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Subjective experience, job satisfaction, and professional actions of music teachers as a predictor of flow state
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Subjective experience, job satisfaction, and professional actions of music teachers as a predictor of flow state
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Content
Subjective Experience, Job Satisfaction, and Professional Actions of Music Teachers as a
Predictor of Flow State
by
Michael J. de Vries
A Dissertation Presented to the
FACULTY OF THE USC THORNTON SCHOOL OF MUSIC
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF MUSICAL ARTS
MUSIC TEACHING AND LEARNING
May 2023
Copyright 2023 Michael J. de Vries
ii
Acknowledgements
A special thank you to all the following people who played a crucial role in this endeavor:
Dr. Peter Webster who acted as my main mentor throughout this entire process and retained the
role to see me through all the way to the end even after his official retirement. His advice and
direction were my guiding light throughout the whole process.
Dr. Beatriz Ilari who generously took the official position as my dissertation committee chair
upon Dr. Webster’s retirement. Even more so, I took much of my coursework with Dr. Ilari and I
can easily credit her as one of my main influences at USC.
Dr. William Coppola and Dr. Candice Mattio who both graciously served on my dissertation
committee and provided insightful feedback that strengthened my study.
Vice Dean Dr. Rotem Gilbert for approving the purchase of ExpiWell for use in the MTAL
department which was vital for the implementation of this study.
All the professors, staff, and fellow students whom I have had the pleasure of working with
throughout these years at the University of Southern California, as well as during my formative
years at California Baptist University.
My High School Choir Director George Giorgetti whose influence and mentorship sent me down
this career path in the first place.
All my wonderful family and friends who have supported and encouraged me along this journey.
Finally, my wife Stephanie who has put up with my stress and busy schedule for many years
while I worked on this undertaking, even throughout the process of welcoming our baby
daughter Clara into the world.
iii
Table of Contents
Acknowledgements……………....…………………………………….………………………….ii
List of Tables………………………………………………………………….…………......……v
List of Figures…………….………………………………………………………………………vi
Abstract…………………………………………………………………………...……………...vii
Chapter 1: Introduction ................................................................................................................... 1
Narratives of Practice .................................................................................................................. 1
General Description of Study ...................................................................................................... 5
Problem Description and Need for Further Research ................................................................. 5
Purpose Statement ....................................................................................................................... 8
Research Questions ..................................................................................................................... 8
Hypothesis ................................................................................................................................... 8
Definitions of Important Terms/Constructs ................................................................................ 9
Flow Theory Explained ............................................................................................................. 10
Historical Development ......................................................................................................... 10
Models of Flow ...................................................................................................................... 15
Positive Benefits of Flow ...................................................................................................... 17
Organization of the Study ......................................................................................................... 20
Chapter 2: Literature Review ........................................................................................................ 22
Introduction ............................................................................................................................... 22
Measuring Flow......................................................................................................................... 22
Flow Questionnaire................................................................................................................ 23
Standardized Scale ................................................................................................................. 23
Experience Sampling Method ............................................................................................... 24
Calculating Flow Using ESM Data and the Quadrant Model ............................................... 28
Examples of Flow Measures in the General Literature that Guide the Present Study .......... 30
Implications for Technologically Mediated ESM Study of Flow ............................................. 32
Technology in Collecting, Coding, and Analyzing Data....................................................... 32
Reaching and Retaining ESM Participants ............................................................................ 33
Flow Research in Music Teaching and Learning ...................................................................... 36
Music Teaching Practice ....................................................................................................... 36
Music Performance ................................................................................................................ 38
Flow in Well-being/Motivation ............................................................................................. 40
Music Listening ..................................................................................................................... 42
Subjective Experience of Teachers ........................................................................................... 43
Praxis Shock/Survival Mode ................................................................................................. 43
Teacher Career Development ................................................................................................ 46
Student Issues and Classroom Management ......................................................................... 48
Administrative Issues ............................................................................................................ 50
Urban and Suburban Teacher Experience ............................................................................. 51
Summary of Reviewed Literature ............................................................................................. 52
iv
Chapter 3: Design ......................................................................................................................... 54
