Machine Learning Model Building Techniques for Small and Medium-sized University Courses

Abstract

The relation between an educational target and a set of predictors related to the learners and their learning activities in a given learning context can be investigated by predictive Machine Learning (ML) modelling. For courses with many students and with predictors that well reflect the specialties of the courses, the predictive power of “classical” ML models generally meets expectations. At the same time, even large universities have several courses where the small number of students does not allow the use of “classical” ML models, although the need to forecast student performance also appears for these courses. In this study, considering the research on the Applied Statistics course with the participation of 56 full-time students at the University of Dunaújváros, we present various model building techniques that can be used to increase the predictive power of models. We systematically show different model building technics starting from less effective technics to more developed ones. These developed ones are applicable even for small or mid-sized university courses producing a monotonically increasing good performance metrics in time. The conditions and limits of their applicability are also discussed.