Supporting Three-dimensional Learning on Ecosystems Using an Agent-Based Computer Model
Tóm tắt
The Next Generation Science Standards call for engaging K–12 students in three-dimensional learning, in which students make sense of phenomena or solve problems by simultaneously using science and engineering practices (SEPs), crosscutting concepts (CCCs), and disciplinary core ideas (DCIs). Decades of education research suggest agent-based computer models (ABMs) have the potential to support all three dimensions. However, most existing studies focus on using ABMs to support one or two dimensions (i.e., DCIs and/or SEPs). This article presents a mixed-methods study in which 63 sixth-grade students engaged in ABM-supported, three-dimensional learning to explore the causes of severe bark beetle outbreaks in forest ecosystems. Data collected from pre- and post-assessments, students’ written explanations for the outbreak phenomenon, and videos of classroom instruction suggest the ABM of bark beetle outbreaks supported students in using all three dimensions of science learning to make sense of the target phenomenon. Our results show that the ABM-supported unit significantly improved students’ understanding of ecosystem concepts. The largest improvement was observed among previously low-performing students. Furthermore, students engaged in sophisticated science practices, reasoning with the computer-generated data to develop an evidence-based explanation for the target phenomenon. The ABM helped students to make sense of the target phenomenon using five different CCCs. Importantly, our results also show that ABMs enabled students as young as sixth grade to predict system outcomes and better understand the nature of models in science. This study contributes to the field by bridging ABM education literature with three-dimensional science teaching and learning.
Tài liệu tham khảo
Achieve, I. (2016). Using phenomena in NGSS-designed lessons and units. https://www.nextgenscience.org/sites/default/files/Using%20Phenomena%20in%20NGSS.pdf
Allen, M. (2014). Misconceptions in primary science. McGraw-hill Education (U.K.).
American Association for the Advancement of Science (AAAS). (1993). Project 2061: Benchmarks for Science Literacy. Oxford University Press. http://www.project2061.org/publications/bsl/online/index.php
Banchi, H., & Bell, R. (2008). The many levels of inquiry. Science and Children, 46(2), 26–29.
Barth-Cohen, L., & Wittmann, M. (2020). Learning about crosscutting concepts as concepts. 14th International Conference of the Learning Sciences (ICLS), Nashville, Tennessee.
Basu, S., Biswas, G., Sengupta, P., Dickes, A., Kinnebrew, J. S., & Clark, D. (2016). Identifying middle school students’ challenges in computational thinking-based science learning. Research and Practice in Technology Enhanced Learning, 11(1), 1–35.
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.
Bentz, B. J., Régnière, J., Fettig, C. J., Hansen, E. M., Hayes, J. L., Hicke, J. A., Kelsey, R. G., Negrón, J. F., & Seybold, S. J. (2010). Climate change and bark beetles of the western United States and Canada: Direct and indirect effects. BioScience, 60(8), 602–613.
Berland, L. K., & McNeill, K. L. (2010). A learning progression for scientific argumentation: Understanding student work and designing supportive instructional contexts. Science Education, 94(5), 765–793.
Berland, L. K., & Reiser, B. J. (2011). Classroom communities’ adaptations of the practice of scientific argumentation. Science Education, 95(2), 191–216.
Butler, J., Mooney Simmie, G., & O’Grady, A. (2015). An investigation into the prevalence of ecological misconceptions in upper secondary students and implications for pre-service teacher education. European Journal of Teacher Education, 38(3), 300–319.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic Press.
Cole, F. L. (1988). Content analysis: Process and application. Clinical Nurse Specialist, 2(1), 53–57.
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.
Dickes, A. C., & Sengupta, P. (2013). Learning natural selection in 4th grade with multi-agent-based computational models. Research in Science Education, 43(3), 921–953.
Dickes, A. C., Sengupta, P., Farris, A. V., & Basu, S. (2016). Development of mechanistic reasoning and multilevel explanations of ecology in third grade using agent-based models. Science Education, 100(4), 734–776.
Duncan, R. G., Chinn, C. A., & Barzilai, S. (2018). Grasp of evidence: Problematizing and expanding the next generation science standards’ conceptualization of evidence. Journal of Research in Science Teaching, 55(7), 907–937.
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115.
Fick, S. J., Barth-Cohen, L., Rivet, A., Cooper, M., Buell, J., & Badrinarayan, A. (2018). Supporting students’ learning of science content and practices through the intentional incorporation and scaffolding of crosscutting concepts. Summit for examining the potential for crosscutting concepts to support three-dimensional learning.
Furtak, E. M., Badrinarayan, A., Penuel, W. R., Duwe, S., & Patrick-Stuart, R. (2021). Assessment of crosscutting concepts: Creating opportunities for sensemaking. In J. Nordine & O. Lee (Eds.), Crosscutting Concepts: Strengthening Science and Engineering Learning. NSTA Press.
Gogolin, S., & Krüger, D. (2018). Students’ understanding of the nature and purpose of models. Journal of Research in Science Teaching, 55(9), 1313–1338.
