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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Bielza, C., Gómez, M., Shenoy, P.P.: A review of representation issues and modelling challenges with influence diagrams. Omega 39, 227–241 (2011)
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)
Dringus, L.P., Ellis, E.: Using data mining as a strategy for assessing asynchronous discussion forums. Computers & Education 45, 140–160 (2005)
Dringus, L.P., Ellis, E.: Temporal transitions in participation flow in an asynchronous discussion forum. Computers & Education 54(2), 340–349 (2010)
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)
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)
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)
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)
Lacave, C., Díez, F.J.: A review of explanation methods for Bayesian networks. Knowledge Engineering Review 17, 107–127 (2002)
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)
Millán, E., Loboda, T., Pérez de-la Cruz, J.L.: Bayesian networks for student model engineering. Computers & Education 55(4), 1663–1683 (2010)
Niblett, T.: Constructing decision trees in noisy domains. In: EWSL 1987, pp. 67–78 (1987)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)
Pearl, J., Geiger, D., Verma, T.: Conditional independence and its representations. Kybernetika 25, 33–44 (1989)
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)
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)
Swan, K., Shen, J., Hiltz, S.R.: Assessment and collaboration in online learning. Journal of Asynchronous Learning Networks 10(1), 45–62 (2006)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Anaya, A.R., Luque, M. (2013). An Influence Diagram for the Collaboration in E-learning Environments. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Models in Computation and Biology. IWINAC 2013. Lecture Notes in Computer Science, vol 7930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38637-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-38637-4_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38636-7
Online ISBN: 978-3-642-38637-4
eBook Packages: Computer ScienceComputer Science (R0)