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

Authors:
  • Université de Lille, Lille, France
  • Ecole de Psychologues Praticiens de Paris - Institut Catholique de Paris

Abstract and Figures

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. The chapter begins by an overview of flow related research reframed for the purpose of measuring the ex-perience of flow in learning. A longitudinal study of flow experien-ced by students in a Massive Open Online Course (MOOC) is des-cribed. The Flow in Education scale (EduFlow Scale) was used. It is described as well as the results of the study. They illustrate the po-tential 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 heuris-tic learning: the Individually Motivated Community model. The model builds upon three major theories of the self: Self-Determination, Self-Efficacy and Autotelism-Flow.
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LászlóHarmat· FransØrstedAndersen
FredrikUllén· JonWright
GaynorSadlo Editors
Flow
Experience
Empirical Research and Applications
xi
Contents
Part I Flow Experience: General Introduction
1 Flow and Psychological Selection .......................................................... 3
Antonella Delle Fave and Marta Bassi
Part II Flow in Arts and Sports
2 Finding Flow in Music Practice: An Exploratory Study
About Self-Regulated Practice Behaviours and Dispositions
to Flow in Highly Skilled Musicians ...................................................... 23
M. V. Araújo and C. F. Hein
3 The Experience of Flow in Artistic Creation ........................................ 37
Tatiana Chemi
4 Flow in Sport ........................................................................................... 51
Christian Swann
5 Running and Flow: Does Controlled Running
Lead to Flow-States? Testing the Transient
Hypofontality Theory ............................................................................. 65
Oliver Stoll and Jan M. Pithan
Part III Flow, Creativity and Productivity
6 Flow in Creativity: A Review of Potential
Theoretical Conflict ................................................................................ 79
Genevieve M. Cseh
7 Intuition and Flow ................................................................................... 95
Lauri Järvilehto
xii
8 Flow at Work as a Moderator of the Self- Determination
Model of Work Engagement .................................................................. 105
Daniela De Fraga and Giovanni B. Moneta
Part IV Flow in Education
9 The EduFlow Model: A Contribution Toward the
Study of Optimal Learning Environments ........................................... 127
Jean Heutte , Fabien Fenouillet , Jonathan Kaplan ,
Charles Martin-Krumm , and Rémi Bachelet
10 Using ESM to Study Flow in a STEM Project ..................................... 145
Frans Ørsted Andersen
11 Flow, Leisure, and Positive Youth Development .................................. 163
Teresa Freire , Dionísia Tavares , Eliana Silva , and Ana Teixeira
Part V Flow in Every Day Experiences
12 Flow in the Context of Daily Experience Fluctuation .......................... 181
Marta Bassi and Antonella Delle Fave
13 Flow Within Everyday Emotions and Motivations:
A Reversal Theory Perspective .............................................................. 197
Jon Wright
Part VI The Social Flow
14 Social Psychology of Flow: A Situated
Framework for Optimal Experience ..................................................... 215
Marco Boffi , Eleonora Riva , Nicola Rainisio , and Paolo Inghilleri
15 The Application of Team Flow Theory ................................................. 233
Jef J. J. van den Hout , Orin C. Davis , and Bob Walrave
16 New Technologies as Opportunities for Flow
Experience: A Framework for the Analysis ......................................... 249
Stefano Triberti , Alice Chirico , and Giuseppe Riva
Part VII Flow and Personality
17 Flow and Individual Differences – A Phenotypic
Analysis of Data from More than 10,000 Twin Individuals ................ 267
Fredrik Ullén , László Harmat , Töres Theorell , and Guy Madison
18 Optimal Experience and Optimal Identity:
A Multinational Examination at the Personal Identity Level ............. 289
Yanhui Mao , Scott Roberts , and Marino Bonaiuto
Contents
xiii
19 The Flow Experience in Clinical Settings:
Applications in Psychotherapy and Mental Health Rehabilitation .... 309
Eleonora Riva , Teresa Freire , and Marta Bassi
Part VIII Physiological Correlates of the Flow Experience
20 Experimental Paradigms to Investigate Flow- Experience
and Its Psychophysiology: Inspired from Stress
Theory and Research .............................................................................. 329
Tahmine Tozman and Corinna Peifer
21 The Flow Experience Revisited: The Influence
of Skills-Demands-Compatibility on Experiential
and Physiological Indicators .................................................................. 351
Johannes Keller
22 Towards a Neurobiological Understanding of Reduced
Self-Awareness During Flow: An Occupational
Science Perspective ................................................................................. 375
Gaynor Sadlo
Erratum ........................................................................................................... E1
Index ................................................................................................................. 389
Contents
127© Springer International Publishing Switzerland 2016
L. Harmat et al. (eds.), Flow Experience, DOI 10.1007/978-3-319-28634-1_9
Chapter 9
The EduFlow Model: A Contribution Toward
the Study of Optimal Learning Environments
Jean Heutte, Fabien Fenouillet, Jonathan Kaplan, Charles Martin-Krumm,
and Rémi Bachelet
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 chap-
ter 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.
