Advertisement

An Influence Diagram for the Collaboration in E-learning Environments

  • Antonio R. Anaya
  • Manuel Luque
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7930)

Abstract

Influence diagrams have been used as decision support tool in different domains where the uncertainty plays an important role. The domain of collaborative learning environments can be characterized by the difficulties of proposing student collaboration indicators, and by the relationship between these indicators and the psychological and social student behavior. Thus, an analysis of the collaboration process muss take into account the natural uncertainty of the used indicators. For this reason we have built an influence diagram whose network has been created using the obtained findings in previous research. The influence diagram can support with a decision table that informs on the problematic circumstances of the target student to be recommended.

Keywords

Bayesian Network Collaborative Learning Decision Table Student Interaction Decision Node 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anaya, A.R., Boticario, J.G.: Application of machine learning techniques to analyze student interactions and improve the collaboration process. Expert Systems with Applications 38(2), 1171–1181 (2011)CrossRefGoogle Scholar
  2. 2.
    Anaya, A.R., Boticario, J.G.: Content-free collaborative learning modeling using data mining. User Modeling and User-adapted Interaction 21(1-2), 181–216 (2011)CrossRefGoogle Scholar
  3. 3.
    Arias, M., Díez, F.J., Palacios, M.P.: OpenMarkovXML. A format for encoding probabilistic graphical models. Technical Report CISIAD-10-04, UNED, Madrid, Spain (2010)Google Scholar
  4. 4.
    Bielza, C., Gómez, M., Shenoy, P.P.: A review of representation issues and modelling challenges with influence diagrams. Omega 39, 227–241 (2011)CrossRefGoogle Scholar
  5. 5.
    Daradoumis, T., Juan, A.A., Lera-López, F., Faulin, J.: Using collaboration strategies to support the monitoring of online collaborative learning activity. In: Lytras, M.D., Ordonez De Pablos, P., Avison, D., Sipior, J., Jin, Q., Leal, W., Uden, L., Thomas, M., Cervai, S., Horner, D. (eds.) TECH-EDUCATION 2010. CCIS, vol. 73, pp. 271–277. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Dringus, L.P., Ellis, E.: Using data mining as a strategy for assessing asynchronous discussion forums. Computers & Education 45, 140–160 (2005)CrossRefGoogle Scholar
  7. 7.
    Dringus, L.P., Ellis, E.: Temporal transitions in participation flow in an asynchronous discussion forum. Computers & Education 54(2), 340–349 (2010)CrossRefGoogle Scholar
  8. 8.
    Gaudioso, E., Montero, M., Talavera, L., Hernandez del Olmo, F.: Supporting teachers in collaborative student modeling: A framework and an implementation. Expert Systems with Applications 36, 2260–2265 (2009)CrossRefGoogle Scholar
  9. 9.
    Howard, R.A., Matheson, J.E.: Influence diagrams. In: Howard, R.A., Matheson, J.E. (eds.) Readings on the Principles and Applications of Decision Analysis, pp. 719–762. Strategic Decisions Group, Menlo Park (1984)Google Scholar
  10. 10.
    Johnson, D.W., Johnson, R.: Cooperation and the use of technology. In: Handbook of Research on Educational Communications and Technology, pp. 401–424. Taylor & Francis, Abington (2004)Google Scholar
  11. 11.
    Jordan, L.E.: Transforming the student experience at a distance: Designing for collaborative online learning. Engineering Education: Journal of the Higher Education Academy Engineering Subject Centre 4(2) (2009)Google Scholar
  12. 12.
    Lacave, C., Díez, F.J.: A review of explanation methods for Bayesian networks. Knowledge Engineering Review 17, 107–127 (2002)CrossRefGoogle Scholar
  13. 13.
    Lacave, C., Luque, M., Díez, F.J.: Explanation of Bayesian networks and influence diagrams in Elvira. IEEE Transactions on Systems, Man and Cybernetics—Part B: Cybernetics 37, 952–965 (2007)CrossRefGoogle Scholar
  14. 14.
    Millán, E., Loboda, T., Pérez de-la Cruz, J.L.: Bayesian networks for student model engineering. Computers & Education 55(4), 1663–1683 (2010)CrossRefGoogle Scholar
  15. 15.
    Niblett, T.: Constructing decision trees in noisy domains. In: EWSL 1987, pp. 67–78 (1987)Google Scholar
  16. 16.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)Google Scholar
  17. 17.
    Pearl, J., Geiger, D., Verma, T.: Conditional independence and its representations. Kybernetika 25, 33–44 (1989)MathSciNetGoogle Scholar
  18. 18.
    Perera, D., Kay, J., Yacef, K., Koprinska, I.: Mining learners’ traces from an online collaboration tool. In: Proceedings of the 13th International Conference of Artificial Intelligence in Education, Workshop Educational Data Mining, Marina del Rey, CA. USA, pp. 60–69 (July 2007)Google Scholar
  19. 19.
    Romero, C., Ventura, S.: Educational data mining: A review of the state-of-the-art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40(6), 601–618 (2010)CrossRefGoogle Scholar
  20. 20.
    Swan, K., Shen, J., Hiltz, S.R.: Assessment and collaboration in online learning. Journal of Asynchronous Learning Networks 10(1), 45–62 (2006)Google Scholar
  21. 21.
    Talavera, L., Gaudioso, E.: Mining student data to characterize similar behavior groups in unstructured collaboration spaces. In: Proceedings of the Workshop on Artificial Intelligence in CSCL, 16th European Conference on Artificial Intelligence (ECAI 2004), Valencia, Spain, pp. 17–23 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Antonio R. Anaya
    • 1
  • Manuel Luque
    • 1
  1. 1.Dept. of Artificial IntelligenceUNEDMadridSpain

Personalised recommendations