Advertisement

Implications of Neuroimaging for Educational Research

Chapter

Abstract

Neural functions are fundamental to learning, instruction, and performance. Although tremendous progress has been made in neuroscience in the past two decades, its applications in educational research are just beginning to be realized. This review focuses on selected technologies, methods, and findings from neuroscience that have important implications for educational sciences. Specifically, this chapter discusses conceptual and empirical research on the use, implications, and limitations of neuroimaging techniques such as continuous electroencephalography, event-related potentials, and functional magnetic resonance imaging in the domains of language and reading, mathematics learning, problem solving, cognitive load, and affective processes in learning. Neuroimaging has enabled scientists to open “the black box” of neural activity that underlies learning. It seems timely, therefore, to consider how educational researchers may employ the increased understanding of brain function to explore educational questions.

Keywords

Neuroscience Neuroimaging EEG ERP fMRI fNIRS 

Notes

Acknowledgements

Pavlo Antonenko is grateful to the National Aeronautics and Space Administration for providing financial support for a portion of this work (#NNX10AV03A and #NNX08AJ14A).

References

  1. Anderson, J. R., Betts, S., Ferris, J. L., & Fincham, J. M. (2011). Cognitive and metacognitive activity in mathematical problem solving: Prefrontal and parietal patterns. Cognitive, Affective, and Behavioral Neuroscience, 11(1), 52–67.CrossRefGoogle Scholar
  2. Anderson, J. R., Carter, C. S., Fincham, J. M., Qin, Y., Ravizza, S. M., & Rosenberg-Lee, M. (2008). Using fMRI to test models of complex cognition. Cognitive Science, 32, 1323–1348.CrossRefGoogle Scholar
  3. *Andreassi, J. L. (2007). Psychophysiology: Human behavior and physiological response. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  4. Ansari, D., & Coch, D. (2006). Bridges over troubled waters: Education and cognitive neuroscience. Trends in Cognitive Sciences, 10, 146–151.CrossRefGoogle Scholar
  5. Antonenko, P., & Niederhauser, D. (2010). The influence of leads on cognitive load and learning in a hypertext-assisted learning environment. Computers in Human Behavior, 26(2), 140–150.CrossRefGoogle Scholar
  6. Antonenko, P., Paas, F., Grabner, R., & van Gog, T. (2010). Using electroencephalography (EEG) to measure cognitive load. Educational Psychology Review, 22, 425–438.CrossRefGoogle Scholar
  7. *Basar, E., (1999). Brain function and oscillations. Berlin: Springer.Google Scholar
  8. Bear, M. F., Connors, B., & Paradiso, M. A. (2006). Neuroscience: Exploring the brain (3rd ed.). New York, NY: Williams & Wilkins.Google Scholar
  9. *Berntson, G. G., & Cacioppo, J. T. (2009). Handbook of neuroscience for the behavioral sciences. New York, NY: Wiley.Google Scholar
  10. Boekaerts, M. (2003). Toward a model that integrates motivation, affect, and learning. British Journal of Educational Psychology Monograph Series, II, 173–189.Google Scholar
  11. Bowden, E. M., & Beeman, M. J. (1998). Getting the right idea: Semantic activation in the right hemisphere may help solve insight problems. Psychological Science, 9, 435–440.CrossRefGoogle Scholar
  12. Brodmann, K. (1909). Vegleichende Lokalisationslehre der Grosshirnde. Barth, Leipzig.Google Scholar
  13. Bruer, J. T. (1997). Education and the brain: A bridge too far. Educational Researcher, 26(8), 4–16.CrossRefGoogle Scholar
  14. Bruer, J. T. (1999). The myth of the first three years: A new understanding of early brain development and lifelong learning. New York, NY: The Free Press.Google Scholar
