On predicting learning styles in conversational intelligent tutoring systems using fuzzy classification trees

Oscar is a conversational intelligent tutoring system (CITS) which dynamically predicts and adapts to a student´s learning style throughout the tutoring conversation. Oscar aims to mimic a human tutor to improve the effectiveness of the learning experience by leading a natural language tutorial and adapting material to suit an individual´s learning style. Prediction of learning style is undertaken through capturing independent variables during the conversation. The variable with the highest value determines the individuals learning style. This paper proposes a new method which uses a fuzzy classification tree to build a fuzzy predictive model using these variables which are captured through natural language dialogue Experiments have been undertaken on two of the learning style dimensions: perception (sensory-intuitive) and understanding (sequential-global). Early results show the model has substantially increased the predictive accuracy of the Oscar CITS and discovered some interesting relationships amongst these variables.