Advances in the Use of Neuroscience Methods in Research on Learning and Instruction

Main Article Content

Bert De Smedt

Abstract

Cognitive neuroscience offers a series of tools and methodologies that allow researchers in the field of learning and instruction to complement and extend the knowledge they have accumulated through decades of behavioral research. The appropriateness of these methods depends on the research question at hand. Cognitive neuroscience methods allow researchers to investigate specific cognitive processes in a very detailed way, a goal in some but not all fields of the learning sciences. This value added will be illustrated in three ways, with examples in field of mathematics learning. Firstly, cognitive neuroscience methods allow one to understand learning at the biological level. Secondly, these methods can help to measure processes that are difficult to access by means of behavioral techniques. Finally, and more indirectly, neuroimaging data can be used as an input for research on learning and instruction. I will end this contribution by highlighting the challenges of applying neuroscience methods to research on learning and instruction.

Article Details

How to Cite
De Smedt, B. (2014). Advances in the Use of Neuroscience Methods in Research on Learning and Instruction. Frontline Learning Research, 2(4), 7-14. https://doi.org/10.14786/flr.v2i4.115
Section
Articles
Author Biography

Bert De Smedt, Faculty of Psychology and Educational Sciences University of Leuven

Faculty, Faculty of Psychology and Educational Sciences

 

