Skip to main content

Rules from Cognition for Conceptual Modelling

  • Conference paper
Conceptual Modeling (ER 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7532))

Included in the following conference series:

Abstract

Conceptual Modelling is a cognitive intensive process. Prior research has acknowledged the importance of cognitive theories and their implications for Conceptual Modelling. Several authors have developed hypotheses to give modellers a hint how to improve their models. Although much effort has been made, researchers and practitioners cannot easily apply or broaden these hypotheses. Yet, they are forced to spend a lot of review work, as a comprehensive overview about past research is missing. With this paper we give a review of hypotheses developed from Cognition for Conceptual Modelling.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Larkin, J., Simon, H.: Why a diagram is (sometimes) worth ten thousand words. Cognitive Science 11(1), 65–100 (1987)

    Article  Google Scholar 

  2. Moody, D.: What makes a good diagram? improving the cognitive effectiveness of diagrams in is development. ADBIS 492, 481–492 (2007)

    Google Scholar 

  3. Rockwell, S., Bajaj, A.: Cogeval: Applying cognitive theories to evaluate conceptual models. Advanced Topics in Database Research 4, 255–282 (2004)

    Google Scholar 

  4. Gemino, A., Wand, Y.: Complexity and clarity in conceptual modeling: Comparison of mandatory and optional properties. Data & Knowledge Engineering 55(3), 301–326 (2005)

    Article  Google Scholar 

  5. Khatri, V., Vessey, I., Ramesh, V., Clay, P., Park, S.: Understanding conceptual schemas: Exploring the role of application and IS domain knowledge. ISR 17(1), 81–99 (2006)

    Article  Google Scholar 

  6. Recker, J., Dreiling, A.: Does it matter which process modelling language we teach or use? an experimental study on understanding process modelling languages without formal education. In: ACIS 2007, pp. 356–366 (2007)

    Google Scholar 

  7. Gemino, A., Wand, Y.: A framework for empirical evaluation of conceptual modeling techniques. Requirements Engineering 9(4), 248–260 (2004)

    Article  Google Scholar 

  8. Parsons, J.: Effects of local versus global schema diagrams on verification and communication in conceptual data modeling. JMIS 19(3), 155–183 (2002)

    Google Scholar 

  9. Moody, D.L.: Cognitive Load Effects on End User Understanding of Conceptual Models: An Experimental Analysis. In: Benczúr, A.A., Demetrovics, J., Gottlob, G. (eds.) ADBIS 2004. LNCS, vol. 3255, pp. 129–143. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  10. Vessey, I., Galletta, D.: Cognitive fit: An empirical study of information acquisition. ISR 2(1), 63–84 (1991)

    Article  Google Scholar 

  11. Agarwal, R., Sinha, A., Tanniru, M.: Cognitive fit in requirements modeling: A study of object and process methodologies. JMIS 13(2), 137–162 (1996)

    Google Scholar 

  12. Gemino, A.: Empirical comparisons of animation and narration in requirements validation. Requirements Engineering 9(3), 153–168 (2004)

    Article  Google Scholar 

  13. Kim, J., Hahn, J., Hahn, H.: How do we understand a system with (so) many diagrams? cognitive integration processes in diagrammatic reasoning. ISR 11(3), 284–303 (2000)

    Article  Google Scholar 

  14. Bodart, F., Patel, A., Sim, M., Weber, R.: Should the optional property construct be used in conceptual modeling? ISR 12(4), 384–405 (2001)

    Article  Google Scholar 

  15. Weber, R.: Are attributes entities? a study of database designers’ memory structures. ISR 7(2), 137–162 (1996)

    Article  Google Scholar 

  16. Bajaj, A.: The effect of the number of concepts on the readability of schemas: an empirical study with data models. Requirements Engineering 9(4), 261–270 (2004)

    Article  Google Scholar 

  17. Moody, D.: Comparative Evaluation of Large Data Model Representation Methods: The Analyst’s Perspective. In: Spaccapietra, S., March, S.T., Kambayashi, Y. (eds.) ER 2002. LNCS, vol. 2503, p. 214. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  18. Kahneman, D.: Attention and effort. Prentice-Hall, Englewood Cliffs, NJ (1973)

    Google Scholar 

  19. Miller, G.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63(2), 81 (1956)

    Article  Google Scholar 

  20. Craik, F., Lockhart, R.: Levels of processing: A framework for memory research. J. of Verbal Learning and Verbal Behavior 11(6), 671–684 (1972)

    Article  Google Scholar 

  21. Kintsch, W., Vipond, D.: Reading comprehension and readability in educational practice and psychological theory, Erlbaum, Hillsdale, NJ (1979)

    Google Scholar 

  22. Moody, D.: The physics of notations: Toward a scientific basis for constructing visual notations in software engineering. IEEE Transactions on Software Engineering 35(6), 756–779 (2009)

    Article  Google Scholar 

  23. Shannon, C., Weaver, W.: The mathematical theory of communication, vol. 19. University of Illinois Press Urban (1962)

    Google Scholar 

  24. Bertin, J.: Semiology of graphics: Diagrams, networks, maps. Wisconsin Press (1983)

    Google Scholar 

  25. Newell, A., Simon, H.: Human problem solving. Prentice-Hall (1972)

    Google Scholar 

  26. Goodman, N.: Languages of Art: An Approach to a Theory of Symbols. Bobbs-Merrill Co. (1968)

    Google Scholar 

  27. Parsons, J., Wand, Y.: Using cognitive principles to guide classification in information systems modeling. MISQ 32(4), 839–868 (2008)

    Google Scholar 

  28. Hegarty, M., Just, M.: Constructing mental models of machines from text and diagrams. J. of Memory and Language 32, 717–742 (1993)

    Article  Google Scholar 

  29. Mayer, R.: Models for understanding. Rev. of Educat. Research 59(1), 43–64 (1989)

    Google Scholar 

  30. Lohse, G.: A cognitive model for understanding graphical perception. Human-Computer Interaction 8(4), 353–388 (1993)

    Article  Google Scholar 

  31. Palmer, S., Rock, I.: Rethinking perceptual organization: The role of uniform connectness. Psychonomic Bulletin & Review 1(1), 29–55 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Stark, J., Esswein, W. (2012). Rules from Cognition for Conceptual Modelling. In: Atzeni, P., Cheung, D., Ram, S. (eds) Conceptual Modeling. ER 2012. Lecture Notes in Computer Science, vol 7532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34002-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34002-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34001-7

  • Online ISBN: 978-3-642-34002-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics