Profiling Student Learning Styles with Multilayer Perceptron Neural Networks

Abstract

Student profiling is central to the move from 'one size fits all' computer-aided learning systems to intelligent tutoring systems which adapt to meet the needs of different students. This paper proposes a new method for profiling student learning styles for a conversational intelligent tutoring system (CITS) which utilizes a Mulitlayer Perceptron Artificial Neural Network (MLP-ANN). Throughout an automated conversational tutorial with a CITS, aspects of student behaviour are dynamically captured and input to a Learning Styles Predictor agent to profile an individual's learning style. The proposed method will incorporate a MLP-ANN to combine a set of behaviour traits extracted from the tutoring conversation to improve the accuracy of the learning styles prediction. The paper describes experiments conducted with real students in a live teaching/learning environment for profiling two Felder and Silverman learning styles dimensions. The results show that MLP-ANNs can predict learning styles with an accuracy of 84-89%.