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Inducing and Tracking Confusion with Contradictions during Complex Learning

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IJAIE Volume 22, Number 1, ISSN 1560-4292


Cognitive disequilibrium and its affiliated affective state of confusion have been found to positively correlate with learning, presumably due to the effortful cognitive activities that accompany their experience. Although confusion naturally occurs in several learning contexts, we hypothesize that it can be induced and scaffolded to increase learning opportunities. We addressed the possibility of confusion induction in a study where learners engaged in trialogues on research methods concepts with animated tutor and student agents. Confusion was induced by staging disagreements and contradictions between the animated agents, and then inviting the (human) learners to provide their opinions. Self-reports of confusion indicated that the contradictions were successful at inducing confusion in the minds of the learners. A second, more objective, method of tracking learners' confusion consisted of analyzing learners' performance on forced-choice questions that were embedded after contradictions. This measure was also found to be revealing of learners' underlying confusion. The contradictions alone did not result in enhanced learning gains. However, when confusion had been successfully induced, learners who were presented with contradictions did show improved learning compared to a no-contradiction control. Theoretical and applied implications along with possible future directions are discussed.


Lehman, B., D'Mello, S., Strain, A., Mills, C., Gross, M., Dobbins, A., Wallace, P., Millis, K. & Graesser, A. (2013). Inducing and Tracking Confusion with Contradictions during Complex Learning. International Journal of Artificial Intelligence in Education, 22(1), 85-105. Retrieved September 17, 2019 from .

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Cited By

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  • More confusion and frustration, better learning: The impact of erroneous examples

    J. Elizabeth Richey, Carnegie Mellon University, United States; Juan Miguel L. Andres-Bray, University of Pennsylvania, United States; Michael Mogessie, Carnegie Mellon University, United States; Richard Scruggs & Juliana M.A.L. Andres, University of Pennsylvania, United States; Jon R. Star, Harvard University, United States; Ryan S. Baker, University of Pennsylvania, United States; Bruce M. McLaren, Carnegie Mellon University, United States

    Computers & Education Vol. 139, No. 1 (October 2019) pp. 173–190

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