nStudy: Software for Learning Analytics about Processes for Self-Regulated Learning


  • Philip H Winne Simon Fraser University http://orcid.org/0000-0001-5133-7525
  • Kenny Teng Simon Fraser University
  • Daniel Chang Simon Fraser University
  • Michael Pin-Chuan Lin Simon Fraser University
  • Zahia Marzouk Simon Fraser University
  • John C Nesbit Simon Fraser University
  • Alexandra Patzak Simon Fraser University
  • Mladen Raković Simon Fraser University
  • Donya Samadi Simon Fraser University
  • Jovita Vytasek Simon Fraser University




Self-regulated learning, Metacognition, Trace data


Data used in learning analytics rarely provide strong and clear signals about how learners process content. As a result, learning as a process is not clearly described for learners or for learning scientists. Gašević, Dawson, and Siemens (2015) urged data be sought that more straightforwardly describe processes in terms of events within learning episodes. They recommended building on Winne’s (1982) characterization of traces — ambient data gathered as learners study that more clearly represent which operations learners apply to which information — and his COPES model of a learning event — conditions, operations, products, evaluations, standards (Winne, 1997). We designed and describe an open source, open access, scalable software system called nStudy that responds to their challenge. nStudy gathers data that trace cognition, metacognition, and motivation as processes that are operationally captured as learners operate on information using nStudy’s tools. nStudy can be configured to support learners’ evolving self-regulated learning, a process akin to personally focused, self-directed learning science.

Author Biography

Philip H Winne, Simon Fraser University

Professor, Faculty of Education, Simon Fraser University


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How to Cite

Winne, P. H., Teng, K., Chang, D., Lin, M. P.-C., Marzouk, Z., Nesbit, J. C., Patzak, A., Raković, M., Samadi, D., & Vytasek, J. (2019). nStudy: Software for Learning Analytics about Processes for Self-Regulated Learning. Journal of Learning Analytics, 6(2), 95–106. https://doi.org/10.18608/jla.2019.62.7