Assessing Neural Synchrony in Tutoring Dyads

  • Bradly Stone
  • Anna Skinner
  • Maja Stikic
  • Robin Johnson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8534)


The current study examined synchronous psychophysiological monitoring across a tutor and tutee during a spatial reasoning video game, Tetris®. We hypothesized that increased synchrony across tutor-tutee would correlate with increased performance (i.e. increased learning. A teaming platform enabled simultaneous electroencephalogram (EEG) and electrocardiogram (ECG) acquisition for the tutor-tutee dyad throughout the gaming sessions, using the B-Alert® X10 EEG system (Advanced Brain Monitoring, Inc, Carlsbad, CA). A sample of n = 15 healthy participants as tutees with a single tutor across all dyads completed the protocol with each tutee playing 3 rounds of Tetris®. Initial results indicate small, significant, correlations in psychophysiological metrics that increased with experience. Exploratory stepwise regressions found the correlations explained more variance in performance than individual tutee/tutor psychophysiological metrics. These data imply that synchrony on a psychophysiological level between tutor and tutee impact tutee performance. Further examination of more complex synchrony metrics is required.


Neurophysiology EEG ECG Neural Synchrony 


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  1. 1.
    Michael, J.: Where’s the evidence that active learning works? Advances in Physiology Education 30(4), 159–167 (2006)Google Scholar
  2. 2.
    Chi, M.T., Bassok, M.: Learning from examples via self-explanations. Knowing, learning, and instruction: Essays in honor of Robert Glaser, pp. 251–282 (1989)Google Scholar
  3. 3.
    Chi, M.T., et al.: Eliciting self-explanations improves understanding. Cognitive Science 18(3), 439–477 (1994)Google Scholar
  4. 4.
    Pirolli, P., Recker, M.: Learning strategies and transfer in the domain of programming. Cognition and Instruction 12(3), 235–275 (1994)CrossRefGoogle Scholar
  5. 5.
    Cohen, P.A., Kulik, J.A., Kulik, C.-L.C.: Educational outcomes of tutoring: A meta-analysis of findings. American Educational Research Journal 19(2), 237–248 (1982)CrossRefGoogle Scholar
  6. 6.
    Lemke, J.L.: Talking science: Language, learning, and values. ERIC (1990)Google Scholar
  7. 7.
    Fawcett, L.M., Garton, A.F.: The effect of peer collaboration on children’s problem-solving ability. British Journal of Educational Psychology 75(2), 157–169 (2005)CrossRefGoogle Scholar
  8. 8.
    Fletcher, J.: Evidence for learning from technology-assisted instruction. Technology Applications in Education: A Learning View. Lawrence Erlbaum Associates, Hillsdale (2003)Google Scholar
  9. 9.
    Ai-Lim Lee, E., Wong, K.W., Fung, C.C.: How does desktop virtual reality enhance learning outcomes? A structural equation modeling approach. Computers & Education 55(4), 1424–1442 (2010)CrossRefGoogle Scholar
  10. 10.
    Delacruz, G.C., Chung, G., Baker, E.L.: Validity evidence for games as assessment environments. In: 9th International Conference of the Learning Sciences, Chicago (2010)Google Scholar
  11. 11.
    Kulik, J.A., Fletcher, J.D.: Effectiveness of Intelligent Tutoring Systems (IDA Document D-4664). Institute for Defense Analyses, Alexandria (2012)Google Scholar
  12. 12.
    Raphael, G., et al.: I-NET®: Interactive Neuro-Educational Technology to Accelerate Skill Learning. In: 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, Minnesota (2009)Google Scholar
  13. 13.
    Berka, C., et al.: Accelerating Training Using Interactive Neuro-Educational Technologies: Aplications to Archery, Golf, and Rifle Marksmanship. International Journal of Sports and Society 1(4), 87–104 (2010)Google Scholar
  14. 14.
    Stephens, G.J., Silbert, L.J., Hasson, U.: Speaker–listener neural coupling underlies successful communication. Proceedings of the National Academy of Sciences 107(32), 14425–14430 (2010)CrossRefGoogle Scholar
  15. 15.
    Stevens, R., et al.: Modeling the neurodynamic complexity of submarine navigation teams. Computational and Mathematical Organization Theory, 1–24 (2012)Google Scholar
  16. 16.
    Stevens, R., et al.: Cognitive neurophysiologic synchronies: What can they contribute to the study of teamwork. Human Factors (2012) (in Press)Google Scholar
  17. 17.
    Stevens, R.H., Galloway, T., Berka, C., Sprang, M.: Can neurophysiologic synchronies provide a platform for adapting team performance? In: Schmorrow, D.D., Estabrooke, I.V., Grootjen, M. (eds.) FAC 2009. LNCS, vol. 5638, pp. 658–667. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. 18.
    Englund, C., et al.: Unified Tri-Service Cognitive Performance Assessment Battery (UTC-PAB). 1. Design and Specification of the Battery. DTIC Document (1987)Google Scholar
  19. 19.
    Kabat, M.H., et al.: Construct validity of selected Automated Neuropsychological Assessment Metrics (ANAM) battery measures. The Clinical Neuropsychologist 15(4), 498–507 (2001)CrossRefGoogle Scholar
  20. 20.
    Lindstedt, J.K., Gray, W.D.: Extreme Expertise: Exploring Expert Behavior in Tetris.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bradly Stone
    • 1
  • Anna Skinner
    • 2
  • Maja Stikic
    • 1
  • Robin Johnson
    • 1
  1. 1.Advanced Brain Monitoring, Inc. CarlsbadUSA
  2. 2.AnthoTronix. Silver SpringUSA

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