Abstract
The properties and functions of complex systems apply across a variety of domains of science and are at the heart of the solutions to many global crises facing the world today. As such, understanding of complex systems has been increasingly recognized as a core goal of science education. Learning about complexity-related concepts and phenomena is persistently considered difficult for many students—even at the undergraduate level, but traditional pedagogical approaches have been unsuccessful in teaching complex systems effectively. Evidence indicates that agent-based participatory simulations can be promising for this purpose. Using mixed methods, cross-case analysis, we examined how undergraduates experience changes in their understanding of the watershed as a complex ecosystem with the use of a particular agent-based participatory simulation called the UVA Bay Game. While one of the cases yielded evidence of nonsignificant quantitative change between pre- and post-simulation concept maps, this study observed an overall positive increase of complex system understanding through both concept map analysis and narrative reflections on learning. Our findings extend the current understanding of the role of participatory agent-based simulations in teaching and learning about complex systems in classrooms. Implications and limitations are discussed.
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Availability of Data and Material
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
Notes
Access to the software can be obtained by contacting its creator, Dr. Dirk Ifenthaler, by email at ifenthaler@uni-mannheim.de.
References
Basu, S., Sengupta, P., & Biswas, G. (2015). A scaffolding framework to support learning of emergent phenomena using multi-agent-based simulation environments. Research in Science Education, 45(2), 293–324.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate—A new and powerful approach to multiple testing. Journal of the Royal Statistical Society B, 57, 289–300.
Ben-Zvi Assaraf, O., & Orion, N. (2005). Development of system thinking skills in the context of earth system education. Journal of Research in Science Teaching, 42, 518–560.
Berland, L. K., & Lee, V. R. (2012). In pursuit of consensus: Disagreement and legitimization during small-group argumentation. International Journal of Science Education, 34(12), 1857–1882.
Blumschein, P., Hung, W., Jonassen, D., & Strobel, J. (Eds.). (2009). Model-based approaches to learning. Sense Publishers.
Brandstädter, K., Harms, U., & Großschedl, J. (2012). Assessing system thinking through different concept-mapping practices. International Journal of Science Education, 34(14), 2147–2170.
Centola, D., Wilensky, U., & McKenzie, E. (2000). Survival of the groupiest: Facilitating students’ understanding of the multiple levels of fitness through multi-agent modeling-The EACH Project. International Journal of Complex Systems, 377.
Chesapeake Bay Program. (n.d.). Discover the Chesapeake. Retrieved September 21, 2020, from https://www.chesapeakebay.net/discover
Chi, M. T. H. (2000). Misunderstanding emergent processes as causal. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
Chi, M. T. H. (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Sciences, 14, 161–199.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Academic Press.
Creswell, J. W. (2007). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Prentice Hall.
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.
Dubovi, I., & Lee, V. R. (2019). Instructional support for learning with agent-based simulations: A tale of vicarious and guided exploration learning approaches. Computers & Education, 142, 103644.
Feltovich, P. J., Coulson, R. L., & Spiro, R. J. (2001). Learners’ (mis)understanding of important and difficult concepts. In K. D. Forbus & P. J. Feltovich (Eds.), Smart machines in education: The coming revolution in educational technology (pp. 349–375). AAAI/MIT Press.
Feltovich, P. J., Spiro, R. J., & Coulson, R. L. (1993). Learning, teaching, and testing for complex conceptual understanding. Test theory for a new generation of tests, 181–217.
Funke, J. (1991). Solving complex problems: Exploration and control of complex problems. In R. Sternberg & P. Frensch (Eds.), Complex problem solving: Principles and mechanisms (pp. 185–222). Lawrence Erlbaum.
Herbel-Eisenmann, B., Lubienski, S., & Id-Deen, L. (2006). Reconsidering the study of mathematics instructional practices: The importance of curricular context in understanding local and global teacher change. Journal of Mathematics Teacher Education, 9, 313–345.
Hmelo-Silver, C. E., & Azevedo, R. (2006). Understanding complex systems: Some core challenges. The Journal of the Learning Sciences, 15(1), 53–61.
