Intelligent Authoring of ’Graph of Microworlds’ for Adaptive Learning with Microworlds

In science education, it is important to sequence a set of microworlds (which means a system and its model limited from educational viewpoint) of various complexity adaptively to the context of learning. We previously proposed Graph of Microworlds (GMW), a framework for indexing a set of microworlds based on their models. By using GMW, it is possible to adaptively select the microworld a student should learn next, and to assist him in transferring between microworlds. However, it isn’t easy to describe GMW because an author must have the expertise in the process of modeling. In this research, we propose a method for semi-automating the description of GMW by introducing the compositional modeling mechanism. Our method assists an author in generating a set of indexed microworlds and also in considering educational meanings of the relations between them. We present how to design such a function and also illustrate how it works. A preliminary test with a prototype system showed the effectiveness of our method.