Show Me How You Click, and I’ll Tell You What You Can: Predicting User Competence and Performance by Mouse Interaction Parameters

  • Christiane AttigEmail author
  • Ester Then
  • Josef F. Krems
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)


Automatically detecting and adapting to user competence is a promising approach for advancing human-technology interaction. With the present work, we demonstrate that perceived user competence and performance can be predicted by easily ascertainable low-level mouse interaction parameters with considerable amounts of explained variance. N = 71 users with varying competence interacted with a statistical software while mouse interaction parameters were recorded. Results showed that perceived task competence could best be predicted by clicks per second, maximum mouse velocity, and average duration of pauses > 150 ms (R2 = .39). Perceived system competence could best be predicted by clicks per second, maximum mouse acceleration, and average number of pauses > 150 ms (R2 = .28). Performance could best be predicted by clicks per second, maximum mouse velocity, and average number of pauses > 150 ms (R2 = .50). Results imply that assessing low-level mouse interaction parameters could be a feasible approach for automatic detection of user competence and performance.


Adaptive systems User competence Mouse interaction 



This research was funded by the European Social Fund and the Free State of Saxony under Grant No. 100269974. We want to thank our student assistants Daniel Götz, Katharina Schulzeck and Sabine Wollenberg for their support in data collection and manuscript preparation.


  1. 1.
    D’Mello, S.K.: A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. J. Educ. Psychol. 105, 1082–1099 (2013)CrossRefGoogle Scholar
  2. 2.
    Calvo, R.A., D’Mello, S.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE T. Aff. Comput. 1, 18–37 (2010)CrossRefGoogle Scholar
  3. 3.
    Lavie, T., Meyer, J.: Benefits and costs of adaptive user interfaces. Int. J. Hum. Comput. Int. 68, 508–524 (2010)CrossRefGoogle Scholar
  4. 4.
    Grossman, T., Fitzmaurice, G.: An investigation of metrics for the in situ detection of software expertise. Hum. Comput. Int. 30, 64–102 (2015)CrossRefGoogle Scholar
  5. 5.
    Magnisalis, I., Demetriadis, S., Karakostas, A.: Adaptive and intelligent systems for collaborative learning support: a review of the field. IEEE T. Learn. Technol. 4, 5–20 (2011)CrossRefGoogle Scholar
  6. 6.
    Sengpiel, M., Jochems, N.: Development of the (adaptive) computer literacy scale (CLS). In: Lindgaard, G., Moore, D. (eds.) The Proceedings of the 19th Triennial Congress of the International Ergonomics Association (2015)Google Scholar
  7. 7.
    Hurst, A., Hudson, S. E., Mankoff, J.: Dynamic detection of novice vs. skilled use without a task model. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 271–280). ACM, San Jose (2007)Google Scholar
  8. 8.
    Ghazarian, A., Noorhosseini, S.M.: Automatic detection of users’ skill levels using high-frequency user interface events. User Model. User-Adap. 20, 109–146 (2010)CrossRefGoogle Scholar
  9. 9.
    Ghazarian, A., Ghazarian, A.: Pauses in man-machine interactions: a clue to users’ skill levels and their user interface requirements. Int. J. Cog. Perfor. Supp. 1, 82–102 (2013)CrossRefGoogle Scholar
  10. 10.
    IBM Corp.: IBM SPSS Statistics for Windows (Version 24.0). IBM Corp., Armonk (2016)Google Scholar
  11. 11.
    Leijten, M., Van Waes, L.: Keystroke logging in writing research: using inputlog to analyze writing processes. Writ. Commun. 30, 358–392 (2013)CrossRefGoogle Scholar
  12. 12.
    LimeSurvey GmbH: LimeSurvey—An Open Source Survey Tool. LimeSurvey GmbH, Hamburg (2003)Google Scholar
  13. 13.
    Card, S.K., Moran, T.P., Newell, A.: The Keystroke-level model for user performance time with interactive systems. Commun. ACM 23, 396–410 (1980)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Chemnitz University of Technology, Cognitive and Engineering PsychologyChemnitzGermany

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