**Learner Modeling Based on Bayesian Networks**

Anouar Tadlaoui Mouenis, Aammou Souhaib and Khaldi Mohamed

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/61021

#### **Abstract**

[7] Stahl G. Meaning and interpretation in collaboration. In: Proceedings of the Interna‐ tional Conference on Computer Supported Collaborative Learning. 2003. Bergen,

[8] Brown R.E. The process of community-building in distance learning classes. Journal

[9] Kim J., & Ryu, H. Evaluation of teaching and learning system. Korea National Open

[10] Rha I., Hong S. A study on exploring at the process model of online learning com‐ munity development. Journal of Educational Technology. 2004;19(3):101-122.

[11] Rubinstein M. Patterns of Problem Solving. Englewood Cliffs. NJ: Prentice-hall, Inc.

[12] Oren A., Mioduser D., Nachmias R. The development of social climate in virtual learning discussion groups. International Review of Research in Open and Distance Learning. 2002;3(1). Available from http://www.irrodl.org/index.php/irrodl/article/

[13] Moore M.G. Three types of interaction. American Journal of Distance Education,

[14] Lee H.-J. New perspectives of theoretical research in web-based distance education: Beyond Moore's Concepts. Korean Journal of Educational Research. 2004;42(1):

[15] Fulford C.P., Zhang, S. Perceptions of interaction: The critical predictor in distance

education. The American Journal of Distance Education. 1993; 7(3):8-21.

of Asynchronous learning networks. 2001;5(2):18-35.

164 E-Learning - Instructional Design, Organizational Strategy and Management

University Press: Seoul; 2000.

view/80/154 [Accessed: 2015-07-11].

Norway.

1975.

1989;3(2):1-6.

137-168.

The work presented in this chapter lies within Learner modeling in an adaptive ed‐ ucational system construed as a computational modeling of the learner. All actions of the learner in a learning situation on an adaptive hypermedia systems are not limited to valid or invalid actions (true and false), but they are a set of actions that characterize the learning path of his formation. Thus, we cannot represent the infor‐ mation from the system of each learner using relative data. It requires putting our work in a probabilistic context due to the changes in the learner model information during formation. We propose in this work to use Bayesian networks as a probabil‐ istic framework to resolve the issue of dynamic management and update of the learner model. The experiments and results presented in this work are arguments in favor of our hypothesis, and can also promote reusing the modeling obtained through different systems and similar modeling situations.

**Keywords:** Learner model, adaptive Hypermedia educational systems, Bayesian networks, Cognitive diagnosis, Uncertainty
