Exploring the Adaptation to Learning Styles: The Case of AdaptiveLesson Module for Moodle

  • Jelena Nakić
  • Sabine Graf
  • Andrina Granić
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7946)


Understanding learners and acknowledging diversities in their learning behavior is the key to design effective e-learning systems. This paper presents an innovative solution focused on adaptation to learning styles in the context of the learning management system Moodle. A new activity module named AdaptiveLesson has been developed as an extension of the Lesson module. It simplifies the interface for teachers who are creating lesson content while on the other hand provides students with individually adapted on-line courses in respect to their learning styles. Modifications in adaptive lessons with respect to regular lesson are described and the mechanism for adaptation to learning styles is presented. In order to evaluate the effectiveness of the proposed solution, a pilot evaluation of an on-line course developed by the AdaptiveLesson module has been conducted. The experiment is based on the comparison of an adaptive and an equivalent regular course. Results are discussed and guidelines for further research are established.


e-learning systems Moodle lessons learning styles adaptive course 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blackboard, , (last accessed February 1, 2013)
  2. 2.
    Brusilovsky, P.: Adaptive Hypermedia. User Modeling and User-Adapted Interaction 11(1-2), 87–110 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Brusilovsky, P., Millán, E.: User Models for Adaptive Hypermedia and Adaptive Educational Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 3–53. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Carver, C.A., Howard, R.A., Lane, W.D.: Addressing different learning styles through course hypermedia. IEEE Transactions on Education 42, 33–38 (1999)CrossRefGoogle Scholar
  5. 5.
    Chin, D.: Empirical evaluation of user models and user-adapted systems. User Modeling and User-Adapted Interaction 11(1-2), 181–194 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Claroline, (last accessed February 1, 2013)
  7. 7.
    Felder, R.M., Silverman, L.K.: Learning and Teaching Styles in Engineering Education. Engineering Education 78(7), 674–681 (1988)Google Scholar
  8. 8.
    Felder, R.M., Soloman, B.A.: Index of learning styles questionnaire (1997), (retrieved January 11, 2013)
  9. 9.
    Graf, S.: Providing adaptive courses in learning management systems with respect to learning styles. In: Proceedings of the World Conference on E-learning in Corporate, Government, Healthcare, and Higher Education (E-learn), pp. 2576–2583. AACE Press, Chesapeake (2007)Google Scholar
  10. 10.
    Graf, S., Liu, T.C.: Kinshuk: Analysis of learners’ navigational behaviour and their learning styles in an online course. Journal of Computer Assisted Learning 26(2), 116–131 (2010)CrossRefGoogle Scholar
  11. 11.
    Graf, S., Liu, T.-C., Kinshuk, C.N.-S., Yang, S.J.H.: Learning Styles and Cognitive Traits - Their Relationship and its Benefits in Web-based Educational Systems. Computers in Human Behavior 25(6), 1280–1289 (2009)CrossRefGoogle Scholar
  12. 12.
    Granić, A., Nakić, J.: Enhancing the Learning Experience: Preliminary Framework for User Individual Differences. In: Leitner, G., Hitz, M., Holzinger, A. (eds.) USAB 2010. LNCS, vol. 6389, pp. 384–399. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Honey, P., Mumford, A.: The Manual of Learning Styles. Peter Honey Publications, Maidenhead (1992)Google Scholar
  14. 14.
    Höök, K.: Steps to take before intelligent user interfaces become real. Interacting with Computers 12(4), 409–426 (2000)CrossRefGoogle Scholar
  15. 15.
    Ilias, (last accessed February 1, 2013)
  16. 16.
