Tracing Student Activity Patterns in E-Learning Environments: Insights into Academic Performance

Evgenia Paxinou (), Georgios Feretzakis, Rozita Tsoni, Dimitrios Karapiperis, Dimitrios Kalles and Vassilios S. Verykios ()
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Evgenia Paxinou: School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
Georgios Feretzakis: School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
Rozita Tsoni: School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
Dimitrios Karapiperis: School of Science and Technology, International Hellenic University, 57001 Thermi, Greece
Dimitrios Kalles: School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
Vassilios S. Verykios: School of Science and Technology, Hellenic Open University, 26335 Patras, Greece

Future Internet, 2024, vol. 16, issue 6, 1-15

Abstract: In distance learning educational environments like Moodle, students interact with their tutors, their peers, and the provided educational material through various means. Due to advancements in learning analytics, students’ transitions within Moodle generate digital trace data that outline learners’ self-directed learning paths and reveal information about their academic behavior within a course. These learning paths can be depicted as sequences of transitions between various states, such as completing quizzes, submitting assignments, downloading files, and participating in forum discussions, among others. Considering that a specific learning path summarizes the students’ trajectory in a course during an academic year, we analyzed data on students’ actions extracted from Moodle logs to investigate how the distribution of user actions within different Moodle resources can impact academic achievements. Our analysis was conducted using a Markov Chain Model, whereby transition matrices were constructed to identify steady states, and eigenvectors were calculated. Correlations were explored between specific states in users’ eigenvectors and their final grades, which were used as a proxy of academic performance. Our findings offer valuable insights into the relationship between student actions, link weight vectors, and academic performance, in an attempt to optimize students’ learning paths, tutors’ guidance, and course structures in the Moodle environment.

Keywords: Moodle; Markov Chain Model; learning analytics; self-directed learning; eigenvector; academic performance (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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