasutake, Koichi; Yamakawa, Osamu; Tagawa, Takahiro

2014-01-01

In this paper, we introduce a mathematical model for collaborative learning and the answering process for multiple-choice questions. The collaborative learning model is inspired by the Ising spin model and the model for answering multiple-choice questions is based on their difficulty level. An intensive simulation study predicts the possibility of…

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…

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…

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...

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

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...

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…

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.

Working Memory and Distributed Vocabulary Learning.

Science.gov (United States)

Atkins, Paul W. B.; Baddeley, Alan D.

1998-01-01

Tested the hypothesis that individual differences in immediate-verbal-memory span predict success in second-language vocabulary acquisition. In the two-session study, adult subjects learned 56 English-Finnish translations. Tested one week later, subjects were less likely to remember those words they had difficulty learning, even though they had…

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.

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.

Working Memory in Students with Mathematical Difficulties

Science.gov (United States)

Nur, I. R. D.; Herman, T.; Ningsih, S.

2018-04-01

Learning process is the activities that has important role because this process is one of the all factors that establish students success in learning. oftentimes we find so many students get the difficulties when they study mathematics. This condition is not only because of the outside factor but also it comes from the inside. The purpose of this research is to analyze and give the representation how students working memory happened in physical education students for basic statistics subjects which have mathematical difficulties. The subjects are 4 students which have a mathematical difficulties. The research method is case study and when the describe about students working memory are explanated deeply with naturalistic observation. Based on this research, it was founded that 4 students have a working memory deficit in three components. The components are phonological loop, visuospatial sketchpad, dan episodic buffer.

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…

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

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.

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.

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

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.

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…

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 ...

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).

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.

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.

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…

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.

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

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

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…

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.

[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.

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.

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.

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.

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

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...

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…

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…

[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.

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.

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.

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.

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.

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…

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 (a_max/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.

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

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…

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…

Grammatical Templates: Improving Text Difficulty Evaluation for Language Learners

OpenAIRE

Wang, Shuhan; Andersen, Erik

2016-01-01

Language students are most engaged while reading texts at an appropriate difficulty level. However, existing methods of evaluating text difficulty focus mainly on vocabulary and do not prioritize grammatical features, hence they do not work well for language learners with limited knowledge of grammar. In this paper, we introduce grammatical templates, the expert-identified units of grammar that students learn from class, as an important feature of text difficulty evaluation. Experimental clas...

Effectiveness of a self-regulated remedial program for handwriting difficulties.

Science.gov (United States)

Van Waelvelde, Hilde; De Roubaix, Amy; Steppe, Lien; Troubleyn, Evy; De Mey, Barbara; Dewitte, Griet; Debrabant, Julie; Van de Velde, Dominique

2017-09-01

Handwriting difficulties may have pervasive effects on a child's school performance. I Can! is a remedial handwriting program with a focus on self-regulated learning and applying motor learning principles combined with a behavioural approach. It is developed for typically developing children with handwriting problems. The study aim was to evaluate the program's effectiveness. Thirty-one children aged 7-8 year participated in a cross-over study. Handwriting quality and speed were repeatedly assessed by means of the Systematic Screening of Handwriting Difficulties test. Difficulties addressed were fluency in letter formation, fluency in letter connections, letter height, regularity of letter height, space between words, and line path. Mixed model analysis revealed improved quality of writing and speed for all children but significantly more improvement in handwriting quality for the children participating in the program. Although writing speed improved over time, no additional effects of the program occurred. 'I Can!' is found to be an effective instructive program to ameliorate handwriting quality in typically developing children with handwriting difficulties. The program's success was by a therapy burst of only 7 weeks focusing on the child's self-regulated learning capacities, within an individualized education plan according to their needs and goals.

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.

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.

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.

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…

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.

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.

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

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…

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…

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

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.

Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment.

Science.gov (United States)

Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Gerjets, Peter; Spüler, Martin

2017-01-01

In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.

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.

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.

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…

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.

Grammar-based feature generation for time-series prediction

CERN Document Server

De Silva, Anthony Mihirana

2015-01-01

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

Introduction to machine learning.

Science.gov (United States)

Baştanlar, Yalin; Ozuysal, Mustafa

2014-01-01

The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.

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

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.

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

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/.

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....

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.

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.

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

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...

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Science.gov (United States)

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

Students’ Learning Obstacles and Alternative Solution in Counting Rules Learning Levels Senior High School

Directory of Open Access Journals (Sweden)

M A Jatmiko

2017-12-01

Full Text Available The counting rules is a topic in mathematics senior high school. In the learning process, teachers often find students who have difficulties in learning this topic. Knowing the characteristics of students' learning difficulties and analyzing the causes is important for the teacher, as an effort in trying to reflect the learning process and as a reference in constructing alternative learning solutions which appropriate to anticipate students’ learning obstacles. This study uses qualitative methods and involves 70 students of class XII as research subjects. The data collection techniques used in this study is diagnostic test instrument about learning difficulties in counting rules, observation, and interview. The data used to know the learning difficulties experienced by students, the causes of learning difficulties, and to develop alternative learning solutions. From the results of data analysis, the results of diagnostic tests researcher found some obstacles faced by students, such as students get confused in describing the definition, students difficulties in understanding the procedure of solving multiplication rules. Based on those problems, researcher analyzed the causes of these difficulties and make hypothetical learning trajectory as an alternative solution in counting rules learning.

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.

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…

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

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).

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.

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.

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.

Conservation Abilities, Visuospatial Skills, and Numerosity Processing Speed: Association With Math Achievement and Math Difficulties in Elementary School Children.

Science.gov (United States)

Lambert, Katharina; Spinath, Birgit

The aim of the present study was to investigate the associations between elementary school children's mathematical achievement and their conservation abilities, visuospatial skills, and numerosity processing speed. We also assessed differences in these abilities between children with different types of learning problems. In Study 1 ( N = 229), we investigated second to fourth graders and in Study 2 ( N = 120), third and fourth graders. Analyses revealed significant contributions of numerosity processing speed and visuospatial skills to math achievement beyond IQ. Conservation abilities were predictive in Study 1 only. Children with math difficulties showed lower visuospatial skills and conservation abilities than children with typical achievement levels and children with reading and/or spelling difficulties, whereas children with combined difficulties explicitly showed low conservation abilities. These findings provide further evidence for the relations between children's math skills and their visuospatial skills, conservation abilities, and processing speed and contribute to the understanding of deficits that are specific to mathematical difficulties.

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

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.

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

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…

Students’ difficulties in solving linear equation problems

Science.gov (United States)

Wati, S.; Fitriana, L.; Mardiyana

2018-03-01

A linear equation is an algebra material that exists in junior high school to university. It is a very important material for students in order to learn more advanced mathematics topics. Therefore, linear equation material is essential to be mastered. However, the result of 2016 national examination in Indonesia showed that students’ achievement in solving linear equation problem was low. This fact became a background to investigate students’ difficulties in solving linear equation problems. This study used qualitative descriptive method. An individual written test on linear equation tasks was administered, followed by interviews. Twenty-one sample students of grade VIII of SMPIT Insan Kamil Karanganyar did the written test, and 6 of them were interviewed afterward. The result showed that students with high mathematics achievement donot have difficulties, students with medium mathematics achievement have factual difficulties, and students with low mathematics achievement have factual, conceptual, operational, and principle difficulties. Based on the result there is a need of meaningfulness teaching strategy to help students to overcome difficulties in solving linear equation problems.

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...

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.

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.

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

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

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...

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.

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.

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,…

Evidence-based interventions for reading and language difficulties: creating a virtuous circle.

