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.
Original languageEnglish
Title of host publicationProceeding LAK '18 Proceedings of the 8th International Conference on Learning Analytics and Knowledge
PublisherAssociation of Computing Machinery
Number of pages200
ISBN (Electronic)978-1-4503-6400-3
Publication statusPublished - Mar 2018
Event8th International Conference on Learning Analytics and Knowledge - Sydney, Australia
Duration: 5 Mar 20189 Mar 2018


Conference8th International Conference on Learning Analytics and Knowledge
Abbreviated titleLAK '18


  • self regulation
  • learning strategies
  • blended learning
  • clickstream activity
  • Learning Analytics

Fingerprint Dive into the research topics of 'Finding traces of self-regulated learning in activity streams'. Together they form a unique fingerprint.

Cite this