  15. Bruer, J. T. (2006). Points of view: On the implications of neuroscience research for science teaching and learning: Are there any? A skeptical theme and variations: The primacy of psychology in the science of learning. CBE Life Sciences Education, 5(2), 104–110.CrossRefGoogle Scholar
  16. Carnegie Task Force. (1996). Years of promise: A comprehensive learning strategy for America’s children. New York, NY: Carnegie Corporation of New York.Google Scholar
  17. Clark, R. E. (2010). Cognitive and neuroscience research on learning and instruction: Recent insights about the impact of non-conscious knowledge on problem solving, higher order thinking skills and interactive cyber-learning environments. International Conference on Education Research (ICER), Seoul, South Korea.Google Scholar
  18. Dash, P. K., Herbert, A. E., & Runyan, J. D. (2004). A unified theory for systems and cellular memory consolidation. Brain Research Reviews, 45, 30–37.CrossRefGoogle Scholar
  19. De Jong, T., Van Gog, T., Jenks, K., Manlove, S., Van Hell, J. G., Jolles, J., et al. (2009). Explorations in learning and the brain: On the potential of cognitive neuroscience for educational science. Berlin: Springer.Google Scholar
  20. Dehaene, S. (1997). The number sense: How the mind creates mathematics. New York, NY: Oxford University Press.Google Scholar
  21. Dehaene, S. (2009). Reading in the brain: The science and evolution of a human invention. New York, NY: Penguin.Google Scholar
  22. Dehaene, S., Spelke, E., Stanescu, R., Pinel, P., & Tsivkin, S. (1999). Sources of mathematical thinking: Behavioral and brain-imaging evidence. Science, 284, 970–974.CrossRefGoogle Scholar
  23. Diamond, M. C. (2001). Response of the brain to enrichment. Annals of the Brazilian Academy of Sciences, 73(2).Google Scholar
  24. Dolcos, F., & Cabeza, R. (2002). Event-related potentials of emotional memory: Encoding pleas- ant, unpleasant, and neutral pictures. Cognitive, Affective, and Behavioral Neuroscience, 2, 252–263.CrossRefGoogle Scholar
  25. Fischer, K. W. (2009). Mind, brain, and education: Building a scientific groundwork for learning and teaching. Mind, Brain, and Education, 3, 2–15.Google Scholar
  26. *Fischer, K. W., Daniel, D. B., Immordino-Yang, M. H., Stern, E., Battro, A., & Koizumi, H. (2007). Why mind, brain, and education? Why now? Mind, Brain, and Education, 1, 1–2.Google Scholar
  27. Fledge, J., & Fletcher, K. (1992). Talker and listener effects on the perception of degree of foreign accent. Journal of the Acoustical Society of America, 91, 370–389.CrossRefGoogle Scholar
  28. Friedman, D., Cycowicz, Y. M., & Gaeta, H. (2001). The novelty P3: An event-related brain potential (ERP) sign of the brain’s evaluation of novelty. Neurosceince and Biobehavioral Reviews, 25, 355–373.CrossRefGoogle Scholar
  29. Gerlic, I., & Jausovec, N. (1999). Multimedia: Differences in cognitive processes observed with EEG. Educational Technology Research and Development, 47(3), 5–14.CrossRefGoogle Scholar
  30. Gerson, A., Parra, L. C., & Sajda, P. (2005). Cortical origins of response time variability during rapid discrimination of visual objects. Neuroimage, 28, 342–353.CrossRefGoogle Scholar
  31. Gevins, A. S., & Smith, M. E. (2008). EEG in neuroergonomics. In R. Parasuraman & M. Rizzo (Eds.), Neuroergonomics: The brain at work. New York, NY: Oxford University Press.Google Scholar
  32. Goleman, D. (1995). Emotional intelligence. New York, NY: Bantam Books.Google Scholar
  33. Gopnik, A., Meltzoff, A. N., & Kuhl, P. K. (1999). The scientist in the crib: Minds, brains and how children learn. New York, NY: Harper Collins.Google Scholar
  34. Goswami, U. (2004). Neuroscience and education. British Journal of Educational Psychology, 74, 1–14.CrossRefGoogle Scholar
  35. Goswami, U. (2006). Neuroscience and education: From research to practice? Nature Reviews Neuroscience, 7, 406–411.CrossRefGoogle Scholar
