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

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.

[1]  Andrew J. Martin,et al.  Long and short measures of flow: the construct validity of the FSS-2, DFS-2, and new brief counterparts. , 2008, Journal of sport & exercise psychology.

[2]  J. Heutte.  Persister dans la conception de son environnement personnel d’apprentissage : Contributions et complémentarités de trois théories du self (autodétermination, auto-efficacité, autotélisme-flow) , 2014 .

[3]  Barbara M. Byrne,et al.  Structural equation modeling with AMOS , 2010 .

[4]  A. Bandura.  Self-Efficacy: The Exercise of Control , 1997, Journal of Cognitive Psychotherapy.

[5]  Sheng-Chin Yu,et al.  journal homepage: www.elsevier.com/locate/compedu , 2022 .

[6]  M. Csíkszentmihályi.  The Evolving Self: A Psychology for the Third Millennium , 1993 .

[7]  M. Csíkszentmihályi,et al.  Flow Theory and Research , 2009 .

[8]  Clint A. Bowers,et al.  Measuring the flow experience of gamers: An evaluation of the DFS-2 , 2012, Comput. Hum. Behav..

[9]  P. Bentler,et al.  Comparative fit indexes in structural models. , 1990, Psychological bulletin.

[10]  E. Deci,et al.  “Facilitating optimal motivation and psychological well-being across life’s domains”: Correction to Deci and Ryan (2008). , 2008 .

[11]  S. Engel.  Thought and Language , 1964 .

[12]  F. Fenouillet.  Les théories de la motivation , 2012 .

[13]  Regina Vollmeyer,et al.  Motivational Effects on Self-Regulated Learning with Different Tasks , 2006 .

[14]  M. Csíkszentmihályi,et al.  Positive psychology. An introduction. , 2000, The American psychologist.

[15]  M. Csíkszentmihályi.  Flow: The Psychology of Optimal Experience , 1990 .

[16]  M. Bassi,et al.  Psychological Selection and Optimal Experience Across Cultures: Social Empowerment through Personal Growth , 2010 .

[17]  F. Rheinberg,et al.  Motivation and Action: Intrinsic Motivation and Flow , 2020 .

[18]  M. Csíkszentmihályi,et al.  Optimal learning experience in digital environments: Theoretical concepts, measure and modelisation , 2014 .

[19]  J. Heutte.  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. , 2011 .

[20]  T. Abel,et al.  Mind, Self, and Society , 1934 .

[21]  M. Csíkszentmihályi,et al.  The Concept of Flow , 2014 .

[22]  Pierre-André Caron,et al.  MOOC User Persistence - Lessons from French Educational Policy Adoption and Deployment of a Pilot Course , 2014, LTEC@KMO.

[23]  A. Bakker.  The work-related flow inventory: Construction and initial validation of the WOLF , 2008 .

[24]  M. Csíkszentmihályi,et al.  Middle School Students’ Motivation and Quality of Experience: A Comparison of Montessori and Traditional School Environments , 2005, American Journal of Education.

[25]  J. H. Steiger.  Structural Model Evaluation and Modification: An Interval Estimation Approach. , 1990, Multivariate behavioral research.

[26]  A. Bandura.  Social Foundations of Thought and Action: A Social Cognitive Theory , 1985 .

[27]  R. Keith Sawyer,et al.  Group genius : the creative power of collaboration , 2007 .

[28]  Giovanni B. Moneta,et al.  On the Measurement and Conceptualization of Flow , 2012 .

[29]  Donna L. Hoffman,et al.  Flow Online: Lessons Learned and Future Prospects , 2009 .

[30]  David J. Shernoff,et al.  Flow in Schools Cultivating Engaged Learners and Optimal Learning Environments , 2008 .

[31]  E. Deci,et al.  The "What" and "Why" of Goal Pursuits: Human Needs and the Self-Determination of Behavior , 2000 .

[32]  L. Tucker,et al.  A reliability coefficient for maximum likelihood factor analysis , 1973 .

[33]  Donna L. Hoffman,et al.  Measuring the Customer Experience in Online Environments: A Structural Modeling Approach , 2000 .

[34]  S. Richer,et al.  Construction et validation de l'échelle du sentiment d'appartenance sociale (ÉSAS) , 1998 .

[35]  R. Eklund,et al.  Assessing Flow in Physical Activity: The Flow State Scale-2 and Dispositional Flow Scale-2 , 2002 .

[36]  Johannes Keller,et al.  The Flow Model Revisited , 2012 .

[37]  S. Engeser,et al.  Historical Lines and an Overview of Current Research on Flow , 2021, Advances in Flow Research.

[38]  Shelly L. Gable,et al.  What (and Why) is Positive Psychology? , 2005 .

[39]  P. Bentler,et al.  Evaluating model fit. , 1995 .

[40]  David J. Shernoff,et al.  Student engagement in high school classrooms from the perspective of flow theory. , 2003 .

[41]  M. Csíkszentmihályi,et al.  Optimal experience in work and leisure. , 1989, Journal of personality and social psychology.

[42]  Michel Galaup,et al.  Etude des déterminants psychologiques de la persistance dans l'usage d'un jeu sérieux : évaluation de l'environnement optimal d'apprentissage avec Mecagenius? , 2014, ArXiv.

[43]  H. Marsh,et al.  Development and Validation of a Scale to Measure Optimal Experience: The Flow State Scale , 1996 .

[44]  Elena Karahanna,et al.  Time Flies When You're Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage , 2000, MIS Q..

[45]  D. Eichel.  Beyond Boredom And Anxiety Experiencing Flow In Work And Play , 2016 .