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Cano, Alberto; Leonard, John D. – IEEE Transactions on Learning Technologies, 2019
Early warning systems have been progressively implemented in higher education institutions to predict student performance. However, they usually fail at effectively integrating the many information sources available at universities to make more accurate and timely predictions, they often lack decision-making reasoning to motivate the reasons…
Descriptors: Progress Monitoring, At Risk Students, Disproportionate Representation, Underachievement
Jokhan, Anjeela; Sharma, Bibhya; Singh, Shaveen – Studies in Higher Education, 2019
Early warning systems are being used to assist students in their studies as well as understanding student behaviour and performance better. A home-grown EWS plug-in for Moodle was used to predict the student performance in a first year IT literacy course at University of the South Pacific. The alert tool was designed to capture student logins,…
Descriptors: Higher Education, College Students, Online Courses, At Risk Students