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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)
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Floris, Francesco; Marchisio, Marina; Sacchet, Matteo; Rabellino, Sergio – International Association for Development of the Information Society, 2020
Open Online Courses can serve different purposes: in the case of Orient@mente at the University of Torino, they aim at facilitating the transition from secondary to tertiary education with automatic evaluation tests that students can try in order to understand their capabilities in - and their attitude towards - certain disciplines, and with…
Descriptors: Learning Analytics, Academic Failure, Universities, Student Adjustment