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References

  • American Association for the Advancement of Science (1989). Science for all Americans. New York: Oxford University Press.
  • Barr, R. A., & Caplan, L. J. (1987). Category representations and their implications for category structure. Memory & Cognition, 15, 397418.
  • Bereiter, C. (1985). Toward a solution of the learning paradox. Review of Educational Research, 55, 201226.
  • Bower, G. H., Black, J. B., & Turner, T. J. (1979). Scripts in memory for text. Cognitive Psychology, 11, 177220.
  • Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications. Review of Research in Education, 24, 61100.
  • Cabrera, D. (2006). A roadmap for complex systems education: Concept mapping research on what educational and science leaders think are the components of a complexity science curriculum. Presented at the Santa Fe Institute Meeting of Educational and Science Leaders. Santa Fe, NM.
  • Casti, J. L. (1997). Would-be worlds: How simulation is changing the frontiers of science. New York: John Wiley.
  • Cheng, P. W. (1997). From covariation to Causation: A causal power theory. Psychological Review, 104, 367405.
  • Chi, M. T. H. (1997). Creativity: Shifting across ontological categories flexibly. In T. B. Ward, S. M. Smith, & J. Vaid (Eds.), Conceptual structures and processes: Emergence, discovery and change (pp. 209234). Washington, DC: American Psychological Association.
  • Chi, M. T. H. (2005). Common sense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Sciences, 14, 161199.
  • Chi, M. T. H. (2008). Three types of conceptual change: Belief revision, mental model transformation, and categorical shift. In S. Vosniadou (Ed.), Handbook of research on conceptual change (pp. 6182). Hillsdale, NJ: Erlbaum.
  • Chi, M. T. H., Bassok, M., Lewis, M., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145182.
  • Chi, M. T. H., de Leeuw, N., Chiu, M. H., & LaVancher, C. (1994a). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439477.
  • Chi, M. T. H., & Hausmann, R. G. M. (2003). Do radical discoveries require ontological shifts?. In L. V. Shavinina (Ed.), International handbook on innovation (pp. 430444). Oxford, England: Elsevier.
  • Chi, M. T. H., Kristensen, A. K., & Roscoe, R. (in press). Misunderstanding emergent causal mechanism in natural selection. In K. Rosengren, S. Brem, M. Evans, & G. Sinatra (Eds.), Evolution challenges: Integrating research and practice in teaching and learning about evolution. New York: Oxford University Press.
  • Chi, M. T. H., & Ohlsson, S. (2005). Complex declarative learning. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 371399). New York: Cambridge University Press.
  • Chi, M. T. H., & Roscoe, R. D. (2002). The processes and challenges of conceptual change. In M. Limon & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice (pp. 327). The Netherlands: Kluwer Academic Publishers.
  • Chi, M. T. H., Slotta, J. D., & de Leeuw, N. (1994b). From things to processes: A theory of conceptual change for learning science concepts. Learning and Instruction, 4, 2743.
  • Clement, J. (1993). Using bridging analogies and anchoring intuitions to deal with students’ preconceptions in physics. Journal of Research in Science Teaching, 30, 12411257.
  • Clement, J., Brown, D. E., & Zietsman, A. (1989). Not all preconceptions are misconceptions: Finding ‘anchoring conceptions’ for grounding instruction on students’ intuitions. International Journal of Science Education, 11, 554565.
  • Closset, J. L. (1983). Sequential reasoning in electricity. In Research on physics education: Proceedings of the First International Workshop (pp. 313319). Paris: Editions du Centre National de Recherche Scientifique.
  • Confrey, J. (1990). A review of the research in student conceptions in mathematics, science and programming. In C. B. Cazdan (Ed.), Review of research in education, Vol. 16 (pp. 356). Washington, DC: American Educational Research Association.
  • Driver, R. (1987). Promoting conceptual change in classroom settings: The experience of the children’s learning in science project. In J. D. Novak (Ed.), Proceeding of the second international seminar on misconceptions and educational strategies in science and mathematics (Vol. II, pp. 97107). Ithaca, NY: Department of Education, Cornell University.
  • Duit, R. (2008). Bibliography – CTCSE: Students’ and teachers’ conceptions and science education [accessed on October 19, 2011]. Available at http://www.ipn.uni-kiel.de/aktuell/stcse/stcse.html.
  • Dupin, J. J., & Johsua, S. (1984). Teaching electricity: Interactive evolution of representations, models, and experiments in a class situation. In R. Duit, W. Jung, & C. von Rhoneck (Eds.), Proceedings of the international workshop of aspects of understanding electricity (pp. 331341). Kiel, Germany: Institut für die Pädagogik der Naturwissenschaften an der Universität Kiel.
