Revising Learner Misconceptions Without Feedback: Prompting for Reflection on Anomalous Facts

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The Internet has enabled learning at scale, from Massive Open Online Courses (MOOCs) to Wikipedia. But online learners may become passive, instead of actively constructing knowledge and revising their beliefs in light of new facts. Instructors cannot directly diagnose thousands of learners' misconceptions and provide remedial tutoring. This paper investigates how instructors can prompt learners to reflect on facts that are anomalies with respect to their existing misconceptions, and how to choose these anomalies and prompts to guide learners to revise incorrect beliefs without any feedback. We conducted two randomized experiments with online crowd workers learning statistics. Results show that prompts to explain why these anomalies are true drive revision towards correct beliefs. But prompts to simply articulate thoughts about anomalies have no effect on learning. Furthermore, we find that explaining multiple anomalies is more effective than explaining only one, but the anomalies should rule out multiple misconceptions simultaneously.
Publisher
ACM Special Interest Group on Computer-Human Interaction (SIGCHI)
Issue Date
2016-05-11
Language
English
Citation

34th Annual CHI Conference on Human Factors in Computing Systems, CHI 2016, pp.470 - 474

DOI
10.1145/2858036.2858361
URI
http://hdl.handle.net/10203/238694
Appears in Collection
CS-Conference Papers(학술회의논문)
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