Abstract
The basic objective of process mining is to discover, monitor, and improve process models by extracting knowledge from event logs using different techniques. In order to enhance the quality of process models, several works in literature used trace clustering, where a trace represents a sequence of events of the same process instance (user). In this research paper, we attempt to discover process models in the educational domain so as to refine learning resource recommendation. For this reason, we propose to apply trace clustering on event log extracted from the learning platform Moodle. Indeed, to the best of our knowledge, trace clustering has not yet been applied in the educational domain. From this perspective, we performed several experiments with various clustering algorithms to retain the most performant one. Subsequently, we applied a process discovery algorithm, namely heuristic miner. The results revealed that the quality of the discovered process models is better once trace clustering is considered.
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“User Guide.” Gaussian mixture models. Web. ©2007 - 2022. scikit-learn developers.
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Acknowledgment
This work was financially supported by the PHC Utique program of the French Ministry of Foreign Affairs and Ministry of higher education and research and the Tunisian Ministry of higher education and scientific research in the CMCU project number 22G1403.
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Hachicha, W., Ghorbel, L., Champagnat, R., Zayani, C.A. (2023). Trace Clustering Based on Activity Profile for Process Discovery in Education. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_54
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