Trends and Applications of Data Mining for Improving Student Learning: A Systematic Mapping of Educational Data Mining Research in the Philippines

Charles Darwin O. Godinez^1, and Laila S. Lomibao¹

¹University of Science and Technology of Southern Philippines, Cagayan de Oro City, Philippines

Cite this paper:
Charles Darwin O. Godinez and Laila S. Lomibao. Trends and Applications of Data Mining for Improving Student Learning: A Systematic Mapping of Educational Data Mining Research in the Philippines. Journal of Innovations in Teaching and Learning. 2025; 5(2):103-111. doi: 10.12691/jitl-5-2-3

Abstract

While traditional assessments offer snapshots of student knowledge, it often fails to capture the dynamic nature of learning and the intricate interplay of students’ cognitive and metacognitive skills. With this, the burgeoning field of Educational Data Mining (EDM) research provides promise for innovative assessment that can provide deeper insights into student learning and inform effective teaching strategies. However, there are limited EDM research in Philippine educational systems that existing literature reviews on EDM applications often have broader geographical scope and may not capture the unique challenges and opportunities with the Philippine educational systems. This systematic mapping analyzed existing research on EDM applications to predict or improve student performance within Philippine educational contexts. Eighteen (18) EDM research in the Philippines were evaluated. The results showed that current Philippine EDM research focuses to make predictions or classifications of student performance or behavior, such as academic achievement, online learning adaptation, procrastination, and learning styles. Specifically in mathematics education, current EDM research explored broader applications focusing on general academic performance, online learning effectiveness, and student risk factors. A wide range of student data attributes such as age, sex, and parents’ education for demographic data, online learning skills for behavioral data, and overall grades and GPA for performance data are actively utilized in Philippine EDM research. A variety of algorithms lead by traditional algorithms – Naïve Bayes and Decision Trees, have been successfully employed to carry out EDM tasks. The study also identified challenges related to data quality, availability, and ethical considerations. By identifying these trends and applications, this study can inform future efforts to leverage educational data for improving learning outcomes within the Philippine educational system.

Keywords:
literature review Mathematics Education predicting student performance

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