Science & Education

, Volume 23, Issue 4, pp 897–921 | Cite as

Epistemological Issues Concerning Computer Simulations in Science and Their Implications for Science Education

  • Ileana M. Greca
  • Eugenia Seoane
  • Irene Arriassecq
Article

Abstract

Computers and simulations represent an undeniable aspect of daily scientific life, the use of simulations being comparable to the introduction of the microscope and the telescope, in the development of knowledge. In science education, simulations have been proposed for over three decades as useful tools to improve the conceptual understanding of students and the development of scientific capabilities. However, various epistemological aspects that relate to simulations have received little attention. Although the absence of this discussion is due to various factors, among which the relatively recent interest in the analysis of longstanding epistemological questions concerning the use of simulations, the inclusion of this discussion on the research agenda in science education appears relevant, if we wish to educate scientifically literate students in a vision of the nature of science closer to the work conducted by researchers today. In this paper we review some contemporary thoughts emerging from philosophy of science about simulations in science and set out questions that we consider of relevance for discussion in science education, in particular related with model-based learning and experimental work.

Keywords

Science Education Climate Science Virtual Experiment Virtual Laboratory Epistemological Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.

References

  1. Baser, M. (2006). Effects of conceptual change and traditional confirmatory simulations on pre-service teachers’ understanding of direct current circuits. Journal of Science Education and Technology, 15(5–6), 367–381.Google Scholar
  2. Bayraktar, S. (2002). A meta-analysis of the effectiveness of computer-assisted instruction in science education. Journal of Research on Technology in Education, 34(2), 173–188.Google Scholar
  3. Benzi, M. (2009). The history of numerical linear algebra. SIAM conference on applied liner algebra.Google Scholar
  4. Brukner, C., & Zeilinger, A. (2005). Quantum physics as a science of information. In A. Elitzur, S. Dolev, & N. Kolenda (Eds.), Quo vadis quantum mechanics?. Berlin: Springer.Google Scholar
  5. Buckley, B. C., Gobert, J. D., Kindfield, A. C. H., Horwitz, P., Tinker, R. F., Gerlits, B., et al. (2004). Model-based teaching and learning with BioLogica™: What do they learn? How do they learn? How do we know? Journal of Science Education and Technology, 13, 23–41.CrossRefGoogle Scholar
  6. Cartwright, N. (1983). How the laws of physics lie. Oxford, New York: Clarendon Press.CrossRefGoogle Scholar
  7. Cartwright, N. (1999). Models and the limits of theory: quantum Hamiltonians and the BCS model of superconductivity. In M. S. Morgan & M. Morrison (Eds.), Models as mediators: perspective on natural and social science. Cambridge: Cambridge University Press.Google Scholar
  8. Chinn, C. A., & Malhotra, B. A. (2002). Epistemologically authentic inquiry in schools: A theoretical framework for evaluating inquiry tasks. Science Education, 86, 175–218.CrossRefGoogle Scholar
  9. Christian, W., & Belloni, M. (2001). Physlets: Teaching physics with interactive curricular material. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  10. Confrey, J., & Doerr, H. (1994). Student modelers. Interactive Learning Environments, 4(3), 199–217.CrossRefGoogle Scholar
  11. Crippen, K. J., Archambault, L. M. & Kern, C. L. (2012). The nature of laboratory learning experiences in secondary science online. Research in Science Education. Online First doi: 10.1007/s11165-012-9301-6.
  12. de Jong, T., Linn, M. C., & Zacharias, C. Z. (2013). Physical and virtual laboratories in science and engineering education. Science, 340(6130), 305–308.Google Scholar
  13. de Jong, T., Martin, E., Zamarro, J.-M., Esquembre, F., Swaak, J., & van Joolingen, W. R. (1999). The integration of computer simulation and learning support: an example from the physics domain of collisions. Journal of Research in Science Teaching, 36, 597–615.CrossRefGoogle Scholar
  14. de Jong, T., & Njoo, M. (1992). Learning and instruction with computer simulation: Learning processes involved. In E. de Corte, M. C. Linn, H. Mandl, & L. Verschaffel (Eds.), Computer-based learning environments and problem solving (pp. 411–427). Berlin: Springer.CrossRefGoogle Scholar
