Case-Based Reasoning: Instructional Materials



Increasing AI Project Effectiveness with Reusable Code Frameworks: A Case Study Using IUCBRF

AAAI Conferences

Instructors' ability to assign artificial intelligence programming projects is limited by the time the projects may require. This problem is often exacerbated by the need for students to develop significant system infrastructure, requiring them to spend time addressing issues which may be orthogonal to the AI course's core pedagogical goals. This paper argues that such problems can be alleviated by basing coding assignments on paradigm-specific frameworks, collections of reusable code designed to be extended and applied to a variety of specific problems. In addition, frameworks can provide a basis for further student research or application of projects to real-world domains, providing additional motivation. This paper illustrates the application of a framework-based approach to teaching case-based reasoning (CBR), introducing the Indiana University Case-Based Reasoning Framework (IUCBRF), discussing its design, and presenting sample exercises that take advantage of the framework's characteristics.



Authoring Simulation-based Intelligent Tutoring Systems

AAAI Conferences

In complex domains, instruction is often complicated by the need for the student to master a variety of concepts and to apply them in unique situations and in different sequences. In these kinds of domains, the student must develop not only a competence in the relevant facts and skills, but also an understanding of the concepts underlying these procedures. Instructional courses must be attuned to the trainee's background and needs, motivate him to develop an accurate and thorough understanding of the subject matter, and then effectively verify the correctness of his understanding and remediate inaccuracies. When students are required to be flexible in their understanding of principles and potential applications, the most effective teaching strategy is to maximize the role of the teacher to a one-on-one interaction. In fact, [Bloom 1984] describes the two-sigma problem as the fact that students receiving one-on-one instruction perform two standard deviations better than students receiving conventional instruction.