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Wei Dai; Jionghao Lin; Flora Ji-Yoon Jin; Yi-Shan Tsai; Namrata Srivastava; Pierre Le Bodic; Dragan Gasevic; Guanliang Chen – Journal of Learning Analytics, 2025
Supporting academically at-risk students has attracted much attention in the field of learning analytics. However, much of the research in this area has focused on developing advanced machine learning models to predict students' academic performance, which alone is insufficient to improve student learning without the implementation of timely…
Descriptors: Learning Analytics, Identification, At Risk Students, Feedback (Response)
Aghababyan, Ani; Martin, Taylor; Janisiewicz, Philip; Close, Kevin – Journal of Learning Analytics, 2016
Learning analytics is an emerging discipline and, as such, benefits from new tools and methodological approaches. This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the second annual Learning Analytics Summer Institute in Cambridge, Massachusetts, on 30 June 2014. Specifically, this paper…
Descriptors: Educational Research, Data Collection, Data Analysis, Workshops

Peer reviewed
