This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.

Highlights A wide variety of tracking and detection tools for computer vision-based GMA exist. A “method-of-choice” for automated GMA does not yet exist. Large expert-annotated valid datasets are urgently needed. The prerequisites of classic GMA is indispensable for developing automated solutions. A future augmented GMA shall combine human expertise with computerised tools.

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