Application of Wearable Technology for the Acquisition of Learning Motivation in an Adaptive E-Learning Platform

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 795)


Motivated learning is the prerequisite for a deep processing of learning content and a long retention performance, as well as the basis for joy of learning and persistent interest. The SensoMot-project (“Sensor Measures of Motivation for Adaptive Learning”) aims at identifying critical motivational incidents during adaptive e-learning sessions in the context of university courses of micro- and nano-technology through sensory acquisition with current consumer wearables. These critical motivational incidents will be used to adapt learning content at runtime and thus enhance motivation.


Adaptive e-learning Motivation Physiological data Sensory acquisition Wearable technology 



Part of the authors’ work has been supported by the German Federal Ministry for Education and Research (BMBF) within the joint project SensoMot under grant no. 16SV7516, within the program “Tangible Learning”.


  1. 1.
    Choe, E.K., Lee, N.B., Lee, B., Pratt, W., Kientz, J.A.: Understanding quantified-selfers’ practices in collecting and exploring personal data. In: Jones, M., Palanque, P., Schmidt, A., Grossman, T. (eds.) CHI 2014, One of a CHInd. Conference proceedings: Toronto, Canada, 26 April – 1 May 2014, the 32nd Annual ACM Conference on Human Factors in Computing Systems, the 32nd annual ACM conference, Toronto, Ontario, Canada, pp. 1143–1152. Association for Computing Machinery, New York (2014).
  2. 2.
    Hou, M., Banbury, S., Burns, C.: Intelligent Adaptive Systems: An Interaction-Centered Design Perspective. CRC Press, Boca Raton (2015)Google Scholar
  3. 3.
    Trepte, S., Reinecke, L.: Medienpsychologie. Kohlhammer-Urban-Taschenbücher, vol. 726, 1st edn. Kohlhammer, Stuttgart (2013)Google Scholar
  4. 4.
    Ravaja, N.: Contributions of psychophysiology to media research: review and recommendations. Media Psychol. 6, 193–235 (2004). Scholar
  5. 5.
    Williamson, J., Liu, Q., Lu, F., Mohrman, W., Li, K., Dick, R., Shang, L.: Data sensing and analysis: challenges for wearables. In: The 20th Asia and South Pacific Design Automation Conference, 2015 20th Asia and South Pacific Design Automation Conference (ASP-DAC), Chiba, Japan, 19 January 2015 – 22 January 2015, pp. 136–141. IEEE (2015).
  6. 6.
    Park, S., Chung, K., Jayaraman, S.: Wearables. In: Sazonov, E. (ed.) Wearable Sensors, pp. 1–23. Academic Press, Cambridge (2014)Google Scholar
  7. 7.
    Jiang, H., Chen, X., Zhang, S., Zhang, X., Kong, W., Zhang, T.: Software for wearable devices: challenges and opportunities. In: 2015 IEEE 39th Annual Computer Software and Applications Conference, 2015 IEEE 39th Annual Computer Software and Applications Conference (COMPSAC), Taichung, Taiwan, 01 July 2015 – 05 July 2015, pp. 592–597. IEEE (2015).
  8. 8.
    Tarakji, A.M.: Analysis of fitness wearables. Unpublished Master Thesis, Technische Universität Ilmenau (2016)Google Scholar
  9. 9.
    Lemay, M., Bertschi, M., Sola, J., Renevey, P., Parak, J., Korhonen, I.: Application of optical heart rate monitoring. In: Sazonov, E., Neuman, M.R. (eds.) Wearable Sensors. Fundamentals, Implementations and Applications, pp. 105–129. Academic Press, Cambridge (2014)Google Scholar
  10. 10.
    Lee, J., Matsumura, K., Yamakoshi, K.-i., Rolfe, P., Tanaka, S., Yamakoshi, T.: Comparison between red, green and blue light reflection photoplethysmography for heart rate monitoring during motion. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society (2013).
  11. 11.
    Appelhans, B.M., Luecken, L.J.: Heart rate variability as an index of regulated emotional responding. Rev. Gen. Psychol. 10, 229 (2006). Scholar
  12. 12.
    Boucsein, W.: Electrodermal Activity. Springer, Boston (2012)CrossRefGoogle Scholar
  13. 13.
    Westerink, J.H.D.M., van den Broek, E.L., Schut, M.H., van Herk, J., Tuinenbreijer, K.: Computing emotion awareness through galvanic skin response and facial electromyography. In: Toolenaar, F., Westerink, J.H.D.M., Ouwerkerk, M., Overbeek, T.J.M., Pasveer, W.F., Ruyter, B. de (eds.) Probing Experience, vol. 8, pp. 149–162. Springer, Dordrecht (2008). Philips ResearchCrossRefGoogle Scholar
  14. 14.
    Radüntz, T.: Kontinuierliche Bewertung psychischer Beanspruchung an informationsintensiven Arbeitsplätzen auf Basis des Elektroenzephalogramms. Humboldt-Universität zu Berlin (2016)Google Scholar
  15. 15.
    Birbaumer, N., Schmidt, R.F.: Biologische Psychologie, 7th edn. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indices of operator engagement in automated task. Biol. Psychol. 40(1–2), 187–195 (1995)CrossRefGoogle Scholar
  17. 17.
    Schneider, O., Martens, T., Bauer, M., Ott-Kroner, A., Dick, U., Dorochevsky, M.: SensoMot – sensorische erfassung von motivationsindikatoren zur steuerung adaptiver lerninhalte. In: Igel, C., Ullrich, C., Wessner, M. (eds.) Bildungsräume 2017, pp. 267–272. Gesellschaft für Informatik, Bonn (2017)Google Scholar
  18. 18.
    Rubin, J., Chisnell, D.: Handbook of Usability Testing: How to Plan, Design, and Conduct Effective Tests, 2nd edn. Wiley, Indianapolis (2008)Google Scholar
  19. 19.
    Nielsen, J.: Usability Engineering. Kaufmann, Amsterdam (2010)zbMATHGoogle Scholar
  20. 20.
    Matthews, G., Joyner, L., Gilliland, K., Campbell, S.E., Falconer, S., Huggins, J.: Validation of a comprehensive stress state questionnaire: Towards a state big three. Pers. psychol. Eur. 7, 335–350 (1999)Google Scholar
  21. 21.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. In: Human Mental Workload, Advances in Psychology, vol. 52, pp. 139–183. Elsevier (1988)Google Scholar
  22. 22.
    Hartson, R., Pyla, P.S.: The UX Book: Process and Guidelines for Ensuring a Quality User Experience, 1st edn. Morgan Kaufmann, Amsterdam (2012)Google Scholar
  23. 23.
    Cline, A.: Agile Development in the Real World. Apress, Berkeley (2015)CrossRefGoogle Scholar
  24. 24.
    Göring, K.: Analyse der Umwelt von Wearables mittels Techniken des strategischen Managements. Unpublished Master Thesis, Technische Universität Ilmenau (2017)Google Scholar
  25. 25.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology. a comparison of two theoretical models. Manage. Sci. 35, 982–1003 (1989). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Ilmenau University of TechnologyIlmenauGermany
  2. 2.Medical School HamburgHamburgGermany

Personalised recommendations