  • Ferrari, M., & Chi, M. T. H. (1998). The nature of naive explanations of natural selection. International Journal of Science Education, 20(10), 12311256.
  • Gadgil, S., Nokes, T. J., & Chi, M. T. H. (in press). Effectiveness of holistic mental model confrontation in driving conceptual change. Learning and Instruction, 22, 4761.
  • Gell-Mann, M. (1994). The Quark and the Jaguar: Adventures in the simple and the complex. London: Little, Brown and Company.
  • Gentner, D., & Kurtz, K. (2005). Relational categories. In W. K. Ahn, R. L. Goldstone, B. C. Love, A. B. Markman, & P. W. Wolff (Eds.), Categorization inside and outside the lab (pp. 151175). Washington, DC: APA.
  • Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15, 138.
  • Goldstone, R.L. (1994). The role of similarity in categorization: Providing a groundwork. Cognition, 52, 125157.
  • Goldstone, R.L., & Wilensky, U. (2008). Promoting transfer by grounding complex systems principles. The Journal of the Learning Sciences, 17, 465516.
  • Grotzer, T. A., & Sudbury, M. (2000). Moving beyond underlying linear causal models of electrical circuits. Paper presented at the Annual Conference of the National Association for Research in Science Teaching, New Orleans.
  • Gupta, A., Hammer, D., & Redish, E. F. (2010). The case for dynamic models of learners’ ontologies in physics. Journal of the Learning Sciences, 19(3), 285321.
  • Hmelo-Silver, C. E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behavior, and functions. Cognitive Science, 28, 127138.
  • Hofstein, A., & Welch, W. W. (1984). The stability of attitudes towards science between junior and senior high school. Research in Science and Technological Education, 2, 131138.
  • Jacobson, M. J. (2001). Problem solving, cognition, and complex systems: Differences between experts and novices. Complexity, 6, 4149.
  • 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, 1134.
  • James, R. K., & Smith, S. (1985). Alienation of student from science in grades 4–12. Science Education, 70, 3945.
  • de Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2009). Towards a framework for attention cueing in instructional animations: Guidelines for research and design. Educational Psychology Review, 21(2), 113140.
  • LaBarbera, M., & Vogel, S. (1982). The design of fluid transport systems in organisms. American Scientist, 70, 5460.
  • Levy, S. T., & Wilensky, U. (2008). Inventing a “mid-level” to make ends meet: Reasoning between the levels of complexity. Cognition and Instruction, 26, 147.
  • Levy, S. T., & Wilensky, U. (2009). Students’ learning with the Connected Chemistry (CC1) curriculum: Navigating the complexities of the particulate world. Journal of Science Education and Technology, 18(3), 243254.
  • Licht, P. (1987). A strategy to deal with conceptual and reasoning problems in introductory electricity education. In J. Novak (Ed.), Proceedings of the 2nd international seminar “Misconceptions and Educational Strategies in Science and Mathematics” (Vol. II., pp. 275284). Ithaca, NY: Cornell University.
  • Limon, M. (2001). On the cognitive conflict as an instructional strategy for conceptual change: A critical appraisal. Learning and Instruction, 11, 357380.
  • Linn, M. C., & Hsi, S. (2000). Computers, teachers, peers: Science learning partners. Mahwah, NJ: Lawrence Erlbaum Associates.
  • Loewenstein, J., Thompson, L., & Gentner, D. (1999). Analogical encoding facilitates knowledge transfer in negotiation. Psychonomic Bulletin & Review, 6, 586597.
  • Lohmann, S. (1994). Dynamics of informational cascades: The Monday Demonstration in Leipzig, East Germany, 1989–91. World Politics, 47, 42101.
  • Lowe, R. K. (2003). Animation and learning: Selective processing of information in dynamic graphics. Learning and Instruction, 13, 156176.
  • Mandler, J. M., & Johnson, N. S. (1977). Remembrance of things parsed: Story structure and recall. Cognitive Psychology, 9(1), 111151.
  • Marek, E. (1986). Understanding and misunderstandings of biology concepts. American Biology Teacher, 48, 3740.
  • Marton, F. (2006). Sameness and difference in transfer. The Journal of the Learning Sciences, 15, 501538.
  • Maton, A., Hopkins, J., Johnson, S., LaHart, D., Warner, M. Q., & Wright, J. D. (1995). Exploring life science. Englewood Cliffs, NJ: Prentice Hall.