Hmelo-Silver, C. E., Liu, L., Gray, S., & Jordan, R. (2015). Using representational tools to learn about complex systems: A tale of two classrooms. Journal of Research in Science Teaching, 52(1), 6–35.
Ifenthaler, D. (2014). AKOVIA: Automated knowledge visualization and assessment. Technology, Knowledge and Learning, 19, 241–248.
Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15, 11–34.
Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38(5), 365–379.
Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development, 45, 65–94.
Kauffman, S. A. (1993). The origins of order: Self-organization and selection in evolution. Oxford University Press.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.
Klopfer, E., Yoon, S., & Perry, J. (2005). Using palm technology in participatory simulations of complex systems: A new take on ubiquitous and accessible mobile computing. Journal of Science Education and Technology, 14, 285–297.
Kumar, V., Tissenbaum, M. B., & Kim, T. (2021). Procedural collaboration in educational games: Supporting complex system understandings in immersive whole class simulations. Communication Studies, 72(6), 994–1016.
Learmonth, G., Smith, D. E., Sherman, W. H., White, M. A., & Plank, J. (2011). A practical approach to the complex problem of environmental sustainability: The UVA Bay Game. The Innovation Journal: The Public Sector Innovation Journal, 16 (1), Article 4.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage Publications.
Meadows, D. H., & Wright, D. (2008). Thinking in systems: A primer. Chelsea Green Publishing.
Nakagawa, S. (2004). A farewell to Bonferroni: The problems of low statistical power and publication bias. Behavioral Ecology, 15(6), 1044–1045.
National Science Foundation. (2009). Transitions and tipping points in complex environmental systems. National Science Foundation.
New Jersey Department of Education. (2006). New Jersey core curriculum content standards for science.
Novak, J. D. (1990). Concept mapping: A useful tool for science education. Journal of Research in Science Teaching, 27, 937–949.
Oliveira, A., Feyzi Behnagh, R., Ni, L., Mohsinah, A. A., Burgess, K. J., & Guo, L. (2019). Emerging technologies as pedagogical tools for teaching and learning science: A literature review. Human Behavior and Emerging Technologies, 1(2), 149–160.
Ottino, J. M. (2004). Engineering complex systems. Nature, 427, 399.
Penner, D. E. (2000). Explaining systems: Investigating middle school participants’ understanding of emergent phenomena. Journal of Research in Science Teaching, 37, 784–806.
Pirnay-Dummer, P., Ifenthaler, D., & Spector, J. M. (2010). Highly integrated model assessment technology and tools. Educational Technology Research and Development, 58, 3–18.
Porter, A., Garet, M., Desimone, L., Yoon, K., & Birman, B. (2000). Does professional development change teaching practice? Results from a three-year study. American Institutes of Research.
Rates, C. A., Mulvey, B. K., & Feldon, D. F. (2016). Promoting conceptual change for complex systems understanding: Outcomes of an agent-based participatory simulation. Journal of Science Education and Technology, 25(4), 610–627.
Resnick, M. (1997). Turtles, termites, and traffic jams: Explorations in massively parallel microworlds. The MIT Press.
Resnick, M., & Wilensky, U. (1993). Beyond the deterministic, centralized mindsets: A new thinking for new science. Paper presented at the annual meeting of the American Educational Research Association, Atlanta, GA.
Rutten, N., Van Joolingen, W. R., & Van Der Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers & Education, 58(1), 136–153.
Seel, N. M., & Blumschein, P. (2009). Modeling and simulation in learning and instruction: A theoretical perspective. In Model-based approaches to learning, 2–14. Leiden, the Netherlands: Brill Sense.
Sengupta, P., & Wilensky, U. (2009). Learning electricity with NIELS: Thinking with electrons and thinking in levels. International Journal of Computers for Mathematical Learning, 14, 21–50.
Sitzmann, T. (2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64(2), 489–528.