J. Heutte (*)
Univ. Lille, EA 4354 – CIREL (Centre Interuniversitaire de Recherche
en Education de Lille), Lille 59000, France
e-mail: jean.heutte@univ-lille1.fr
F. Fenouillet
Chart-UPON - EA 4004, University of Nanterre, Nanterre, France
J. Kaplan
Chart-UPON - EA 4004, University of Nanterre, Nanterre, France
ECP-EA 4571, ISPEF, Université Lumière Lyon 2, Lyon, France
C. Martin-Krumm
APEMAC EA 4360 UDL, Metz, France
Institut de Recherche Biomédicale des Armées (IRBA), Brétigny, France
IFEPS Angers Les Ponts de Cé, Les Ponts de Cé, France
R. Bachelet
Centrale Lille, Université Lille Nord de France, Lille, France
128
9.1 Introduction
Flow or autotelic experience (providing well-being through the task activity itself)
is “the experience of complete absorption in the present moment” (Nakamura and
Csikszentmihalyi 2009, p. 195). Heutte et al. (2013), during their work within the
Positive Psychology movement that was recently initiated with Seligman and
Csikszentmihalyi (2000) and in accordance with Gable and Haidt’s definition
(2005), proposed that Positive Educational Psychology could be designated as the
scientific study of the conditions and processes that contribute to the flourishing or
optimal functioning of:
sLearners, people working in education or training and all players who are part of
the educational community;
sActual or virtual communities in which the above-mentioned people learn or
work;
sEducation and training systems, organizations or arrangements.
New prospects for research in the domain open when considering these three levels:
people, groups, and institutions. On each of these levels the intimate and social
dimensions of pleasure associated with understanding and making oneself under-
stood are explored in a quest for optimal learning environments. Optimal learning
environments are defined by Heutte (2014) as environments that support a state of
flow in the process of learning.
According to Nakamura and Csikszentmihalyi (2009, p. 195) conditions for
reaching a state of flow include:
1. A balance between perceived skills and perceived task demands,1 or opportuni-
ties for action, that stretch but do not overmatch existing skills;
2. Clear proximal goals;
3. Immediate feedback.
In consideration of these conditions, Autotelism-Flow is a subjective experience
that is characterized by the combination of distinct (experiential) states that co-
occur during engagement in a skill-related activity (see Chap. 11 by Freire et al. in
this volume) (Nakamura and Csikszentmihalyi 2009), specifically including:
1. An intense and focused concentration on the present moment;
2. The merging of action and awareness;
3. The loss of self-consciousness (i.e. loss of awareness of oneself as a social
player);
1 According to Keller and Landäußer (2012) and Rheinberg (2008), the term ‘demands’ is more
appropriate than the term ‘challenge’. Keller and Landäußer argue that “the propositions regarding
the boundary conditions of flow experience can be simplified and reduced […to] the antecedent
factor ‘Perceived fit of skill and task demands’” (2012, p. 53). “In any case, a sense of control is
definitely one of the most important components of the flow experience, whether or not an ‘objec-
tive’ assessment justifies such feelings” (Csikszentmihalyi 1975, p. 46).
J. Heutte et al.
129
4. A strong feeling of control, specifically over one’s actions, characterized by a
feeling of ability to deal with the situation and a feeling that one knows how to
deal with whatever comes next;
5. Distortion of the temporal experience, or a modified experience of time (typi-
cally, a sense that time has passed faster than usual);
6. Experience of the activity as intrinsically rewarding (enjoyment and well-being
rooted in the activity itself) such that often the end-goal is just an excuse for the
process (the autotelic experience).
This chapter offers to present a model of flow in educational contexts, referred to as
the EduFlow Model (Heutte et al. 2014a). The chapter begins with a description of
the conceptual dimensions of flow. A subgroup of these dimensions was used in the
Flow in Education Scale (EduFlow Scale). The dimensions are then presented.
What follows is the description of a study that used the EduFlow Scale derived from
the model. This study explored the relations between the EduFlow Model dimen-
sions using Structural Equation Modelling (SEM) to analyze learners’ flow in a
Massive Open Online Course (MOOC).
Most researchers concur with the conceptualization of flow as an autotelic expe-
rience (see Chap. 11 by Freire et al. in this volume). The intuitive understanding of
the concept requires an operational definition nevertheless; an operation somewhat
more complicated with varying results (see Hoffman and Novak 2009, for a review).
Engeser and Schiepe-Tiska (2012) pointed out that there is a certain level of dis-
agreement among researchers as to how flow should be measured. “Indeed, over the
past 35 years, researchers have kept developing and validating new measurement
instruments for flow, and modifying and revalidating established ones, which indi-
cates that a gold measurement standard for flow has yet to be achieved” (Moneta
2012, p. 23).