  15. de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179–201.CrossRefGoogle Scholar
  16. Doerr, H. (1996). STELLA: Ten years later. A review of the literature. International Journal of Computers for Mathematical Learning, 1(2), 201–224.CrossRefGoogle Scholar
  17. Doerr, H. (1997). Experiment, simulation and analysis: An integrated instructional approach to the concept of force. International Journal of Science Education, 19(3), 265–282.CrossRefGoogle Scholar
  18. Dowling, D. (1999). Experimenting on theories. Science in Context, 12, 261–273.CrossRefGoogle Scholar
  19. Durán, J. M. (2013). Explaining simulated phenomena. A defense of the epistemic power of computer simulations. PhD Thesis. Stuttgart University.Google Scholar
  20. Fox Keller, E. (2003). Models, simulations and “computer experiments”. In H. Radder (Ed.), The philosophy of scientific experimentation. Pittsburgh: Pittsburgh University Press.Google Scholar
  21. Frigg, R., & Reiss, J. (2009). The philosophy of simulation: Hot issues or same old stew? Synthese, 169, 593–613.CrossRefGoogle Scholar
  22. Galison, P. (1987). How experiments end. Chicago: University of Chicago Press.Google Scholar
  23. Galison, P. (1996). Computer simulation and the trading zone. In P. Galison & D. J. Stump (Eds.), The disunity of science: boundaries, contexts, and power. Stanford: Stanford University Press.Google Scholar
  24. Gière, R. (1999). Science without laws. Chicago: University of Chicago Press.Google Scholar
  25. Gilbert, J. K., Boulter, C. J. & Elmer, R. (2000). Positioning models in science education and in design and technology education. In: J. K. Gilbert, & C. J. Boulter (Eds.), Developing models in science education (pp. 3–17). Dordrecht: Kluwer.Google Scholar
  26. Guala, F. (2005). The methodology of experimental economics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  27. Guillemot, H. (2010). Connections between simulations and observation in climate computer modeling. Scientist′s practices and “bottom-up epistemology” lessons. Studies in History and Philosophy of Modern Physics, 41, 242–252.CrossRefGoogle Scholar
  28. Hacking, I. (1983). Representing and intervening. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  29. Halloun, I. (2007). Mediated modeling in science education. Science & Education, 16(7), 653–697.CrossRefGoogle Scholar
  30. Hargrave, C. P., & Kenton, J. M. (2000). Pre instructional simulations: Implications for science classroom teaching. Journal of Computers in Mathematics and Science Teaching, 19(1), 47–58.Google Scholar
  31. Harper, W. L. (2011). Isaac Newton’s scientific method: Turning data into evidence about gravity and cosmology. Oxford: Oxford University Press.CrossRefGoogle Scholar
  32. Hennessy, S. (2006). Integrating technology into teaching and learning of school science: A situated perspective on pedagogical issues in research. Studies in Science Education, 42, 1–50.CrossRefGoogle Scholar
  33. Hooker, C. A. (2011). Introduction to philosophy of complex systems. Part B. In C. A. Hooker (Ed.), Philosophy of complex systems, Vol. 10 (Handbook of the Philosophy of Science) Oxford: Elsevier.Google Scholar
  34. Hsu, Y.-S., & Thomas, R. A. (2002). The impacts of a web-aided instructional simulation on science learning. International Journal of Science Education, 24, 955–979.CrossRefGoogle Scholar
  35. Hughes, R. I. G. (1999). The Ising model, computer simulation, and universal physics. In M. S. Morgan & M. Morrison (Eds.), Models as mediators. Cambridge: Cambridge University Press.Google Scholar
  36. Humphreys, P. (2004). Extending ourselves: Computational science, empiricism, and scientific method. New York: Oxford University Press.CrossRefGoogle Scholar
  37. Huppert, J., & Lazarowitz, R. (2002). Computer simulations in the high school: Students’ cognitive stages, science process skills and academic achievement in microbiology. International Journal of Science Education, 24, 803–821.CrossRefGoogle Scholar
  38. Izquierdo, M., & Adúriz-Bravo, A. (2003). Epistemological foundations of school science. Science & Education, 12(1), 27–43.CrossRefGoogle Scholar
  39. Jaakkola, T., Nurmi, S., & Veermans, K. (2011). A comparison of students’ conceptual understanding of electric circuits in simulation only and simulation-laboratory contexts. Journal of Research in Science Teaching, 48(1), 71–93.CrossRefGoogle Scholar
