Tool for Moodle course assessment based on structure and standard logs

The subject of this paper is a practical task: aligning the intended structure of an online course with actual user activity recorded in the standard event log of a Moodle system. The aim is to describe an original method for producing a consolidated course evaluation report and its implementation as a local plugin suitable both for a teacher’s course-level self-check and for administrative cross-course monitoring. The tasks include formalizing input data, selecting a metric set, defining access and report-storage rules, and ensuring reproducible calculations within a selected calendar interval. The methodology is engineering-oriented: the input includes course element descriptions and role- and period-filtered records from the standard log; the output includes numerical indicators for structure, activity type composition, content use, student reach, and temporal activity stability, along with a weighted aggregate score with an explicit formula and a list of low-engagement elements grouped by module type, excluding decorative labels. The result is a working extension with interfaces for running evaluations, viewing reports, deleting reports, and executing batch runs in a separate administrative mode. The conclusion confirms the practical value of the tool for teachers’ self-assessment of whether designed course elements are actually used, and for institutional monitoring, with an explicit limitation: the indicators reflect observed usage patterns and structural consistency with logs rather than learning outcomes. The novelty lies in the combined use of these metrics, role-specific access scenarios, and transparent score presentation in the report.

1. Romero C., Ventura S. Educational data mining and learning analytics: An updated survey // Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2020. Vol. 10, no. 3. https://doi.org/10.1002/widm.1355.

2. Тербушева Е. А., Пиотровская К. Р. Аналитический потенциал платформы Moodle для мониторинга качества персонифицированного обучения // Общество. Коммуникация. Образование. 2021. Т. 12, № 4. С. 19–34. https://doi.org/10.18721/JHSS.12402.

3. Шаимов Н. Д., Ломазова И. А., Мицюк А. А. и др. Анализ академической успеваемости студентов с использованием журналов событий электронной образовательной среды // Моделирование и анализ информационных систем. 2022. Т. 29, № 4. С. 286–314. https://doi.org/10.18255/1818-1015-2022-4-286-314.

4. Карманова Е. В., Захарова Я. М., Киселев А. В. Интеллектуальный анализ образовательных результатов студентов университета с использованием LMS Moodle // Информатика и образование. 2024. Т. 39, № 3. С. 16–28. https://doi.org/10.32517/0234-0453-2024-39-3-16-28.

5. Pan Z., Biegley L., Taylor A. et al. A systematic review of learning analytics: Incorporated instructional interventions on learning management systems // Journal of Learning Analytics. 2024. Vol. 11, no. 2. P. 52–72. https://doi.org/10.18608/jla.2023.8093.

6. Aljohani N. R., Daud A., Abbasi R. A. et al. An integrated framework for course adapted student learning analytics dashboard // Computers in Human Behavior. 2019. Vol. 92. P. 679–690. https://doi.org/10.1016/j.chb.2018.03.035.

7. López-Zambrano J., Lara J. A., Romero C. Towards portability of models for predicting students’ final performance in university courses starting from Moodle logs // Applied Sciences. 2020. Vol. 10, no. 1. https://doi.org/10.3390/app10010354.

8. Bodily R., Verbert K. Review of research on student-facing learning analytics dashboards and educational recommender systems // IEEE Transactions on Learning Technologies. 2017. Vol. 10, no. 4. P. 405–418. https://doi.org/10.1109/TLT.2017.2740172.

9. Shannon C. E. A mathematical theory of communication // The Bell System Technical Journal. 1948. Vol. 27, no. 3. P. 379–423.

10. Prinsloo P., Slade S., Khalil M. The answer is (not only) technological: Considering student data privacy in learning analytics // British Journal of Educational Technology. 2022. Vol. 53, no. 4. P. 876–893. https://doi.org/10.1111/bjet.13216.