Breaking the Flow
Examining the Link between Flow and Learning in
Computer-Mediated Learning Environments
Daniel P. Auld
1,2
and Fran C. Blumberg
1
1
Graduate School of Education, Fordham University, 113 West 60
th
St., New York, U.S.A.
2
Student Academic Success Programs, John Jay College of Criminal Justice – CUNY, New York, U.S.A.
Keywords: Flow, Learning, Computer-Mediated Learning Environments.
Abstract: In the context of research concerning computer-mediated learning environments (CMLEs), the construct of
flow, or optimal experience, has been positively linked with students' learning outcomes, such as affective
and cognitive perceptions of learning and the development of academic skills. However, this linkage is
compromised by inconsistent characterizations of flow across studies and divergent measures of when flow
may have occurred during learning. Further, characterizations of learning have differed across studies (i.e.
self-reported attitudes about one's learning experience or one's academic achievement). In this paper, we
review these inconsistencies and discuss how meta-analysis may be one means by which we can examine
whether flow does impact learning within CMLEs, given the differing operationalizations of flow and
learning that are found within the extant literature.
1 INTRODUCTION
The concept of flow, or optimal experience, as first
introduced by Mihalyi Csikszentmihalyi
(Csikszentmihalyi, 1990), has been examined in the
context of diverse activities such as sports,
classroom learning, and more recently online and
computer-mediated learning environments (de
Freitas and Neumann, 2009; Liao, 2006; Shin, 2006;
Voiskounsky, 2008). Flow can be characterized as a
state in which individuals are “in the zone” or
immersed in the task at hand, such that concerns
about performance become less salient. Further,
Csikszentmihalyi (1990) has likened flow to optimal
performance, positive feelings of well-being, and
enjoyment. In the academic realm, flow has been
associated with enhanced academic performance,
particularly in traditional classroom settings. For
example, high school students who reported
experiencing flow while writing English essays
submitted better work and were more engaged in the
activity than students who did not report as such
(Larson, 1988). In the online learning realm, Liao
(2006) found that college students who reported
being in flow during their online courses were more
likely to engage in online course activities than those
who were not in flow.
The linkage between factors such as flow and
learning has garnered much research attention
particularly in computer-mediated learning contexts
(Konradt, Filip, and Hoffman, 2003; Liao, 2006;
Shin, 2006), which includes learning via mobile
device applications and online learning modules.
These contexts are becoming increasingly ubiquitous
in higher education (Allen and Seaman, 2010).
Online learning in this context refers to courses
whereby all or a significant portion of the instruction
and learning activities are presented via the Internet.
Findings show that learning outcomes in online
courses are comparable to, or in some cases,
superior to learning in traditional classroom
environments (Allen, Mabry, Mattrey, Bourhis,
Titsworth and Burrell, 2004). One contributing
factor to enhanced academic performance in online
learning environments is the engagement they evoke
(Arbaugh, 2010). In fact, some researchers contend
that online learning environments or computer-
mediated environments (CMEs) in general promote
flow (Chen, Wigand, and Nilan, 1998; Hoffman and
Novak, 1996; Liao, 2006). One striking issue that
has plagued this work, however, is the diversity of
operationalizations and measurements of flow. This
situation compromises conclusions that can be
461
P. Auld D. and C. Blumberg F..
Breaking the Flow - Examining the Link between Flow and Learning in Computer-Mediated Learning Environments.
DOI: 10.5220/0004414304610468
In Proceedings of the 5th International Conference on Computer Supported Education (CSEDU-2013), pages 461-468
ISBN: 978-989-8565-53-2
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
drawn about how flow may impact learning in these
environments.
