Teaching for Long-Term Memory

  • Elena NechitaEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 486)


A major goal of education is to help students store information in long-term memory and use that information on later occasions, in the most efficient manner. This chapter investigates the use of analogy as a strategy for encoding information in long-term memory. The results of a study concerning the ability of students to use analogy when learning computer science are presented.


Multiagent System Semantic Network Retrieval Practice Target Problem Data Flow Graph 
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.



This research was supported by the project entitled Hybrid Medical Complex SystemsComplexMediSys (2011–2012), a bilateral research project between Romania and Slovakia.


  1. 1.
    Ashworth, E.J.: Signification and modes of signifying in thirteenth-century logic: a preface to aquinas on analogy. Medieval Philosophy Theology 1, 39–67 (1991)Google Scholar
  2. 2.
    Anderson, J.M.: Structural analogy and universal grammar. Lingua 116(5), 601–633 (2006)Google Scholar
  3. 3.
    Atmar, W.: Notes on the simulation of evolution. IEEE Trans. Neural Networks 5(1), 130–147 (1994)CrossRefGoogle Scholar
  4. 4.
    Aubusson, P.J., Harrison, A.G., Ritchie, S.M. (eds.): Metaphor and analogy in science education. Springer (2006)Google Scholar
  5. 5.
    Bear, M.F., Connors, B.W., Paradiso, M.: Neuroscience: exploring the brain, 3rd ed. Baltimore, MD: Lippincott, Williams and Wilkins (2006)Google Scholar
  6. 6.
    Boden, M.: The creative mind: myths and mechanisms, 2nd edn. Routledge, London (2004)Google Scholar
  7. 7.
    Boudewijns, Z.S., Kleele, T., Mansvelder, H.D., Sakmann, B., de Kock, C.P., Oberlaender, M.: Semi-automated three-dimensional reconstructions of individual neurons reveal cell type-specific circuits in cortex. Commun Integrative Biol 4(4), 486–488 (2011) doi: 10.4161/cib.4.4.15670 Google Scholar
  8. 8.
    Davies, J., Nersessian, N.J., Ashok, K.G.: Visual models in analogical problem solving. Found. Sci. 10, 133–152 (2005)CrossRefGoogle Scholar
  9. 9.
    Farrell, S., Lewandowsky, S.: Computational modeling in cognition: principles and practice. SAGE Publications (2011)Google Scholar
  10. 10.
    AnA, Fingelkurts, AlA, Fingelkurts: Persistent operational synchrony within brain default-mode network and self-processing operations in healthy subjects. Brain Cogn. 75(2), 79–90 (2011)CrossRefGoogle Scholar
  11. 11.
    Fusi, S.: Long term memory: encoding and storing strategies of the brain. Neurocomputing 38–40, 1223–1228 (2001)CrossRefGoogle Scholar
  12. 12.
    Gentner, D., Holyoak, K.J., Kokinov, B. (eds.): The analogical mind: perspectives from cognitive science. MIT Press, Cambridge (2001)Google Scholar
  13. 13.
    Gick, M.L., Holyoak, K.J.: Analogical problem solving. Cogn. Psychol. 12, 306–355 (1980)CrossRefGoogle Scholar
  14. 14.
    Heuer, R.J.: Psychology of intelligence analysis. Center for the Study of Intelligence, USA (1999)Google Scholar
  15. 15.
    Iantovics, B.: Agent-based medical diagnosis systems. Comput. Inf. 27(4), 593–625 (2008)Google Scholar
  16. 16.
    Johansson, C.: An attractor memory model of neocortex, Ph. D. Thesis, (2006) ISBN 91-7178-461-6, ISSN-1653-5723, ISRN-KTH/CSC/A–06/14–SE, School of Computer Science and Communication, Royal Institute of Technology, SwedenGoogle Scholar
  17. 17.
    Johnson-Laird, P.N.: The computer and the mind. Harvard University Press, Cambridge Mass (1988)Google Scholar
  18. 18.
    Kanerva, P.: Dual role of analogy in the design of a cognitive computer. In: Holyoak, K.J., Gentner, D., Kokinov, B.N. (eds.) Advances in analogy research: integration of theory and data from the cognitive, computational, and neural sciences, pp. 164–170. New Bulgarian University Press, Sofia (1998)Google Scholar
  19. 19.
    Kompus, K.: How the past becomes present: neural mechanisms governing retrieval from episodic memory. University dissertation from Umea (2010)
  20. 20.
    Little, J.: Analogy in science: where do we go from here? Rhetoric Soc. Q. 30(1), 69–92 (2000)CrossRefGoogle Scholar
  21. 21.
    Northcutt, R.G.: Understanding vertebrate brain evolution. Integr. Comp. Biol. 42(4), 743–756 (2002)CrossRefGoogle Scholar
  22. 22.
    Polya, G.: How to solve it. A new aspect of mathematical method. Princeton University Press, New Jersey (1957)Google Scholar
  23. 23.
    Salvucci, D.D., Anderson, J.R.: Integrating analogical mapping and general problem solving: the path-mapping theory. Cognitive Sci. 25, 67–110 (2001)CrossRefGoogle Scholar
  24. 24.
    Solomon, I.: Analogical transfer and functional fixedness in the science classroom. J. Educ. Res. 87, 371–377 (1994)CrossRefGoogle Scholar
  25. 25.
    Sowa, J.F. (ed.): Principles of semantic networks: explorations in the representation of knowledge. Morgan Kaufmann Publishers, San Mateo (1991)zbMATHGoogle Scholar
  26. 26.
    Stephan, K.E., Riera, J.J., Deco, G., Horwitz, B.: The brain connectivity workshops: moving the frontiers of computational systems neuroscience. NeuroImage 42, 1–9 (2008)CrossRefGoogle Scholar
  27. 27.
    Sweller, J.: Cognitive load during problem-solving: effects on learning. Cognitive Sci. 12(2), 257–285 (1988)CrossRefGoogle Scholar
  28. 28.
    Turner, M.: The literary mind. Oxford University Press, New York (1996)Google Scholar
  29. 29.
    Vosniadou, S., Ortony, A. (eds.): Similarity and analogical reasoning. Cambridge University Press (1989)Google Scholar
  30. 30.
    Wolfe, P.: Brain matters: translating research into classroom practice, 2nd edn. Association for Supervision and Curriculum Development, Alexandria (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University “Vasile Alecsandri” of BacăuBacăuRomania

Personalised recommendations