Learning through modeling and self explanations

In many domains, especially in science, learning involves the acquisition and construction of models. In this project the focus is on students who acquire knowledge in science domains by modeling with the help of a computer tool. Systematic research into the effectiveness of these modeling environments has only recently started to emerge. What is clear from the related area of inquiry learning is that modeling is a process that needs careful sequencing and instructional support. In this project we investigate aspects of sequencing and support and in doing so we apply principles from explanation-based learning to learning by modeling. Explanation based learning (often in combination with worked-out problems) has proven to be a highly effective way of learning. In our case, the graphical structure of models can be used to generate step-by-step explanations of the behavior of the model. Explanations can be given in qualitative or quantitative terms, before or after simulating the model, and based on given or self-created models. In the proposed project we investigate how different uses of prompts and worked-out models in combination with self-explanation can improve learning with modeling and how this compares to more traditional instructional approaches.