Having analyzed 30 different flow categorizations, Hoffman and Novak (2009)
found significant disparities concerning both (1) the definition/conceptualization of
flow dimensions, and (2) the antecedents, the experience of flow and its effects. A
challenge for researchers lies with building adequate measurement instruments for
both the experience and its effects. The difficulty is related to the fact that the expe-
rience of flow is described as one in which the subject loses self-awareness as a
result of being highly focused and absorbed in the activity at hand. This makes self-
reports of flow effects difficult. Furthermore, due to fluctuation in the state, there is
a risk of disturbing the fine balance needed to achieve or sustain the state by intrud-
ing upon the subject if intervening. This would be the case if micro-interventions
during the subject’s activity were used to observe the state and its effects. To date,
various measurement instruments have been created and used to study the highly
unstable and subjective phenomenon of flow in an attempt to overcome the chal-
lenges posed by its measurement. Qualitative methods have been applied using
interviews, questionnaires and the Experience Sampling Method (ESM;
Csikszentmihalyi and Larson 1984; Csikszentmihalyi and LeFevre 1989; Nakamura
and Csikszentmihalyi 2009).
9 The EduFlow Model: A Contribution Toward the Study of Optimal Learning…
130
Mayers’ flow scale (1978) is one of the first self-administered questionnaires
designed to estimate the frequency at which a person feels each one of the nine flow
dimensions initially described (Csikszentmihalyi 1990). The instrument was fre-
quently used for repeated-measures designs (e.g. Delle Fave and Massimini 1992)
in order to assess differences in the state of flow depending on the activity being
carried out and at various moments during that activity. More recently, a general
flow scale was developed on the basis of existing questionnaires to be applied in
specific contexts such as during sport activities (Jackson and Marsh 1996; Jackson
and Eklund 2002) and psychotherapy (Parks 1996).
According to Rheinberg et al. (2003), the intrusive nature and the time expendi-
ture associated with using the ESM put the flow process itself at risk of being inter-
rupted. Using other common measurement instruments presents the same
inconvenience. It is hence recommended to use flow measurement instruments that
use short questionnaires such as the Flow-Kurzskala Scale (see Chap. 10 by
Andersen in this volume) (FKS, Rheinberg et al. 2003). Vollmeyer and Rheinberg
(2006) estimated that FKS is a measurement scale that could be adapted to numer-
ous contexts. In order to facilitate the measurement of flow in studies in which flow
is not the central concept, Jackson et al. (2008)) validated a shorter version of FKS
containing only nine items, one for each dimension. However, this does not enable
researchers to structurally separate dimensions from observed data. More recently,
Bakker (2008) developed a 13-item flow at work scale, the Work-Related Flow
Inventory (WOLF). This scale includes three dimensions: work absorption, work
enjoyment, and intrinsic work motivation. The scale does not account for the condi-
tions of flow occurrences.
9.2 Choice of Dimensions to Include in Measures
of Autotelism-Flow in Educational Contexts
Delle Fave et al. (2011) affirm that it is extremely important that the measurement
instruments adhere strictly to the nine original dimensions of flow. This is the posi-
tion that many have adopted. The Flow-State Scale (FSS2), a nine dimensions scale
originally elaborated by Jackson and Eklund (2002, 2004) for sports activities, has
been tested and used in various contexts: theater; creative or performing arts; work
and music; online shopping; and, video gaming.
Procci et al. (2012) nevertheless identify limits to strictly and systematically
applying the framework of the nine original dimensions. For Procci and colleagues
flow differences observed in latent factor correlations are due to contextual contin-
gencies and variations in activities. Heutte et al. (2014b) observe that barely half of
the nine dimensions of FFS2 are actually perceived by learners in educational
context. Flow perception appears to vary and is context bound. For example, in
activities that are less physically strenuous or demanding, cognitive aspects are
more salient and more inter-related. Variations in context do not invalidate the
J. Heutte et al.
131
theoretical framework comprised of the nine components of flow. It is plausible that
the nine components are simply not equally prominent in all contexts.
Hoffman and Novak (2009) noted that researchers have been confronted with the
dilemma between privileging a short unidimensional scale that enables to quickly
capture a general state of flow; and, to include all flow components. In the former
the trade-off is losing resolution; in the latter, overburdening respondents, which
may lead to loss of accuracy and incomplete responses. This may explain why
numerous flow researchers have chosen to collapse dimensions, or drop some in a
quest for more compact questionnaires to suit specific research contexts or when
focusing on particular dimensions of interest.
When constructing the Flow in Education Scale (EduFlow Scale), Heutte et al.
(2014a) were confronted with a similar dilemma. They chose to select some of the
nine dimensions while leaving seemingly less relevant ones out. The dimensions
that were retained are:
sCognitive absorption (related concepts include: focused immersion (Agarwal
and Karahanna 2000; Fu et al. 2009); work absorption (Bakker 2008); and con-
centration (Jackson and Eklund 2002; Novak et al. 2000; Shernoff et al. 2003)).
sTime transformation (Jackson and Eklund 2002; also referred to as temporal dis-
sociation Agarwal and Karahanna 2000; Novak et al. 2000);
sAutotelic activity and well-being (related to the concept of enjoyment: Agarwal
and Karahanna 2000; Bakker 2008; Novak et al. 2000; Shernoff et al. 2003).
9.3 Cognitive Absorption: When Nothing Can Disturb
an Activity Centered on Understanding
According to Csikszentmihalyi, an autotelic activity or experience of flow is char-
acterized by numerous persons as one of the best moments in their lives. These are
experiences in which actions unfold with an extraordinary impression of fluidity,
painless effort and a strong sense of ease. Descriptions include being completely
engaged in the activity to a degree that nothing external could disturb the subject.
