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Cognitive Load Levels While Learning with or Without a Pedagogical Agent

  • Madlen Müller-WuttkeEmail author
  • Nicholas H. Müller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11590)

Abstract

An eye-tracking study examining the benefits of a proactive human-computer-interaction has been conducted. Regarding the beneficial aspects of knowledge transfer, the data shows a clear benefit once the system has been enhanced by the so called electronic educational instance which tracks a user’s gaze and thereby infers whether or not someone is actually looking at a screen while the e-learning software is conveying its knowledge. To further show how a physiological input might be used as a form of input, current literature of smoothing pupillary response data is discussed and a preliminary tool is presented. Due to this, pupillary data can be used to indicate cognitive load levels while learning and would therefore allow a proactive system to change the e-learning program accordingly.

Keywords

Pedagogical agent E-learning Proactive system Cognitive load 

References

  1. 1.
    Heidig, S., Clarebout, G.: Do pedagogical agents make a difference to student motivation and learning? Educ. Res. Rev. 6, 27–54 (2011)CrossRefGoogle Scholar
  2. 2.
    Lusk, M.M., Atkinson, R.K.: Animated pedagogical agents: does their degree of embodiment impact learning from static or animated worked examples? Appl. Cogn. Psychol. 21, 747–764 (2007)CrossRefGoogle Scholar
  3. 3.
    Wang, N., Johnson, W.L., Mayer, R.E., Risso, P., Shaw, E., Collins, H.: The politeness effect: pedagogical agents and learning outcomes. Int. J. Hum.-Comput. Stud. 66, 98–112 (2008)CrossRefGoogle Scholar
  4. 4.
    Veletsianos, G.: How do learners respond to pedagogical agents that deliver social-oriented non-task messages? Impact on student learning, perceptions, and experiences. Comput. Hum. Behav. 28, 275–283 (2012)CrossRefGoogle Scholar
  5. 5.
    Rosch, J.L., Vogel-Walcutt, J.J.: A review of eye-tracking applications as tools for training. Cogn. Technol. Work 15(3), 313–327 (2012)CrossRefGoogle Scholar
  6. 6.
    Mathot, S., et al.: Safe and sensible preprocessing and baseline correction of pupil-size data. Behav. Res. Methods 50(1), 94–106 (2018).  https://doi.org/10.3758/s13428-017-1007-2CrossRefGoogle Scholar
  7. 7.
    Mathot, S.: A simple way to reconstruct pupil size during eye blinks (2013).  https://doi.org/10.6084/m9.figshare.688001.v1
  8. 8.
    Wuttke, M., Heidt, M., Rosenthal, P., Ohler, P., Wuttke, M., Müller, N.H.: Proactive functions of a pedagogical agent – steps for implementing a social catalyst function. In: Zaphiris, P., Ioannou, A. (eds.) LCT 2016. LNCS, vol. 9753, pp. 573–580. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39483-1_52CrossRefGoogle Scholar
  9. 9.
    Wuttke, M.: Pro-active pedagogical agents. In: Fakultät für Informatik (ed.) Proceedings of International Summer Workshop Computer Science, pp. 59–62, July 2013Google Scholar
  10. 10.
    Wuttke, M., Heidt, M.: Beyond presentation - employing proactive intelligent agents as social catalysts. In: Kurosu, M., Ioannou, A. (eds.) HCI 2014. LNCS, vol. 8511, pp. 182–190. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-07230-2_18CrossRefGoogle Scholar
  11. 11.
    Reeves, B., Nass, C.: The Media Equation. How People Treat Computers, Televisions, and New Media Like Real People and Places. Cambridge University Press, New York (1996)Google Scholar
  12. 12.