Initial characterizations of flow in CMEs were
drawn from Hoffman and Novak (1996) whose
characterization of the components of flow as
experienced during web browsing were adapted by
other researchers. Hoffman and Novak’s model of
flow replicated much of Csikszentmihalyi’s original
formulation (as discussed below) and constructs
derived from the media literature including
interactivity and telepresence. Researchers (Chen, et
al., 1998; Choi, Kim and Kim, 2007; Novak,
Hoffman and Yung 2000) then modified Hoffman
and Novak’s (1996) framework to investigate flow
in CMEs often with far less extensive
characterizations of flow that fell far short of
Csikszentmihalyi’s original formulation (Liu, et al.,
2011). Thus, diverse characterizations of flow have
been reflected in the CME literature, which includes
similarly diverse means of measuring flow. For
example, researchers have assessed flow experiences
via surveys that span diverse time frames in which
individuals may attain and lose the flow state (Choi,
et al., 2007; Konradt, et al., 2003; Liao, 2006; Liu, et
al., 2011; Shin, 2006) or may follow by several
months after a given activity has occurred (Choi, et
al., 2007; Liao, 2006). This situation limits
conclusions about whether flow does influence
learning in CMEs.
The fundamental question remains whether flow
is experienced during CMEs generally and CMLEs
(computer mediated learning environments) more
specifically, and how the experience of flow may
affect learning. Given the lack of consensus about
how flow occurs in CMLEs, conclusions about
whether CMLEs allow for flow or at minimum,
learner engagement, are not readily drawn. Further,
the body of literature concerning CMLEs and flow
spans diverse disciplines that tend to perpetuate,
within discipline, a particular conceptualization of
the flow construct. In this paper, we review these
issues and make suggestions for clarifying the flow
construct as linked to learning outcomes within
CMLEs.
2 FLOW AND ITS
CHARACTERIZATION
Flow refers to a state of optimal performance
whereby individuals feel in control of their behavior
while engaged in motivating activities, and report
extreme enjoyment or self-transcendence
(Csikszentmihalyi, 1988). According to
Csikszentmihalyi (1988), flow is experienced across
diverse domains and activities, is all-absorbing, and
seemingly automatic despite occurring during
cognitively demanding tasks (Csikszentmihalyi,
1988). He further noted that those experiencing flow
were more likely to re-experience it. Therefore, flow
is best characterized as cyclical, whereby its
attainment is positively correlated with the desire
and likelihood of re-attaining it.
According to Csikszentmihalyi (1988; 1990),
flow is comprised of nine major components
reflecting the general categories of antecedents,
experiences, and effects or consequences. One
antecedent includes a balance between perceived
skills required to complete an activity and optimal
challenge whereby the activity is neither too easy
nor too difficult. When one’s skills are low and
challenges posed by the task are too easy, an
individual may experience apathy. If one’s skills are
high, but challenges posed by the task are too easy,
an individual may experience boredom. Similarly,
when one perceives one’s skills as insufficient given
the demands of the tasks, the individual may
experience anxiety and abandon the task.
Accordingly, ideal flow situations are those in which
the challenges become progressively difficult as
one’s skills improve. Two further antecedents
include a clear, attainable goal and unequivocal
feedback from the situation.
Aspects of the flow experience entail the
merging of action and awareness, which is
accompanied by focused concentration that
culminates in the paradox of control
(Csikszentmihalyi, 1988). Here, the individual feels
in control of his actions during a task, despite a
seeming automaticity and effortlessness to his
behaviors. The three flow effects include the loss of
self-consciousness, the transformation or distortion
of time, and a resulting enjoyable experience.
Csikszentmihalyi first examined flow within the
context of athletic performance (Csikszentmihalyi,
1988; Csikszentmihalyi, 1990). For example, while
interviewing high-achieving athletes about their
performance in their respective sports,
Csikszentmihalyi (1988, 1990) noticed that each
described key accomplishments in similar ways: the
loss of self-consciousness, the sensation of being
carried or flowing on a current, and the feeling of
being present in the moment despite exposing their
bodies to stressful physical circumstances.
Participants also reported feeling compelled to re-
engage in these activities when they accomplished
their goals, if only for the opportunity to achieve
CSEDU2013-5thInternationalConferenceonComputerSupportedEducation
462
new goals and the self-fulfillment that accompanied
their success. For example, Sato (1988) found that
Japanese adolescents’ motorcycle riding produced
positive feelings of well-being among those engaged
in the activity, a sense of community with fellow
riders, and pride from applying their skills to the
challenges of the rides.
