Advertisement

Applying Augmented Cognition to Flip-Flop Methodology

  • Jan StelovskyEmail author
  • Randall K. Minas
  • Umida Stelovska
  • John Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9744)

Abstract

The Flip-Flop instructional methodology involves students in creating quizzes synchronized with video recordings of lectures. While students create questions, which involves generating right and wrong answers, feedback for the answers, hints and links leading to relevant resources, they get deeply involved with the content presented in the lecture screencasts. We propose to conduct a wide range of experiments testing the effectiveness of this approach – from simple surveys, evaluations of time spent creating quizzes and assessment of their quality to extensive longitudinal studies of the students’ emotional responses and cognitive load using a electroencephalography (EEG), electrodermal activity (EDA), heart rate variability (HRV) and facial electromyography (EMG).

Keywords

Instructional methods Inverted classroom Educational technology Augmented cognition Cognitive neuroscience Psychophysiological methods 

References

  1. 1.
    Gibbs, F.A., Gibbs, E.L.: Atlas of Electroencephalography. F.A. Gibbs, Boston City Hospital, Oxford (1941)zbMATHGoogle Scholar
  2. 2.
    Harmon-Jones, E., Peterson, C.K.: Electroencephalographic methods in social and personality psychology. In: Harmon-Jones, E., Beer, J.S. (eds.) Methods and Social Neuroscience, pp. 170–197. The Guilford Press, New York (2009)Google Scholar
  3. 3.
    Andreassi, J.L.: Psychophysiology: Human Behavior & Physiological Response, 5th edn. Lawrence Erlbaum Associates, Mahwah (2007)Google Scholar
  4. 4.
    Potter, R.F., Bolls, P.D.: Psychophysiological Measurement and Meaning: Cognitive and Emotional Processing of Media. Routledge, New York (2011)Google Scholar
  5. 5.
    Minas, R.K., Potter, R.F., Dennis, A.R., Bartelt, V., Bae, S.: Putting on the thinking cap: using neurois to understand information processing biases in virtual teams. J. Manag. Inf. Syst. 30, 49–82 (2014)CrossRefGoogle Scholar
  6. 6.
    Dietrich, A., Kanso, R.: A review of EEG, erp, and neuroimaging studies of creativity and insight. Psychol. Bull. 136, 822–848 (2010)CrossRefGoogle Scholar
  7. 7.
    Petsche, H.: Approaches to verbal, visual and musical creativity by EEG coherence analysis. Int. J. Psychophysiol. 24, 145–159 (1996)CrossRefGoogle Scholar
  8. 8.
    Fink, A., Schwab, D., Papousek, I.: Sensitivity of EEG upper alpha activity to cognitive and affective creativity interventions. Int. J. Psychophysiol. 82, 233–239 (2011)CrossRefGoogle Scholar
  9. 9.
    Martindale, C., Hasenfus, N.: EEG differences as a function of creativity, stage of the creative process, and effort to be original. Biol. Psychol. 6, 157–167 (1978)CrossRefGoogle Scholar
  10. 10.
    Fink, A., Neubauer, A.C.: EEG alpha oscillations during the performance of verbal creativity tasks: differential effects of sex and verbal intelligence. Int. J. Psychophysiol. 62, 46–53 (2006)CrossRefGoogle Scholar
  11. 11.
    Radel, R., Davranche, K., Fournier, M., Dietrich, A.: The role of (dis) inhibition in creativity: decreased inhibition improves idea generation. Cognition 134, 110–120 (2015)CrossRefGoogle Scholar
  12. 12.
    Fink, A., Benedek, M.: EEG alpha power and creative ideation. Neurosci. Biobehav. Rev. 44, 111–123 (2014)CrossRefGoogle Scholar
  13. 13.
    Mumford, M.D., Whetzel, D.L.: Insight, creativity, and cognition: on Sternberg and Davidson’s the nature of insight. Creativity Res. J. 9, 103 (1996)CrossRefGoogle Scholar
  14. 14.
    Lavric, A., Forstmeier, S., Rippon, G.: Differences in working memory involvement in analytical and creative tasks: an ERP study. NeuroReport 11, 1613–1618 (2000)CrossRefGoogle Scholar
  15. 15.
    Nijstad, B.A., Stroebe, D.: How the group affects the mind: a cognitive model of idea generation in groups. Pers. Soc. Psychol. Rev. 10, 186–213 (2006)CrossRefGoogle Scholar
  16. 16.
    Ericsson, K.A., Simon, H.A.: Protocol Analysis. MIT Press, Cambridge (1993)Google Scholar
  17. 17.
    Bowden, E.M., Jung-Beeman, M., Fleck, J., Kounios, J.: New approaches to demystifying insight. Trends Cogn. Sci. 9, 322–328 (2005)CrossRefGoogle Scholar
  18. 18.
    Wager, T.D., Jonides, J., Reading, S.: Neuroimaging studies of shifting attention: a meta-analysis. NeuroImage 22, 1679–1693 (2004)CrossRefGoogle Scholar
