Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students

TL;DR

This study investigates algorithmic bias in predicting Filipino students' academic performance using LMS data, finding no significant bias across regional backgrounds with robust model performance.

Contribution

It provides the first thorough analysis of regional bias in educational models within an Asian context, using extensive LMS data from a large Filipino university.

Findings

No unfair bias detected across regional groups

High model accuracy with AUC of 0.75 and F1-score of 0.79

Comprehensive bias evaluation using multiple metrics

Abstract

Algorithmic bias is a major issue in machine learning models in educational contexts. However, it has not yet been studied thoroughly in Asian learning contexts, and only limited work has considered algorithmic bias based on regional (sub-national) background. As a step towards addressing this gap, this paper examines the population of 5,986 students at a large university in the Philippines, investigating algorithmic bias based on students' regional background. The university used the Canvas learning management system (LMS) in its online courses across a broad range of domains. Over the period of three semesters, we collected 48.7 million log records of the students' activity in Canvas. We used these logs to train binary classification models that predict student grades from the LMS activity. The best-performing model reached AUC of 0.75 and weighted F1-score of 0.79. Subsequently, we examined the data for bias based on students' region. Evaluation using three metrics: AUC, weighted F1-score, and MADD showed consistent results across all demographic groups. Thus, no unfairness was observed against a particular student group in the grade predictions.