Bridging the Conceptualization of Youth with Intellectual Disabilities to Sentencing under the YCJA

The current study examined how disability and the concepts of risk, need and responsivity are understood by criminal justice professionals and inform their perceptions of young offenders with ID at sentencing under the ‘different but equal’ philosophy. Semi-structured interviews were conducted with 11 lawyers and 8 mental health workers across 6 major urban areas in Ontario. Participants primarily perceived ID through a medical discourse, overlooking social and structural barriers that, in some cases, may hinder adherence to sentencing dispositions. Specifically, participants discussed balancing the reduced culpability of offenders (e.g., intent) – justifying lenient sentencing – with public safety concerns (i.e., ID viewed as a barrier to rehabilitation) – justifying increasing the severity of sentences. Participants assessed clients with ID and their risks, needs and responsivity within the context of other legal factors: criminal history, severity of the offence, and YCJA objectives. Participants articulated the importance of tailored courthouse identification programs, services/funding, and education/training.

Examining Predictors of Quality of Life of Adults with Intellectual Disabilities After Deinstitutionalization

This study examined if a person’s quality of life could be predicted by six relevant factors in a sample of 114 individuals with intellectual disability who had moved from institutional settings to community living settings within Ontario. Further, two aspects of self-efficacy were tested to see if they moderated the relationship between the possible predictors and the quality of life indicator. The initial multiple regression model accounted for a very small amount of the variance in the outcome (r2 = .08). The second analysis included decision-making as a predictor (r2 = .35) but did not find it to be moderator. The third analysis used opportunities for change as a predictor (r2 = .28), and as a moderator with two significant interaction terms, health and years in an institutional setting (r2 = .35). These findings support the often-theorized influence of self-efficacy on quality of life for individuals with intellectual disability.

Creating and Enabling a Sustainable Livelihood for Persons with Intellectual Disabilities: A Comparative Case Study of Ghana and Canada.

Abstract Despite the plethora of published studies on rights, including employment rights, for persons with intellectual disabilities (Hatton, 2002; Tarulli, et al., 2004; Ward & Stewart, 2008), relatively few have discussed their applicability to individuals with intellectual disabilities to facilitate their full involvement in socio-economic development. This study explored the mechanisms facilitating and inhibiting the full participation of persons with intellectual disabilities in the area of employment through a comparative case analysis of policies and practices in Ontario, Canada (a developed country) and in Ghana (a developing country) both of which are signatories to the United Nations Convention on the Rights of Persons with Disabilities (UNCRPD). The study employed targeted recruitment based on the nature of the research which is a combination of policy and practice investigation.

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.

Social skills of children with different disabilities: Assessment and implications for interventions

This study characterizes the differences and similarities in the repertoire of social skills of children from 12 different categories of special educational needs: autism, hearing impairment, mild intellectual disabilities, moderate intellectual disabilities, visual impairment, phonological disorder, learning disabilities, giftedness and talent, externalizing behavior problems, internalizing behavior problems, internalizing and externalizing behavior problems and attention deficit hyperactivity disorder. Teachers of 120 students in regular and special schools, aged between 6 and 14 years old, from four Brazilian states, responded to the Social Skills Rating System. Children with ADHD, autism, internalizing and externalizing behavior problems and externalizing behavior problems presented comparatively lower frequency of social skills. The intervention needs of each evaluated category are discussed.

Simple strategies that work! : helpful hints for all educators of students with Asperger syndrome, high-functioning autism, and relates disabilities.

Recurso que ofrece enfoques y estrategias que los profesores pueden utilizar para ayudar a sus estudiantes con síndrome de Asperger y autismo en el camino hacia el éxito. Analiza los problemas que pueden surgir en el aula de inclusión y cómo los educadores pueden hacer adaptaciones para atender a sus alumnos con autismo sin interferir en las rutinas del aula estándar. Incluye información sobre lo que puede causar ansiedad en el estudiante con estas discapacidades, los posibles incrementos en los problemas de comportamiento, y lo que el profesor puede hacer para ayudar. Cuenta con diez estrategias acompañadas de ejemplos y razones por las que son importantes usarlas.

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

Explaining grammatical difficulties in intellectual disabilities

Resumen en inglés. Resumen basado en el de la publicación