Journal of Science Education and Technology

, Volume 27, Issue 2, pp 114–130 | Cite as

Exploring the Use of Electroencephalography to Gather Objective Evidence of Cognitive Processing During Problem Solving

  • Thomas Delahunty
  • Niall Seery
  • Raymond Lynch


Currently, there is significant interest being directed towards the development of STEM education to meet economic and societal demands. While economic concerns can be a powerful driving force in advancing the STEM agenda, care must be taken that such economic imperative does not promote research approaches that overemphasize pragmatic application at the expense of augmenting the fundamental knowledge base of the discipline. This can be seen in the predominance of studies investigating problem solving approaches and procedures, while neglecting representational and conceptual processes, within the literature. Complementing concerns about STEM graduates’ problem solving capabilities, raised within the pertinent literature, this paper discusses a novel methodological approach aimed at investigating the cognitive elements of problem conceptualization. The intention is to demonstrate a novel method of data collection that overcomes some of the limitations cited in classic problem solving research while balancing a search for fundamental understanding with the possibility of application. The methodology described in this study employs an electroencephalographic (EEG) headset, as part of a mixed methods approach, to gather objective evidence of students’ cognitive processing during problem solving epochs. The method described provides rich evidence of students’ cognitive representations of problems during episodes of applied reasoning. The reliability and validity of the EEG method is supported by the stability of the findings across the triangulated data sources. The paper presents a novel method in the context of research within STEM education and demonstrates an effective procedure for gathering rich evidence of cognitive processing during the early stages of problem conceptualization.


