Analysis of Learning Behavior in a Programming Course using Process Mining and Sequential Pattern Mining

This full paper of the research-to-practice category addresses the problem of discovering patterns in students' learning paths in an introductory programming course. Student interactions in courses delivered through learning management systems (LMS) are stored in event logs. Each event corresponds to some action performed by students on course content. Process Mining (PM) techniques allow extracting knowledge from the event logs generating the trace, that is, the ordered set of events of a given student in a course. PM can also obtain the process models generated from the traces. Process models show where students started and ended their paths, enabling them to characterize possible behaviours. It shows that each student can behave differently, generating different sequences. Applying Sequential Pattern Mining (SPM) techniques make it possible to extract sequential patterns and discover sequences of similar events. Thus, this article aims to present the results of applying PM and SPM techniques to verify and analyze the students' learning paths in an introductory programming course. For this, a log of Moodle events was collected, which went through data selection and cleaning, and segmentation. The Moodle event log stores data that allows exploring information on three levels: (1) activity-type, (2) activity, and (3) action in the activity. Thus, the study aimed to investigate which activity-types, activities, and actions were performed and in what order. Among the applied techniques are those to obtain: event log statistics, process models with the Directly-Follows Graph algorithm and event sequences with the Generalized Sequential Patterns algorithm. The results showed that the students had distinct behaviors when accessing and performed the activities. Overall, we highlight the following two main findings that enabled a better understanding of the different behaviors: (i) verifying process models of Levels 2 and 3 attributes allows to deepen the analysis in the flow of the events of the activities; (ii) The subsequences of interest extracted via SPM can complement the analysis of the students' behaviors.