'I want to educate school age children' : producing early childhood teacher professional identities

The Australian Government’s current workforce reforms in early childhood education and care (ECEC) include a major shift in qualification requirements. The new requirement is that university four-year degree-qualified teachers are employed in before-school contexts, including child care. Ironically, recent research studies show that, in Australia, the very preservice teachers who are enrolled in these degree programs have a reluctance to work in childcare. This article reports on part of a larger study which is inquiring into how early childhood teacher professional identities are discursively produced, and provides a partial mapping of the literature. One preservice teacher’s comment provides the starting point, and the paper locates some the discourses that are accessible to preservice teachers as they prepare for the early years workforce. An awareness of the discursive field provides a sound background for preparing early childhood teachers. A challenge for the field is to consider which discourses are dominant, and how they potentially work to privilege work in some ECEC contexts over others.

Emotional development among early school-age children: Gender differences in the role of problem behaviours

There has been an increasing focus on social and emotional development in educational programmes in early childhood as both variables are believed to influence behavioural outcomes in the classroom. However, relationships between social and emotional development and behaviour in early childhood have rarely been explored. This article sets out to investigate the conceptualisation of these variables and their inter-relationships. Structural equation models were used to assess if differences exist between boys and girls in relation to social and emotional competences, which could affect the relative success of such programmes. This article is based on cross-sectional data collected from 749 four- to six-year-olds and their teachers. The findings generally supported the hypothesised relationships between social and emotional development variables and prosocial behaviour (including internalising behaviour) for boys and girls. However, some gender differences were noted in externalising behaviour, which teachers often consider to be most significant due to its potentially disruptive nature in the classroom.

In what extent can toy premiums promote healthy eating habits?: A study with school-age children

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Using stimulus equivalence to teach monetary skills to school-age children with autism

The present study evaluated the use of stimulus equivalence in teaching monetary skills to school aged children with autism. An AB within-subject design with periodic probes was used. At pretest, three participants demonstrated relation DA, an auditory-visual relation (matching dictated coin values to printed coin prices). Using a three-choice match-to-sample procedure, with a multi-component intervention package, these participants were taught two trained relations, BA (matching coins to printed prices) and CA (matching coin combinations to printed prices). Two participants achieved positive tests of equivalence, and the third participant demonstrated emergent performances with a symmetric and transitive relation. In addition, two participants were able to show generalization of learned skills with a parent, in a second naturalistic setting. The present research replicates and extends the results of previous studies by demonstrating that stimulus equivalence can be used to teach an adaptive skill to children with autism.

Using fuction-based CBT with parent involvement to treat OCD in two school-age children with high-functioning autism

Obsessive Compulsive Disorder (OCD) involves excessive worry coupled with engaging in rituals that are believed to help alleviate the worry. Pervasive Developmental Disorders (PODs) are characterized by impairments in social interaction, communication, and the presence of repetitive and/or restrictive behaviours (American Psychiatric Association, 2000). Research suggests that as many as 81% of children with a POD also meet criteria for a diagnosis ofOCD. Currently, only a handful of studies have investigated the use of Cognitive Behavioural Therapy (CBT) in treating OCD in children with autism (Reaven & Hepburn, 2003 ; Sze & Wood, 2007; Lehmkuhl, Storch, Bodtish & Geflken, 2008). In these case studies. the use of a multi-modal CBT treatment package was successful in alleviating OCD behaviours. The current study used function-based CBT with parent involvement and behavioural supplements to treat 2 children with POD and OCD. Using a multiple baseline design across behaviours and participants, parents reported that their child 's anxiety was alleviated and these gains were maintained at 6-month follow-up. According to results of the Children 's Yale-Brown Obsessive Compulsive Scale (Goodman, Price, Rasmussen, Riddle, & Rapoport, 1986) from preto post-test, OCD behaviours of the children decreased II"om the severe to the mild range. In addition, the parents rated the family's level of interference related to their child 's OCD as substantially lower. Last, the CBT treatment received high ratings of consumer satisfaction.

Soil-Transmitted Helminth Infections, Nutrition and Growth in School-age Children from Rural Communities in Honduras

Background: Soil-transmitted helminth (STH) infections are endemic in Honduras but their impact on children’s health is not well studied. Objectives: To evaluate the prevalence and intensity of STH infections and their association with nutrition and growth in a sample of Honduran children. Methodology: A cross-sectional study was done among Honduran rural school-age children in 2011. Blood and stool samples and anthropometric measurements were obtained to determine nutritional status, STH infection and growth status, respectively. Results: The STH prevalence among 320 studied children was 72.5%. Prevalence by species was 30%, 67% and 16% for Ascaris, Trichuris and 16% hookworms, respectively. High intensity infections were associated with decreased growth scores but regardless of intensity, co-infections negatively affected growth indicators. Conclusions: The health burden of STH infections is related to high parasitic load but also to the presence of low-intensity concurrent infections. The synergistic effects of polyparasitism in underprivileged children warrants more attention.

Soil-Transmitted Helminth Infections and Nutritional Status in School-age Children from Rural Communities in Honduras

Background: Soil-transmitted helminth (STH) infections are endemic in Honduras and efforts are underway to decrease their transmission. However, current evidence is lacking in regards to their prevalence, intensity and their impact on children’s health. Objectives: To evaluate the prevalence and intensity of STH infections and their association with nutritional status in a sample of Honduran children. Methodology: A cross-sectional study was done among school-age children residing in rural communities in Honduras, in 2011. Demographic data was obtained, hemoglobin and protein concentrations were determined in blood samples and STH infections investigated in single-stool samples by Kato-Katz. Anthropometric measurements were taken to calculate heightfor- age (HAZ), BMI-for-age (BAZ) and weight-for-age (WAZ) to determine stunting, thinness and underweight, respectively. Results: Among 320 children studied (48% girls, aged 7–14 years, mean 9.7661.4) an overall STH prevalence of 72.5% was found. Children .10 years of age were generally more infected than 7–10 year-olds (p = 0.015). Prevalence was 30%, 67% and 16% for Ascaris, Trichuris and hookworms, respectively. Moderate-to-heavy infections as well as polyparasitism were common among the infected children (36% and 44%, respectively). Polyparasitism was four times more likely to occur in children attending schools with absent or annual deworming schedules than in pupils attending schools deworming twice a year (p,0.001). Stunting was observed in 5.6% of children and it was associated with increasing age. Also, 2.2% of studied children were thin, 1.3% underweight and 2.2% had anemia. Moderate-to-heavy infections and polyparasitism were significantly associated with decreased values in WAZ and marginally associated with decreased values in HAZ. Conclusions: STH infections remain a public health concern in Honduras and despite current efforts were highly prevalent in the studied community. The role of multiparasite STH infections in undermining children’s nutritional status warrants more research.

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.

A performance test for school age children wtih a language handicap

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

Noise exposure in school-age children

This paper discusses a study to determine the average level of noise exposure for school children on a typical school day.