Maternal depression symptoms in early childhood and children's cognitive performance at school entry : the role of maternal guidance

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

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.

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.

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.

Working memory in school-aged children: Exhibition intervals and distracters effects in Memonum computerized test

This study assessed visual working memory through Memonum computerized test in schoolchildren. The effects of three exposure times (1, 4 and 8 seconds) have been evaluated, and the presentation of a distractor on the mnemonic performance in the test Memonum in 72 children from a college in the metropolitan area of Bucaramanga, Colombia, aged between 8 and 11 in grades third, fourth and fifth grade. It has been found significant difference regarding the exposure time in the variables number of hits and successes accumulated, showing a better mnemonic performance in participants who took the test during 8 seconds compared to children who took the test during 1 second; in addition, the presence of a distractor showed a significant difference regarding the strengths and successes accumulated. Such distractor is considered a stimulus generator interference that disrupts the storage capacity of working memory in children. Additionally, a significant difference was found with respect to the use of mental rehearsal strategy, indicating that participants who took the test in 4 and 8 seconds, respectively, assigned higher scores than children who took the test in 1 second. A long exposure time to stimuli during Memonum test increases the holding capacity. Also, the use of a distractor affects the storage capacity and this, at the same time, increases the school progression due to the use of mnemonic strategies that children use to ensure the memory of the numerical series

Executive functions in school-aged children: age and socioeconomic status effects

Lately, the study of prefrontal executive functions in grade scholars has noticeably increased. The aim of this study is to investigate the influence of age and socioeconomic status (sEs) on executive tasks performance and to analyze those socioeconomic variables that predict a better execution. A sample of 254 children aged between 7 and 12 years from the city of santa Fe, Argentina and belonging to different socioeconomic status were tested. A bat- tery of executive functions sensitive to prefrontal function was used to obtain the results. These in- dicate a significant influence of age and SES on executive functions. The cognitive patterns follow a different path according to the development and sEs effect. Besides, it is revealed a pattern of low cognitive functioning in low-sEs children in all executive functions. Finally, from the variables included in this study, it was found that only the educational level of the mother and the housing conditions are associated to the children’s executive function. The results are discussed in terms of the influence of the cerebral maturation and the envi- ronmental variables in the executive functioning.

Children' s school study : an experimental theory of verbal information and social attitudes. 'Estudio de la escuela primaria: una teoría experimental de la información verbal y las actitudes sociales'.

Determinar los conceptos diferenciales alcanzados por los alumnos de segunda etapa de EGB con respecto a los alumnos de primera etapa en el area de ciencias sociales. Medir mediante una escala de Likertel cambio de actitudes producido como consecuencia de los aprendizajes en el area de humanidades.. La muestra aleatoria la componían 284 alumnos (145 chicos y 139 chicas) de 5õ y 8õ de EGB y 3õ de BUP, alumnos en centros de un centro urbano (Cáceres) y 3 núcleos rurales de la misma provincia.. La investigación se divide en dos bloques, el primero de ellos, de corte más teórico, estudia la cibernética y la teoría de la información como ciencias y su aplicación en el campo de la psicopedagogía. Se determinan también los esquemas generales para la construcción de escalas de medida de actitudes, y se trata de determinar experimentalmente la existencia de diferencias significativas en el caudal lingüístico entre alumnos procedentes de un medio rural y los procedentes de un medio urbano. El segundo bloque, de corte experimental, analiza el concepto de escala usado en ciencias sociales, y se trata de determinar las actitudes que representan un mayor grado de madurez en la dimensión socio-política. Para ello se aplica a los sujetos de la muestra la Social-responsability Scale.. Escala Likert. Quessing-game method (modificado). Social Responsability Scale de Berkowitz y Lutzeman.. Análisis de Varianza. Análisis de correlación. En terminos de transinsformación didáctica puede considerarse que durante la segunda etapa de EGB hay adquisición de conocimientos que suponen un substancial enriquecimiento del caudal lingüístico. El ambiente posibilita y condiciona el enriquecimiento o desarrollo de las facultades individuales. Pueden establecerse correlaciones parciales entre la adquisión de conceptos y la evolución de actitudes..

Young bilingual children in nursery school.

Presenta las conclusiones de un estudio etnolingüístico llevado a cabo en el noreste de Inglaterra sobre el comportamiento lingüístico de niños de tres años de edad durante el primer año en el jardín de infancia. Se trata de niños británicos, pero de familias de inmigrantes de tercera generación, que hablan otros idiomas, además del inglés, en sus hogares y comunidades. Tanto los métodos de investigación como las conclusiones se pueden extrapolar a niños de otras sociedades donde el inglés es el idioma oficial en la enseñanza. Resulta de interés para padres, maestros y otras personas interesadas en las vidas de los niños pequeños.

Why children differ in their mathematical attainment at primary school?.'¿Por qué los niños difieren en su competencia matemática en la escuela primaria?'.

Resúmen tomado parcialmente de la publicación.- El artículo forma parte de un monográfico dedicado a Psicología de las Matemáticas

The effect of nonlinear frequency compression and linear frequency transposition on speech perception in school-aged children

The primary objective of this study is to determine whether nonlinear frequency compression and linear transposition algorithms provide speech perception benefit in school-aged children.

Communication strategies used by children at a school for the deaf

This paper discusses a study to determine the communication strategies used by hearing impaired children and their effectiveness.

A performance test for school age children wtih a language handicap

This paper discusses language and intelligence tests for hearing impaired children.

The relationship of hearing-impaired children's scores on the Beery Visual-Motor Integration Test and the reading subtests of the American School Achievement Test

This paper discusses visual-motor tests and reading tests for hearing impaired children.

Average annual reading growth rate progress of children who are deaf, use a cochlear implant and attend an auditory-oral school

Reading growth rate averages were established for children who are deaf, have a unilateral cochlear implant and attend an auditory-oral school.