3 MEASUREMENT OF FLOW
Since the late 1970s, the standard technique for
measuring flow had been the Experience Sampling
Method (ESM). This tool, first used by
Csikszentmihalyi and colleagues (Csikszentmihalyi
and Larson, 1987; Csikszentmihalyi, Larson, and
Prescott, 1977), was designed to allow real-time
measurement of flow experiences. Specifically,
individuals were given paging devices that randomly
“beeped” during a given interval of time. When the
devices beeped, participants were to stop what they
were doing and answer questions about the activity
in which they were currently engaged. For example,
participants were asked what they were doing, where
they were doing it, their emotional state,
involvement in the activity, perceptions of activity
challenges, perceptions of their skills to meet these
challenges, interest and motivation to engage in the
activity, concentration levels, their sense of self-
consciousness, and control. Csikszentmihalyi and
LeFevre (1989) found, via this technique, that
individuals tended to report experiencing flow more
often while on the job than during leisure activities
despite their greater motivation to engage in leisure
activities. Notably, those who were more engaged in
a given activity when they were beeped reported
happier feelings, greater creativity, concentration,
and satisfaction than those who were less engaged.
Other researchers using ESM and its derivations
have since documented flow in diverse activities
across cultures including daily labor, educational
settings, web navigation, electronic gameplay, and
computerized simulations (Carli, Delle Fave and
Massimini, 1988; Chen, et al., 1998;
Csikszentmihalyi and Csikszentmihalyi, 1988;
Csikszentmihalyi, 1990; Larson, 1988; O’Broin and
Clarke, 2006; Shernoff et al., 2003).
4 FLOW IN TRADITIONAL
LEARNING ENVIRONMENTS
Flow also has been studied in diverse educational
contexts. For example, Shernoff, et al. (2003)
examined flow in the context of student engagement
in classroom activities using data from a three-year
longitudinal study of 526 10th and 12th graders.
These students participated in discussions that were
either teacher-led or student-led and required skills
and challenge levels that varied from low to high.
Participants then completed surveys concerning
aspects of flow such as engagement, attention,
motivation, and enjoyment, with regard to the
activities completed in class and their perceived
performance in these activities. Students’
perceptions of high challenge and skill levels were
associated with greater engagement in their
coursework than when they perceived lower
challenge levels. When experiencing flow, students
reported greater interest, concentration, and
enjoyment than those not experiencing flow
(Shernoff, et al., 2003).
Larson (1988) also found that characteristics of
flow correlated with better research papers produced
by high school students for their junior-year English
class than when these characteristics were absent. In
his study, students were to write 10-page papers over
the term, and to review and to revise their work
based on teacher feedback before submitting final
drafts. Larson (1988) compared students’ survey
responses about their emotional states while writing
their essays with their essay grades to determine
how flow characteristics predicted their
performance. Students who demonstrated aspects of
flow, as reflected by enjoyment in the activity, also
demonstrated effective self-regulation strategies to
stay on task, and achieved their goal of writing well-
structured and well-researched papers according to
their teachers. Students who experienced flow,
regardless of time spent on the task, were more
creative, more efficient and received higher grades
than peers who did not cite flow. Further, those who
did not demonstrate aspects of flow set expectations
that were unrealistically high, were more anxious
about their goals for completing their papers, and
received poorer grades than their counterparts who
reported flow.
Collectively, these findings show that in
traditional learning environments students who
achieve flow as opposed to those who do not, are
more engaged in and attentive during their
schoolwork (Shernoff, et al., 2003), more successful
at achieving their academic goals (Larson, 1988),
better at employing self-regulation strategies
(Larson, 1988), more likely to show gains in self-
esteem following the activity (Shernoff, et al., 2003),
experience greater enjoyment (Shernoff, et al.,
BreakingtheFlow-ExaminingtheLinkbetweenFlowandLearninginComputer-MediatedLearningEnvironments
463
2003), and report less anxiety about their
schoolwork (Larson, 1988). Studies eliciting these
findings, however, are not grounded in all original
nine variables that Csikszentmihalyi cited as
requisite for the flow experience. This situation
reflected a situation whereby researchers made
selective choices about which variables to include in
their investigations of the flow experience. Among
the variables selected most often were the balance of
perceived skills to perceived challenges and
perceived control. Among the variables most often
excluded were feedback, time distortion and loss of
self-consciousness. The selective addition and
deletion of components of Czikszentmihalyi’s model
was notably evident in the literature concerning flow
in the context of CMLEs as discussed below.
5 EXAMINATION OF FLOW IN
CMLES
Given educators’ increasing interest in online
learning, research examining flow in the context of
CMLEs (Chen, et al., 1998; Ghani, 1995;
Voiskounsky, 2008; Webster, Trevino and Ryan,
1993) has become more salient. Across this growing
body of work, very clear distinctions in the
operationalization of flow, the timing of its
assessment, and the characterization of learning have
emerged. Specifically, flow has been described via
characterizations that reflected all, some, or none of
Csikszentmihalyi’s original formulation (1988;
1990). Second, flow has been assessed at variable
times intervals such as during learning activity
sessions (consistent with Csikszentmihalyi’s ESM
approach) or after, sometimes with significant
delays. Finally, although researchers have often
claimed to have measured learning via content or
skills acquired after a given activity (reflecting a
direct learning measure), in most if not all studies as
reviewed below, learning has been assessed via
attitudes about the activity or one’s skills (reflecting
an indirect learning measure). These discrepancies
have ramifications for understanding the linkage
between flow and learning within CMLEs and begin
with defining the flow construct.
5.1 Divergence in the
Operationalization of Flow
Researchers who first studied flow in the context of
CMLEs did not see Csikszentmihalyi’s (1988; 1990)
original formulation as applicable. For example,
Hoffman and Novak (1996) built a model to
examine flow in the navigation of consumer web
sites that started with Csikszentmihalyi’s nine
characteristics and then also incorporated extrinsic
motivation, as demonstrated by goal-directed search
(where search referred to those conducted while
navigating a given website); intrinsic motivation, or
non-directed search; users’ level of involvement in
the task at hand, interactivity of the medium,
vividness of the site, and telepresence or the
“mediated perception of an environment” (see
Steuer, 1991, p. 76). According to Hoffman and
Novak, the attainment of flow was linked to
increased learning, perceived behavioral control,
willingness to explore (in their case, websites), and
positive subjective experiences. They demonstrated
this in studies examining participants’ exploration of
a consumer website (Novak, et al., 2000), whereby
reported experiences of flow were significantly,
positively correlated to respondents’ perceived
skills, perceived challenges of browsing the web
site, telepresence, and to the interactive speed of the
web site.
Despite only a few variables being shown to link
directly to flow, researchers would continue to draw
from and test the work of Hoffman, Novak and
colleagues (Hoffman and Novak, 1996; Novak, et
al., 2000). Some of these researchers who drew on
Hoffman and Novak’s (1996) work used predictors
that seemed unique to CMEs, such as interactivity
(Liao, 2006) and telepresence (Shin, 2006). For
example, Liao (2006) found that within a distance
learning course, interactivity was more predictive of
flow than undergraduate participants’ assessment of
the balance of skills to perceived challenges; a
finding that contradicted the accepted notion that the
balance of skills and challenges is the best predictor
of flow (Chen et al., 1998; Konradt, et al., 2003;
Massimini and Carli, 1988; Pearce, Ainley, and
Howard, 2005). Shin (2006) incorporated
telepresence into a factor analysis to clarify how the
flow antecedents of perceived skills, perceived
challenges, and clearly defined goals, contributed to
the flow experiences of enjoyment, telepresence,
focused attention, engagement and time distortion
within online coursework. The findings showed that
these five experience factors accounted for nearly
60% of the variance in explaining flow. Further,
flow was significantly positively correlated with the
balance of skills and challenges and overall
satisfaction with online courses.