  36. Goswami, U. (2009). Mind, brain, and literacy: Biomarkers as usable knowledge for education. Mind, Brain, and Education, 3, 176–184.CrossRefGoogle Scholar
  37. Haapalainen, E., Kim S., Forlizzi, J., & Dey A. (2010). Psychophysiological measures for assessing cognitive load. Proceedings of the 12th ACM International Conference on Ubiquitous Computing (pp. 301–310).Google Scholar
  38. Hirshfield, L. M., Chauncey, K., Gulotta, R., Girouard, A., Solovey, E. T., Jacob, R. J. K., et al. (2009). Combining electroencephalograph and functional near infrared spectroscopy to explore users’ mental workload. In Lecture Notes in Computer Science (pp. 239–247). Berlin: Springer.Google Scholar
  39. Jobard, G., Crivello, F., & Tzourio-Mazoyer, N. (2003). Evaluation of the dual route theory or reading: A meta-analysis of 35 neuroimaging studies. Neuroimage, 20, 693–712.CrossRefGoogle Scholar
  40. Jolles, J., de Groot, R., van Benthem, J., Dekkers, H., de Glopper, C., & Uijlings, H. (2006). Brain lessons: A contribution to the international debate on brain, learning and education. Maastricht: NeuroPsych.Google Scholar
  41. Jones, E. G., & Mendell, L. M. (1999). Assessing the decade of the brain. Science, 284, 739.CrossRefGoogle Scholar
  42. Jung, K.-J., Prasad, P., Qin, Y., & Anderson, J. R. (2005). Extraction of overt verbal response from acoustic noise from the scanner in fMRI by use of segmented active noise cancellation. Magnetic Resonance Imaging, 53, 739–744.Google Scholar
  43. Jung-Beeman, M., Bowden, E. M., Haberman, J., Frymiare, J. L., Arambel-Liu, S., & Greenblatt, R. (2004). Neural activity when people solve verbal problems with insight. PLoS Biology, 2, 500–510.CrossRefGoogle Scholar
  44. Katz, L., & Frost, R. (1992). The reading process is different for different orthographies: The orthographic depth hypothesis. In R. Frost & L. Katz (Eds.), Orthography, phonology, morphology, and meaning: Advances in psychology (Vol. 94, pp. 67–84). Oxford: North Holland.CrossRefGoogle Scholar
  45. Kerr, D. S., Campbell, L. W., Applegate, M. D., Brodish, A., & Landfield, P. W. (1991). Chronic stress-induced acceleration of electrophysiologic and morphometric biomarkers of hippocampal aging. Journal of Neuroscience, 11, 1316–1326.Google Scholar
  46. Khader, P., Jost, K., Ranganath, C., & Rösler, F. (2010). Theta and alpha oscillations during working memory maintenance predict successful long-term memory encoding. Neuroscience Letters, 468, 339–343.CrossRefGoogle Scholar
  47. Kringelbach, M. L. (2009). Neural basis of mental representations of motivation, emotion and pleasure. In G. G. Berntson & J. T. Cacioppo (Eds.), Handbook of neuroscience for the behavioral sciences (pp. 807–828). New York, NY: Wiley.Google Scholar
  48. Marshall, S. P. (2007). Identifying cognitive state from eye metrics. Aviation, Space, & Environmental Medicine, 78(5), 165–175.CrossRefGoogle Scholar
  49. Martin-Loeches, M., Casado, P., Hernández-Tamames, J. A., & Álvarez-Linera, J. (2008). Brain activation in discourse comprehension: A 3t fMRI study. Neuroimage, 41, 614–622.CrossRefGoogle Scholar
  50. Mayer, R. E. (2008). Learning and instruction (2nd ed.). Upper Saddle River, NJ: Pearson Merrill Prentice Hall.Google Scholar
  51. Menon, V., Mackenzie, K., Rivera, S. M., & Reiss, A. L. (2002). Prefrontal cortex involvement in processing incorrect arithmetic equations: Evidence from event-related fMRI. Human Brain Mapping, 16(3), 119–130.CrossRefGoogle Scholar
  52. Metcalfe, J. (1986). Feeling of knowing in memory and problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 12, 288–294.CrossRefGoogle Scholar
  53. Minnery, B. S., & Fine, M. (2009). Neuroscience and the future of human-computer interaction. Interactions, 16(2), 70–75.CrossRefGoogle Scholar
