CT Topics

Modeling and Simulation

Modeling is the process by which scientists represent ideas about the natural world to each other, and then collaboratively make changes to these representations over time in response to new evidence and understandings. Models are used to represent a system (or parts of a system) under study, to aid in the development of questions and explanations, to generate data that can be used to make predictions, and to communicate ideas to others. Scientific models can take many forms, which sometimes overlap and can be present in a single model.

Scientists devise ways to test competing models and engage in arguments about them. New findings may support or contradict existing models. Models are created, revised and sometimes rejected. They are both explanatory and predictive.

Students too can be expected to evaluate and refine models through a cycle of comparing their predictions with the real world and then adjusting them to gain insights into the idea or concept being modeled. Students should also consider the strengths and limitations of their model.

The goals for working with scientific models is to understand how the model represents a real-life system. When engaging in CT concepts, that underlying definition is consistent, but the way one develops and interacts with physical models versus digital models can be quite different. In the elementary grades, the goal is to lay the foundation for this difference.

Schwarz, Reiser, et. al. (2008) identified a learning progression for scientific models. This model works both for physical and digital models, although best practices are to have students some experience with a physical model, where possible, prior to using, modifying, or creating a digital model.

• Students construct models consistent with prior evidence and theories to illustrate, explain, or predict phenomena.
• Students use models to illustrate, explain, and predict phenomena.
• Students compare and evaluate the ability of different models to accurately represent and account for patterns in phenomena, and to predict new phenomena.
• Students revise models to increase their explanatory and predictive power, taking into account additional evidence or aspects of a phenomenon.

Referring to the learning progression for scientific models outlined above, a high-level sequence for developing a digital models would include

• Students perform controlled experiments.
• Students construct, evaluate, and revise physical models.
• Students use, modify, and/or create digital models from evidence they gathered from the physical model.
• Students compare digital models, physical models, and the original phenomena that the models represent.

Digital models typically represent quite complex systems, and is most likely beyond the capabilities of younger elementary students. However, it is very useful to have even younger students interact with digital models, and allowing them to explore (and asking them to explain) that using digital models provides both benefits and potential drawbacks: they allow exploring scenarios that may be difficult or dangerous to observe or replicate physically (experiments with large populations, over large periods of time, etc.), but are as good as the programming, and can thus represent unrealistic combinations or scenarios (such as incorrect gravitational forces, or having carnivores eating plants).