Evaluation of Recommended Learning Paths Using Process Mining and Log Skeletons: Conceptualization and Insight into an Online Mathematics Course

Abstract

Academic institutions dedicate a substantial effort to ensure the academic success of their students. At the course level, teachers recommend learning paths (RLPs) for students to guarantee the achievement of their learning outcomes. In terms of performance, these kinds of approaches are deemed more effective than others based uniquely on performing a collection of independent activities. However, there is neither systematic means to validate if following the learning path (LP) is effective, nor to assess whether and to what extent students adhere to these recommendations. This article introduces a novel technique for modeling recommended LPs, including not only an evaluation of path utility, but also a quantitative measure of student adherence thereto using process mining, and more precisely, log skeletons. Following an event abstraction process regarding real student-recorded activity, a scoping process is employed to retain the trajectories that adhere to the prescribed LP. The method based on process mining is translated into practice by considering an online university mathematics course. Results confirm the applicability of the method and, in this case, reveal that adhering to the suggested path correlates positively with final grades. Few students strictly follow the prescribed LP, although the vast majority support it. The method can be easily applied to overcome several challenges associated with enhancing academic performance from the learning analytics perspective.