Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course. (October 2019) - Wong, Jacqueline; Khalil, Mohammad; Baars, Martine; de Koning, Björn B.; Paas, Fred
Abstract: Self-regulated learning (SRL) refers to how learners steer their own learning. Supporting SRL has been shown to enhance the use of SRL strategies and learning performance in computer-based learning environments. However, little is known about supporting SRL in Massive Open Online Courses (MOOCs). In this study, weekly SRL prompts were embedded as videos in a MOOC. We employed a sequential pattern mining algorithm, Sequential Pattern Discovery using Equivalence classes (cSPADE), on gathered log data to explore whether differences exist between learners who viewed the SRL-prompt videos and those who did not. Results showed that SRL-prompt viewers interacted with more course activities and completed these activities in a more similar sequential pattern than non SRL-prompt viewers. Also, SRL-prompt viewers tended to follow the course structure, which has been identified as a behavioral characteristic of students who scored higher on SRL (i.e., comprehensive learners) in previous research. Based on the results, implications for supporting SRL in MOOCs are discussed. Highlights: We examined learners' use of self-regulated learning (SRL) prompts in a MOOC. Using sequential pattern mining, sequences of learner activities were examined. Students who viewed more prompts interacted with more course elements. Viewers of SRL-prompts better follow the course structure than non-viewers. Exploring sequences of learner activities potentially informs the design of MOOCs.