In computer science classes you can observe that students are able to solve a problem – say sorting a list – but fail completely writing a programme that does the job. This barrier between intuitive solution and the finding and explication of an algorithm can be overcome, if the programmer learns to analyse her or his own intuitions connected to the task.


Programme Code Declarative Knowledge Python Script Programming Concept Intuitive Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Anderson, J.R.: Cognitive Psychology and Its Implications, 6th edn. Worth Publishers, New York (2004)Google Scholar
  2. 2.
    Beck, K.: Extreme Programming Explained. Addison Wesley, Boston, San Francisco, New York, Toronto, Montreal, London, Munich, Paris, Madrid, Capetown, Sydney, Tokyo, Singapore, Mexico City (1999)Google Scholar
  3. 3.
    Bell, T., Witten, I.H., Fellows, M.: Computer Science Unplugged. Off-line activities and games for all ages (1998),
  4. 4.
    Chiu, M.M.: Using Metaphors to understand and solve arithmetic problems: Novices and experts working with negative numbers. Mathematical Thinking and Learning 3.3, 93–124 (2001)CrossRefGoogle Scholar
  5. 5.
    di Sessa, A.A.: Changing Minds. In: Computers, Learning and Literacy, The MIT Press, Cambridge, London (2001)Google Scholar
  6. 6.
    Fischbein, E.: Intuition in Science and Mathematics. An Educational Approach. D. Reidel Publishing Company, Dordrecht, Boston, Lancaster, Tokyo (1987)Google Scholar
  7. 7.
    Jeffries, R.: What is Extreme Programming (2001),
  8. 8.
    Leiser, D.: Scattered Naive Theories: Why the human mind is isomorphic to the Internet Web. New Ideas in Psychology 19, 175–202 (2001)CrossRefGoogle Scholar
  9. 9.
    Mayer, R.E.: The Psychology of How Novices Learn Computer Programming. In [12], 129–160Google Scholar
  10. 10.
    Perkins, D.N., Hancock, C., Hobbs, R., Martin, F.: Conditions of learning in novice programmers. Educational Technology Center, Harvard University. In: [12]Google Scholar
  11. 11.
    Smith, J.P., di Sessa, A.A., Roschelle, J.: Misconceptions Reconceived: A Constructivist Analysis of Knowledge in Transition. Journal of the Learning Sciences, 3(2) (1993/94)Google Scholar
  12. 12.
    Soloway, E., Spohrer, J.C. (eds.): Studying the Novice Programmer, Hillsdale (1989)Google Scholar
  13. 13.
    Spohrer, J.C., Soloway, E., Pope, E.: A Goal/Plan Analysis of Buggy Pascal Programs. In: [12], 355–399Google Scholar
  14. 14.
    van der Veer, G.C.: Mental Models of Computer Systems: Visual Languages in the Mind. In: Tauber, M.J., Mahling, D.E., Arefi, F. (Hrsg.) (eds.) Cognitive Aspects of Visual Languages and Visual Interfaces, North-Holland, Amsterdam (1994)Google Scholar
  15. 15.
    Weigend, M.: Objektorientierte Programmierung mit Python, 2nd edn. MITP Bonn (2005)Google Scholar
  16. 16.
    Weigend, M.: Intuitive Modelle in der Informatik. In: Friedrich, S. (ed.) Unterrichtskonzepte für informatische Bildung. In: INFOS 2005 Proceeedings, Bonn, pp. 275–284 (2005)Google Scholar
  17. 17.
    Weigend, M.: The Python Visual Sandbox (2006),

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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michael Weigend
    • 1
  1. 1.Holzkamp-Gesamtschule WittenWittenGermany

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