Post hoc identification of student groups: Combining user modeling with cluster analysis

Abstract

This study aims to discover groups of students enrolled in the emergency remote teaching online course based on the various course-related data collected throughout the first year of COVID-19 pandemic. Research was conducted among 222 students enrolled in the course “Business Informatics” at the Faculty of Organization and Informatics of the University of Zagreb in the academic year 2020/2021. Overlays were used to model students’ success on the various quizzes and exams within the course. The k-means clustering was employed to classify students into groups, based on combination of students’ overlay values, frequency of accessing course lessons and the final grades. Three distinct clusters (i.e., students’ groups) were discovered and explained in the given context. The identified groups of students can be used for future adaptations of the online course design in order to improve the retention and their final grades.