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This paper focuses on the study of automated process discovery using the Inductive visual Miner (IvM) and Directly Follows visual Miner (DFvM) algorithms to produce a valid process model for educational process mining in order to understand and predict the learning behavior of students. These models were evaluated on the publicly available xAPI (Experience API or Experience Application Programming Interface) dataset, which is an education dataset intended for tracking students’ classroom activities, participation in online communities, and performance. Experimental results with several performance measures show the effectiveness of the developed process models in helping experts to better understand students’ learning behavioral patterns.

An Education Process Mining Framework: Unveiling Meaningful Information for Understanding Students’ Learning Behavior and Improving Teaching Quality