The European Educational Researcher

Monitoring Mouse Behavior in e-learning Activities to Diagnose Students’ Acceptance Items of Perceived Usefulness and Ease of Use

The European Educational Researcher, Volume 3, Issue 1, February 2020, pp. 21-27
OPEN ACCESS VIEWS: 844 DOWNLOADS: 497 Publication date: 15 Feb 2020
ABSTRACT
This study investigates students’ mouse behavior during their interaction with a web-based experiential learning environment for Computer Science courses. The research focuses on the detection of correlations between the monitored mouse metrics and students’ technology acceptance items of perceived usefulness and ease of use. Findings reveal several significant correlations; in particular, metrics of mouse clicks and hovers can be associated with students’ perceived ease use and perceived usefulness. The findings of this work show an interesting research direction towards the analysis of learners’ mouse behavior during their interaction with interactive and web-based tutoring systems.
KEYWORDS
Experiential learning, Interactive web development courses, Mmouse tracking, Sstudents’ perceived acceptance, Web-based tutoring systems
CITATION (APA)
Tzafilkou, K., & Protogeros, N. (2020). Monitoring Mouse Behavior in e-learning Activities to Diagnose Students’ Acceptance Items of Perceived Usefulness and Ease of Use. The European Educational Researcher, 3(1), 21-27. https://doi.org/10.31757/euer.312
REFERENCES
  1. Arapakis, I., Lalmas, M., & Valkanas, G. (2014). Understanding Within-Content Engagement through Pattern Analysis of Mouse Gestures. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM '14). ACM, New York, NY, USA, 1439-1448.
  2. Bojko, A. (2013). _. Rosenfeld Media, Brooklyn, NY.
  3. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
  4. Dijkstra, M. (2013). The Diagnosis Of Self-Efficacy Using Mouse And Keyboard Input. Master thesis, Utrecht University.
  5. Oestean. (2019, June 11). Women in Digital. Retrieved July 19, 2019, from https://ec.europa.eu/digital-single-market/en/women-ict
  6. Hinbarji, Z. Albatal, R., & Gurrin, C. (2015). Dynamic User Authentication Based on Mouse Movements Curves. MultiMedia Modeling: 21st International Conference, MMM 2015, Sydney, NSW, Australia, January 5-7, 2015, Proceedings, Part II 111 - 122 Springer International Publishing.
  7. Hornbæk, K. & Frøkjær, E. (2003) Reading patterns and usability in visualizations of electronic documents. ACM Transactions on Computer-Human Interaction (TOCHI),\ 10(2):119–149, 2003.
  8. Khan, I.A., Brinkman, W-P., Fine, N. & Hierons, R.M. (2008). Measuring personality from keyboard and mouse use. In Proceedings of the 15th European conference on Cognitive ergonomics: the ergonomics of cool interaction (ECCE '08), Julio Abascal, Inmaculada Fajardo, and Ian Oakley (Eds.). ACM, New York, NY, USA, Article 38, 8 pages.
  9. Leiva T., & A. Hernando, R. (2007). (SMT) Real Time Mouse Tracking Registration and Visualization Tool for Usability Evaluation on Websites. ISBN 978-972-8924-44-7
  10. Marin, E. (2014). Experiential learning: empowering students to take control of their learning by engaging them in an interactive course simulation environment. The 6th International Conference Edu World 2014 “Education Facing Contemporary World Issues”, Procedia - Social and Behavioral Sciences 180, pp. 854 – 859
  11. Mueller, F. & Lockerd, A., (2001). Cheese: Tracking Mouse Movement activity onWebsites, a Tool for User Modeling. Ext. Abstracts CHI. Seattle, Washington, USA, pp1-2.
  12. Nakkabi, Y., Traore, I. & A.A.E. Ahmed. (2010). Improving Mouse Dynamics Biometric Performance Using Variance Reduction via Extractors With Separate Features". In: Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 40.6 (Nov. 2010), pp. 1345-1353.
  13. Rodden, K., & Fu, X. (2007). Exploring how Mouse Movements relate to Eye Movements on Web Search Results Pages. WISI Workshop.
  14. Shapiro, S.S. & Wilk, M.B. (1965). An analysis of variance test for normality (complete samples). Biometrika 52 (3–4), pp.591–611.
  15. Slanzi, G., Jorge A. Balazs, & Juan D. Velsquez. (2017). Combining eye tracking, pupil dilation and EEG analysis for predicting web users click intention. Inf. Fusion 35, C (May 2017), 51-57.
  16. Tzafilkou K., Protogeros, N., & Yakinthos, C. (2014). Mouse Tracking for Web Marketing: Enhancing User Experience in Web Application Software by Measuring Self-Efficacy and Hesitation Levels. International Journal of strategic and Innovative Marketing, 1,4.
  17. Tzafilkou K.; & Protogeros N. (2018). Mouse behavioral patterns and keystroke dynamics in End-User Development. Comput. Hum. Behav. 83, C (June 2018), 288–305.
  18. Tzafilkou, K. and Protogeros, N. (2017). Diagnosing user perception and acceptance using eye tracking in web-based end-user development. Computers in Human Behavior. 72, C (July 2017), 23-37.
  19. Tzafilkou, K., Chouliara, A., Protogeros, N., Karagiannidis, C. & Koumpis, A. (2015). Engaging end-users in creating data-intensive mobile applications: A creative ‘e-learning-by-doing’ approach. International Conference on Interactive Mobile Communication Technologies and Learning (IMCL), IEEE
  20. Zimmermann, P., Guttormsen, S., Danuser, B., & Gomez, P. (2003). Affective computing--a rationale for measuring mood with mouse and keyboard. International journal of occupational safety and ergonomics 9(4): 539-551.
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