Application of fuzzy logic techniques in the BSS1 tutoring system
Journal of Artificial Intelligence in Education
Comparing Two IRT Models for Conjunctive Skills
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
The role of fuzzy logic in the management of uncertainty in expert systems
Fuzzy Sets and Systems
An automatic comparison between knowledge diagnostic techniques
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
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Our aim is to develop a Fuzzy Logic based student model which removes the arbitrary specification of precise numbers and facilitates the modelling at a higher level of abstraction. Fuzzy Logic involves the use of natural language in the form of If-Then statements to demonstrate knowledge of domain experts and hence generates decisions and facilitates human reasoning based on imprecise information coming from the student-computer interaction. Our case study is in geometry. In this paper, we propose a fuzzy logic representation for student modelling and compare it with the Additive Factor Model (AFM) algorithm implemented on DataShop. Two rule-based fuzzy inference systems have been developed that ultimately predict the degree of error a student makes in the next attempt to the problem. Results indicate the rule-based systems achieve levels of accuracy matching that of the AFM algorithm.