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Supporting Instructors in MOOCs: Using cognitive science research to guide pedagogy & instructional design. MIT/EdX
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Supporting Instructors in MOOCs: Using cognitive science research to guide pedagogy & instructional design. MIT/EdX

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Abstract: How can online learning platforms provide useful information about pedagogy to instructors teaching online, while ensuring that course teams are not constrained in leveraging their teaching …

Abstract: How can online learning platforms provide useful information about pedagogy to instructors teaching online, while ensuring that course teams are not constrained in leveraging their teaching expertise to personalize their MOOC? The scientific literature on learning and education provides hundreds of detailed studies, which can be synthesized to identify effective instructional strategies, and mined for examples of how an instructional strategy can be implemented in a specific environment, set of educational materials, or student population. This talk illustrates this approach, by presenting a worksheet guide that supports MOOC designers in using two instructional strategies: increasing student motivation to think through challenges by designing exercises which encourage students to see their intelligence as malleable, and enhancing deep understanding with questions and prompts for students to explain. The talk explains how these two instructional strategies are motivated by both existing literature and recently conducted experimental studies. It also presents the specific details of how the guide is targeted at MOOC instructors and provides them with multiple actionable strategies they can use in their courses.

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  • Not traditional, but ubiquitous
  • TL: give conditions parallel names: explain/write thoughts or explanations/written thoughts. I prefer former.
  • Transcript