  40. Jackson, S., Stratford, S. J., Krajcik, J. S., & Soloway, E. (1996). Making system dynamics modeling accessible to pre-college science students. Interactive Learning Environments, 4(3), 233–257.CrossRefGoogle Scholar
  41. Johnson, A., & Lenhard, J. (2011). Toward a new culture of prediction. Computational modeling in the era of desktop computing. In A. Nordmann, et al. (Eds.), Science transformed? Debating claims of an epochal break. Pittsburgh: University of Pittsburgh Press.Google Scholar
  42. Kaput, J. J. (1995). Creating cyberetic and psychological ramps from the concrete to the abstract: Examples from multiplicate structure. In D. N. Perkins & J. L. Schwartz (Eds.), Software goes to school: Teaching for understanding with new technologies (pp. 130–154). London: Oxford University Press.Google Scholar
  43. Kaufmann, W. J., & Smarr, L. L. (1993). Supercomputing and the transformation of science. New York: Scientific American Library.Google Scholar
  44. Kirschner, P., & Huisman, W. (1998). Dry laboratories in science education; computer-based practical work. International Journal of Science Education, 20, 665–682.CrossRefGoogle Scholar
  45. Klahr, D., Triona, L. M., & Williams, C. (2007). Hands on what? The relative effectiveness of physical versus virtual materials in an engineering design project by middle school children. Journal of Research in Science Teaching, 44, 183–203.CrossRefGoogle Scholar
  46. Koponen, I. T. (2007). Models and modelling in physics education: A critical re-analysis of philosophical underpinnings and suggestions for revisions. Science & Education, 16, 751–773.CrossRefGoogle Scholar
  47. Kukkonen, J., Kärkkäinen, S. Dillon, P., & Keinonen, T. (2013). The effects of scaffolded simulation-based inquiry learning on fifth-graders’ representations of the greenhouse effect. International Journal of Science Education. First on-line, 2013. doi: 10.1080/09500693.2013.782452.
  48. Küppers, G. & Lenhard, J. (2005). Validation of simulation: Patterns in the social and natural sciences. Journal of Artificial Societies and Social Simulation, 8(4)3. http://jasss.soc.surrey.ac.uk/8/4/3.html.
  49. Lederman, N. G., Abd-El-Khalick, F., Bell, R. L., & Schwartz, R. (2002). Views of nature of science questionnaire (VNOS): Toward valid and meaningful assessment of learners’ conceptions of nature of science. Journal of Research in Science Teaching, 39(6), 497–521.CrossRefGoogle Scholar
  50. Lenhard, J. (2010). Computation and simulation. In R. Frodeman, J. T. Klein, & C. Mitcham (Eds.), The oxford handbook on interdisciplinarity (pp. 246–258). Oxford: Oxford University Press.Google Scholar
  51. Liu, H.-C., Andre, T., & Greenbowe, T. (2008). The impact of learner’s prior knowledge on their use of chemistry computer simulations: A case study. Journal of Science Education and Technology, 17, 466–482.CrossRefGoogle Scholar
  52. Lunetta, V. N., Hofstein, A., & Clough, M. (2007). Learning and teaching in the school science laboratory: An analysis of research, theory, and practice. In N. Lederman & S. Abel (Eds.), Handbook of research on science education (pp. 393–441). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  53. Mandinach, E. (1989). Model-building and the use of computer simulation of dynamic systems. Journal of Educational Computing Research, 5(2), 221–243.CrossRefGoogle Scholar
  54. Marshall, J. A., & Young, E. S. (2006). Preservice teachers’ theory development in physical and simulate environments. Journal of Research in Science Teaching, 43(9), 907–937.CrossRefGoogle Scholar
  55. McComas, W. F., & Olson, J. K. (1998). The nature of science in international standards documents. In W. F. McComas (Ed.), The nature of science in science education: Rationales and strategies (pp. 41–52). Dordrecht, The Netherlands: Kluwer.Google Scholar
  56. Mindell, D. (2002). Between human and machine: Feedback, control, and computing before cybernetics. Baltimore, MD: Johns Hopkins University Press.Google Scholar
  57. Monaghan, J. M., & Clement, J. J. (1999). Use of a computer simulation to develop mental simulations for learning relative motion concepts. International Journal of Science Education, 21(9), 921–944.CrossRefGoogle Scholar
  58. Morgan, M. (2003). Experiments without material intervention: Model experiments, virtual experiments and virtually experiments. In Hans. Radder (Ed.), The philosophy of scientific experimentation (pp. 216–235). Pittsburgh: University of Pittsburgh Press.Google Scholar
