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Non-parametric decision trees and online HCI

Published: 27 April 2013 Publication History

Abstract

This paper proposes that online HCI studies (such as web-surveys and remotely monitored usability tests) can benefit from statistical data analysis using modern statistical learning methods such as classification and regression trees (CARTs). Applying CARTs to the often large amount of data yielded by online studies can easily provide clarity concerning the most important effects underlying experimental data in situations where myriad possible factors are under consideration. The feedback provided by such an analysis can also provide valuable reflection on the experimental methodology. We discuss these matters with reference to a study of 1300 participants in a structured experiment concerned with head-interaction techniques for first-person-shooter games.

References

[1]
Breiman, L., Friedman, J., Olshen, R., Stone, C., Steinberg, D., and Colla, P. CART: Classification and regression trees. Wadsworth: Belmont, CA (1983).
[2]
Hastie, T., Tibshirani, R., and Friedman, J. The elements of statistical learning: Data mining, inference, and prediction (2nd edition). http://www-stat.stanford. edu/~tibs/ElemStatLearn/download.html {accessed 14th Feb 2013}.
[3]
R Development Core Team. R: A Language and Environment for Statistical Computing {software}. R Foundation for Statistical Computing, Vienna, Austria, 2012. http://www.R-project.org {accessed 14th Jan 2013}.

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  • (2013)Studying a Head Tracking Technique for First-Person-Shooter Games in a Home SettingHuman-Computer Interaction – INTERACT 201310.1007/978-3-642-40498-6_18(246-263)Online publication date: 2013

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  1. Non-parametric decision trees and online HCI

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    cover image ACM Conferences
    CHI '13: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
    April 2013
    3550 pages
    ISBN:9781450318990
    DOI:10.1145/2470654
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 27 April 2013

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    Author Tags

    1. classification
    2. decision trees
    3. games
    4. non-parametric
    5. online studies
    6. parametric
    7. regression

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    CHI '13 Paper Acceptance Rate 392 of 1,963 submissions, 20%;
    Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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    • (2013)Studying a Head Tracking Technique for First-Person-Shooter Games in a Home SettingHuman-Computer Interaction – INTERACT 201310.1007/978-3-642-40498-6_18(246-263)Online publication date: 2013

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