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Diagrams Affect Choice of Strategy in Probability Problem Solving

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Diagrammatic Representation and Inference (Diagrams 2016)

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Abstract

We investigated whether diagrams influence strategy choice and success in solving elementary combinatorics problems. Generic diagrams (trees or two-way tables) were provided to solvers as aids. Participants’ coded solution strategies revealed that problem solvers tended to utilize mathematical structures and solutions that easily mapped to the diagrams’ visuospatial relations. For example, when provided with an unlabeled N-by-N table, solvers tended to proceed by defining an equally-likely outcome space (an “outcome-search” solution); when provided with a binary tree, solvers tended to adopt a “sequential” solution defining stage-wise simple or conditional probabilities; when provided with an N-ary tree cuing equally-likely outcomes, choices were split between the two solution types. Furthermore, the tree and the table showed different patterns of characteristic errors, and perhaps for this reason, the tree led to higher accuracy for one problem that involved sequential sampling without replacement, while the table was best for the other problem, involving independent events. The results support arguments that the content and structure of diagrams must be congruent to that of the problem at hand and be easily and accurately perceived to be effective, and demonstrate that diagrams can influence strategy choice in problem solving.

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References

  1. Hegarty, M., Kozhevnikov, M.: Types of visual–spatial representations and mathematical problem solving. J. Educ. Psychol. 91(4), 684–689 (1999)

    Article  Google Scholar 

  2. Heiser, J., Tversky, B.: Arrows in comprehending and producing mechanical diagrams. Cogn. Sci. 30, 581–592 (2006)

    Article  Google Scholar 

  3. Manalo, E., Uesaka, Y.: Quantity and quality of diagrams used in math word problem solving: a comparison between New Zealand and Japanese students. In: Paper Presented at the New Zealand Association for Research in Education (NZARE) National Conference, Rotorua, New Zealand, December 2006

    Google Scholar 

  4. Zahner, D., Corter, J.E.: The process of probability problem solving: use of external visual representations. Math. Thinking Learn. 12(2), 177–204 (2010)

    Article  Google Scholar 

  5. Novick, L.R., Catley, K.M.: Reasoning about evolution’s grand patterns: college students’ understanding of the tree of life. Am. Educ. Res. J. 50, 138–177 (2013)

    Article  Google Scholar 

  6. Gattis, M., Holyoak, K.J.: Mapping conceptual to spatial relations in visual reasoning. J. Exp. Psychol. Learn. Mem. Cogn. 22(1), 231–239 (1996)

    Article  Google Scholar 

  7. Simkin, D., Hastie, R.: An information-processing analysis of graph perception. J. Am. Stat. Assoc. 82(398), 454–465 (1987)

    Article  Google Scholar 

  8. Tversky, B., Corter, J.E., Gao, J., Tanaka, Y., Nickerson, J.: People, place, and time: inferences from diagrams. In: Proceedings of the 35th Annual Conference of the Cognitive Science Society, pp. 3593–3597. Cognitive Science Society, Austin (2013)

    Google Scholar 

  9. Gick, M.L., Holyoak, K.J.: Schema induction and analogical transfer. Cogn. Psychol. 15, 1–38 (1983)

    Article  Google Scholar 

  10. Novick, L.R.: Representational transfer in problem solving. Am. Psychol. Soc. 1(2), 128–132 (1990)

    Google Scholar 

  11. Mason, D.L., Corter, J.E., Tversky, B., Nickerson, J.V.: Structure, space and time: some ways that diagrams affect inferences in a planning task. In: Cox, P., Plimmer, B., Rodgers, P. (eds.) Diagrams 2012. LNCS, vol. 7352, pp. 277–290. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  12. Braithwaite, D.W., Goldstone, R.L.: Flexibility in data interpretation: effects of representational format. Front. Psychol. 4, 1–16 (2013)

    Article  Google Scholar 

  13. Zacks, J., Tversky, B.: Bars and lines: a study of graphic communication. Mem. Cogn. 27(6), 1073–1079 (1999)

    Article  Google Scholar 

  14. Markman, A.B.: Knowledge Representation. Lawrence Erlbaum Associates, Mahwah (1999)

    Google Scholar 

  15. Novick, L.R., Hurley, S.M.: To matrix, network, or hierarchy: that is the question. Cogn. Psychol. 42(2), 158–216 (2001)

    Article  Google Scholar 

  16. Gattis, M.: Mapping relational structure in spatial reasoning. Cogn. Sci. 28, 589–610 (2004)

    Article  Google Scholar 

  17. Tversky, B., Kugelmass, S., Winter, A.: Cross-cultural and developmental trends in graphic productions. Cogn. Psychol. 23(4), 515–557 (1991)

