تقرير
Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students
| العنوان: | Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students |
|---|---|
| المؤلفون: | Švábenský, Valdemar, Verger, Mélina, Rodrigo, Maria Mercedes T., Monterozo, Clarence James G., Baker, Ryan S., Saavedra, Miguel Zenon Nicanor Lerias, Lallé, Sébastien, Shimada, Atsushi |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computers and Society, K.3 |
| الوصف: | 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. Comment: Published in proceedings of the 17th Educational Data Mining Conference (EDM 2024) |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2405.09821Test |
| رقم الانضمام: | edsarx.2405.09821 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |