
Contents
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Introduction Introduction
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Neurounderstanding Neurounderstanding
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The Cause of Poor Mathematics Achievement: Developmental Dyscalculia The Cause of Poor Mathematics Achievement: Developmental Dyscalculia
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Numerical Magnitude Processing Numerical Magnitude Processing
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School-Taught Mathematical Skills School-Taught Mathematical Skills
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Arithmetic Arithmetic
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Mathematical Problem-Solving Mathematical Problem-Solving
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Individual Differences in Mathematical Competencies Individual Differences in Mathematical Competencies
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Neuroprediction Neuroprediction
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Neurointervention Neurointervention
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Rationale for Behavioral Educational Interventions Rationale for Behavioral Educational Interventions
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Effects of Education On Biology Effects of Education On Biology
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Biological Interventions that Impact on Education Biological Interventions that Impact on Education
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Cautionary Notes on Applications of Neuroscience to Education Cautionary Notes on Applications of Neuroscience to Education
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Ecological Validity Ecological Validity
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We Do Need Education We Do Need Education
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Unrealistic Expectations Unrealistic Expectations
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Conclusion Conclusion
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Acknowledgments Acknowledgments
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References References
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33 Applications of Neuroscience to Mathematics Education
Get accessBert De Smedt, Parenting and Special Education Research Unit, Faculty of Psychology and Educational Sciences, University of Leuven, Belgium
Roland H. Grabner, Georg-Elias-Müller Institute of Psychology, Georg-August-Universität Göttingen, Germany
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Published:03 November 2014
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Abstract
In this chapter, we explore three types of applications of neuroscience to mathematics education: neurounderstanding, neuroprediction, and neurointervention. Neurounderstanding refers to the idea that neuroscience is generating knowledge on how people acquire mathematical skills and how this learning is reflected at the biological level. Such knowledge might yield a better understanding of the typical and atypical development of school-taught mathematical competencies. Neuroprediction deals with the potential of neuroimaging data to predict future mathematical skill acquisition and response to educational interventions. In neurointervention, we discuss how brain imaging data have been used to ground interventions targeted at mathematics learning and how education shapes the neural circuitry that underlies school-taught mathematics. We additionally elaborate on recently developed neurophysiological interventions that have been shown to affect mathematical learning. While these applications offer exciting opportunities for mathematics education, some potential caveats should be considered, which are discussed at the end of this chapter.
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