Uncovering learning patterns in a MOOC through conformance alignments

Web-based learning is now offered in multiple forms. One of these is the phenomenon of Massive Open Online Courses (MOOCs). Several approaches in Learning Analytics (LA) attempt to analyze and explain students learning patterns in MOOCs. In addition to traditional data mining techniques, online surveys constitute another way used in LA for analyzing students’ learning habits in MOOCs. However, such an approach can be error-prone with data collection. Therefore, we adopt the use of process mining techniques. Process mining techniques provide more robust ways of extracting, analyzing and visualizing students’ activities trail. In this paper, we make use of alignment-based conformance checking to extract and analyse students’ learning patterns in MOOCs. The aim is to provide a guideline and demonstrate how process mining can provide critical insights in tems of students’ learning and quiz submissions behavior, their resulting performance and the correlation therein. Keywords: Learning Analytics, Mooc, Coursera, Educational Data Mining, Process Mining,Online Learning, Conformance Checking