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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
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Wan, Han; Liu, Kangxu; Yu, Qiaoye; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2019
Most educational institutions adopted the hybrid teaching mode through learning management systems. The logging data/clickstream could describe learners' online behavior. Many researchers have used them to predict students' performance, which has led to a diverse set of findings, but how to use insights from captured data to enhance learning…
Descriptors: Educational Practices, Learner Engagement, Identification, Study Habits