    • 1. Supporting Instructors in MOOCs: Using cognitive science research to guide pedagogy & instructional design May, EdX/MIT Joseph Jay Williams josephjaywilliams@stanford.edu www.josephjaywilliams.com/education lytics.stanford.edu Lytics Lab, Graduate School of Education & Office of the Vice Provost for Online Learning Stanford University (Formerly) Graduate School of Education, UC Berkeley
    • 2. Abstract • How can online learning platforms provide useful information about pedagogy to instructors teaching online, while ensuring that course teams are not constrained in leveraging their teaching expertise to personalize their MOOC? The scientific literature on learning and education provides hundreds of detailed studies, which can be synthesized to identify effective instructional strategies, and mined for examples of how an instructional strategy can be implemented in a specific environment, set of educational materials, or student population. This talk illustrates this approach, by presenting a worksheet guide that supports MOOC designers in using two instructional strategies: increasing student motivation to think through challenges by designing exercises which encourage students to see their intelligence as malleable, and enhancing deep understanding with questions and prompts for students to explain. The talk explains how these two instructional strategies are motivated by both existing literature and recently conducted experimental studies. It also presents the specific details of how the guide is targeted at MOOC instructors and provides them with multiple actionable strategies they can use in their courses.
    • 3. Complementary Knowledge in Instruction • • • • Instructors Instructional Designers EdX Researchers: Cognitive & Learning Scientists
    • 4. Overview • I. Incorporating cognitive & learning sciences research • II. Increasing motivation by changing beliefs about intelligence • III. Enhancing understanding by engaging students in generating explanations
    • 5. III. Cognitive & Learning Sciences Research • Synthesizing & Applying Broad Principles & Recent Findings • • • • • Williams, J.J. (2013). Improving Learning in MOOCs by Applying Cognitive Science. Paper presented at the MOOCshop Workshop, International Conference on Artificial Intelligence in Education, Memphis, TN. Pashler, H., Bain, P., Bottge, B., Graesser, A., Koedinger, K., McDaniel, M., Metcalfe, J.: Organizing Instruction and Study to Improve Student Learning (NCER 20072004). Washington, DC: Institute of Education Sciences, U.S. Department of Education (2007) Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., Norman, M. K.: How learning works: Seven research-based principles for smart teaching. Jossey-Bass (2010) Willingham, D. T.: Why Don't Students Like School. Jossey-Bass (2010) Clark, R. C., & Mayer, R. E.: E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. Pfeiffer (2004) • Specific detailed studies
    • 6. II. Increase motivation – change beliefs about intelligence • Many ways to increase motivation • Change students’ beliefs about whether intelligence is fixed or malleable (Dweck, 2011; Yeager & Walton, 2012)
    • 7. Implicit beliefs about Intelligence • On a scale from 1 to 10, how much do you agree that? • Your intelligence is something very basic about you that you can’t change very much. • No matter how much intelligence you have, you can always change it quite a bit. • Fixed Mindset • Growth Mindset (Dweck, 2006)
    • 8. Boost GPA with 2 class lessons? • Teach students an incremental/growth theory (Paunesku, Romero et al, 2012) • Self-fulfilling prophecy • Avoid trying hard and uncomfortable challenges • Avoid asking questions & understanding errors
    • 9. Teach growth mindset of intelligence to increase motivation • Growth mindset related to motivation & learning (Dweck, 2008; Yeager & Walton, 2011) • Embed messages in online Khan Academy exercises • Effect of Growth Mindset beyond encouragement? Jascha Sohl-Dickstein
    • 10. Experimentally manipulate added messages Practice-as-usual Message Growth Mindset Message Positive Some of these problems are hard. Do your best! Remember, the more you practice the smarter you become!
    • 11. Design Practice-as-usual • Growth Mindset Message • Positive Message • • • "Remember, the more you practice the smarter you become.”, "Mistakes help you learn. Think hard to learn from them.” • "Some of these problems are hard. Just do your best." "This might be a tough problem, but we know you can do it.” • 50 000+ students per condition • Dependent measures: – Number of problems attempted – Accuracy
    • 12. Number of Problems Attempted by Students • Unpublished data has been removed from this slide
    • 13. Accuracy • Unpublished data has been removed from this slide
    • 14. Worksheet Guide • https://docs.google.com/document/d/199mzM696pH1SbppUeQ5DQTsE628u00YmIngkKlUZ5M /edit
    • 15. III. Enhance understanding – prompt for explanations • “Teaching is the best way to learn” • Cognitive Psychology, Education, Cognitive Tutors (Chi, 2000; Legare, 2012; Lombrozo, 2012; McNamara, 2004; Murphy, 2000; Siegler, 2002) • Mathematics, Statistics, Physics, Chemistry, Biology • Many age groups • Multiple formats
    • 16. Explanation and Learning • General boost to Learning Engagement • The Subsumptive Constraints Account: Interpret target of why-explanation in terms of a broader generalization (Williams & Lombrozo, 2010) • Discovery & transfer (Williams & Lombrozo, 2010, Cognitive Science) • Use of prior knowledge (Williams & Lombrozo, 2013, Cog. Psych.) • Erroneously overgeneralize at expense of anomalies (Williams et al, 2013, JEP: General) 16
    • 17. Anomalies in Mathematics • Anomalies – contradict existing beliefs • Often ignored by students (Chinn & Brewer, 1993 ) • Effects of explaining anomalies? (Williams, et al 2012; 2013) Williams, Walker & Lombrozo, 2012 17
    • 18. Using concept of statistical deviation for ranking • Statistical concepts: • Introductory & central to many disciplines • Difficult but important in everyday reasoning • Learn to rank using z-scores/standard deviation (Schwartz & Martin, 2004, Belenky & Nokes, 2011) Sarah got 85% in a Sociology class, where the average score was 79%, the average deviation was 3%, the minimum score was 67%, and the maximum score was 90%. Tom got 69% in a Art History class, where the average score was 65%, the average deviation was 8%, the minimum score was 42%, and the maximum score was 87%. Who was ranked higher? XXXX was ranked higher. 18
    • 19. Statistical rules for ranking Sarah was ranked higher. Tom Type of Information Sarah Tom Ranking Rule Use of rule Higher ranked Personal Score • 85% 69% Higher score 85 > 69 Sarah Class Average 79% 65% Greater distance from average (85 – 79) > (69 – 65) Sarah Class Maximum 90% 87% 8% 8% 3% 3% (90 – 85) < (87 – 69) Sarah Class Deviation Closer to maximum More deviations above the average (85-79)/8 < (69-65)/3 Tom Tom • Anomalous observation 19
    • 20. Design Study Items Pre-Test Post-Test 5 ranked student pairs. Make 5 verbal responses Explain: Why was this person ranked higher? ? Write Thoughts: Write your thoughts about this ranking. ? 20
    • 21. Improvement in Accuracy • Unpublished data has been removed from this slide
    • 22. Review • I. Incorporating cognitive & learning sciences research • II. Increasing motivation by changing beliefs about intelligence • III. Enhancing understanding by engaging students in generating explanations

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