Profiling low-proficiency science students in the Philippines using machine learning.

ABSTRACT: Filipino students' performance in global assessments of science literacy has always been low, and this was confirmed again in the PISA 2018, where Filipino learners' average science literacy scores ranked second to last among 78 countries. In this study, machine learning approaches were used to analyze PISA data from the student questionnaire to test models that best identify the poorest-performing Filipino students. The goal was to explore factors that could help identify the students who are vulnerable to very low achievement in science and that could indicate possible targets for reform in science education in the Philippines. The random forest classifier model was found to be the most accurate and more precise, and Shapley Additive Explanations indicated 15 variables that were most imp

SUBMITTER: Bernardo ABI 

PROVIDER: S-EPMC10154750 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature