Which Filipino Students are Being Left Behind in Mathematics? Testing Machine Learning Models to Differentiate Lowest-Performing Filipino Students in Public and Private Schools in the 2018 PISA Mathematics Test

Added Title

DLSU-AKI Working Paper Series 2022-09-085

Authors

Minie Rose C. Lapinid, De La Salle University, ManilaFollow
Macario O. Cordel II, De La Salle University, ManilaFollow
Jude Michael M. Teves, De La Salle University, ManilaFollow
Sashmir A. Yap, De La Salle University, ManilaFollow
Unisse C. Chua, De La Salle University, ManilaFollow
Allan B. I Bernardo, De La Salle University, ManilaFollow

College

Br. Andrew Gonzalez FSC College of Education

Document Type

Article

Publication Date

9-2022

Place of Publication

DLSU-Angelo King Institute for Economic and Business office

Abstract

Filipino students performed poorly in the PISA 2018 mathematics assessment, with more than 50% obtaining scores below the lowest proficiency level. Students from public schools also performed worse compared to their private school counterparts. We used machine learning approaches, specifically binary classification methods, to model the variables that best identified the poor performing students (below Level 1) vs. better performing students (Levels 1 to 6) using the PISA data from a nationally representative sample of 15-year-old Filipino students. We analyzed data from students in private and public schools separately. Several binary classification methods were applied, and the best classification model for both private and public school groups was the Random Forest classifier. The 10 variables with the highest impact on the model were identified for the private and public school groups. Five variables were similarly important in the private and public school models. But there were other distinct variables that relate to students’ motivations, family, and school experiences that were important in identifying the poor performing students in each school type. The results are discussed in relation to the social and social cognitive experiences of students that relate to socioeconomic contexts that differ between public and private schools.

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Recommended Citation

Lapinid, M. C., Cordel, M. O., Teves, J. M., Yap, S. A., Chua, U. C., & Bernardo, A. B. (2022). Which Filipino Students are Being Left Behind in Mathematics? Testing Machine Learning Models to Differentiate Lowest-Performing Filipino Students in Public and Private Schools in the 2018 PISA Mathematics Test. Retrieved from https://animorepository.dlsu.edu.ph/res_aki/4

Keywords

mathematics achievement; machine learning; Philippines; public vs. private schools; school type, socioeconomic differences; PISA

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