Science.gov (United States)

Snowling, Margaret J; Hulme, Charles

2011-03-01

BACKGROUND. Children may experience two very different forms of reading problem: decoding difficulties (dyslexia) and reading comprehension difficulties. Decoding difficulties appear to be caused by problems with phonological (speech sound) processing. Reading comprehension difficulties in contrast appear to be caused by problems with 'higher level' language difficulties including problems with semantics (including deficient knowledge of word meanings) and grammar (knowledge of morphology and syntax). AIMS. We review evidence concerning the nature, causes of, and treatments for children's reading difficulties. We argue that any well-founded educational intervention must be based on a sound theory of the causes of a particular form of learning difficulty, which in turn must be based on an understanding of how a given skill is learned by typically developing children. Such theoretically motivated interventions should in turn be evaluated in randomized controlled trials (RCTs) to establish whether they are effective, and for whom. RESULTS. There is now considerable evidence showing that phonologically based interventions are effective in ameliorating children's word level decoding difficulties, and a smaller evidence base showing that reading and oral language (OL) comprehension difficulties can be ameliorated by suitable interventions to boost vocabulary and broader OL skills. CONCLUSIONS. The process of developing theories about the origins of children's educational difficulties and evaluating theoretically motivated treatments in RCTs, produces a 'virtuous circle' whereby theory informs practice, and the evaluation of effective interventions in turn feeds back to inform and refine theories about the nature and causes of children's reading and language difficulties. ©2010 The British Psychological Society.

Response selection difficulty modulates the behavioral impact of rapidly learnt action effects.

Directory of Open Access Journals (Sweden)

Uta eWolfensteller

2014-12-01

Full Text Available It is well established that we can pick up action effect associations when acting in a free-choice intentional mode. However, it is less clear whether and when action effect associations are learnt and actually affect behavior if we are acting in a forced-choice mode, applying a specific stimulus-response (S-R rule. In the present study, we investigated whether response selection difficulty imposed by S-R rules influences the initial rapid learning and the behavioral expression of previously learnt but weakly practiced action effect associations when those are re-activated by effect exposure. Experiment 1 showed that the rapid acquisition of action effect associations is not directly influenced by response selection difficulty. By contrast, the behavioral expression of re-activated action effect associations is prevented when actions are directly activated by highly over-learnt response cues and thus response selection difficulty is low. However, all three experiments showed that if response selection difficulty is sufficiently high during re-activation, the same action effect associations do influence behavior. Experiment 2 and 3 revealed that the effect of response selection difficulty cannot be fully reduced to giving action effects more time to prime an action, but seems to reflect competition during response selection. Finally, the present data suggest that when multiple novel rules are rapidly learnt in succession, which requires a lot of flexibility, action effect associations continue to influence behavior only if response selection difficulty is sufficiently high. Thus, response selection difficulty might modulate the impact of experiencing multiple learning episodes on action effect expression and learning, possibly via inducing different strategies.

Workplace bullying and sleep difficulties: a 2-year follow-up study.

Science.gov (United States)

Hansen, Ase Marie; Hogh, Annie; Garde, Anne Helene; Persson, Roger

2014-04-01

The aims of the present study were to investigate whether being subjected to bullying and witnessing bullying at the workplace was associated with concurrent sleep difficulties, whether frequently bullied/witnesses have more sleep difficulties than occasionally bullied/witnesses, and whether there were associations between being subjected to bullying or witnessing bullying at the workplace and subsequent sleep difficulties. A total of 3,382 respondents (67 % women and 33 % men) completed a baseline questionnaire about their psychosocial work environment and health. The overall response rate was 46 %. At follow-up 2 years later, 1671 of those responded to a second questionnaire (49 % of the 3,382 respondents at baseline). Sleep difficulties were measured in terms of disturbed sleep, awakening problems, and poor quality of sleep. Bullied persons and witnesses reported more sleep difficulties than those who were neither bullied nor witnesses to bullying at baseline. Frequently bullied/witnesses reported more sleep difficulties than respondents who were occasionally bullied or witnessing bullying at baseline. Further, odds ratios for subsequent sleep difficulties were increased among the occasionally bullied, but not among witnesses. However, the associations weakened when adjusting for sleep difficulties at baseline. Being subjected to occasional bullying at baseline was predictive of subsequent sleep difficulties. Witnessing bullying at baseline did not predict sleep difficulties at follow-up.

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

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.

The Use of Open-Ended Problem-Based Learning Scenarios in an Interdisciplinary Biotechnology Class: Evaluation of a Problem-Based Learning Course Across Three Years

Directory of Open Access Journals (Sweden)

Todd R. Steck

2012-02-01

Full Text Available Use of open-ended Problem-Based Learning (PBL in biology classrooms has been limited by the difficulty in designing problem scenarios such that the content learned in a course can be predicted and controlled, the lack of familiarity of this method of instruction by faculty, and the difficulty in assessment. Here we present the results of a study in which we developed a team-based interdisciplinary course that combined the fields of biology and civil engineering across three years. We used PBL scenarios as the only learning tool, wrote the problem scenarios, and developed the means to assess these courses and the results of that assessment. Our data indicates that PBL changed students’ perception of their learning in content knowledge and promoted a change in students’ learning styles. Although no  statistically significant improvement in problem-solving skills and critical thinking skills was observed, students reported substantial changes in their problem-based learning strategies and critical thinking skills.

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

Machine learning-based methods for prediction of linear B-cell epitopes.

Science.gov (United States)

Wang, Hsin-Wei; Pai, Tun-Wen

2014-01-01

B-cell epitope prediction facilitates immunologists in designing peptide-based vaccine, diagnostic test, disease prevention, treatment, and antibody production. In comparison with T-cell epitope prediction, the performance of variable length B-cell epitope prediction is still yet to be satisfied. Fortunately, due to increasingly available verified epitope databases, bioinformaticians could adopt machine learning-based algorithms on all curated data to design an improved prediction tool for biomedical researchers. Here, we have reviewed related epitope prediction papers, especially those for linear B-cell epitope prediction. It should be noticed that a combination of selected propensity scales and statistics of epitope residues with machine learning-based tools formulated a general way for constructing linear B-cell epitope prediction systems. It is also observed from most of the comparison results that the kernel method of support vector machine (SVM) classifier outperformed other machine learning-based approaches. Hence, in this chapter, except reviewing recently published papers, we have introduced the fundamentals of B-cell epitope and SVM techniques. In addition, an example of linear B-cell prediction system based on physicochemical features and amino acid combinations is illustrated in details.

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.

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.

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.

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.

Why do organizations not learn from incidents? Bottlenecks, causes and conditions for a failure to effectively learn

DEFF Research Database (Denmark)

Drupsteen, Linda; Hasle, Peter

2014-01-01

be studied.Difficulties were identified in multiple steps of the learning process, but most difficulties became visiblewhen planning actions, which is the phase that bridges the gap from incident investigation to actions forimprovement. The main causes for learning difficulties, which were identified...... learn. In sevenorganizations focus groups were held to discuss factors that according to employees contributed to thefailure to learn. By use of a model of the learning from incidents process, the steps, where difficulties forlearning arose, became visible, and the causes for these difficulties could...

Undergraduate Students’ Difficulties in Reading and Constructing Phylogenetic Tree

Science.gov (United States)

Sa'adah, S.; Tapilouw, F. S.; Hidayat, T.

2017-02-01

Representation is a very important communication tool to communicate scientific concepts. Biologists produce phylogenetic representation to express their understanding of evolutionary relationships. The phylogenetic tree is visual representation depict a hypothesis about the evolutionary relationship and widely used in the biological sciences. Phylogenetic tree currently growing for many disciplines in biology. Consequently, learning about phylogenetic tree become an important part of biological education and an interesting area for biology education research. However, research showed many students often struggle with interpreting the information that phylogenetic trees depict. The purpose of this study was to investigate undergraduate students’ difficulties in reading and constructing a phylogenetic tree. The method of this study is a descriptive method. In this study, we used questionnaires, interviews, multiple choice and open-ended questions, reflective journals and observations. The findings showed students experiencing difficulties, especially in constructing a phylogenetic tree. The students’ responds indicated that main reasons for difficulties in constructing a phylogenetic tree are difficult to placing taxa in a phylogenetic tree based on the data provided so that the phylogenetic tree constructed does not describe the actual evolutionary relationship (incorrect relatedness). Students also have difficulties in determining the sister group, character synapomorphy, autapomorphy from data provided (character table) and comparing among phylogenetic tree. According to them building the phylogenetic tree is more difficult than reading the phylogenetic tree. Finding this studies provide information to undergraduate instructor and students to overcome learning difficulties of reading and constructing phylogenetic tree.

