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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.