Manrique, Ruben; Nunes, Bernardo Pereira; Marino, Olga; Casanova, Marco A.; Nurmikko-Fuller, Terhi
Identifying and monitoring students who are likely to dropout is a vital issue for universities. Early detection allows institutions to intervene, addressing problems and retaining students. Prior research into the early detection of at-risk students has opted for the use of predictive models, but a comprehensive assessment of the suitability of different algorithms and approaches is complicated by the large number of variable features that constitute a student's educational experience....[Show more]
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