The Construction of Fuzzy Set and Fuzzy Rule for Mixed Approach in Adaptive Hypermedia Learning System

  • Naomie Salim
  • Norreen Haron
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3942)

Abstract

In this paper, a framework for individualizing the learning material structure in adaptive learning system is introduced. It aims to utilize the learning characteristics and provide a personalized learning environment that exploit pedagogical model and fuzzy logic techniques. The learning material consists of 4 structures; 1) theory, 2) examples 3) exercises and 4) activities. The pedagogical model and learning characteristics are based on the student’s personality factor (Myers-Briggs Type Indicator (MBTI)), whilst the fuzzy logic techniques are used to classify the structure of learning material which is based on student’s personality factors. This paper tend to illustrate the construction of fuzzy set and fuzzy rules to find the best rules based on combination of two approaches; learning style approach and fuzzy logic approach for adapting the content to the user, allowing a learning system to dynamically adapt the choice of possible learning structure through the learning material based on the user’s personality factor.

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References

  1. 1.
    Brusilovsky, P.: Adaptive Hypermedia. User Modeling and User- Adapted Instruction, vol. 1-2, pp. 87–110. Kluwer academic Publishers, Dordrecht (2001)Google Scholar
  2. 2.
    Hashim, S., Yaakub, R.: Terbitan Pertama Psikologi Pembelajaran dan Personaliti. PTS Publications & Distributors Sdn. Bhd (Pelita) Malaysia (2003)Google Scholar
  3. 3.
    Stash, N., Cristeae, A., Bra, P.D.: Authoring of Learning Styles in Adaptive Hypermedia:Problems and Solutions International World Wide Web Conference. In: Proceedings of the 13th international World Wide Web conference on Alternate track papers & poster, pp. 114–123 (2004)Google Scholar
  4. 4.
    Carro, R.M., Pulido, E., Rodríguez, P.: Tangow: Task-Based Adaptive Learner Guidance on the WWW. In: Proc. Second Workshop on Adaptive Systems and User Modeling on the World Wide Web, Canada, pp. 49–57 (1999)Google Scholar
  5. 5.
    Triantafillou, E., Pomportsis, A., Georgiadou, E.: AES-CS: Adaptive Educational System base on cognitive styles. In: Proceedings of the AH 2002 Workshop, Malaga, Spain, pp. 10–20 (2002)Google Scholar
  6. 6.
    Bishop, C.C., Wheeler, D.: The Myers-Briggs Personality Type and Its Relationship to Computer Programming. Journal of Research on Computing in Education 26, 358–371 (1994)Google Scholar
  7. 7.
    Soles, C., Moller, L.: Myers Briggs Type Preferences in Distance Learning Education. International Journal of Educational Technology 2 (2001)Google Scholar
  8. 8.
    Honey, P., Mumford, A.: The Manual of Learning Styles. Maidenhead: PeterHoney (1992)Google Scholar
  9. 9.
    Schroeder, C.C.: New-Students-New LearningStyle (March 7, 2004), http://www.virtualschool.edu/mon/Academia/KierseyLearningStyles.html
  10. 10.
    Negnevitsky, M.: Artificial Intelligence. A Guide to Intelligent Systems, 1st edn. Pearson Education, London (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Naomie Salim
    • 1
  • Norreen Haron
    • 1
  1. 1.Faculty of Computer Science & Information SystemUniversiti Teknologi MalaysiaSkudai

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