STEM education Problem solving Methodological approach Cognition EEG 


  1. Adams, J. L. (2001). Conceptual blockbusting: A guide to better ideas (4th ed.). Cambridge, Massachusetts: Perseus Publishing.Google Scholar
  2. Amadieu, F., vanGog, T., Paas, F., Tricot, A., & Mariné, C. (2009). Effects of prior knowledge and concept-map structure on disorientation, cognitive load, and learning. Learning and Instruction, 19, 376–386.CrossRefGoogle Scholar
  3. Anderson, E. W., Potter, K. C., Matzen, L. E., Shepherd, J. F., Preston, G. A., & Silva, C. T. (2011). A user study of visualization effectiveness using EEG and cognitive load. European Security, 30(3), 791–800.Google Scholar
  4. Ansari, D., & Coch, D. (2006). Bridges over troubled waters: education and cognitive neuroscience. Trends in Cognitive Sciences, 10(4), 146–151.CrossRefGoogle Scholar
  5. Artzt, A. F., & Armour-Thomas, E. (1992). Development of a cognitive-metacognitive framework for protocol analysis of mathematical problem solving in small groups. Cognition and Instruction, 9(2), 137–175.CrossRefGoogle Scholar
  6. Badcock, N. A., Mousikou, P., Mahajan, Y., de Lissa, P., Thie, J., & McArthur, G. (2013). Validation of the Emotiv EPOC® EEG gaming system for measuring research quality auditory ERPs. PeerJ, 1, e38.CrossRefGoogle Scholar
  7. Banich, M. T., & Compton, R. J. (2011). Cognitive neuroscience. Wadsworth: Cengage.Google Scholar
  8. Besterfield-Sacre, M., Cox, M. F., Borrego, M., Beddoes, K., & Zhu, J. (2014). Changing engineering education: views of U.S. faculty, chairs, and deans. Journal of Engineering Education, 103(2), 193–219.CrossRefGoogle Scholar
  9. Bodner, G. M., & Domin, D. S. (2000). Mental models: the role of representations in problem solving in chemistry. Chemistry Education, 4(1), 24–30.Google Scholar
  10. Bogard, T., Liu, M., & Chiang, Y.-h. (2013). Thresholds of knowledge development in complex problem solving: a multiple-case study of advanced learners’ cognitive processes. Educational Technology Research and Development, 61, 465–503.CrossRefGoogle Scholar
  11. Boonen, A. J. H., Wesel, F. v., Jolles, J., & Schoot, M. v. d. (2014). The role of visual representation type, spatial ability, and reading comprehension in word problem solving: an item-level analysis in elementary school children. International Journal of Educational Research, 68, 15–26.CrossRefGoogle Scholar
  12. Bryman, A. (2008). Social research methods (3rd ed.). New York: Oxford University Press.Google Scholar
  13. Byrnes, J. P., & Vu, L. T. (2015). Educational neuroscience: definitional, methodological, and interpretive issues. Wiley Interdisciplinary Reviews: Cognitive Science, 6(3), 221–234.Google Scholar
  14. Cabeza, R., & Nyberg, L. (2000). Imaging cognition II: an empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 12(1), 1–47.CrossRefGoogle Scholar
  15. Call, B. J., Goodridge, W., Villanueva, I., Wan, N. and Jordan, K. (2016). Utilizing electroencephalography measurements for comparison of task-specific neural efficiencies: spatial intelligence tasks. Journal of Visual Experiments, 114.Google Scholar
  16. Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.CrossRefGoogle Scholar
  17. Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education. London: Routledge.Google Scholar
  18. DaSilva, F. L. (2010). EEG: origin and measurement. In C. Mulert & L. Lemieux (Eds.), EEG-fMRI. Berlin: Springer.Google Scholar
  19. Delahunty, T. (2014) Investigating Conceptualisation and the Approach Taken to Solving Convergent Problems: Implications for Instructional Task Design [Ph.D], Unpublished Thesis, University of Limerick Google Scholar
  20. Delahunty, T., Seery, N. and Lynch, R. (2013). Conceptualisation in visuospatial reasoning tasks: a case for exploring. In Engineering Design Graphics Division 68th Mid-Year Conference, Worcester, MA, October 20–22.Google Scholar
  21. Delahunty, T., Seery, N. and Lynch, R. (2015). Spatial skills and success in problem solving within engineering education. In 6th Research in Engineering Education Symposium DIT, July 13–15.Google Scholar
  22. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. The Journal of Neuroscience, 134, 9–21.Google Scholar
  23. Duit, R., & Treagust, D. F. (2012). How can conceptual change contribute to theory and practice in science education? In B. J. Fraser, K. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (pp. 107–118). New York: Springer.CrossRefGoogle Scholar
  24. Esfahani, E. T., & Sundararajan, V. (2012). Classification of primitive shapes using brain-computer interfaces. Computer-Aided Design, 44, 1011–1019.CrossRefGoogle Scholar