  59. Morgan, M. S., & Morrison, M. (Eds.). (1999). Models as mediators: Perspective on natural and social science. Cambridge: Cambridge University Press.Google Scholar
  60. National Research Council. (2006). America’s lab report: Investigations in high school science. Washington, DC: National Academy Press.Google Scholar
  61. Nola, R. (2004). Pendula, models, constructivism and reality. Science & Education, 13, 349–377.CrossRefGoogle Scholar
  62. Norton, S., & Suppe, F. (2001). Why atmospheric modeling is good science. In C. Miller & P. Edwards (Eds.), Changing the atmosphere: Expert knowledge and environmental governance. Cambridge: MIT Press.Google Scholar
  63. Ören, T. I. (2011a). The many facets of simulation through a collection of about 100 definitions. SCS M&S Magazine, 2(2), 82–92.Google Scholar
  64. Ören, T. I. (2011b). A critical review of definitions and about 400 types of modeling and simulation. SCS M&S Magazine, 2(3), 142–151.Google Scholar
  65. Osborne, J. F., Ratcliffe, M., Collins, S., Millar, R., & Duschl, R. (2003). What ‘ideas-about-science’ should be taught in school science? A Delphi study of the ‘expert’ community. Journal of Research in Science Teaching, 40(7), 692–720.CrossRefGoogle Scholar
  66. Özmen, H., Demircioğlu, G., & Coll, R. (2009). A comparative study of the effects of a concept mapping enhanced laboratory experience on Turkish high school students’ understanding of acid-base chemistry. International Journal of Science and Mathematics Education, 7, 1–24.CrossRefGoogle Scholar
  67. Papert, S. (1980). Mindstorms: Children, computers and powerful ideas. New York: Basic Books Inc.Google Scholar
  68. Parker, W. S. (2009). Does matter really matter? Computer simulations, experiments, and materiality. Synthese, 169(3), 483–496.CrossRefGoogle Scholar
  69. Plass, J. L., Milne, C., Homer, B. D., Schwartz, R. N., Hayward, E. O., Jordan, T., et al. (2012). Investigating the effectiveness of computer simulations for chemistry learning. Journal of Research in Science Teaching, 49, 394–419.CrossRefGoogle Scholar
  70. Rohrlich, F. (1991). Computer simulations in the physical sciences. In A. Fine, M. Forbes, & L. Wessels (Eds.), Proceedings of the 1990 philosophy of science association biennial meetings (pp. 507–518). East Lansing, MI: Philosophy of Science Association.Google Scholar
  71. Scalise, K., Timms, M., Moorjani, A., Clark, L., Holtermann, K., & Irvin, P. S. ( 2011). Student learning in science simulations: Design features that promote learning gains. Journal of Research in Science Teaching, 48(9), 1050–1078.Google Scholar
  72. Scheckler, R. K. (2003). Virtual labs: A substitute for traditional labs? International Journal of Developmental Biology, 47, 231–236.Google Scholar
  73. Sensevy, G., Tiberghien, A., Santini, J., Laube, S., & Griggs, P. (2008). An epistemological approach to modeling: Cases studies and implications for science teaching. Science Education, 92(3), 424–446.CrossRefGoogle Scholar
  74. Sins, P. H. M., Savelsbergh, E. R., van Joolingen, W., & van Hout-Wolters, B. (2009). The relation between students’ epistemological understanding of computer models and their cognitive processing in a modeling task. International Journal of Science Education, 31(9), 1205–1229.CrossRefGoogle Scholar
  75. Sismondo, S. (1999). Models, simulations and their objects. Science in Context, 12(2), 247–260.CrossRefGoogle Scholar
  76. Smetana, L. K., & Bell, R. L. (2012). Computer simulations to support science instruction and learning: A critical review of the literature. International Journal of Science Education, 34(9), 1337–1370.CrossRefGoogle Scholar
  77. Snir, J., Smith, C. L., & Raz, G. (2003). Linking phenomena with competing underlying models: A software tool for introducing students to the particulate model. Science Education, 87, 794–830.CrossRefGoogle Scholar
  78. Steed, M. (1992). STELLA, a simulation construction kit: Cognitive process and educational implications. Journal of Computers in Mathematics and Science Teaching, 11, 39–52.Google Scholar
  79. Stratford, S. J., Krajcik, J., & Soloway, E. (1998). Secondary students’ dynamic modeling processes: Analyzing, reasoning about, synthesizing, and testing models of stream ecosystems. Journal of Science Education and Technology, 7(3), 215–234.CrossRefGoogle Scholar
