Journal of Science Education and Technology

, Volume 20, Issue 2, pp 111–115 | Cite as

Effect of Current Electricity Simulation Supported Learning on the Conceptual Understanding of Elementary and Secondary Teachers

  • David Devraj Kumar
  • P. V. Thomas
  • John D. Morris
  • Karen M. Tobias
  • Mary Baker
  • Trudy Jermanovich


This study examined the impact of computer simulation and supported science learning on a teacher’s understanding and conceptual knowledge of current electricity. Pre/Post tests were used to measure the teachers’ concept attainment. Overall, there was a significant and large knowledge difference effect from Pre to Post test. Two interesting interactions were observed during the data analysis. The first was the difference between Elementary and Secondary teachers. Both groups had significant gains, with large effect sizes, but the Elementary teachers (Pre-Mean = 3.70, Post-Mean = 7.51) started lower and ended higher exhibiting a significantly larger gain than the Secondary teachers (Pre-Mean = 4.96, Post-Mean = 6.71). The second interaction was the impact of gender. Both groups showed significant gains, with large effect sizes, but females (Pre-Mean = 3.90, Post-Mean = 7.21) gained significantly more than males (Pre-Mean = 5.13, Post-Mean = 7.01). These results confirm that computer simulation supported science learning can have a positive effect on concept attainment in teachers.


Electricity Simulation Conceptual understanding Elementary Secondary Teachers 



The Florida Atlantic University CPU workshops from where this study emerged were supported by the CPU Project at San Diego State University Center for Research in Mathematics and Science Education with funding from the National Science Foundation, and the Educational Technology Services at Broward County Schools. The authors would like to thank Dr. Fred Goldberg for thoughtful feedback, and Dr. Douglas Huffman, Dr. Kaye Ann Grant and Mr. Michael Hrabak for their assistance throughout various phases of this study.


  1. Akpan JP (2002) Which comes first: computer simulation of dissection or a traditional laboratory practical method of dissection. Electron J Sci Educ 6(4). Available at:
  2. Association for Science Teacher Education (2005) ASTE Position statement on technology in science teacher education. Approved July 2005. Available at:
  3. Barab S, Dede C (2007) Games and immersive participatory simulations for science education: an emerging type of curricula. J Sci Educ Technol 16(1):1–3CrossRefGoogle Scholar
  4. Finkelstein ND, Adams WK, Keller CJ, Kohl PB, Perkins KK, Podolefsky NS, Reid S, LeMaster R (2005) When learning about the real world is better done virtually: a study of substituting computer simulations for laboratory equipment. Phys Rev Spec Topics Phys Educ Res 1(010103):1–8Google Scholar
  5. Huffman D (1998) Interim evaluation report: using computers to construct physics understanding. 1997 Current electricity workshops. Center for Applied Research and Educational Improvement, MinnesotaGoogle Scholar
  6. Huffman D, Butterbaugh D (1999) Evaluation report: using computers to construct physics understanding. Motion and force workshops. Center for Applied Research and Educational Improvement, MinnesotaGoogle Scholar
  7. Huffman D, Goldberg F, Michlin M (2003) Using computers to create constructivist learning environments: Impact on pedagogy. J Comput Math Sci Teach 22(2):151–168Google Scholar
  8. Kennepohl D (2001) Using computer simulations to supplement teaching laboratories in chemistry for distance delivery. J Distance Educ 16(2). Available at (5/9/07):
  9. Kinzer CK, Sherwood RD, Bransford JD (1986) Computer strategies for education. Foundations and content-area applications. Merrill Publishing Company, ColumbusGoogle Scholar
  10. Kumar DD (2001) Computer applications for balancing chemical equations. J Sci Educ Technol 10(4):347–350CrossRefGoogle Scholar
  11. Kumar DD (2004) Analysis of laptop computers in science. Sci Educ Int 15(3):201–208Google Scholar
  12. Kumar DD (2010) Approaches to video anchors in problem-based science learning. J Sci Educ Technol 19(1):13–19CrossRefGoogle Scholar
  13. Kumar DD, Altschuld JW (2003) Science education policy: a symposium. Rev Policy Res 20(4):561–567CrossRefGoogle Scholar
  14. Kumar DD, Crippen KJ (2005) Science education in review: response to secretary’s summit 2004. J Sci Educ Technol 14(2):143–145CrossRefGoogle Scholar
  15. Kumar DD, Sherwood RD (2007) Effect of a problem based simulation on the conceptual understanding of undergraduate science education students. J Sci Educ Technol 16(3):239–246CrossRefGoogle Scholar
  16. Meier DK, Reinhard KJ, Carter DO, Brooks DW (2008) Simulations with elaborated worked example modeling: beneficial effects on schema acquisition. J Sci Educ Technol 17(3):262–273CrossRefGoogle Scholar
  17. Milken Exchange on Education Technology (1999) Will new teachers be prepared to teach in a digital age? A national survey on information technology in teacher education. Milken Family FoundationGoogle Scholar
  18. National Research Council (1996) National science education standards. National Academy Press, WashingtonGoogle Scholar
  19. Otero VK, Johnson A, Goldberg F (1999) How does the computer facilitate the development of physics knowledge by prospective teachers? J Educ 181(2). Available (on 5/4/07) at:
  20. Redish EF, Saul JM, Steinberg RN (1997) On the effectiveness of active-engagement microcomputer-based laboratories. Am J Phys 65(1):45–54CrossRefGoogle Scholar
  21. Sherwood RD (2002) Problem-Based multimedia software for middle grades science: development issues and an initial field study. J Comput Math Sci Teach 21(2):147–165Google Scholar
  22. The National Commission on Teaching, America’s Future (1996) What matters most: teaching for America’s future. Author, New YorkGoogle Scholar
  23. Van Heuvelen A (1997) Using interactive simulations to enhance conceptual development of problem-solving skills. Am Inst Phys Conf Proc 399:1119–1135Google Scholar
  24. Wieman CE, Perkins KK (2006) A powerful tool for teaching science. Nature Phys 2:290–292CrossRefGoogle Scholar
  25. Williamson V, Abraham M (1995) The effects of computer animation on the particulate mental models of college chemistry students. J Res Sci Teach 32:521–534CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • David Devraj Kumar
    • 1
  • P. V. Thomas
    • 2
  • John D. Morris
    • 1
  • Karen M. Tobias
    • 3
  • Mary Baker
    • 4
  • Trudy Jermanovich
    • 4
  1. 1.College of EducationFlorida Atlantic UniversityDavieUSA
  2. 2.Thin Film LabMar Ivanios CollegeTrivandrumIndia
  3. 3.Sheridan Technical CenterHollywoodUSA
  4. 4.School Board of Broward CountyFort LauderdaleUSA

Personalised recommendations