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Data quality challenges in educational process mining: building process-oriented event logs from process-unaware online learning systems

Author

Listed:
  • Rahila Umer
  • Teo Susnjak
  • Anuradha Mathrani
  • Suriadi Suriadi

Abstract

Educational process mining utilises process-oriented event logs to enable discovery of learning practices that can be used for the learner's advantage. However, learning platforms are often process-unaware, therefore do not accurately reflect ongoing learner interactions. We demonstrate how contextually relevant process models can be constructed from process-unaware systems. Using a popular learning management system (Moodle), we have extracted stand-alone activities from the underlying database and formatted it to link the learners' data explicitly to process instances (cases). With a running example that describes quiz-taking activities undertaken by students, we describe how learner interactions can be captured to build process-oriented event logs. This article contributes to the fields of learning analytics and education process mining by providing lessons learned on the extraction and conversion of process-unaware data to event logs for the purpose of analysing online education data.

Suggested Citation

  • Rahila Umer & Teo Susnjak & Anuradha Mathrani & Suriadi Suriadi, 2022. "Data quality challenges in educational process mining: building process-oriented event logs from process-unaware online learning systems," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 39(4), pages 569-592.
  • Handle: RePEc:ids:ijbisy:v:39:y:2022:i:4:p:569-592
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