Why Self-Regulated Learning Makes Instructional Design a Gambit, and How to Benefit

Updated: 4:10pm, 11 Nov, 2022
17 May 2022 (Tue)
via Zoom
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Chair: Prof. Nancy Law, Professor, Faculty of Education, The University of Hong Kong

A basic view of instructional design has three steps with recursion. First, survey learning science to find cognitive operations that foster learning. Second, design conditions so learners use those cognitive operations as they study content. Third, match content learners operate on to content assessed. Repeat this 3-step process over and over to improve the design’s effectiveness. If learners are more than machines, hiding inside this 3-step process are important roles for metacognition and self-regulated learning. From a point of view that takes into account learners’ metacognition and self-regulated learning, instructional designs might be thought of as bets designers make in the “learning casino”. Software we designed, called nStudy, can support a partnership between instructional designers and self-regulated learners who are doing their own research for N=me. A happy result is the opportunity to place less and less risky bets in the learning casino. Like a blackjack player counting cards, keeping track of data and patience are keys to winning the learning game.

About the speaker(s):

Phil Winne is Distinguished SFU Professor of Education at Simon Fraser University, Canada. Struggling to contain his curiosity, Phil’s research ranges over self-regulated learning, metacognition, learning analytics, designing software technologies to advance research and help learners boost achievements, and research methodologies in the learning sciences. He is honored to be an elected Fellow of the Royal Society of Canada, the American Educational Research Association, the American Psychological Association, the Association for Psychological Science, and the Canadian Psychological Association. More information about the speaker can be found at

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