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...

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…

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

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.

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...

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

Pupils' Difficulties: What Can the Teacher Do?

Science.gov (United States)

Dawson, C. J.

1978-01-01

Discusses how the teacher can deal with difficulties pupils of varying ages have in understanding certain chemical ideas. The article does not support using a Piagetian model for science courses in secondary schools. It suggests that Ausubel's learning theory is of much more use to the practicing teacher. (HM)

Mathematics Difficulties: Does One Approach Fit All?

Science.gov (United States)

Gifford, Sue; Rockliffe, Freda

2012-01-01

This article reviews the nature of learning difficulties in mathematics and, in particular, the nature and prevalence of dyscalculia, a condition that affects the acquisition of arithmetical skills. The evidence reviewed suggests that younger children (under the age of 10) often display a combination of problems, including minor physical…

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

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.

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.

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.

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.

Managing PBL difficulties in an industrial engineering and management program

Directory of Open Access Journals (Sweden)

Anabela Alves

2016-08-01

Full Text Available Purpose: Project-Based Learning (PBL is considered to be an active learning methodology which can be used to develop both technical and transversal competences in engineering programs. This methodology demands a great deal of work effort from the students and also from the teachers and it requires a meticulous plan and a well-managed project as well. These activities go far beyond the normal activities in traditional lectures, enabling to outpace the difficulties that spur along the way that may be both complex and demotivating. This methodology has been implemented in the Integrated Master Degree on Industrial Engineering and Management (IEM, at one public university in Portugal, since the 2004/2005 academic year. The aim of this paper is to identify and discuss the main difficulties of the implementation of PBL, mainly from the teachers’ perspectives. Additionally, some effective strategies will be recommended to overcome such difficulties. Design/methodology/approach: The perceptions of the teachers were collected through a survey based on six main themes. The participants in the study include eight teachers from the five courses of the first semester of the first year of the IEM program involved in the 2012/2013 and 2013/2014 editions. Findings: Integration of courses in the project; student assessment; growing number of students in each team and the need of physical spaces for them; and compartmentalized knowledge has emerged as the main difficulties. To overcome these difficulties some key strategies were recommended. Originality/value: A new perspective based on course teachers' views and experiences will deepen the understanding of the problems and provide inputs for the development of strategies that may improve the effectiveness of PBL and introduce changes for its successful implementation. These strategies are intended to be transferable to other contexts, as most of the problems and constraints are common to other active learning

Managing PBL difficulties in an industrial engineering and management program

International Nuclear Information System (INIS)

Alves, A.; Sousa, R.; Moreira, F.; Carvalho, M.A.; Cardoso, E.; Pimenta, P.; Malheiro, T.; Brito, I.; Fernandes, S.; Mesquita, D.

2016-01-01

Project-Based Learning (PBL) is considered to be an active learning methodology which can be used to develop both technical and transversal competences in engineering programs. This methodology demands a great deal of work effort from the students and also from the teachers and it requires a meticulous plan and a well-managed project as well. These activities go far beyond the normal activities in traditional lectures, enabling to outpace the difficulties that spur along the way that may be both complex and demotivating. This methodology has been implemented in the Integrated Master Degree on Industrial Engineering and Management (IEM), at one public university in Portugal, since the 2004/2005 academic year. The aim of this paper is to identify and discuss the main difficulties of the implementation of PBL, mainly from the teachers’ perspectives. Additionally, some effective strategies will be recommended to overcome such difficulties. Design/methodology/approach: The perceptions of the teachers were collected through a survey based on six main themes. The participants in the study include eight teachers from the five courses of the first semester of the first year of the IEM program involved in the 2012/2013 and 2013/2014 editions. Findings: Integration of courses in the project; student assessment; growing number of students in each team and the need of physical spaces for them; and compartmentalized knowledge has emerged as the main difficulties. To overcome these difficulties some key strategies were recommended. Originality/value: A new perspective based on course teachers' views and experiences will deepen the understanding of the problems and provide inputs for the development of strategies that may improve the effectiveness of PBL and introduce changes for its successful implementation. These strategies are intended to be transferable to other contexts, as most of the problems and constraints are common to other active learning approaches. (Author)

Managing PBL difficulties in an industrial engineering and management program

Energy Technology Data Exchange (ETDEWEB)

Alves, A.; Sousa, R.; Moreira, F.; Carvalho, M.A.; Cardoso, E.; Pimenta, P.; Malheiro, T.; Brito, I.; Fernandes, S.; Mesquita, D.

2016-07-01

Project-Based Learning (PBL) is considered to be an active learning methodology which can be used to develop both technical and transversal competences in engineering programs. This methodology demands a great deal of work effort from the students and also from the teachers and it requires a meticulous plan and a well-managed project as well. These activities go far beyond the normal activities in traditional lectures, enabling to outpace the difficulties that spur along the way that may be both complex and demotivating. This methodology has been implemented in the Integrated Master Degree on Industrial Engineering and Management (IEM), at one public university in Portugal, since the 2004/2005 academic year. The aim of this paper is to identify and discuss the main difficulties of the implementation of PBL, mainly from the teachers’ perspectives. Additionally, some effective strategies will be recommended to overcome such difficulties. Design/methodology/approach: The perceptions of the teachers were collected through a survey based on six main themes. The participants in the study include eight teachers from the five courses of the first semester of the first year of the IEM program involved in the 2012/2013 and 2013/2014 editions. Findings: Integration of courses in the project; student assessment; growing number of students in each team and the need of physical spaces for them; and compartmentalized knowledge has emerged as the main difficulties. To overcome these difficulties some key strategies were recommended. Originality/value: A new perspective based on course teachers' views and experiences will deepen the understanding of the problems and provide inputs for the development of strategies that may improve the effectiveness of PBL and introduce changes for its successful implementation. These strategies are intended to be transferable to other contexts, as most of the problems and constraints are common to other active learning approaches. (Author)

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.

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.

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.

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.

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…

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.

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

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.

Correlation between oro and hypopharynx shape and position with endotracheal intubation difficulty

Directory of Open Access Journals (Sweden)

Daher Rabadi

2014-12-01

Full Text Available Background and objective: Prediction of intubation difficulty can save patients from major preoperative morbidity or mortality. The purpose of this paper is to assess the correlation between oro-hypo pharynx position, neck size, and length with endotracheal intubation difficulty. The study also explored the diagnostic value of Friedman Staging System in prediction cases with difficult intubation. Method: The consecutive 500 ASA (I, II adult patients undergoing elective surgery were evaluated for oro and hypopharynx shape and position by modified Mallampati, Cormack and Lehane score as well as Friedman obstructive sleep apnea classification systems. Neck circumference and length were also measured. All cases were intubated by a single anesthesiologist who was uninformed of the above evaluation and graded intubation difficulty in visual analog score. Correlation between these findings and difficulty of intubation was assessed. Sensitivity, Specificity, Positive and Negative Predictive Values were also reported. Results: Cormack-Lehane grade had the strongest correlation with difficulty of intubation followed by Friedman palate position. Friedman palate position was the most sensitive and had higher positive and negative predictive values than modified Mallampati classification. Cormack-Lehane grade was found to be the most specific with the highest negative predictive value among the four studied classifications. Conclusion: Friedman palate position is a more useful, valuable and sensitive test compared to the modified Mallampati screening test for pre-anesthetic prediction of difficult intubation where its involvement in Multivariate model may raise the accuracy and diagnostic value of preoperative assessment of difficult airway.

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

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

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.

Sequence-based prediction of protein protein interaction using a deep-learning algorithm.

