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The EduFlow Model: A Contribution Toward the Study of Optimal Learning Environments

  • Jean Heutte
  • Fabien Fenouillet
  • Jonathan Kaplan
  • Charles Martin-Krumm
  • Rémi Bachelet
Chapter

Abstract

The intention of the following chapter is to shed light on primary factors that play a role in defining what we coin as an optimal learning environment, an environment that buttresses an experience of flow for learners (see Chap.  10 by Andersen in this volume). The chapter begins with an overview of flow related research reframed for the purpose of measuring the experience of flow in learning. A longitudinal study of flow experienced by students undertaking a Massive Open Online Course (MOOC) is described. The Flow in Education scale (EduFlow Scale) used in the study is described and the results of the study presented. The results illustrate the potential value and relevance of measuring flow in learning as well as the relation to the extended concept of cognitive absorption. We conclude the chapter with a presentation of a model of heuristic learning: the Individually Motivated Community model. The model builds upon three major theories of the self: Self-Determination, Self-Efficacy and Autotelism-Flow.

Keywords

Community Flow MOOC Optimal learning environment Positive educational psychology 

References

  1. Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694.CrossRefGoogle Scholar
  2. Andersen, F. O. (2005). International trends in primary school education: An overview based on case studies in Finland, Denmark & Japan. Billund: Lego Learning Institute.Google Scholar
  3. Bachelet R. (2014). Data and success rates of MOOC GdP3, website read 01/15/2015. http://goo.gl/StCLX9
  4. Bakker, A. B. (2008). The work-related flow inventory: Construction and initial validation of the WOLF. Journal of Vocational Behavior, 72, 400–414.CrossRefGoogle Scholar
  5. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs: Prentice-Hall.Google Scholar
  6. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.Google Scholar
  7. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.CrossRefPubMedGoogle Scholar
  8. Byrne, B. M. (1994). Structural equation modeling with EQS and EQS/windows. Thousand Oaks: Sage Publications.Google Scholar
  9. Caron, P. A., Heutte, J., & Rosselle, M. (2014). Présentation d’une méthode et d’outils pour évaluer les perceptions des apprenants dans un MOOC., JOCAIR 2014, Paris, France, 27 juin.Google Scholar
  10. Csikszentmihalyi, M. (1975). Beyond boredom and anxiety: Experiencing flow in work and play. San Francisco: Jossey-Bass.Google Scholar
  11. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper and Row.Google Scholar
  12. Csikszentmihalyi, M. (1993). The evolving self. A psychology for the 3rd millennium. New York: Harper Collins.Google Scholar
  13. Csikszentmihalyi, M., & Larson, R. (1984). Being adolescent. New York: Basic Books.Google Scholar
  14. Csikszentmihalyi, M., & LeFevre, J. (1989). Optimal experience in work and leisure. Journal of Personality and Social Psychology, 56(5), 815–822.CrossRefPubMedGoogle Scholar
  15. Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227–268.CrossRefGoogle Scholar
  16. Deci, E. L., & Ryan, R. M. (2008). Facilitating optimal motivation and psychological well-being across life’s domains. Canadian Psychology, 49, 14–23.CrossRefGoogle Scholar
  17. Delle Fave, A., & Massimini, F. (1992). The ESM and the measurement of clinical change: A case of anxiety disorder. The experience of psychopathology: Investigating mental disorders in their natural settings, 280.Google Scholar
  18. Delle Fave, A., Massimini, F., & Bassi, M. (2011). Psychological selection and optimal experience across cultures: Social empowerment through personal growth. New York: Springer.CrossRefGoogle Scholar
  19. Engeser, S., & Schiepe-Tiska, A. (2012). Historical lines and overview of current research. In S. Engeser (Ed.), Introduction to flow research. New York: Springer.Google Scholar
  20. Fenouillet, F. (2012). Les théories de la motivation. Paris: Dunod.CrossRefGoogle Scholar
