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Abstract
Educational researchers commonly agree that self-regulation learning strategies are context specific. As such, the emergence of asynchronous online courses in higher education has created a need for developing a new approach that can be used to investigate self-regulated learning strategies. The purpose of this study was to use learning analytics to examine students self-regulated learning in an asynchronous online course. Log data from 284 undergraduate students enrolled in an asynchronous online statistics course were utilized to identify clusters of students according to their self-regulated learning patterns. As a result, the students were classified into one of three clusters that showed distinct learning patterns. The learning patterns of students having different self-regulated learning profiles were examined through the analysis of the contribution of each weeks log variables to the probability of being classified into each of the three clusters. The results revealed that learning profiles and patterns differed between the clusters. This study concluded by proposing instructional strategies that can be used to support self-regulated learning processes in asynchronous online courses.