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Leveraging Big Data to Help Each Learner and Accelerate Learning Science


by Philip H. Winne — 2017

Background: Today’s gold standard for identifying what works, the randomized controlled trial, poorly serves each and any individual learner. Elements of my argument provide grounds for proposed remedies in cases where software can log extensive data about operations each learner applies to learn and each bit of information to which a learner applies those operations.

Purpose of Study: Analyses of such big data can produce learning analytics that provide raw material for self-regulating learners, for instructors to productively adapt instructional designs, and for learning scientists to advance learning science. I describe an example of such a software system, nStudy.

Research Design: I describe and analyze features of nStudy, including bookmarks, quotes, notes, and and note artifacts that can be used to generate trace data.

Results: By using software like nStudy as they study, learners can partner with instructors and learning scientists in a symbiotic and progressive ecology of authentic experimentation.

Conclusion: I argue that software technologies like nStudy offer significant value in supporting learners and advancing learning science. A rationale and recommendations for this approach arise from my critique of pseudo-random controlled trials.



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Cite This Article as: Teachers College Record Volume 119 Number 3, 2017, p. 1-24
http://www.tcrecord.org ID Number: 21769, Date Accessed: 12/10/2017 9:15:19 PM

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About the Author
  • Philip Winne
    Simon Fraser University
    E-mail Author
    PHILIP H. WINNE is Professor and Canada Research Chair in Self-Regulated Learning and Learning Technologies; and Associate Dean, Graduate Studies and Research in the Faculty of Education, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.
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