Manuscript received August 2, 2022; revised September 25, 2022; accepted October 24, 2022.
Abstract—Many traditional educational management
models are being switched or shifted into online platforms; thus,
assessing behavioral aspects of learners is essential to improving
the quality of online teaching and learning processes. Currently,
a problem in managing online teaching of courses is that
instructors do not have the appropriate tools and techniques to
be fully aware of students’ behavioral patterns in a data-driven
and process-aware approach. This study is divided into three
main parts. In the first part, a dataset of online students is
transformed and preprocessed. In the second part, the Fuzzy
Miner algorithm supported by Fluxicon Disco is applied to the
dataset to understand the learning process of the students in
terms of the duration and length of the tutorial videos watched
online (i.e., fully watched, partially watched, paused, and
resumed intervals) and in terms of the frequencies of all
activities. In the third part, a comparison between behavioral
patterns of high-performance group of students versus their
low-performance counterparts attending the same course was
conducted, and we used the Dotted Chart Analysis technique
supported by ProM to conduct and make the comparisons. The
results of the study showed significant differences between the
two groups in terms of the duration spent on the tutorial videos
and in terms of the sequence and order of the activities
performed and executed. The findings of the research can be
used by instructors, administrators, and educational managers
to improve the course curriculum management process or to
boost effective coaching and teaching styles, leading to the
optimization of students’ learning process by increasing
educators’ awareness about students’ weaknesses and
strengths.
Index Terms—Education data mining, education process
mining, data analytics, learning behavior, collecting event logs,
e-learning system, process discovery, student learning behavior.
Anake Nammakhunt, Parham Porouhan, and Wichian Premchaiswadi are
with Graduate School of Information Technology, Siam University,
Bangkok, Thailand.
*Correspondence: anake_cc@thonburi-u.ac.th
Cite: Anake Nammakhunt*, Parham Porouhan, and Wichian Premchaiswadi, "Creating and Collecting e-Learning Event Logs to Analyze Learning Behavior of Students through Process Mining," International Journal of Information and Education Technology vol. 13, no. 2, pp. 211-222, 2023.
Copyright © 2023 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).