Others would omit variables, such as the
perceived balance of skills and challenges that were
common in flow studies and instead examined the
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appeal of the activities themselves. For example,
Ryu and Parsons (2012) assessed the link between
flow and learning using a mobile device application
via a seven-question Likert scale drawing on two
variables from Csikszentmihalyi (1990) to predict
flow, which were cognitive curiosity, and intrinsic
interest, and added risk-taking behavior, or attempts
to explore aspects of the learning environment not
required by the task instructions. Flow was
indirectly linked to learning through each of these
three predictors demonstrating participants’
voluntary willingness to explore the environment
further, greater importance given to the activity, and
more motivation to learn.
Still other researchers examined flow with
respect to only one of Csikszentmihalyi’s (1988;
1990) original nine predictors of flow; the balance of
perceived skills and perceived challenges
(Csikszentmihalyi and LeFevre, 1989; Konradt, et
al., 2003; Liu, et al., 2011; Pearce, et al., 2005). For
example, Liu, et al., (2011) examined flow and
learning through use and acquisition of problem-
solving strategies among 110 first-year university
computer science students constructing railway
system simulations follow lecture or lab activities.
The authors hypothesized that students in flow
would show better problem-solving strategies than
students who were anxious or bored; essentially, the
antithesis of flow. Findings indicated that flow was
more likely to occur when the students were actively
engaged in building simulations than passively
involved in the lectures. Specifically, when building
simulations, over 55% of students achieved flow;
21% of students achieved flow when attending
lectures. Liu, et al., (2011) offered a unique
contribution to the literature in that they
demonstrated that flow was correlated to a direct
learning outcome, namely that participants in flow
appropriately transferred successful problem-solving
strategies to new situations more often than those
who did not achieve flow. However, this finding was
only marginally significant.
Choi, et al., (2007) assessed flow using
participants’ self-report of whether they had
experienced it, their ratings of its frequency, and its
intensity. Participants were asked to answer survey
questions two to three months following the end of
the course to determine whether flow impacted
individuals’ self-efficacy with technology while
using an e-learning system. Students’ self-reports of
flow were significantly, positively correlated with
their attitudes towards or satisfaction with e-learning
and with technology self-efficacy.
Notably,
one set of researchers (Pearce, et al.,
2005) evaluated flow in two ways; the first entailed
a situated measurement that assessed flow
immediately after each of seven learning activities
by asking participants to rate their perceived skills to
meet the perceived challenges (Massimini and Carli,
1988; Konradt, et al., 2003). From these seven
skill/challenge ratios the researchers tallied a final,
in-situ score for flow. Pearce et al., (2005) also
assessed flow a second way using a post-hoc
questionnaire following all seven activities. Flow
was operationalized by the variables of control,
enjoyment and engagement. Surprisingly, the post-
hoc measure of flow did not correlate with the in-
situ measure of students’ flow states. This finding
was perplexing as the balance of perceived skills to
perceived challenges as used in the in situ measure,
and control, enjoyment, and engagement from the
post-hoc measure are all variables that have been
established as predictors of flow and should have
yielded a positive correlation. Therefore, the authors
re-examined participants’ post hoc reports of flow
and found that they were correlated with the most
memorable of the seven activities that participants
had just experienced. When the researchers were
comparing the post-hoc measure to a summed total
of the seven distinct in situ measures they had
conflated flow and non-flow moments. Thus, the
predictors of flow were less important than the
timing of the flow assessment to the supposedly
flow-inducing activities.
5.2 Measurement and Timing of Flow
within CMLEs
The timing of flow measurement is critical to the
accuracy of individuals’ self-report. If it is too
delayed, memories fade leaving reports of the
experience in doubt. In ESM, participants’
perceptions of flow states are assessed as soon as
they receive the alert to report on their state.
