Inquiry learning is a didactic approach in which students acquire knowledge and skills through processes of theory building and experimentation. Computer modeling and simulation can play a prominent role within this approach. Students construct representations of physical systems using modeling. Using simulation, they execute these representations to study the phenomena or systems modeled. However, the modeling task is complex, and students can fail to create adequate models, which prevents effective learning. This necessitates supportive measures to scaffold the modeling processes. In this paper, we address the issue of designing such support through the development of intelligent advice to be incorporated in modeling environments. The advice is based on the definition of a family of reference solutions for each modeling problem, on the comparison of the reference solutions with the students' solutions, and on the use of an advice knowledge base. This advice guides the students to the construction of a better solution, helping them acquire the knowledge required for successful modeling and for the correction of modeling mistakes. In a collaborative session, having the advice encourages discussion between students about the advice and the best way of proceeding. Empirical validations of the advice approach are presented.
|Journal||Simulation : transactions of the Society for Modeling and Simulation International|
|Publication status||Published - 2006|
- intelligent solution analysis
- Inquiry learning
- System Dynamics modeling