Abstract—The most critical challenge in analyzing the data of
Massive Open Online Courses (MOOC) using process mining
techniques is storing event logs in appropriate formats. In this
study, an innovative approach for extraction of MOOC data is
described. Thereafter, several process-discovery techniques, i.e.,
Dotted Chart Analysis, Fuzzy Miner, and Social Network Miner,
are applied to the extracted MOOC data. In addition,
behavioral studies of high- and low-performance students
taking online courses are conducted. These studies considered i)
overall behavioral statistics, ii) identification of bottlenecks and
loopback behavior through frequency- and
time-performance-based approaches, and iii) working together
relationships. The results indicated that there are significant
behavioral differences between the two groups. We expect that
the results of this study will help educators understand
students’ behavioral patterns and better organize online course
Index Terms—Process mining, event log, fuzzy miner, social network, dotted chart analysis.
Poohridate Arpasat, Parham Porouhan, and Wichian Premchaiswadi are with Graduate School of Information Technology, Siam University, Bangkok, Thailand (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
Nucharee Premchaiswadi is with College of Creative Design and Entertainment Technology, Dhurakij Pundit University, Bangkok, Thailand (e-mail: email@example.com).
Cite: Poohridate Arpasat, Nucharee Premchaiswadi, Parham Porouhan, and Wichian Premchaiswadi, "Applying Process Mining to Analyze the Behavior of Learners in Online Courses," International Journal of Information and Education Technology vol. 11, no. 10, pp. 436-443, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).