Introduction ............................................................................................................................... 54
Research Questions and Hypothesis ......................................................................................... 54
Research Methodology .............................................................................................................. 55
Study Timeline ...................................................................................................................... 59
Pilot Study ............................................................................................................................. 59
Participants and Their Recruitment ....................................................................................... 62
Initial Steps in Data Collection .............................................................................................. 64
Variables ................................................................................................................................ 64
Survey Design and Data Collection ....................................................................................... 65
Data Collection ...................................................................................................................... 67
Statistical Procedures ............................................................................................................. 68
Summary ................................................................................................................................... 72
Chapter 4: Results ......................................................................................................................... 74
Introduction ............................................................................................................................... 74
Demographics and Descriptive Statistics .................................................................................. 75
Approach to Data Analysis ....................................................................................................... 77
Results ....................................................................................................................................... 80
Summary of Results .................................................................................................................. 87
Summary ................................................................................................................................... 88
Chapter 5: Conclusion and Implications ....................................................................................... 89
Introduction ............................................................................................................................... 89
Overall Summary of Results ..................................................................................................... 89
Discussion of Findings .............................................................................................................. 90
Research Question 1 .............................................................................................................. 90
Research Question 2 .............................................................................................................. 97
Research Question 3 ............................................................................................................ 103
Qualitative Responses ............................................................................................................. 105
Further Recommendations for Informed Practice and Continued Research ........................... 107
Conclusion ............................................................................................................................... 111
References ................................................................................................................................... 113
Appendix A ................................................................................................................................. 122
Appendix B ................................................................................................................................. 124
Appendix C ................................................................................................................................. 126
v
List of Tables
Table 3.1: Quadrant Model……………………………………………………………………… 65
Table 3.2: Data Collection Tally of Flow States 1-4 and Actions 1-8…………………………... 68
Table 4.1: Participant Demographic Information……………………………………………….. 75
Table 4.2: Initial Survey Questions 14-27 with Average Rating………………………………... 77
Table 4.3: Quadrant Model with Flow State Number Codes…………………………………… 78
Table 4.4: Initial Survey Predictor Variables Codes and Text………………………………….. 79
Table 4.5: Action Codes and their Descriptions………………………………………………… 79
Table 4.6: Regression Model: Flow State 4: The Optimal State of Flow…………………….…. 81
Table 4.7: Regression Model: Flow State 3: Control/Relaxation………………………….……. 82
Table 4.8: Regression Model: Flow State 2: Stress/Anxiety……………………………….…… 83
Table 4.9: Regression Model: Flow State 1: Boredom/Apathy…………………………….…… 84
Table 4.10: Regression Model: Flow States and Corresponding Action……..……………….… 85
vi
List of Figures
Figure 1.1: Original Model of Flow .............................................................................................. 16
Figure 1.2: 8-Channel Model of Flow .......................................................................................... 17
Figure 3.1: ExpiWell Survey Programming ................................................................................. 58
Figure 3.2: ExpiWell Scheduling .................................................................................................. 58
vii
Abstract
Measures of job satisfaction and preparedness from an initial survey with music teachers
(n=40) were used to predict flow states using Csikszentmihalyi’s theory of flow. Flow state and
corresponding professional action data were collected through experience sampling. Multiple
linear regression was used to show relationships between demographic information, satisfaction,
and preparedness measures with each flow state from the quadrant model. Correlation was also
found between reported flow state and corresponding professional actions. It was found that the
optimal state of flow was correlated with higher satisfaction with student population and pay,
higher levels of isolation, and lower levels of satisfaction with parental and community support.
The state of control/relaxation was correlated with satisfaction with teacher preparation program.
Additionally, the professional action of conducting rehearsal/performance was associated with
the optimal flow state, stress/anxiety, and boredom/apathy. The action of teaching concept(s)
was correlated with the optimal state of flow and the state of stress/anxiety. The action of
planning/preparing instruction was correlated with the optimal state of flow and the state of
control/relaxation.
