| In this thesis, I present an intelligent tutoring system, called BITS, based on the Bayesian network approach to support the students in learning computer programming. My system takes full advantage of Bayesian networks, which are a formal framework for uncertainty management in Artificial Intelligence based on probability theory. I discuss how to employ Bayesian networks as an inference engine to guide the students' learning processes. In addition, I describe the architecture of BITS and the role of each module in the system. Whereas many tutoring systems are static HTML Web pages of a class textbook or lecture notes, my intelligent system can help a student navigate through the online course materials, recommend learning goals, and generate appropriate reading sequences. Thus, I believe that BITS is very useful to any work applying Bayesian networks as a model for developing adaptive Web-based learning systems for various courses. BITS has been implemented and is used in the summer 2004 session of CS110, which is the initial computer programming course at the University of Regina. (Abstract shortened by UMI.)... |
