Predicting Math Student Success in the Initial Phase of College With Sparse Information Using Approaches From Statistical Learning - Kilian, Pascal; Loose, Frank; Kelava, Augustin (Frontiers in Education, 2020)
In math teacher education, dropout research relies mostly on frameworks which carry out intensive variable collections leading to a lack of practical applicability. The aim of this study is to provide not only good dropout predictions, but also to generate interpretable and practicable results together with easy-to-understand recommendations with a special focus on the group of teacher candidates. As proof-of-concept, a sparse feature space together with machine learning methods is used for prediction of dropout, wherein the most predictive features have to be identified. Interpretability can be reached by introducing risk groups for the students. Implications for interventions are discussed.