Abstract:
In this chapter, 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) in 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 and the significance of the cases' input features. We have also built a simulation to comprehensively test the learning behavior of our agent. Our design of agent learning, adaptation of a solution through CBR, and simulation is based on a set of domain-specific and independent assumptions. 3.1 Introduction Traditionally, learning materials are delivered in a textual format and on paper. For example, a learning module on a topic may include a description (or a tutorial) of the topic, a few examples illustrating the topic, and one or more exercise problems to gauge how well the students have achieved the expected understanding of the topic. The delivery mechanism of these learning materials has traditionally been via textbooks and/or instructions provided by a teacher. A teacher, for example , may provide a few pages of notes about a topic, explain the topic for a few minutes, discuss a couple of examples, and then give some exercise problems as homework. During the delivery, students ask questions and the teacher attempts to answer the questions accordingly. Thus, the delivery is interactive: the teacher learns how well the students have mastered the topic, and the students clarify their understanding of the topic. In a traditional classroom of a relatively small size, the above scenario is feasible. However, when e-learning approaches such as distance learning and asynchronous learning are involved, or in the case of a large class size, the traditional delivery mechanism is often not feasible. In this chapter, we propose an intelligent agent that delivers learning materials based on the usage history of the learning materials, the student static background