The pleasure of being engaged in the activity and persistence in it, both related to
the intrinsic nature of the subject’s engagement, are characteristic of total immer-
sion, a hallmark of flow (Nakamura and Csikszentmihalyi 2009).
Autonomous motivation is not the only type related to activity that produces an
experience of flow. Many activities are engaged in voluntarily even when motiva-
tion is extrinsically controlled (Deci and Ryan 2000). Once engaged in these, one
often reaches a state of deep concentration as a result of embracing the activity fully
in an attempt to do as best as one can.
Different types of extrinsic motivation affect regulations with various degrees of
autonomy. Introjected regulation can ensue from internal prods and pressures such
as threats of guilt or self-esteem-relevant contingencies. When internalization of
externally controlled regulations are higher, such as when social values are espoused
9 The EduFlow Model: A Contribution Toward the Study of Optimal Learning…
132
and acquire value, integration by the subject can lead to identified regulation, or
when identification with the values is full, lead to integrated regulation (Deci and
Ryan 2000, 2008).
We suggest that the central concept of Cognitive Absorption (CA) can be
expanded to any activity related to comprehension. After discussion with Ruy
Agarwall and Elena Karahanna, and with their permission, we extended the concept
of CA (Agarwal and Karahanna 2000) to include a state of deep involvement in an
activity focused on understanding with—or without—software (Heutte 2014). CA
corresponds to a deep state of commitment connected to a total-focus episode (opti-
mal experience). The cognitive activity entirely absorbs (or focuses) cognitive
resources, to the point that nothing but understanding matters. It has an immediate
consequence: nothing can disrupt concentration as it is exclusively committed to the
process of understanding. Thus, we define CA as an exclusive focus of attention,
extreme in intensity and peaceful, related to a state of total concentration during an
activity (Heutte 2011). CA can be portrayed as an invading interest for the activity.
It can in some way be said to occur beyond the subject’s will when the subject is
caught up in the activity.
9.4 Loss of Self-Consciousness: The Social Dimension
of Flow
Heutte and associates (2014a) and Heutte (2011, 2014) retained the dimension Loss
of Self-Consciousness (LSC) while regarding it as a social dimension. Here the
interest lies in the role that other persons are playing in personal agency. This inter-
est includes the role of individual motivation in collective activity (see Chap. 11 by
Freire et al. in this volume). Considering controlled motivation, social factors need
to be accounted for in processes of internalization and integration of regulations.
Social factors include learners’ socialization, feelings of belonging and relatedness
(Deci and Ryan 2000; Heutte 2011, 2014; Richer and Vallerand 1998).
LSC produces serenity together with no concern with oneself. It is characterized
by a feeling of growing beyond the boundaries of ego. The blurring occurs when
“one needs to step out of the cocoon of personal goals and confront larger issues in
the public arena” (Csikszentmihalyi 1993, p. 293). The following descriptions are
relevant too: LSC «marks the fading of Mead's ‘me’ from awareness» (Nakamura
and Csikszentmihalyi 2002, p. 92). Nakamura and Csikszentmihalyi (2009, p. 195)
describe this as a «loss of awareness of oneself as a social actor». We suggest that
this dimension of flow brings the dynamic relationship between the social world and
cognitive development to the fore (Mead 1934).
Blending egos (Sawyer 2007) occurs when people recognize that ingenuity results
from the performance of the entire group. Ingenuity is not attributable to the sole
performance of any one of the group’s individual members. In their studies on the
effects of flow in educational context, Shernoff and Csikszentmihalyi (2009) demon-
strated that students experiencing flow were more likely to perceive their classmates
as friends. Peers also spent more time in academic activities such as individual and
J. Heutte et al.
133
group work (Rathunde and Csikszentmihalyi 2005), demonstrated more cooperation
(Andersen 2005), as well as had a higher sense of belongingness (Johnson 2004).
The works of these authors led Heutte et al. (2014a) to conclude that LSC represents
the social dimension of flow. In reference to social developmental psychology, which
considers social interaction as key to individual cognitive development (e.g.,
Vygotsky 1962; Bandura 1997), LSC emerges as an essential criterion in relation to
environmental and personal factors. Among factors that influence learner engage-
ment are school contexts that may foster to varying degrees optimal learning experi-
ences (See Chap. 10 by Andersen in this volume) (Shernoff et al. 2003).
9.5 The EduFlow Scale: An Instrument in the Service
of Studying Optimal Learning Environments
Heutte et al. (2014a) opted to study four dimensions for the assessment of optimal
learning environments i.e. environments that support learners’ flow. To this end the
EduFlow Scale was developed. The four dimensions it measures are:
sFlowD1: Cognitive absorption—increased concentration and immersion in the
task.
sFlowD2: Time transformation—alteration in the perception of time, sometimes
leading to a lengthened duration of immersion in the task.
sFlowD3: Loss of self-consciousness—lack of self-concern related to an increase
in importance of the psycho-social dimension of learning.
sFlowD4: Autotelic experience—well-being during task performance resulting
from purpose in the task itself that enhances persistence and the desire to engage
in the activity again.
