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

arXiv: https://arxiv.org/abs/2405.09821 · HTML: https://mlsanity.com/paper/2405.09821/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 5 of 5)

- [Algorithmic Fairness in Education](https://mlsanity.com/paper/2007.05443/) (2020)
- [Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs](https://mlsanity.com/paper/2207.00551/) (2022)
- [Towards Equity and Algorithmic Fairness in Student Grade Prediction](https://mlsanity.com/paper/2105.06604/) (2021)
- [Evaluation of Fairness Trade-offs in Predicting Student Success](https://mlsanity.com/paper/2007.00088/) (2020)
- [Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models](https://mlsanity.com/paper/2305.15342/) (2023)

## Built on by (most cited first, 1 of 1)

- [Toward Sufficient Statistical Power in Algorithmic Bias Assessment: A Test for ABROCA](https://mlsanity.com/paper/2501.04683/) (2025)

## Abstract (arXiv)

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.