Evaluating Web Based Instructional Models Using Association Rule Mining

  • Enrique García
  • Cristóbal Romero
  • Sebastián Ventura
  • Carlos de Castro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5535)


In this paper we describe an Integrated Development System for Instructional Model for E-learning (INDESIME) to create and to maintain instructional models using adaptive technologies and collaborative tools. An authoring tool has also been developed for helping to non-programming users to create Learning Management Systems (LMSs) courses that implement a specific instructional model. Data mining techniques are proposed to evaluate the e-learning courses generated from the model. We have tested the degree of effectiveness of our system using Moodle courses. The courses topics tested are based on the European Computer Driving Licence Foundation catalogue.


instructional design learning management systems authoring tools and methods data mining association rules 


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  1. 1.
    WebCT Inc. Learning management system WebCT (2005),
  2. 2.
    BlackBoard Inc. Learning management system BlackBoard (2005),
  3. 3.
    WBT Systems. Learning management system TopClass (2005),
  4. 4.
    Dougiamas, M., et al.: Course management system MOODLE (2005),
  5. 5.
    Leidhold, W., et al.: Learning management system ILIAS (2005),
  6. 6.
    Gay, G., et al.: Learning management system ATutor (2005),
  7. 7.
    Brusilovsky, P.: Adaptive and Intelligent Web-based Educational Systems. International Journal of Artificial Intelligence in Education 13, 159–169 (2003)Google Scholar
  8. 8.
    Brusilovsky, P., Schwarz, E., Weber, G.: ELM-ART: An intelligent tutoring system on World Wide Web. In: Lesgold, A.M., Frasson, C., Gauthier, G. (eds.) ITS 1996. LNCS, vol. 1086, pp. 261–269. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  9. 9.
    Brusilovsky, P., Schwarz, E., Weber, G.: ELM-ART: An intelligent tutoring system on World Wide Web. In: Third International Conference on Intelligent Tutoring Systems, pp. 261–269 (1995)Google Scholar
  10. 10.
    Brusilovsky, P., Eklund, J., Schwarz, E.: Web-based education for all: A tool for developing adaptive courseware. Computer Networks and ISDN Systems 30(1-7), 291–300 (1998)CrossRefGoogle Scholar
  11. 11.
    Carro, R.M., Pulido, E., Rodrígues, P.: TANGOW. Computer Science Report, Eindhoven University of Technology. pp. 49–57 (1999)Google Scholar
  12. 12.
    De Bra, P., Calvi, L.: AHA! An Open Adaptive Hypermedia Architecture. The New Review of Hypermedia and Multimedia, vol. 4, pp. 115–139. Taylor Graham Publishers (1998)Google Scholar
  13. 13.
    Weber, G.: ART-WEB. University of Trier, Trier (1999)Google Scholar
  14. 14.
    Romero, C., Ventura, S.: Educational data mining: A survey from 1995 to 2005. International Journal Experts Systems with Applications 33(1), 135–146 (2007)CrossRefGoogle Scholar
  15. 15.
    Reigeluth, C.: A new paradigm of instructional theory, vol. II. Lawrence Erlbaum Associates Inc., Mahwah (1999)Google Scholar
  16. 16.
    Gros, B., et al.: Instructional Design and the Authoring of Multimedia and Hypermedia Systems: Does a Marriage make Sense? Educational Technology 1(37), 48–56 (1997)Google Scholar
  17. 17.
    D’Angelo, G.: From didactics to e-didactics. E-learning paradigms, models and techniques. Liguori Editori (2007)Google Scholar
  18. 18.
    Guardià, L., Sangrà, A.: Instructional design and learning objects; towards a model for the design of online learning evaluation activities. In: Proceedings of the First Pluri-Disciplinary Symposium on Design, Evaluation and Description of Reusable Learning Contents, Guadalajara, Spain (2004)Google Scholar
  19. 19.
    Chiappe, A.: Modelo de diseño instruccional basado en objetos de aprendizaje. Universidad de la Sabana, Colombia (2006),
  20. 20.
    Advanced Distributed Learning. Shareable content object reference model (SCORM): The SCORM overview (2005),
  21. 21.
    Wu, H., Houben, G.J., De Bra, P.: AHAM: A Dexter-based Reference Model for Adaptive Hypermedia. In: Proceedings of the ACM Conference on Hypertext and Hypermedia, Darmstadt, Germany, pp. 147–156 (1999)Google Scholar
  22. 22.
    Zheng, Z., et al.: Real world performance of association rules. In: Proceedings of the Sixth ACM-SIGKDD (2001)Google Scholar
  23. 23.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. Proceedings of 20th VLDB CVonf. Santiago de Chile (1996)Google Scholar
  24. 24.
    Scheffer, T.: Finding Association Rules That Trade Support Optimally against Confidence. Intelligent Data Analysis 9(4), 381–395 (2005)Google Scholar
  25. 25.
    García, E., Romero, C., Ventura, S., de Castro, C.: An architecture for making recommendations to courseware authors through association rule mining and collaborative filtering. UMUAI: User Modelling and User Adapted Interaction 19(1-2), 99–132 (2009)Google Scholar
  26. 26.
    Advanced Distributed Learning. Shareable content object reference model (SCORM): The SCORM overview (2009),

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Enrique García
    • 1
  • Cristóbal Romero
    • 1
  • Sebastián Ventura
    • 1
  • Carlos de Castro
    • 1
  1. 1.Escuela Politécnica SuperiorUniversidad de CórdobaCórdobaSpain

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