Data Quality Challenges in Educational Process Mining: Building Process-Oriented Event Logs from Process-Unaware Online Learning Systems

Educational process mining utilizes 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.

Keywords

learning analytics, process mining, quiz-taking behaviour, learning management system, education data, process instance, data quality

Citation

International Journal of Business Information Systems, 2022, 39 (4), pp. 569 - 592

URI

https://hdl.handle.net/10179/18270

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