Discovering and Mapping LMS Course Usage Patterns to Learning Outcomes

Abstract

The widespread use of Learning Management Systems in higher education, a growing adoption of e-learning, distance, hybrid and blended learning puts much pressure onto both students, to achieve learning goals and lecturers, to design high quality online courses. Lecturers typically evaluate how much the students have achieved at the end of the course. This exploratory study attempts to uncover the relationship between usage behavior and students’ grades, i.e. what are the online course usage patterns performed by higher graded students in contrast to lower graded ones. The core data is of the analysis are the event logs extracted from the online course Modeling and simulation at the Faculty of informatics in Pula and mapped to the students’ grades accumulated from the final exams, assignments, projects and class tasks. Process mining techniques were used for process discovery and process model analysis. A set of procedures were developed (within the R programming environment) to analyze the discovered process models. The findings indicate that a better understanding of online course usage patterns and its relationship with learning outcomes can be used to develop intelligent systems (recommender systems, intelligent agents, intelligent personal assistants etc.) that can improve students’ learning process.