Simple Mouse Attribute Analysis

  • Jennifer Matthiesen
  • Michael B. HolteEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11589)


This work investigates the potential bivariate correlations between selected pattern related mouse attributes and a set of factors for the determination of the satisfaction with the usability. To examine this, a prototype tool for the analyzation and characterization of mouse attributes, Simple Mouse Attribute Analysis (SMATA), within the usage of a cloud-based vertical business software solution for managing soft data, was designed and implemented. A questionnaire was conducted to evaluate the users’ satisfaction with the usability. Following, the potential correlation between those properties was investigated. The findings revealed several statistically significant correlations between the factors of satisfaction with the usability and the examined mouse attributes. Mouse attributes like the number of direct movement, the number of long direct movements, the number of made pauses, as well as the covered distance and the total time of the session could be associated with the perception of the system usefulness, the information and interface quality and the overall impression. The objective of this study was to point out a new interesting research direction of using implicit gathered user data from one of the default communication channels in HCI: the computer mouse.


Mouse attributes Mouse behaviour patterns HCI Satisfaction Usability ECM 



The research leading to these results has been conducted in collaboration with WorkPoint A/S.


  1. 1.
    Mueller, F., Lockerd, A.: Cheese: tracking mouse movement activity on websites, a tool for user modeling. In: CHI 2001, Extended Abstracts on Human Factors in Computing Systems, pp. 279–280 (2001)Google Scholar
  2. 2.
    Tzafilkou, K., Protogeros, N.: Mouse behavioral patterns and keystroke dynamics in end-user development: what can they tell us about users’ behavioral attributes? In: Computers in Human Behavior, pp. 288–305, 5 February 2018Google Scholar
  3. 3.
    de Vicente, A., Pain, H.: Informing the detection of the students’ motivational state: an empirical study. In: Cerri, S.A., Gouardères, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 933–943. Springer, Heidelberg (2002). Scholar
  4. 4.
    McQuiggan, S.W., Lester, J.C.: Diagnosing self-efficacy in intelligent tutoring systems: an empirical study. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 565–574. Springer, Heidelberg (2006). Scholar
  5. 5.
    Rodden, K.X., Fu, A.A., Spiro, I.: Eye-mouse coordination patterns on web search results pages. In: CHI 2008 Extended Abstracts on Human Factors in Computing Systems, pp. 2997–3002 (2008)Google Scholar
  6. 6.
    Hinbarji, Z., Albatal, R., Gurrin, C.: Dynamic user authentication based on mouse movements curves. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015. LNCS, vol. 8936, pp. 111–122. Springer, Cham (2015). Scholar
  7. 7.
    Ferreira, S., Arroyo, E., Tarrago, R., Blat, J.: Applying mouse tracking to investigate patterns of mouse movements in web forms. Universitat Pompeu Fabra, Pompeu (2010)Google Scholar
  8. 8.
    Zimmermann, P., Guttormsen, S., Danuser, B., Gomez, P.: Affective computing–a rationale for measuring mood with mouse and keyboard. Int. J. Occup. Saf. Ergon. 9(4), 539–551 (2003)CrossRefGoogle Scholar
  9. 9.
    Tanjim-Al-Akib, M., Ashik, L.K., Al-Walid-Shaiket, H., Chowdhury, K.: User-modeling and recommendation based on mouse-tracking for e-commerce websites. In: 19th International Conference on Computer and Information Technology, Dhaka, Bangladesh (2016)Google Scholar
  10. 10.
    Navalpakkam, V., Churchill, E.: Mouse tracking: measuring and predicting users’ experience of web-based content. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2012), pp. 2936–2972 (2012)Google Scholar
  11. 11.
    Tzafilkou, K., Protogeros, N., Yakinthos, C.: Mouse tracking for web marketing: enhancing user experience in web application software by measuring self-efficacy and hesitation levels. Int. J. Strateg. Innov. Mark. 1, 233–247 (2014)Google Scholar
  12. 12.
    Mitakos, T., Almaliotis, I., Demerouti, A.: An auditing approach for ERP systems examining human factors that influence ERP user satisfaction. Informatica Economică 14(1), 78–92 (2010)Google Scholar
  13. 13.
    Brooke, J.: SUS - a quick and dirty usability scale. In: Usability Evaluation in Industry, pp. 189–194. Taylor & Francis, London (1996)Google Scholar