1
Chapter 1: Introduction
It is useful to remember occasionally that life unfolds as a chain of subjective
experiences. Whatever else life might be, the only evidence we have of it, the only direct
data to which we have access, is the succession of events in consciousness. The quality of
these experiences determines whether and to what extent life was worth living.
(Csikszentmihalyi, 2014, p. 209)
Narratives of Practice
Narrative 1: The concert is in three days. Everything is locking into place. You are passed
the rehearsal stages of learning notes, talking about the shape of the music, the diction,
and all the other details. The music is difficult, but there has been a large amount of time
spent mastering it. At this point, it is all memorized and you are just creating music. It is
very musically satisfying. Complete focus is on the music and the interaction between the
ensemble and conductor is instantaneous and seamless. The emotion and drama of the
music is tangible. Unfortunately, sense of time snaps back to reality when the obnoxious
dismissal bell interrupts just a couple of minutes before finishing the piece. Everyone
wants to keep going, but the passing period is short, so everyone keeps singing as
personal items are grabbed and people shuffle out the door. Standing there now in a silent
empty space, the contrast of the preceding moment is stark. Thoughts drift back to the
many responsibilities waiting for attention. A sigh is released before heading back to the
solitude and bureaucracy of the office duties that are awaiting.
Narrative 2: Another piece is needed for the concert. Several new pieces have been in the
works, but one more is needed to round out the concert. The perfect piece was in the
2
library. A personal favorite that you have done multiple times over the years is exactly
what is needed. You know the piece like the back of your hand, and you know it will be a
winner with the students and the audience alike. Starting rehearsal with the song, you
immediately jump to the section at the end that is the most difficult. The student to your
left is starting to fidget and beginning to gently raise their hand in a way that you can tell
they need to go to the restroom but do not want to interrupt. While continuing to play the
piano with your left hand, eye contact is made with the student and a motion made with
the right hand indicating to the student to grab the bathroom pass and go. You already
know the basses are going to come in on the wrong note right here, so you start with
focus on that exact moment. You then move on to the other spots you know will be
trouble if not addressed. A glance at the clock reminds you that there are seven minutes
left in the class. You know you have just enough time to go over two more things. With
exactly two minutes left, you covered exactly what you wanted and inform the students to
clean up right on schedule. Isn’t it nice when everything goes exactly as planned?
Narrative 3: This beginning ensemble is making your hair go gray far too early. This
group is like Dr. Jekyll and Mr. Hyde. You have no idea which version of the ensemble is
going to show on any given today. On Friday you ended the rehearsal with optimism. The
day before that you searched for comfort in imagining existence in an alternate universe
where corporal punishment was still acceptable. Maybe that wasn’t the most productive
thing, but your frustration had to come out somewhere. This Monday morning it seems as
if the entire choir is sleep deprived. Also, a few of the best singers are absent because of a
field trip that nobody told you about. The volume of the singers ranges somewhere
3
between a church mouse and a tree falling in the forest with nobody there to witness it.
Sure, it is Monday morning, but there is a concert in 3 days! You are doing your best act
to be positive and get some energy in the room, but anger is starting to sneak out. A
couple of minutes later, you must stop in the middle of singing because you randomly
hear rap music. First you assume someone is getting a phone call and forgot to put their
phone on silent. Then you realize it is far worse than that. You are dumbfounded as your
eyes lock with the girl who is listening to headphones during rehearsal. “Are you
seriously listening to your headphones during rehearsal?” First, she tries to explain how it
is possible for her to listen to her extremely loud music through headphones while still
simultaneously listening and participating in the rehearsal. “Are you kidding me?” is
quickly followed by her actual explanation informing you that she hates the song, the
class, and doesn’t care. After dealing with this student, you must apologize to the class
because it takes you a little time to control your emotion and refocus back on what is
even happening in the class. How has it only been 15 minutes?
