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Cited by 252

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Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems

Kim, Jun H., Gunn, Daniel V., Schuh, Eric, Phillips, Bruce, Pagulayan, Randy J., Wixon, Dennis·2008·Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Abstract

Automatic recording of user behavior within a system (instrumentation) to develop and test theories has a rich history in psychology and system design. Often, researchers analyze instrumented behavior in isolation from other data. The problem with collecting instrumented behaviors without attitudinal, demographic, and contextual data is that researchers have no way to answer the 'why' behind the 'what'. We have combined the collection and analysis of behavioral instrumentation with other HCI methods to develop a system for Tracking Real-Time User Experience (TRUE). Using two case studies as examples, we demonstrate how we have evolved instrumentation methodology and analysis to extensively improve the design of video games. It is our hope that TRUE is adopted and adapted by the broader HCI community, becoming a useful tool for gaining deep insights into user behavior and improvement of design for other complex systems.

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Metadata

Authors
Kim, J. H., Gunn, D. V., Schuh, E., Phillips, B., Pagulayan, R. J., & Wixon, D.
Venue
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2008-04-06
Pages
443-452
OpenAlex
W2142777385
Cited by
252
References
0
FWCI
14.43
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems. https://app.cochat.ai/papers/10.1145/1357054.1357126
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Kim, J. H., Gunn, D. V., Schuh, E., Phillips, B., Pagulayan, R. J., & Wixon, D. (2008). Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/1357054.1357126

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.