F8-FY5-4 - Educational Process Mining for Verifying Student Learning Paths in an Introductory Programming Course
2. Research-to-Practice Full PaperEdson Pinheiro Pimentel1, Lucas Vieira de Oliveira1, Juliana Cristina Braga1, Itana Stiubiener1
1 Federal University of ABC
Introduction
This full paper of the research-to-practice category addresses the problem of organizing instructional materials and assessment activities in e-learning courses and their effects on learning outcomes. Usually, the teacher organizes the course sequence according to his didactic-pedagogical strategies and expects this help to guide the student through his learning process in the course. However, unless restrictions are imposed, students may choose to follow different paths than those indicated in the material's organization. A question emerges from this context: what are the impacts on the students learning outcomes when they take learning paths other than expected by the teacher? In Virtual Learning Environments, student's interaction with course materials can be stored in the so-called event logs. With the support of Educational Process Mining, it is possible to track the path of how and what specific actions students perform during learning, resulting in process models and historical statistical information. This paper aims to present the application results of PM techniques to verify the students learning paths in an introductory programming course. We used a Moodle event log containing 24605 events collected from 73 undergraduate students. For experiments, we divided this original log file into five other segments of datasets among passed and failed students variations. Techniques to obtain statistical information, Heuristic Miner algorithm to process discovery, and other techniques were applied from the implementations available in ProM Framework and scripts based on PM4Py library. The results showed that overall approved and failed students took different paths and event numbers to perform activities in the course. Besides, we obtained control-flows and frequencies of the activities and connections, thus making it possible to identify the dependencies, which resources started or ended the process, among other things. The analysis of these results provides general and specific information on students' learning paths and can help teachers observe students' behavior patterns and progress.