@inproceedings{6a2efe4f25e34bac97aabd7f2b19165b,
title = "Revising learner misconceptions without feedback: Prompting for reflection on anomalies",
abstract = "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.",
keywords = "Explanation, MOOCs, Online learning, Prompts",
author = "Williams, {Joseph Jay} and Tania Lombrozo and Anne Hsu and Bernd Huber and Juho Kim",
year = "2016",
month = may,
day = "7",
doi = "10.1145/2858036.2858361",
language = "English (US)",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
pages = "470--474",
booktitle = "CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems",
note = "34th Annual Conference on Human Factors in Computing Systems, CHI 2016 ; Conference date: 07-05-2016 Through 12-05-2016",
}