Science.gov (United States)

Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng

2017-05-25

Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

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

Mastoidectomy: anatomical parameters x surgical difficulty

Directory of Open Access Journals (Sweden)

Pereira Júnior, Anastácio Rodrigues

2012-01-01

Full Text Available Introduction: The lowered temporal meninges and/ or anterior sigmoid sinus are contiditions that can determine surgical difficulties in performing mastoidectomy. Objective: To correlate in the tomography the extent of the prolapse of the sigmoid sinus and of temporal meninges with the surgical difficulty in the mastoidectomy. Method: The tomographic measurements of prolapse sigmoid and of temporal meninges were correlated with the presence or non-presence of the surgical difficulty observed during the mastoidectomy procedure in patients with ostomatoiditis chronic (n=30. Form of study: Contemporary cohort transverse. Results: In 10 patients were observed surgical difficulty distributed as: due to prolapse of the sigmoid sinus (n = 2 or temporal meninges prolapse (n = 7 or both (n = 1. In patients in which the surgical difficulty was due to sigmoid sinus prolapse, the tomography distance of the anterior border of the sigmoid sinus to posterior wall of external auditory canal was lower than 9 mm. In patients in which surgical difficulty was due to temporal meninges prolapse, the tomographic distance to the upper plane of the petrous bone was 7 mm. Conclusion: The computerized tomography distance between the temporal meninges and the upper plane of the petrous bone 7 mm and the distance of the anterior border of the sigmoid sinus to posterior wall of external auditory canal was lower than 9 mm are predictive to the surgical difficulties to perform mastoidectomy.

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.

The use of machine learning and nonlinear statistical tools for ADME prediction.

Science.gov (United States)

Sakiyama, Yojiro

2009-02-01

Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future.

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)

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.

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…

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...

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…

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.

Theory of mind selectively predicts preschoolers' knowledge-based selective word learning.

Science.gov (United States)

Brosseau-Liard, Patricia; Penney, Danielle; Poulin-Dubois, Diane

2015-11-01

Children can selectively attend to various attributes of a model, such as past accuracy or physical strength, to guide their social learning. There is a debate regarding whether a relation exists between theory-of-mind skills and selective learning. We hypothesized that high performance on theory-of-mind tasks would predict preference for learning new words from accurate informants (an epistemic attribute), but not from physically strong informants (a non-epistemic attribute). Three- and 4-year-olds (N = 65) completed two selective learning tasks, and their theory-of-mind abilities were assessed. As expected, performance on a theory-of-mind battery predicted children's preference to learn from more accurate informants but not from physically stronger informants. Results thus suggest that preschoolers with more advanced theory of mind have a better understanding of knowledge and apply that understanding to guide their selection of informants. This work has important implications for research on children's developing social cognition and early learning. © 2015 The British Psychological Society.

Theory of mind selectively predicts preschoolers’ knowledge-based selective word learning

Science.gov (United States)

Brosseau-Liard, Patricia; Penney, Danielle; Poulin-Dubois, Diane

2015-01-01

Children can selectively attend to various attributes of a model, such as past accuracy or physical strength, to guide their social learning. There is a debate regarding whether a relation exists between theory-of-mind skills and selective learning. We hypothesized that high performance on theory-of-mind tasks would predict preference for learning new words from accurate informants (an epistemic attribute), but not from physically strong informants (a non-epistemic attribute). Three- and 4-year-olds (N = 65) completed two selective learning tasks, and their theory of mind abilities were assessed. As expected, performance on a theory-of-mind battery predicted children’s preference to learn from more accurate informants but not from physically stronger informants. Results thus suggest that preschoolers with more advanced theory of mind have a better understanding of knowledge and apply that understanding to guide their selection of informants. This work has important implications for research on children’s developing social cognition and early learning. PMID:26211504

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.

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.

Procedural learning and dyslexia.

Science.gov (United States)

Nicolson, R I; Fawcett, A J; Brookes, R L; Needle, J

2010-08-01

Three major 'neural systems', specialized for different types of information processing, are the sensory, declarative, and procedural systems. It has been proposed (Trends Neurosci., 30(4), 135-141) that dyslexia may be attributable to impaired function in the procedural system together with intact declarative function. We provide a brief overview of the increasing evidence relating to the hypothesis, noting that the framework involves two main claims: first that 'neural systems' provides a productive level of description avoiding the underspecificity of cognitive descriptions and the overspecificity of brain structural accounts; and second that a distinctive feature of procedural learning is its extended time course, covering from minutes to months. In this article, we focus on the second claim. Three studies-speeded single word reading, long-term response learning, and overnight skill consolidation-are reviewed which together provide clear evidence of difficulties in procedural learning for individuals with dyslexia, even when the tasks are outside the literacy domain. The educational implications of the results are then discussed, and in particular the potential difficulties that impaired overnight procedural consolidation would entail. It is proposed that response to intervention could be better predicted if diagnostic tests on the different forms of learning were first undertaken. 2010 John Wiley & Sons, Ltd.

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.

Early Identification of Reading Comprehension Difficulties

Science.gov (United States)

Catts, Hugh W.; Nielsen, Diane Corcoran; Bridges, Mindy Sittner; Liu, Yi-Syuan

2016-01-01

Most research on early identification of reading disabilities has focused on word reading problems and little attention has been given to reading comprehension difficulties. In this study, we investigated whether measures of language ability and/or response to language intervention in kindergarten uniquely predicted reading comprehension…

Current management for word finding difficulties by speech-language therapists in South African remedial schools.

Science.gov (United States)

de Rauville, Ingrid; Chetty, Sandhya; Pahl, Jenny

2006-01-01

Word finding difficulties frequently found in learners with language learning difficulties (Casby, 1992) are an integral part of Speech-Language Therapists' management role when working with learning disabled children. This study investigated current management for word finding difficulties by 70 Speech-Language Therapists in South African remedial schools. A descriptive survey design using a quantitative and qualitative approach was used. A questionnaire and follow-up focus group discussion were used to collect data. Results highlighted the use of the Renfrew Word Finding Scale (Renfrew, 1972, 1995) as the most frequently used formal assessment tool. Language sample analysis and discourse analysis were the most frequently used informal assessment procedures. Formal intervention programmes were generally not used. Phonetic, phonemic or phonological cueing were the most frequently used therapeutic strategies. The authors note strengths and raise concerns about current management for word finding difficulties in South African remedial schools, particularly in terms of bilingualism. Opportunities are highlighted regarding the development of assessment and intervention measures relevant to the diverse learning disabled population in South Africa.

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.

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…

Perceptual discrimination difficulty and familiarity in the Uncanny Valley: more like a "Happy Valley".

Science.gov (United States)

Cheetham, Marcus; Suter, Pascal; Jancke, Lutz

2014-01-01

The Uncanny Valley Hypothesis (UVH) predicts that greater difficulty perceptually discriminating between categorically ambiguous human and humanlike characters (e.g., highly realistic robot) evokes negatively valenced (i.e., uncanny) affect. An ABX perceptual discrimination task and signal detection analysis was used to examine the profile of perceptual discrimination (PD) difficulty along the UVH' dimension of human likeness (DHL). This was represented using avatar-to-human morph continua. Rejecting the implicitly assumed profile of PD difficulty underlying the UVH' prediction, Experiment 1 showed that PD difficulty was reduced for categorically ambiguous faces but, notably, enhanced for human faces. Rejecting the UVH' predicted relationship between PD difficulty and negative affect (assessed in terms of the UVH' familiarity dimension), Experiment 2 demonstrated that greater PD difficulty correlates with more positively valenced affect. Critically, this effect was strongest for the ambiguous faces, suggesting a correlative relationship between PD difficulty and feelings of familiarity more consistent with the metaphor happy valley. This relationship is also consistent with a fluency amplification instead of the hitherto proposed hedonic fluency account of affect along the DHL. Experiment 3 found no evidence that the asymmetry in the profile of PD along the DHL is attributable to a differential processing bias (cf. other-race effect), i.e., processing avatars at a category level but human faces at an individual level. In conclusion, the present data for static faces show clear effects that, however, strongly challenge the UVH' implicitly assumed profile of PD difficulty along the DHL and the predicted relationship between this and feelings of familiarity.

Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

Science.gov (United States)

Patel, Nihir; Wang, Jason T L

2015-10-01

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

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.

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.

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 ...

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.

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 ...

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.

Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery.

Directory of Open Access Journals (Sweden)

Rubén Armañanzas

Full Text Available Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE. Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

Science.gov (United States)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

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.

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

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.

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.

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.

Hebbian learning and predictive mirror neurons for actions, sensations and emotions.

Science.gov (United States)

Keysers, Christian; 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 how mirror neurons become a dynamic system that performs active inferences about the actions of others and allows joint actions despite sensorimotor delays. We explore how this system performs a projection of the self onto others, with egocentric biases to contribute to mind-reading. Finally, we argue that Hebbian learning predicts mirror-like neurons for sensations and emotions and review evidence for the presence of such vicarious activations outside the motor system.

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."

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

Science.gov (United States)

Pearce, Marcus T

2018-05-11

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

On-Line, Self-Learning, Predictive Tool for Determining Payload Thermal Response

Science.gov (United States)

Jen, Chian-Li; Tilwick, Leon

2000-01-01

This paper will present the results of a joint ManTech / Goddard R&D effort, currently under way, to develop and test a computer based, on-line, predictive simulation model for use by facility operators to predict the thermal response of a payload during thermal vacuum testing. Thermal response was identified as an area that could benefit from the algorithms developed by Dr. Jeri for complex computer simulations. Most thermal vacuum test setups are unique since no two payloads have the same thermal properties. This requires that the operators depend on their past experiences to conduct the test which requires time for them to learn how the payload responds while at the same time limiting any risk of exceeding hot or cold temperature limits. The predictive tool being developed is intended to be used with the new Thermal Vacuum Data System (TVDS) developed at Goddard for the Thermal Vacuum Test Operations group. This model can learn the thermal response of the payload by reading a few data points from the TVDS, accepting the payload's current temperature as the initial condition for prediction. The model can then be used as a predictive tool to estimate the future payload temperatures according to a predetermined shroud temperature profile. If the error of prediction is too big, the model can be asked to re-learn the new situation on-line in real-time and give a new prediction. Based on some preliminary tests, we feel this predictive model can forecast the payload temperature of the entire test cycle within 5 degrees Celsius after it has learned 3 times during the beginning of the test. The tool will allow the operator to play "what-if' experiments to decide what is his best shroud temperature set-point control strategy. This tool will save money by minimizing guess work and optimizing transitions as well as making the testing process safer and easier to conduct.

Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

Science.gov (United States)

Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

2018-03-01

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

Difficulty Systematized Evaluation of Vocal Folds Exposure in Microsurgery of the Larynx

Directory of Open Access Journals (Sweden)

Ballin, Annelyse Cristine

2010-09-01

Full Text Available Introduction: Several studies addressing preoperative factors that predict difficulty of endotracheal intubation graduated by anesthesiologists, for the scale of the Cormack-Lehane. These parameters were evaluated for the difficulty of location of the laryngoscope in microsurgery of the larynx. There is not a standard scale of difficulty targeted to surgeons of the larynx. Objective: Create a standard scale of difficulty leasing the laryngoscope during microsurgery of the larynx, with a focus on exposure of the vocal folds (vocal cords to evaluate which clinical parameters predict difficulty of exposure of their vocal folds and verify the improvement of laryngeal exposure with the hanger of the laryngoscope. Method: A prospective randomized study, 57 patients undergoing laryngeal microsurgery. The preoperative parameters were evaluated: three epidemiological data, two of history and 13 physical examination. Intraoperatively: the anesthesiologist evaluated the Cormack-Lehane score and the surgeon evaluated according to the proposed scale, before and after placement of the hanger. Results and Conclusion: Several parameters showed sensitivity, specificity, positive predictive value for high inadequate exposure of the larynx. But only distance hiomentual <6.05 cm (p = 0.003 and 2 classes of Cormack-Lehane (p = 0.04 with statistical significance and high sensitivity of 100% and 81% respectively. The use of the hanger of laryngoscope laryngeal exposure improved significantly (p = 0.04. The proposed scale standardizes the visualization and grades the difficulty of exposure of their vocal folds, facilitating comparisons between studies and communication between otolaryngologists.

PEDLA: predicting enhancers with a deep learning-based algorithmic framework.

Science.gov (United States)

Liu, Feng; Li, Hao; Ren, Chao; Bo, Xiaochen; Shu, Wenjie

2016-06-22

Transcriptional enhancers are non-coding segments of DNA that play a central role in the spatiotemporal regulation of gene expression programs. However, systematically and precisely predicting enhancers remain a major challenge. Although existing methods have achieved some success in enhancer prediction, they still suffer from many issues. We developed a deep learning-based algorithmic framework named PEDLA (https://github.com/wenjiegroup/PEDLA), which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues. We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 cells, and demonstrated that PEDLA framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction. We further extended PEDLA to iteratively learn from 22 training cell types/tissues. Our results showed that PEDLA manifested superior performance consistency in both training and independent test sets. On average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) of sensitivity and specificity across 22 training cell types/tissues, as well as 95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. Together, our work illustrates the power of harnessing state-of-the-art deep learning techniques to consistently identify regulatory elements at a genome-wide scale from massively heterogeneous data across diverse cell types/tissues.

Learning how to learn: Meta-learning strategies for the challenges of learning pharmacology.

Science.gov (United States)

Alton, Suzanne

2016-03-01

Nursing students have difficulty with pharmacology courses because of the complicated nomenclature and the difficulty of applying drug information to actual patient care. As part of a new pharmacology course being created, meta-learning strategies designed to diminish the difficulties of learning this difficult content were part of the course pedagogy. Strategies were demonstrated, reviewed in class, and implemented through homework assignments. The setting was an Academic Health Center's School of Nursing in the southern United States. Participants were third-year nursing students in an undergraduate nursing program. Surveys of students' opinions of learning gains were conducted at the end of the course over several semesters. In addition, pharmacology scores on a standardized exit exam were compared prior to implementing the course and after. Students reported learning dry material more easily, having greater confidence, and finding substantial value in the learning strategies. Students indicated the most helpful strategies, in descending order, as follows: making charts to compare and contrast drugs and drug classes, writing out drug flash cards, making or reviewing creative projects, prioritizing information, making or using visual study aids, and using time and repetition to space learning. Implementation of the new course improved pharmacology scores on a standardized exit exam from 67.0% to 74.3%. Overall response to learning strategies was positive, and the increase in the pharmacology standardized exit exam scores demonstrated the effectiveness of this instructional approach. Copyright © 2016 Elsevier Ltd. All rights reserved.

Arithmetic difficulties in children with cerebral palsy are related to executive function and working memory

NARCIS (Netherlands)

Jenks, K.M.; Moor, J.M.H. de; Lieshout, E.C.D.M. van

2009-01-01

Background - Although it is believed that children with cerebral palsy are at high risk for learning difficulties and arithmetic difficulties in particular, few studies have investigated this issue. Methods - Arithmetic ability was longitudinally assessed in children with cerebral palsy in special

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.

Analysis of junior high school students' difficulty in resolving rectangular conceptual problems

Science.gov (United States)

Utami, Aliksia Kristiana Dwi; Mardiyana, Pramudya, Ikrar

2017-08-01

Geometry is one part of the mathematics that must be learned in school and it has important effects on the development of creative thinking skills of learners, but in fact, there are some difficulties experienced by the students. This research focuses on analysis difficulty in resolving rectangular conceptual problems among junior high school students in every creative thinking skills level. This research used a descriptive method aimed to identify the difficulties and cause of the difficulties experienced by five students. The difficulties are associated with rectangular shapes and related problems. Data collection was done based on students' work through test, interview, and observations. The result revealed that student' difficulties in understanding the rectangular concept can be found at every creative thinking skills level. The difficulties are identifying the objects rectangular in the daily life except for a rectangle and square, analyzing the properties of rectangular shapes, and seeing the interrelationships between figures.

Divided attention: an undesirable difficulty in memory retention.

