Abstract
Early childhood is a critical part of a child’s development as it involves physical, cognitive, and psychological development. In the educational domain, especially early childhood education, there is rich data available that we could leverage to determine the development stage of a child and hidden patterns of a child’s learning ability or disability. This study investigates which data mining classification technique will be most suitable in building a predictive model that can identify the social, cognitive, and emotional stages of a child. The authors compared J48, Naïve Bayes, random forest, support vector machines (SVM), and k-nearest neighbors (KNN) classifiers using performance measures like Kappa statistics, receiver operating characteristic (ROC), root-mean-square error (RMSE), and mean absolute error (MAE) using a data mining analytical tool called WEKA. The authors also compared the accuracy measures like true positive (TP) rate, false positive (FP) rate, precision, recall, and F-measure. The results indicate that the J48 classifier has a better classification accuracy and prediction rating over other tested algorithms using the early childhood dataset.
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Ikponmwosa, A., Vajjhala, N.R., Rakshit, S., Longe, O. (2022). Examining Data Mining Classification Techniques for Predicting Early Childhood Development in Nigeria. In: Borah, S., Mishra, S.K., Mishra, B.K., Balas, V.E., Polkowski, Z. (eds) Advances in Data Science and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 86. Springer, Singapore. https://doi.org/10.1007/978-981-16-5685-9_6
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DOI: https://doi.org/10.1007/978-981-16-5685-9_6
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