Citekey: @Winne2016-as

Winne, P. (2016). Self-regulated learning. SFU Educational Review, 1(1).



Cool to see Winne articulate assumptions of his research program. Everyone should do this :) Neat! (p. 1)

Several assumptions undergird my research program. First, one way people learn relies on innate features of our cognitive architecture. Like Pavlov’s dogs, who learned a novel stimulus (a bell) could forecast food or Skinner’s pigeons who learned to behave in particular ways (pecking button on a wall) under particular conditions (when a red light but not a green light was illuminated) to obtain food, we assemble information in the world to create knowledge in our minds. Second, people also learn using cognitive tools. These tactics and strategies are kinds of information of a procedural sort. One example is using mnemonics, like ROY G BIV for colors in the visible spectrum of light in order of wavelength. Another is using schemas that describe a task, like checking whether an explanation of an effect is complete by identifying its cause, describing boundaries within which the cause produces the effect, and providing a rationale for the causal relation that conforms to a larger conceptual structure, a theory. Third, students have goals for their learning. Goals can be primal, like dogs’ and pigeons’ hunger. Also, goals can have human sophistication, such as figuring out how to study with minimal effort and time. Fourth – here is where my research has focused for the past quarter century – students are learning scientists. Like “professional” learning scientists, students construct organized accounts about knowledge and about various learning mechanisms – working memory, forgetting. They use these to design theories about why learning works as it does (p. 1)

impoverished toolkit of learning tactics and strategies (p. 2)

Why ‘learning science’ instead of ‘learning sciences’? Wondering about Phil’s thoughts behind this choice. (p. 2)

Can learning science help? I believe so. Facets of my research program identify and track the particular effects of such difficulties that beset learners. I have developed and tested models of innate processes learners have. Mine is a small set of operations on information that help learners to become SMART: searching, monitoring, assembling, rehearsing and translating (Winne, 2005). Second, I have cataloged cognitive tools learners use as well as tools learning science has discovered that are backed by empirical evidence they work (Winne, 2013). Third, I have traced goals learners have (Zhou & Winne, 2012). Fourth, I have designed and built support systems – software called nStudy – to help learners become better learning scientists (Roll & Winne, 2015; Winne, 2010; Winne & Baker, 2013). (p. 2)

Alongside my theoretical and empirical work, nStudy is one of my most significant endeavors – see As well as being software that supports learning per se, it is a scientific instrument that records every observable action a learner carries out while studying online content. (p. 2)

Winne, P. H. (2005). Researching and promoting self-regulated learning using software technologies. In P. Tomlinson, J. Dockrell, & P. H. Winne. (Eds.). (2005). Pedagogy – Teaching for learning. Monograph Series II: Psychological Aspects of Education, 3 (pp. 91-105). Leicester, UK: The British Psychological Society. (p. 3)

Winne, P. H. (2010). Bootstrapping learner’s self-regulated learning. Psychological Test and Assessment Modeling, 52, 472-490. (p. 3)

Winne, P. H. (2013). Learning strategies, study skills and self-regulated learning in postsecondary education. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research. Volume 28 (pp. 377-403). Dordrecht: Springer. (p. 3)

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. (p. 3)

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Bodong Chen, University of Minnesota

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