Can you explain the surprising result? About students' arguments in the context of risk literacy before and after working with a simulation
Résumé
Risk literacy as the understanding and evaluation of risks for informed decision making is increasingly relevant in today's society. Updating conditional probabilities based on new information, i.e., Bayesian reasoning, is fundamental for risk literacy as risks are understood as negatively connotated events and their corresponding probabilities. In this paper, we provide a novel approach to measuring Bayesian reasoning (and hence one aspect of risk literacy) by asking for explanations regarding surprising results. Our analysis includes three aspects: First, we introduce a coding system which can be used to categorize students' answers into misconceptions or adequate arguments with varying elaborateness. Secondly, we use this coding system to test if working with a computer-simulation affects students' arguments of the surprising results. The results are encouraging to infer that misconceptions can be reduced by using the simulation.
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