    Article  Google Scholar 

  18. Shah, P., Mayer, R.E., Hegarty, M.: Graphs as aids to knowledge construction: Signaling techniques for guiding the process of graph comprehension. J. Educ. Psychol. 91(4), 680–702 (1999)

    Article  Google Scholar 

  19. Tversky, B., Zacks, J., Lee, P., Heiser, J.: Lines, blobs, crosses and arrows: diagrammatic communication with schematic figures. In: Anderson, M., Cheng, P., Haarslev, V. (eds.) Diagrams 2000. LNCS (LNAI), vol. 1889, pp. 221–230. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  20. Russell, W.E.: The use of visual devices in probability problem solving. Doctoral dissertation, Columbia University, Dissertation Abstracts International, 61, 1333 (2000)

    Google Scholar 

  21. Corter, J.E., Zahner, D.C.: Use of external visual representations in probability problem solving. Stat. Educ. Res. J. 6(1), 22–50 (2007)

    Google Scholar 

  22. Bobek, E.J., Corter, J.E.: Effects of problem difficulty and student expertise on the utility of provided diagrams in probability problem solving. In: Ohlsson, S., Catrambone, R. (eds.) Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 2650–2655. Cognitive Science Society, Austin (2010)

    Google Scholar 

  23. Ainsworth, S.: The functions of multiple representations. Comput. Educ. 33(2), 131–152 (1999)

    Article  MathSciNet  Google Scholar 

  24. Novick, L.R., Hurley, S.M., Francis, M.: Evidence for abstract, schematic knowledge of three spatial diagram representations. Mem. Cogn. 27(2), 288–308 (1999)

    Article  Google Scholar 

  25. Novick, L.R., Hmelo, C.E.: Transferring symbolic representations across nonisomorphic problems. J. Exp. Psychol. Learn. Mem. Cogn. 20(6), 1296–1321 (1994)

    Article  Google Scholar 

  26. Gugga, S.S., Corter, J.E.: Effects of temporal and causal schemas on probability problem solving. In: Bello, P., Guarini, M., Scassellati, B. (eds.) Proceedings of the 36th Annual Conference of the Cognitive Science Society, pp. 2650–2655. Cognitive Science Society, Austin (2014)

    Google Scholar 

  27. Tversky, B., Morrison, J.B., Betrancourt, M.: Animation: can it facilitate? Int. J. Hum. Comput. Stud. 57(4), 247–262 (2002)

    Article  Google Scholar 

  28. Larkin, J.H., Simon, H.A.: Why a diagram is (sometimes) worth ten thousand words. Cogn. Sci. 11, 65–99 (1987)

    Article  Google Scholar 

  29. Gentner, D., Markman, A.B.: Structure mapping in analogy and similarity. Am. Psychol. 52(1), 45–56 (1997)

    Article  Google Scholar 

  30. Tversky, B.: Visualizations of thought. Top. Cogn. Sci. 3, 499–535 (2011)

    Article  Google Scholar 

  31. Tversky, B., Corter, J.E., Yu, L., Mason, D.L., Nickerson, J.V.: Representing category and continuum: visualizing thought. In: Cox, P., Plimmer, B., Rodgers, P. (eds.) Diagrams 2012. LNCS, vol. 7352, pp. 23–34. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  32. Bauer, M.I., Johnson-Laird, P.N.: How diagrams can improve reasoning. Psychol. Sci. 6, 372–378 (1993)

    Article  Google Scholar 

  33. Glenberg, A.M., Langston, W.E.: Comprehension of illustrated text: pictures help to build mental models. J. Mem. Lang. 31, 129–151 (1992)

    Article  Google Scholar 

  34. Pinker, S.: A theory of graph comprehension. In: Freedle, R. (ed.) Artificial Intelligence and the Future of Testing, pp. 73–126. Erlbaum, Hillsdale (1990)

    Google Scholar 

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Correspondence to Chenmu Xing .

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Xing, C., Corter, J.E., Zahner, D. (2016). Diagrams Affect Choice of Strategy in Probability Problem Solving. In: Jamnik, M., Uesaka, Y., Elzer Schwartz, S. (eds) Diagrammatic Representation and Inference. Diagrams 2016. Lecture Notes in Computer Science(), vol 9781. Springer, Cham. https://doi.org/10.1007/978-3-319-42333-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-42333-3_1

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  • Publisher Name: Springer, Cham

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