Making predictions about chemical reactivity: Assumptions and heuristics

Journal of Research in Science Teaching

Volumn 50, Issue 6, 2013, Pages 748-767

  Maeyer, Jenine ^a   Talanquer, Vicente ^b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b University of Arizona   (United States)

Author keywords

assumptions; chemistry; cognitive constraints; college students; heuristics; reasoning

Indexed keywords

ASSUMPTIONS; COGNITIVE CONSTRAINTS; COLLEGE STUDENTS; HEURISTICS; REASONING;

CHEMISTRY; DECISION MAKING;

STUDENTS;

EID: 84880572557     PISSN: 00224308     EISSN: 10982736     Source Type: Journal    
DOI: 10.1002/tea.21092     Document Type: Article

References (54)

1. American Association for the Advancement of Science (AAAS). (1993). Benchmarks for science literacy. Washington, DC: Oxford University Press.
2. Andersson, B., (1986). The experimental gestalt of causation: A common core to pupils' preconceptions in science. European Journal of Science Education, 8 (2), 155-171.
3. Ben-Zvi, R., Bat-Sheva, E., &, Silberstein, J., (1986). Is an atom of copper malleable ? Journal of Chemical Education, 63 (1), 64-66.
4. Bhattacharyya, G., (2006). Practitioner development in organic chemistry: How graduate students conceptualize organic acids. Chemistry Education Research and Practice, 7 (4), 240-247. (Pubitemid 44643724)
5. Bowdle, B., &, Gentner, D., (2005). The career of metaphor. Psychological Review 112 (1), 193-216. (Pubitemid 40131525)
6. Brown, D. E., &, Hammer, D., (2008). Conceptual change in physics. In, S. Vosniadou, (Ed.), International handbook of research on conceptual change (pp. 127-154). New York: Routledge.
7. Brown, N. J. S., Nagashima, S. O., Fu, A., Timms, M., &, Wilson, M., (2010). A framework for analyzing scientific reasoning in assessments. Educational Assessment, 15, 142-174.
8. Charmaz, K., (2006). Constructing grounded theory: A practical guide through qualitative analysis. Thousand Oaks, CA: Sage Publications.
9. Chi, M. T. H., (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. The Journal of the Learning Sciences 14 (2), 161-199. (Pubitemid 40551444)
10. Chi, M. T. H., (2008). Three kinds of conceptual change: Belief revision, mental model transformation, and ontological shift. In, S. Vosniadou, (Ed.), International handbook of research on conceptual change (pp. 61-82). New York, NY: Routledge.
11. Chi, M. T. H., Roscoe, R. D., Slotta, J. D., Roy, M., &, Chase, C. C., (2011). Misconceived causal explanations for emergent processes. Cognitive Science, 36 (1), 1-61.
12. Claesgens, J., Scalise, K., Wilson, M., &, Stacy, A., (2009). Mapping student understanding in chemistry: The perspectives of chemists. Science Education, 93, 56-85.
13. Cooper, M. M., Grove, N., Underwood, S., &, Klymkowsky, M. W., (2010). Lost in Lewis structures: An investigation of student difficulties in developing representational competence. Journal of Chemical Education 87 (8), 869-874.
14. diSessa, A. A., (1993). Toward an epistemology of physics. Cognition and Instruction, 10, 165-255.
15. Gelman, S. A., (2009). Learning from others: Children's construction of concepts. Annual Review of Psychology, 60, 115-140.
16. Gigerenzer, G., &, Gaissmaier, W., (2011). Heuristic decision-making. Annual Review of Psychology, 62, 451-482.
17. Gilbert, J. K., &, Treagust, D., (Eds.), (2009). Multiple representations in chemical education. The Netherlands: Springer.
18. Gilovich, T., Griffin, D., &, Kahneman, D., (Eds.), (2002). Heuristics and biases: The psychology of intuitive judgment. Cambridge UK: Cambridge University Press.
19. Giner-Sorolla, R., &, Chaiken, S., (1997). Selective use of heuristic and systematic processing under defense motivation. Personality and Social Psychology Bulletin, 23, 84-97.
20. Hatano, G., &, Inagaki, K., (2000). Domain-specific constraints on conceptual development. International Journal of Behavioral Development, 24 (3), 267-275.
21. Heckler, A. F., (2011). The ubiquitous patterns of incorrect answers to science questions: The Role of automatic, bottom-up processes. In, Mestre, J. P., &, Ross, B. H., (Eds.), Psychology of learning and motivation: Cognition in education (Vol. 55, pp. 227-268), Oxford: Academic Press.
22. Hoffmann, R., &, Laszlo, P., (1991). Representation in chemistry. Angewandte Chemie International Edition, 30 (1), 1-16.
23. Keil, F. C., (1990). Constraints on constraints: Surveying the epigenetic landscape. Cognitive Science, 14 (1), 135-168.
24. Kelemen, D., &, Rosset, E., (2009). The human function compunction: Teleological explanations in adults. Cognition, 111, 138-143.
25. Kellman, P. J., Massey, C. M., &, Son, J. Y., (2010). Perceptual learning modules in mathematics: Enhancing students' pattern recognition, structure extraction, and fluency. Topics in Cognitive Science, 2, 285-305.
26. Kind, V., (2004). Beyond appearances: Students' misconceptions about basic chemical ideas. (2nd ed.). London: Royal Society of Chemistry.