Science.gov (United States)

Gaspelin, Nicholas; Ruthruff, Eric; Pashler, Harold

2013-10-01

How can we improve memory retention? A large body of research has suggested that difficulty encountered during learning, such as when practice sessions are distributed rather than massed, can enhance later memory performance (see R. A. Bjork & E. L. Bjork, 1992). Here, we investigated whether divided attention during retrieval practice can also constitute a desirable difficulty. Following two initial study phases and one test phase with Swahili-English word pairs (e.g., vuvi-snake), we manipulated whether items were tested again under full or divided attention. Two days later, participants were brought back for a final cued-recall test (e.g., vuvi-?). Across three experiments (combined N = 122), we found no evidence that dividing attention while practicing retrieval enhances memory retention. This finding raises the question of why many types of difficulty during practice do improve long-term retention, but dividing attention does not.

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…

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.

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.

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.

A Case Study on Learning Difficulties and Corresponding Supports for Learning in cMOOCs | Une étude de cas sur les difficultés d’apprentissage et le soutien correspondant pour l’apprentissage dans les cMOOC

Directory of Open Access Journals (Sweden)

Shuang Li

2016-04-01

Full Text Available 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 presents a case study on student difficulties and support requirements at the beginning of a cMOOC. Content analysis of messages posted by learners and instructors in four main online course learning spaces including Moodle, blogs, Facebook and Twitter was conducted. Three questions are explored in this paper: (1 What kinds of difficulties do learners encounter at the beginning of a cMOOC?; (2 Which of these difficulties are typical for most learners?; and (3 How are these difficulties responded to and supported in the cMOOC environment? Based on the research results of this study, we provide some reflections on learning support for cMOOCs and a discussion of the research itself in the last part of the paper. Les cMOOC, qui s’appuient sur une théorie pédagogique connectiviste, soulèvent des défis pour les apprenants ainsi que des occasions de questionnement de soi. Des études préalables ont démontré que les apprenants des cMOOC peuvent connaître des difficultés d’apprentissage, mais ces études n’offrent pas d’exploration empirique en profondeur des difficultés des élèves ni des stratégies de soutien. Cet article présente une étude de cas sur les difficultés des élèves et les besoins de soutien au début d’un cMOOC. On a procédé à l’analyse du contenu des messages publiés par les apprenants et les instructeurs dans les quatre principaux espaces en ligne pour l’apprentissage, c’est-à-dire Moodle, les blogues, Facebook et Twitter. Cet article explore trois questions : (1 Quels types de difficultés rencontrent les apprenants au début d’un cMOOC?; (2 Parmi ces difficult

The difficulties of teacher in teaching geometry for mental retardation students

Science.gov (United States)

Shomad, Z. A.; Kusmayadi, T. A.; Riyadi

2018-03-01

The purpose of this research is to find out the problems faced by teachers in teaching materials on mental retardation students. It focused on the difficulties faced by the teacher in teaching geometry. A qualitative method with field study approach used in this study. The subjects in this research are the teacher and mild mental retardation students. There are six teachers and six students involve as the subject which is chosen by purposive sampling. The data of this research is the observation and interview against teachers and mental retardation students. The data was analyzed qualitatively with Miles and Huberman steps. The results of this research show that mental retardation students have less attention to the materials, less special books or learning media props, difficult in the set the students, and the difficulty in choosing the material that suits the student needs and the condition of mental retardation students. There's not much pay attention to the children with special need, particularly mental retardation student. Thus, this study can help analyze the difficulties teachers so that learning math for mental retardation students more optimal.

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 ...

Predicting effects of noncoding variants with deep learning-based sequence model.

Science.gov (United States)

Zhou, Jian; Troyanskaya, Olga G

2015-10-01

Identifying functional effects of noncoding variants is a major challenge in human genetics. To predict the noncoding-variant effects de novo from sequence, we developed a deep learning-based algorithmic framework, DeepSEA (http://deepsea.princeton.edu/), that directly learns a regulatory sequence code from large-scale chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. We further used this capability to improve prioritization of functional variants including expression quantitative trait loci (eQTLs) and disease-associated variants.

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.

Opponent appetitive-aversive neural processes underlie predictive learning of pain relief.

Science.gov (United States)

Seymour, Ben; O'Doherty, John P; Koltzenburg, Martin; Wiech, Katja; Frackowiak, Richard; Friston, Karl; Dolan, Raymond

2005-09-01

Termination of a painful or unpleasant event can be rewarding. However, whether the brain treats relief in a similar way as it treats natural reward is unclear, and the neural processes that underlie its representation as a motivational goal remain poorly understood. We used fMRI (functional magnetic resonance imaging) to investigate how humans learn to generate expectations of pain relief. Using a pavlovian conditioning procedure, we show that subjects experiencing prolonged experimentally induced pain can be conditioned to predict pain relief. This proceeds in a manner consistent with contemporary reward-learning theory (average reward/loss reinforcement learning), reflected by neural activity in the amygdala and midbrain. Furthermore, these reward-like learning signals are mirrored by opposite aversion-like signals in lateral orbitofrontal cortex and anterior cingulate cortex. This dual coding has parallels to 'opponent process' theories in psychology and promotes a formal account of prediction and expectation during pain.

Arithmetic difficulties in children with cerebral palsy are related to executive function and working memory.

NARCIS (Netherlands)

Jenks, K.M.; Moor, J.M.H. de; Lieshout, E.C. van

2009-01-01

BACKGROUND: Although it is believed that children with cerebral palsy are at high risk for learning difficulties and arithmetic difficulties in particular, few studies have investigated this issue. METHODS: Arithmetic ability was longitudinally assessed in children with cerebral palsy in special (n

Decision tree-based learning to predict patient controlled analgesia consumption and readjustment

Directory of Open Access Journals (Sweden)

Hu Yuh-Jyh

2012-11-01

Full Text Available Abstract Background Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA, which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment. Methods The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction. Results The prediction accuracies of total analgesic consumption (continuous dose and PCA dose and PCA analgesic requirement (PCA dose only by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works. Conclusion This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA

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.

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....

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.

Learning Convex Inference of Marginals

OpenAIRE

Domke, Justin

2012-01-01

Graphical models trained using maximum likelihood are a common tool for probabilistic inference of marginal distributions. However, this approach suffers difficulties when either the inference process or the model is approximate. In this paper, the inference process is first defined to be the minimization of a convex function, inspired by free energy approximations. Learning is then done directly in terms of the performance of the inference process at univariate marginal prediction. The main ...

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.

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.

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.

Resting-state qEEG predicts rate of second language learning in adults.

Science.gov (United States)

Prat, Chantel S; Yamasaki, Brianna L; Kluender, Reina A; Stocco, Andrea

2016-01-01

Understanding the neurobiological basis of individual differences in second language acquisition (SLA) is important for research on bilingualism, learning, and neural plasticity. The current study used quantitative electroencephalography (qEEG) to predict SLA in college-aged individuals. Baseline, eyes-closed resting-state qEEG was used to predict language learning rate during eight weeks of French exposure using an immersive, virtual scenario software. Individual qEEG indices predicted up to 60% of the variability in SLA, whereas behavioral indices of fluid intelligence, executive functioning, and working-memory capacity were not correlated with learning rate. Specifically, power in beta and low-gamma frequency ranges over right temporoparietal regions were strongly positively correlated with SLA. These results highlight the utility of resting-state EEG for studying the neurobiological basis of SLA in a relatively construct-free, paradigm-independent manner. Published by Elsevier Inc.

Multi-population genomic prediction using a multi-task Bayesian learning model.

