An Intuitionistic Fuzzy Approach to Classify the User Based on an Assessment of the Learner’s Knowledge Level in E-Learning Decision-Making


Mukta Goyal, Divakar Yadav, Alka Tripathi, Journal of Information Processing Systems Vol. 13, No. 1, pp. 57-67, Feb. 2017  

10.3745/JIPS.04.0011
Keywords: Domain Model, E-learning, E-Learning Environment, Fuzzy Rules, Intuitionistic Fuzzy Set, User modeling
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Abstract

In this paper, Atanassov’s intuitionistic fuzzy set theory is used to handle the uncertainty of students’ knowledgeon domain concepts in an E-learning system. Their knowledge on these domain concepts has been collected from tests that were conducted during their learning phase. Atanassov’s intuitionistic fuzzy user model is proposed to deal with vagueness in the user’s knowledge description in domain concepts. The user model uses Atanassov’s intuitionistic fuzzy sets for knowledge representation and linguistic rules for updating the user model. The scores obtained by each student were collected in this model and the decision about the students’ knowledge acquisition for each concept whether completely learned, completely known, partially known or completely unknown were placed into the information table. Finally, it has been found that the proposed scheme is more appropriate than the fuzzy scheme.


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Cite this article
[APA Style]
Mukta Goyal, Divakar Yadav, & Alka Tripathi (2017). An Intuitionistic Fuzzy Approach to Classify the User Based on an Assessment of the Learner’s Knowledge Level in E-Learning Decision-Making. Journal of Information Processing Systems, 13(1), 57-67. DOI: 10.3745/JIPS.04.0011.

[IEEE Style]
M. Goyal, D. Yadav and A. Tripathi, "An Intuitionistic Fuzzy Approach to Classify the User Based on an Assessment of the Learner’s Knowledge Level in E-Learning Decision-Making," Journal of Information Processing Systems, vol. 13, no. 1, pp. 57-67, 2017. DOI: 10.3745/JIPS.04.0011.

[ACM Style]
Mukta Goyal, Divakar Yadav, and Alka Tripathi. 2017. An Intuitionistic Fuzzy Approach to Classify the User Based on an Assessment of the Learner’s Knowledge Level in E-Learning Decision-Making. Journal of Information Processing Systems, 13, 1, (2017), 57-67. DOI: 10.3745/JIPS.04.0011.