Is asking students to generate predictions an effective technique to induce meaningful cognitive conflict and to facilitate conceptual change?
Final Report Abstract
Triggering a cognitive conflict is a popular strategy to achieve conceptual change in learners. One difficulty with this strategy in practice is that the cognitive conflict is often not perceived by learners as significant enough to challenge existing misconceptions. In the first phase of the project, we wanted to test in a series of experiments whether, how and for whom the generation of predictions prior to the presentation of conflicting information leads to a significant cognitive conflict and facilitates conceptual change. The results suggest that the generation of predictions facilitates conceptual change in children and that this is related to the surprise triggered by incorrectly predicted outcomes. Surprise was measured via the pupillary surprise response.Results further suggest that the magnitude of the prediction-induced surprise response depends on both the size of the prediction error and the degree of confidence in the prediction. In the second phase of the project, the aim was to assess more precisely the effect of making predictions on surprise and the subsequent revision of misconceptions. To this end, we tested whether the effect of generating predictions can be captured using the Bayesian inference framework, which accounts for confidence and prediction error in the revision of beliefs. We found that when children's prior beliefs are captured, their belief revision in the prediction condition comes close to that of an optimal Bayesian learner. We further found that making predictions leads children to a more ‘optimal’ learning trajectory (in the Bayesian sense). These results provide empirical support but also refinements for recent versions of the ‘Theory Theory’ by emphasizing the importance of the learning activity in which children are involved. In summary, the results of our studies contribute to a better understanding of the strategy of generating predictions as well as to the understanding of conceptual learning processes in childhood more generally.
Publications
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Cognitive Prerequisites for Generative Learning: Why Some Learning Strategies Are More Effective Than Others. Child Development, 92(1), 258-272.
Breitwieser, Jasmin & Brod, Garvin
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Tackling Scientific Misconceptions: The Element of Surprise. Child Development, 92(5), 2128-2141.
Theobald, Maria & Brod, Garvin
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Explicitly predicting outcomes enhances learning of expectancy-violating information. Psychonomic Bulletin & Review, 29(6), 2192-2201.
Brod, Garvin; Greve, Andrea; Jolles, Dietsje; Theobald, Maria & Galeano-Keiner, Elena M.
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Predicting vs. guessing: the role of confidence for pupillometric markers of curiosity and surprise. Cognition and Emotion, 36(4), 731-740.
Theobald, Maria; Galeano-Keiner, Elena & Brod, Garvin
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Priors, Progressions, and Predictions in Science Learning: Theory-Based Bayesian Models of Children’s Revising Beliefs of Water Displacement. IEEE Transactions on Cognitive and Developmental Systems, 15(3), 1487-1500.
Colantonio, Joseph A.; Bascandziev, Igor; Theobald, Maria; Brod, Garvin & Bonawitz, Elizabeth
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Seeing the Error in My “Bayes”: A Quantified Degree of Belief Change Correlates with Children’s Pupillary Surprise Responses Following Explicit Predictions. Entropy, 25(2), 211.
Colantonio, Joseph; Bascandziev, Igor; Theobald, Maria; Brod, Garvin & Bonawitz, Elizabeth
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Do reflection prompts promote children's conflict monitoring and revision of misconceptions?. Child Development, 95(4), e253-e269.
Theobald, Maria; Colantonio, Joseph; Bascandziev, Igor; Bonawitz, Elizabeth & Brod, Garvin
