Causal model progressions as a foundation for intelligent learning environments
Artificial Intelligence - Special issue on artificial intelligence and learning environments
Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide
Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Contrasting a system dynamics model and an agent-based model of food web evolution
MABS'06 Proceedings of the 2006 international conference on Multi-agent-based simulation VII
Creative Model Construction in Scientists and Students: The Role of Imagery, Analogy, and Mental Simulation
ICALT '11 Proceedings of the 2011 IEEE 11th International Conference on Advanced Learning Technologies
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Scientists use both conceptual models and executable simulations to help them make sense of the world. Models and simulations each have unique affordances and limitations, and it is useful to leverage their affordances to mitigate their respective limitations. One way to do this is by generating the simulations based on the conceptual models, preserving the capacity for rapid revision and knowledge sharing allowed by the conceptual models while extending them to provide the repeated testing and feedback of the simulations. In this paper, we present an interactive system called MILAfiS for generating agent-based simulations from conceptual models of ecological systems. Designed with STEM education in mind, this user-centered interface design allows the user to construct a Component-Mechanism-Phenomenon conceptual model of a complex system, and then compile the conceptual model into an executable NetLogo simulation. In this paper, we present the results of a pilot study with this interface with about 50 middle school students in the context of learning about ecosystems.