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Direct linkYang, Zongkai; Yang, Juan; Rice, Kerry; Hung, Jui-Long; Du, Xu – IEEE Transactions on Learning Technologies, 2020
This article proposes two innovative approaches, the one-channel learning image recognition and the three-channel learning image recognition, to convert student's course involvements into images for early warning predictive analysis. Multiple experiments with 5235 students and 576 absolute/1728 relative input variables were conducted to verify…
Descriptors: Distance Education, At Risk Students, Artificial Intelligence, Man Machine Systems
An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Course
Baneres, David; Rodriguez-Gonzalez, M. Elena; Serra, Montse – IEEE Transactions on Learning Technologies, 2019
Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management…
Descriptors: Prediction, Feedback (Response), At Risk Students, College Freshmen
