Article

Expertise & spatial reasoning in advanced scientific problem solving

Authors:

Sonali RajeAuthors Info & Claims

ICLS'08: Proceedings of the 8th international conference on International conference for the learning sciences - Volume 2

Pages 366 - 373

Published: 24 June 2008 Publication History

Abstract

Visualization and other forms of spatial cognition are considered fundamental to learning and problem solving in science. This assumption is especially prevalent in organic chemistry where imagistic reasoning is considered to be a primary cognitive activity. While previous research has shown that students are aware of several analytical heuristics and imagistic strategies for problem solving, there have been no studies exploring how experts in organic chemistry approach problem solving. Here, we identify problem solving strategies employed by ten chemistry experts to solve undergraduate organic chemistry assessment tasks. Our findings suggest that experts employ a range of imagistic and analytical strategies for reasoning about spatial information and prefer, on average, to use analytical strategies.

References

[1]

Alexander, P., & Judy, J. E. (1992). The interaction of domain-specific and strategic knowledge in academic performance. Review of Educational Research, 58(4), 375-404.

[2]

Barnea, N., & Dori, Y. J. (1999). High school chemistry students' performance and gender differences in a computerized molecular modeling learning environment. Journal of Science Education and Technology, 8(4), 257-271.

[3]

Becvar, L. A., Hollan, J., & Hutchins, E. (2005). Hands as molecules: Representational gestures used for developing theory in a scientific laboratory. Semiotica, 156(1/4), 89-112.

[4]

Clement, J. (2005). Transfer of imagery and runnability from analogies to models in experts. Paper presented at the Annual Meeting of the American Educational Research Association, Montreal, Quebec.

[5]

Craig, D. L., Nersessian, N. J., & Catrambone, R. (2002). Perceptual simulation in analogical problem solving. In L. Magnani & N. J. Nersessian (Eds.), Model-Based Reasoning: Science, Technology, & Values (pp. 167-191). New York: Kluwer Academic, pp. 167-191). New York: Kluwer Academic.

[6]

Ealy, J. B. (2004). Students' understanding is enhanced through molecular modeling. Journal of Science Education and Technology, 13(4).

[7]

Ege, S. N. (2003). Organic chemistry: Structure and reactivity (5th ed.). Boston: Houghton Mifflin.

[8]

Ericcson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87, 215-251.

[9]

Ferk, V., Vrtacnik, M., Blejec, A., & Gril, A. (2003). Students' understanding of molecular structure representations. International Journal of Science Education, 25(10), 1227-1245.

[10]

Feyereisen, P., & Havard, I. (1999). Mental imagery and production of hand gestures while speaking in younger and older adults. Journal of Nonverbal Behavior, 23(2), 153-171.

[11]

Fogg, P. (2005, January 28). Harvard's president wonders aloud about women in science and math. The Chronicle of Higher Education, p. A12.

[12]

Habraken, C. L. (1996). Perceptions of chemistry: Why is the common perception of chemistry, the most visual of sciences, so distorted? Journal of Science Education and Technology, 5(3), 193-201.

[13]

Hadar, U., & Butterworth, B. (1997). Iconic gestures, imagery, and word retrieval in speech. Semiotica, 115, 147-172.

[14]

Kozma, R., & Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 34(9), 949-968.

[15]

Kuhn, D., Garcia-Mila, M., Zohar, A., & Andersen, C. (1995). Strategies of knowledge acquisition. Society for Research in Child Development Monographs, 4(60, Serial No. 245).

[16]

Mathewson, J. H. (1999). Visual-spatial thinking: An aspect of science overlooked by educators. Science Education, 83, 33-54.

[17]

Narayanan, N. H., Suwa, M., & Motoda, H. (1995). Behavior hypothesis from schematic diagrams. In J. Glasgow, N. H. Narayanan & B. Chandrasekaran (Eds.), Diagrammatic reasoning: Cognitive and computational perspectives, pp. 501-534). Menlo Park, CA: The AAAI Press.

