Handbook of Learning Analytics

Chapter 21

Handbook of Learning Analytics
First Edition

Learning Analytics for Self-Regulated Learning

Philip H. Winne


The Winne-Hadwin (1998) model of self-regulated learning (SRL), elaborated by Winne’s (2011, in press) model of cognitive operations, provides a framework for conceptualizing key issues concerning kinds of data and analyses of data for generating learning analytics about SRL. Trace data are recommended as observable indicators that support valid inferences about a learner’s metacognitive monitoring and metacognitive control that constitute SRL. Characteristics of instrumentation for gathering ambient trace data via software learners can use to carry out everyday studying are described. Critical issues are discussed regarding what to trace about SRL, attributes of instrumentation for gathering ambient trace data, computational issues arising when analyzing trace and complementary data, the scheduling and delivery of learning analytics, and kinds of information to convey in learning analytics that support productive SRL.

Export Citation: Plain Text (APA)     BIBTeX     RIS

Supplementary Material

No Supplementary Material Available

References (27)

Arnold, K. E., & Pistilli, M. D. (2012). Course Signals at Purdue. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 267–270). New York: ACM. doi:10.1145/2330601.2330666

Azevedo, R., Moos, D. C., Johnson, A. M., & Chauncey, A. D. (2010). Measuring cognitive and metacognitive regulatory processes during hypermedia learning: Issues and challenges. Educational Psychologist, 45(4), 210–223.

Baker, R. S. J. d., & Winne, P. H. (Eds.). (2013). Educational data mining on motivation, metacognition, and self-regulated learning [Special issue]. Journal of Educational Data Mining, 5(1).

Cooper, H., Nye, B., Charlton, K., Lindsay, J., & Greathouse, S. (1996). The effects of summer vacation on achievement test scores: A narrative and meta-analytic review. Review of Educational Research, 66(3), 227–268.

Delaney, P. F., Verkoeijen, P. P., & Spirgel, A. (2010). Spacing and testing effects: A deeply critical, lengthy, and at times discursive review of the literature. Psychology of Learning and Motivation, 53, 63–147.

Dunlosky, J., & Rawson, K. A. (2015). Practice tests, spaced practice, and successive relearning: Tips for classroom use and for guiding students’ learning. Scholarship of Teaching and Learning in Psychology, 1(1), 72–78.

Educause (n.d.) Next generation learning initiative. http://nextgenlearning.org

Eckerson, W. W. (2006). Performance dashboards: Measuring, monitoring, and managing your business. Hoboken, NJ: John Wiley & Sons.

Elias, T. (2011, January). Learning analytics: Definitions, processes and potential. http://learninganalytics.net/LearningAnalyticsDefinitionsProcessesPotential.pdf

Murayama, K., Miyatsu, T., Buchli, D., & Storm, B. C. (2014). Forgetting as a consequence of retrieval: A meta-analytic review of retrieval-induced forgetting. Psychological Bulletin, 140(5), 1383–1409.

Roll, I., & Winne, P. H. (2015a). Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics, 2(1), 7–12.

Roll, I., & Winne, P. H. (Eds.). (2015b). Self-regulated learning and learning analytics [Special issue]. Journal of Learning Analytics, 2(1).

Siemens, G. (2010, August 25). What are learning analytics? http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/

Webb, E. J., Campbell, D. T., Schwartz, R. D., & Sechrest, L. (1966). Unobtrusive measures. Skokie, IL: Rand-McNally.
Winne, P. H. (1995). Inherent details in self-regulated learning. Educational Psychologist, 30, 173–187.

Winne, P. H. (1997). Experimenting to bootstrap self-regulated learning. Journal of Educational Psychology, 89, 397–410.

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

Winne, P. H. (2010b). Improving measurements of self-regulated learning. Educational Psychologist, 45, 267–276.

Winne, P. H. (2011). A cognitive and metacognitive analysis of self-regulated learning. In B. J. Zimmerman and D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 15–32). New York: Routledge.

Winne, P. H. (2014). Issues in researching self-regulated learning as patterns of events. Metacognition Learning, 9(2), 229–237.

Winne, P. H. (2017). Leveraging big data to help each learner upgrade learning and accelerate learning science. Teachers College Record, 119(3). http://www.tcrecord.org/Content.asp?ContentId=21769.

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

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.

Winne, P. H., Gupta, L., & Nesbit, J. C. (1994). Exploring individual differences in studying strategies using graph theoretic statistics. Alberta Journal of Educational Research, 40, 177–193.

Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Mahwah, NJ: Lawrence Erlbaum Associates.

Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). Orlando, FL: Academic Press.

Zhou, M., Xu, Y., Nesbit, J. C., & Winne, P. H. (2011). Sequential pattern analysis of learning logs: Methodology and applications. In C. Romero, S. Ventura, S. R. Viola, M. Pechenizkiy & R. Baker (Eds.), Handbook of educational data mining (pp. 107–121). Boca Raton, FL: CRC Press.

About this Chapter

Learning Analytics for Self-Regulated Learning

Book Title
Handbook of Learning Analytics

pp. 241-249




Society for Learning Analytics Research

Philip H. Winne

Author Affiliations
Faculty of Education, Simon Fraser University, Canada

Charles Lang1
George Siemens2
Alyssa Wise3
Dragan Gašević4

Editor Affiliations
1. Teachers College, Columbia University, USA
2. LINK Research Lab, University of Texas at Arlington, USA
3. Learning Analytics Research Network, New York University, USA
4. Schools of Education and Informatics, University of Edinburgh, UK


Register | Lost Password