Narrative 4: After arriving at your third school for the day, you get settled in the
room and have a few minutes until the 6
th
grade band students will be released to see you
for their single class every two weeks. You faceplant into the desk to rest your eyes. This
is going to be the 19
th
time you taught this same lesson and 7
th
school you visited before
you start the rotation all over again. At this point, Hot Cross Buns is ingrained into your
psyche, and you sympathize with Bill Murray in Groundhog Day. The kids are sweet as
always, but you are tired. It is that point in the semester when you seriously need a break.
Plus, with everything going on in life, you got very little sleep last night. The weekend is
4
almost here. Here come the students. After 18 times teaching this lesson, your autopilot
has this covered. You have just one more hour to go. Hot Cross Buns. Hot Cross…
“Johnny that note is supposed to be a C… Where does your finger go? That’s right!” You
snicker to yourself because Johnny is absolutely amazed that you magically knew that
note was supposed to be a C. “How did you know that? You weren’t even looking!” It
must be because you have reached the pinnacle of musical brilliance. Back to the task at
hand. Hot Cross Buns. Hot Cross Buns… 59 more minutes. Steak sounds good for
dinner. Hot Cross... I don’t remember if we have any green beans left... Buns. It’s been a
while since I made it to the grocery store. Hot… Maybe we will just go out to eat…
Cross… I’ll give them something to practice individually for a minute so I can sneak in a
text to just meet me at the restaurant after work... Buns. “Good job, Johnny!” I must
make sure we get cleaned up on time so I can get out of here before dismissal traffic gets
too bad. My goodness, 58 more minutes!
Each of these narratives illustrates events that show different states of experience inspired
by actual events in the author’s own teaching practice. Furthermore, they are examples of the
four different states of flow, the theory that underpins this study. The narratives of a music
teacher in practice show respectively each of the states of flow: (1) the optimal state of flow,
often referred to simply as “flow”, (2) control/relaxation, (3) stress/anxiety, and (4)
boredom/apathy. For other music teachers and for many others, these narratives are likely to be
relatable stories that can be used as a reference for understanding the application of psychologist
Csikszentmihalyi’s theory of flow (Csikszentmihalyi, 1990, 1997, 2015) in the practice of a
music teacher.
5
This chapter provides an explanation of this dissertation and its overall structure. After
providing a general description of the study, a description of the problem meant to be addressed
along with the need for further research is offered, together with a purpose statement and a set of
research questions. The hypotheses are then explained along with a definition of important terms.
Finally, as a background for this work, a description of the theory of flow is offered, starting with
the historical development of the theory from its inception to current practice, as well as the
many benefits of being in the optimal state of flow. Chapter 2 extends this background further by
reviewing important work with flow and selected music studies most relevant to the present
study.
General Description of Study
This study examined selected experiences of music teachers through the lens of the
psychological theory of flow (Csikszentmihalyi, 1990, 1997, 2015). Music teachers were
recruited to participate in an experience sampling survey that described their actions as well as
their feelings about those actions over a period of two weeks. Paired with an initial survey of
demographic information and feelings on various items identified from research related to job
satisfaction and preparedness, this data provided both a description of current music teacher
feelings as well as an opportunity to use correlational statistics with sub-groups of music
teachers. The use of Csikszentmihalyi’s theory of optimal experience gave a theoretical lens
through which to analyze the self-reported experience of the music teachers. This data provided
information useful for music teacher training, support, and advocacy.
