Interactive Instruction in Bayesian Inference

Bibliographic Data

ID15215941
AuthorsAzam Khan (0000-0002-1816-5714, Autodesk ResearchCanada, corresponding author), Simon Breslav (Autodesk ResearchCanada), Kasper Hornbæk (0000-0001-8742-1198, University of CopenhagenDenmark)
Year2016
Volume33
Issue3
Pages207-233
Publication date2016-07-08
Open AccessNo
TypeARTICLE
VenueHuman-Computer Interaction (JOURNAL)
Journal identifiersISSN: 0737-0024 • E-ISSN: 1532-7051
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/07370024.2016.1203264
OpenAlexW2468423936
LanguageEN
Citations received1
References cited29

An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions at significantly improved rates. Nonetheless, in novel interactivity conditions, performance was lowered suggesting that more interaction can add more difficulty for participants. Overall, a leap forward in accuracy was found, with more than twice the participant accuracy of previous work. This indicates that an instructional approach to improving human performance in Bayesian inference is a promising direction

Bayesian inference · Bayesian probability · Bayesian statistics · Inference · Machine learning · Advanced Text Analysis Techniques · Computer Science · Data Visualization and Analytics · Visual and Cognitive Learning Processes · Artificial Intelligence

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