Maldonado Mahauad, Jorge JavierPerez Sanagustín, MarKizilcec, ReneMuñoz Gama, Jorge2018-10-192018-10-1920180747-5632https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85034415787&origin=inwardBig data in education offers unprecedented opportunities to support learners and advance research in the learning sciences. Analysis of observed behaviour using computational methods can uncover patterns that reflect theoretically established processes, such as those involved in self-regulated learning (SRL). This research addresses the question of how to integrate this bottom-up approach of mining behavioural patterns with the traditional top- down approach of using validated self-reporting instruments. Using process mining, we extracted interaction sequences from fine-grained behavioural traces for 3458 learners across three Massive Open Online Courses. We identified six distinct interaction sequence patterns. We matched each interaction sequence pattern with one or more theory-based SRL strategies and identified three clusters of learners. First, Comprehensive Learners …Big data in education offers unprecedented opportunities to support learners and advance research in the learning sciences. Analysis of observed behaviour using computational methods can uncover patterns that reflect theoretically established processes, such as those involved in self-regulated learning (SRL). This research addresses the question of how to integrate this bottom-up approach of mining behavioural patterns with the traditional top- down approach of using validated self-reporting instruments. Using process mining, we extracted interaction sequences from fine-grained behavioural traces for 3458 learners across three Massive Open Online Courses. We identified six distinct interaction sequence patterns. We matched each interaction sequence pattern with one or more theory-based SRL strategies and identified three clusters of learners. First, Comprehensive Learners …es-ESLearning StrategiesMassive Open Online CoursesProcess MiningSelf-Regulated LearningARTÍCULO10.1016/j.chb.2017.11.011