nStudy: software for learning analytics about learning processes and self-regulated learning

Philip H. Winne, Kenny Teng, Daniel Chang, Michael Pin Chuan Lin, Zahia Marzouk, John C. Nesbit, Alexandra Patzak, Mladen Rakovic, Donya Samadi, Jovita Vytasek

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)95-106
Number of pages12
JournalJournal of Learning Analytics
Issue number2
Publication statusPublished - 23 Jul 2019
Externally publishedYes


  • Cognition
  • Metacognition
  • Self-regulated learning
  • Trace data

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