Interactive Instruction in Bayesian Inference

Research output: Contribution to journalJournal articlepeer-review

Standard

Interactive Instruction in Bayesian Inference. / Khan, Azam; Breslav, Simon; Hornbæk, Kasper.

In: Human-Computer Interaction, Vol. 33, 2018, p. 207–233.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Khan, A, Breslav, S & Hornbæk, K 2018, 'Interactive Instruction in Bayesian Inference', Human-Computer Interaction, vol. 33, pp. 207–233. https://doi.org/10.1080/07370024.2016.1203264

APA

Khan, A., Breslav, S., & Hornbæk, K. (2018). Interactive Instruction in Bayesian Inference. Human-Computer Interaction, 33, 207–233. https://doi.org/10.1080/07370024.2016.1203264

Vancouver

Khan A, Breslav S, Hornbæk K. Interactive Instruction in Bayesian Inference. Human-Computer Interaction. 2018;33:207–233. https://doi.org/10.1080/07370024.2016.1203264

Author

Khan, Azam ; Breslav, Simon ; Hornbæk, Kasper. / Interactive Instruction in Bayesian Inference. In: Human-Computer Interaction. 2018 ; Vol. 33. pp. 207–233.

Bibtex

@article{2ce8f115920c47428e310122ad36ef6b,
title = "Interactive Instruction in Bayesian Inference",
abstract = "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{\textquoteright}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.",
author = "Azam Khan and Simon Breslav and Kasper Hornb{\ae}k",
year = "2018",
doi = "10.1080/07370024.2016.1203264",
language = "English",
volume = "33",
pages = "207–233",
journal = "Human-Computer Interaction",
issn = "0737-0024",
publisher = "Taylor & Francis Online",

}

RIS

TY - JOUR

T1 - Interactive Instruction in Bayesian Inference

AU - Khan, Azam

AU - Breslav, Simon

AU - Hornbæk, Kasper

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

U2 - 10.1080/07370024.2016.1203264

DO - 10.1080/07370024.2016.1203264

M3 - Journal article

AN - SCOPUS:85003890143

VL - 33

SP - 207

EP - 233

JO - Human-Computer Interaction

JF - Human-Computer Interaction

SN - 0737-0024

ER -

ID: 179048505