  21. Fenouillet, F., Martin-Krumm, C., Heutte, J., & Besançon, M. (2014). An urgent call for change: Flow, motivation and well-being in French School students. 7th European Conference on Positive Psychology (ECPP), Amsterdam, The Netherlands.Google Scholar
  22. Fu, F. L., Su, R. C., & Yu, S. C. (2009). Egameflow: A scale to measure learners’ enjoyment of e-learning games. Computers & Education, 52(1), 101–112.CrossRefGoogle Scholar
  23. Gable, S. L., & Haidt, J. (2005). What (and why) is positive psychology? Review of General Psychology, 9(2), 103.CrossRefGoogle Scholar
  24. Heutte, J. (2011). La part du collectif dans la motivation et son impact sur le bien-être comme médiateur de la réussite des étudiants: Complémentarités et contributions entre l’autodétermination, l’auto-efficacité et l’autotélisme. (Unpublished Ph. D. thesis). Université Paris Ouest-Nanterre-La Défense (France).Google Scholar
  25. Heutte, J. (2014) Persister dans la conception de son environnement personnel d’apprentissage: Contributions et complémentarités de trois théories du self. STICEF, 21, ISSN: 1764-7223Google Scholar
  26. Heutte J.,Fenouillet F., Martin-Krumm C. (2013). Contribution de la psychologie positive au pilotage de l’innovation. Congrès Francophone de Psychologie Positive, Metz, France.Google Scholar
  27. Heutte, J., Fenouillet, F., Boniwell, I., Martin-Krumm, C., & Csikszentmihalyi, M. (2014a). Optimal learning experience in digital environments: Theoretical concepts, measure and modelisation, SymposiumDigital Learning in 21st Century Universities”. Georgia Institute of Technology (Georgia Tech), Atlanta, GA. Google Scholar
  28. Heutte, J., Galaup, M., Lelardeux, C., Lagarrigue, P., & Fenouillet, F. (2014b). Etude des déterminants psychologiques de la persistance dans l’usage d’un jeu sérieux: évaluation de l’environnement optimal d’apprentissage avec Mecagenius. STICEF, 21, ISSN: 1764–7223Google Scholar
  29. Heutte, J., Kaplan, J., Fenouillet, F., Caron, P. A., & Rosselle, M. (2014c). MOOC user persistence—lessons from French educational policy adoption and deployment of a pilot course. In L. Uden, J. Sinclair, Y.-H. Tao, & D. Liberona (Ed.), Learning technology for education in cloud. MOOC and Big Data (LTEC’14), Communications in Computer and Information Science. 446: 13–24. Springer.Google Scholar
  30. Hoffman, D. L., & Novak, T. P. (2009). Flow online: Lessons learned and future prospects. Journal of Interactive Marketing, 23(1), 23–34.CrossRefGoogle Scholar
  31. Hu, L. T., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76–99). Thousand Oaks: Sage.Google Scholar
  32. Jackson, S. A., & Eklund, R. C. (2002). Assessing flow in physical activity: The flow stateScale-2 and dispositional flow state scale-2. Journal of Sport and Exercise Psychology, 24, 133–115.CrossRefGoogle Scholar
  33. Jackson, S. A., & Eklund, R. C. (2004). The flow scale manual. Morgantown: Fitness Information Technology.Google Scholar
  34. Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The flow state scale. Journal of Sport and Exercise Psychology, 18, 17–35.CrossRefGoogle Scholar
  35. Jackson, S. A., Martin, A. J., & Eklund, R. C. (2008). Long and short measures of flow: The construct validity of the FSS-2, DFS-2, and new brief counterparts. Journal of Sport and Exercise Psychology, 30, 561–587.CrossRefPubMedGoogle Scholar
  36. Johnson, L. S. (2004). Academic engagement from the perspective of flow theory: A comparative analysis of nontraditional and traditional schools. Unpublished doctoral dissertation, Northern Illinois University, DeKalb.Google Scholar
  37. Keller, J., & Landhäußer, A. (2012). The flow model revisited. In S. Engeser (Ed.), Advances in flow research (pp. 51–64). New York: Springer.CrossRefGoogle Scholar
  38. Maddux, J. E. (2002). Self-efficacy: The power of believing you can. Handbook of positive psychology (pp. 277–287). New York: Oxford University Press.Google Scholar
  39. Mayers, P. (1978). Flow in adolescence and its relevation to school experience. Unpublished doctoral dissertation, University of Chicago. In C. R. Snyder, & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 89–105). Oxford: University Press.Google Scholar
