982 resultados para Learning Disability (LD).
Resumo:
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
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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
Resumo:
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.
Resumo:
Explanations of the marked individual differences in elementary school mathematical achievement and mathematical learning disability (MLD or dyscalculia) have involved domain-general factors (working memory, reasoning, processing speed and oral language) and numerical factors that include single-digit processing efficiency and multi-digit skills such as number system knowledge and estimation. This study of third graders (N = 258) finds both domain-general and numerical factors contribute independently to explaining variation in three significant arithmetic skills: basic calculation fluency, written multi-digit computation, and arithmetic word problems. Estimation accuracy and number system knowledge show the strongest associations with every skill and their contributions are both independent of each other and other factors. Different domain-general factors independently account for variation in each skill. Numeral comparison, a single digit processing skill, uniquely accounts for variation in basic calculation. Subsamples of children with MLD (at or below 10th percentile, n = 29) are compared with low achievement (LA, 11th to 25th percentiles, n = 42) and typical achievement (above 25th percentile, n = 187). Examination of these and subsets with persistent difficulties supports a multiple deficits view of number difficulties: most children with number difficulties exhibit deficits in both domain-general and numerical factors. The only factor deficit common to all persistent MLD children is in multi-digit skills. These findings indicate that many factors matter but multi-digit skills matter most in third grade mathematical achievement.
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The authors describe on a Brazilian girl with coronal synostosis, facial asymmetry, ptosis, brachydactyly, significant learning difficulties, recurrent scalp infections with marked hair loss, and elevated serum immunoglobulin E. Standard lymphocyte karyotype showed a small additional segment in 7p21[46,XX,add(7)(p21)]. Deletion of the TWIST1 gene, detected by Multiplex Ligation Probe-dependent Amplification (MPLA) and array-CGH, was consistent with phenotype of SaethreChotzen syndrome. Array CGH also showed deletion of four other genes at 7p21.1 (SNX13, PRPS1L1, HD9C9, and FERD3L) and the deletion of six genes (CACNA2D2, C3orf18, HEMK1, CISH, MAPKAPK3, and DOCK3) at 3p21.31. Our case reinforces FERD3L as candidate gene for intellectual disability and suggested that genes located in 3p21.3 can be related to hyper IgE phenotype. (C) 2012 Wiley Periodicals, Inc.
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O presente trabalho tem por justificativa compreender como os professores percebem a não aprendizagem, esse entendimento faz-se necessário entender para poder lidar com essa temática, cada vez mais latente nas escolas. Os objetivos do estudo são: compreender qual o pressuposto epistemológico que predomina na prática docente dos professores de anos iniciais; interpretar como se consolidam os processos de diagnóstico e seus encaminhamentos; e investigar quais as estratégias elaboradas pela escola para trabalhar com alunos diagnosticados com dificuldades de aprendizagem (DA) em sala de aula. A pesquisa possui caráter qualitativo, sendo utilizado como método de coleta de dados o grupo focal e como método de análise dos dados o Discurso do Sujeito Coletivo (DSC). O contexto do estudo é uma amostra representativa das escolas públicas da rede municipal de ensino regular do Ensino Fundamental da zona urbana da cidade do Rio Grande, RS. Os professores indicaram, em suas falas, indÃcios de uma concepção empirista, apontando vestÃgios a respeito da transmissão de conhecimento, bem como indicações de uma concepção construtivista. De modo geral, os professores destacaram ao longo da interação a importância da famÃlia inserida no contexto escolar e no que acontece na sala de aula com as crianças. Enfatizaram também que ao longo de sua formação não tiveram conhecimentos que poderiam servir de base para auxiliar em sua prática. Ao identificarem crianças com DA em sua sala de aula, os professores relataram que os encaminham para um atendimento especifico na escola, a sala de recursos. Desse modo, analisar as concepções dos professores e fazê-los problematizar sobre sua prática pode ser uma estratégia para encarar e diminuir o processo de não aprendizagem.
