dc.creatorMaldonado Mahauad, Jorge Javier
dc.creatorPerez Sanagustín, Mar
dc.creatorKizilcec, Rene
dc.creatorMuñoz Gama, Jorge
dc.date.accessioned2018-10-19T18:03:47Z
dc.date.accessioned2022-10-20T23:33:48Z
dc.date.available2018-10-19T18:03:47Z
dc.date.available2022-10-20T23:33:48Z
dc.date.created2018-10-19T18:03:47Z
dc.date.issued2018
dc.identifier0747-5632
dc.identifierhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85034415787&origin=inward
dc.identifier10.1016/j.chb.2017.11.011
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4618155
dc.description.abstractBig 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 …
dc.languagees_ES
dc.sourceComputers in Human Behavior
dc.subjectLearning Strategies
dc.subjectMassive Open Online Courses
dc.subjectProcess Mining
dc.subjectSelf-Regulated Learning
dc.titleMining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses
dc.typeARTÍCULO


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