Implementation of an Adaptive Mechanism in Moodle Based on a Hybrid Dynamic User Model

  • Ioannis KaragiannisEmail author
  • Maya Satratzemi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 916)


Learning styles summarizes the concept that individuals have different learning preferences and they learn better when they receive information in their preferred way. Even though learning styles have been subjected to some criticism, they can play an important role in adaptive e-learning systems. In order to overcome the drawbacks of the traditional detection method, educational data mining techniques have been implemented in these systems for the automatic detection of students’ learning styles. The purpose of this paper is to present the implementation of an adaptive mechanism in Moodle. The proposed mechanism is based on a hybrid dynamic user model that is built with techniques that are based both on learner knowledge and behavior. An evaluation study was conducted in order to examine the effectiveness of the proposed mechanism. The results were encouraging since they indicated that our extension affected students’ motivation and performance. In addition, the precision attained by the proposed automatic detection approach was rather positive.


Learning management systems Adaptive systems Learning styles Automatic detection User modeling 


  1. 1.
    Akbulut, Y., Cardak, C.S.: Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011. Comput. Educ. 58, 835–842 (2012)CrossRefGoogle Scholar
  2. 2.
    Bachari, E., Abelwahed, H., Adnani, M.: E-learning personalization based on dynamic learners’ preference. Int. J. Comput. Sci. Inf. Technol. 3(3), 200–216 (2011)Google Scholar
  3. 3.
    Bernard, J., Chang, T.W., Popescu, E., Graf, S.: Learning style identifier: Improving the precision of learning style identification through computational intelligence algorithms. Expert Syst. Appl. 75, 94–108 (2017)CrossRefGoogle Scholar
  4. 4.
    Brusilovsky, P.: Adaptive hypermedia. User Model. User Adap. Interact. 11, 87–110 (2001)CrossRefGoogle Scholar
  5. 5.
    Cabada, R.Z., Estrada, M.L.B., García, C.A.R.: EDUCA: a web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network. Expert Syst. Appl. 38(8), 9522–9529 (2011)Google Scholar
  6. 6.
    Crockett, K., Latham, A., Whitton, N.: On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees. Int. J. Hum Comput Stud. 97, 98–115 (2017)CrossRefGoogle Scholar
  7. 7.
    Dorça, F.A., Lima, L.V., Fernandes, M.A., Lopes, C.R.: Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: An experimental analysis. Expert Syst. Appl. 40(6), 2092–2101 (2013)CrossRefGoogle Scholar
  8. 8.
    Dung, P.Q., Florea, A.M.: An approach for detecting learning styles in learning management systems based on learners’ behaviours. In: International Conference on Education and Management Innovation, pp. 171–177. IACSIT Press, Singapore (2012)Google Scholar
  9. 9.
    Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1988)Google Scholar
  10. 10.
    Felder, R.M., Soloman, B.A.: Index of Learning Styles Questionnaire.
  11. 11.
    Felder, R.M., Spurlin, J.: Applications, reliability and validity of the index of learning styles. Int. J. Eng. Educ. 21(1), 103–112 (2005)Google Scholar
  12. 12.
    Feldman, J., Monteserin, A., Amandi, A.: Automatic detection of learning styles: state of the art. Artif. Intell. Rev. 44(2), 157–186 (2015)CrossRefGoogle Scholar
  13. 13.
    García, P., Amandi, A., Schiaffino, S.N., Campo, M.R.: Evaluating Bayesian networks’ precision for detecting students’ learning styles. Comput. Educ. 49(3), 794–808 (2007)CrossRefGoogle Scholar
  14. 14.
    Graf, S.: Adaptivity in learning management systems focusing on learning styles. Ph.D. dissertation, Faculty of Informatics, Vienna University of Technology, Austria (2007)Google Scholar
  15. 15.
    Karagiannis, I., Satratzemi, M.: Enhancing adaptivity in moodle: framework and evaluation study. In: Auer, M., Guralnick, D., Uhomoibhi, J. (eds.) Interactive Collaborative Learning ICL 2016. Advances in Intelligent Systems and Computing, vol. 545, pp. 575–589. Springer, Cham (2017)Google Scholar
  16. 16.
    Kirschner, P.A.: Stop propagating the learning styles myth. Comput. Educ. 106, 166–171 (2017)CrossRefGoogle Scholar
  17. 17.
    Liyanage, M.P.P., Gunawardena, K.S.L., Hirakawa, M.: Using learning styles to enhance learning management systems. ICTer 7(2), 1–10 (2014)Google Scholar
  18. 18.
    Popescu, E., Badica, C.: Creating a personalized artificial intelligence course: WELSA case study. Int. J. Inf. Syst. Soc. Change 2(1), 31–47 (2011)Google Scholar
  19. 19.
    Thalmann, S.: Adaptation criteria for the personalised delivery of learning materials: a multi-stage empirical investigation. Australas. J. Educ. Technol. 30(1), 45–60 (2014)CrossRefGoogle Scholar
  20. 20.
    Truong, H.M.: Integrating learning styles and adaptive e-learning system: current developments, problems and opportunities. Comput. Hum. Behav. 55, 1185–1193 (2016)CrossRefGoogle Scholar
  21. 21.
    Yang, T.C., Hwang, G.J., Yang, S.J.H.: Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. J. Educ. Technol. Soc. 16(4), 185–200 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Applied InformaticsUniversity of MacedoniaThessalonikiGreece

Personalised recommendations