    Jovanović, D., Milošević, D., Žižović, M.: INDeLER: eLearning Personalization by Mapping Student’s learning Style and Preference to Metadata. International Journal of Emerging Technologies in Learning 3(4), 41–50 (2008)Google Scholar
  17. 17.
    Kuljis, J., Liu, F.: A comparison of learning style theories on the suitability for elearning. In: Proceedings of the IASTED Conference on Web Technologies, Applicationsand Services (2005)Google Scholar
  18. 18.
    Litzinger, T.A., Lee, S.H., Wise, J.C., Felder, R.M.: A Study of the Reliability and Validity of the Felder-Solomon Index of Learning Styles. In: Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition (2005)Google Scholar
  19. 19.
    Livesay, G.A., Dee, K.C., Nauman, E.A., Hites, L.S.: Engineering student learning styles: a statistical analysis using Felder’s Index of Learning Styles. Presented at the Annual Conference of the American Society for Engineering Education, Montreal, Canada (June 2002)Google Scholar
  20. 20.
    Magoulas, G.D., Chen, S.Y., Papanikolaou, K.A.: Integrating layered and heuristic evaluation for adaptive learning environments. In: Brusilovsky, P., Corbett, A.T., de Rosis, F. (eds.) UM 2003. LNCS, vol. 2702, pp. 5–14. Springer, Heidelberg (2003)Google Scholar
  21. 21.
    Moodle, (last accessed February 1, 2013)
  22. 22.
    Moodle Docs, (last accessed February 1, 2013)
  23. 23.
    Nakić, J., Marangunić, N., Granić, A.: Learning Styles and Navigation Patterns in Web-Based Education. In: Stephanidis, C. (ed.) Universal Access in HCI, Part IV, HCII 2011. LNCS, vol. 6768, pp. 587–596. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  24. 24.
    Paramythis, A., Weibelzahl, S., Masthoff, J.: Layered Evaluation of Interactive Adaptive Systems: Framework and Formative Methods. User Modeling and User-Adapted Interaction 20(5), 383–453 (2010)CrossRefGoogle Scholar
  25. 25.
    Sakai, (last accessed February 1, 2013)
  26. 26.
    Schiaffino, S., Garcia, P., Amandi, A.: eTeacher: providing personalized assistance to e-learning students. Computers & Education 51(4), 1744–1754 (2008)CrossRefGoogle Scholar
  27. 27.
    Tarpin-Bernard, F., Marfisi-Schottman, I., Habieb-Mammar, H.: AnAmeter: The first steps to evaluating adaptation. In: 6th Workshop on User-Centred Design and Evaluation of Adaptive Systems at UMAP 2009, pp. 11–20. CEUR, Trento (2009)Google Scholar
  28. 28.
    Tobar, C.M.: Yet Another Evaluation Framework. In: Brusilovsky, P., Corbett, A.T., de Rosis, F. (eds.) UM 2003. LNCS, vol. 2702, pp. 15–24. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  29. 29.
    Tuckman, B.W.: Conducting Educational Research, 5th edn. Wadsworth Group (1999)Google Scholar
  30. 30.
    Van Zwanenberg, N., Wilkinson, L.J., Anderson, A.: Felder and Silverman’s Index of Learning Styles and Honey and Mumford’s Learning Styles Questionnaire: how do they compare and do they predict academic performance? Educational Psychology 20, 365–380 (2000)CrossRefGoogle Scholar
  31. 31.
    Weibelzahl, S.: Problems and pitfalls in evaluating adaptive systems. In: 4th Workshop on the Evaluation of Adaptive Systems at UM 2005, Edinburgh, UK, pp. 57–66 (2005)Google Scholar
  32. 32.
    Weibelzahl, S., Lippitsch, S., Weber, G.: Advantages, opportunities, and limits of empirical evaluations: Evaluating adaptive systems. Künstliche Intelligenz 3(2), 17–20 (2002)Google Scholar
  33. 33.
    Zywno, M.S.: A contribution to validation of score meaning for Felder–Soloman’s Index of Learning Styles. Presented at the Annual Conference of the American Society for Engineering Education, Nashville, USA (June 2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jelena Nakić
    • 1
  • Sabine Graf
    • 2
  • Andrina Granić
    • 1
  1. 1.Faculty of ScienceUniversity of SplitSplitCroatia
  2. 2.School of Computing & Information SystemsAthabasca UniversityEdmontonCanada

Personalised recommendations