Authors:
Noura Joudieh
1
;
Wil van der Aalst
2
;
Ronan Champagnat
1
;
Mourad Rabah
1
and
Samuel Nowakowski
3
Affiliations:
1
L3i, La Rochelle University, La Rochelle, France
;
2
PADS, RWTH Aachen University, Aachen, Germany
;
3
LORIA, Lorraine University, Nancy, France
Keyword(s):
Process Mining, Educational Process Mining, Moodle, Learning Analytics, Quality of Education.
Abstract:
Learning Management Systems like Moodle generate detailed logs from student interactions, offering significant potential for learning analytics and educational process mining. However, raw logs capture interaction-based actions rather than actual learning processes, limiting their pedagogical relevance. To address this, we developed Moodle2EventLog, a tool that automates the cleaning, preprocessing, and semantic enrichment of Moodle logs. The tool operates in two modules: the first cleans and structures logs by generating event logs with key elements (case IDs, activities, timestamps), and the second enriches them by grouping low-level events into context-aware sub-processes and maps them to ”Semantic Activities” based on Bloom’s Taxonomy. We tested Moodle2EventLog on logs from 65 Computer Science courses at Frederick University (471 students) from 2018–2022, and one course from La Rochelle University (36 students) in 2023, which serves as the use case in this paper. The enriched log
s enabled deeper pedagogical analysis, such as identifying learning phase frequencies, studying specific activities and resource usage, and extracting semantically informed learner profiles linked to performance. Evaluation and instructor feedback validated the tool’s effectiveness, demonstrating its ability to transform raw logs into pedagogically rich data, enabling the discovery of learning paths and providing insights unattainable with original Moodle logs.
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