Problem Description and Need for Further Research
Music teachers have difficult but rewarding jobs. In order to do their work well, they
must have a variety of well-developed skills. Past research has shown that the teaching
6
profession as a whole has high attrition rates, especially within the first several years. Merrow
(1999) found that 20% of teachers leave the classroom after 3 years, while 50% leave after 5
years. While all careers do have attrition rates, Ingersoll (2002) found that teachers have a
turnover rate that is approximately 4% higher when compared to other careers. There is some
evidence that this problem is even worse for music teachers. In an analysis of hundreds of
thousands of teachers, Gardner (2010) found that music teachers were less likely to continue in
their current position when compared to teachers of other subjects, with 82.2% continuing
compared to 87.9% in other subjects. Furthermore, Gardner found that music teachers who left
their position were far more likely to report being satisfied in their new career (86.8%) than
teachers of other subjects who left for new careers (65.9%). Ballantyne (2007) found that many
beginning music teachers suffer trauma at the beginning of their career, and are forced into
praxis shock, which relates to higher rates of dropout. Ballantyne (2007) also found from her
research that there is a notion amongst music teachers that any music teacher who does their job
well has a high likelihood of burnout in 4-5 years. Music teachers who burnout or otherwise feel
unprepared to do their jobs are likely to leave the profession. Furthermore, additional research is
needed to support music teachers throughout their careers. Conway (2012) states:
…in the area of career cycle, other than documenting the existence of such a concept,
music education researchers know little about the characteristics of music teachers in
various "stages," thus we know very little about how to best provide support for music
teachers throughout their careers. (p. 74)
More research is needed to identify what causes music teachers to experience these conditions
and how to better support them. This is true not only in the beginning of their career, but in the
stages throughout.
7
Teacher turnover also has an impact on students and the school community. As noted,
Gardner (2010) showed that music teachers have higher rates of turnover than teachers of other
subjects. Ronfeldt, Loeb, and Wyckoff (2013) provided evidence that the turnover rate of
teachers affects not only achievement measures of students, but also that it can negatively change
the dynamics of the entire school community. Music teachers are usually in the unique position
of running entire programs that reach far beyond just a single academic class. It is logical to
assume that the impact of a music teacher leaving could have an even greater effect on the
environment of the school, especially on the music students who may be in music classes for
multiple consecutive years (Kloss, 2013). The experience of teachers needs to be investigated to
help these professionals and in turn their students. In turn, this research is designed to be useful
in determining policy and practice in music teacher training and support.
Flow has been identified as a theory that can assist in improving music teaching and
learning. The following studies, which will be analyzed in further detail in Chapter 2, provide a
conceptual base and need for the present study. Keay (2018) directly suggested that the areas of
music teaching and flow have a wealth of opportunities for further research. Gunderson (2003)
conducted a study, with direct advice from Csikszentmihalyi, designed to determine if teachers
who are in the optimal state of flow are more effective teachers. Gunderson found that effective
teachers experiences were correlated with the optimal state of flow. Bakker (2005) found that
work factors such as support and autonomy are predictors of flow amongst music teachers.
Furthermore, Hektner, Schmidt, and Csikszentmihalyi (2007) suggested that advanced statistical
methods, such as regression, are suitable for the further advancement of analyzing experience
sampling data and exploring relationships between variables. This study seeks to extend this field
of research based on this work.
8
Purpose Statement
The purpose of this research was to correlate subjective experience of music teachers
based on Csikszentmihalyi’s theory of flow with variables from demographic information and
measures of satisfaction and preparedness that have been previously identified as issues for
music teachers. The reported “flow states” were also correlated with their corresponding
professional actions to show which are likely to be occurring during different professional
actions.
Research Questions
1. What are the trends in the subjective experience of music teachers when professional
actions are correlated with flow states?
2. Are there notable trends in the subjective experience of music teachers based on (a) years
of experience, (b) type of music classes taught, (c) means of teaching transmission, and
(d) measures of job satisfaction and preparedness?
3. Do music teachers who experience the optimal state of flow more frequently report more
job satisfaction and success?
Hypothesis
It was expected that music teachers’ reports of satisfaction and preparedness with their
current job as measured by initial survey would be positively correlated with higher rates of flow
as measured by a series of experience sampling data. It was also expected that defined sub-
groups of teachers would have different levels of relationship as predicted by related literature.
The descriptive experience of music teachers would likely align with previous research in the
field of music teacher education, showing the need for continued advancement and progress on
9
known issues such as student behavior/issues, classroom management, administrative support,
school environment, and location.
