Elaborative interrogation as a learning technique for students with learning disabilities

This study examined whether or not students with learning disabilities could effectively use a question and answer strategy known as elaborative interrogation. This technique involved students answering why they thought facts based on familiar animal stories were true. Thirty students from a provincial demonstration high school (for students with learning disabilities) were assigned to one of two study conditions, (a) elaborative interrogation or (b) reading for understanding. Three students, one from the experimental condition and two from the control did not complete the study. Both conditions required that the students learn 36 facts concerning six familiar animals. Immediately following the study session the students completed a free-recall test, a matched association test and a questionnaire regarding their perceived difficulty of the animal stories. After 30 days a matched association test was completed. The oneway ANOVA, 2 x 2 split plot ANOVA and Tukey's Honestly Significant Test were used to determine significance. There was no significant difference in the two conditions for free recall retention. There were significant differences in the elaborative interrogation condition for the immediate matched association test and for the 30-day matched association test. The probability of the students' responses in the elaborative interrogation were measured to determine the effects of adequate responses on long-term retention. It was found that the adequate responses were more likely to promote retention than inadequate responses. In conclusion, long-term retention of factual information was significantly better in the elaborative interrogation condition in comparison to the reading for understanding control. For future research, the dependent measure, free recall should be given both verbally and in written format. In addition, extra time should be allowed for processing of the new information to occur.

Making the transition from secondary to post-secondary education : a retrospective study of students with learning disabilities

The purpose of this study was to investigate what students with Learning Disabilities perceive are the personal characteristics they possess and services they require to assist them to complete secondary school and to continue their education in a postsecondary setting. Twenty-one students (12 female and 9 male) participated in the study which consisted of an interview and completion of a questionnaire. The central findings were as follows: 1) the participants perceived that personal characteristics were important in secondary school and still remain of importance at th~ postsecondary level; 2) Many of the typical accommodations and services supposed to be provided in secondary schools were not provided to the participants in this study; 3) the participants believed that they had more academic than social problems. Recommendations for future research in this field are based on findings related to the transition of LD students from secondary school to postsecondary education.

Breaking the silence : exploring the workplace experiences of six women with learning disabilities

In the current economic climate, employees are expected to upgrade their skills in order to remain productive and competitive in the workplace, and many women with learning disabilities! may feel doubly challenged when dealing with such expectations. Although the number of people with reported learning disabilities who enter the workforce is expected to increase, a dearth of research focuses on work-related experiences of women with learning disabilities; consequently, employers and educators often are unaware ofthe obstacles and demands facing such individuals. This qualitative narrative study sheds light on the work experiences of women with diagnosed or suspected learning disabilities. The study used semistructured interviews to explore their perspectives and reflections on learnlng in order to: (a) raise awareness of the needs of women with learning disabilities, (b) enhance their opportunities to learn in the workplace, and (c) draw attention to the need for improvement of inclusiveness in the workplace, especially for hidden disabilities. Study findings reveal that participants' learning was influenced by work relationships, the learning environments, self-determination, and taking personal responsibility. Moreover, the main accommodation requested was to have supportive and understanding work relationships and environments. Recommendations are made for future research and workplace improvements, most notably that no employees should be left behind through an employee-centered approach.

Self-Regulated Learning and Children At-Risk for Learning Disabilities: Using Motivational Tactics to Support "Reading Rocks!"

This project explored self-regulation among children impacted by leaming disabilities. More specifically, this thesis examined whether a remedial literacy program called Reading Rocks! offered by the Leaming Disabilities Association of Niagara Region, provided participating children opportunities to set goals, develop strategies to meet these goals, and provide intemal and extemal feedback- all processes associated with a model of self-regulated leaming as pioneered by Butler and Winne (1995) and Winne and Hadwin (1999). In this thesis, I triangulate the data through the combination of three different methodologies. Firstly, I describe the various elements of the Reading Rocks! program. Secondly, I analyze the data gathered through three semi-structured interviews with three parents of children that participated in the Reading Rocks! program to demonstrate whether the program provides opportunities for children to self-regulate their learning. Thirdly, I also analyze photographic evidence of the motivational workstation boards created by the tutors and children to further illustrate how Reading Rocks! promotes self-regulatory processes among children.

