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Download full textRodríguez, M. Elena; Guerrero-Roldán, Ana Elena; Baneres, David; Karadeniz, Abdulkadir – International Review of Research in Open and Distributed Learning, 2022
This work discusses a nudging intervention mechanism combined with an artificial intelligence (AI) system for early detection of learners' risk of failing or dropping out. Different types of personalized nudges were designed according to educational principles and the learners' risk classification. The impact on learners' performance, dropout…
Descriptors: Artificial Intelligence, Electronic Learning, College Students, Intervention
An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Course
Direct linkBaneres, 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
