Analysis of Student Learning Pattern in Learning Management System (LMS) using Heuristic Mining a Process Mining Approach

Abstract

The COVID-19 pandemic has had a significant impact on educational organizations, one of which is the Telkom University educational institute. Telkom University using Learning Management System (LMS) during pandemic covid-19. The use of an LMS will have a huge number of data records stored in the form of event logs. Opportunities to process data recorded in event logs must be maximized to improve Telkom University's LMS continuously. In this study, the process mining method conducted using event logs from Website Application Development (WAD) and Enterprise System (ES) courses to compare student learning patterns between programming courses and non-programming courses. Process mining conducted using PROM 5.2 and the Heuristic Miner plugins. The modeling process uses a heuristic miner algorithm because of its ability to present the main behavior recorded in the event log well by analyzing conformance checking. The result of conformance checking shows a fitness value of 0.887 for WAD and 0.847 for ES. It means the process model can represent the event log well. WAD's value of advanced behavioral appropriateness and degree of model flexibility are 0.594 and 0.411, whereas ES 0.181 and 0.155. The value of the structure shows 1.0 for the two subjects. After obtaining a process model, the next step is to analyze student learning patterns based on the frequency of access to the LMS. With this research, it is hoped that this research can contribute to adding new insights regarding the use of event logs in the field of education and knowing the activities carried out by students through LMS learning media, as well as providing visualization of student behavior patterns in the LMS (Learning Management System) of Telkom University.