Process mining encompasses a suite of techniques aimed at analyzing event data to gain insights and improve operational processes. One way of achieving this is to discover the driving process of the activities that occurred in a system. Technically, process discovery algorithms are used to transform an event log into a process model which is representative of the activities registered in the given system. This study explores the application of process discovery methods to better understand the learning processes in an introductory programming course for first-year Computer Science BSc students. A total of 52 practical problems were assigned as out-of-class activities via GitHub Classroom, resulting in 2789 commits from 59 students. These commits, along with the students’ exam grades, were recorded in an object-centric event log, subsequently converted into a casebased log for analysis using the PM4Py program library. The study had two primary goals: first, to identify the characteristics of successful learning strategies by comparing process models of students who passed versus those who failed the programming exam; and second, to identify bottlenecks that hindered student progress. By employing the Heuristic Miner and Inductive Miner algorithms, we developed and contrasted learning process models, revealing significant patterns and obstacles within the educational process. The findings provide valuable insights into the factors that contribute to effective learning and suggest areas for enhancing our teaching methodologies.
Varga et al. (Wed,) studied this question.