[18]

Nathan, M. J., Stephens, A. C., Masarik, K., Alibali, M. W., & Koedinger, K. R. (2002). Representational fluency in the middle school: A classroom study. In D. S. Mewborn, P. Sztajn, D. Y. White, H. G. Wiegel, R. L. Bryant & K. Nooney (Eds.), Proceedings of the twenty-fourth annual meeting of the North American chapter of the international group for the psychology of mathematics education pp. 463-472). Columbus, OH: ERIC.

[19]

Nersessian, N. J. (2002). The cognitive basis of model-based reasoning in science. In P. Carruthers, S. Stich & M. Siegal (Eds.), The cognitive basis of science, pp. 133-153). Cambridge, MA: Cambridge University Press.

[20]

Qin, Y., & Simon, H. A. (1992). Imagery as process representation in problem solving. Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, 1050-1055.

[21]

Schunn, C. D., & Reder, L. M. (2001). Another source of individual differences: Strategy adaptivity to changing rates of success. Journal of Experimental Psychology: General, 130, 59-76.

[22]

Science, A. A. f. t. A. o. (1990). Science for all Americans. New York: Oxford University Press.

[23]

Siegler, R. S., & Chen, Z. (2002). Development of rules and strategies: Balancing the old and the new. Journal of Experimental Child Psychology, 81, 446-457.

[24]

Siegler, R. S., & Croweley, K. (1991). The microgenetic method: A direct means for studying cognitive development. American Psychologist, 46(6), 606-620.

[25]

Siegler, R. S., & Svetina, M. (2006). What leads children to adopt new strategies? A microgenetic/Cross-sectional study of class inclusion. Child Development, 77(4), 997-1015.

[26]

Stieff, M. (2004). A localized model of spatial cognition in chemistry. Unpublished Doctoral Dissertation, Northwestern University, Evanston, IL.

[27]

Stieff, M. (2005). Visualization and diagrammatic reasoning in genuine scientific problem solving. In T. Barkowsky, C. Freksa, M. Hegarty & R. Loew (Eds.), Reasoning with mental and external diagrams: computation modeling and spatial assistance, Vol. AAAI Tech. Rep. SS-05-06, pp. 121-126). Menlo Park, CA: AAAI Press.

[28]

Stieff, M. (2007). Mental rotation and diagrammatic reasoning in science. Learning and Instruction, 17(2), 219-234.

[29]

Strauss, S., & Corbin, J. (1994). Grounded theory methodology: an overview. In N. Denzin & Y. Lincoln (Eds.), Handbook of Qualitative Research, pp. 273-285). Thousand Oaks: Sage Publications.

[30]

Taagepera, M., & Noori, S. (2000). Mapping students' thinking patterns in learning organic chemistry by the use of knowledge space theory. Journal of Chemical Education, 77(9), 1224-1229.

[31]

Wu, H.-k., & Shah, P. (2004). Exploring visuospatial thinking in chemistry learning. Science Education, 88(3), 465-492.

Cited By

Margulieux L(2020)Spatial encoding strategy theory: the relationship between spatial skill and STEM achievementACM Inroads10.1145/338189111:1(65-75)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3381891
Margulieux LMcCartney RPetersen ARobins AMoskal AMcCartney RRobins A(2019)Spatial Encoding Strategy TheoryProceedings of the 2019 ACM Conference on International Computing Education Research10.1145/3291279.3339414(81-90)Online publication date: 30-Jul-2019
https://dl.acm.org/doi/10.1145/3291279.3339414

Index Terms

Expertise & spatial reasoning in advanced scientific problem solving
1. Applied computing
2. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Cognitive robotics
    2. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science

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ICLS'08: Proceedings of the 8th international conference on International conference for the learning sciences - Volume 2

June 2008

523 pages

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International Society of the Learning Sciences

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Published: 24 June 2008

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Cited By

Margulieux L(2020)Spatial encoding strategy theory: the relationship between spatial skill and STEM achievementACM Inroads10.1145/338189111:1(65-75)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3381891
Margulieux LMcCartney RPetersen ARobins AMoskal AMcCartney RRobins A(2019)Spatial Encoding Strategy TheoryProceedings of the 2019 ACM Conference on International Computing Education Research10.1145/3291279.3339414(81-90)Online publication date: 30-Jul-2019
https://dl.acm.org/doi/10.1145/3291279.3339414

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