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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