Goldstone, R. L., & Wilensky, U. (2008). Promoting transfer by grounding complex systems principles. The Journal of the Learning Sciences, 17(4), 465–516.
Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 11(3), 255–274.
Gu, X., & Blackmore, K. (2015). A systematic review of agent-based modelling and simulation applications in the higher education domain. Higher Education Research & Development, 34(5), 883–898.
Hmelo-Silver, C. E., Eberbach, C., & Jordan, R. (2014). Technology-supported inquiry for learning about aquatic ecosystems. Eurasia Journal of Mathematics, Science and Technology Education, 10(5), 405–413.
Jacobson, M. J. (2001). Problem solving, cognition, and complex systems: Differences between experts and novices. Complexity, 6(3), 41–49.
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26.
Jones, T., & Laughlin, T. (2009). Learning to measure biodiversity: Two agent-based models that simulate sampling methods & provide data for calculating diversity indices. The American Biology Teacher, 71(7), 406–410.
Krajcik, J., McNeill, K. L., & Reiser, B. J. (2008). Learning-goals-driven design model: Developing curriculum materials that align with national standards and incorporate project-based pedagogy. Science Education, 92(1), 1–32.
Logan, J. A., Macfarlane, W. W., & Willcox, L. (2010). Whitebark pine vulnerability to climate-driven mountain pine beetle disturbance in the Greater Yellowstone Ecosystem. Ecological Applications, 20(4), 895–902.
Macal, C. M. (2016). Everything you need to know about agent-based modelling and simulation. Journal of Simulation, 10(2), 144–156.
Macal, C. M., & North, M. J. (2010). Toward teaching agent-based simulation. Proceedings of the 2010 Winter Simulation Conference,
McNeill, K. L., & Krajcik, J. (2008). Inquiry and scientific explanations: Helping students use evidence and reasoning. In Science as Inquiry in the Secondary Setting (pp. 121–134).
Morrison, J. B., Tversky, B., & Betrancourt, M. (2000). Animation: Does it facilitate learning. AAAI spring symposium on smart graphics.
Munson, B. H. (1994). Ecological misconceptions. The Journal of Environmental Education, 25(4), 30–34.
National Research Council. (1996). National science education standards. National Academies Press.
National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.
NGSS Lead States. (2013). Next generation science standards: For states, by states. The National Academies Press.
Nordine, J., & Lee, O. (2021). Crosscutting concepts: Strengthening science and engineering learning. NSTA Press.
Pallant, A., & Lee, H.-S. (2015). Constructing scientific arguments using evidence from dynamic computational climate models. Journal of Science Education and Technology, 24(2–3), 378–395.
Quinn, H. (2021). The role of crosscutting concepts in three-dimensional science learning. In Crosscutting Concepts: Strengthening Science and Engineering Learning (pp. xi-xix).
Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. The Journal of the Learning Sciences, 13(3), 273–304.
Rosenthal, R. (1991). Meta-analytic procedures for social sciences. Sage.
Samon, S., & Levy, S. T. (2017). Micro–macro compatibility: When does a complex systems approach strongly benefit science learning? Science Education, 101(6), 985–1014.
Schwarz, C. V., Passmore, C., & Reiser, B. J. (2017). Helping students make sense of the world using next generation science and engineering practices. NSTA Press.
Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380.
Sengupta, P., & Wilensky, U. (2009). Learning electricity with NIELS: Thinking with electrons and thinking in levels. International Journal of Computers for Mathematical Learning, 14(1), 21–50.
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.
Tan, J., & Biswas, G. (2007). Simulation-based game learning environments: Building and sustaining a fish tank. 2007 First IEEE International Workshop on Digital Game and Intelligent Toy Enhanced Learning (DIGITEL'07).
Varma, K., & Linn, M. C. (2012). Using interactive technology to support students’ understanding of the greenhouse effect and global warming. Journal of Science Education and Technology, 21(4), 453–464.
Vattam, S. S., Goel, A. K., Rugaber, S., Hmelo-Silver, C. E., Jordan, R., Gray, S., & Sinha, S. (2011). Understanding complex natural systems by articulating structure-behavior-function models. Educational Technology & Society, 14(1), 66–81.
Whitley, E., & Ball, J. (2002). Statistics review 6: Nonparametric methods. Critical Care, 6(6), 509–513.
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. (2006). Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories—An embodied modeling approach. Cognition and Instruction, 24(2), 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(1), 3–19.
Xiang, L., & Mitchell, A. (2019). Investigating bark beetle outbreaks: Using computer models to visualize changes in a forest ecosystem. Science Scope, 42(6), 65–76.
Xiang, L., & Passmore, C. (2015). A framework for model-based inquiry through agent-based programming. Journal of Science Education and Technology, 24(2), 311–329.
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.
Yoon, S. A., Koehler-Yom, J., Anderson, E., Oztok, M., Klopfer, E., Schoenfeld, I., Wendel, D., Sheldon, J., & Scheintaub, H. (2015). Impacts on student understanding of scientific practices and crosscutting themes through an NGSS–designed computer-supported curriculum and instruction project. In International Society of the Learning Sciences, Inc.[ISLS].