Cookies, Spreadsheets, and Modeling: Dynamic, Interactive, Visual Science and Math

1. Cookies, Spreadsheets, and Modeling: Dynamic, Interactive, Visual Science and Math A Preview of the PSC CAST Professional Development Modules using Excel Scott A. Sinex Prince George’s Community College Presented at Network Connections, Pittsburgh, PA on 27 October 2011

2. The agenda for today • The Pittsburgh Supercomputing Center’s CAST Professional Development Modules • Building a mathematical model with cookies • Using spreadsheet simulations to enhance a model • A taste of constructing a simulation in Excel

3. Computation and Science for Teachers (CAST) • A program to infuse computational reasoning into secondary math and science instruction • A collaboration of the Pittsburgh Supercomputing Center (PSC), the Maryland Virtual High School (MVHS) and the Southwest PA Math & Science Collaborative (MSC) • A Professional Development Experience for middle and high school math and science teachers.

4. The Pittsburgh Supercomputing Center • A University-based computing and research organization serving scientists and researchers across the nation. • Mission: Provide state of the art high performance computing environments for solving large-scale computational problems in all fields of science such as: • Violent storm modeling • Molecular biology • Origins of the universe • New educational mission: introduce tools of computational scientists to secondary school math and science teachers PSC offices are located at 300 S. Craig St., on the campus of Carnegie Mellon University in Pittsburgh, Pennsylvania.

5. CAST Goals The goals of the CAST program are to: • Increase the use of computational reasoningto support theory and experimentation in scientific inquiry. • Increase the use of interactive computational toolssuch as modeling and simulation to support the teaching of scientific and mathematical concepts. • Improve the learning experience and engagementof students in math and science.

6. What is Computational Reasoning? • Understanding how to analyze, visualize and represent data using mathematical and computational tools • Using computer models to support theory and experimentation in scientific inquiry • Using models and simulations as interactive tools for understanding complex concepts in science and mathematics

7. Why Computational Reasoning? • Addresses Common Core Standards in Mathematics • Standards for Mathematical Practices • MODEL WITH MATHEMATICS • Reason abstractly and quantitatively • Use appropriate tools strategically • Look for and express regularity in repeated reasoning • Standards for Mathematical Content • Making Inferences and Justifying Conclusions • Understand and evaluate random processes underlying statistical experiments • Make inferences and justify conclusions from sample surveys, experiments and observational studies. http://www.corestandards.org/assets/CCSSI_Math%20Standards.pdf

8. Why Computational Reasoning? • Supports Science Practices recommended by the 2011 Framework for K-12 Science Education • Developing and using models • Using mathematics, information and computer technology, and computational thinking • Supports teaching science as inquiry by providing: • Models of real world events that are difficult to demonstrate in wet lab experiments • Opportunities for careful observation and analysis of scientific investigations • The ability to test hypotheses, analyze results, form explanations, judge the logic and consistency of conclusions, and predict future outcomes. http://www7.nationalacademies.org/bose/Standards_Framework_Homepage.html

9. CAST Two-Track Program Track one – seven modules on how to USE models (already available over the web) in the classroom Track two – five modules on how to CREATE or customize your own models for the classroom Both tracks explore three modeling tools: • Excel models - Today a taste of this tool! • Agent models • System or Aggregate models

10. The big picture… Multiple representations A multivariable approach

11. Goals for today

12. Our hypothesis… • Is there a relationship between the height of a stack of Oreo cookies and the number of cookies in the stack? • If so, how could we find this relationship?

13. Collect the data • Stack cookies • Measure to nearest 0.1cm • Open cookie_stack.xls • Enter into “just add data” Excelet

14. “Just add data” Excelet

15. What does the mathematical model or equation mean?

16. What about errors in the model? Where can errors originate? Measurement Manufacturing

17. More about errors What should the y-intercept be for the mathematical model? Why doesn’t the mathematical model have a y-intercept of zero?

18. Scatter in the data

19. If we repeated the experiment but mixed regular Oreos with Double-Stuf Oreos, how would our results turn out?

20. Building a simulation • Let’s construct a model for the behavior of the quadratic equation • Our multivariable equation y = ax2 + bx + c • How does the graph behave when we vary a, b, and c?

21. Set-up screen

22. Simulation of quadratic equation

23. What did you learn today?

24. Want to learn even more? • PSC CAST PD modules http://www.psc.edu/eot/cast • Developer’s Guide to Exceletshttp://academic.pgcc.edu/~ssinex/excelets

25. For more info… • For PSC CAST Professional Development Modules: Cheryl Begandybegandy@psc.edu • For Excelets: Scott Sinexssinex@pgcc.edu THANKS FOR ATTENDING TODAY