Using Mouse and Keyboard Dynamics to Detect Cognitive Stress During Mental Arithmetic

Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 591)

Abstract

To build a personalized e-learning system that can deliver adaptive learning content based on student’s cognitive effort and efficiency, it is important to develop a construct that can help measuring perceived mental state, such as stress and cognitive load. The construct must be able to be quantified, computerized and automated. Our research investigates how mouse and keyboard dynamics analyses could be used to detect cognitive stress, which is induced by high mental arithmetic demand with time pressure, without using intrusive and expensive equipment. The research findings suggest that when task demand increased, task error, task duration, passive attempt, stress perception and mouse idle duration may increase, while mouse speed, left mouse click and keystroke speed decreased. The significant effects of task demand and time pressure on mouse and keystroke behaviours suggest that stress evaluation from these input devices is potentially useful for designing an adaptive e-learning system.

Keywords

Stress detection Keyboard dynamics Mouse dynamics Mental arithmetic Adaptive e-learning 

References

  1. 1.
    Beilock, S.L., Ramirez, G.: On the interplay of emotion and cognitive control: implications for enhancing academic achievement. Psychol. Learn. Motiv. Adv. Res. Theory 55, 137 (2011)CrossRefGoogle Scholar
  2. 2.
    Paas, F.G.W.C., Van Merriënboer, J.J.G.: Instructional control of cognitive load in the training of complex cognitive tasks. Educ. Psychol. Rev. 6, 351–371 (1994)CrossRefGoogle Scholar
  3. 3.
    Kirschner, P.A.: Cognitive load theory: implications of cognitive load theory on the design of learning. Learn. Instr. 12, 1–10 (2002)CrossRefGoogle Scholar
  4. 4.
    Chatzara, K., Karagiannidis, C., Stamatis, D.: Students attitude and learning effectiveness of emotional agents. 2010 IEEE 10th international conference on advanced learning technologies (ICALT), pp. 558–559 (2010)Google Scholar
  5. 5.
    Selye, H.: The Stress in Life. McGraw-Hill, New Jersey (1956)Google Scholar
  6. 6.
    Setz, C., Arnrich, B., Schumm, J., La Marca, R., Troster, G., Ehlert, U.: Discriminating stress from cognitive load using a wearable EDA device. IEEE Trans. Inf. Technol. Biomed. 14, 410–417 (2010)CrossRefGoogle Scholar
  7. 7.
    Owen, A.M., McMillan, K.M., Laird, A.R., Bullmore, E.: N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum. Brain Mapp. 25, 46–59 (2005)CrossRefGoogle Scholar
  8. 8.
    Sloan, R.P., Korten, J.B., Myers, M.M.: Components of heart rate reactivity during mental arithmetic with and without speaking. Physiol. Behav. 50, 1039–1045 (1991)CrossRefGoogle Scholar
  9. 9.
    Imbo, I., Vandierendonck, A.: Do multiplication and division strategies rely on executive and phonological working memory resources? Mem. Cognit. 35, 1759–1771 (2007)CrossRefGoogle Scholar
  10. 10.
    Sweller, J., Ayres, P., Kalyuga, S.: Cognitive Load Theory. Springer, Berlin (2011)CrossRefGoogle Scholar
  11. 11.
    Weinberg, A., Ferri, J., Hajcak, G.: Interactions between attention and emotion. Handb. Cogn. Emot. 35 (2013)Google Scholar
  12. 12.
    Hajcak, G., Dunning, J.P., Foti, D.: Neural response to emotional pictures is unaffected by concurrent task difficulty: an event-related potential study. Behav. Neurosci. 121, 1156 (2007)CrossRefGoogle Scholar
  13. 13.
    Lim, Y.M., Ayesh, A., Stacey, M.: The effects of menu design on users’ emotions, search performance and mouse behaviour. In: Patel, S., Wang, Y., Kinsner, W., Patel, D., Fariello, G., and Zadeh, L.A. (eds.) IEEE 13th international conference on cognitive informatics & cognitive computing, pp. 541–549. IEEE, London (2014)Google Scholar
  14. 14.
    Tsoulouhas, G., Georgiou, D., Karakos, A.: Detection of learner’s™ affective state based on mouse movements. J. Comput. 3, 9–18 (2011)Google Scholar
  15. 15.
    Pusara, M., Brodley, C.E.: User Re-authentication via Mouse Movements. In: Proceedings of the 2004 ACM workshop on visualization and data mining for computer security, pp. 1–8, ACM, New York (2004)Google Scholar
  16. 16.
    Shen, C., Cai, Z., Guan, X., Du, Y., Maxion, R.: User authentication through mouse dynamics. IEEE Trans. Inf. Forensics Secur. 8(1), 16–30 (2012)CrossRefGoogle Scholar
  17. 17.
    Shen, C., Cai, Z., Guan, X.: Continuous authentication for mouse dynamics: a pattern-growth approach. Dependable systems and networks (DSN). 42nd annual IEEE/IFIP international conference on 2012, pp. 1–12 (2012)Google Scholar
  18. 18.
    Shen, C., Cai, Z., Guan, X., Sha, H., Du, J.: Feature Analysis of Mouse Dynamics in Identity Authentication and Monitoring. In: Proceedings of the 2009 IEEE international conference on communications, pp. 673–677, IEEE Press, Piscataway (2009)Google Scholar
  19. 19.
    Vizer, L.M.: Detecting Cognitive and Physical Stress Through Typing Behavior. In: Proceedings of the 27th international conference extended abstracts on human factors in computing systems CHI EA 09. 3113 (2009)Google Scholar
  20. 20.
    Filho, J.R.M., Freire, E.O.: On the equalization of keystroke timing histograms. Pattern Recogn. Lett. 27, 1440–1446 (2006)CrossRefGoogle Scholar
  21. 21.
    Cavoukian, A.: Privacy by design: origins, meaning, and prospects for assuring privacy and trust in the information era. In: Yee, G. (ed.) Privacy protection measures and technologies in business organizations: aspects and standards, pp. 170–208. IGI Global, Hershey (2012)Google Scholar
  22. 22.
  23. 23.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Applied Sciences and ComputingTunku Abdul Rahman University CollegeKuala LumpurMalaysia
  2. 2.Faculty of TechnologyDe Montfort UniversityLeicesterUK

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