A Case Study of the "Who is NOBODY?" Project: A Character Development Program for Children with Learning Disabilities

This research study explored a support system for children with learning disabilities. The Learning Disabilities Association of Niagara Region (LDANR) recently expanded its Better Emotional and Social Times (B.E.S.T.) program to incorporate an innovative, character education initiative called the “Who is NOBODY?” program. The objective of this qualitative case study was two-fold. First, the study aimed to support the LDANR in assessing the efficacy of the “Who is NOBODY?” program, providing the LDANR with empirical support for their programs. Second, the study enabled a more in-depth understanding of how to best support children with LD in regards to their social and emotional well-being. The study explored the “Who is NOBODY?” program through three lenses: design, implementation, and experiences of participating children. Three primary themes emerged from these three data lenses: positive character traits, prosocial behaviour, and strong self-efficacy – leading to the promotion of strong character development and self-esteem. Taken together, the “Who is NOBODY?” program was shown to be a successful remediation program for supporting vulnerable children with LD.

Strategies and Techniques for Teaching Children with Learning Disabilities: A Case Study of the Spring Reading Program

This case study explored strategies and techniques in order to assist individuals with learning disabilities in their academic achievement. Of particular focus was how a literacy-based program, titled The Spring Reading Program, utilizes effective tactics and approaches that result in academic growth. The Spring Reading Program, offered by the Learning Disabilities Association of Niagara Region (LDANR) and partnered with John McNamara from Brock University, supports children with reading disabilities academically. In addition, the program helps children increase their confidence and motivation towards literacy. I began this study by outlining the importance of reading followed by and exploration of what educators and researchers have demonstrated regarding effective literacy instruction for children with learning disabilities. I studied effective strategies and techniques in the Spring Reading Program by conducting a qualitative case study of the program. This case study subsequently presents in depth, 4 specific strategies: Hands-on activities, motivation, engagement, and one-on-one instruction. Each strategy demonstrates its effectiveness through literature and examples from the Spring Reading Program.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

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.

Evaluating Greek primary school textbooks used to teach students with learning disabilities

Evaluaci??n de los libros de texto de Educaci??n Primaria griegos utilizados en la ense??anza de los estudiantes con dificultades de aprendizaje. La evaluaci??n de los libros de texto en cuanto a su cumplimiento de las normas basadas en la evidencia de dise??o instruccional, y en cuanto a su idoneidad para acomodar las diversas necesidades educativas de los diversos grupos de la poblaci??n escolar, se considera un medio importante de mejorar la calidad de los servicios educativos incluyendo a estudiantes con discapacidades de aprendizaje. En el presente trabajo, se explican los resultados de las evaluaciones de los libros de texto de Lengua y Matem??ticas que se utilizan en los tres primeros grados de la escuela griega primaria para ense??ar a los estudiantes con y sin dificultades de aprendizaje. La evaluaci??n se bas?? en los siguientes criterios: claridad de objetivos de instrucci??n, el examen de conocimientos previos, explicitaci??n de las explicaciones de instrucci??n, la suficiencia de los ejemplos de ense??anza, la introducci??n de conceptos adicionales y capacidades, la adecuaci??n de la pr??ctica guiada, la eficacia de la pr??ctica independiente, y la adecuaci??n de los conocimientos. Seg??n los resultados, los libros de texto no cumplen en cuatro de los ocho criterios revisados, en concreto los criterios de la claridad de los objetivos de instrucci??n, la explicitud de las explicaciones de instrucci??n, la introducci??n de conceptos adicionales y habilidades, y la conveniencia de revisar los conocimientos. Bas??ndose en estos resultados, el punto de vista puede considerarse que los libros de texto evaluados presentan considerables deficiencias e insuficiencias, lo que exige la aplicaci??n de modificaciones sustanciales en varios par??metros de dise??o de la instrucci??n cuando se utilizan para ense??ar a los estudiantes con dificultades de aprendizaje. Se discuten los efectos de estas deficiencias.

Supporting Students with Learning Disabilities to Explore Linear Relationships using Online Learning Objects. 'Apoyando a estudiantes con problemas de aprendizaje para explorar relaciones lineales mediante el uso de objetos de aprendizaje en línea'.

Resumen basado en el de la publicación

Applicability of the Test of Visual Perceptual Abilities (TVPA) in diagnosing learning disabilities in hearing-impaired children

This paper studies the validity of the Test of Visual Perceptual Abilities (TVPA) as an indicator of learning problems in hearing-impaired children and how it correlates with other measures of learning disabilities.