nStudy: Software for Learning Analytics about Processes for Self-Regulated Learning
Keywords: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.
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/10.1037/0022-06188.8.131.527
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
How to Cite
LicenseAuthors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) license that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).