Representing Complex Problems: A Representational Epistemic Approach

Learning to Solve Complex Scientific Problems

Volumn , Issue , 2017, Pages 97-130

  Cheng, Peter C H ^a   Barone, Rossano ^a  

^a UNIVERSITY OF SUSSEX   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 56549094379     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9781315091938-5     Document Type: Chapter

References (40)

1. Barone, R., & Cheng, R C.-H. (2004). Representations for problem solving: On the benefits of integrated structure. In E. Banissi, K. Borner, C. Chen, M. Dastbaz, G. Clapwortht, F. A., E. Izquierdo, C. Maple, J. Roberts, C. Moore, A. Ursyn, & J. J. Zhang (Eds.), Proceedings of the 8th international conference on information visualisation (pp. 575-580). Los Alamitos, CA: Institute of Electrical and Electronics Engineers.
2. Barwise, J., & Etchemendy, J. (1995). Heterogenous logic. In J. Glasgow, N. H. Narayanan, & B. Chandrasekaran (Eds.), Diagrammatic reasoning: Cognitive and computational perspectives (pp. 211-234). Menlo Park, CA: American Assosciation for Artificial Intelligence Press.
3. Bertin, J. (1983). Seminology of graphics: Diagrams, networks, maps. Madison: University of Wisconsin Press.
4. Burns, C. M., & Hajdukiewicz, J. R. (2004). Ecological interface design. Boca Raton, FL: CRC Press.
5. Card, S., MacKinlay, J., & Shneiderman, B. (Eds.). (1999). Readings in information visualization: Using vision to think. Mahwah, NJ: Lawrence Erlbaum Associates.
6. Cheng, P. C.-H. (1999). Interactive law encoding diagrams for learning and instruction. Learning and Instruction, 9, 309-326.
7. Cheng, P. C.-H. (2002). Electrifying diagrams for learning: Principles for effective representational systems. Cognitive Science, 26, 685-736.
8. Cheng, P. C. H. (2003). Diagrammatic re-codification of probability theory: A representational epistemological study. In R. Alterman & D. Kirsh (Eds.), Proceedings of the twenty fifth annual conference of the cognitive science society (pp. 234-239). Boston: Cognitive Science Society.
9. Cheng, P. C.-H. (2004). Why diagrams are (sometimes) six times easier than words: Benefits beyond locational indexing. In A. Blackwell, K. Marriot, & A. Shimojima (Eds.), Diagrammatic representation and inference: Third international conference, diagrams 2004 (pp. 242-254). Berlin, Germany: Springer-Verlag.
10. Cheng, P. C.-H., Barone, R., Cowling, P. I., & Ahmadi, S. (2002). Opening the information bottleneck in complex scheduling problems with a novel representation: Stark diagrams. In M. Hegarty, B. Meyer, & N. H. Narayanan (Eds.), Diagrammatic representations and inference: Second international conference, diagrams 2002 (pp. 264-278). Berlin, Germany: Springer-Verlag.
11. Cheng, P. C.-H., Barone, R., Pappa, N., Wilson, J. R., Cauvain, S. P., & Young, L. S. (2006). Understanding bakery scheduling: Diverse methods for convergent constraints in user-centred design. In P. D. Bust (Ed.), Contemporary ergonomics (pp. 45-49). London: Taylor & Francis.
12. Cheng, P. C.-H., & Shipstone, D. M. (2003). Supporting learning and promoting conceptual change with box and avow diagrams. Part 1: Representational design and instructional approaches. International Journal of Science Education, 25, 193-204.
13. Cheng, P. C.-H., & Simon, H. A. (1995). Scientific discovery and creative reasoning with diagrams. In S. Smith, T. Ward, & R. Finke (Eds.), The creative cognition approach (pp. 205-228). Cambridge, MA: MIT Press.
14. Chi, M. T. H., Claser, R., & Farr, M. J. (Eds.). (1988). The nature of expertise. Hillsdale, NJ: Lawrence Erlbaum Associates.
15. Ellis, B. (1968). Basic concepts of measurement. Cambridge, England: Cambridge University Press.
16. Engelhardt, J. (2002). The language of graphics. Amsterdam: Institute for Logic, Language, and Computation, University of Amsterdam.
17. Evans, J. (1989). Bias in human reasoning: Causes and consequences. Hillsdale, NJ: Lawrence Erlbaum Associates.
18. Glasgow, J., Narayanan, N. H., & Chandrasekaran, B. (Eds.). (1995). Diagrammatic reasoning: Cognitive and computational perspectives. Menlo Park, CA: American Association for Artificial Intelligence Press.
19. Green, T. R. G. (1989). Cognitive dimensions of notations. In A. Sutcliffe & L. Maclaulay (Eds.), People and computers 5. Cambridge, England: Cambridge University Press.
20. Kaplan, C. A., & Simon, H. A. (1990). In search of insight. Cognitive Psychology, 22, 374-419.
21. Klahr, D., & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science, 12, 1-48.
22. Koedinger, K. R., & Anderson, J. R. (1990). Abstract planning and perceptual chunks: Elements of expertise in geometry. Cognitive Science, 14, 511-550.
23. Kosslyn, S. M. (1989). Understanding charts and graphs. Applied Cognitive Psychology, 3, 185-226.
24. Kotovsky, K., Hayes, J. R., & Simon, H. A. (1985). Why are some problems hard? Cognitive Psychology, 17, 248-294.
25. Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65-99.
26. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice Hall.
27. Palmer, S. E. (1978). Fundamental aspects of cognitive representation. In E. Rosch & B. B. Lloyd (Eds.), Cognition and categorization, (pp. 259-303). Hillsdale, NJ: Lawrence Erlbaum Associates.
28. Pinker, S. (1990). A theory of graph comprehension. In R. Freedle (Ed.), Artificial intelligence and the future of testing (pp. 73-126). Hillsdale, NJ: Lawrence Erlbaum Associates.
29. Scaife, M., & Rogers, Y. (1996). External cognition: How do graphical representations work? International Journal of Human-Computer Studies, 45, 185-213.
30. Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence, 90, 79-133.
31. Sloman, A. (1985). Why we need many knowledge representation formalisms. In M. Bramer (Ed.), Research and developments in expert systems (pp. 163-183). Cambridge, England: Cambridge University Press.
32. Simon, H. A. (1973). The structure of ill-structured problems. Artificial Intelligence, 4, 181-201.
33. Simon, H. A., & Lea, G. (1974). Problem solving and rule induction: A unified view. In L. W. Gregg (Ed.), Knowledge and cognition (pp. 105-127). Hillsdale, NJ: Lawrence Erlbaum Associates.
34. Stenning, K., & Oberlander, J. (1995). A cognitive theory of graphical and linguistic reasoning: Logic and implementation. Cognitive Science, 19, 97-140.
35. Ware, C. (1999). Information visualisation: Perception for design. San Francisco: Kaufmann.
36. Wood, D. J. (1988). How children think and learn. Oxford, England: Blackwell.
37. VanLehn, K. (1989). Problem solving and cognitive skill acquisition. In M. Posner (Ed.), Foundations of cognitive science (pp. 527-580). Cambridge, MA.: Bradford/MIT Press.
38. Zhang, J. (1996). A representational analysis of relational information displays. International Journal of Human-Computer Studies, 45, 59-74.
39. Zhang, J. (1997). The nature of external representations in problem solving. Cognitive Science, 21, 179-217.
40. Zhang, J., & Norman, D. A. (1994). A representational analysis of numeration systems. Cognition, 57, 271-295.