Publication | Journal of Human-Computer Interaction 2016
Interactive Instruction in Bayesian Inference
Abstract
Interactive Instruction in Bayesian Inference
Azam Khan, Simon Breslav, Kasper Hornbaek
Journal of Human-Computer Interaction 2016
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 pre-training. 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.
Download publicationRelated Resources
2024
Make-A-Shape: a Ten-Million-scale 3D Shape ModelTrained on 10 million 3D shapes, our model exhibits the capability to…
2023
Recently Published by Autodesk ResearchersA selection of recently published papers by Autodesk Researchers…
2012
Exploring the Collective Categorization of Biological Information for Biomimetic DesignCategorizing biological information can be subjective and ambiguous,…
2018
Digital Dérive: Reconstructing Urban Environments based on Human ExperienceThis paper describes a novel method for reconstructing urban…
Get in touch
Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.
Contact us