Science.gov (United States)

Chen, Liuhong; Li, Changxi; Miller, Stephen; Schenkel, Flavio

2014-05-03

Genomic prediction in multiple populations can be viewed as a multi-task learning problem where tasks are to derive prediction equations for each population and multi-task learning property can be improved by sharing information across populations. The goal of this study was to develop a multi-task Bayesian learning model for multi-population genomic prediction with a strategy to effectively share information across populations. Simulation studies and real data from Holstein and Ayrshire dairy breeds with phenotypes on five milk production traits were used to evaluate the proposed multi-task Bayesian learning model and compare with a single-task model and a simple data pooling method. A multi-task Bayesian learning model was proposed for multi-population genomic prediction. Information was shared across populations through a common set of latent indicator variables while SNP effects were allowed to vary in different populations. Both simulation studies and real data analysis showed the effectiveness of the multi-task model in improving genomic prediction accuracy for the smaller Ayshire breed. Simulation studies suggested that the multi-task model was most effective when the number of QTL was small (n = 20), with an increase of accuracy by up to 0.09 when QTL effects were lowly correlated between two populations (ρ = 0.2), and up to 0.16 when QTL effects were highly correlated (ρ = 0.8). When QTL genotypes were included for training and validation, the improvements were 0.16 and 0.22, respectively, for scenarios of the low and high correlation of QTL effects between two populations. When the number of QTL was large (n = 200), improvement was small with a maximum of 0.02 when QTL genotypes were not included for genomic prediction. Reduction in accuracy was observed for the simple pooling method when the number of QTL was small and correlation of QTL effects between the two populations was low. For the real data, the multi-task model achieved an

Performance monitoring in obsessive-compulsive undergraduates: Effects of task difficulty.

Science.gov (United States)

Riesel, Anja; Richter, Anika; Kaufmann, Christian; Kathmann, Norbert; Endrass, Tanja

2015-08-01

Both obsessive-compulsive disorder and subclinical obsessive-compulsive (OC) symptoms seem to be associated with hyperactive error-related brain activity. The current study examined performance monitoring in subjects with subclinical OC symptoms using a new task with different levels of difficulty. Nineteen subjects with high and 18 subjects with low OC characteristics performed a random dot cinematogram (RDC) task with three levels of difficulty. The high and low OC groups did not differ in error-related negativity (ERN), correct-related negativity (CRN) and performance irrespective of task difficulty. The amplitude of the ERN decreased with increasing difficulty whereas the magnitude of CRN did not vary. ERN and CRN approached in size and topography with increasing difficulty, which suggests that errors and correct responses are processed more similarly. These results add to a growing number of studies that fail to replicate hyperactive performance monitoring in individuals with OC symptoms in task with higher difficulty or requiring learning. Together with these findings our results suggest that the relationship between OC symptoms and performance monitoring may be sensitive to type of task and task characteristics and cannot be observed in a RDC that differs from typically used tasks in difficulty and the amount of response-conflict. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

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…

Do sophisticated epistemic beliefs predict meaningful learning? Findings from a structural equation model of undergraduate biology learning

Science.gov (United States)

Lee, Silvia Wen-Yu; Liang, Jyh-Chong; Tsai, Chin-Chung

2016-10-01

This study investigated the relationships among college students' epistemic beliefs in biology (EBB), conceptions of learning biology (COLB), and strategies of learning biology (SLB). EBB includes four dimensions, namely 'multiple-source,' 'uncertainty,' 'development,' and 'justification.' COLB is further divided into 'constructivist' and 'reproductive' conceptions, while SLB represents deep strategies and surface learning strategies. Questionnaire responses were gathered from 303 college students. The results of the confirmatory factor analysis and structural equation modelling showed acceptable model fits. Mediation testing further revealed two paths with complete mediation. In sum, students' epistemic beliefs of 'uncertainty' and 'justification' in biology were statistically significant in explaining the constructivist and reproductive COLB, respectively; and 'uncertainty' was statistically significant in explaining the deep SLB as well. The results of mediation testing further revealed that 'uncertainty' predicted surface strategies through the mediation of 'reproductive' conceptions; and the relationship between 'justification' and deep strategies was mediated by 'constructivist' COLB. This study provides evidence for the essential roles some epistemic beliefs play in predicting students' learning.

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.

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

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...

Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model.

Science.gov (United States)

Aridhi, Sabeur; Sghaier, Haïtham; Zoghlami, Manel; Maddouri, Mondher; Nguifo, Engelbert Mephu

2016-01-01

Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. In this context, in silico methods of phenotypic prediction and genotype-phenotype relationship discovery are limited. In this work, we analyzed basal DNA repair proteins of most known proteome sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts this bacterial phenotype. We formulated the problem of predicting bacterial ionizing radiation resistance (IRR) as a multiple-instance learning (MIL) problem, and we proposed a novel approach for this purpose. We provide a MIL-based prediction system that classifies a bacterium to either IRRB or IRSB. The experimental results of the proposed system are satisfactory with 91.5% of successful predictions.

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…

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.

Science.gov (United States)

Xia, Zheng; Wu, Ling-Yun; Zhou, Xiaobo; Wong, Stephen T C

2010-09-13

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

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…

A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

International Nuclear Information System (INIS)

Tang, Ling; Yu, Lean; Wang, Shuai; Li, Jianping; Wang, Shouyang

2012-01-01

Highlights: ► A hybrid ensemble learning paradigm integrating EEMD and LSSVR is proposed. ► The hybrid ensemble method is useful to predict time series with high volatility. ► The ensemble method can be used for both one-step and multi-step ahead forecasting. - Abstract: In this paper, a novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EEMD) and least squares support vector regression (LSSVR) is proposed for nuclear energy consumption forecasting, based on the principle of “decomposition and ensemble”. This hybrid ensemble learning paradigm is formulated specifically to address difficulties in modeling nuclear energy consumption, which has inherently high volatility, complexity and irregularity. In the proposed hybrid ensemble learning paradigm, EEMD, as a competitive decomposition method, is first applied to decompose original data of nuclear energy consumption (i.e. a difficult task) into a number of independent intrinsic mode functions (IMFs) of original data (i.e. some relatively easy subtasks). Then LSSVR, as a powerful forecasting tool, is implemented to predict all extracted IMFs independently. Finally, these predicted IMFs are aggregated into an ensemble result as final prediction, using another LSSVR. For illustration and verification purposes, the proposed learning paradigm is used to predict nuclear energy consumption in China. Empirical results demonstrate that the novel hybrid ensemble learning paradigm can outperform some other popular forecasting models in both level prediction and directional forecasting, indicating that it is a promising tool to predict complex time series with high volatility and irregularity.

The Predictive Value of Ultrasound Learning Curves Across Simulated and Clinical Settings

DEFF Research Database (Denmark)

Madsen, Mette E; Nørgaard, Lone N; Tabor, Ann

2017-01-01

OBJECTIVES: The aim of the study was to explore whether learning curves on a virtual-reality (VR) sonographic simulator can be used to predict subsequent learning curves on a physical mannequin and learning curves during clinical training. METHODS: Twenty midwives completed a simulation-based tra......OBJECTIVES: The aim of the study was to explore whether learning curves on a virtual-reality (VR) sonographic simulator can be used to predict subsequent learning curves on a physical mannequin and learning curves during clinical training. METHODS: Twenty midwives completed a simulation......-based training program in transvaginal sonography. The training was conducted on a VR simulator as well as on a physical mannequin. A subgroup of 6 participants underwent subsequent clinical training. During each of the 3 steps, the participants' performance was assessed using instruments with established...... settings. RESULTS: A good correlation was found between time needed to achieve predefined performance levels on the VR simulator and the physical mannequin (Pearson correlation coefficient .78; P VR simulator correlated well to the clinical performance scores (Pearson...

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…

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.

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.

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.

Enhancing Expectations of Cooperative Learning Use through Initial Teacher Training

Science.gov (United States)

Duran Gisbert, David; Corcelles Seuba, Mariona; Flores Coll, Marta

2017-01-01

Despite its relevance and evidence support, Cooperative Learning (CL) is a challenge for all educational systems due to the difficulties in its implementation. The objective of this study is to identify the effect of Primary Education initial teacher training in the prediction of future CL use. Two groups of 44 and 45 students were conceptually…

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...

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…

Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

Science.gov (United States)

Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

2015-08-01

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

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

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.

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.

