Interactive Instruction in Bayesian Inference

AbstractAn 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 publication

Related Resources

See what’s new.



Using AI to Optimize Construction Design

How can we leverage AI to make construction design processes more…



Egocentric Analysis of Dynamic Networks with EgoLines

The egocentric analysis of dynamic networks focuses ondiscovering the…



Robotic assembly of timber joints using reinforcement learning

In architectural construction, automated robotic assembly is…



A hybrid lattice Boltzmann-molecular dynamics-immersed boundary method model for the simulation of composite foams

Small fillers (e…

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