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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
Mulholland, P.; Anastopoulou, S.; Collins, T.; Feisst, M.; Gaved, M.; Kerawalla, L.; Paxton, M.; Scanlon, E.; Sharples, M.; Wright, M. – IEEE Transactions on Learning Technologies, 2012
This paper describes the development of nQuire, a software application to guide personal inquiry learning. nQuire provides teacher support for authoring, orchestrating, and monitoring inquiries as well as student support for carrying out, configuring, and reviewing inquiries. nQuire allows inquiries to be scripted and configured in various ways,…
Descriptors: Inquiry, Active Learning, Computer Uses in Education, Computer Software