A fuzzy model for predicting learning styles using behavioral cues in an conversational intelligent tutoring system

This paper proposes a new model for predicting student learning styles for conversational intelligent tutoring systems (CITS). The learning styles are predicted from behavior cues extracted during conversation obtained during automated CITS tutorials. The heart of the model is a fuzzy rule base determined automatically from existing tutorial data with membership function boundaries optimized by a genetic algorithm. The zero-order Sugeno fuzzy inference model is utilized to predict the Felder and Silverman learning styles in two of the learning style dimensions: perception (sensory-intuitive) and understanding (sequential-global). This work is motivated by the changing nature of both education and learners and the need to provided personalized tutoring on demand. The model is incorporated into an existing CITS and evaluated using undergraduate University students. The experimental results have shown strong predictive accuracy when compared with existing approaches to delivery of personalized tutorials and have received good student feedback.