Learners' epistemic criteria for good scientific models

Journal of Research in Science Teaching

Volumn 48, Issue 5, 2011, Pages 486-511

  Pluta, William J ^a   Chinn, Clark A ^a   Duncan, Ravit Golan ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

epistemic criteria; modeling; nature of science; reasoning

Indexed keywords

MODELS; PHILOSOPHICAL ASPECTS;

EPISTEMIC CRITERIA; INQUIRY-BASED SCIENCE; MIDDLE SCHOOL STUDENTS; MODEL FIT; MODEL-BASED REASONING; NATURE OF SCIENCE; REASONING; SCIENTIFIC PRODUCTS;

STUDENTS;

EID: 79953861733     PISSN: 00224308     EISSN: 10982736     Source Type: Journal    
DOI: 10.1002/tea.20415     Document Type: Article

References (90)

1. Abd-El-Khalick, F., Bell, R.L., &, Lederman, N.G., (1998). The nature of science and instructional practice: Making the unnatural natural. Science Education, 82, 417-436.
2. Alston, W.P., (2005). Beyond "justification": Dimensions of epistemic evaluation. Ithaca, NY: Cornell University Press.
3. Anderson, R.C., Nguyen-Jahiel, K., McNurlen, B., Archodidou, A., Kim, S., Reznitskaya, A., Tillmanns, M., &, Gilbert, L., (2001). The snowball phenomenon: Spread of ways of talking and ways of thinking across groups of children. Cognition and Instruction, 19, 1-46. (Pubitemid 33582610)
4. Bazerman, C., (1988). Shaping written knowledge: The genre and activity of the experimental article in science. Madison: University of Wisconsin Press.
5. Bell, P., &, Linn, M.C., (2000). Scientific arguments as learning artifacts: Designing for learning from the web with KIE. International Journal of Science Education, 22, 797-817.
6. Bishop, M.A., &, Trout, J.D., (2005). Epistemology and the psychology of human judgment. Oxford: Oxford University Press.
7. Blumenfeld, P., Kempler, T., &, Krajcik, J., (2006). Motivation and cognitive engagement in learning environments. In, R.K. Sawyer, (Ed.), The Cambridge handbook of the learning sciences (pp. 475-488). Cambridge, UK: Cambridge University Press.
8. BrÃ¥ten, I., Britt, M.A., StrÃmsÃ, H.I., &, Rouet, J.-F., (2011). The role of epistemic beliefs in the comprehension of multiple expository texts: Toward an integrated model. Educational Psychologist, 46, 48-70.
9. Brown, A.L., &, Campione, J.C., (1996). Psychological theory and the design of innovative learning environments: On procedures, principles, and systems. In, L. Schauble, &, R. Glaser, (Eds.), Innovations in learning: New environments for education (pp. 289-325). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
10. Carey, S., &, Smith, C.L., (1993). On understanding the nature of scientific knowledge. Educational Psychologist, 28, 235-251.
11. Cartwright, N., (1983). How the laws of physics lie. Oxford, England: Oxford University Press.
12. Chinn, C.A., &, Brewer, W.F., (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63, 1-49.
13. Chinn, C.A., &, Brewer, W.F., (2001). Models of data: A theory of how people evaluate data. Cognition and Instruction, 19, 323-393. (Pubitemid 33576225)
14. Chinn, C.A., &, Malhotra, B.A., (2002). Epistemologically authentic reasoning in schools: A theoretical framework for evaluating inquiry tasks. Science Education, 86, 175-218. (Pubitemid 37230332)
15. Chinn, C.A., &, Samarapungavan, A., (2009). Conceptual change-Multiple routes, multiple mechanisms: A commentary on Ohlsson (2009). Educational Psychologist, 44, 47-57.
16. Chinn, C.A., Buckland, L.A., &, Samarapungavan, A., (2010) Integrating philosophy intolearning sciences research on epistemic cognition. In K. Gomez, L. Lyons, & J. Radinsky (Eds.), Learning in the Disciplines: Proceedings of the 9th International Conference of the Learning Sciences (ICLS 2010)-Volume 2, Short Papers, Symposia, and Selected Abstracts. International Society of the Learning Sciences: Chicago IL.
17. Chinn, C.A., Buckland, L.A., &, Samarapungavan, A., (in press). Expanding the dimensions of research on epistemic cognition: Applying philosophy to psychology and education. Educational Psychologist.
