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. © 2012 Springer-Verlag.
CITATION STYLE
Goel, G., Lallé, S., & Luengo, V. (2012). Fuzzy logic representation for student modelling: Case study on geometry. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7315 LNCS, pp. 428–433). https://doi.org/10.1007/978-3-642-30950-2_55
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