How to Select an Example? A Comparison of Selection Strategies in Example-Based Learning

  • Sebastian Gross
  • Bassam Mokbel
  • Barbara Hammer
  • Niels Pinkwart
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)


In this paper, we investigate an Intelligent Tutoring System (ITS) for Java programming that implements an example-based learning approach. The approach does not require an explicit formalization of the domain knowledge but automatically identifies appropriate examples from a data set consisting of learners’ solution attempts and sample solution steps created by experts. In a field experiment conducted in an introductory course for Java programming, we examined four example selection strategies for selecting appropriate examples for feedback provision and analyzed how learners’ solution attempts changed depending on the selection strategy. The results indicate that solutions created by experts are more beneficial to support learning than solution attempts of other learners, and that examples modeling steps of problem solving are more appropriate for very beginners than complete sample solutions.


intelligent tutoring system example-based learning programming 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aleven, V., Mclaren, B.M., Sewall, J., Koedinger, K.R.: A new paradigm for intelligent tutoring systems: example-tracing tutors. International Journal of Artificial Intelligence in Education, 105–154 (2009)Google Scholar
  2. 2.
    Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive tutors: Lessons learned. Journal of the Learning Sciences 4(2), 167–207 (1995)CrossRefGoogle Scholar
  3. 3.
    Brusilovsky, P., Yudelson, M.: From webex to navex: Interactive access to annotated program examples. Proceedings of the IEEE 96(6), 990–999 (2008)CrossRefGoogle Scholar
  4. 4.
    Chi, M.T.H., Bassok, M., Lewis, M.W., Reimann, P., Glaser, R.: Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science 13(2), 145–182 (1989)CrossRefGoogle Scholar
  5. 5.
    Cilibrasi, R., Vitanyi, P.: Clustering by compression. IEEE Transactions on Information Theory 51(4), 1523–1545 (2005)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Dzikovska, M.O., Nielsen, R.D., Brew, C.: Towards effective tutorial feedback for explanation questions: A dataset and baselines. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2012, pp. 200–210. Association for Computational Linguistics, Stroudsburg (2012)Google Scholar
  7. 7.
    Gross, S., Mokbel, B., Hammer, B., Pinkwart, N.: Towards a domain-independent its middleware architecture. In: Chen, N.-S., Huang, R., Kinshuk,, Li, Y., Sampson, D.G. (eds.) Proceedings of the 13th IEEE International Conference on Advanced Learning Technologies (ICALT), pp. 408–409 (2013)Google Scholar
  8. 8.
    Große, C.S., Renkl, A.: Finding and fixing errors in worked examples: Can this foster learning outcomes? Learning and Instruction 17(6), 612–634 (2007)CrossRefGoogle Scholar
  9. 9.
    Lynch, C., Ashley, K.D., Pinkwart, N., Aleven, V.: Concepts, structures, and goals: Redefining ill-definedness. Int. J. of Artif. Intell. Ed. 19(3), 253–266 (2009)Google Scholar
  10. 10.
    Mitrovic, A., Koedinger, K.R., Martin, B.: A comparative analysis of cognitive tutoring and constraint-based modeling. In: Brusilovsky, P., Corbett, A., de Rosis, F. (eds.) UM 2003. LNCS, vol. 2702, pp. 313–322. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Mokbel, B., Gross, S., Paassen, B., Pinkwart, N., Hammer, B.: Domain-independent proximity measures in intelligent tutoring systems. In: D’Mello, S.K., Calvo, R.A., Olney, A. (eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM), pp. 334–335 (2013)Google Scholar
  12. 12.
    Sweller, J., Ayres, P., Kalyuga, S.: Cognitive Load Theory. Explorations in the Learning Sciences, Instructional Systems and Performance Technologies. Springer (2011)Google Scholar
  13. 13.
    Walker, E., Ogan, A., Aleven, V., Jones, C.: Two approaches for providing adaptive support for discussion in an ill-defined domain. In: Proceedings of a Workshop at ITS 2008, Montreal, Canada, June 23, pp. 1–12 ( 2008)Google Scholar
  14. 14.
    Wittwer, J., Renkl, A.: How effective are instructional explanations in example-based learning? a meta-analytic review. Educational Psychology Review 22(4), 393–409 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sebastian Gross
    • 1
  • Bassam Mokbel
    • 2
  • Barbara Hammer
    • 2
  • Niels Pinkwart
    • 1
  1. 1.Humboldt-Universität zu BerlinGermany
  2. 2.Bielefeld UniversityGermany

Personalised recommendations