18. Chinn, C.A., Duschl, R.A., Duncan, R.G., Pluta, W.J., &, Buckland, L.A., (2008). A microgenetic classroom study of learning to reason scientifically through modeling and argumentation. In ICLS 2008: Proceedings of International Society of the Learning Sciences: Lulu.
19. Chinn, C.A., Pluta, W.J., Buckland, L.A., Rogat, T.K., Difranco, J., &, Witham, S., (2010). Promoting reasoning: A microgenetic study of middle-school students learning through model-based inquiry. Symposium presented at the annual meeting of the American Educational Research Association, Denver, CO.
20. Clement, J., (2000). Model based learning as a key research area for science education. International Journal of Science Education, 22, 1041-1053.
21. Driver, R., Squires, A., Rushworth, P., &, Wood-Robinson, V., (1994). Making sense of secondary science. London: Routledge.
22. Duschl, R., (2008). Science education in three-part harmony: Balancing conceptual, epistemic, and social learning goals. Review of Research in Education, 32, 268-291.
23. Duschl, R.A., (1990). Restructuring science education. The importance of theories and their development. New York: Teachers' College Press.
24. Engle, R.A., &, Conant, F.R., (2002). Guiding principles for fostering productive disciplinary engagement: Explaining an emergent argument in a community of learners classroom. Cognition and Instruction, 20, 399-483. (Pubitemid 36593949)
25. Engle, R.A., Nguyen, P.D., &, Mendelson, A., (in press). The influence of framing on transfer: Initial evidence from a tutoring experiment. Instructional Science.
26. Giere, R.N., (1988). Explaining science: A cognitive approach. Chicago: University of Chicago Press.
27. Giere, R.N., (2004). How models are used to represent reality. Philosophy of Science, 71, 742-752.
28. Giere, R.N., (2006). Scientific perspectivism. Chicago: University of Chicago Press.
29. Godfrey-Smith, P., (2006). The strategy of model-based science. Biology and Philosophy, 21, 725-740.
30. Goldman, A.I., (1999). Knowledge in a social world. Oxford, UK: Oxford University Press.
31. Greene, J.A., &, Azevedo, R., (2010). The measurement of learners' self-regulated cognitive and metacognitive processes while using computer-based learning environments. Educational Psychologist, 45, 203-209.
32. Greene, J.A., Muis, K.R., &, Pieschl, S., (2010). The role of epistemic beliefs in students' self-regulated learning with computer-based learning environments: Conceptual and methodlogical issues. Educational Psychologist, 45, 245-257.
33. Grosslight, L., Unger, C., Jay, E., &, Smith, C.L., (1991). Understanding models and their use in science: Conceptions of middle and high school students and experts. Journal of Research in Science Teaching, 28, 799-822.
34. Gutwill, J.P., Frederiksen, J.R., &, White, B.Y., (1999). Making their own connections: Students' understanding of multiple models in basic electricity. Cognition and Instruction, 17, 249-282.
35. Haack, S., (2003). Defending science: Between scientism and cynicism. Amherst, NY: Prometheus Books.
36. Harrison, A.G., &, Treagust, D.F., (2000). Learning about atoms, molecules and chemical bonds: A case study of multiple-model use in grade 11 chemistry. Science Education, 84, 352-381.
37. Hogan, K., &, Maglienti, M., (2001). Comparing the epistemological underpinnings of students' and scientists' reasoning about conclusions. Journal of Research in Science Teaching, 38, 663-687. (Pubitemid 33655585)
38. Kitcher, P., (1981). Explanatory unification. Philosophy of Science, 48, 507-531.
39. Kitcher, P., (1993). The advancement of science. Oxford: Oxford University Press.
40. Kitcher, P., (2001). Science, truth, and democracy. Oxford: Oxford University Press.
41. Kuhn, T.S., (1977). The essential tension. Chicago: University of Chicago Press.
