Learning Functional Models of Aquaria: The ACT Project on Ecosystem Learning in Middle School Science
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The ACT project is an ongoing collaboration among learning, cognitive, computing and biological scientists at Georgia Institute of Technology and Rutgers University, focusing on learning functional models of ecosystems in middle school science. In particular, ACT (for Aquarium Construction Toolkit) is an interactive learning environment for stimulating and scaffolding construction of Structure-Behavior-Function (SBF) models to reason about classroom aquaria. Initial results from deployment of ACT in several classrooms with a few hundred middle school children indicate statistically significant improvement in identification of the structure, behaviors and functions of classroom aquaria as well as appropriation of SBF modeling by some middle school teachers for modeling other natural systems. In this article, we summarize and review the main results from ACT on learning about SBF models of ecosystems in middle school science and describe self-regulated learning in ACT, while also looking ahead and outlining the design of a metacognitive ACT toolkit.
KeywordsMiddle School Functional Model Middle School Student Model Table Middle School Teacher
This paper has benefited from discussions with Julia Svoboda. We also thank Steven Gray at Rutgers University for his contributions to early parts of this work. We are grateful to the United States National Science Foundation [Grant (#0632519)] and the United States Institute for Education Sciences (Grant #R305A090210) for their support of their work.
- Aleven, V., McLaren, B., Roll, I., & Koedinger, K. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. Proceedings of the 7th International Conference on Intelligent Tutoring Systems (ITS 2004) (pp. 227–239). Berlin: Springer Verlag.Google Scholar
- Chandrasekaran, B. (1994). Functional representations and causal processes. In M. Yovits (Ed.), Advances in computers (pp. 73–143). Waltham, MA: Academic Press.Google Scholar
- Clement, J. (2008). Creative Model Construction in Scientists and Students: The Role of Imagery, Analogy, and Mental Simulation. Dordrecht: Springer.Google Scholar
- Dawes, J. (2000). Tropical aquarium fish: A step by step guide to setting up and maintaining a freshwater or marine aquarium. Sterling publishing, New York City.Google Scholar
- Duschl, R., & Grandy, R. (2008). Teaching scientific inquiry: Recommendations from research and implementation. Rotterdam, Netherlands: Sense Publications.Google Scholar
- Goel, A., Gomez, A., Grue, N., Murdock, W., Recker, M., & Govindaraj, T. (1996). Towards design learning environments—Explaining how devices work. In Proceedings of International Conference on Intelligent Tutoring Systems. Springer, Cognitive Science Society, ACM, International Society of the learning Sciences, Montreal, Canada.Google Scholar
- Goel, A., Rugaber, S., & Vattam, S. (2009). Structure, behavior and function of complex systems: The SBF modeling language. International Journal of AI in Engineering Design, Analysis and Manufacturing, 23, 23–35. Special Issue on Developing and Using Engineering Ontologies.Google Scholar
- Goel, A., Vattam, S., Rugaber, S., Joyner, D., Hmelo-Silver, C., Jordan R, et al. (2010) Learning functional and causal abstractions of classroom Aquaria. In Proceedings of 32nd Annual Meeting of the Cognitive Science Society. Springer, Cognitive Science Society, ACM, International Society of the learning Sciences, Portland, Oregon.Google Scholar
- Hmelo-Silver, C., Jordan, R., Honwad, S., Eberbach, C., Sinha, S., Goel, A, et al. (2011). Foregrounding behaviors and functions to promote ecosystem understanding. In Proceedings of 9th Hawaii International Conference on Education. London: Routledge.Google Scholar
- Hmelo-Silver, C., Liu, L., Gray, S., Finkelstein, H., & Schwartz, R. (2007). Enacting things differently: Using NetLogo models to learn about complex systems. Paper presented at biennial meeting of European Association for Research on Learning and Instruction. Budapest, Hungary.Google Scholar
- Honwad, S., Hmelo-Silver, C., Jordan, R., Eberbach, C., Gray, S., Sinha, S, et al. (2010). Connecting the visible to the invisible: Helping middle school children understand complex ecosystem processes. In Proceedings of 32nd Annual Meeting of the Cognitive Science Society. Springer, Cognitive Science Society, ACM, International Society of the learning Sciences, Portland, Oregon.Google Scholar
- Lehrer, R., & Schauble, L. (2006). Cultivating model-based reasoning in science education. In R. Sawyer (Ed.), The Cambridge handbook of the learning sciences. New York: Cambridge University Press.Google Scholar
- Murdock, J. W., & Goel, A. (2001). Learning about constraints by reflection. In Procs. Fourteenth Canadian Conference on Artificial Intelligence (AI-01). In Stroulia and S. Matwin (Eds.), LNAI 2056, (pp. 131–140). Berlin: Springer-Verlag.Google Scholar
- National Research Council (NRC). (1996). National science education standards. Washington, DC: National Academy Press.Google Scholar
- Nersessian, N. (2008). Creating scientific concepts. Cambridge, MA: MIT Press.Google Scholar
- New Jersey Department of Education. (2006). Core Curriculum Content Standards. In State of New Jersey Department of Education. Retrieved June 19, 2008, from http://www.state.nj.us/education/cccs/.
- Rasmussen, J. (1986). Information processing and human-machine interaction. New York: North-Holland.Google Scholar
- Robbins, J., & Redmiles, D. (1999). Cognitive Support, UML Adherence, and XMI Interchange in Argo/UML. In Proceedings of Conference on Construction of Software Engineering Tools (CoSET 99) (pp. 61–70). Springer, Cognitive Science Society, ACM, International Society of the learning Sciences, Los Angeles, California.Google Scholar
- Roschelle, J. (1996). Designing for cognitive communication: Epistemic Fidely or mediating collaborative inquiry. In D. Day & D. Kovacs (Eds.), Computer, communication and mental models. London, UK: Taylor & Francis.Google Scholar
- Simon, H. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106(6), 467–482.Google Scholar
- Simon, H. (1999). Can there be a science of complex systems? In Y. Bar-Yam (Ed.), Unifying themes in complex systems (pp. 3–14). Cambridge, MA: Perseus.Google Scholar
- Sinha, S., Gray, S., Hmelo-Silver, C., Jordan, R., Honwad, S., Eberbach, C, et al. (2010). Appropriating conceptual representations: A case of transfer in a middle school science teacher. In Proceedings of 9th International Conference of the Learning Sciences. Springer, Cognitive Science Society, ACM, International Society of the learning Sciences, Chicago. June 28–29, 2010.Google Scholar
- Stadelman, P., & Finley, L. (2003). Tropical fish: setting up and taking care of aquariums made easy: Expert advice for new aquarists. Hauppauge, New York: Barron’s Educational Series.Google Scholar
- Stansbury, E. (1999). The simplified classroom aquarium: A teacher’s guide to operating and maintaining a small classroom aquarium. Springfield: Charles C. Thomas publisher.Google Scholar
- Stewart, J., Cartier, J., & Passmore, C. (2005). Developing understanding through model-based inquiry. In M. S. Donovan & J. Bransford (Eds.), How people learn. Washington, DC: National Research Council.Google Scholar
- Vattam, S., Goel, A., Rugaber, S., Hmelo-Silver, C., Jordan, R., Gray, S., et al. (2011). Understanding complex natural systems by articulating structure-behavior-function models. Special issue on Creative Design, 14(1), 166–181.Google Scholar
- Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University. Evanston, IL.