Skip to main content
Log in

Learning to Think and Communicate with Diagrams: 14 Questions to Consider

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

This paper looks at the particular role which diagrammatic representations, and external representations more generally, play within an educational context. In particular, it considers the way in which the demands on diagrammatic representational systems in educational settings differ with respect to other settings (e.g. professional): in some instances, these demands are increased, while in others, the demands are markedly different.

The paper considers three key issues: the question of whether diagrams make certain tasks easier (and whether this is desirable from an educational point of view), the generalisation and transfer of diagrammatic skills once learnt, and the possible problems associated with simultaneously learning domain knowledge and a novel representational system.

The paper then considers a number of sub-issues, and concludes by highlighting areas of particular interest for future AI research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Ainsworth, S., Wood, D. & Bibby, P. (1996). Coordinating Multiple Representations in Computer Based Learning Environments. In Brna, P., Paiva, A. and Self, J.A. (eds.) Proceedings of the European Conference on Artificial Intelligence in Education, 336–342. Lisbon: Edicões Colibri.

    Google Scholar 

  • Baker M. & Lund, K. (1996). Flexibly Structuring the Interaction in a CSCL Environment. In Brna, P., Paiva, A. and Self, J.A. (eds.), Proceedings of the European Conference on Artificial Intelligence in Education, 401–407. Lisbon: Edicões Colibri.

    Google Scholar 

  • Barwise, J. (1993). Heterogeneous Reasoning. In Allwein, G. and Barwise, J. (eds.) Working Papers on Diagrams and Logic, 211–234. Indiana: Visual Inference Laboratory, Indiana University.

    Google Scholar 

  • Barwise, J. & Etchemendy, J. (1994). Hyperproof. Cambridge: CSLI Publications, Cambridge University Press.

    Google Scholar 

  • Brayshaw, M. (1993). MRE: A Flexible and Customisable Program Visualisation Architecture. In Diaper, D. et al. (ed.) People and Computers VIII. Cambridge: Cambridge University Press.

    Google Scholar 

  • Brna, P. (1996). Can't See the Words for the Tree: Interpretation and Graphical Representations. In Proceedings of the Colloquium on Thinking with Diagrams, Digest No: 96/010, 1/1–1/3. London: IEE.

    Google Scholar 

  • Brna, P. (1998). Collaborative Virtual Learning Environments for Concept Learning. International Journal of Continuing Engineering Education and Life-Long Learning 8(2). To appear.

  • Brna, P. & Aspin, R. (In press). Collaboration in a Virtual World: Support for Conceptual Learning? Education and Information Technology.

  • Brna, P. & Burton, M. (1997). The Computer Modelling of Students Collaborating in Learning About Energy. Journal of Computer Assisted Learning 13(3): 193–204.

    Google Scholar 

  • Brown, J.S., Collins, A. & Duguid, P. (1989). Situated Cognition and the Culture of Learning. Educational Researcher 17: 32–41.

    Google Scholar 

  • Cheng, P. (1996). Thinking, Expertise and Diagrams that Encode Laws. In Proceedings of the Colloquium on Thinking with Diagrams, Digest No: 96/010, 10/1–10/3. London: IEE.

    Google Scholar 

  • Chi, M.T.H., Bassok, M., Lewis, M.W., Reimann, P. & Glaser, R. (1989). Self Explanations: How Students Study and Use Examples in Learning to Solve Problems. Cognitive Science 13: 145–182.

    Google Scholar 

  • Chi, M.T.H., Feltovich, P.J. & Glaser, R. (1981). Categorization and Representation of Physics Problems by Experts and Novices. Cognitive Science 5: 121–152.

    Google Scholar 

  • Cox, R. (1996). Analytical Reasoning with Multiple External Representations. Unpublished Ph.D. thesis, Department of Artificial Intelligence, University of Edinburgh.

  • Cox, R. (1997). Representation Interpretation versus Representation Construction: An ILEbased Study Using SwitchERII. In Proceedings of the 8th World Conference of the Artificial Intelligence on Education Society (AI-ED97), 434–441. Amsterdam: IOS.

    Google Scholar 

  • Cox, R. (In press). The Roles of Externalisation in Reasoning with Self-constructed Representations. Learning and Instruction.

  • Cox, R. & Brna, P. (1995). Analytical Reasoning with External Representations: Supporting the Stages of Selection, Construction and Use. Journal of AI in Education 6(2/3): 239–302.

    Google Scholar 

  • Cox, R., McKendree, J., Tobin, R., Lee, J. & Mayes, T. (In press). Vicarious Learning from Dialogue and Discourse: A Controlled Comparison. Instructional Science.

  • Cox, R., Stenning, K. & Oberlander, J. (1994). Graphical Effects in Learning Logic: Reasoning, Representation and Individual Differences. In Ram, A. and Eiselt, K. (eds.) Proceedings of the 16th Annual Conference of the Cognitive Science Society, 188–198. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Cronbach, L.J. & Snow, R.E. (1977). Aptitudes and Instructional Methods: A Handbook on Research on Interactions. New York: Irvington.

    Google Scholar 

  • Giere, R. (1988). Explaining Science: A Cognitive Approach. Chicago: The University of Chicago Press.

    Google Scholar 

  • Grossen, G. & Carnine, D. (1990). Diagramming a Logic Strategy: Effects on Difficult Problem Types and Transfer. Learning Disability Quarterly 13: 168–182.

    Google Scholar 

  • Hall, R., Kibler, D., Wenger, E. & Truxaw, C. (1989). Exploring the Episodic Structure of Algebra Story Problem Solving. Cognition and Instruction 6(3): 223–283.

