Intelligent Tutoring System: Hierarchical Rule as a Knowledge Representation and Adaptive Pedagogical Model

摘要

In this study, we present a new rule structure called Hierarchical Rule (HR) to be an effective knowledge representation in Intelligent Tutoring System (ITS) and in Intelligent Educational Systems (IES). The structure of the rule shall expedite the process of inference and allow the system to work in forward as well as backward chaining. The HR structure will help in putting the knowledge in a very systematic way, which will lead to well structured system. This representation can be used very effectively in the pedagogical model to inform the system, which method should be followed up with the current user. The pedagogical model shall benefit from the user ?s model (history of the user) to choose the proper explanation method. We also present an algorithm in the novel form to represent the HRs using the neural network s to enhance the performance of the rule system. We call this algorithm HRANN. In addition to that, a general method concerning the adaptation of pedagogical model is introduced. This method is mainly depend on competitive learning (unsupervised learning) if enough number of examples is not given. The system ?s performance will keep improving as long as the system is working for various users. The system shall benefit from its experience. In case enough number of examples are provided, the traditional method of backpropagation algorithm can be used.