Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Verbal proficiency as fitness indicator: Experimental and comparative research on the evolutionary psychology of language and verbal displays

Summary: Recent research on the evolution of language and verbal displays (e.g., Miller, 1999, 2000a, 2000b, 2002) indicated that language is not only the result of natural selection but serves as a sexually-selected fitness indicator that is an adaptation showing an individual’s suitability as a reproductive mate. Thus, language could be placed within the framework of concepts such as the handicap principle (Zahavi, 1975). There are several reasons for this position: Many linguistic traits are highly heritable (Stromswold, 2001, 2005), while naturally-selected traits are only marginally heritable (Miller, 2000a); men are more prone to verbal displays than women, who in turn judge the displays (Dunbar, 1996; Locke & Bogin, 2006; Lange, in press; Miller, 2000a; Rosenberg & Tunney, 2008); verbal proficiency universally raises especially male status (Brown, 1991); many linguistic features are handicaps (Miller, 2000a) in the Zahavian sense; most literature is produced by men at reproduction-relevant age (Miller, 1999). However, neither an experimental study investigating the causal relation between verbal proficiency and attractiveness, nor a study showing a correlation between markers of literary and mating success existed. In the current studies, it was aimed to fill these gaps. In the first one, I conducted a laboratory experiment. Videos in which an actor and an actress performed verbal self-presentations were the stimuli for counter-sex participants. Content was always alike, but the videos differed on three levels of verbal proficiency. Predictions were, among others, that (1) verbal proficiency increases mate value, but that (2) this applies more to male than to female mate value due to assumed past sex-different selection pressures causing women to be very demanding in mate choice (Trivers, 1972). After running a two-factorial analysis of variance with the variables sex and verbal proficiency as factors, the first hypothesis was supported with high effect size. For the second hypothesis, there was only a trend going in the predicted direction. Furthermore, it became evident that verbal proficiency affects long-term more than short-term mate value. In the second study, verbal proficiency as a menstrual cycle-dependent mate choice criterion was investigated. Basically the same materials as in the former study were used with only marginal changes in the used questionnaire. The hypothesis was that fertile women rate high verbal proficiency in men higher than non-fertile women because of verbal proficiency being a potential indicator of “good genes”. However, no significant result could be obtained in support of the hypothesis in the current study. In the third study, the hypotheses were: (1) most literature is produced by men at reproduction-relevant age. (2) The more works of high literary quality a male writer produces, the more mates and children he has. (3) Lyricists have higher mating success than non-lyric writers because of poetic language being a larger handicap than other forms of language. (4) Writing literature increases a man’s status insofar that his offspring shows a significantly higher male-to-female sex ratio than in the general population, as the Trivers-Willard hypothesis (Trivers & Willard, 1973) applied to literature predicts. In order to test these hypotheses, two famous literary canons were chosen. Extensive biographical research was conducted on the writers’ mating successes. The first hypothesis was confirmed; the second one, controlling for life age, only for number of mates but not entirely regarding number of children. The latter finding was discussed with respect to, among others, the availability of effective contraception especially in the 20th century. The third hypothesis was not satisfactorily supported. The fourth hypothesis was partially supported. For the 20th century part of the German list, the secondary sex ratio differed with high statistical significance from the ratio assumed to be valid for a general population.

Explorations of the Practical Issues of Learning Prediction-Control Tasks Using Temporal Difference Learning Methods

There has been recent interest in using temporal difference learning methods to attack problems of prediction and control. While these algorithms have been brought to bear on many problems, they remain poorly understood. It is the purpose of this thesis to further explore these algorithms, presenting a framework for viewing them and raising a number of practical issues and exploring those issues in the context of several case studies. This includes applying the TD(lambda) algorithm to: 1) learning to play tic-tac-toe from the outcome of self-play and of play against a perfectly-playing opponent and 2) learning simple one-dimensional segmentation tasks.

The Informational Complexity of Learning from Examples

This thesis attempts to quantify the amount of information needed to learn certain tasks. The tasks chosen vary from learning functions in a Sobolev space using radial basis function networks to learning grammars in the principles and parameters framework of modern linguistic theory. These problems are analyzed from the perspective of computational learning theory and certain unifying perspectives emerge.