Resumo:
Throughout their schooling experiences, students with learning disabilities (LD) face numerous academic and socioemotional challenges. Some of these individuals rise above these obstacles to obtain a postsecondary education and become professionals. Recently, there have been a number of individuals with learning disabilities who have chosen a career in teaching. There is a lack of research that documents the experiences of teachers with learning disabilities. The purpose of this qualitative study is to gain an understanding of the challenges that the teachers with learning disabilities strive to overcome and the supports that they receive ^^^ch facilitate their inception into teaching. Four teachers with learning disabilities were the participants in this collective case study research. Data were collected through semistructured interviews. These data were coded, collapsed into themes, and the results were presented in a narrative form. The resultant 9 themes are: (a) Perspectives on School Experiences, (b) Identification and Effective Accommodations, (c) Isolation, Frustration, and Support, (d) Awareness of Learning Disability at Age 18, (e) Disclosure of Learning Disability, (f) Negative Impact of the Learning Disability Label, (g) Desire, Drive, and Obstacles, (h) Empathy, Compassion, and Self-Concept, and (i) Critical Views of Colleagues. The themes reflect the common experiences among participants. The discussion brings forth new information that is not found in other research. The impHcations of this research will interest teacher federations, parents of students with LD, teachers, and educational researchers.
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Children of parents with learning difficulties (LD) are at risk for a variety of developmental problems including behavioural and psychiatric disorders. However, there are no empirically supported programs to prevent behavioural and psychiatric problems in these children. The purpose of the study was to test the effectiveness of a parenting intervention designed to teach parents with learning difficulties positive child behaviour management strategies. A multiple baseline across skills design was used with two parents, who were taught three skills: 1) clear instructions, 2) recognition of compliance and 3) correction of noncompliance. Training scores improved on each skill and maintained at a 1-month follow-up. Scores on generalization cards were high and showed maintenance, but improvements in parenting skills in the naturalistic environment were low at posttest and follow-up. Increases were seen in child compliance at posttest and 1-month follow-up. Results of pre-post social validity measures were also generally positive.
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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
Resumo:
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.
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The paper presents the findings of a research study carried out in Ireland in 2006 (Murphy et al., 2007) which explored the meaning of dependence and independence for older people with a disability. The research adopted a grounded theory approach; purposive sampling was used initially with some relational sampling towards the latter interviews. The sample was comprised of 143 older people with one of six disabilities: stroke (n=20), arthritis (20), depression (20), sensory disability (20), a learning disability (24), and dementia (18). All participants lived at home, some participants required high levels of help in activities of living while others were mostly independent. An interview schedule was used to guide interviews, all of which were tape recorded and transcribed. Data was collected on levels of dependence and independence using the Katz scale. Participants recorded high levels of independence in relation to transferring (93%), toileting (92%), dressing (87%), continence (87%) and feeding (98%). The main area of dependence where participants required assistance from others was with bathing (77%). The constant comparative technique was used to analyze qualitative data. The findings of the study would suggest that participants personal definition of their independence or dependence shifted relative to others and/or improvement or worsening of their capacity People were aware of the difference between independence and dependence, but these two concepts were not always perceived as opposites. It was possible to be independent and dependent at the same time. People valued being able to do things for themselves, accepted help when necessary but wanted to reciprocate when possible. Participants used varied coping strategies to regain and retain control of their lives. Strategies to promote older peoples independence and self esteem will be explored in this paper.
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This framework is to help people and organisations (e.g. Learning Disability Partnership Boards) work towards ensuring that local services are culturally competent/appropriate.
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This factsheet on learning disability has been compiled by the Department of Health South East. It highlights the health inequalities experienced by those with learning disabilities and summarises the main health-related issues. It details key publications such as 'Valuing People Now', which was launched in Jan-09, and provides information about strategies, resources and national drivers including Local Area Agreements. It can serve as a useful tool for PCTs, commissioners, those involved in service development, and those with a public health remit in order to improve practice and health outcomes among those with learning disability at both local and regional levels. For further information contact: Jonathan Campion (jonathan.campion@dh.gsi.gov.uk) or Jo Nurse (jo.nurse@dh.gsi.gov.uk)
Resumo:
Collage is a pattern-based visual design authoring tool for the creation of collaborative learning scripts computationally modelled with IMS Learning Design (LD). The pattern-based visual approach aims to provide teachers with design ideas that are based on broadly accepted practices. Besides, it seeks hiding the LD notation so that teachers can easily create their own designs. The use of visual representations supports both the understanding of the design ideas and the usability of the authoring tool. This paper presents a multicase study comprising three different cases that evaluate the approach from different perspectives. The first case includes workshops where teachers use Collage. A second case implies the design of a scenario proposed by a third-party using related approaches. The third case analyzes a situation where students follow a design created with Collage. The cross-case analysis provides a global understanding of the possibilities and limitations of the pattern-based visual design approach.