The EduFlow Scale has been tested in various educational contexts with students
ranging from primary school to university including face-to-face learning (Heutte
2011; Fenouillet et al. 2014) and online learning (Caron et al. 2014; Heutte et al.
2014b, 2014c). From these studies the EduFlow Model was derived (cf. Fig. 9.1).
The EduFlow Scale has several advantages: it can be used in different educa-
tional contexts; it is a 12-item scale reducing respondent burden; and, it differenti-
ates four dimensions of flow that are relevant to cognitive processes (there are three
items per dimension).
9.6 Personal and Collective Self-Efficacy: The Power
of Believing You Can
Drawing upon inspiration from Bandura (1986, p. 391), we contend that personal as
well as collective self-efficacy refer to people’s judgments of their personal and col-
lective capabilities to organize and execute courses of action required to attain
9 The EduFlow Model: A Contribution Toward the Study of Optimal Learning…
134
designated goals (Heutte 2011). Self-efficacy beliefs are central to human agency.
Strongly perceived self- and collective efficacy mediate human accomplishment
and contribute to personal and collective well-being. People who are confident in
their capabilities approach difficult tasks as challenges to be mastered rather than as
threats to be avoided. Of course, personal efficacy and collective efficacy go hand-
in- hand because a «collection of inveterate self-doubters is not easily forged into a
collectively efficacious force» (Bandura 1997, p. 480). Individuals are socially
embedded and are rarely alone in facing problems, difficulties, or challenges.
«Simply stated, collective efficacy is the extent to which we believe that we can
work together effectively to accomplish our shared goals» (Maddux 2002, p. 284).
A high sense of personal and collective self-efficacy increases learner readiness
to invest effort, and supports persistence and willingness to face difficulties. It helps
learners recover volition after an experience judged as negative. Heutte (2011) dem-
onstrated that Cognitive Absorption (CA), autotelic activity and well-being are
linked to personal and collective self-efficacy beliefs. Under certain circumstances
when learners interact, a feeling of collective self-efficacy can more saliently affect
CA than individual self-efficacy can (cf. Perceived Self and Collective Efficacy
Flow Contributions Model [PSCEFC], Heutte 2011, p. 184). Heutte (2011, 2014)
named the Volitional Loop of Perceived Collective-Efficacy (VLPCE) a virtuous
loop in which Perceived Collective Efficacy (PCE) leads to CA which leads to an
autotelic experience—well-being, leading again to PCE and so on. The VLPCE
may well be one of the best drivers of persistence in working or learning with
others.
Fig. 9.1 Ecological validity of the EduFlow model. Relationships between different dimensions
of flow in education (Heutte et al. 2014a). Note: D1 FlowD1—cognitive absorption. D2 FlowD2—
time transformation. D3 FlowD3—loss of self-consciousness. D4 FlowD4—autotelic
experience—well-being
J. Heutte et al.
135
9.7 Project Management MOOC
Project Management MOOC Session 1 was the first xMOOC to be organized in
France. It was developed from a preexisting open courseware website. Enrollment
opened January 11, 2013. Courses began March 18 the same year and offered two
individual tracks: Basic and Advanced. A Team Project track was later opened too.
Voluteers ran the MOOC and support was provided by École Centrale de Lille who
sponsored the project. Technical support for the MOOC application—Canvas was
provided by Unow, a French startup company.
Project Management MOOC Session 3 followed the same principles, but added
seven optional modules (see Fig. 9.2). Basic and advanced tracks were launched in
March 2014. The tracks lasted 5 weeks each with an extra week for final exams.
There were 11,827 enrollees out of which 5,899 completed at least one quiz of
the Basic track.
Success rates (Bachelet 2014) were:
sEntry rate 50 %. The ratio is the number of students who submitted at least one
short quiz divided by the total number of enrollees.
sBasic track success rate: 38 %. The ratio is calculated by dividing the number of
students who submitted at least one short quiz by the number of students
successfully terminating the Basic track. Basic track success was determined
from an average score made up of at least a 70 % success score on course mate-
rial tests plus at least a 60 % success score on the final examination.
The MOOC required 25 h of study for the Basic track and 45 for the Advanced
track. These included assignments and peer assessments. The additional Team track
required at least 25 extra hours of study. Students were entitled to European Credit
Transfer and Accumulation System (ECTS) credits, awarded by École Centrale
Lille after the final assessment.
Fig. 9.2 Video courses on the Project Management MOOC
9 The EduFlow Model: A Contribution Toward the Study of Optimal Learning…
136
9.7.1 Procedure
Data collection was carried out between March and May 2014. Two online ques-
tionnaires using LimeSurvey (version 2.05) were administered:
sQuestionnaire One (Q1) contained 33 groups of questions spread over three
screen-pages.
sQuestionnaire Two (Q2) contained 42 groups of questions spread over three
screen-pages.
