Approach, Technologies and Goals This paper describes a set of educational tools designed to support central cognitive skills such as argument analysis and construction, cooperative negotation, collaborative writing and scientific inquiry. In building these tools we drew upon multi-media technologies, interactive video and object-oriented programming techniques. Our approach however, was not motivated by the technologies but rather by a desire to embed our educational objectives in situations which were intrinsically interesting to the students. This approach, alluded to in the title of the paper, came out of our intention to have the student feel good about her personal interests, and further to have her feel good about her intellectual competence as a vehicle for furthering her interests.

Analogical design and invention is a central task in human cognition. Often during the process the designer/inventor gets stuck; backs off from the problem; and only later, after having put the problem aside, discovers that some famihar plan can be used in a novel way to solve the problem. W e describe a system which uses a causal case memory to check the side effects, preconditions, etc. of incoming events in order to model this phenomenon. The method used makes this work relevant to case-based reasoning as well as design. It also forms a companion issue to execution-time planning.

We describe elements of a cognitive theory of analogical reasoning. The theory was developed using protocol data and has been implemented as a computer model. In order to constrain the theory, it has been developed within a problem-solving context, reflecting the purpose of analogical reasoning. This has allowed us to develop: A purpose-constrained mapping process which makes learning and debugging more tractable; An evaluation process that actively searches for bugs; And a debugging process that maintains functional aspects of base models, while adding target-appropriate causal explanations. The active, knowledge-based elements of our theory are characteristic of mechanisms needed to model complex problem-solving.

Descriptions of scientific and technical systems take a number of different forms. Depending upon thepurpose of a description, it may focus on a system's behavior, causaliiy, physical or functional topology,or structural composition. An analogical explanation used to teach someone about such a system is alsotypically geared to one or another of these purposes. In this paper we describe some research leading tothe development of a theory of the role of explanatory model types in the generation of analogicalmappings. The work is motivated by the larger question of how explanations presented as analogies areapplied by students learning about new domains. Our long term goals are (1) the development of atheory of purpose-guided analogical learning, based on a coherent taxonomy of mental model types, and(2) the development of a theory of the integration of partial mental models during learning, usingprinciples for relating different explanatory model types.