  25. Eysenck, M. W., & Keane, M. T. (2010). Cognitive psychology: A student’s handbook (6th ed.). Sussex: Psychology Press.Google Scholar
  26. Fink, A., Grabner, R. H., Benedek, M., Reishofer, G., Hauswirth, V., Fally, M., Neuper, C., Ebner, F., & Neubauer, A. C. (2009). The creative brain: investigation of brain activity during creative problem solving. Human Brain Mapping, 30, 734–748.CrossRefGoogle Scholar
  27. Freeman, W. J. and Quiroga, R. Q. (2013). Frequency analysis. In Imaging Brain Function with EEG (pp. 21–36). Springer.Google Scholar
  28. Gick, M. L. (1986). Problem-solving strategies. Educational Psychologist, 21(1–2), 99–120.CrossRefGoogle Scholar
  29. Gill, H. S., & O'Boyle, M. W. (2003). Generating an image from an ambiguous visual input: an electroencephalographic (EEG) investigation. Brain and Cognition, 51, 287–293.CrossRefGoogle Scholar
  30. Goldin, G. A. (1997). Chapter 4: observing mathematical problem solving through task-based interviews, Journal for Research in Mathematics Education. Monograph, 40–177.Google Scholar
  31. Goméz, A., Moreno, A., Pazos, J., & Sierra-Alonso, A. (2000). Knowledge maps: an essential technique for conceptualisation. Data and Knowledge Engineering, 33, 169–190.CrossRefGoogle Scholar
  32. Hahn, U., & Chater, N. (1998). Similarity and rules: distinct? Exhaustive? Empirically distinguishable? Cognition, 65(2), 197–230.CrossRefGoogle Scholar
  33. Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage: a theory of cognitive interest in science learning. Journal of Education & Psychology, 90(3), 414.CrossRefGoogle Scholar
  34. Hegarty, M., & Kozhevnikov, M. (1999). Types of visual-spatial representations and mathematical problem solving. Journal of Education & Psychology, 91(4), 684–689.CrossRefGoogle Scholar
  35. Jakel, F., & Schreiber, C. (2013). Introspection in problem solving. Journal of Problem Solving, 6(1), 20–33.CrossRefGoogle Scholar
  36. Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: a research paradigm whose time has come. Educational Research, 33(7), 14–26.CrossRefGoogle Scholar
  37. Jonassen, D. H. (1997). Instructional design models for well-structured and III-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65–94.CrossRefGoogle Scholar
  38. Kimbell, R. and Stables, K. (2008). Researching design learning: Issues and findings from two decades of research and development. Springer Science and Business Media B.V.Google Scholar
  39. Kirsh, D. (2009). Problem solving and situated cognition. In P. Robbins & M. Aydede (Eds.), The Cambridge handbook of situated cognition. New York: Cambridge University Press.Google Scholar
  40. Klimesch, W. (1997). EEG-alpha rhythms and memory processes. International Journal of Psychophysiology, 26, 319–340.CrossRefGoogle Scholar
  41. Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews, 29(2), 169–195.CrossRefGoogle Scholar
  42. Lai, M.-L., Tsai, M.-J., Yang, F.-Y., Hsu, C.-Y., Liu, T.-C., Lee, S. W.-Y., Lee, M.-H., Chiou, G.-L., Liang, J.-C., & Tsai, C.-C. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational Research Review, 10, 90–115.CrossRefGoogle Scholar
  43. Liu, C.-J., & Shen, M.-H. (2011). The influence of different representations on solving concentration problems at elementary school. Journal of Science Education and Technology, 20, 621–629.CrossRefGoogle Scholar
  44. Luttrell, W. (2010). Qualitative educational research: Readings in reflexive methodology and transformative practice. New York: Routledge.Google Scholar
  45. Lyle, J. (2003). Stimulated recall: a report on its use in naturalistic research. British Educational Research Journal, 29(6), 861–878.CrossRefGoogle Scholar
  46. Madill, A., Jordan, A., & Shirley, C. (2000). Objectivity and reliability in qualitative analysis: realist, contextualist and radical constructionist epistemologies. British Journal of Psychology, 91, 1–20.CrossRefGoogle Scholar
  47. Maquet, P. (2001). The role of sleep in learning and memory. Science, 294(5544), 1048–1052.CrossRefGoogle Scholar
  48. Mayer, R. E. (2010). Unique contributions of eye-tracking research to the study of learning with graphics. Learning and Instruction, 20, 167–171.CrossRefGoogle Scholar
  49. McCormick, R., & Davidson, M. (2009). Problem solving and the tyranny of product outcomes. Journal of Design and Technology Education, 1(3), 230–241.Google Scholar
  50. McGilchrist, I. (2009). The master and his emissary: The divided brain and the making of the western world. England: Yale University Press.Google Scholar
