Advertisement

Adaptive E-learning for Supporting Motivation in the Context of Engineering Science

  • Mathias BauerEmail author
  • Cassandra Bräuer
  • Jacqueline Schuldt
  • Heidi Krömker
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 785)

Abstract

Current e-learning environments rely on adaptive user-centered approaches rather than static learning material, that is presented to the learner in a linear way, to maintain motivation and prevent learning blocks. The goal of the research project “SensoMot - Sensor Measures of Motivation for Adaptive Learning” is to detect critical motivational incidents based on sensor data and self-reports. By deriving suitable adaptation mechanisms, the learning process should be controlled to match the learner’s motivation. Learning blocks should be detected at an early stage by means of unobtrusive, non-reactive sensors and the learning contents should be adapted accordingly. The focus of the present study is especially on the adaptation in e-learning for supporting motivation in the context of engineering science.

Keywords

Adaptation Adaptive E-learning Engineering science Focus groups Motivation Layered evaluation User as wizard 

Notes

Acknowledgments

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”.

References

  1. 1.
    Krömker, H., Hoffmann, M., Huntemann, N.: Anwendungsorientierte Fachlandkarten: Fallbeispiel Nanotechnologie. In: 10. Ingenieurpädagogische Regionaltagung 2015 - Anwendungsorientierung und Wissenschaftsorientierung in der Ingenieurbildung, Eindhoven, Niederlande, 5 November 2015Google Scholar
  2. 2.
    Niegemann, H.M., Domagk, S., Hessel, S., Hein, A., Hupfer, M., Zobel, A.: Kompendium Multimediales Lernen. X.media.press. Springer, Berlin (2008)Google Scholar
  3. 3.
    Rheinberg, F., Vollmeyer, R., Rollett, W.: Motivation and action in self-regulated learning. In: Boekaerts, M., Pintrich, P.R., Zeidner, M. (eds.) Handbook of Selfregulation, pp. 503–529. Academic Press, London (2000)Google Scholar
  4. 4.
    Bandura, A.: Self-efficacy. Toward a unifying theory of behavioral change. Psychol. Rev. 84(2), 191–215 (1977)CrossRefGoogle Scholar
  5. 5.
    Krapp, A., Hidi, S., Renninger, K.A.: Interest, learning and development. In: Renninger, K.A., Hidi, S., Krapp, A. (eds.) Role of interest in learning and development, pp. 3–25 (1992)Google Scholar
  6. 6.
    Rheinberg, F., Vollmeyer, R.: Motivation, 8th edn., vol. 555. Urban-Taschenbücher. Kohlhammer, Stuttgart (2012)Google Scholar
  7. 7.
    Brophy, J.E.: Motivating Students to Learn, 3rd edn. Routledge, New York (2010)Google Scholar
  8. 8.
    Hartnett, M.: Motivation in Online Education. Springer, New York (2016)CrossRefGoogle Scholar
  9. 9.
    Krömker, H., Hoffmann, M., Huntemann, N.: Wissensstrukturierung für das Lernen in den Ingenieurwissenschaften. In: Kammasch, G., Klaffke, H., Knutzen, S. (eds.) Technische Bildung im Spannungsfeld zwischen beruflicher und akademischer Bildung. Die Vielfalt der Wege zu technischer Bildung: Referate der 11. Ingenieurpädagogischen Regionaltagung 2016 an der Technischen Universität Hamburg, pp. 101–108. IPW, Berlin (2017)Google Scholar
  10. 10.
    Leutner, D.: Adaptivität und Adaptierbarkeit multimedialer Lehr- und Informationssysteme. In: Issing, L.J., Klimsa, P. (eds.) Information und Lernen mit Multimedia und Internet. Lehrbuch für Studium und Praxis, 3rd edn., pp. 115–126. Beltz, Weinheim (2005)Google Scholar
  11. 11.
    Brusilovsky, P.: Adaptive hypermedia. From intelligent tutoring systems to web-based education. In: Goos, G., Hartmanis, J., van Leeuwen, J., Gauthier, G., Frasson, C., VanLehn, K. (eds.) Intelligent Tutoring Systems. LNCS, vol. 1839, pp. 1–7. Springer, Heidelberg (2000)Google Scholar
  12. 12.
    Knutov, E., de Bra, P., Pechenizkiy, M.: AH 12 years later. A comprehensive survey of adaptive hypermedia methods and techniques. New Rev. Hypermedia Multimed. (2009).  https://doi.org/10.1080/13614560902801608CrossRefGoogle Scholar
  13. 13.
    Brusilovsky, P.: Methods and techniques of adaptive hypermedia. In: Brusilovsky, P., Kobsa, A., Vassileva, J. (eds.) Adaptive Hypertext and Hypermedia, pp. 1–43. Springer, Dordrecht (1998)CrossRefGoogle Scholar
  14. 14.
    Paramythis, A., Weibelzahl, S., Masthoff, J.: Layered evaluation of interactive adaptive systems. Framework and formative methods. User Model User Adap. Interact. 20(5), 383–453 (2010)CrossRefGoogle Scholar
  15. 15.
    Brusilovsky, P., Karagiannidis, C., Sampson, D.: The benefits of layered evaluation of adaptive applications and services. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds.) User Modeling 2001. 8th International Conference, UM 2001 Sonthofen, Germany, 13–17 July 2001 Proceedings. LNCS, vol. 2109, pp. 1–8. Springer, Heidelberg (2001)Google Scholar
  16. 16.
    Domagk, S.: Pädagogische Agenten in multimedialen Lernumgebungen. Empirische Studien zum Einfluss der Sympathie auf Motivation und Lernerfolg. Zugl.: Erfurt, Univ., Diss., 2007. Wissensprozesse und digitale Medien, vol. 9. Logos, Berlin (2008)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Mathias Bauer
    • 1
  • Cassandra Bräuer
    • 1
  • Jacqueline Schuldt
    • 1
  • Heidi Krömker
    • 1
  1. 1.Ilmenau University of TechnologyIlmenauGermany

Personalised recommendations