However, ESM has been criticized because it
removes participants from the state to be assessed
(Weber, et al., 2009). A survey of the literature
indicates that researchers have assessed flow using
both in-situ and post-hoc measures. For example,
Pearce, et al., 2005 examined flow in-situ, following
each of the seven potentially-flow inducing
activities, and after the entire set of seven learning
activities. Other researchers have assessed flow in
non-situated ways and with significant time delays.
In fact, Ryu and Parsons (2012) assessed flow days
after the flow-inducing activities occurred and Choi,
et al. (2007) and Liao (2006) did so months after
these activities ended.
BreakingtheFlow-ExaminingtheLinkbetweenFlowandLearninginComputer-MediatedLearningEnvironments
465
Variants in the timing of flow may be an artifact
of researchers’ efforts to adapt measurement of flow
to web-based environments. For example, Chen et
al, (1998) adapted ESM for the web such that a
survey assessing flow would appear directly on the
screen on which the respondents already were
viewing. In their study, their flow conceptualization
was consistent with that offered by
Csikszentmihalyi. As part of their investigation, they
designed a questionnaire to “pop up” randomly and
frequently during web browsing sessions as
participants navigated different sites. The
researchers found evidence of flow in users’
experiences, particularly, when the users perceived
themselves as able to navigate a given site.
Researchers also have adapted ESM for use
within learning activities occurring on computers or
within CMLEs. For example, O’Broin and Clarke
(2006) adapted the Chen, et al. (1998) pop-up web
survey to design a computer-based and mobile
application which recorded students’ assessment of
their perceived skills, perceived challenges; clarity
of the activity’s goals; understanding of feedback;
meaningfulness of the activity; amounts of
concentration; and feelings of control. Findings
showed that participants were in flow 81% of the
time when engaged in a given task.
Capturing the flow state as close in time to when
it likely occurred is the goal of many studying flow
(Chen et al, 1998; Csikszentmihalyi and Lefevre,
1989). Conceivably, this is because measurement
accuracy should be higher when more closely
situated to the flow experience. For example, Pearce
et al. (2005), in the study described above, noticed
that participants who did well on the learning
activities demonstrated skill growth as the
scaffolded challenges increased. Specifically, the
skill/challenge ratios following the first activity, the
fourth activity (which was highly-challenging and
thus, presumably memorable), and the sixth and
seventh activities significantly correlated with the
post-hoc measure of flow. The authors concluded
that primacy effects, recency effects, and highly
salient events, such as greatly challenging tasks,
predominated participants’ overall assessment of
their experiences reporting their flow states after all
activities had been completed. This conclusion
highlights the importance of contextualizing flow
measurement to particular moments in time and not
generally as flow may vary during the course of an
activity or over the course of a set of related
activities.
Additional criticisms of flow measurement
include assessing flow as related to an activity in
general and not to a specific moment when flow may
have occurred (Weber, et al., 2009). For example,
Shin (2006) and Liao (2006) assessed students’ flow
states as related to perceptions of their online
coursework overall rather than specific activities
where they may or may not have been in flow. The
use of ESM and adapted ESM techniques has greatly
improved flow measurement by allowing researchers
to assess flow immediately after having experienced
it (Csikszentmihalyi and Larson, 1987;
Csikszentmihalyi and LeFevre, 1989). However,
studies that utilized adapted ESM techniques did not
examine flow’s link to learning (O’Broin and
Clarke, 2006) and studies that utilized other forms of
in situ measurement of flow did not establish links to
direct learning outcomes (Pearce, et al., 2005). There
is inconsistency about what flow is and despite clear
recommendations in the research specifying that
flow should be measured as close in time to the
supposedly flow-inducing moments, researchers
measure flow in non-situated ways and with
significant delays following the activities. Therefore,
conclusions about flow’s linkage to learning are
dubious.
5.3 Flow Linkages to Learning
Outcomes within CMLEs
Findings that examine the linkage between flow and
learning outcomes, especially direct learning
outcomes, are limited. For example, Liu, et al.,
(2011) as noted above, demonstrated flow’s link to
successful use of problem-solving strategies.
