Advertisement

GeNeDis 2016 pp 181-191 | Cite as

The Effectiveness of Neurofeedback Training in Algorithmic Thinking Skills Enhancement

Conference paper
First Online:
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 988)

Abstract

Although research on learning difficulties are overall in an advanced stage, studies related to algorithmic thinking difficulties are limited, since interest in this field has been recently raised. In this paper, an interactive evaluation screener enhanced with neurofeedback elements, referring to algorithmic tasks solving evaluation, is proposed. The effect of HCI, color, narration and neurofeedback elements effect was evaluated in the case of algorithmic tasks assessment. Results suggest the enhanced performance in the case of neurofeedback trained group in terms of total correct and optimal algorithmic tasks solution. Furthermore, findings suggest that skills, concerning the way that an algorithm is conceived, designed, applied and evaluated are essentially improved.

Keywords

Neurofeedback Learning disabilities Alpha waves Beta waves Algorithmic thinking Neuroeducation 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Katzir, T., and J. Pare-Blagoev. 2006. Applying Cognitive Neuroscience Research to Education: The Case of Literacy. Educational Psychologist 41 (1): 53–74.CrossRefGoogle Scholar
  2. 2.
    Hocutt, A.M. 1996. Effectiveness of Special Education: Is Placement the Critical Factor? The Future of Children, Special Education for Students with Disabilities 6 (1): 77–102.Google Scholar
  3. 3.
    Plerou, A. 2014. Dealing with Dyscalculia Over Time. International Conference on Information Communication Technologies in Education.Google Scholar
  4. 4.
    Wang, S.-K., and T.C. Reeves. 2006. The Effects of a Web-Based Learning Environment on Student Motivation in a High School Earth Science Course. Educational Technology Research and Development 54 (6): 597–621.CrossRefGoogle Scholar
  5. 5.
    Green, A., and K.S. Eklundh. 2003. Designing for Learnability in Human-Robot Communication. IEEE Transactions on Industrial Electronics 50 (4): 644–650.CrossRefGoogle Scholar
  6. 6.
    Hereford, J., and W. Winn. 1994. Non-Speech Sound in Human-Computer Interaction: A Review and Design Guidelines. Journal of Educational Computing Research 11 (3): 211–233.CrossRefGoogle Scholar
  7. 7.
    Johnson, W.L., J.W. Rickel, and J.C. Lester. 2000. Animated Pedagogical Agents: Face-to-Face Interaction in Interactive Learning Environments. International Journal of Artificial Intelligence in Education 11: 47–78.Google Scholar
  8. 8.
    Vernon, D., et al. 2003. The Effect of Training Distinct Neurofeedback Protocols on Aspects of Cognitive Performance. International Journal of Psychophysiology 47 (1): 75–85.CrossRefPubMedGoogle Scholar
  9. 9.
    Basar, E., et al. 1995. Time and Frequency Analysis of the Brain’s Distributed Gamma-Band System. IEEE Engineering in Medicine and Biology Magazine 14 (4): 400–410.CrossRefGoogle Scholar
  10. 10.
    Batty, M.J., et al. 2006. Relaxation Strategies and Enhancement of Hypnotic Susceptibility: EEG Neurofeedback, Progressive Muscle Relaxation and Self-Hypnosis. Brain Research Bulletin 71 (1): 83–90.CrossRefPubMedGoogle Scholar
  11. 11.
    Vernon, D., A. Frick, and J. Gruzelier. 2004. Neurofeedback as a Treatment for ADHD: A Methodological Review with Implications for Future Research. Journal of Neurotherapy 8 (2): 53–82.CrossRefGoogle Scholar
  12. 12.
    Surjono, H.D. 2015. The Effects of Multimedia and Learning on Student Achievement in Online Electronics Course. The Turkish Online Journal of Educational Technology 14 (1): 166–122.Google Scholar
  13. 13.
    Brown, C.M. 1998. Human-Computer Interface Design Guidelines.Google Scholar
  14. 14.
    Adams, F.M., and C.E. Osgood. 1973. A Cross-Cultural Study of the Affective Meanings of Color. Journal of Cross-Cultural Psychology 4 (2): 135–156.CrossRefGoogle Scholar
  15. 15.
    Farley, F.H., and A.P. Grant. 1976. Arousal and Cognition: Memory for Color Versus Black and White Multimedia Presentation. The Journal of Psychology 94 (1): 147–150.CrossRefGoogle Scholar
  16. 16.
    Dzulkifli, M.A., and M.F. Mustafar. 2013. The Influence of Colour on Memory Performance: A Review. The Malaysian Journal of Medical Sciences: MJMS 20 (2): 3–9.PubMedPubMedCentralGoogle Scholar
  17. 17.
    Deterding, S., et al. 2011. From Game Design Elements to Gamefulness. Proceedings of the 15th International Academic MindTrek Conference on Envisioning Future Media Environments - MindTrek ’11. New York, NY, USA: ACM Press, p. 9.Google Scholar
  18. 18.
    StataCorp L.P. 2007. Stata Data Analysis and Statistical SOFTWARE. In Special Edition Release 10.Google Scholar
  19. 19.
    Futschek, G. 2006. Algorithmic Thinking: The Key for Understanding Computer Science. In Informatics Education – The Bridge between Using and Understanding Computers, Lecture Notes in Computer Science, edited by R.T. Mittermeir, 159–168. Berlin, Heidelberg: Springer.Google Scholar
  20. 20.
    Voskoglou, M.G., and S. Buckley. 2012. Problem Solving and Computational Thinking in a Learning Environment. Preprint arXiv:1212.0750.Google Scholar
  21. 21.
    Schoenfeld, A. 1992. Learning to Think Mathematically: Problem Solving, Metacognition, and Sense Making in Mathematics. In Handbook of Research on Mathematics Teaching and Learning, 334–370.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Informatics, Bioinformatics and Human Electrophysiology LaboratoryIonian UniversityCorfuGreece
  2. 2.Department of InformaticsIonian UniversityCorfuGreece

Personalised recommendations

Cite paper

Buy options