On the Software Engineering Aspects of Educational Intelligence

  • Thanasis Hadzilacos
  • Dimitris Kalles
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)


Students who enroll in the undergraduate program on informatics at the Hellenic Open University (HOU) demonstrate significant difficulties in advancing beyond the introductory courses. We use decision trees and genetic algorithms to analyze their academic performance throughout an academic year. Based on the accuracy of the generated rules, we analyze the educational impact of specific tutoring practices and reflect on some software engineering issues involved in the development of organization-wide measurement systems.


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  1. 1.
    Open Leaning, Special issue on Student retention in open and distance learning. 19:1 (2004)Google Scholar
  2. 2.
    Xenos, M., Pierrakeas, C., Pintelas, P.: A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University. Computers & Education 39, 361–377 (2002)CrossRefGoogle Scholar
  3. 3.
    Kotsiantis, S., Pierrakeas, C., Pintelas, P.: Predicting students performance in distance learning using Machine Learning techniques. Applied Artificial Intelligence 18(5), 411–426 (2004)CrossRefGoogle Scholar
  4. 4.
    Witten, I., Frank, E.: Data mining: practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Mateo (2000)Google Scholar
  5. 5.
    Kalles, D., Pierrakeas, C.: Analyzing student performance in distance learning with genetic algorithms and decision trees (to appear in: Applied Artificial Intelligence) (2006)Google Scholar
  6. 6.
    Papagelis, A., Kalles, D.: Breeding decision trees using evolutionary techniques. In: Proceedings of the International Conference on Machine Learning, Williamstown, Massachusetts, pp. 393–400. Morgan Kaufmann, San Francisco (2001)Google Scholar
  7. 7.
    Kalles, D., Pierrakeas, C.: Using Genetic Algorithms and Decision Trees for a posteriori Analysis and Evaluation of Tutoring Practices based on Student Failure Models. In: 3rd IFIP conference on Artificial Intelligence Applications and Innovations, Athens, Greece (2006)Google Scholar
  8. 8.
    Hadzilacos, T., Kalles, D., Pierrakeas, C., Xenos, M.: On Small Data Sets Revealing Big Differences. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds.) SETN 2006. LNCS (LNAI), vol. 3955, pp. 512–515. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Werth, L.H.: Predicting student performance in a beginning computer science class. In: Proceedings of the 17th SIGCSE technical symposium on Computer science education, Cincinnati, OH, pp. 138–143 (1986)Google Scholar
  10. 10.
    Minaei-Bidogli, B., Kashy, D.A., Kortemeyer, G., Punch, W.F.: Predicting student performance: an application of data mining methods with the educational web-based system LON-CAPA. In: Proceedings of the 33rd ASEE/IEEE Frontiers in Education conference, Boulder, CO (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thanasis Hadzilacos
    • 1
    • 2
  • Dimitris Kalles
    • 1
  1. 1.Hellenic Open UniversityPatrasGreece
  2. 2.Research Academic Computer Technology InstitutePatrasGreece

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