    Google Scholar 

  • Kaput, J.J. (1987). Towards a Theory of Symbol Use inMathematics. In Janvier, C. (ed.) Problems of Representation in the Teaching and Learning of Mathematics, 159–196. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Kashihara, A., Okabe, M., Hirashima, T. & Jun'ichi, T. (1996). A Self-explanation Assistance with Diagram Tailoring. In Brna, P., Paiva, A. and Self, J.A. (eds.) Proceedings of the European Conference on Artificial Intelligence in Education, 122–128. Lisbon: Edicões Colibri.

    Google Scholar 

  • Kintsch, W. (1988). The Role of Knowledge in Discourse Comprehension: A Constructionintegration Model. Psychological Review 95: 163–182.

    Google Scholar 

  • Kitajima, M. & Polson, P.G. (1996). A Comprehension-based Model of Exploration. In Human Factors in Computing Systems: CHI'96 Conference Proceedings, 324–331.

  • Kitajima, M. & Polson, P.G. (in press). A Comprehension-based Model of Exploration. Human-Computer Interaction.

  • Lowe, R.K. (1989). Search Strategies and Inference in the Exploration of Scientific Diagrams. Educational Psychology 9(1): 27–44.

    Google Scholar 

  • Lowe, R.K. (1994). Diagram Prediction and Higher Order Structures in Mental Representation. Research in Science Education 24: 208–216.

    Google Scholar 

  • Lowe, R.K. (1994). Selectivity in Diagrams; Reading Beyond the Lines. Educational Psychology 14: 467–491.

    Google Scholar 

  • Lowe, R.K. (1995). Supporting Conceptual Change in the Interpretation of Meteorological Diagrams. In Aarnoutse C. et al. (eds.) Abstracts of the 6th Conference of the European Association for Research on Learning and Instruction, 89–90, Tilburg, The Netherlands: MesoConsult.

    Google Scholar 

  • Lowe, R.K. (1996a). Background Knowledge and the Construction of a Situational Representation from a Diagram. European Journal of Psychology of Education 11(4): 377–397.

    Google Scholar 

  • Lowe, R.K. (1996b). Pictorial Information Design for Schools. Information Design Journal 8: 233–243.

    Google Scholar 

  • Mayer, R.E. & Sims, V.K. (1994). For Whom is a Picture Worth a Thousand Words? Extensions of a Dual-coding Theory of Multimedia Learning. Journal of Educational Psychology 86: 389–401.

    Google Scholar 

  • Oberlander, J., Cox, R., Monaghan, P., Stenning, K. & Tobin, R. (1996). Individual Differences in Proof Structures Following Multimodal Logic Teaching. In Proceedings of the 18th Annual Conference of the Cognitive Science Society, 201–206. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Rickel, J. & Johnson, W.L. (1997). Mixed-initiative Interaction between Pedagogical Agents and Students in Virtual Environments. In Proceedings of AAAI Spring Symposium on Computational Models for Mixed Initiative Interaction.

  • Rieman, J., Young, R.M. & Howes, A. (1996). A Dual-space Model of Iteratively Deepening Exploratory Learning. International Journal of Human-Computer Studies 44: 743–775.

    Google Scholar 

  • Rumelhart, D.E. & Norman, D.A. (1978). Accretion, Tuning and Restructuring: Three Modes of Learning. In Cotton, J.W. and Klatzky, R.L., (eds.) Semantic Factors of Cognition. New York: Lawrence Erlbaum.

    Google Scholar 

  • Salomon, G. (1994). Interaction of Media, Cognition and Learning. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Schnotz, W. & Grzondziel, H. (1996). Effects of Visualizations on the Structure and Applications of Mental Models. Paper presented at the XXVI International Conference of Psychology in Montreal, August 16th-21st, 1996.

  • Schnotz, W., Picard, E. & Hron, A. (1993). How Do Successful and Unsuccessful Learners Use Text and Graphics? Learning and Instruction 20(3): 181–199.

    Google Scholar 

  • Schwarz, B. & Dreyfus, T. (1993). Measuring Integration of Information in Multirepresentational Software. Interactive Learning Environments 3(3): 177–198.

    Google Scholar 

  • Shrager, J. (1989). Reinterpretation and the Perceptual Microstructure of Conceptual Knowledge: Cognition Considered as a Perceptual Skill. In Proceedings of the 11th Annual Conference of the Cognitive Science Society.

  • Stenning, K. & Oberlander, J. (1995). A Cognitive Theory of Graphical and Linguistic Reasoning: Logic and Implementation. Cognitive Science 19(1): 97–140.

    Google Scholar 

  • Van der Pal, J. (1996). The Balance of Situated Action and Formal Instruction for Learning Conditional Reasoning. Chicago: The University of Chicago Press.

    Google Scholar 

  • Whitelock, D., Brna, P. & Holland, S. (1996). What is the Value of Virtual Reality for Conceptual Learning? Towards a Theoretical Framework. In Brna, P., Paiva, A. and Self, J.A. (eds.) Proceedings of the European Conference on Artificial Intelligence in Education, 136–141. Lisbon: Edicões Colibri.

    Google Scholar 

  • Wilkin, B. (1994). The Self-explanation Effect with Self-generated Diagrams. Technical Report No. CSM-9, School of Education, University of California at Berkeley.

  • Zhang, J. & Norman, D.A. (1994). Representations in Distributed Cognitive Tasks. Cognitive Science 18(1): 87–122.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brna, P., Cox, R. & Good, J. Learning to Think and Communicate with Diagrams: 14 Questions to Consider. Artificial Intelligence Review 15, 115–134 (2001). https://doi.org/10.1023/A:1006584801959

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1006584801959

Navigation