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

Authors: Rahila Umer; Teo Susnjak; Anuradha Mathrani; Suriadi Suriadi

Addresses: Department of Computer Science, Faculty of Information and Communication Technology, Balochistan University of Information Technology, Engineering and Management Sciences, Pakistan ' School of Natural and Computational Sciences, Massey University, Auckland, New Zealand ' School of Natural and Computational Sciences, Massey University, Auckland, New Zealand ' Information Systems School, Queensland University of Technology, Brisbane, Australia

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.

Keywords: learning analytics; process mining; quiz-taking behaviour; learning management system; education data; process instance; data quality.

DOI: 10.1504/IJBIS.2020.10027967

International Journal of Business Information Systems, 2022 Vol.39 No.4, pp.569 - 592

Received: 23 Jun 2019
Accepted: 02 Sep 2019

Published online: 16 May 2022 *

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