  19. 19.
    Amodio, D.M., Frith, C.D.: Meeting of minds: the medial frontal cortex and social cognition. Nat. Rev. Neurosci. 7, 268–277 (2006, print)Google Scholar
  20. 20.
    Kounios, J., Fleck, J.I., Green, D.L., Payne, L., Stevenson, J.L., Bowden, E.M., Jung-Beeman, M.: The origins of insight in resting-state brain activity. Neuropsychologia 46, 281–291 (2008)CrossRefGoogle Scholar
  21. 21.
    Kounios, J., Frymiare, J.L., Bowden, E.M., Fleck, J.I., Subramaniam, K., Parrish, T.B., Jung-Beeman, M.: The prepared mind: neural activity prior to problem presentation predicts subsequent solution by sudden insight. Psychol. Sci. 17, 882–890 (2006)CrossRefGoogle Scholar
  22. 22.
    Sandkühler, S., Bhattacharya, J.: Deconstructing insight: EEG correlates of insightful problem solving. PLoS ONE 3, e1459 (2008)CrossRefGoogle Scholar
  23. 23.
    Baddeley, A.: Working Memory. Science 255, 556–559 (1992)CrossRefGoogle Scholar
  24. 24.
    Conway, A.R.A., Engle, R.W.: Working memory and retrieval: a resource-dependent inhibition model. J. Exp. Psychol.: Gen. 123, 354–373 (1994)CrossRefGoogle Scholar
  25. 25.
    Welsh, M.C., Satterlee-Cartmell, T., Stine, M.: Towers of Hanoi and London: contribution of working memory and inhibition to performance. Brain Cogn. 41, 231–242 (1999)CrossRefGoogle Scholar
  26. 26.
    D’Esposito, M.: From cognitive to neural models of working memory. Philo. Trans. Roy. Soc. B: Biol. Sci. 362, 761–772 (2007)CrossRefGoogle Scholar
  27. 27.
    D’Esposito, M., Detre, J.A., Alsop, D.C., Shin, R.K., Atlas, S., Grossman, M.: The neural basis of the central executive system of working memory. Nature 378, 279–281 (1995)CrossRefGoogle Scholar
  28. 28.
    Curtis, C.E., D’Esposito, M.: Persistent activity in the prefrontal cortex during working memory. Trends Cogn. Sci. 7, 415–423 (2003)CrossRefGoogle Scholar
  29. 29.
    Gevins, A., Smith, M.E., McEvoy, L., Yu, D.: High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb. Cortex 7, 374–385 (1997)CrossRefGoogle Scholar
  30. 30.
    Hoptman, M.J., Davidson, R.J.: Baseline EEG asymmetries and performance on neuropsychological tasks. Neuropsychologia 36, 1343–1353 (1998)CrossRefGoogle Scholar
  31. 31.
    Herwig, U., Satrapi, P., Schönfeldt-Lecuona, C.: Using the international 10–20 EEG system for positioning of transcranial magnetic stimulation. Brain Topogr. 16, 95–99 (2003)CrossRefGoogle Scholar
  32. 32.
    Newmann, F.M.: Student Engagement and Achievement in American Secondary Schools. ERIC (1992)Google Scholar
  33. 33.
    Andreassi, J.L., Filipovic, S.R.: Psychophysiology: Human Behavior and Physiological Response. Elsevier, Philadelphia (2001)Google Scholar
  34. 34.
    Crosby, M.E., Auernheimer, B., Aschwanden, C., Ikehara, C.: Physiological data feedback for application in distance education. Presented at the Proceedings of the 2001 Workshop on Perceptive User Interfaces, Orlando, Florida, USA (2001)Google Scholar
  35. 35.
    Vick, R.M., Ikehara, C.S.: Methodological issues of real time data acquisition from multiple sources of physiological data. In: Proceedings of the 36th Annual Hawaii International Conference on System Sciences, p. 7 (2003)Google Scholar
  36. 36.
    Ikehara, C., Crosby, M.E.: A real-time cognitive load in educational multimedia. In: Proceedings of the 2003 World Conference on Educational Multimedia, Hypermedia & Telecommunications, Honolulu, HI (2003)Google Scholar
  37. 37.
    Colmenarez, A.J., Xiong, Z., Huang, T.S.: Facial Analysis from Continuous Video with Applications to Human-Computer Interface, vol. 2. Springer Science & Business Media, Heidelberg (2006)Google Scholar
  38. 38.
    Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C.M., Kazemzadeh, A., Lee, S., Neumann, U., Narayanan, S.: Analysis of emotion recognition using facial expressions, speech and multimodal information. Presented at the Proceedings of the 6th International Conference on Multimodal Interfaces, State College, PA, USA (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jan Stelovsky
    • 1
    Email author
  • Randall K. Minas
    • 2
  • Umida Stelovska
    • 3
  • John Wu
    • 3
  1. 1.Department of Information and Computer SciencesUniversity of Hawaii at ManoaHonoluluUSA
  2. 2.Shidler College of BusinessUniversity of Hawaii at ManoaHonoluluUSA
  3. 3.parWinr, Inc.SunnyvaleUSA

Personalised recommendations