Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students
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.
Keywords
Cite
@article{arxiv.2405.09821,
title = {Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students},
author = {Valdemar Švábenský and Mélina Verger and Maria Mercedes T. Rodrigo and Clarence James G. Monterozo and Ryan S. Baker and Miguel Zenon Nicanor Lerias Saavedra and Sébastien Lallé and Atsushi Shimada},
journal= {arXiv preprint arXiv:2405.09821},
year = {2024}
}
Comments
Published in proceedings of the 17th Educational Data Mining Conference (EDM 2024)