  51. Middleton, H. (2008). Examining design thinking: visual and verbal protocol analysis. In H. Middleton (Ed.), Researching technology education: Methods and techniques. Netherlands: Sense.Google Scholar
  52. Molle, M., Marshall, L., Wolf, B., Fehm, H. L., & Born, J. (1999). EEG complexity and performance measures of creative thinking. Psychophysiology, 36, 95–104.CrossRefGoogle Scholar
  53. Montague, M., Rosenzweig, C., & Krawec, J. (2011). Metacognitive strategy use of eighth-grade students with and without learning disabilities during mathematical problem solving: a think-aloud analysis. Journal of Learning Disabilities, 44(6), 508–520.CrossRefGoogle Scholar
  54. National Academy of Engineering. (2004). The engineer of 2020: Visions of engineering in the new century. Washington, DC: National Academy of Engineering.Google Scholar
  55. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs: Prentice-Hall.Google Scholar
  56. Novick, L. R., & Bassok, M. (2005). Problem solving. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 321–369). New York: Cambridge University Press.Google Scholar
  57. O'Donoghue, J. and Kooij, H. V.-D. (2007). Assessing adults’ quantitative skills—the INULIS project. In The 14th International Conference of Adult Learning Mathematics (ALM), limerick (pp. 251-263).Google Scholar
  58. OECD. (2002). Understanding the brain: Towards a new learning science. Paris: OECD Publications Service.Google Scholar
  59. OECD. (2004). Learning for tomorrow's world: First results from PISA 2003 [online], available: Accessed 05/10/2012.
  60. Ohlsson, S. (2012). The problems with problem solving: reflections on the rise, current status, and possible future of a cognitive research paradigm. The Journal of Problem Solving, 5(1), 101–128.CrossRefGoogle Scholar
  61. Onton, J., & Makeig, S. (2006). Information-based modeling of event-related brain dynamics. In C. Neuper & W. Klimesch (Eds.), Event-related dynamics of brain oscillations (pp. 99–120). Oxford: Elsevier Science.CrossRefGoogle Scholar
  62. Osaka, M. (1984). Peak alpha frequency of EEG during a mental task: task difficulty and hemispheric differences. Psychophysiology, 21(1), 101–105.CrossRefGoogle Scholar
  63. Pfurtscheller, G. (1992). Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest. Electroencephalography and Clinical Neurophysiology, 83(1), 62–69.CrossRefGoogle Scholar
  64. Pfurtscheller, G., & Silva, F. H. L. D. (1999). Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 110, 1842–1857.CrossRefGoogle Scholar
  65. Razoumnikova, O. M. (2000). Functional organization of different brain areas during convergent and divergent thinking: an EEG investigation. Cognitive Brain Research, 10, 11–18.CrossRefGoogle Scholar
  66. Rescher, B., & Rapplesberger, P. (1999). Gender dependent EEG-changes during a mental rotation task. International Journal of Psychophysiology, 33, 209–222.CrossRefGoogle Scholar
  67. Rowan, A. J., & Tolunsky, E. (2003). Primer of EEG. Pennsylvania: Butterworth Heinemann.Google Scholar
  68. Schnotz, W., Baadte, C., Muller, A., & Rasch, R. (2010). Creative thinking and problem solving with depictive and descriptive representations. In L. Verschaffel, E. DeCorte, T. deJong, & J. Ellen (Eds.), Use of representations in reasoning and problem solving (pp. 11–35). New York: Routledge.Google Scholar
  69. Sigman, M., Pena, M., Goldin, A. P., & Ribeiro, S. (2014). Neuroscience and education: prime time to build the bridge. Nature Neuroscience, 17(4), 497–502.CrossRefGoogle Scholar
  70. Stillings, N. A., Weisler, S. E., Chase, C. H., Feinstein, M. H., Garfield, J. L., & Rissland, E. L. (1995). Cognitive science: An introduction. London: MIT Press.Google Scholar
  71. Stokes, D. E. (1997). Pasteur’s quadrant: Basic science and technological innovation. Washington D.C.: Brookings Institution Press.Google Scholar
  72. Uddin, L. Q., Kelly, A. M. C., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2008). Functional connectivity of default mode network components: correlation, anticorrelation, and causality. Human Brain Mapping, 30(2), 625–637.CrossRefGoogle Scholar
  73. Ward, J. (2010). The student’s guide to cognitive neuroscience (2nd ed.). East Sussex: Psychology Press.Google Scholar
  74. Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R. (2008). The seductive allure of neuroscience explanations. Journal of Cognitive Neuroscience, 20(3), 470–477.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.University of NebraskaLincolnUSA
  2. 2.Athlone Institute of Technology, Ireland and KTH Royal Institute of TechnologyStockholmSweden
  3. 3.University of LimerickLimerickIreland

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