  14. 14.
    Nielsen, J.: Usability 101: Introduction to Usability, 4 January 2012.
  15. 15.
    International Organisation for Standardisation, “ISO9241 Ergonomic, Part 11: Guidance on usability,” International Organisation for Standardisation, Geneva, Switzerland (1998)Google Scholar
  16. 16.
    Frøkjær, E., Hertzum, M., Hornbæk, K.: Measuring usability: are effectiveness, efficiency, and satisfaction really correlated? In: CHI 2000 Proceedings of the SIGCHI Conference on Human Factors in Computing Systema, pp. 345–352, 01–06 April 2000Google Scholar
  17. 17.
    Loewenstein, G.: The psychology of curiosity: a review and reinterpretation. Psychol. Bull. 116(1), 75–98 (1994)CrossRefGoogle Scholar
  18. 18.
    Bandura, A.: Self-efficacy: toward a unifying theory of behavioural change. Psychol. Rev. 8(2), 191–215 (1977)CrossRefGoogle Scholar
  19. 19.
    Blackwell, A.F., Rode, J.A., Toye, E.F.: How do we program the home? Gender, attention investment, and the psychology of programming at home. Int. J. Hum. Comput. Stud. 67, 324–341 (2009)CrossRefGoogle Scholar
  20. 20.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340 (1989)CrossRefGoogle Scholar
  21. 21.
    Atterer, R., Wnuk, M.S.A.: Knowing the user’s every move - user activity tracking for website usability evaluation and implicit interaction. In: Proceedings of the 15th International Conference on World Wide Web, Edinburgh, Scotland (2006)Google Scholar
  22. 22.
    Leiva, L.A., Vivó, R.: A gesture inference methodology for user evaluation based on mouse activity tracking. In: Proceedings of the IADIS International Conference on Interfaces and HCI, pp. 18–26 (2008)Google Scholar
  23. 23.
    Chen, M., Anderson, J.R., Sohn, M.: What can a mouse cursor tell us more? Correlation of eye/mouse movements on web browsing. In: Extended Abstracts CHI 2001, pp. 281–282 (2001)Google Scholar
  24. 24.
    Cooke, L.: Is the mouse a “poor man’s eye tracker”? Usability and Information Design Magazine, pp. 252–255 (2006)Google Scholar
  25. 25.
    Griffiths, L., Chen, Z.: Investigating the differences in web browsing behaviour of chinese and european users using mouse tracking. In: Aykin, N. (ed.) UI-HCII 2007. LNCS, vol. 4559, pp. 502–512. Springer, Heidelberg (2007). Scholar
  26. 26.
    ClickTale Ltd.: ClickTale: Basic System Manual, 1 February 2013Google Scholar
  27. 27.
    Leiva, L.A., Vivó, R.: (SMT) real time mouse tracking registration and visualization tool for usability evaluation on websites. In: Proceedings of IADIS WWW/Internet, pp. 187–192 (2007)Google Scholar
  28. 28.
    Churruca, S.L.: Comparative study of cursor movement patterns between a touchpad and a mouse devices. Master thesis, UPF (2011)Google Scholar
  29. 29.
  30. 30.
    Dijkstra, M.: The diagnosis of self-efficacy using mouse and keyboard input. Faculty of Science theses, Utrecht (2013)Google Scholar
  31. 31.
    Leiva, L., Vivó, R.: Web browsing behavior analysis and interactive hypervideo. ACM Trans. Web (TWEB) 7(4), 20–28 (2013)Google Scholar
  32. 32.
    Fitts, P.M.: The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47, 381–391 (1954)CrossRefGoogle Scholar
  33. 33.
    Naumann, A., et al.: Intuitive use of user interfaces: defining a vague concept. In: Harris, D. (ed.) EPCE 2007. LNCS (LNAI), vol. 4562, pp. 128–136. Springer, Heidelberg (2007). Scholar
  34. 34.
    Lewis, J.R.: IBM Computer Usability Satisfaction Questionnaires: Psychometric Evaluation and Instructions for Use. Human Factors Group, Boca Raton (1995)Google Scholar
  35. 35.
    Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Erlbaum, Hillsdale (1988)zbMATHGoogle Scholar
  36. 36.
    Zahoor, S., Rajput, D., Bedekar, M., Kosamkar, P.: Inferring web page relevancy through keyboard and mouse usage. In: International Conference on Computing Communication Control and Automation, pp. 474–478 (2015)Google Scholar
  37. 37.
    Keir, P.J., Bach, J.M., Rempel, D.: Effects of computer mouse design and task on carpal tunnel pressure. Ergonomics 42(10), 1350–1360 (1999)CrossRefGoogle Scholar
  38. 38.
    Gerling, K.M., Klauser, M., Niesenhaus, J.: Measuring the impact of game controllers on player experience in FPS games. In: MindTrek 2011 Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, Tampere, Finland (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Architecture, Design and Media TechnologyAalborg UniversityEsbjergDenmark

Personalised recommendations