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Authors: Iyad Suleiman
Keywords: machine learning, school readiness, socio-economic data, data mining, socio-demographic data, script languages, Python, R Language
Abstract: Study the school readiness is an interesting domain that has attracted the attention of the public and private sectors in education. Researchers have developed some techniques for assessing the readiness of preschool kids to start school. Here we benefit from an integrated approach which combines data mining and social network analysis towards a robust solution. The main objective of this study is to explore the socio-demographic variables (age, gender, parents' education, parents' work status, and class and neighborhood peers influence, Supportive Family , Health Status, Family Problems, Motivation, Family Problems, Gaming Devices, Sleeping quality, School Support, Extended Family Support) and Average Marks, data that may impact the school readiness. This paper proposes to apply three models of Data Mining Techniques using R and Python Script Languages to predict school readiness. Real data on 148 Primary School children was used from Life school for Creativity and Excellence a private school located in Ramah village, and white-box classification, clustering and association methods, such as induction rules were employed. Experiments attempt to improve their accuracy for predicting which children might fail or dropout by first, using all the available attributes; next, selecting the best attributes; and finally, rebalancing data and using cost sensitive classification. The outcomes have been compared and the models with the best results are shown.