Advertisement

Technology, Knowledge and Learning

, Volume 20, Issue 3, pp 339–355 | Cite as

Cortical Activations During a Computer-Based Fraction Learning Game: Preliminary Results from a Pilot Study

  • Joseph M. Baker
  • Taylor Martin
  • Ani Aghababyan
  • Armen Armaghanyan
  • Ronald Gillam
Original research
First Online:

Abstract

Advances in educational neuroscience have made it possible for researchers to conduct studies that observe concurrent behavioral (i.e., task performance) and neural (i.e., brain activation) responses to naturalistic educational activities. Such studies are important because they help educators, clinicians, and researchers to better understand the etiology of both typical and atypical math processing. Because of its ease of use and robust tolerance of movement, functional near-infrared spectroscopy (fNIRS) provides a brain-imaging platform that is optimally suited for such studies. To that end, the focus of the current research is to use fNIRS to help better understand the neural signatures associated with real-world math learning activities. For example, the computer game “Refraction” was designed as a fun and engaging method to improve fraction knowledge in children. Data collected in previous studies have identified significant correlations between Refraction play and improvements in fraction knowledge. Here we provide the results of a pilot study that describes participants’ cortical activations in response to Refraction play. As hypothesized, Refraction play resulted in increases in parietal cortical activations at levels above those measured during spatial-specific activities. Moreover, our results were similar to another fNIRS study by Dresler et al. (J Neural Transm 116(12): 1689–1700, 2009), where children read Arabic numeral addition equations compared to written equations. Our results provide a valuable proof-of-concept for the use of Refraction within a large-scale fNIRS-based longitudinal study of fraction learning.

Keywords

Neuroscience Neuroimaging NIRS Education Mathematics Educational neuroscience 
This is a preview of subscription content, log in to check access.

