Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-24T01:31:54.082Z Has data issue: false hasContentIssue false
  • English
  • Français

Educational Sciences: A Crossroad for Dialogue among Disciplines

Published online by Cambridge University Press:  24 January 2018

Erik De Corte
Erik De Corte*
Affiliation:
Center for Instructional Psychology and Technology (CIP&T), University of Leuven, Dekenstraat 2 – 3773, B-3000 Leuven, Belgium. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This article illustrates that due to the complexity of educational practices and of the educational system, their scientific study constitutes a crossroads for dialogue and possible conflicts among a variety of disciplines. The article focuses on school education. A first illustration shows how analyzing and improving classroom practices requires collaboration with and among different sub-disciplines of psychology. In the next section the recent domain of educational neuroscience is discussed as a crossroads of educational science, psychology and neuroscience. Thereafter, it is argued that research on mathematics education calls on the contribution of many disciplines such as mathematics, pedagogy, the psychology of cognition and math-related beliefs, and anthropology. The final example focuses on educational technology that requires interaction between educational science, psychology, computer science, economics, etc.

Type
Conflicts and Dialogues between Science and Humanities
Information
European Review , Volume 26 , Issue 2 , May 2018 , pp. 262 - 271
Copyright
© Academia Europaea 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. The Royal Society (2011) Brain Waves Module 2: Neuroscience. Implications for Education and Lifelong Learning (London: The Royal Society)Google Scholar
2. Mareschal, D., Butterworth, B. and Tolmie, A. (2014) Educational Neuroscience (Oxford, UK: Wiley Blackwell)Google Scholar
3. Dehaene, S. and Cohen, L. (1995) Towards an anatomical and functional model of number processing. Mathematical Cognition, 1, pp. 83–120.Google Scholar
4. De Smedt, B. and Grabner, R. (2015) Applications of neuroscience to mathematics education. In A. Dowker and R. Cohen-Kadosh, (Eds) Oxford Handbook of Mathematical Cognition (Oxford, UK: Oxford University Press), pp. 613–636.Google Scholar
5. Butterworth, B. and Varma, S. (2014) Mathematical development. In D. Mareschal, B. Butterworth and A. Tolmie, (Eds) Educational Neuroscience (Oxford, UK: Wiley Blackwell), pp. 201–236.Google Scholar
6. Butterworth, B., Varma, S. and Laurillard, D. (2011) Dyscalculia: From brain to education. Science, 332, pp. 1049–1053.Google Scholar
7. OECD (2010) The High Cost of Low Educational Performance: The Long-run Economic Impact of Improving Educational Outcomes (Paris: OECD), p. 17.Google Scholar
8. Ansari, D. (2008) Effects of development and enculturation on number representation in the brain. Nature Reviews Neuroscience, 9, pp. 278–291.Google Scholar
9. Bruer, J.T. (1997) Education and the brain: A bridge too far. Educational Researcher, 26(8), pp. 4–16.Google Scholar
10. Thorndike, E.L. (1922) The Psychology of Arithmetic (New York: Macmillan)CrossRefGoogle Scholar
11. Freudenthal, H. (1991) Revisiting Mathematics Education (Dordrecht, The Netherlands: Kluwer)Google Scholar
12. Schoenfeld, A.H. (1985) Mathematical Problem Solving (Orlando, FL: Academic Press), p. 45.Google Scholar
13. Picker, S.H. and Berry, J.S. (2000) Investigating pupils’ images of mathematicians. Educational Studies in Mathematics, 43, pp. 65–94.CrossRefGoogle Scholar
14. Lampert, M. (1990) When the problem is not the question and the solution is not the answer. American Educational Research Journal, 27, pp. 29–63.Google Scholar
15. d’Ambrosio, U. (1985) Ethnomathematics and its place in the history and pedagogy of mathematics. For the Learning of Mathematics, 5, pp. 44–48.Google Scholar
16. Nunes, T.N., Schliemann, A.D. and Carraher, D.W. (1993) Street Mathematics and School Mathematics (Cambridge, UK: Cambridge University Press)Google Scholar
17. Cuban, L. (1986) Teachers and Machines: The Classroom Use of Technology since 1920 (New York: Columbia University Teachers College Press)Google Scholar
18. Darrow, B. (1932) Radio: The Assistant Teacher (Columbus, Ohio: R.G. Adams & Company), p. 79.Google Scholar
19. Mayer, R.E. (2010) Learning with technology. In H. Dumont, D. Istance and F. Benavides, (Eds) The Nature of Learning. Using Research to Inspire Practice (Paris: OECD Publishing), pp. 179–198.Google Scholar
20. Jansen, D. and Schuwer, R. (2015) Institutional MOOC Strategies in Europe. Status Report Based on a Mapping Survey Conducted in October – December 2015 (Heerlen, The Netherlands: EADTU), p. 13.Google Scholar
21. Laurillard, D. (2016) How should professors adapt to the changing digital education environment. In E. De Corte, L. Engwall and U. Teichler, (Eds) From Books to MOOCs? Emerging Models of Learning and Teaching in Higher Education, Wenner-Gren International Series, Volume 88 (London: Portland Press Ltd), pp. 3–15.Google Scholar
22. Fleming, B. (2013) Learning from MOOCs: Unmasking the Weaknesses of Online Education. http://www.eduventures.com/2013/04/learning-from-moocs-unmasking-the-weaknesses-of-online-education.Google Scholar
23. Corte, E. De, Engwall, I. and Teichler, U. (Eds) (2016) From Books to MOOCs? Emerging Models of Learning and Teaching in Higher Education, Wenner-Gren International Series, Volume 88 (London: Portland Press Ltd)Google Scholar
24. Berliner, D.C. (2002) Educational research: The hardest science of all. Educational Researcher, 31(8), pp. 18–20, p. 19.CrossRefGoogle Scholar