It should be noted that before and during the execution of this study, the changes in
school environments due to the COVID-19 pandemic could have affected teaching and may have
become a substantial and unexpected factor. Many music teachers were forced into online or
hybrid models. Experience levels and fluency with these different modes of teaching may have
been entirely incongruent with some teacher’s previous job experience. With this unprecedented
development, the level of technological fluency would likely be a major factor for many music
teachers and could reshuffle some of the expected correlations from this study and previous
research.
Definitions of Important Terms/Constructs
Flow: Csikszentmihalyi’s theory of flow is the psychological theory of optimal experience. For
this study, flow is operationally defined as a psychological state in which the ratio of challenge
and skill of a given task are both above the average for an individual.
Flow States: This study used the quadrant model of flow (see Table 3.1, Chapter 3), which
identifies the four flow states based on the relative balance of reported challenge and skill of a
particular action (Nakamura & Csikszentmihalyi, 2014). Each flow state is categorized by
challenge and skill being above or below average. The four states are: (1) the optimal state of
flow with above average challenge and above average skill, (2) the state of control/relaxation
below average challenge and above average skill, (3) the state of stress/anxiety with above
average challenge and below average skill, and (4) the state of boredom/apathy with below
average challenge and below average skill. Each of these four flow states were used as a
dependent variable in regression analysis.
10
Professional Actions: These are the reported actions being taken by the participant music
teachers during experience sampling. These actions have been categorized into four music
categories and four non-musical categories for a total of eight. The eight categories of
professional actions are: (1) conducting rehearsal/performance, (2) teaching concept(s), (3)
planning/preparing instruction, (4) evaluating student work, (5) school administrative duties, (6)
fundraising activities, (7) event planning/coordinating, and (8) dealing with student
issues/behavior. Data categorized into these eight professional actions were collected in
experience sampling along with corresponding flow states and used as predictor variables.
Beginning Teacher: A beginning teacher is defined as a teacher with less than 5 years of
experience. This person is deemed to be in the initial phase of their career.
Praxis Shock: This is the state that a teacher feels or experiences when they have trouble coping
with the realities of their job. Teachers in this state are often referred to as being in survival
mode.
Subjective Experience of Teachers: This is defined as perceived and reported feelings of
teachers. It is recognized that their self-reports reflect their own personal thoughts and feelings
and that these are valid.
Flow Theory Explained
Historical Development
The theory of flow had its origins in the positive psychology movement which gained
steam in the post-World War II era (Froh, 2004). During this time, there was an increasing
number of psychologists who were becoming dissatisfied with the common behaviorist
approach. These psychologists became increasingly interested in the positive aspects of life.
11
Researchers such as Abraham Maslow, who is now well known for his theory of the hierarchy of
needs, became prominent in this era. The change toward positivity can clearly be seen in this
quotation by Maslow (1954):
The science of psychology has been far more successful on the negative than on the
positive side; it has revealed to us much about man’s shortcomings, his illnesses, his sins,
but little about his potentialities, his virtues, his achievable aspirations, or his full
psychological height. It is as if psychology had voluntarily restricted itself to only half its
rightful jurisdiction, and that the darker, meaner half. (p. 354)
Although the field of positive psychology is typically seen as formally starting in the late 1990s,
Froh (2004) described how the roots of positive psychology go back even further than the mid-
20
th
century. Near the turn of the century, William James (1842-1910) argued that subjective
experience must be studied to fully view optimal functioning of humans. For this reason, some
consider William James to be the first positive psychologist (Froh, 2004). However, the leaders
in the positive psychology movement, Martin Seligman and Mihaly Csikszentmihalyi, have
distanced themselves from this era due to their disagreement with the humanist approach and
their view that it does not use a strong enough scientific basis (Froh, 2004). Despite differences
in theoretical foundation, Csikszentmihalyi nonetheless developed his theory of flow by focusing
on similar ideas of positive human experience.
Personal experience is the subjective reality of one’s life. Historically, psychological
research has focused on the behavior of people rather than their emotions. This historical focus
has given supremacy to behavior over experience (Csikszentmihalyi, 2014). Csikszentmihalyi
described how when one deals with other people, their behavior does take precedence over their
inner states. However, that is only because their behavior has a direct impact on one’s own
12
personal experience. When looking at others, what someone does is usually of more interest than
how someone feels; however when dealing with oneself, how one feels tends to take precedence.
