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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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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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Cukurova, Mutlu; Khan-Galaria, Madiha; Millán, Eva; Luckin, Rose – Journal of Learning Analytics, 2022
One-to-one online tutoring provided by human tutors can improve students' learning outcomes. However, monitoring the quality of such tutoring is a significant challenge. In this paper, we propose a learning analytics approach to monitoring online one-to-one tutoring quality. The approach analyzes teacher behaviours and classifies tutoring sessions…
Descriptors: Learning Analytics, Tutoring, Educational Quality, Behavior Patterns
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Pishtari, Gerti; Prieto, Luis P.; Rodriguez-Triana, Maria Jesus; Martinez-Maldonado, Roberto – Journal of Learning Analytics, 2022
This research was triggered by the identified need in literature for large-scale studies about the kinds of designs that teachers create for mobile learning (m-learning). These studies require analyses of large datasets of learning designs. The common approach followed by researchers when analyzing designs has been to manually classify them…
Descriptors: Scaling, Classification, Context Effect, Telecommunications
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Lämsä, Joni; Uribe, Pablo; Jiménez, Abelino; Caballero, Daniela; Hämäläinen, Raija; Araya, Roberto – Journal of Learning Analytics, 2021
Scholars have applied automatic content analysis to study computer-mediated communication in computer-supported collaborative learning (CSCL). Since CSCL also takes place in face-to-face interactions, we studied the automatic coding accuracy of manually transcribed face-to-face communication. We conducted our study in an authentic higher-education…
Descriptors: Cooperative Learning, Computer Assisted Instruction, Synchronous Communication, Learning Analytics