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 content.
Highlights
Many countries have recognized the importance of information and communication technology (ICT) in education and online courses)
E.g., students' backgrounds and learning goals [7], can be used for behavioral analysis of learners Three process-discovery algorithms were used, i.e., i) Dotted Chart Analysis algorithm supported by ProM Software, ii) Fuzzy Miner algorithm supported by Disco Fluxicon Software [9], and iii) Social Network Miner algorithm, supported by ProM Software [10], [13]
Massive Open Online Courses (MOOC) refers to free online courses available via the Web, thereby providing learning opportunities for an unlimited number of participants In general, MOOCs have many advantages compared with existing traditional teaching/learning approaches
Summary
Many countries have recognized the importance of information and communication technology (ICT) in education and online courses). Mukala et al [7] explored students' learning behavior based on the techniques of Dotted Chart Analysis and conformance diagnostics They modeled and analyzed different study parts obtained from Massive Open Online Courses (MOOC) data, using a case study from the Eindhoven University of Technology. This paper aims to analyze and compare high- and low-performance study behavior using process mining based on online courses at a University in Thailand. E.g., students' backgrounds and learning goals [7], can be used for behavioral analysis of learners Three process-discovery algorithms were used, i.e., i) Dotted Chart Analysis algorithm supported by ProM Software, ii) Fuzzy Miner algorithm supported by Disco Fluxicon Software [9], and iii) Social Network Miner algorithm, (based on the Working Together metric) supported by ProM Software [10], [13]
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