  80. Suárez, M. (1999). The role of models in application of scientific theories: Epistemological implications. In M. S. Morgan & M. Morrison (Eds.), Models as mediators. Cambridge: Cambridge University Press.Google Scholar
  81. Sundberg, M. (2010a). Organizing simulation code collectives. Science Studies, 23, 37–57.Google Scholar
  82. Sundberg, M. (2010b). Cultures of simulations vs. cultures of calculations? The development of simulation practices in meteorology and astrophysics. Studies in History and Philosophy of Modern Physics, 41, 273–281.CrossRefGoogle Scholar
  83. Tao, P., & Gunstone, R. (1999). The process of conceptual change in force and motion during computer supported physics instruction. Journal of Research in Science Teaching, 36, 859–882.CrossRefGoogle Scholar
  84. Teodoro, V. D. (2002). Modellus: Learning physics with mathematical modeling. PhD Thesis.Google Scholar
  85. Thornton, R. K. (1987). Tools for scientific thinking-microcomputer-based laboratories for physics teaching. Physics Education, 22, 230–238.CrossRefGoogle Scholar
  86. Triona, L., & Klahr, D. (2003). Point and click or grab and heft: Comparing the influence of physical and virtual instructional materials on elementary school students’ ability to design experiments. Cognition and Instruction, 21, 149–173.CrossRefGoogle Scholar
  87. Turkle, S. (1995). Life on the screen. Identity in the age of the internet. New York: Touchstone.Google Scholar
  88. Turkle, S. (2009). Simulation and its discontents. Cambridge: MIT Press.Google Scholar
  89. van Fraassen, B. C. (1980). The scientific image. Oxford, UK: Oxford University Press.CrossRefGoogle Scholar
  90. Waight, N., Liu, X., Gregorius, R., Smith, E. & Park, M. (2013). Teacher conceptions and approaches associated with an immersive instructional implementation of computer-based models and assessment in a secondary chemistry classroom. International Journal of Science Education, On line First. doi: 09500693.2013.787506
  91. Wheeler, J. A. (1990). Information, physics, quantum: The search for links. In W. Zurek (Ed.), Complexity, entropy, and the physics of information. Redwood City, CA: Addison-Wesley.Google Scholar
  92. White, B. Y. (1984). Designing computer games to help physics students understand Newton’s laws of motion. Cognition and Instruction, 1(1), 69–108.CrossRefGoogle Scholar
  93. White, B., & Frederiksen, J. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3–118.CrossRefGoogle Scholar
  94. Windschitl, M. (2001). Using simulations in the middle school: Does assertiveness of dyad partners influence conceptual change? International Journal of Science Education, 23(1), 17–32.Google Scholar
  95. Winsberg, E. (1999). Sanctioning models: The epistemology of simulation. Science in Context, 12, 2.Google Scholar
  96. Winsberg, E. (2003). Simulated Experiments: Methodology for a virtual world. Philosophy of Science, 70, 105–125.Google Scholar
  97. Winsberg, E. (2010). Science in the age of computer simulation. Chicago: The University of Chicago Press.CrossRefGoogle Scholar
  98. Wong, S. L., & Hodson, D. (2009). From the horse’s mouth: What scientists say about scientific investigation and scientific knowledge. Science Education, 93, 109–130.CrossRefGoogle Scholar
  99. Zacharia, Z. C. (2003). Beliefs, attitudes, and intentions of science teachers regarding the educational use of computer simulations and inquiry-based experiments in physics. Journal of Research in Science Teaching, 40, 792–823.CrossRefGoogle Scholar
  100. Zacharia, Z. C., & Anderson, O. R. (2003). The effects of an interactive computer-based simulations prior to performing a laboratory inquiry-based experiments on students’ conceptual understanding of physics. American Journal of Physics, 71, 618–629.CrossRefGoogle Scholar
  101. Zacharia, Z. C., Olympiou, G., & Papaevripidou, M. (2008). Effects of experimenting with physical and virtual manipulatives on students’ conceptual understanding in heat and temperature. Journal of Research in Science Teaching, 45(9), 1021–1035.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Ileana M. Greca
    • 1
  • Eugenia Seoane
    • 2
  • Irene Arriassecq
    • 3
  1. 1.Dpto. de Didácticas EspecíficasUniversidad de BurgosBurgosSpain
  2. 2.Fac. de Cs. Exactas - ECienTecUniversidad Nacional del Centro de la Provincia de Buenos AiresBuenos AiresArgentina
  3. 3.Fac. de Cs. Exactas - ECienTecUniversidad Nacional del Centro de la Provincia de Buenos Aires - CONICETBuenos AiresArgentina

Personalised recommendations