Q1 was administered just after the MOOC began, during the first week. A reminder
e-mail was sent 2 weeks later. Q2 was administered 6 weeks after the MOOC began
with a reminder e-mail following 1 week later.The two questionnaires contained
questions from the following scales:
sEduFlow Scale containing its four dimensions.
sPSE: Perceived Self-Efficacy beliefs in university courses (Heutte 2011, adapted
from Schwarzer and Jerusalem 1995)
sPCE: Perceived Collective-Efficacy beliefs in university courses (Heutte 2011,
adapted from Schwarzer and Jerusalem 1995)
sFor each questionnaire, 7-point Likert-type items were used ranging from
strongly disagree to strongly agree.
Distractors questions were also included to ensure that respondents paid attention
(e.g. Do not answer this question. Move on to the next).
Eleven thousand eight hundred and twenty seven invitation e-mails were sent out
to invite MOOC students to participate.
9.8 Results
9.8.1 Data Analysis
sQ1 (Mean response time 23 min.): Access to the questionnaire was registered
7,313 times. Four thousand seven hundred twenty seven respondents reached the
third screen-page of the questionnaire without answering distractor questions.
sQ2 (Mean response time 25 min.): Access to the questionnaire was registered
3,410 times. Two thousand one hundred thirty five respondents reached the third
screen-page of the questionnaire without answering distractor questions.
9.8.2 Flow Change
Between the first and the second questionnaire, all of the flow dimensions were exhib-
ited by students who persisted in the course for more than 6 weeks (see Table 9.1).
J. Heutte et al.
137
There is an increase of flow that is statistically significant in all dimensions with
a slightly stronger increase for Loss of self-consciousness. The effects (η2 /ETA-
squared) nevertheless remain very small (see Table 9.1):
sη2 = 0.03 for FlowD1-Cognitive absorption;
sη2 = 0.02 for FlowD2-Time transformation;
sη2 = 0.03 for FlowD3-Loss of self-consciousness;
sη2 = 0.01 for FlowD4-Autotelic experience—well-being.
The analysis suggests that student volition during the MOOC is linked to relative
stability over time in all flow dimensions. Results also suggest that the MOOC
enabled an optimal learning experience for those students who responded to the
questionnaires, but this may have not been the case for those who dropped out.
9.8.3 Self and Collective Efficacy: Flow Contributions
(PSCEFC) Model
The analysis of data using SEM enabled validation of the Self and Collective
Efficacy—Flow Contributions (PSCEFC) Model for Cognitive Absorption and
Autotelic Experience—Well-Being dimensions (D1 and D4). The following omni-
bus chi-square tests and fit indices were used: Comparative Fit Index (CFI; Bentler
1990), Tucker Lewis Index (TLI; Tucker and Lewis 1973), Root Mean Square Error
of Approximation (RMSEA; Steiger 1990) and Standardized Root Mean Square
Residual (SRMR). In the tested models (see Fig. 9.3), all fit indices were acceptable
(Byrne 1994; Hu and Bentler 1995).
The standardized regression coefficients (R2) suggest that the PSCEFC Model
contributes to explaining variance of FlowD1—Cognitive Absorption (63< R2 <69)
and of FlowD4—Autotelic Experience—Well-Being (41< R2 <43) in an educa-
tional context.
Consistent with Heutte (2014) expectation, results cohere with the Volitional
Loop of Perceived Collective-Efficacy (see in blue, Fig. 9.3). Standardized regres-
sion coefficients (R2) are somewhat lower than expected in relation to the effect of
Table 9.1 Flow change over time in the Project Management MOOC
Factor Survey M SE F P η2N
Flow D1 Q1 4.93 0.92 F(11,847) = 63.67 <0.001 0.03 1,848
Q2 5.08 0.84
Flow D2 Q1 4.92 1.22 F(11,840) = 36.84 <0.001 0.02 1,841
Q2 5.09 1.15
Flow D3 Q1 4.06 1.54 F(11,841) = 56.13 <0.001 0.03 1,842
Q2 4.32 1.44
Flow D4 Q1 5.06 1.11 F(11,833) = 8.51 <0.01 0.01 1,834
Q2 5.13 1.07
9 The EduFlow Model: A Contribution Toward the Study of Optimal Learning…
138
Perceived Collective-Efficacy on FlowD1—Cognitive Absorption (0.29< R2 <0.35)
and FlowD4—Autotelic Experience—Well-Being on Perceived Collective-Efficacy
(0.09< R2 <0.11). This can perhaps be explained by student assignments being pri-
marily individual rather than collective in the Project Management MOOC.
9.9 Conclusions and Prospects
This chapter has highlighted the relevance of using a flow in education model such
as EduFlow to advance the study of optimal learning environments. Furthermore,
the results from the research conducted with learners in the MOOC fit our extended
conception of Cognitive Absorption. Cognitive Absorption emerges as a central
characteristic in flow experiences within the context of learning (Heutte 2011,
2014). The model underpins the EduFlow Scale Construct. The EduFlow Scale has
three advantages:
Q1
(N=3.735)
Q2
(N=1.707)
D4
R2=.41
D4
R2=.43
D1
R2=.63
D1
R2=.69
PCE
R2=.49
PCE
R2=.47
PSE PSE
Khi2(147)=2397.80, p<.001
CFI=.93;
TLI=.92;
RMSEA=.064;
SRMR=.037;
Khi2(147)=1102.84, p<.001
CFI=.95;
TLI=.94;
RMSEA=.062;
SRMR=.036;
Structural Equation Models
.62
∗∗∗
.11∗∗∗
.35∗∗∗
.63***
.50*** .61∗∗∗
.62∗∗∗
.29∗∗∗
.09∗∗∗
.64∗∗∗
p <.05
∗∗ p <.01
∗∗∗ p <.001
Fig. 9.3 The volitional loop of perceived collective-efficacy in Project Management MOOC, at
start (Q1) and 6 weeks later (Q2). Note: D1 FlowD1-cognitive absorption. D4 FlowD4-autotelic
experience—well-being. PSE perceived self-efficacy, PCE perceived collective-efficacy
