Moodle Learning Analytics: how to extract log data efficiently ?
(2025) , 73 pages
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- Authors
- Abstract
- This report describes a learning analytics project carried out within a Belgian university as part of the individual career development of an IT specialist working on the Moodle platform. The project focuses on designing a learning analytics tool based on Moodle data, while addressing major technical challenges related to the platform’s large log tables and the need for optimized database structures and queries. Unlike many learning analytics initiatives aimed at teachers or institutional leaders, this project is student-centered. Drawing on pedagogical research on student engagement, it proposes a “Social Flow” view that displays students’ most frequent actions in order to enhance engagement. The report presents the conceptual foundations of the project, the Moodle plugin architecture required for its implementation, and an analysis of existing Moodle learning analytics plugins, including an approach inspired by Thomas Dondorf’s PhD work. It then details the main technical challenges: data storage, data retrieval and optimization, exclusion of non-relevant data, and performance tuning of database queries. All queries were tested and optimized using a copy of a very large Moodle database, though no live user testing was conducted. An evaluation framework is proposed to assess student interest, usability, and performance, but user testing was not carried out due to a professional career change. The report concludes by summarizing key principles for efficient data processing in Moodle learning analytics projects.
- Affiliations
Citations
Motte, I. (2025). Moodle Learning Analytics: how to extract log data efficiently ?