www.kips.or.kr Copyright© 2017 KIPS 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*, and Alka Tripathi** 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. Keywords Domain Model, E-Learning, E-Learning Environment, Fuzzy Rules, Intuitionistic Fuzzy Set, User Modeling 1. Introduction Personalization of the E-learning system depends on the learner’s knowledge, background, and interests [1]. Learner modeling is a process in which information about the learner is collected and updated. Assessment is one of the strategic objectives of the E-learning system, which results in finding the value of the knowledge acquired by students [2]. Assessing a learner’s knowledge level under uncertain conditions is not effective due to there being insufficient information available on the learner’s responses to the test items [3]. Reliable student modeling comes via careful student assessment. It is the process that allows the expert to diagnose the learner’s mental state and knowledge status in order to check the efficiency of teaching and to detect possible learning deficiencies [4]. An overlay model, which is a popular form of the structural model, represents the degree to which the user knows about a domain fragment [5]. Course sequences should facilitate input from not only content authors, but also from instructional designers and knowledge domain experts. Human decision-making can be modeled and simulated through soft computing approaches in an E-learning system [6,19]. Generalized nets are ※ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Manuscript received October 18, 2013; accepted May 14, 2014; onlinefirst December 31, 2014. Corresponding Author: Divakar Yadav (divakar.yadav@jiit.ac.in) * Dept. of Computer Science & Engineering, Jaypee Institute of Information Technology, Noida 201307, Uttar Pradesh, India (mukta.goyal20@gmail.com, divakar.yadav@jiit.ac.in) ** Dept. of Mathematics, Jaypee Institute of Information Technology, Noida 201307, Uttar Pradesh, India (alka.choubey@gmail.com) J Inf Process Syst, Vol.13, No.1, pp.57~67, February 2017 ISSN 1976-913X (Print) https://doi.org/10.3745/JIPS.04.0011 ISSN 2092-805X (Electronic)