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

Published on Jul 23, 2019in Journal of learning Analytics
· DOI :10.18608/JLA.2019.62.7
Philip H. Winne68
Estimated H-index: 68
(SFU: Simon Fraser University),
Kenny Teng1
Estimated H-index: 1
(SFU: Simon Fraser University)
+ 7 AuthorsJovita Vytasek8
Estimated H-index: 8
(SFU: Simon Fraser University)
Sources
Abstract
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. Gasevic, 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.
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