Designing context-aware adaptive units of learning based on IMS-LD standard

Learning contents adaptation has been a subject of interest in the research area of the adaptive hypermedia systems. Defining which variables and which standards can be considered to model adaptive content delivery processes is one of the main challenges in pedagogical design over e-learning environments. In this paper some specifications, architectures and technologies that can be used in contents adaptation processes considering characteristics of the context are described and a proposal to integrate some of these characteristics in the design of units of learning using adaptation conditions in a structure of IMS-Learning Design (IMS-LD) is presented. The key contribution of this work is the generation of instructional designs considering the context, which can be used in Learning Management Systems (LMSs) and diverse mobile devices

Models of Learning and Motivation

A brief skim through educational theory intended for students registered on a single module in Technology Enhanced Learning. Startes with Blooms taxonomy, travles through instructivism and constructivism and on to theories of motivation/

Accessibility in the Design of Learning Resources

These slides accompany a seminar delivered on 20 May 2016 by Jane Warren (Southampton Education School) and Adam Warren (Institute for Learning Innovation and Development). A recording of the lecture can be viewed here: http://tinyurl.com/zp8u3lq

Impact on the quality of learning as a result of the use of computers in schools

The educational software and computer assisted learning has been used in schools to promote the interest of students in new ways of thinking and learning so it can be useful in the reading learning process. Experimental studies performed in preschool and school age population have shown a better yield and a positive effect in reading, mathematics and cognitive skills in children who use educative software for fi fteen to twenty minutes a day periods. The goal of this study was to evaluate the progression in verbal, visual-motor integration and reading skills in children who were using educational software to compare them with a group in traditional pedagogic methodology. Results: All children were evaluated before using any kind of pedagogic approach. Initial evaluation revealed a lower–age score in all applied test. 11% of them were at high risk for learning disorders. There was a second evaluation that showed a significant positive change compared with the fi rst one. Nevertheless, despite some items, there were no general differences comparing the groups according if they were using or not a computer. In conclusion, policies on using educational software and computers must be revaluated due to the fact that children in our public schools come from a deprived environment with a lack of opportunities to use technologies.

Historical perspective of the Psychology of motivation

The psychology of motivation has a long tradition and history in psychology. In fact, we consider that, to a certain extent, understanding the history of the psychology of motivation is understanding great part of which has been psychology itself, since the main target of psychology was, and is, to try to explain behaviour, and the aim of psychology of motivation is to find out the causes of behaviour. In its long passage to the present time, there have been three perspectives that have monopolized most of the investigation: the biological, the behavioural and the cognitive. They are not excluding. Each one of them has been predominant in certain stages, although the same attention was paid to the other two. Nowadays, the biological and cognitive perspectives are those that receive greater attention from the investigators. The historical direction in the study of the psychology of motivation represents an important solution to know how the events that have given rise to the present consideration about the psychology of motivation were forged. To know the past helps us to understand the present, at the same time it allows us to hypothesise with great probability of success which will be the future in the study object.

Motivation, the Engine of Learning

This essay centers in motivation as a fundamental aspect of learning and in the double way sense that this relation must have. It defines the word “motivation” and the manner how relationship between students and teachers come about in the game of helping out or reduce motivation, and thus learning. It also defines the reason why teachers must help build up the so called “intrinsic motivation”, ending with the importance this has and how it can be achieved.

Secuenciamiento de actividades educativas orientado a la reutilización y la auto-organización en tutoría inteligente. ' 'Sequencing of learning activities oriented towards reuse and auto-organization for intelligent tutoring systems' '.

Se pretende la elaboración una plataforma capaz de implementar sistemas de tutoría inteligente, orientada al problema de la adaptación del secuenciamiento; la creación de una herramienta o método que defina secuenciamientos adaptativos de material educativo; el desarrollo, empleando la herramienta anterior, de uno o más tutores y adaptar las técnicas de inteligencia de enjambre al campo de los cursos impartidos a través de Internet, Elearning.. En primer lugar se analiza el estado de la cuestión, destacando los aspectos novedosos. A continuación se presentan las aportaciones de la investigación. Por último, se exponen las conclusiones y las líneas de investigación abiertas. También se incluyen apéndices con información adicional que puede ser relevante para algunos lectores. Se investiga el uso de técnicas de inteligencia de enjambre para obtener sistemas educativos robustos y con capacidad de autoorganización. Las lecciones aprendidas de los sistemas cristalizan en la creación de un módulo para SIT.. Las principales contribuciones son: el diseño de una plataforma para el desarrollo de Sistemas de Tutoría Inteligentes (ITS); se presenta una herramienta para la adaptación de secuencias de unidades de aprendizaje, grafos de secuenciamiento (Sequencing Graphs, SG) y se analizan las iniciativas actuales relacionadas con las técnicas de enjambre en educación. Estas técnicas tienen un interés especial para los sistemas de elearning dada su complejidad. .