  40. Mead, G.-H. (1934). Mind, self, and society. Chicago: University of Chicago Press.Google Scholar
  41. Moneta, G. B. (2012). On the measurement and conceptualization of flow. In S. Engeser (Ed.), Advances in. Flow research (pp. 23–50). New York: Springer.CrossRefGoogle Scholar
  42. Nakamura, J., & Csikszentmihalyi, M. (2002). The concept of flow. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 89–105). Oxford: Oxford University Press.Google Scholar
  43. Nakamura, J., & Csikszentmihalyi, M. (2009). Flow theory and research. In S. J. Lopez & C. R. Snyder (Eds.), Handbook of positive psychology (pp. 195–206). New York: Oxford University Press.Google Scholar
  44. Novak, T. P., Hoffman, D. L., & Yung, Y. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22–42.CrossRefGoogle Scholar
  45. Parks, B. (1996). Flow, boredom and anxiety in therapeutic work. Unpublished doctoral dissertation, University of Chicago.Google Scholar
  46. Procci, K., Singer, A. R., Levy, K. R., & Bowers, C. (2012). Measuring the flow experience of gamers: An evaluation of the DFS-2. Computers in Human Behavior, 28(6), 2306–2312.CrossRefGoogle Scholar
  47. Rathunde, K., & Csikszentmihalyi, M. (2005). Middle school students’ motivation and quality of experience: A comparison of Montessori and traditional school environments. American Journal of Education, 111(3), 341–371.CrossRefGoogle Scholar
  48. Rheinberg, F. (2008). Intrinsic motivation and flow-experience. In H. Heckhausen & J. Heckhausen (Eds.), Motivation and action (pp. 323–348). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  49. Rheinberg, F., Vollmeyer, R., & Engeser, S. (2003). Die Erfassung des Flow-Erlebens. In J. Stiensmeier-Pelster & F. Rheinberg (Eds.), Diagnostik von Motivation und Selbstkonzept (pp. 261–279). Göttingen: Hogrefe.Google Scholar
  50. Richer, S. F., & Vallerand, R. J. (1998). Construction et validation de l’échelle du sentiment d’appartenance sociale (ÉSAS). European Review of Applied Psychology, 48(2), 129–138.Google Scholar
  51. Sawyer, R. K. (2007). Group genius: The creative power of collaboration. New York: Basic Books.Google Scholar
  52. Schwarzer, R., & Jerusalem, M. (1995). Generalized self-efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 35–37). Windsor: NFER-NELSON.Google Scholar
  53. Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55(1), 5–14.CrossRefPubMedGoogle Scholar
  54. Shernoff, D. J., & Csikszentmihalyi, M. (2009). Flow in schools: Cultivating engaged learners and optimal learning environments. In R. C. Gilman, E. S. Heubner, & M. J. Furlong (Eds.), Handbook of positive psychology in schools (pp. 131–145). New York: Routledge.Google Scholar
  55. Shernoff, D. J., Csikszentmihalyi, M., Schneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18, 158–76.CrossRefGoogle Scholar
  56. Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioural Research, 25, 173–180.CrossRefGoogle Scholar
  57. Tucker, L. R., & Lewis, C. (1973). The reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10.CrossRefGoogle Scholar
  58. Vollmeyer, R., & Rheinberg, F. (2006). Motivational effects on self-regulated learning with different tasks. Educational Psychology Review, 18(3), 239–253.CrossRefGoogle Scholar
  59. Vygotsky, L. -S. (1962). Thought and language. MIT Press, Cambridge, Massachusetts.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jean Heutte
    • 1
  • Fabien Fenouillet
    • 2
  • Jonathan Kaplan
    • 2
    • 3
  • Charles Martin-Krumm
    • 4
    • 5
    • 6
  • Rémi Bachelet
    • 7
  1. 1.Univ. Lille, EA 4354 – CIREL (Centre Interuniversitaire de Recherche en Education de Lille)LilleFrance
  2. 2.Chart-UPON - EA 4004University of NanterreNanterreFrance
  3. 3.ECP-EA 4571, ISPEFUniversité Lumière Lyon 2LyonFrance
  4. 4.APEMAC EA 4360 UDLMetzFrance
  5. 5.Institut de Recherche Biomédicale des Armées (IRBA)BrétignyFrance
  6. 6.IFEPS Angers Les Ponts de CéLes Ponts de CéFrance
  7. 7.Centrale LilleUniversité Lille Nord de FranceLilleFrance

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