27. Klaczynski, P. A., (2004). A dual-process model of adolescent development: Implications for decision-making, reasoning, and identity. In, Kail, R. V., (Ed.), Advances in child development and behavior (pp. 73-123), San Diego, CA: Academic Press.
28. Kozma, R., &, Russell, J., (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 43 (9), 949-968. (Pubitemid 127458975)
29. Kruglanski, A. W., &, Gigerenzer, G., (2011). Intuitive and deliberative judgments are based on common principles. Psychological Review, 118, 97-109.
30. Maeyer, J., &, Talanquer, V., (2010). The role of intuitive heuristics in students' thinking: Ranking chemical substances. Science Education, 94, 963-984.
31. McClary, L. M., &, Bretz, S. L., (2012). Development and assessment of a diagnostic tool to identify organic chemistry students' alternative conceptions related to acid strength. International Journal of Science Education, 34 (5), 2317-2341.
32. McClary, L., &, Talanquer, V., (2011a). Heuristic reasoning in chemistry: Making decisions about acid strength. International Journal of Science Education, 3 (10), 1433-1454.
33. McClary, L., &, Talanquer, V., (2011b). College students' mental models of acids and acid strength. Journal of Research in Science Teaching, 48 (4), 396-413.
34. National Research Council (NRC). (1996). National science education standards. Washington, DC: National Academy Press.
35. National Research Council (NRC). (2003). Beyond the molecular frontier: Challenges for chemistry and chemical engineering. Washington, DC: National Academy Press.
36. National Research Council (NRC). (2011). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. committee on a conceptual framework for new K-12 science education standards. Washington, DC: The National Academies Press.
37. Osman, M., &, Stavy, R., (2006). Development of intuitive rules: Evaluating the application of the dual system framework in understanding children's intuitive reasoning. Psychonomic Bulletin & Review, 13 (6), 935-953.
38. Penner, D. E., (2000). Explaining systems: Investigating middle school students' understanding of emergent phenomena. Journal of Research in Science Teaching 37 (8), 784-806.
39. Perfors, A., Tenenbaum, J. B., Griffiths, T. L., &, Xu, F., (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition, 120, 302-321.
40. Redish, E. F., (2004). A theoretical framework for physics education research: Modeling student thinking. In, Redish, E. F., &, Vicentini, M., (Eds.), Proceedings of the international school of physics, "Enrico Fermi" course CLVI Amsterdam: IOS Press.
41. Shah, A. K., &, Oppenheimer, D. M., (2008). Heuristics made easy: An effort-reduction framework. Psychological Bulletin, 134 (2), 207-222.
42. Sloman, S. A., (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119 (1), 3-22. (Pubitemid 126021382)
43. Spelke, E. S., &, Kinzler, K. D., (2007). Core knowledge. Developmental Science, 10, 89-96.
44. Stains, M., &, Talanquer, V., (2007). Classification schemes used by chemistry students to identify chemical substances. International Journal of Science Education, 29 (5), 643-667.
45. Stavy, R., &, Tirosh, D., (2000). How students (mis-)understand science and mathematics: Intuitive rules. New York, NY: Teachers College Press.
46. Taber, K. S., (2001). Building the structural concepts of chemistry: Some considerations from educational research. Chemistry Education: Research and Practice in Europe, 2 (2), 123-158.
47. Taber, K. S., &, García Franco, A., (2010). Learning processes in chemistry: Drawing upon cognitive resources to learn about the particulate structure of matter. Journal of the Learning Sciences, 19 (1), 99-142.
48. Talanquer, V., (2006). Commonsense chemistry: A model for understanding students' alternative conceptions. Journal of Chemical Education, 83 (5), 812-816.
49. Talanquer, V., (2008). Students' predictions about the sensory properties of chemical compounds: Additive versus emergent frameworks. Science Education, 92 (1), 96-114.
50. Tro, N. J., (2010). Chemistry: A molecular approach. (2nd ed.). Upper Saddle River, NJ: Prentice Hall.
51. von Aufschnaiter, C., &, von Aufschnaiter, S., (2003). Theoretical framework and empirical evidence of students' cognitive processes in three dimensions of content, complexity, and time. Journal of Research in Science Teaching, 40 (7), 616-648.
52. Vosniadou, S., (1994). Capturing and modeling the process of conceptual change. Learning and Instruction, 4, 45-69.
53. Vosniadou, S., &, Ortony, A., (Eds.), (1989). Similarity and analogical reasoning. New York: Cambridge University Press.
54. Vosniadou, S., Vamvakoussi, X., &, Skopeliti, I., (2008). The framework theory approach to the problem of conceptual change. In, Vosniadou, S., (Ed.), International handbook of research on conceptual change (pp. 3-34). New York: Routledge.