International Journal of Science, Technology and Society
Volume 1, Issue 1, July 2013, Pages: 9-18
Received: May 17, 2013;
Published: Jun. 10, 2013
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Osman Yaşar, State University of New York, The College at Brockport, Brockport, New York, USA
We present a computational pedagogy approach to teaching an interdisciplinary science course. Modeling and simulation tools allow us to introduce a science topic from a simplistic framework and then move into details after learners gain a level of interest to help them endure the hardships and frustration of deeper learning. More than 90% of students in course surveys state that modeling improved their understanding of science concepts. Students appear to appreciatelearning not only the use of simulation tools to design and conduct science experiments, but also basic programming skills to simulate a science experiment using a simple algebraic equation, new = old + change. A strong link is established between computational and natural sciences. Students learn in a simplistic framework how laws of nature act as the source of change.
Teaching Science through Computation, International Journal of Science, Technology and Society.
Vol. 1, No. 1,
2013, pp. 9-18.
Guzdial, Mark. (2009). Teaching computing to everyone. Communications of the ACM,Vol. 52, No. 5, 1-3.
Computing Curricula.(2005). A Cooperative Project of the Association for Computing Machinery, the Association for Information Sciences, and the IEEE Computer Society. http://www.computer.org/portal/web/education/Curricula.
S & E Indicators. National Science Board. 1996 and 2010. http://www.nsf.gov/statistics/.
BLS Report. (2010). The Bureau of Labor Statistics. Occupational Employment Statistics. http://www.bls.gov/oes/.
AIP Survey. (2010). Important Knowledge & Skills Used on the Job. American Institute of Physics. http://www.bls.gov/oes/2010/may/stem.htm.
Landau, R. (2006). Computational Physics: A Better Model for Physics Education? IEEE Comp. in Sci & Eng., 8 (5), 22-30.
Swanson Survey. (2010). A Survey of Computational Science Education. By C. Swanson. The Krell Institute, http://www2.krellinst.org/services/technology/CSE_survey/.
NAP Report. (2007). Rising Above The Gathering Storm. Washington, D.C.: The National Academy Press. http://www.nap.edu/.
NAP Report. (2010). Rising Above The Gathering Storm, Revisited: Washington, D.C.: The National Academy Press. http://www.nap.edu/.
National Science Foundation, Math and Science Partnership (MSP) Program. http://www.nsf.gov.
Cuny, J. (2011). Transforming Computer Science Education in High School. IEEE Computer, 44 (6), 107-109.
Sjøberg, S. and Schreiner, C. (2005). How do learners in different cultures relate to science and technology? Results and perspectives from the project ROSE.Asia Pacific Forum on Science Learning &Teaching, 6, 1-16.
The College Board. (2011). AP CS Principles Course. http://www.csprinciples.org. Also see June 2012 issue of ACM Inroads.
NGSS (Next Generation Science Standards). (2013). http://www.nextgenscience.org/.
Wing, J. M. (2006). Computational Thinking, Communications of the ACM, Vol. 49, No. 3, 33-35.
Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience. New York: Harper Collins.
NAP Report. (2000). How People Learn: Brain, Mind, and School. Washington. D.C: The National Academies Press. http://www.nap.edu/.
Tenenbaum, J. B., Kemp, C., Griffiths, T. L. & Goodman, N. D. (2011). How to Grow a Mind: Statistics, Structure, and Abstraction. Science, 331, 1279-1285.
NSF Report. (2008). Fostering Learning in the Networked World. National Science Foundation. http://www.nsf.gov/pubs/2008/nsf08204/nsf08204.pdf.
NSTA Report (2008). Technology in the Secondary Science Classroom. National Science Teachers Association. (Eds) Bell, L. R., Gess-Newsome, J., and Luft, J. Washington, DC.
Yaşar, O., Rajasethupathy, K., Tuzun, R., McCoy, A. and Harkin, J. (2000). A New Perspective on Computational Science Education, IEEE Comp. in Sci & Eng, 5 (2), 74-79.
Yaşar, O. (2001). Computational Science Education: Standards, Learning Outcomes and Assessment. Lecture Notes in Computer Science, 2073, 1159-1169.
Yaşar, O. and Landau, R. (2003). Elements of Computational Science & Eng. Education, SIAM Review, 45, 787-805.
Yaşar, O. (2004). C-MST Pedagogical Approach to Math and Science Education. Lecture Notes in Comp Sci, 3045, 807-816.
Yaşar, O., Little, L., Tuzun, R. Rajasethupathy, K., Maliekal, J. and Tahar, M. (2006). Computational Math, Science, and Technology, Lecture Notes in Comp Science, 3992, 169-176.
Yaşar, O., Maliekal, J., Little, L. J. and Jones, D. (2006). Computational Technology Approach to Math and Science Education. IEEE Comp. in Sci & Eng., 8 (3), 76-81.
Yaşar, O., Maliekal, J., Little, L. J. (2013). An interdisciplinary approach to professional development for secondary school math, science, and technology teachers. Submitted to J. Computational Science Education.
Creswell, J. W. (2012). Educational Research: Planning, Conducting and Evaluating Quantitative and Qualitative Research. 4th Ed. Pearson Education, Inc.
Fincher, S. and Petre, M. (2005). Computer Science Education Research. Taylor&Francis e-Library: London and New York.
Goode, J. and Margolis, J. (2011). Exploring computer science: A case study of school reform. Transactions on Computing Education. 11(2).