Abstract
Arab junior high school science teachers with minimal simulations background were closely examined for their inquiry performance using three consecutive simulations. The simulations modelled the same phenomenon - changes in a food chain population sizes, but composed of different elements (animal, plants) and different levels of complexity expressed in the number of variables for manipulation (i.e., 2, 4, and 6 variables). Half of the teachers experienced the three simulations in an ascending order of complexity (from 2 to 6 variables) whereas the other half - in a descending order of complexity (from 6 to 2 variables). The order effect was examined as related to teachers’ knowledge and beliefs about simulations and their use in teaching, teachers’ disciplinary knowledge, and their inquiry procedures. Data were collected via pre-post-questionnaires, observations, video-recordings of computer screens, audio-recordings of teachers’ think aloud and post individual interviews. Findings suggested that the teachers exposed to an ascending complexity were better able to construct a more comprehensive and accurate mental model of an effective simulation inquiry and of the population size phenomenon than did teachers who began with the highest complexity. Implications for developing a pedagogy for teaching with simulations are discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anderson, J. R. (Ed.). (1981). Cognitive skills and their acquisition (2011th ed.). Routledge.
Basu, S., Dickes, A., Kinnebrew, J. S., Sengupta, P., & Biswas, G. (2013). CTSiM: A computational thinking environment for learning science through simulation and modeling. In Proceedings of the 5th international conference on computer supported education (pp. 369–378). Aachen.
Beggrow, E. P., Ha, M., Nehm, R. H., Pearl, D., & Boone, W. J. (2014). Assessing scientific practices using machine-learning methods: How closely do they match clinical interview performance? Journal of Science Education and Technology, 23(1), 160–182.
Brucker, B., Scheiter, K., & Gerjets, P. (2014). Learning with dynamic and static visualizations: Realistic details only benefit learners with high visuospatial abilities. Computers in Human Behavior, 36, 330–339.
Bryce, C. M., Baliga, V. B., De Nesnera, K. L., Fiack, D., Goetz, K., Tarjan, L. M., & Ash, D. (2016). Exploring models in the biology classroom. The American Biology Teacher, 78(1), 35–42.
Charles, E. S., & d’Apollonia, S. (2004). Developing a conceptual framework to explain emergent causality: Overcoming ontological beliefs to achieve conceptual change. In K. Forbus, D. Gentner, & T. Reiger (Eds.), Proceedings of the 26th annual cognitive science society. Lawrence Erlbaum Associates.
Donnelly, D. F., Linn, M. C., & Ludvigsen, S. (2014). Impacts and characteristics of computer-based science inquiry learning environments for precollege students. Review of Educational Research, 84(4), 572–608.
Eilam, B. (2012). System thinking and feeding relations: Learning with a live ecosystem model. Instructional Science, 40(2), 213–239.
Eilam, B., & Reisfeld, D. (2017). A curriculum unit for promoting complex system thinking: The case of combined system dynamics and agent-based models for population growth. Journal of Advances in Education Research, 2(2), 39–60. https://doi.org/10.22606/jaer,2017.22001
Gerard, L. F., Varma, K., Corliss, S. B., & Linn, M. C. (2011). Professional development for technology-enhanced inquiry science. Review of Educational Research, 81(3), 408–448.
Goldman, S. R., Greenleaf, C., Yukhymenko-Lescroart, M., Brown, W., Ko, M. L. M., Emig, J. M., Wallace, P., Blaum, D., & Britt, M. A. (2019). Explanatory modeling in science through text-based investigation: Testing the efficacy of the project READI intervention approach. American Educational Research Journal, 56(4), 1148–1216.
Goldstone, R. L., & Sakamoto, Y. (2003). The transfer of abstract principles governing complex adaptive systems. Cognitive Psychology, 46(4), 414–466.
Greca, I. M., Seoane, E., & Arriassecq, I. (2014). Epistemological issues concerning computer simulations in science and their implications for science education. Science & Education, 23(4), 897–921.
Hegarty, M. (2004). Dynamic visualization and learning: Getting to the difficult questions. Learning and Instruction, 14, 343–351.
Henze, I., van Driel, J., & Verloop, N. (2007). Science teachers’ knowledge about teaching models and modeling in the context of a new syllabus on public understanding of science. Research in Science Education, 37(2), 99–122.
Hinton, M. E., & Nakhleh, M. (1999). Students’ microscopic, macroscopic, and symbolic representations of chemical reactions. The Chemical Educator, 4(4), 1–29.
Hmelo-Silver, C. E., & Azevedo, R. (2006). Understanding complex systems: Some core challenges. The Journal of the Learning Sciences, 15(1), 53–61.
Jacobson, M. J., Kapur, M., So, H.-J., & Lee, J. (2011). The ontologies of complexity and learning about complex systems. Instructional Science, 39, 763–783.
Jimoyiannis, A. (2010). Designing and implementing an integrated technological pedagogical science knowledge framework for science teacher’s professional development. Computers & Education, 55(3), 1259–1269.
Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1(2), 112–133.
Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.
Kapur, M. (2015). Learning from productive failure. Learning Research and Practice, 1(1), 51–65.
Khan, S. (2011). New pedagogies on teaching science with computer simulations. Journal of Science Education and Technology, 20(3), 215–232.
Kornhauser, D., Rand, W., & Wilensky, U. (2007). Visualization tools for agent-based modeling in NetLogo (pp. 15–17). Agent2007.
