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

Authors

  • 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

DOI:

https://doi.org/10.18608/jla.2019.62.7

Keywords:

Self-regulated learning, Metacognition, Trace data

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.

Author Biography

Philip H Winne, Simon Fraser University

Professor, Faculty of Education, Simon Fraser University

References

Baddeley, A. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63, 1–29. http://dx.doi.org/10.1146/annurev-psych-120710-100422

Bisra, K., Liu, Q., Nesbit, J. C., Salimi, F., & Winne, P. H. (2018). Inducing self-explanation: A meta-analysis. Educational Psychology Review, 30(3), 703–725. http://dx.doi.org/10.1007/s10648-018-9434-x

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. http://dx.doi.org/10.1177/1529100612453266

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x

Gier, V. S., Kreiner, D. S., & Natz-Gonzalez, A. (2009). Harmful effects of preexisting inappropriate highlighting on reading comprehension and metacognitive accuracy. The Journal of General Psychology, 136(3), 287–302. http://dx.doi.org/10.3200/GENP.136.3.287-302

Miyatsu, T., Nguyen, K., & McDaniel, M. A. (2018). Five popular study strategies: Their pitfalls and optimal implementations. Perspectives on Psychological Science, 13(3), 390–407. http://dx.doi.org/10.1177/1745691617710510

Orlando, V. P., Caverly, D. C., Swetnam, L. A., & Flippo, R. F. (1989). Text demands in college classes: An investigation. Forum for Reading, 21(1), 43–49.

Salomon, G., & Perkins, D. N. (1989). Rocky roads to transfer: Rethinking mechanisms of a neglected phenomenon. Educational Psychologist, 24, 113–142. http://dx.doi.org/10.1207/s15326985ep2402_1

Winne, P. H. (1982). Minimizing the black box problem to enhance the validity of theories about instructional effects. Instructional Science, 11, 13–28. http://dx.doi.org/10.1007/BF00120978

Winne, P. H. (1987). Why process–product research cannot explain process–product findings and a proposed remedy: The cognitive mediational paradigm. Teaching and Teacher Education, 3, 333–356. https://doi.org/10.1016/0742-051X(87)90025-4

Winne, P. H. (1997). Experimenting to bootstrap self-regulated learning. Journal of Educational Psychology, 89, 397–410. http://dx.doi.org/10.1037/0022-0663.89.3.397

Winne, P. H. (2010). Bootstrapping learner’s self-regulated learning. Psychological Test and Assessment Modeling, 52, 472–490.

Winne, P. H. (2017a). Learning analytics for self-regulated learning. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (pp. 241–249). Beaumont, AB: Society for Learning Analytics Research. http://dx.doi.org/10.18608/hla17.021

Winne, P. H. (2017b). Leveraging big data to help each learner upgrade learning and accelerate learning science. Teachers College Record, 119(3), 1–24.

Winne, P. H. (2018). Cognition and metacognition in self-regulated learning. In D. Schunk & J. Greene (Eds.), Handbook of self-regulation of learning and performance. (2nd ed., pp. 36–48). New York: Routledge.

Winne, P. H. (2019). Enhancing self-regulated learning for information problem solving with ambient big data gathered by nStudy. In O. O. Adesope & A. G. Rud (Eds.), Contemporary technologies in education: Maximizing student engagement, motivation, and learning (pp. 145–162). New York: Palgrave Macmillan. http://dx.doi.org/10.1007/978-3-319-89680-9_8

Winne, P. H., & Baker, R. S. J. d. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. Journal of Educational Data Mining, 5(1), 1–8. Retrieved from https://jedm.educationaldatamining.org/index.php/JEDM/article/view/28

Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. In D. H. Schunk & B. J. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297–314). Mahwah, NJ: Lawrence Erlbaum Associates.

Winne, P. H., & Marzouk, Z. (2019). Learning strategies and self-regulated learning. In J. Dunlosky & K. Rawson (Eds.), Cambridge handbook of cognition and education (pp. 696–715). New York: Cambridge University Press. http://dx.doi.org/10.1017/9781108235631.028

Winne, P. H., Nesbit, J. C., & Popowich, F. (2017). nStudy: A system for researching information problem solving. Technology, Knowledge and Learning, 22(3), 369–376. http://dx.doi.org/10.1007/s10758-017-9327-y

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Published

2019-07-23

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

Issue

Vol. 6 No. 2 (2019): Human-Centred Learning Analytics

Section

Data and Tool Reports

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