Bayesian Reasoning is both a fundamental idea of probability and a key model in applied sciences for evaluating situations of uncertainty. Bayesian Reasoning may be defined as the dealing with, and understanding of, Bayesian situations. This includes various aspects such as calculating a conditional probability (performance), assessing the effects of changes to the parameters of a formula on the result (covariation) and adequately interpreting and explaining the results of a formula (communication). Bayesian Reasoning is crucial in several non-mathematical disciplines such as medicine and law. However, even experts from these domains struggle to reason in a Bayesian manner. Therefore, it is desirable to develop a training course for this specific audience regarding the different aspects of Bayesian Reasoning. In this paper, we present an evidence-based development of such training courses by considering relevant prior research on successful strategies for Bayesian Reasoning (e.g., natural frequencies and adequate visualizations) and on the 4C/ID model as a promising instructional approach. The results of a formative evaluation are described, which show that students from the target audience (i.e., medicine or law) increased their Bayesian Reasoning skills and found taking part in the training courses to be relevant and fruitful for their professional expertise.
Patients need to be informed correctly and comprehensibly about the implications of their medical test results. Reasoning in such situations, where, for example, a medical test result is used to make inferences on a particular disease, is called Bayesian reasoning. Prior research mostly concentrated on the ability to correctly calculate risks in Bayesian situations (so-called performance) and repeatedly demonstrated that performance is very low—even among medical experts. The need to also study communication within Bayesian situations has been brought forward. Here, we broaden the focus of Bayesian reasoning and present first insights into a study where medical students participated in a training course on the aspect of performance and show that this already improves the ability to judge doctor–patient communication within Bayesian situations.
Questions involving Bayesian Reasoning often arise in events of everyday life, such as assessing the results of a breathalyser test or a medical diagnostic test. Bayesian Reasoning is perceived to be difficult, but visualisations are known to support it. However, prior research on visualisations for Bayesian Reasoning has only rarely addressed the issue on how to design such visualisations in the most effective way according to research on multimedia learning. In this article, we present a concise overview on subject-didactical considerations, together with the most fundamental research of both Bayesian Reasoning and multimedia learning. Building on these aspects, we provide a step-by-step development of the design of visualisations which support Bayesian problems, particularly for so-called double-trees and unit squares.
There have been intensive research efforts to improve Bayesian reasoning over the last 25 years. Much of this research focuses solely on improving performance on Bayesian tasks. In addition to performance, however, it is also important to establish an understanding of the effect on the positive predictive value when parameters of Bayesian formula are changed. We call this ability “covariation” in Bayesian tasks. To this end, training courses were developed to support understanding of covariation, based on strategies that have been proven helpful by previous studies concerning performance, using: (a) natural frequencies and (b) visualisations, i.e., double trees and unit squares. Results of a comparative study in a pre-, post-, and follow-up test design show that the developed training courses can improve understanding of covariation.
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