J. Heutte et al.
139
sIt is suited to flow measurement in various educational contexts;
sIt is short;
sIt differentiates between four dimensions of flow that are related to cognitive
process.
9.9.1 Further Exploration of the Social Conditions
for Flow in Education
According to Fenouillet (2012), the explanatory level of group motivation is neces-
sarily bound to the motivation of the individuals that compose it. Most theories refer
to motivation and volition on the individual level. The challenge lies in applying
these constructs to group processes. Heutte addresses the challenge using contribu-
tions from major theories of the self (Self-Determination, Self-Efficacy, and
Autotelism-Flow). His proposal for a Heuristic Model of the Individually Motivated
Community2 (HMIMC, Heutte 2011, 2014) encompasses these three theories (see
Fig. 9.4), although they do not share their views according to the Integrative Model
of Motivation (Fenouillet 2012):
2 Modèle heuristique du collectif individuellement motivé, in French (MHCIM, Heutte 2011,
2014).
Fig. 9.4 Dynamic of the heuristic model of the individually motivated community (Heutte 2014)
9 The EduFlow Model: A Contribution Toward the Study of Optimal Learning…
140
sDeci and Ryan (2000) consider the need for relatedness3 to affect behavior
orientation.
sBandura (1997)) considers predictions of self -and collective-efficacy to affect
engagement in tasks or activities.
sCsikszentmihalyi (1990) considers flow outcome to affect behavioral persistence
in activities.
Well-being encompasses these three major theories (Heutte 2011).
QIRr: Quality of interpersonal relationships with those who are responsible for the working
or learning conditions (responsible)
QIRa: Quality of interpersonal relationships between those who work or learn together (in
activity)
ACCr: Feelings of acceptance, and relatedness with those who are responsible for the
working or learning conditions (responsible)
ACCa: Feelings of acceptance, and relatedness between those who work or learn together
(in activity)
PSE: Perceived self-efficacy
PCE: Perceived collective-efficacy
FlowD1: Cognitive absorption
FlowD2: Time transformation
FlowD3: Loss of self-consciousness
FlowD4: Autotelic experience—well-being
From a socio-cognitive standpoint, the environment composed of one’s peers,
needs to be accounted for. A learner will benefit from an upturned social climate
through interactions with peers, which will support learner motivation and sustain
volition. Flow and a sense of enjoyment provide for an optimal learning experience
that in turn will contribute to the collective endeavor and will boost a sense of col-
lective self-efficacy.
Being able to make choices is fundamental to autonomy—a basic psychological
need. It affects well-being and contributes to self- and collective-efficacy. Feeling
autonomous prepares the ground for engaging in an activity and supports persis-
tence of effort to share, acquire, and construct knowledge.Directions for future
exploration may include:
sExploring cognitive absorption, autotelic activity and well-being in educational
contexts.
sCarrying out comparative studies of mediating factors of flow that learners and
instructors experience.
sStudying the ecological validity of the EduFlow Model as well as the heuristic
model of the Individually Motivated Collective.
The EduFlow Model should prove useful to studies of cognitive activities in a vari-
ety of environments including learning within organizational contexts (Heutte
3 In balance with two other basic psychological needs, autonomy and competence.
J. Heutte et al.
141
2014). Exploration on the personal, collective and institutional levels broadens our
understanding by accounting for the intricate relations of factors within processes
that are context bound, such as learning and knowledge creation.
References
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.
Andersen, F. O. (2005). International trends in primary school education: An overview based on
case studies in Finland, Denmark & Japan. Billund: Lego Learning Institute.
Bachelet R. (2014). Data and success rates of MOOC GdP3, website read 01/15/2015. http://goo.
gl/StCLX9
Bakker, A. B. (2008). The work-related flow inventory: Construction and initial validation of the
WOLF. Journal of Vocational Behavior, 72, 400–414.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood
Cliffs: Prentice-Hall.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107,
238–246.
Byrne, B. M. (1994). Structural equation modeling with EQS and EQS/windows. Thousand Oaks:
Sage Publications.
Caron, P. A., Heutte, J., & Rosselle, M. (2014). Présentation d’une méthode et d’outils pour éval-
uer les perceptions des apprenants dans un MOOC., JOCAIR 2014, Paris, France, 27 juin.
Csikszentmihalyi, M. (1975). Beyond boredom and anxiety: Experiencing flow in work and play.
San Francisco: Jossey-Bass.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper and
Row.