Sommer, C., & Lücken, M. (2010). System competence—Are elementary students able to deal with a biological system? NorDiNa, 6(2), 125–143.
Spiro, R. J., Feltovich, P. J., Jacobson, M. J., & Coulson, R. L. (1992). Cognitive flexibility, constructivism, and hypertext: Random access instruction for advanced knowledge acquisition in ill-structured domains. Constructivism and the technology of instruction: A conversation, 57–75.
Stroup, W. M., & Wilensky, U. (2014). On the embedded complementarity of agent-based and aggregate reasoning in students’ developing understanding of dynamic systems. Technology, Knowledge and Learning, 19(1–2), 19–52.
Teddlie, C., & Tashakkori, A. (2009). Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioral sciences. Sage Publications Inc.
Tofel-Grehl, C., Jex, E., Searle, K., Ball, D., Zhao, X., & Bunnell, G. (2020). Electrifying: One teacher’s discursive and instructional changes through engagement in e-textiles to teach science content. Contemporary Issues in Technology and Teacher Education, 20(2), 293–314.
U.Va. Bay Game. (n.d.). About The Game. Retrieved September 21, 2020, from https://web.arch.virginia.edu/baygame/about/
Virginia Department of Education. (2020). Environmental literacy. https://www.doe.virginia.gov/instruction/environmental_literacy/index.shtml
Vitale, J. M., McBride, E., & Linn, M. C. (2016). Distinguishing complex ideas about climate change: Knowledge integration vs. specific guidance. International Journal of Science Education, 38(9), 1548–1569.
Vosniadou, S. (2013). Conceptual change in learning and instruction: The framework theory approach. In S. Vosniadou (Ed.), The international handbook of conceptual change (2nd ed., pp. 11–30). Routledge.
Vosniadou, S. (2014). Examining cognitive development from a conceptual change point of view: The framework theory approach. European Journal of Developmental Psychology, 11(6), 645–661.
Vosniadou, S. (2019). The development of students’ understanding of science. Frontiers in Education, 4, 32.
Vosniadou, S., & Skopeliti, I. (2014). Conceptual change from the framework theory side of the fence. Science & Education, 23, 1427–1445.
Vosniadou, S., Ioannides, C., Dimitrakopoulou, A., & Papademetriou, E. (2001). Designing learning environments to promote conceptual change in science. Learning and Instruction, 11(4–5), 381–419.
Weinerth, K., Koenig, V., Brunner, M., & Martin, R. (2014). Concept maps: A useful and usable tool for computer-based knowledge assessment? A literature review with a focus on usability. Computers & Education, 78, 201–209.
Wilensky, U. (1999). Center for connected learning and computer-based modeling. Northwestern University.
Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo. MIT Press.
Wilensky, U., & Reisman, K. (1998). Learning biology through constructing and testing computational theories—An embodied modeling approach. Y. Bar-Yam. In Second international conference on complex systems (pp. 171–209).
Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and Technology, 8, 3–19.
Wilensky, U., & Stroup, W. (1999). Learning through participatory simulations: Network-based design for systems learning in classrooms computer supported collaborative learning. Stanford University.
Yin, R. K. (2009). Case study research: Design and methods (4th ed.) Thousand Oaks, CA: Sage publications.
Yin, Y., Vanides, J., Ruiz-Primo, M. A., Ayala, C. C., & Shavelson, R. J. (2005). Comparison of two concept-mapping techniques: Implications for scoring, interpretation, and use. Journal of Research in Science Teaching, 42(2), 166–184.
Yoon, S. A., Goh, S.-E., & Park, M. (2018). Teaching and learning about complex systems in K–12 science education: A review of empirical studies 1995–2015. Review of Educational Research, 88(2), 285–325.
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Jeong, S., Elliott, J.B., Feng, Z. et al. Understanding Complex Ecosystems Through an Agent-Based Participatory Watershed Simulation. J Sci Educ Technol 31, 691–705 (2022). https://doi.org/10.1007/s10956-022-09987-8
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DOI: https://doi.org/10.1007/s10956-022-09987-8