Volume 1 Number 4 (Oct. 2011)
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IJEEEE 2011 Vol.1(4): 280-286 ISSN: 2010-3654
DOI: 10.7763/IJEEEE.2011.V1.46

Bayesian Network Based Intelligent Advice Generation for Self-Instructional e-Learner

P. Kuila, C. Basak, and S. Roy

Abstract—This paper presents an agent based Intelligent Tutoring System (ITS). The agent plays the role of an advisor for an e-learner to facilitate the learner to achieve his learning objective. It provides advices to assist an e-learner while solving problems that are normally provided by human experts. A Bayesian network is employed to construct the possible solution states of a problem with the statistics of mistakes; those can be committed by a learner in their problem solving process. The agent will collect statistical data from Bayesian network on learner’s mistakes and the way in which one learner may commit a mistake. On the basis of the statistical data on the learner’s behaviour, especially with respect to his tendency to commit an error, the agent anticipates the point of difficulty during a problem solving session and accordingly guides the learners to solve the given problem free of errors. The Bayesian network is trained with training data, prior information (e.g., expert knowledge, casual relationships, and estimated graph topology or network structure) and the parameters of the joint probability distribution.

Index Terms—Intelligent advisory system (ias), intelligent agent, Bayesian network

The authors are with the department of Computer Science and Engineering from NITTTR’ Kolkata.india (e-mail: pratyay_kuila@yahoo.com.)

Cite: P. Kuila, C. Basak, and S. Roy, "Bayesian Network Based Intelligent Advice Generation for Self-Instructional e-Learner ," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 1, no. 4, pp. 280-286, 2011.

General Information

ISSN: 2010-3654 (Online)
Abbreviated Title: Int. J. e-Educ. e-Bus. e-Manag. e-Learn.
Frequency: Quarterly
Editor-in-Chief: Prof. Kuan-Chou Chen
Executive Editor: Ms. Nancy Lau
Abstracting/ Indexing: EBSCO, Google Scholar, Electronic Journals Library, QUALIS, ProQuest, INSPEC (IET)
E-mail: ijeeee@iap.org
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