Analysing student behaviour in a learning management system using a process mining approach

Abstract

Online learning implementation has been growing year by year across countries, including Indonesia. Many higher education institutions use a Learning Management System (LMS) to facilitate online learning. Unfortunately, many issues arise during online learning implementation, such as a lack of student behaviour monitoring. This study adopts an educational process mining technique to conduct weekly assessments of student behaviour during one semester. The study was undertaken in the following steps: problem identification, literature review, design of study context, log data collection from LMS, log data filtering, event data grouping, conversion of LMS logs to event logs, clustering, and process model discovery. The following findings were revealed in this research: the most frequently accessed features were course material, assignments, and forums; students accessed the LMS most frequently on lecture days; the number of student activities decreased in line with fewer instructions from lecturers; students who attained the best grades most frequently accessed the LMS, and vice versa; and high-achieving students had a more complex process model than other students. Therefore, this research suggests that systematic teaching strategies have a broader impact on student engagement and performance.

https://doi.org/10.34105/j.kmel.2022.14.005