References

  1. Ansari, D., Garcia, N., Lucas, E., Hamon, K., & Dhital, B. (2005). Neural correlates of symbolic number processing in children and adults. NeuroReport, 16, 1769–1773.CrossRefGoogle Scholar
  2. Arsalidou, M., & Taylor, M. J. (2011). Is 2 + 2 = 4? Meta-analysis of brain areas needed for numbers and calculation. NeuroImage, 54, 2382–2393.CrossRefGoogle Scholar
  3. Baker, S., Petrick-Smith, C., Martin, T., Aghababyan, A., Popovic, Z., Andersen, E., et al. (2013). Learning fractions through splitting in an online game. Paper presented at the American Educational Research Association Annual Conference, San Francisco, CA.Google Scholar
  4. Bodner, M., & Shaw, G. L. (2004). Symmetry math video game used to train profound spatial-temporal reasoning abilities equivalent to dynamical knot theory. In R. T. Sharp & P. Winternitz (Eds.), Symmetry in physics: In memory of Robert T. Sharp (Vol. 34, pp. 189–201). Providence, RI: American Mathematical Society.Google Scholar
  5. Butterworth, B., & Kovas, Y. (2013). Understanding neurocognitive development can improve education for all. Science, 340(6130), 300–305.CrossRefGoogle Scholar
  6. Cantlon, J. F., & Li, R. (2013). Neural activity during natural viewing of Sesame Street statistically predicts tests scores in early childhood. PLoS Biology, 11(1), e1001462.CrossRefGoogle Scholar
  7. Center for Game Science (2013). Refraction [Softward}. Available from http://centerforgamescience.org/.
  8. Cui, X., Bryant, D. M., & Reiss, A. L. (2012). NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation. NeuroImage, 59, 2430–2437.CrossRefGoogle Scholar
  9. Dehaene, S. (1997). The number sense: How the mind creates mathematics. New York, NY: Oxford University Press.Google Scholar
  10. Dresler, T., Obersteiner, A., Schecklmann, M., Vogel, A. C. M., Ehlis, A. C., Richter, M. M., et al. (2009). Arithmetic tasks in different formats and their influence on behavior and brain oxygenation as assessed with near-infrared spectroscopy (NIRS): A study involving primary and secondary school children. Jouranl of Neural Transmittion, 116(12), 1689–1700.CrossRefGoogle Scholar
  11. Emerson, R. W., & Cantlon, J. F. (2014). Continuity and change in children’s longitudinal neural responses to numbers. Developmental Science, 18(2), 1–13.Google Scholar
  12. Harvey, B. M., Klein, B. P., Petridou, N., & Dumoulin, S. O. (2013). Topographic representation of numerosity in the human parietal cortex. Science, 341, 1123–1126.CrossRefGoogle Scholar
  13. Hu, W., Bodner, M., Jones, E. G., Peterson, M. R., & Shaw, G. L. (2004). Data mining of mathematical reasoning data relevant to large innate spatial-temporal reasoning abilities in children: Implications for data driven education. In Society for neuroscience Abst 34th annual meeting.Google Scholar
  14. Jordan, K. E., & Baker, J. M. (2011). Multisensory information boosts numerical matching abilities in young children. Developmental Science, 14(2), 205–213.Google Scholar
  15. Kawashima, R., Taira, M., Okita, K., Inoue, K., Tajima, N., Yoshida, H., et al. (2004). A functional MRI study of simple arithmetic: A comparison between children and adults. Cognitive Brain Research, 18(3), 227–233.CrossRefGoogle Scholar
  16. Kesler, S. R., Sheau, K., Koovakkattu, D., & Reiss, A. L. (2011). Changes in frontal-parietal activation and math skills performance following adaptive number sense training: Preliminary results from a pilot study. Neuropsychological Rehabilitation, 21(4), 433–454.CrossRefGoogle Scholar
  17. Kucian, K., von Aster, M., Loenneker, T., & Martin, E. (2008). Development of neural networks for exact and approximate calculation: A fMRI study. Developmental Neuropsychology, 33(4), 447–473.CrossRefGoogle Scholar
  18. Martin, T., Petrick-Smith, C., Forsgren, N.F., Aghababyan, A., Janisiewicz, P., & Baker, S. (2014). Learning fractions by Splitting: Using learning analytics to illustrate the development of mathematical understanding. (Mansucript submitted for publication).Google Scholar
  19. Martin, T., Smith, C. P., Andersen, E., Liu, Y. E., & Popovic, Z. (2012). Refraction time: Making split decisions in an online fraction game. In American Educational Research Association Annual Meeting, AERA.Google Scholar
  20. Menon, V., Rivera, S. M., White, C. D., Glover, G. H., & Reiss, A. L. (2000). Dissociating prefrontal and parietal cortex activation during arithmetic processing. NeuroImage, 12(4), 357–365.CrossRefGoogle Scholar
  21. Moyer-Packenham, P., Baker, J. M., Westenskow, A., Anderson, K., Shumway, J. F., & Jordan, K. E. (2014). Predictors of achievement when virtual manipulatives are used for mathematics instruction. REDIMAT- Journal of Research in Mathematics Education, 3(2), 121–150.Google Scholar
  22. Moyer-Packenham, P., Baker, J. M., Westenskow, A., Rodzon, K., Anderson, K., Shumway, J., & Jordan, K. (2013). A study comparing virtual manipulations with other instructional treatments in third- and fourth-grade classrooms. Journal of Education, 193(2), 25–39.Google Scholar
  23. Okamoto, M., Dan, H., Sakamoto, K., Takeo, K., Shimizu, K., Kohno, S., et al. (2004). Three-dimensional probabilistic anatomical crani-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping. NeuroImage, 21, 99–111.CrossRefGoogle Scholar
  24. Plichta, M. M., Heinzel, S., Ehlis, A. C., Pauli, P., & Fallgatter, A. J. (2007). Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: A parametric validation study. Neuroimage, 35, 625–634.CrossRefGoogle Scholar
  25. Plichta, M. M., Herrmann, M. J., Baehne, C. G., Ehlis, A.-C., Richter, M. M., Pauli, P., & Fallgatter A. J. (2006). Event-related functional near-infrared spectroscopy (fNIRS): Are the measurements reliable? NeuroImage, 31, 116–124.Google Scholar
  26. Rivera, S. M., Reiss, A. L., Eckert, M. A., & Menon, V. (2005). Developmental changes in mental arithmetic: Evidence for increased functional specialization in the left inferior parietal cortex. Cerebral Cortex, 15(11), 1779–1790.CrossRefGoogle Scholar
  27. Rosenberg-Lee, M., Barth, M., & Menon, V. (2011). What difference does a year of schooling make? Maturation of brain response and connectivity between 2nd and 3rd grades during arithmetic problem solving. NeuroImage, 57, 796–808.CrossRefGoogle Scholar
  28. Worsley, K. J., & Friston, K. J. (1995). Analysis of fMRI time-series revisited-again. NeuroImage, 2, 173–181.CrossRefGoogle Scholar
  29. Ye, J. C., Tak, S., Jang, K. E., Jung, J., & Jang, J. (2009). NIRS-SPM: Statistical parametric mapping or near-infrared spectroscopy. NeuroImage, 44, 428–447.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Joseph M. Baker
    • 1
  • Taylor Martin
    • 2
  • Ani Aghababyan
    • 2
  • Armen Armaghanyan
    • 3
  • Ronald Gillam
    • 4
  1. 1.Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral SciencesStanford University School of MedicineStanfordUSA
  2. 2.Advanced Learning Lab, Instructional Technology & Learning Sciences DepartmentUtah State UniversityLoganUSA
  3. 3.Multisensory Cognition Lab, Department of PsychologyUtah State UniversityLoganUSA
  4. 4.LEAP fNIRS Lab, Communicative Disorders & Deaf EducationUtah State UniversityLoganUSA

Personalised recommendations

Cite article

Buy options