1. NSDL/NSTA Web Seminar: Learning By Doing—Computational Science
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NSDL/NSTA Web Seminar:
Learning By Doing—Computational Science

2. Robert introduces Patricia and Bethany

3.
Access to large collections of simulations and interactives
Resources for 21st century learning skills
Still intro, mention of Pathways and NSDL
CSERD: NSDL Computational Science Pathway

4. What is Computational Science?

5. Computational Science…
uses models to demonstrate and visualize concepts
New Moon?
Full Moon?

6. Computational Science…
uses real-life situations to teach inquiry-based math and science concepts
Estimator: The number of stars is greater or less than 340
Revised Slide 16
Greater than 340?
Less than 340?

7. Computational Science…
helps students learn how to use technology to solve problems
Topples
Stands

8. Computational Science…
uses interactive tools to observe and analyze data

9. Interactive Activities
Computational Science Education Reference Desk (CSERD)
Interactive Activities
Discussions
Lesson Plans
Suggestions & Help

10. Skill level with use of technology in classroom?
Intermediate
Advanced
Novice
Skill level with use of technology in classroom?
Combine math and science together in your curriculum?
Rarely to Never
Occasionally
On a regular basis

12. Modern science and mathematics are more concerned with
pattern recognition and characterization
than with mere symbol manipulation.

13. Recognition The pahomnnehil pweor of the hmuan mnid.
Aoccdrnig to a rehcaseerr at Cmabirdge Uinervtisy, it deosn’t mttaer in waht oredr the ltteers in a wrod are, the olny imprmoetnt tihng is taht the frist and lsat ltteer be at the rghit pclae. The rset can be a total mses and you can sitll raed it wouthit porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a whlohe.
Anizamg huh?

14. Rabbits and Wolves: Population Dynamics
Characterization
Rabbits and Wolves: Population Dynamics

15. Add after Slide 24

16. Symbol Manipulation

17.
100+ interactive math based activities
Lesson plans
Discussions
Worksheets
Open-ended explorations
Dictionary
Standards alignments

18. Predict: How much of the forest will burn?
Given: If 1 tree is on fire there is a 100% probability that the tree next to it will catch.
Predict: How much of the forest will burn?
Exactly 0%
0%-50%
50%-100%
Exactly 100%

19. Predict: How much of the forest will burn?
Given: If 1 tree is on fire there is a 0% probability that the tree next to it will catch.
Predict: How much of the forest will burn?
Exactly 0%
0%-50%
50%-100%
Exactly 100%

20. As a scatter plot our data looks like:
Data Points
0, 0
1, 100
Interactivate’s Scatter Plot Activity

21. Poll Question:
Given: If 1 tree is on fire there is a 50% probability that the tree next to it will catch.
Predict: How much of the forest will burn?

22. You try it! Try using the Fire! Interactive a few times on your own
We will now take you to this link:
Try using the Fire! Interactive a few times on your own
We will bring you back in 3
minutes… ask for help by pushing *6 if you need it!

23. Data from 10 Burns at 50% Probability of Burn
Trial #
% Burned 1 1% 2
27% 3
54% 4
65% 5
41%
Trial #
% Burned 6
44% 7
69% 8
43% 9
60% 10
45%
MEAN from 10 trials: ~45%

24. As a scatter plot our data looks like:
Red Data Points
(0, 0)
(1, 100)
Blue Data Points
(0.5, 45)
Hmm… How could we find the overall pattern?
Interactivate’s Scatter Plot Activity

25. Recognizing the Pattern
Resulting graph in green from averaging 10 burns at each of the following probabilities:
0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1

26. Would you have predicted that pattern?
Computers help us to model and simulate real world phenomena so we can better understand the world around us.
The model is only as good as the design.
How does the Fire!! model relate to the real world and how does it not?

27. Possible Content Addressed:
Mathematics
Fractions / Percents
Scatter Plots,
Graphing Coordinate Pairs
Reading / Interpreting
Graphs
Mean / Average
Probability
Randomness
Science
Experimental Design
Experimental Control
Independent and
Dependent Variables
Conjecture and
Hypothesis
Chaos

28. More Robust Models Interactivate’s Directable Fire!!
(not pictured)
Interactivate’s A Better Fire!!
(pictured below)

29. Go to http://nsdl.org and click on the K-12 audience page
Download this seminar’s annotated module for resources from this seminar and more!

30. THANK YOU! Patricia Jacobs Bethany Hudnutt Robert Payo http://nsdl.org
Patricia Jacobs
THANK YOU!
Bethany Hudnutt
Robert Payo

31. National Science Teachers Association
Gerry Wheeler, Executive Director
Frank Owens, Associate Executive Director Conferences and Programs
Al Byers, Assistant Executive Director e-Learning
NSTA Web Seminars
Flavio Mendez, Program Manager
Jeff Layman, Technical Coordinator
Judith Lopes, Administrative Assistant
Susan Hurstcalderone, Volunteer Chat Moderator
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