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Assessing the Effectiveness of Self-Regulated Learning in MOOCs using Behavioural Sequence Data
Min LAN, Jingyan Lu, Nikos Mattheos

Last modified: 2017-05-02

Abstract


Online course instructors intend to stimulate online learners’ effective self-regulated learning (SRL) to improve learning performance through instructional design. However, in an online learning setting, such as MOOC, self-directed learners are demanded more self-regulatory capability to solve learning problems. Knowing how these learners learn in SRL loop will contribute to more effective course design and learning support. In this study, according to Zimmerman’s three-stage SRL model, the learners from the first dental MOOC on Coursera platform were differentiated into more effective self-regulating learners (effective-SRLers) and less effective self-regulating learners (ineffective-SRLers) based on the criteria of three SRL phases behavioural sequence patterns. The clickstream data of 5014 learners was analyzed on behavioural learning sequence through n-gram algorithm. Persistence and grade were compared between these two different types of learners. The results showed us that effective-SRLers persisted longer and performed better than ineffective-SRLers on a significant level. The relationship among persistence, achievement, learning behavioural sequences and online learning design was discussed. The limitations were mentioned and suggestions for future studies were given.