    Lester, J.C., Converse, S.A., Kahler, S.E., Barlow, S.T., Stone, B.A., Bhogal, R.S.: The persona effect: affective impact of animated pedagogical agents. In: Pemberton, S. (ed.) Human Factors in Computing Systems: CHI 1997 Conference Proceedings, pp. 359–366. ACM Press, New York (1997)Google Scholar
  13. 13.
    Wuttke, M.: Proaktive Agenten im Lernkontext. Die Auswirkungen neuer Inputkanäle in der lernstoffvermittelnden Mensch-Computer-Interaktion. Dissertationsschrift. Universitätsbibliothek Technische Universität Chemnitz, Chemnitz (2018)Google Scholar
  14. 14.
    Wuttke, M., Völkel, S., Ohler, P., Müller, N.H.: Analytical steps for the validation of a natural user interface. In: Zaphiris, P., Ioannou, A. (eds.) LCT 2017. LNCS, vol. 10295, pp. 55–63. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-58509-3_6CrossRefGoogle Scholar
  15. 15.
    Louwerse, M.M., Graesser, A.C., McNamara, D.S., Lu, S.: Embodied conversational agents as conversational partners. Appl. Cogn. Psychol. 23(9), 1244–1255 (2008)CrossRefGoogle Scholar
  16. 16.
    Yeh, Y.Y., Wickens, C.D.: Dissociation of performance and subjective measures of workload. Hum. Factors: J. Hum. Factors Ergon. Soc. 30(1), 111–120 (1988)CrossRefGoogle Scholar
  17. 17.
    Paas, F.G.W.C., Merrienboer, V., Jeroen, J.G.: Instructional control of cognitive load in the training of complex cognitive tasks. Educ. Psychol. Rev. 6(4), 351–371 (1994)CrossRefGoogle Scholar
  18. 18.
    Just, M.A., Carpenter, P.A., Miyake, A.: Neuroindices of cognitive workload: neuroimaging, pupillometric and event-related potential studies of brain work. Theor. Issues Ergon. Sci. 4(1-2), 56–88 (2003)CrossRefGoogle Scholar
  19. 19.
    Duchowski, A.T.: Eye Tracking Methodology Theory and Practice. Springer, London (2007).  https://doi.org/10.1007/978-1-84628-609-4CrossRefzbMATHGoogle Scholar
  20. 20.
    Bente, G.: Erfassung und Analyse des Blickverhaltens. In: Mangold, R., Vorderer, P., Bente, G. (eds.) Lehrbuch der Medienpsychologie, pp. 297–324. Hogrefe-Verlag, Göttingen (2004)Google Scholar
  21. 21.
    SMI - SensoMotoric Instruments: SMI - SensoMotoric Instruments (2008). RED/RED250: http://www.smivision.com/en/gaze-eye-tracking-systems/products/red-red250.html. Accessed 01 May 2017
  22. 22.
    Sweller, J., Chandler, P.: Why some material is difficult to learn. Cogn. Instr. 12(3), 185–233 (1994)CrossRefGoogle Scholar
  23. 23.
    Sweller, J.: Cognitive load theory, learning difficulty, and instructional design. Learn. Instr. 4(4), 295–312 (1994)CrossRefGoogle Scholar
  24. 24.
    Sweller, J.: Element interactivity and intrinsic, extraneous, and germane cognitive load. Educ. Psychol. Rev. 22(2), 123–138 (2010).  https://doi.org/10.1007/s10648-010-9128-5CrossRefGoogle Scholar
  25. 25.
    Schroeder, N.L.: The influence of a pedagogical agent on learners cognitive load. Educ. Technol. Soc. 20(4), 138–147 (2017)MathSciNetGoogle Scholar
  26. 26.
    Niegemann, H.M., Domagk, S., Hessel, S., Hein, A., Hupfer, M., Zobel, A.: Kompendium multimediales Lernen. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-37226-4CrossRefGoogle Scholar
  27. 27.
    Schroeder, N.L., Adesope, O.O., Gilbert, R.B.: How effective are pedagogical agents for learning? A meta-analytic review. J. Educ. Comput. Res. 49(1), 1–39 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Technische Universität ChemnitzChemnitzGermany
  2. 2.Socio-Informatics and Societal Aspects of Digitalization, Faculty of Computer Science and Business Information SystemsUniversity of Applied Sciences Würzburg-SchweinfurtWürzburgGermany

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