  • McDaniel, M. A., Anderson, J. L., Derbish, M. H., & Morrisette, N. (2007). Testing the testing effect in the classroom. European Journal of Cognitive Psychology, 19, 494513.
  • Medin, D. L., Goldstone, R. L., & Gentner, D. (1993). Respects for similarity. Psychological Review, 100, 254278.
  • Medin, D. L., Lynch, E. B., & Solomon, K. O. (2000). Are there kinds of concepts? Annual Review of Psychology, 51, 121147.
  • Meir, E., Perry, J., Stal, D., Maruca, S., & Klopfer, E. (2005). How effective are simulated molecular-level experiments for teaching diffusion and osmosis? Cell Biology Education, 4, 235248.
  • Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309326.
  • Odom, A. L. (1995). Secondary and college biology students’ misconceptions about diffusion and osmosis. American Biology Teacher, 57, 409415.
  • Perkins, D. N., & Grotzer, T. A. (2005). Dimensions of causal understanding: The role of complex causal models in students’ understanding of science. Studies in Science Education, 41, 117165.
  • Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66, 211227.
  • Reed, S. K., & Bolstad, C. (1991). Use of examples and procedures in problem solving. Journal of Experimental Psychology: Learning, Memory, & Cognition, 17, 753766.
  • Reiner, M., Slotta, J. D., Chi, M. T. H., & Resnick, L. B. (2000). Naive physics reasoning: A commitment to substance-based conceptions. Cognition and Instruction, 18, 134.
  • Resnick, M. (1994). Turtles, termites and traffic jams: Explorations in massively parallel microworlds. Cambridge, MA: MIT Press.
  • Rumelhart, D. E. (1978). Schemata: The building blocks of cognition. In R. Spiro, B. Bruce, & W. Brewer (Eds.), Theoretical issues in reading comprehension (pp. 3858). Hillsdale, NJ: Erlbaum.
  • Sanger, M., Brecheisen, D. M., & Hynek, B. M. (2001). Can computer animations affect college biology students’ conceptions about diffusion and osmosis? American Biology Teacher, 63, 104107.
  • Schank, R., & Abelson, R. (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Schelling, T. C. (1978). Micromotives and macrobehavior. New York: W.W. Norton and Co.
  • Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition and Instruction, 16, 47552.
  • 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), 2150.
  • diSessa, A. (1993). Towards an epistemology of physics. Cognition and Instruction, 10, 105225.
  • Shipstone, D. (1984). A study of children’s understanding of electricity in simple DC circuits. European Journal of Science Education, 6, 185198.
  • Slotta, J. D., & Chi, M. T. H. (2006). Helping students understand the challenging topics in science through ontology training. Cognition and Instruction, 24, 261289.
  • Slotta, J. D., Chi, M. T. H., & Joram, E. (1995). Assessing students’ misclassifications of physics concepts: An ontological basis for conceptual change. Cognition and Instruction, 13, 373400.
  • Smith, E. E., & Medin, D. L. (1981). Categories and concepts. Cambridge, MA: Harvard University Press.
  • Stein, D. W., & Glenn, C. G. (1979). An analysis of story comprehension in elementary school children. In R. Freedle (Ed.), Advances in discourse processes: Vol. 2. New directions in discourse processing (pp. 53119). Norwood, NJ: Ablex.
  • Torney-Purta, J. (1994). Dimensions of adolescents’ reasoning about political and historical issues: Ontological switches, developmental processes, and situated learning. In M. Carretero & J. F. Voss (Eds.), Cognitive and instructional processes in history and the social sciences (pp. 103122). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Trefil, J., Calvo, R. A., & Cutler, K. (2006). McDougal Littell science: Life science. Boston: Houghton Mifflin Company.
  • Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human-Computer Studies, 57, 247262.
  • Waldrop, M. M. (1992). Complexity: The emerging science at the edge of order and chaos. New York: Simon & Schuster.
  • White, B., & Frederiksen, J. (1990). Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42, 99157.
  • Whitesides, G. M., & Ismagilov, R. F. (1999). Complexity in chemistry. Science, 284, 8992.
  • 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, 171209.
  • 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), 319.
  • Yang, D., Roman, A. S., Streveler, R., Miller, R., Slotta, J., & Chi, M. T. H. (2010). Repairing student misconceptions using ontology training: A study with junior and senior undergraduate engineering students. Presented at the American Society of Engineering Education. Louisville, KY.
  • Zuckerman, J. T. (1994). Problem solvers’ conceptions about osmosis. The American Biology Teacher, 56, 2225.