Is asking students to generate predictions an effective technique to induce meaningful cognitive conflict and to facilitate conceptual change?

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Teaching science is challenging because it entails changing persistent and pervasive misconceptions in students. Recent research suggests that generating predictions is a promising strategy that facilitates children’s revision of misconceptions. This facilitation has been found to be related to an enhanced physiological surprise response to expectancy-violating outcomes when children generated an explicit prediction beforehand. The magnitude of the surprise response has further been shown to depend on participants’ confidence in their prediction as well as on the size of the prediction error. The overarching goal of the second project phase is to more precisely capture the effect of generating predictions on surprise and the subsequent revision of misconceptions. To this end, we will build a formal model of each child’s prior beliefs and compare their physiological surprise response and belief revision to an optimal Bayesian learner. The Bayesian inference framework is particularly useful for this purpose because it offers a formal model of the interplay between confidence, prediction error, and belief revision. It tells us precisely how, according to probability theory, expectancy-violating outcomes should be used to update beliefs and models (i.e., rational inductive inference). We can thus test a) whether children who engage their prior models by making an explicit prediction make better (i.e., closer to statistically optimal) use of conflicting evidence than children who do not engage their prior models beforehand, and b) what roles confidence and prediction error play for this benefit to occur.