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First-year student success in Engineering Bachelor programs is well-studied. Both traditional statistical modelling and machine learning approaches have been used to study what makes students successful. While statistical modelling helps to obtain population-wide patterns, they often fail to create accurate predictions for individual students. Predictive machine learning algorithms can create accurate predictions but often fail to create interpretable insights. This paper compares a statistical modelling and machine learning approach for predicting first-year student success. The case study focuses on first-year Bachelor of Engineering Science students from KU Leuven between 2015-2017 and relates first-semester academic achievement to prior education, learning and study strategies, effort level, and preference for time pressure. ispartof: pages:322-329 ispartof: Proceedings of the 46th SEFI Annual Conference 2018 vol:46 pages:322-329 ispartof: SEFI conference location:Copenhagen, Denmark date:17 Sep - 21 Sep 2018 status: published |
