Teaching computation and science in the context of scientific inquiry and problem solving promotes interest in STEM and increases appreciation for computation in science. The work presented here is the result of multi-institutional and multi-disciplinary collaboration among Computational Physics educator, Science & Math educator, Computational Science Education foundation, Computational Biologist, two community college science teachers, and CS usability expert. We have created a collection of modules that have been piloted in a pre-service education course and are currently being modified for use in an online course for pre- and in- service teachers. The Computational Scientific Thinking & Modeling course will provide practical computation integrated into the scientific problem-solving paradigm. We assume that the students have varied knowledge of physics, biology, algebra, and Calculus1 at a high school level. The following module topics have been selected for the online course: Exponential Decay and Growth, Logistic Growth, Computer Precision, Predator Prey Models, Projectile Motion with Drag, Random Numbers, Random Walk. Students learn to create models and perform computations using Excel, Python language or Vensim simulation software. Our modules start with a scientific problem and then lead the students through its solution via a computational science approach. A typical module includes: Learning Objectives/Skills/Activities, Scientific problem, Concept map and system statements, Computational model, Background information on the computational model, Simulating the model, and Assessment. We found that the module topics are easily described in the context of physical examples. Yet biological examples are less obvious. The pilot of the Exponential Decay and Growth and Logistic Growth revealed that the science was masked in the process of learning the software and the students desired a greater understanding of computation in science. In this poster we present the modules that have been piloted.
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