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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.09821 |
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| _version_ | 1866909253067866112 |
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| author | Švábenský, Valdemar Verger, Mélina Rodrigo, Maria Mercedes T. Monterozo, Clarence James G. Baker, Ryan S. Saavedra, Miguel Zenon Nicanor Lerias Lallé, Sébastien Shimada, Atsushi |
| author_facet | Švábenský, Valdemar Verger, Mélina Rodrigo, Maria Mercedes T. Monterozo, Clarence James G. Baker, Ryan S. Saavedra, Miguel Zenon Nicanor Lerias Lallé, Sébastien Shimada, Atsushi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09821 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students Švábenský, Valdemar Verger, Mélina Rodrigo, Maria Mercedes T. Monterozo, Clarence James G. Baker, Ryan S. Saavedra, Miguel Zenon Nicanor Lerias Lallé, Sébastien Shimada, Atsushi Machine Learning Computers and Society K.3 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. |
| title | Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students |
| topic | Machine Learning Computers and Society K.3 |
| url | https://arxiv.org/abs/2405.09821 |