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Li, Warren; Sun, Kaiwen; Schaub, Florian; Brooks, Christopher – International Journal of Artificial Intelligence in Education, 2022
Use of university students' educational data for learning analytics has spurred a debate about whether and how to provide students with agency regarding data collection and use. A concern is that students opting out of learning analytics may skew predictive models, in particular if certain student populations disproportionately opt out and biases…
Descriptors: College Students, Learning Analytics, Student Attitudes, Informed Consent
Jamiu Adekunle Idowu – International Journal of Artificial Intelligence in Education, 2024
This systematic literature review investigates the fairness of machine learning algorithms in educational settings, focusing on recent studies and their proposed solutions to address biases. Applications analyzed include student dropout prediction, performance prediction, forum post classification, and recommender systems. We identify common…
Descriptors: Algorithms, Dropouts, Prediction, Academic Achievement