Efficient Prediction of Low-Visibility Events at Airports Using Machine-Learning Regression

Science.gov (United States)

Cornejo-Bueno, L.; Casanova-Mateo, C.; Sanz-Justo, J.; Cerro-Prada, E.; Salcedo-Sanz, S.

2017-11-01

We address the prediction of low-visibility events at airports using machine-learning regression. The proposed model successfully forecasts low-visibility events in terms of the runway visual range at the airport, with the use of support-vector regression, neural networks (multi-layer perceptrons and extreme-learning machines) and Gaussian-process algorithms. We assess the performance of these algorithms based on real data collected at the Valladolid airport, Spain. We also propose a study of the atmospheric variables measured at a nearby tower related to low-visibility atmospheric conditions, since they are considered as the inputs of the different regressors. A pre-processing procedure of these input variables with wavelet transforms is also described. The results show that the proposed machine-learning algorithms are able to predict low-visibility events well. The Gaussian process is the best algorithm among those analyzed, obtaining over 98% of the correct classification rate in low-visibility events when the runway visual range is {>}1000 m, and about 80% under this threshold. The performance of all the machine-learning algorithms tested is clearly affected in extreme low-visibility conditions ({algorithm performance in daytime and nighttime conditions, and for different prediction time horizons.

Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

Science.gov (United States)

Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

2015-01-01

Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

The evolutionary basis of human social learning.

Science.gov (United States)

Morgan, T J H; Rendell, L E; Ehn, M; Hoppitt, W; Laland, K N

2012-02-22

Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules.

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...

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.

Historical maintenance relevant information road-map for a self-learning maintenance prediction procedural approach

Science.gov (United States)

Morales, Francisco J.; Reyes, Antonio; Cáceres, Noelia; Romero, Luis M.; Benitez, Francisco G.; Morgado, Joao; Duarte, Emanuel; Martins, Teresa

2017-09-01

A large percentage of transport infrastructures are composed of linear assets, such as roads and rail tracks. The large social and economic relevance of these constructions force the stakeholders to ensure a prolonged health/durability. Even though, inevitable malfunctioning, breaking down, and out-of-service periods arise randomly during the life cycle of the infrastructure. Predictive maintenance techniques tend to diminish the appearance of unpredicted failures and the execution of needed corrective interventions, envisaging the adequate interventions to be conducted before failures show up. This communication presents: i) A procedural approach, to be conducted, in order to collect the relevant information regarding the evolving state condition of the assets involved in all maintenance interventions; this reported and stored information constitutes a rich historical data base to train Machine Learning algorithms in order to generate reliable predictions of the interventions to be carried out in further time scenarios. ii) A schematic flow chart of the automatic learning procedure. iii) Self-learning rules from automatic learning from false positive/negatives. The description, testing, automatic learning approach and the outcomes of a pilot case are presented; finally some conclusions are outlined regarding the methodology proposed for improving the self-learning predictive capability.

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)

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

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…

Baseline performance and learning rate of conceptual and perceptual skill-learning tasks: the effect of moderate to severe traumatic brain injury.

Science.gov (United States)

Vakil, Eli; Lev-Ran Galon, Carmit

2014-01-01

Existing literature presents a complex and inconsistent picture of the specific deficiencies involved in skill learning following traumatic brain injury (TBI). In an attempt to address this difficulty, individuals with moderate to severe TBI (n = 29) and a control group (n = 29) were tested with two different skill-learning tasks: conceptual (i.e., Tower of Hanoi Puzzle, TOHP) and perceptual (i.e., mirror reading, MR). Based on previous studies of the effect of divided attention on these tasks and findings regarding the effect of TBI on conceptual and perceptual priming tasks, it was predicted that the group with TBI would show impaired baseline performance compared to controls in the TOHP task though their learning rate would be maintained, while both baseline performance and learning rate on the MR task would be maintained. Consistent with our predictions, overall baseline performance of the group with TBI was impaired in the TOHP test, while the learning rate was not. The learning rate on the MR task was preserved but, contrary to our prediction, response time of the group with TBI was slower than that of controls. The pattern of results observed in the present study was interpreted to possibly reflect an impairment of both the frontal lobes as well as that of diffuse axonal injury, which is well documented as being affected by TBI. The former impairment affects baseline performance of the conceptual learning skill, while the latter affects the overall slower performance of the perceptual learning skill.

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.

Multi-level machine learning prediction of protein–protein interactions in Saccharomyces cerevisiae

Directory of Open Access Journals (Sweden)

Julian Zubek

2015-07-01

Full Text Available Accurate identification of protein–protein interactions (PPI is the key step in understanding proteins’ biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein–protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein–protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC. Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent.

Analysis of deep learning methods for blind protein contact prediction in CASP12.

Science.gov (United States)

Wang, Sheng; Sun, Siqi; Xu, Jinbo

2018-03-01

Here we present the results of protein contact prediction achieved in CASP12 by our RaptorX-Contact server, which is an early implementation of our deep learning method for contact prediction. On a set of 38 free-modeling target domains with a median family size of around 58 effective sequences, our server obtained an average top L/5 long- and medium-range contact accuracy of 47% and 44%, respectively (L = length). A complete implementation has an average accuracy of 59% and 57%, respectively. Our deep learning method formulates contact prediction as a pixel-level image labeling problem and simultaneously predicts all residue pairs of a protein using a combination of two deep residual neural networks, taking as input the residue conservation information, predicted secondary structure and solvent accessibility, contact potential, and coevolution information. Our approach differs from existing methods mainly in (1) formulating contact prediction as a pixel-level image labeling problem instead of an image-level classification problem; (2) simultaneously predicting all contacts of an individual protein to make effective use of contact occurrence patterns; and (3) integrating both one-dimensional and two-dimensional deep convolutional neural networks to effectively learn complex sequence-structure relationship including high-order residue correlation. This paper discusses the RaptorX-Contact pipeline, both contact prediction and contact-based folding results, and finally the strength and weakness of our method. © 2017 Wiley Periodicals, Inc.

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.

Are Faculty Predictions or Item Taxonomies Useful for Estimating the Outcome of Multiple-Choice Examinations?

Science.gov (United States)

Kibble, Jonathan D.; Johnson, Teresa

2011-01-01

The purpose of this study was to evaluate whether multiple-choice item difficulty could be predicted either by a subjective judgment by the question author or by applying a learning taxonomy to the items. Eight physiology faculty members teaching an upper-level undergraduate human physiology course consented to participate in the study. The…

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy.

Science.gov (United States)

Mani, Subramani; Chen, Yukun; Li, Xia; Arlinghaus, Lori; Chakravarthy, A Bapsi; Abramson, Vandana; Bhave, Sandeep R; Levy, Mia A; Xu, Hua; Yankeelov, Thomas E

2013-01-01

To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building. The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82. With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem. Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC.

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...

Tracking orthographic learning in children with different types of dyslexia

Directory of Open Access Journals (Sweden)

Hua-Chen eWang

2014-07-01

Full Text Available Previous studies have found that children with reading difficulties need more exposures to acquire the representations needed to support fluent reading than typically developing readers (e.g., Ehri & Saltmarsh, 1995. Building on existing orthographic learning paradigms, we report on an investigation of orthographic learning in poor readers using a new learning task tracking both the accuracy (untimed exposure duration and fluency (200ms exposure duration of learning novel words over trials. In study 1, we used the paradigm to examine orthographic learning in children with specific poor reader profiles (9 with a surface profile, 9 a phonological profile and 9 age-matched controls. Both profiles showed improvement over the learning cycles, but the children with surface profile showed impaired orthographic learning in spelling and orthographic choice tasks. Study 2 explored predictors of orthographic learning in a group of 91 poor readers using the same outcome measures as in Study 1. Consistent with earlier findings in typically developing readers, phonological decoding skill predicted orthographic learning. Moreover, orthographic knowledge significantly predicted orthographic learning over and beyond phonological decoding. The two studies provide insights into how poor readers learn novel words, and how their learning process may be compromised by less proficient orthographic and/or phonological skills.

Can Excess Bilirubin Levels Cause Learning Difficulties?