Predicting Students' Dropout Indicators in Public School using Data Mining Approaches

Abstract

Currently, one of the challenges of academic institutions is dropout student issues. It is critical to recognize which students are at risk of losing in school and what are the underlying factors of dropout. The study of students' dropout in Lawigan National High School enables teachers to identify the influential factors of dropout cases in school preemptively, to respond to it immediately, and to assist prospective dropout students in continuing studies and gaining knowledge for a better future. Lawigan National High School is a public school supervised with the Department of Education. The said school holds all the records of the students. The study aims to identify the underlying factors of dropout students that need to have an intervention to lessen the value of dropouts. The Weka Experiment Environment platform used to run simulations with machine learning algorithms from ready datasets. Two classification algorithms C4.5 and Nave Bayes (NB) tested on a dataset containing student academic and demographic details, e.g., age, gender, marital status, written, performance, and quarterly examination grade, attendance percentage, home distance, parents' income, mothers' education, fathers' education, and dropout status, using a 10-fold cross-validation to estimate generalization accuracy. The experiment result shows that the top indicator for students' dropout cases is the academic performance with 98.9474% accuracy as perceived by the C4.5 algorithm, as shown in Table

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