  54. National Council for Accreditation of Teacher Education. (2010). The road less traveled: How the developmental sciences can prepare educators to improve student achievement. Retrieved from: http://www.ncate.org/dotnetnuke/LinkClick.aspx?fileticket=gY3FtiptMSo%3D&tabid=706
  55. National Reading Panel. (2000). Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction (NIH Publication No. 00-4769). Washington, DC: U.S. Government Printing Office.Google Scholar
  56. National Research Council. (2005). How students learn: History, mathematics, and science in the classroom. Washington, DC: National Academies Press.Google Scholar
  57. Neville, H. J., & Bruer, J. T. (2001). Language processing: How experience affects brain organization. In D. B. Bailey Jr., J. T. Bruer, F. J. Symons, & J. W. Lichtman (Eds.), Critical thinking about critical periods (pp. 151–172). Baltimore, MD: Paul H. Brookes.Google Scholar
  58. Office of Economic Cooperation and Development. (2002). Understanding the brain: Towards a new learning science. Paris: Office of Economic Cooperation and Development.Google Scholar
  59. *Office of Economic Cooperation and Development. (2007). Understanding the brain: The birth of a learning science. Paris: Office of Economic Cooperation and Development.Google Scholar
  60. Paas, F., Tuovinen, J. E., Tabbers, H. K., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.CrossRefGoogle Scholar
  61. Paulesu, E., McCrory, E., Fazio, F., Menoncello, L., Brunswick, N., Cappa, S., et al. (2000). A cultural effect on brain function. Nature Neuroscience, 3(1), 91–96.CrossRefGoogle Scholar
  62. Pfurtscheller, G., & Lopes da Silva, F. (2005). EEG event-related desynchronization (ERD) and event-related synchronization (ERS). In E. Niedermeyer & F. Lopes da Silva (Eds.), Electroencephalography, basic principles, clinical applications and related fields (pp. 1003–1016). Philadelphia, PA: Lippincott Williams and Wilkins.Google Scholar
  63. Qin, Y., Carter, C. S., Silk, E., Stenger, V. A., Fissell, K., Goode, A., et al. (2004). The change of the brain activation patterns as children learn algebra equation solving. Proceedings of National Academy of Sciences, 101(15), 5686–5691.CrossRefGoogle Scholar
  64. Ravizza, S. M., Anderson, J. R., & Carter, C. S. (2008). Errors of mathematical processing: The relationship of accuracy to neural regions associated with retrieval or representation of the problem state. Brain Research, 1238, 118–126.CrossRefGoogle Scholar
  65. Sandkuhler, S., & Bhattacharya, J. (2008). Deconstructing insight: EEG correlates of insightful problem solving. PLoS One, 3(1), 1–12.CrossRefGoogle Scholar
  66. Sapolsky, R. M., Romero, L. M., & Munck, A. U. (2000). How do glucocorticoids influence stress-responses? Integrating permissive, suppressive, stimulatory, and adaptive actions. Endocrine Reviews, 21, 55–89.CrossRefGoogle Scholar
  67. Schultheis, H., & Jameson, A. (2004). Assessing cognitive load in adaptive hypermedia systems: Physiological and behavioral methods. In P. De Bra & W. Nejdl (Eds.), Adaptive hypermedia and adaptive web-based systems (pp. 225–234). Berlin: Springer.CrossRefGoogle Scholar
  68. Shaywitz, B., Shaywitz, S., Pugh, K., Mencl, W., Fulbright, R., & Skudlarski, P. (2002). Disruption of posterior brain systems for reading in children with developmental dyslexia. Biological Psychiatry, 52(2), 101–110.CrossRefGoogle Scholar
  69. Shi, Y., Ruiz, N., Taib, R., Choi, E., & Chen, F. (2007). Galvanic skin response (GSR) as an index of cognitive load. Proceedings of the SIG CHI Conference on Human Factors in Computing Systems (pp. 2651–2656). San Jose, CA.Google Scholar
  70. Simpson, J. R., Öngür, D., Akbudak, E., Conturo, T. E., Ollinger, J. M., & Snyder, A. Z. (2000). The emotional modulation of cognitive processing: An fMRI study. Journal of Cognitive Neuroscience, 12, 157–170.CrossRefGoogle Scholar
