Skip to main content

Measuring User Experience in Situ: Use Emotion Data to Assess User Experience

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 607))

Abstract

User Experience is an increasingly critical factor in product success. This study evaluates a new method that uses emotion detection software to capture the emotional data of users in real time while performing specific tasks within an app, and explores how this new method can complement traditional user experience methods such as questionnaires. Our study included N = 200 who were asked to complete three tasks within a health test app while emotion data was captured using the integrated emotion detection software. Results when clustering these emotion data show that the user experience acquired in the task is consistent with the emotion data valence. In addition, we found that a user emotion profile can be established to better characterize user experience of the participants during each task. This may help designers to better target users and product design.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Zhou, F., Lei, B., Liu, Y., et al.: Affective parameter shaping in user experience prospect evaluation based on hierarchical Bayesian estimation. J. Expert Syst. Appl. 78, 1–15 (2017)

    Article  Google Scholar 

  2. Ahn H.: Modeling and analysis of affective influences on human experience, prediction, decision making, and behavior. J. Massachusetts Institute of Technology (2010)

    Google Scholar 

  3. Ahn, H., Picard, R.W.: Affective-cognitive learning and decision making: a motivational reward framework for affective agents. In: International Conference on Affective Computing and Intelligent Interaction, pp. 866–873. Springer (2005)

    Google Scholar 

  4. Bargas, J.A., Hornbaek, K.: Old wine in new bottles or novel challenges? A critical analysis of empirical studies of user experience. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1–10. ACM Press, New York (2011)

    Google Scholar 

  5. Hassenzahl, M., Tractinsky, N.: User experience – a research agenda. J. Behav. Inf. Technol. 25(2), 91–97 (2006)

    Article  Google Scholar 

  6. Norman, D.A.: Emotional Design. Basic Books, New York (2004)

    Google Scholar 

  7. Saariluomaand, P., Jokinen, J.P.P.: Emotional dimensions of user experience: a user psychological analysis. J. Int. J. Hum. Comput. Interact. 30(4), 303–320 (2014)

    Article  Google Scholar 

  8. Thüring, M., Mahlke, S.: Usability, aesthetics and emotions in human–technology interaction. J. Int. J. Psychol. 42(4), 253–264 (2007)

    Article  Google Scholar 

  9. Bargas-Avila, J.A., Hornbaek, K.: Old wine in new bottles or novel challenges: a critical analysis of empirical studies of user experience. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2689–2698. ACM Press, New York (2011)

    Google Scholar 

  10. Zhou, F., Qu, X., Helander, M.G., Jiao, J.R.: Affect prediction from physiological measures via visual stimuli. J. Int. J. Hum. Comput. Stud. 69, 801–819 (2011)

    Article  Google Scholar 

  11. Gross, J.J.: Emotion regulation: affective, cognitive, and social consequences. J. Psychophysiol. 39, 281–291 (2011)

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by UXLab of school of arts and media of Tongji university. We would like to express our gratitude to: Yujia Wang, Huiyan Chen, Ziwei Qian, Chaojie Wang, and Cuiqiong Cheng.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fang You .

Editor information

Editors and Affiliations

Appendix

Appendix

Fig. 1.
figure 1

The emotion state and detailed description of each user in each task.

Fig. 2.
figure 2

In the scatter plot, each point represents the mean of a kind of a control in task one of all users. At the same time, the bar graph corresponds to the user experience questionnaire and represents the number of users selecting each option.

Fig. 3.
figure 3

In the scatter plot, each point represents the mean of a kind of a control in task two of all users. At the same time, the bar graph corresponds to the user experience questionnaire and represents the number of users selecting each option.

Fig. 4.
figure 4

In the scatter plot, each point represents the mean of a kind of a control in task three of all users. At the same time, the bar graph corresponds to the user experience questionnaire and represents the number of users selecting each option.

Fig. 5.
figure 5

User emotion profile

Fig. 6.
figure 6

Structure of this paper

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Chen, Y., You, F., Wang, J., Schroeter, R. (2018). Measuring User Experience in Situ: Use Emotion Data to Assess User Experience. In: Ahram, T., Falcão, C. (eds) Advances in Usability and User Experience. AHFE 2017. Advances in Intelligent Systems and Computing, vol 607. Springer, Cham. https://doi.org/10.1007/978-3-319-60492-3_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60492-3_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60491-6

  • Online ISBN: 978-3-319-60492-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics