Comparing dropout intentions of math students on trainee teacher courses versus bachelor of science courses using intensive longitudinal data

In the past, high dropout rates of university math students have been recurrently observed, causing psychological and economic costs for individuals and society. In this article, we draw on prospective, intensive longitudinal data (ILD) collected at a large German university to examine the effects of stable inter-individual differences (e.g., general math competencies) and intra-individual changes (e.g., motivational states) on the intention to drop out of math studies, an important precursor for actual dropout. Given the ongoing discussion on whether student teachers differ from other types of students in their characteristics (e.g., with respect to cognitive abilities), we were particularly interested in differences in dropout intentions between first-year math B.Sc. and B.Ed. students, who attend the same introductory lectures. Using recent residual dynamic structural equation modeling techniques (RDSEM) we find that dropout intentions of math students in their first semester depend on both baseline characteristics and motivational changeable states which occur during the course. Furthermore, it is shown that B.Sc. and B.Ed. students differ regarding their intra-individual effects and have different trajectories of dropout intentions over time such that they cannot be assumed to be a homogeneous group. The results suggest that the two groups require differential treatment concerning the prevention of early dropouts.