  71. Snow, C. E., Burns, M. S., & Griffin, P. (1998). Preventing reading difficulties in young children. Washington, DC: National Academy Press.Google Scholar
  72. Soller, A., & Stevens, R. (2007). Applications of stochastic analyses for collaborative learning and cognitive assessment. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 217–253). Charlotte, NC: Information Age.Google Scholar
  73. Stevens, R. H., Galloway, T., & Berka, C. (2007). Allocation of time, workload, engagement and distraction as students acquire problem solving skills. In D. Schmorrow, D. Nicholson, J. Drexler, & L. Reeves (Eds.), Foundations of augmented cognition (pp. 128–137). Arlington, VA: Strategic Analysis.Google Scholar
  74. Stevens, R. H., Galloway, T., Berka, C., Johnson, R., & Sprang, M. (2008). Assessing student’s mental representations of complex problem spaces with EEG technologies. Proceedings of the Human Factors and Ergonomics Society 52nd Annual Meeting (pp. 22–26). New York.Google Scholar
  75. Stuss, D. T., & Knight, R. T. (2002). Principles of frontal lobe function. New York, NY: Oxford University Press.CrossRefGoogle Scholar
  76. Sweller, J. (2010). Element interactivity and intrinsic, extraneous and germane cognitive load. Educational Psychology Review, 22, 123–138.CrossRefGoogle Scholar
  77. Sweller, J., van Merriënboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–295.CrossRefGoogle Scholar
  78. Sylwester, R. (2010). A child’s brain: The need for nurture. Thousand Oaks, CA: Corwin Press.Google Scholar
  79. Terry, R. D., DeTeresa, R., & Hansen, L. A. (1987). Neocortical cell counts in normal human adult aging. Annals of Neurology, 21, 530–539.CrossRefGoogle Scholar
  80. Thompson, P. M., & Toga, A. W., (2000). Handbook of medical imaging: Processing and analysis. Academic Press, San Diego.CrossRefGoogle Scholar
  81. Tosetti, P., Nagy, Z., & Bernard, V. (2008). Brain research EU funding (2002–2008). Retrieved from ftp://ftp.cordis.europa.eu/pub/fp7/docs/brain-research-eu-funding_en.pdf
  82. Turkeltaub, P. E., Gareau, L., Flowers, D. L., Zeffiro, T. A., & Eden, G. F. (2003). Development of neural mechanisms for reading. Nature Neuroscience, 6(7), 767–773.CrossRefGoogle Scholar
  83. Ullsperger, M., & Debener, S. (2010). Simultaneous EEG and fMRI: Recording, analysis, and application. New York, NY: Oxford University Press.Google Scholar
  84. Varma, S., McCandliss, B. D., & Schwartz, D. L. (2008). Scientific and pragmatic challenges for bridging education and neuroscience. Educational Researcher, 37(3), 140–152.CrossRefGoogle Scholar
  85. Wilson, G. F., & Russell, C. A. (2003). Real-time assessment of mental workload using psychophysiological measures and artificial neural networks. Human Factors, 45, 635–643.CrossRefGoogle Scholar
  86. Xie, B., & Salvendy, G. (2000). Review and reappraisal of modeling and predicting mental workload in single- and multi-task environments. Work and Stress, 14(1), 74–99.CrossRefGoogle Scholar
  87. Zago, L., Pesenti, M., Mellet, E., Crivello, F., Mazoyer, B., & Tzourio-Mazoyer, N. (2001). Neural correlates of simple and complex mental calculation. Neuroimage, 13, 314–327.CrossRefGoogle Scholar
  88. Ziegler, J. C., & Goswami, U. (2006). Becoming literate in different languages: Similar problems, different solutions. Developmental Science, 9, 429–436.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Pavlo D. Antonenko
    • 1
  • Tamara van Gog
    • 2
  • Fred Paas
    • 3
    • 2
  1. 1.School of Teaching and LearningUniversity of FloridaGainesvilleUSA
  2. 2.Institute of PsychologyErasmus University RotterdamRotterdamThe Netherlands
  3. 3.Interdisciplinary Educational Research InstituteUniversity of WollongongWollongongAustralia

Personalised recommendations