  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.
