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Raymond A. Opoku; Bo Pei; Wanli Xing – Journal of Learning Analytics, 2025
While high-accuracy machine learning (ML) models for predicting student learning performance have been widely explored, their deployment in real educational settings can lead to unintended harm if the predictions are biased. This study systematically examines the trade-offs between prediction accuracy and fairness in ML models trained on the…
Descriptors: Prediction, Accuracy, Electronic Learning, Artificial Intelligence
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Patterson, Chris R.; York, Emily; Maxham, Danielle; Molina, Rudy; Mabrey, Paul, III – Journal of Learning Analytics, 2023
The anticipation, inclusion, responsiveness, and reflexivity (AIRR) framework (Stilgoe et al., 2013) is a novel framework that has helped those in science and technology fields shift their focus from products to the processes used to create those products. However, the framework has not been known to be applied to the development and…
Descriptors: Learning Analytics, Innovation, School Holding Power, At Risk Students
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Swauger, Shea; Kalir, Remi – Journal of Learning Analytics, 2023
This article advances an abolitionist reframing of learning analytics (LA) that explores the benefits of productive disorientation, considers potential harms and care made possible by LA, and suggests the abolitionist imagination as an important educational practice. By applying abolitionist concepts to LA, we propose it may be feasible to open…
Descriptors: Learning Analytics, Justice, Imagination, Futures (of Society)
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Yueqiao Jin; Vanessa Echeverria; Lixiang Yan; Linxuan Zhao; Riordan Alfredo; Yi-Shan Tsai; Dragan Gasevic; Roberto Martinez-Maldonado – Journal of Learning Analytics, 2024
Multimodal learning analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across…
Descriptors: Learning Analytics, Accountability, Ethics, Artificial Intelligence
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Gedrimiene, Egle; Celik, Ismail; Mäkitalo, Kati; Muukkonen, Hanni – Journal of Learning Analytics, 2023
Transparency and trustworthiness are among the key requirements for the ethical use of learning analytics (LA) and artificial intelligence (AI) in the context of social inclusion and equity. However, research on these issues pertaining to users is lacking, leaving it unclear as to how transparent and trustworthy current LA tools are for their…
Descriptors: Learning Analytics, Accountability, Trust (Psychology), Artificial Intelligence
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Selwyn, Neil – Journal of Learning Analytics, 2019
This article summarizes some emerging concerns as learning analytics become implemented throughout education. The article takes a sociotechnical perspective -- positioning learning analytics as shaped by a range of social, cultural, political, and economic factors. In this manner, various concerns are outlined regarding the propensity of learning…
Descriptors: Learning Analytics, Criticism, Politics of Education, Educational Objectives