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Mangaroska, Katerina; Martinez-Maldonado, Roberto; Vesin, Boban; Gaševic, Dragan – Journal of Computer Assisted Learning, 2021
Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA…
Descriptors: Computer Science Education, Student Attitudes, Learning Modalities, Learning Analytics
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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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Sugden, Nicole; Brunton, Robyn; MacDonald, Jasmine; Yeo, Michelle; Hicks, Ben – Australasian Journal of Educational Technology, 2021
There is growing demand for online learning activities that offer flexibility for students to study anywhere, anytime, as online students fit study around work and family commitments. We designed a series of online activities and evaluated how, where, and with what devices students used the activities, as well as their levels of engagement and…
Descriptors: Learning Activities, Learner Engagement, Online Courses, Handheld Devices