Case-based learning mechanisms to deliver learning materials

In this paper, we discuss an integrated framework of case-based learning (CBL) in an agent that intelligently delivers learning materials to students. The agent customizes its delivery strategy for each student based on the student´s background profile and his or her interactions with the graphic user interface (GUI) to our system, and based on the usage history of the learning materials. The agent´s decision-making process is powered by case-based reasoning (CBR). To improve its reasoning process, our agent learns the differences between good cases (cases with a good solution for its problem space) and bad cases (cases with a bad solution for its problem space). It also meta-learns adaptation heuristics, the significance of input features of the cases, and the weights of a content graph for symbolic feature values. We have also built a simulation to comprehensively test the learning behavior of our agent.