However, these researchers assessed students’
perceptions of flow following a six-week lecture
period, and later following a two-week period of
building simulations thereby conflating flow and
non-flow moments within each assessment and
neglecting appropriate measurement of the
construct. Therefore, this situation compromises
conclusions about whether it was flow that was
linked to learning, or another aspect of the
instructional situation unrelated to participant’s flow
levels.
Far greater evidence of flow’s linkage to indirect
than direct learning outcomes is reflected in the
literature. For example, flow has been shown to
yield greater satisfaction with learning (Choi, et al.,
2007; Liao, 2006; Shin, 2006), self-efficacy within
the medium (Choi, et al., 2007), intentions to engage
in the learning environment again in the future
(Liao, 2006), exploratory use of the environment
(Liao, 2006; Ryu and Parson, 2012), and motivation
to learn and involve oneself in the activity (Ryu and
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466
Parsons, 2012). Compromising the linkage between
flow and indirect learning in these situations is that
flow was assessed either independent of a given
learning activity (Liao, 2006; Shin, 2006) or long
after the activity concluded (Liao, 2006; Ryu and
Parsons). A consistent definition of flow and a
situated and timely measurement of flow’s impact
on learning outcomes would reduce doubts about
claims of flow’s linkages to learning,
6 CONCLUSIONS
The fundamental set of questions that emerges from
the literature is whether flow occurs within CMLEs,
and if so, how best to measure it and facilitate it so
as to impact learning. The promise of flow is that it
is all-absorbing engagement in a task and it
motivates individuals to engage in an activity, to
exceed their current skills, and to continually
increase their expertise in this domain. This situation
is desirable in both formal and informal education
contexts.
However, characterizations of flow in CMEs and
CMLEs have been inconsistent. Similarly,
researchers' measurement of flow have deviated
widely from Csikszentmihalyi and colleagues'
(Csikszentmihalyi and Larson, 1987;
Csikszentmihalyi, et al., 1977) goal to capture the
flow experience as close in time to its occurrence.
As noted above, many researchers have assessed
flow long after its occurrence has passed.
As part of our efforts to clearly identify trends in
these differing definitions of flow and its
measurement as linked to learning within CMLEs,
we suggest the use of meta-analysis, as currently
being undertaken in our work. The goal of our meta-
analysis is to extensively examine the pool of
relevant studies to determine if the divergent
methods of defining and measuring flow
demonstrate consistent impact on direct or indirect
learning outcomes. It is anticipated that given the
diversity of flow characterizations and measurement
that a homogeneous effect, which would signify one
universal impact of flow on learning across all
studies, is highly unlikely. More likely, the overall
meta-analytic effect size will demonstrate
heterogeneity, whereby some flow characteristics
might demonstrate stronger links to direct and
indirect learning outcomes as compared to other
flow characteristics that demonstrate weaker links to
learning.
Given that learning might be influenced more by
a small number of flow characteristics, such as the
balance of skills to challenges and the interactivity
of the CMLE, the meta-analysis would recommend
flow characteristics and combinations of those
characteristics that demonstrate the kinds of learning
gains that are possible when learners actually
achieve the flow state. Further, certain situated
measurements of flow might demonstrate greater
occurrence of flow, or increase the certainty that
flow actually occurred as compared to significantly
delayed measurements of flow that might produce
further doubts to flow’s association with learning.
Since flow is difficult to capture the more time has
elapsed and the more general the measurement is to
flow moments, even without the benefit of our meta-
analysis, one may conclude that researchers should
avoid non-situated and delayed measures of flow.
By offering an operationalization of flow that
demonstrates its ability to impact learning within
CMLEs and situating its measurement close in time
to its occurrence, this meta-analysis would offer a
starting point for more consistency in this area of the
literature. Thus, examining flow's link to learning
outcomes within CMLEs could be more easily
compared across studies because when flow is
discussed it is certain that there is a consistent
definition and reliable measure of flow.
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