Citation

Büchter T, Steib N, Böcherer-Linder K, Eichler A, Krauss S, Binder K, Vogel M. Educ. Sci. (Basel) 2022; 12(11): e739.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/educsci12110739

PMID

unavailable

Abstract

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.

Keywords: Ethanol impaired driving

Language: en

Keywords

Bayesian Reasoning; double-tree; multimedia learning; unit square; visualisation