Potential applications of cognitive neuroscience to mathematics education
- 1.2k Downloads
In this editorial, we revisit the ZDM special issue on Cognitive Neuroscience and Mathematics Learning that was published about 5 years ago by providing a snapshot of how research at the intersection of cognitive neuroscience and mathematics education is flourishing at this point in time. This is illustrated by nine empirical papers and two commentaries, the authors of which also commented on the special issue 5 years ago. In this editorial, we briefly discuss applications from neuroscience to education (in the field of mathematics learning) from a methodological and more theoretical point of view and we provide a very brief overview of the contributions in this special issue by linking them to such applications from neuroscience to education.
- Ansari, D., & Lyons, I. (2016). Cognitive neuroscience and mathematics learning: How far have we come? Where do we need to go? ZDM Mathematics Learning. doi: 10.1007/s11858-016-0782-z.
- Babai, R., Nattiv, L., & Stavy, R. (2016). Comparison of perimeters: improving students’ performance by increasing the salience of the relevant variable. ZDM Mathematics Education. doi: 10.1007/s11858-016-0766-z.
- Bowers, J. S. (2016). The practical and principled problems with educational neuroscience. Psychological Review. doi: 10.1037/rev0000025.
- De Smedt, B., & Grabner, R. (2015). Applications of neuroscience to mathematics education. In A. Dowker & R. Cohen-Kadosh (Eds.), Oxford handbook of mathematical cognition (pp. 613–636). Oxford, United Kingdom: Oxford University Press.Google Scholar
- De Smedt, B., Noel, M. P., Gilmore, C., & Ansari, D. (2013). The relationship between symbolic and non-symbolic numerical magnitude processing and the typical and atypical development of mathematics: a review of evidence from brain and behavior. Trends in Neuroscience and Education, 2, 48–55. doi: 10.1016/j.tine.2013.06.001.CrossRefGoogle Scholar
- 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.Google Scholar
- Leikin, R., Waisman, I., & Leikin, M. (2016). Does solving insight-based problems differ from solving learning-based problems? Some evidence from an ERP study. ZDM Mathematics Education. doi: 10.1007/s11858-016-0767-y.
- Merkley, R., Shimi, A., & Scerif, G. (2016). Electrophysiological markers of newly acquired symbolic numerical representations: the role of magnitude and ordinal information. ZDM Mathematics Education. doi: 10.1007/s11858-015-0751-y.
- Obersteiner, A., & Tumpek, C. (2016). Measuring fraction comparison strategies with eye-tracking. ZDM Mathematics Education. doi: 10.1007/s11858-015-0742-z.
- Pollack, C., Leon, S. L., & Star, J. R. (2016). Exploring mental representations for literal symbols using priming and comparison distance effects. ZDM Mathematics Education. doi: 10.1007/s11858-015-0745-9.
- Schillinger, F., De Smedt, B., & Grabner, R. H. (2016). When errors count: an EEG study on numerical error monitoring under performance pressure. ZDM Mathematics Education. doi: 10.1007/s11858-015-0746-8.
- Schneider, M., Beeres, K., Coban, L., Merz, S., Schmidt, S., Stricker, J., & De Smedt, B. (2016). Associations of non-symbolic and symbolic numerical magnitude processing with mathematical competence: a meta-analysis. Developmental Science. doi: 10.1111/desc.12372.
- Spüler, M., Walter, C., Rosentiel, W., Moeller, K., & Klein, E. (2016). EEG-based prediction of cognitive workload induced by arithmetic: a step towards online adaptation in numerical learning. ZDM Mathematics Education. doi: 10.1007/s11858-015-0754-8.
- Supekar, K., Swigart, A. G., 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 of the United States of America, 110(20), 8230–8235. doi: 10.1073/pnas.1222154110.CrossRefGoogle Scholar
- Verschaffel, L., Lehtinen, E., & Van Dooren, W. (2016). Neuroscientific studies of mathematical thinking and learning: A critical look from a mathematics education viewpoint. ZDM Mathematics Education. doi: 10.1007/s11858-016-0781-0.
- Vogel, S., Keller, C., Koschutnig, G., Ebner, F., Dohle, S., Siegrist, M., & Grabner, R. H. (2016). The neural correlates of health risk perception in individuals with low and high numeracy. ZDM Mathematics Education. doi: 10.1007/s11858-016-0761-4.
- Waisman, I., Leikin, M., & Leikin, R. (2016). Brain activity associated with logical inferences in geometry: focusing on students with different levels of ability. ZDM Mathematics Education. doi: 10.1007/s11858-016-0760-5.