Education Commons^™

Addressing The Poor Mathematics Performance Of Filipino Learners: Beyond Curricular And Instructional Interventions, Minie Rose C. Lapinid, Macario O. Cordell Ii, Jude Michael Teves, Sashimir A. Yap, Unisse Chua Ms., Allan B.I. Bernardo

Angelo King Institute for Economic and Business Studies (AKI)

This study aimed to determine predictive models that would identify the most important predictor variables for students in the lowest proficiency group in public schools and private schools. After experimenting with different machine learning approaches, the random forest classifier (SVM) models were found to perform most accurately (Lundberg & Lee, 2017). To identify the most important predictors of being a poor performer in mathematics, Shapley values were generated, which produces a ranked list of several features in descending order. To manage complexity in comparing the key variables for private and public student performance classification, the 10 most significant features for …