42. Laudan, L., (1977). Progress and its problems. Berkeley, CA: University of California Press.
43. Laudan, L., (1984). Science and values: The aims of science and their role in scientific debate. Berkeley: University of California Press.
44. Laudan, L., Donovan, A., Laudan, R., Barker, P., Brown, H., Leplin, J., Thagard, P., &, Wykstra, S., (1986). Scientific change: Philosophical models and historical research. Synthese, 69, 141-223.
45. Lederman, N.G., Abd-El-Khalick, F., Bell, R.L., &, Schwartz, R.S., (2002). Views of nature of science questionnaire: Toward valid and meaningful assessment of learners' conceptions of nature of science. Journal of Research in Science Teaching, 39, 497-521.
46. Lehrer, R., &, Schauble, L., (2004). Modeling natural variation through distribution. American Educational Research Journal, 41, 635-679. (Pubitemid 40071347)
47. Lehrer, R., &, Schauble, L., (2006). Cultivating model-based reasoning in science education. In, R. Keith Sawyer, (Ed.), Cambridge handbook of the learning sciences (pp. 371-387). Cambridge: Cambridge University Press.
48. Longino, H.E., (1990). Science as social knowledge: Values and objectivity in scientific inquiry. Princeton, NJ: Princeton University Press.
49. Longino, H.E., (2002). The fate of knowledge. Princeton, NJ: Princeton University Press.
50. Louca, L., Elby, A., Hammer, D., &, Kagey, T., (2004). Epistemological resources: Applying a new epistemological framework to science instruction. Educational Psychologist, 39, 57-68. (Pubitemid 38422909)
51. Machamer, P., Darden, D., &, Craver, C., (2000). Thinking about mechanisms. Philosophy of Science, 67, 1-25.
52. Mayr, E., (1982). The growth of biological thought: Diversity, evolution, and inheritance. Cambridge, MA: Harvard University Press.
53. McNeill, K.L., Lizotte, D.J., Krajcik, J., &, Marx, R.W., (2006). Supporting students' construction of scientific explanations by fading scaffolds in instructional materials. Journal of the Learning sciences, 15, 153-191. (Pubitemid 43602584)
54. National Research Council. (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: National Academies Press.
55. Nersessian, N., (2002). The cognitive basis of model-based reasoning in science. In, P. Carruthers, S. Stich, &, M. Siegal, (Eds.), The cognitive basis of science (pp. 17-34). Cambridge: Cambridge University Press.
56. Newton-Smith, W.H., (1981). The rationality of science. Boston: Routledge & Kegan Paul.
57. Niiniluoto, I., (2002). Critical scientific realism. Oxford, England: Oxford University Press.
58. Penner, D.E., (2000). Explaining systems: Investigating middle school students' understanding of emergent phenomena. Journal of Research in Science Teaching, 37, 784-806.
59. Popper, K.R., (1959). Logic of scientific discovery. London: Hutchinson.
60. Penner, D.E., Giles, N.D., Lehrer, R., &, Schauble, L., (1997). Building functional models: Designing an elbow. Journal of Research in Science Teaching, 34, 125-143. (Pubitemid 127460359)
61. Pluta, W.J., Buckland, L.A., Chinn, C.A., Duschl, R.A., &, Duncan, R.G., (2008). Learning to evaluate scientific models. In ICLS 2008: Proceedings of International Society of the Learning Sciences. Lulu.
62. Raghavan, K., &, Glaser, R., (1995). Model-based analysis and reasoning in science: The MARS curriculum. Science Education, 79, 37-61.
63. Raghavan, K., Sartoris, M.L., &, Glaser, R., (1998). The impact of the MARS curriculum: The mass unit. Science Education, 82, 53-91. (Pubitemid 128367587)
64. Samarapungavan, A., (1992). Children's judgments in theory choice tasks: Scientific rationality in childhood. Cognition, 45, 1-32.
65. Sandoval, W.A., (2005). Understanding students' practical epistemologies and their influence on learning through inquiry. Science Education, 89, 634-656. (Pubitemid 41360021)
66. Sandoval, W.A., &, Reiser, B.J., (2004). Explanation-driven inquiry: Integrating conceptual and epistemic scaffolds for scientific inquiry. Science Education, 88, 345-372. (Pubitemid 38652408)
