Designing Visualisations for Bayesian Problems According to Multimedia Principles

Autor: Theresa Büchter, Nicole Steib, Katharina Böcherer-Linder, Andreas Eichler, Stefan Krauss, Karin Binder, Markus Vogel
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Education Sciences, Vol 12, Iss 11, p 739 (2022)
Druh dokumentu: article
ISSN: 2227-7102
DOI: 10.3390/educsci12110739
Popis: 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.
Databáze: Directory of Open Access Journals