Abstract
In math teacher education, dropout research relies mostly on frameworks which carry out extensive variable collections leading to a lack of practical applicability. We investigate the completion of a first semester course as a dropout indicator and thereby provide not only good predictions, but also generate interpretable and practicable results together with easy-to-understand recommendations. 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.
Highlights
High dropout rates in mathematics and general in the STEM fields—more precisely, in Germany, the so-called MINT disciplines—are not a new phenomenon
We summarize that with appropriate methods the success in the Analysis 1 lecture can be correctly predicted for 75% of the students, only with the knowledge of their GPA, their math grade in the final exam in school, the test result of the TIMSS items, the school type and their number of semesters and study program
We conclude that teacher candidates start with adverse prerequisites concerning math specific performance measures
Summary
High dropout rates in mathematics and general in the STEM fields (science, technology, engineering and mathematics)—more precisely, in Germany, the so-called MINT disciplines (mathematics, informatics, science and technology)—are not a new phenomenon. This paper focuses on the investigation of the initial phase of the study program, more precisely the Analysis 1 (Calculus 1) lecture which is a typical (and mandatory) start in the mathematics study program. This means the subject of research in this paper is not dropouts (from university or the study program) but dropouts and success in this lecture in the sense of a non-completion rate. For the sake of clarity, throughout the rest of the paper we will use the term dropout when talking about the general problem and non-completion for the dependent variable in our study
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