Lamb, R. L., Annetta, L., Firestone, J., & Etopio, E. (2018). A meta-analysis with examination of moderators of student cognition, affect, and learning outcomes while using serious educational games, serious games, and simulations. Computers in Human Behavior, 80, 158–167. https://doi.org/10.1016/j.chb.2017.10.040
Landriscina, F. (2013). Simulation and learning: A model-centered approach (pp. 47–89). Springer.
Lee, Y. S., Dervent, F., Ko, B., Wang, T., & Ward, P. (2016). Measuring pedagogical content knowledge in pre service teachers in physical education. Research Quarterly for Exercise and Sport, 87(S2), A115.
Mayer, R. E. (2009). Multi-Media learning (2nd ed.). Cambridge University Press.
Mayoh, J., & Onwuegbuzie, A. J. (2015). Toward a conceptualization of mixed methods phenomenological research. Journal of Mixed Methods Research, 9(1), 91–107.
Merchant, N. (2019). Virtual experiments and simulations in science classroom. Williams Honors College, Honors Research Projects., 972. https://ideaexchange.uakron.edu/honors_research_projects/972
Opfer, V. D., & Pedder, D. (2011). Conceptualizing teacher professional learning. Review of Educational Research, 81(3), 376–407.
Pathak, S. A., Jacobson, M. J., Kim, B., Zhang, B. H., & Feng, D. (2008). Learning the physics of electricity with agent-based models: paradox of productive failure. In T. W. Chan, G. Biswas, F.C. Chen, C. Chou, M. Jacobson, Kinshuk, F. Klett, C. K. Looi, T. Mitrovic, R. Mizoguchi, K. Nakabayashi, P. Reimann, D. Suthers, s. Yang & J. C. Yang (Eds.), International Conference on Computers in Education (pp. 221–228).
Resnick, M., & Wilensky, U. (1998). Diving into complexity: Developing problematic decentralized thinking through role-playing activities. The Journal of the Learning Sciences, 7, 153–172.
Ruebush, L., Sulikowski, M., & North, S. (2009). A simple exercise reveals the way students think about scientific modeling. Journal of College Science Teaching, 38(3), 18.
Sauve, L., Renaud, L., Kaufman, D., & Marquis, J. S. (2007). Distinguishing between games and simulation: A systematic review. Education Technology & Society, 10(3), 247–256.
Scanlon, E., Anastopoulou, S., Kerawalla, L., & Mulholland, P. (2011). How technology resources can be used to represent personal inquiry and support students’ understanding of it across contexts. Journal of Computer Assisted Learning, 27(6), 516–529.
Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 97–118). Cambridge University Press.
Scheiter, K., Gerjets, P., Huk, T., Imhof, B., & Kammerer, Y. (2009). The effects of realism in learning with dynamic visualizations. Learning and Instruction, 19(6), 481–494.
Sevinc, S., & Lesh, R. (2018). Training mathematics teachers for realistic math problems: A case of modeling-based teacher education courses. ZDM Mathematics Education (Zentralblatt für Didaktik der Mathematik), 50(1–2), 301–314.
Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Teacher, 15(2), 4–14.
Stern, L., Barnea, N., & Shauli, S. (2008). The effect of a computerized simulation on middle school students’ understanding of the kinetic molecular theory. Journal of Science Education and Technology, 17(4), 305–315.
Stinken-Rösner, L. (2020). Simulations in science education. Progress in Science Education, 3(1), 26–34. https://doi.org/10.25321/prise.2020.996
Tasquier, G., Levrini, O., & Dillon, J. (2016). Exploring students’ epistemological knowledge of models and modeling in science: Result from a teaching/learning experience on climate change. International Journal of Science Education, 38(4), 539–563. https://doi.org/10.1080/09500693.2016.1148828
Toh, P. L. L., & Kapur, M. (2017). Is having more prerequisite knowledge better for learning from productive failure? Instructional Science, 45(3), 377–394.
Tondeur, J., Van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: A systematic review of qualitative evidence. Educational Technology Research and Development, 65(3), 555–575.
van der Meij, J., & de Jong, T. (2006). Supporting students’ learning with multiple representations in a dynamic simulation-based learning environment. Learning and Instruction, 16(3), 199–212.
Vlachopoulos, D., & Makri, A. (2017). The effect of games and simulations on higher education: A systematic literature review. International Journal of Educational Technology in Higher Education, 14(22), 1–33. https://doi.org/10.1186/s41239-017-0062-1
Vo, T., Forbes, C. T., Zangori, L., & Schwarz, C. V. (2015). International Journal of Science Education, 37(15), 2411–2432.
Watson, G., Butterfield, J., Curran, R., & Craig, C. (2010). Do dynamic work instructions provide an advantage over static instructions in a small-scale assembly task? Learning and Instruction, 20(1), 84–93.
Wen, C. T., Chang, C. J., Chang, M. H., Chiang, S. H. F., Liu, C. C., Hwang, F. K., & Tsai, C. C. (2018). The learning analytics of model-based learning facilitated by a problem-solving simulation game. Instructional Science, 46(6), 847–867.
Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling. Modeling natural, social, and engineered complex systems with NetLogo. MIT press.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Eilam, B., Omar, S.Y. (2022). Science Teachers’ Construction of Knowledge About Simulations and Population Size Via Performing Inquiry with Simulations of Growing Vs. Descending Levels of Complexity. In: Ben Zvi Assaraf, O., Knippels, MC.P.J. (eds) Fostering Understanding of Complex Systems in Biology Education. Contributions from Biology Education Research. Springer, Cham. https://doi.org/10.1007/978-3-030-98144-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-98144-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-98143-3
Online ISBN: 978-3-030-98144-0
eBook Packages: EducationEducation (R0)