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The principle of dynamical similitude—the belief that the same behavior may be exhibited by very different systems—allows us to use mathematical models from physics to understand psychological phenomena. Sometimes, model choice is straightforward. For example, the two-frequency resonance map can be used to make predictions about the performance of multifrequency ratios in phys- ical, chemical, physiological and social behavior. Sometimes, we have to dig deeper into our dynamical toolbox to select an appro- priate technique. An overview is provided of other methods, including mass-spring modeling and multifractal analysis, that have been applied successfully to various psychological phenomena. A final demonstration of dynamical similitude comes from the use of the same multifractal method that was used to extract team-level experience from the neurophysiological data of individual team members to the analysis of a large scale economic phenomenon, the stock market index. Continual development of analytical methods that are informed by and can be applied to other sciences allows us to treat psychological phenomena as continuous with the rest of the natural world.
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Scaffolded Training Environment for Physics Programming (STEPP): Modeling High School Physics using Concept Maps and State Machines
We are a year into the development of a software tool for modeling and simulation (M&S) of 1D and 2D kinematics consistent with Newton's laws of motion. Our goal has been to introduce modeling and computational thinking into learning high-school physics. There are two main contributions from an M&S perspective: (1) the use of conceptual modeling, and (2) the application of Finite State Machines (FSMs) to model physical behavior. Both of these techniques have been used by the M&S community to model high-level "soft systems" and discrete events. However, they have not been used to teach physics and represent ways in which M&S can improve physics education. We introduce the NSF-sponsored STEPP project along with its hypothesis and goals. We also describe the development of the three STEPP modules, the server architecture, the assessment plan, and the expected outcomes.
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- Award ID(s):
- 1741756
- PAR ID:
- 10322847
- Date Published:
- Journal Name:
- Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3316480.3325513
