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

Main Article Content

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

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. 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.

Article Details

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
Section
Data and Tool Reports
Author Biography

Philip H Winne, Simon Fraser University

Professor, Faculty of Education, Simon Fraser University

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