Cognitive Modeling of Personalized Software Design Styles: A Case Study in E-Learning

  • Mauro Marinilli
Part of the Advanced Information and Knowledge Processing book series (AI&KP)


This chapter discusses an approach to knowledge representation and processing based on representing information at a metamodel level and adapting it to the current user at various levels of abstraction. In this way both run-time data and program code are adapted to the user. Thanks to this approach, it is possible to model sophisticated concepts in a direct and natural way, avoiding technological details. We employed this technique for developing a user-adapted system for teaching object-oriented design patterns (OODP) by leveraging on existing technologies (software generation facilities, modeling languages, specific and general standard metamodels). The design of the prototype was drawn from an ad-hoc student cognitive model. The prototype is empirically evaluated and the findings discussed.


Design Pattern Cognitive Modeling Class Diagram Schema Match Relevance Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Aaraodt, A., Plaza, E. (1994) Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Amsterdam, NL: Artificial Intelligence Communications.Google Scholar
  2. 2.
    Alexander, C, et al. (1977) A Pattern Language: Towns, Buildings, Construction. New York: Oxford University Press.Google Scholar
  3. 3.
    Anderson, J.R. (2002) Spanning seven orders of magnitude: a challenge for cognitive modeling. Cognitive Science, 26.Google Scholar
  4. 4.
    Anderson, J.R. (1983) The Architecture of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
  5. 5.
    Beck, K., et al. (1996) Industrial experience with design patterns. In: Proceedings of the 18th International Conference on Software Engineering, ICSE’96.Google Scholar
  6. 6.
    Bézivin, J. (2001) From object composition to model transformation with the MDA. In Proceedings of TOOLS USA, Vol. IEEE TOOLS-39, Santa Barbara, CA.Google Scholar
  7. 7.
    Chin, D. (2001) Empirical evaluation of user models and user-adapted systems. Journal of User Modeling and User-Adapted Interaction, 11: 181–194.CrossRefGoogle Scholar
  8. 8.
    Clark, T., Evans, A., Sammut, P., Willans, J. (2004) Applied Metamodelling. A Foundation for Language Driven Development. Version 0.1. Xactium Technical Report.Google Scholar
  9. 9.
    Do, H.-H., Melnik, S., Rahm, E. (2002) Comparison of Schema Matching Evaluations. Scholar
  10. 10.
    Frankel, D. (2003) Model Driven Architecture: Applying MDA to Enterprise Computing. New York: J. Wiley.Google Scholar
  11. 11.
    Gamma, E., Helm, R., Johnson, R., Vlissldes, J. (1993) Design patterns: abstraction and reuse of object-oriented design. In: Proceedings of European Conference on Object-Oriented Programming (ECOOP). Kaiserslautern, Germany: Springer-Verlag.Google Scholar
  12. 12.
    Gavras, A., Belaunde, M, Ferreira Pires, F., Almeida J.P.A. (2003) Towards an MDA-based development methodology for distributed applications.Google Scholar
  13. 13.
    Gibson, J.J. (1977) The theory of affordances. In: Shaw, R.E., Bransford, J., (eds.). Perceiving, Acting, and Knowing. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  14. 14.
    Hartson, H.R. (1999) Cognitive, Physical, and Perceptual Affordances in Interaction Design. Technical Report. Department of Computer Science, Virginia Tech. http://,%20as%20appeared.pdf.Google Scholar
  15. 16.
    Koper, R. (2001) Modeling Units of Study from aPedagogical Perspective—The Pedagogical Meta-Model Behind EML. Heerlen, NL: Open University of the Netherlands.Google Scholar
  16. 17.
    Marinilli, M., Micarelli, A. (2005) Generative programming driven by user models. In: Proceedings of Tenth International Conference on User Modeling UM05, Edinburgh, UK.Google Scholar
  17. 18.
    Norman, D.A. (1988) The Psychology of Everyday Things. New York: Basic Books.Google Scholar
  18. 19.
    Nytun, J.P., Prinz, (2004) A Metalevel Representation and Philosophical Ontology. In: ECOOP 2004 Workshop: Philosophy, Ontology, and Information Systems, Oslo, Norway.Google Scholar
  19. 20.
    Schmidt, D. (1995) Using Design Patterns to Develop Reusable Object-Oriented Communication Software. Communication of ACM, 38(10).Google Scholar
  20. 21.
    Stuurman, S., Florijn, G. (2004) Experiences with Teaching Design Patterns. In: Proceedings of ITICSE’04, Leeds, United Kingdom.Google Scholar
  21. 22.
    Wilcoxon, F. (1945) Individual comparisons by ranking methods. Biometrica Bulletin, 1: 80–83.CrossRefGoogle Scholar
  22. 23.
    Witten, H.I., Eibe F. (1999) Data Mining: Practical Machine Learning Tools and Techniques with Java implementation. Morgan Kaufmann.Google Scholar
  23. 24.
    Jeremic, Z., Devedzic, V., Gasevic, D. (2004) An Intelligent Tutoring System for Learning Design Patterns, ICALT Joensuu, Finland.Google Scholar
  24. 25.
    Milo, T., Zohar, S. (1998) Using Schema Matching to Simplify Heterogeneous Data Translation. VLDB, New York, USA.Google Scholar
  25. 26.
    Yan, L.L., Miller, R.J., Haas, L.M., Fagin, R. (2001) Data-Driven Understanding and Refinement of Schema Mappings. SIGMOD, Santa Barbara, CA, USA.Google Scholar
  26. 27.
    Melnik, S., Garcia-Molina, H., Rahm, E. (2001) Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching, Stanford, CA, USA.Google Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Mauro Marinilli
    • 1
  1. 1.Università Roma TreRomaItaly

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