Assessing the Effectiveness of Self-Regulated Learning in MOOCs Using Macro-level Behavioural Sequence Data

Learners are demanded more self-regulatory capability to carry on effective online learning. Online course instructors attempt to stimulate online learners’ effective self-regulated learning (SRL) to support effective learning and enhance achievement. Knowing how the online learners learn in SRL loop will contribute to the effective course design and scaffolding. In this study, the learners from an edX MOOC were differentiated into more effective selfregulated learners (SRLers) and less effective SRLers based on the criteria of three SRL phases behavioural sequence patterns. The clickstream data of 5764 learners was analyzed on macro-level behavioural learning sequence through ngram algorithm. Persistence and grade were compared among the different types of learners. The results showed us that more effective SRLers persisted longer and performed better than less effective SRLers on a significant level.