67. Scardamalia, M., &, Bereiter, C., (2006). Knowledge building: Theory, pedagogy, and technology. In, K. Sawyer, (Ed.), Cambridge handbook of the learning sciences (pp. 97-118). New York: Cambridge University Press.
68. Scherr, R.E., &, Hammer, D., (2009). Student behavior and epistemological framing: Examples from collaborative active-learning activities in physics. Cognition and Instruction, 27, 147-174.
69. Schwarz, C.V., Reiser, B.J., Davis, E.A., Kenyon, L., Achér, A., Fortus, D., Schwartz, Y., Hug, B., &, Krajcik, J., (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46, 632-654.
70. Schwarz, C.V., &, White, B.Y., (2005). Metamodeling knowledge: Developing students' understanding of scientific modeling. Cognition and Instruction, 23, 165-205. (Pubitemid 40855338)
71. Smith, C.L., Maclin, D., Houghton, C., &, Hennessey, M.G., (2000). Sixth-grade students' epistemologies of science: The impact of school science experiences on epistemological development. Cognition and Instruction, 18, 349-422.
72. Snir, J., Smith, C.L., &, Raz, G., (2003). Linking phenomena with competing underlying models: A software tool for introducing students to the particulate model of matter. Science Education, 87, 794-830. (Pubitemid 38614480)
73. Sober, E., (1981). The principle of parsimony. British Journal of Philosophy of Science, 32, 145-156.
74. Sober, E., (1988). Reconstructing the past: Parsimony, evolution, and inference. Cambridge, MA: MIT Press.
75. Solomon, M., (2001). Social empiricism. Cambridge, MA: MIT Press.
76. Staley, K.W., (2004). The evidence for the top quark: Objectivity and bias in collaborative experimentation. Cambridge: Cambridge University Press.
77. Stewart, J., Hafner, R., Johnson, S., &, Finkel, E., (1992). Science as model building: Computers and high-school genetics. Educational Psychologist, 27, 317-336.
78. Suarez, M., (2003). Scientific representation: Against similarity and isomorphism. International Studies in the Philosophy of Science, 17, 225-244.
79. Suthers, D.D., &, Hundhausen, C.D., (2003). An experimental study of the effects of representational guidance on collaborative learning processes. Journal of the Learning Sciences, 12, 183-218. (Pubitemid 36539896)
80. Toth, E.E., Suthers, D.D., &, Lesgold, A.M., (2002). " Mapping to know": The effects of representational guidance and reflective assessment on scientific inquiry. Science Education, 86, 264-286. (Pubitemid 37230335)
81. Toulmin, S., (1958). The uses of argument. Cambridge: Cambridge University Press.
82. Treagust, D., Chittleborough, G., &, Mamiala, T., (2002). Students' understanding of the role of scientific models in learning science. International Journal of Science Education, 24, 357-368.
83. van Fraassen, B.C., (1980). The scientific image. Oxford: Oxford University Press.
84. Wellman, H.M., &, Gelman, S.A., (1992). Cognitive development: Foundational theories of core domains. Annual Review of Psychology, 43, 337-375.
85. White, P.A., (1988). Causal processing: Origins and development. Psychological Bulletin, 104, 36-52.
86. White, B.Y., (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction, 10, 1-100.
87. White, B.Y., &, Frederiksen, J.R., (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16, 3-118. (Pubitemid 128343737)
88. Windschitl, M., Thompson, J., &, Braaten, M., (2008). How novice science teachers appropriate epistemic discourses around model-based inquiry for use in classrooms. Cognition and Instruction, 26, 310-378.
89. Zimmerman, C., (2007). The development of scientific thinking skills in elementary and middle school. Developmental Review, 27, 172-223.
90. Zimmerman, C., Raghavan, K., &, Sartoris, M.L., (2003). The impact of the MARS curriculum on students' ability to coordinate theory and evidence. International Journal of Science Education, 25, 1247-1271. (Pubitemid 37496973)