References

Anderson, J. R., Bothell, D., Fincham, J. M., Anderson, A. R., Poole, B., & Qin, Y. L. (2011). Brain regions engaged by part- and whole-task performance in a video game: A model-based test of the decomposition hypothesis. Journal of Cognitive Neuroscience, 23, 3983-3997.
Ansari, D. (2008). Effects of development and enculturation on number representation in the brain. Nature Reviews Neuroscience, 9, 278-291.
Ansari, D. (2010).Neurocognitive approaches to developmental disorders of numerical and mathematical cognition: The perils of neglecting the role of development. Learning and Individual Differences, 20, 123-129.
Ansari, D., De Smedt, B., & Grabner, R. (2012). Neuroeducation - a critical overview of an emerging field. Neuroethics, 5, 105-117.
Arsalidou, M., & Taylor, M. J. (2011). Is 2 + 2 = 4? Meta-analyses of brain areas needed for numbers and calculations. Neuroimage, 54, 2382-2393.
Aue, T., Lavelle, L. A., & Cacioppo, J. T. (2009).Great expectations: What can fMRI research tell us about psychological phenomena? International Journal of Psychophysiology, 73, 10-16.
Beck, D. M. (2010).The appeal of the brain in the popular press. Perspectives on Psychological Science, 5, 762-766.
Butterworth, B., Varma, S., & Laurillard, D. (2011). Dyscalculia: From brain to education. Science, 332, 1049-1053.
Cacioppo, J. T., Berntson, G. G., & Nusbaum, H. C. (2008).Neuroimaging as a new tool in the toolbox of psychological science. Current Directions in Psychological Science,17, 62-67.
De Bie, H. M. A., Boersma, M., Wattjes, M. P., Adriaanse, S., Vermeulen, R. J., Oostrom, K. J., Huisman, J., Veltman, D. J., & Delemarre-Van De Waal, H. A. (2010).Preparing children with a mock scanner training protocol results in high quality structural and functional MRI scans. European Journal of Pediatrics,169, 1079-1085.
De Smedt, B., Ansari, D., Grabner, R. H., Hannula, M. M., Schneider, M., & Verschaffel, L. (2010).Cognitive neuroscience meets mathematics education. Educational Research Review, 5, 97-105.
De Smedt, B., Ansari, D., Grabner, R.H., Hannula-Sormunen, M., Schneider, M., & Verschaffel, L. (2011).Cognitive neuroscience meets mathematics education: it takes two to tango. Educational Research Review, 6, 232-237.
De Smedt, B., & Grabner, R. H. (in press). Applications of neuroscience to mathematics education. In A. Dowker & R. Cohen-Kadosh (Eds.) The Oxford handbook of mathematical cognition. Oxford: Oxford University Press.
De Smedt, B., Grabner, R. H., & Studer, B. (2009).Oscillatory EEG correlates of arithmetic strategy use in addition and subtraction. Experimental Brain Research, 195, 635-642.
De Smedt, B., Holloway, I. D., & Ansari, D. (2011). Effects of problem size and arithmetic operation on brain activation during calculation in children with varying levels of arithmetical fluency. Neuroimage ,57, 771-781.
De Smedt, B., Noėl, M. P., Gilmore, C., & Ansari, D. (2013).The relationship between symbolic and non-symbolic numerical magnitude processing skills and the typical and atypical development of mathematics: a review of evidence from brain and behavior. Trends in Neuroscience and Education, 2, 48-55.
Dehaene, S., Piazza, M., Pinel, P., & Cohen, L. (2003).Three parietal circuits for number processing. Cognitive Neuropsychology,20, 487-506.
Dick, F., Lloyd-Fox, S., Blasi, A., Elwell, C., & Mills, D. (2014). Neuroimaging methods. In D. Mareschal, B. Butterworth, & A. Tolmie (Eds.) Educational neuroscience. (pp. 13-45). Malden, MA: Wiley-Blackwell.
Grabner, R. & De Smedt, B. (2011). Neurophysiological evidence for the validity of verbal strategy reports in mental arithmetic. Biological Psychology, 87, 128-136.
Grabner, R. H., & De Smedt, B. (2012).Oscillatory EEG correlates of arithmetic strategies: a training study. Frontiers in Psychology, 3(428), 1-11.
Keller, T. A., & Just, M. A. (2009).Altering cortical connectivity: remediation-induced changes in the white matter of poor readers. Neuron, 64, 624-631.
Kirk, E. P., & Ashcraft, M. H. (2001). Telling stories: The perils and promise of using verbal reports to study math strategies. Journal of Experimental Psychology-Learning Memory and Cognition,27, 157-175.
Lieberman, M. D., Schreiber, D., & Ochsner, K. N. (2003).Is political cognition like riding a bicycle? How cognitive neuroscience can inform research on political thinking. Political Psychology,24, 681-704.
Matjeko, A. A., Price, G. R., Mazzocco, M. M. M., & Ansari, D. (2013).Individual differences in left parietal white matter predict scores on the Preliminary Scholastic Aptitude Test. Neuroimage, 66, 604-610.
Nosworthy, N., Bugden, S., Archibald, L., Evans, B., & Ansari, A. (2013).A two-minute paper-and-pencil test of symbolic and nonsymbolic numerical magnitude processing explains variability in primary school children's arithmetic competence. Plos ONE, 8, e67918
Price, G. R., & Ansari, D. (2013). Dyscalculia. In O. Dulac & M. Lassonde (Eds.) Handbook of Clinical Neurology (pp. 241-244). London: Elsevier.
Price, G. R., Mazzocco, M. M. M., & Ansari, D. (2013).Why mental arithmetic counts: Brain activation during single digit arithmetic predicts high school math scores. Journal of Neuroscience,33, 156-163.
Redcay, E., Dodell-Feder, D., Pearrow, M. J., Mavros, P. L., Kleiner, M., Gabrieli, J. D. E., & Saxe, R. (2010).Live face-to-face interaction during fMRI: A new tool for social cognitive neuroscience. Neuroimage, 50, 1639-1647.
Siegler, R. S. (1996). Emerging minds: The process of change in children's thinking. New York, NY: Oxford University Press.
Siegler, R. S. (2009). Improving the numerical understanding of children from low-income families. Child Development Perspectives, 3, 118-124.
Squire, L. R., Berg, D., Bloom, F. E., Du Lac, S., Ghosh, A., & Spitzer, N. C. (2013) Fundamental neuroscience (4th ed.). Oxford, UK: Academic Press.
Stern, E., & Schneider, M. (2010).A digital road map analogy of the relationship between neuroscience and educational research. ZDM - The International Journal on Mathematics Education, 42, 511-514.
Supekar, K., Swigart, A. J., Tenison, C., Jolles, D. D., Rosenberg-Lee, M., Fuchs, L., & Menon, V.(2013). Neural predictors of individual differences in response to math tutoring in primary-grade school children. Proceedings of the National Academy of Sciences, 110, 8230-8235.
Ward, J. (2006). The student's guide to cognitive neuroscience. New York: Psychology Press.
Zamarian, L., Ischebeck, A., & Delazer, M. (2009).Neuroscience of learning arithmetic-Evidence from brain imaging studies. Neuroscience and Biobehavioral Reviews, 33, 909-925.