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
2003
Real-Time Fluid Dynamics for GamesIn this paper we present a simple and rapid implementation of a fluid…
2012
Modeling and Simulation of Skeletal Muscle for Computer Graphics: A SurveyMuscles provide physiological functions to drive body movement and…
2012
Functional maps: a flexible representation of maps between shapesWe present a novel representation of maps between pairs of shapes that…
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