Csikszentmihalyi, M. (1993). The evolving self. A psychology for the 3rd millennium. New York:
Harper Collins.
Csikszentmihalyi, M., & Larson, R. (1984). Being adolescent. New York: Basic Books.
Csikszentmihalyi, M., & LeFevre, J. (1989). Optimal experience in work and leisure. Journal of
Personality and Social Psychology, 56(5), 815–822.
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.
Deci, E. L., & Ryan, R. M. (2008). Facilitating optimal motivation and psychological well-being
across life’s domains. Canadian Psychology, 49, 14–23.
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.
Delle Fave, A., Massimini, F., & Bassi, M. (2011). Psychological selection and optimal experience
across cultures: Social empowerment through personal growth. New York: Springer.
Engeser, S., & Schiepe-Tiska, A. (2012). Historical lines and overview of current research. In
S. Engeser (Ed.), Introduction to flow research. New York: Springer.
Fenouillet, F. (2012). Les théories de la motivation. Paris: Dunod.
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.
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.
Gable, S. L., & Haidt, J. (2005). What (and why) is positive psychology? Review of General
Psychology, 9(2), 103.
9 The EduFlow Model: A Contribution Toward the Study of Optimal Learning…
142
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).
Heutte, J. (2014) Persister dans la conception de son environnement personnel d’apprentissage:
Contributions et complémentarités de trois théories du self. STICEF, 21, ISSN: 1764-7223
Heutte J.,Fenouillet F., Martin-Krumm C. (2013). Contribution de la psychologie positive au pilot-
age de l’innovation. Congrès Francophone de Psychologie Positive, Metz, France.
Heutte, J., Fenouillet, F., Boniwell, I., Martin-Krumm, C., & Csikszentmihalyi, M. (2014a).
Optimal learning experience in digital environments: Theoretical concepts, measure and mod-
elisation, SymposiumDigital Learning in 21st Century Universities”. Georgia Institute of
Technology (Georgia Tech), Atlanta, GA.
Heutte, J., Galaup, M., Lelardeux, C., Lagarrigue, P., & Fenouillet, F. (2014b). Etude des détermi-
nants psychologiques de la persistance dans l’usage d’un jeu sérieux: évaluation de
l’environnement optimal d’apprentissage avec Mecagenius. STICEF, 21, ISSN: 1764–7223
Heutte, J., Kaplan, J., Fenouillet, F., Caron, P. A., & Rosselle, M. (2014c). MOOC user persis-
tence—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.
Hoffman, D. L., & Novak, T. P. (2009). Flow online: Lessons learned and future prospects. Journal
of Interactive Marketing, 23(1), 23–34.
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.
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.
Jackson, S. A., & Eklund, R. C. (2004). The flow scale manual. Morgantown: Fitness Information
Technology.
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.
Jackson, S. A., Martin, A. J., & Eklund, R. C. (2008). Long and short measures of flow: The con-
struct validity of the FSS-2, DFS-2, and new brief counterparts. Journal of Sport and Exercise
Psychology, 30, 561–587.
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.
Keller, J., & Landhäußer, A. (2012). The flow model revisited. In S. Engeser (Ed.), Advances in
flow research (pp. 51–64). New York: Springer.
Maddux, J. E. (2002). Self-efficacy: The power of believing you can. Handbook of positive psychol-
ogy (pp. 277–287). New York: Oxford University Press.
Mayers, P. (1978). Flow in adolescence and its relevation to school experience. Unpublished doc-
toral dissertation, University of Chicago. In C. R. Snyder, & S. J. Lopez (Eds.), Handbook of
positive psychology (pp. 89–105). Oxford: University Press.
Mead, G.-H. (1934). Mind, self, and society. Chicago: University of Chicago Press.
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.
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.
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.
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.
J. Heutte et al.
143
Parks, B. (1996). Flow, boredom and anxiety in therapeutic work. Unpublished doctoral disserta-
tion, University of Chicago.
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.
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.
Rheinberg, F. (2008). Intrinsic motivation and flow-experience. In H. Heckhausen & J. Heckhausen
(Eds.), Motivation and action (pp. 323–348). Cambridge: Cambridge University Press.
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.
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.
Sawyer, R. K. (2007). Group genius: The creative power of collaboration. New York: Basic Books.
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.
Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction.
American Psychologist, 55(1), 5–14.
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.
Shernoff, D. J., Csikszentmihalyi, M., Schneider, B., & Shernoff, E. S. (2003). Student engage-
ment in high school classrooms from the perspective of flow theory. School Psychology
Quarterly, 18, 158–76.
Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation
approach. Multivariate Behavioural Research, 25, 173–180.
Tucker, L. R., & Lewis, C. (1973). The reliability coefficient for maximum likelihood factor analy-
sis. Psychometrika, 38, 1–10.
Vollmeyer, R., & Rheinberg, F. (2006). Motivational effects on self-regulated learning with differ-
ent tasks. Educational Psychology Review, 18(3), 239–253.
Vygotsky, L. -S. (1962). Thought and language. MIT Press, Cambridge, Massachusetts.
9 The EduFlow Model: A Contribution Toward the Study of Optimal Learning…
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