In Csikszentmihalyi’s (2014) words, “we are all behaviorists when facing outwards, but turn
phenomenologists as soon as we reflect” (p. 209). Studying only what a person does has not
provided definite insight into what the person thinks or feels. Csikszentmihalyi (2014) stated that
subjective feelings are a much better measure of someone’s condition than any behavioral
measure. The traditional arguments of behaviorism that state that science must operate in an
objective manner with only what is tangible and verifiable is questioned. Csikszentmihalyi
(2014) explained, “…emphasis on objective qualities in science is only appropriate when dealing
with objects. It is misplaced when subjectivity is the paramount feature of the object
investigated” (p. 210). In other words, human beings view their entire life through their
subjective experience, therefore it is necessary to explore this experience to have insights about
them. The further exploration of these ideas on subjective experience is what led
Csikszentmihalyi to developing his theory of flow.
Flow received its name from people repeatedly describing their optimal experiences as
“being in the flow.” This was noted by Csikszentmihalyi as he conducted interview research. In
1975, Csikszentmihalyi first sought to answer the question of why people, such as rock climbers,
chess players, athletes, and artists would engage in time-consuming, difficult, and even
dangerous activities (Csikszentmihalyi, Abuhamdeh, & Nakamura, 2014). His finding was that
the participants frequently described a subjective state in which they found great joy and wished
to repeat this state. This optimal experience they described—given the name “flow”—occurs
during the times when people feel exhilarated and in control, like they are the master of their
fate, and their sense of time in the moment has dissipated (Csikszentmihalyi, 1990). People find
13
so much satisfaction being in the flow state that some will do the activity solely for the sake of
the experience with less concern for what it will produce.
The optimal flow state, as described by Csikszentmihalyi, Abuhamdeh, and Nakamura
(2014, pp. 230-231), has three distinct attributes that are common no matter the activity with
which a person is engaged. First, there is a merging of actions and awareness. During the flow
state, the consciousness moves into an atypical state where attentional resources are completely
focused on the task at hand and only the immediate enters awareness. In a usual state of activity,
self-consciousness often intrudes, and the self-reflective process interjects into awareness. In
other words, one is involved in conscious metacognition, actively discussing the activity within
their own mind. In flow, the internal discussion is eliminated, and external feedback is turned
into immediate response and action.
The second aspect of the state of flow is a sense of control. During flow, normal dangers
and worries seem to disappear even though the activity requires a high level of precision and/or
danger. Csikszentmihalyi, Abuhamdeh, and Nakamura (2014) recalled that rock climbers
reported feeling safer performing a dangerous maneuver than doing an ordinary task such as
crossing the street, because during the rock-climbing task they feel as if they were in control,
whereas in crossing the street, they felt left in the hands of fate. Whether they were realistically
safer in such an activity or not is not of consequence for flow, but rather the fact that they felt
such a sense of control.
The third attribute of the state of flow is an altered sense of time. A state of boredom
tends to make one more aware of the passage of time, whereas the flow state obscures it. A
person in flow becomes so invested in the activity that no attention is given to the experience of
the passage of time and therefore time is sensed as passing by quickly. Csikszentmihalyi,
14
Abuhamdeh and Nakamura (2014) noted that there are exceptions to the requirement of an
altered sense of time, when the activity itself requires a precise knowledge of time, such as the
time clock for an athlete.
Like the three attributes, the state of flow also has three prescribed conditions that must
exist for the state to occur (Csikszentmihalyi, Abuhamdeh, & Nakamura, 2014, pp. 232-235).
First, the activity in which a person is engaging in must have a clear set of goals. The goals give
the person a clear direction and purpose for doing the activity that allows them to channel their
attention towards reaching the goal(s). The second condition is that there must be a balance
between the perceived challenges and perceived skills. The key in this condition is that this
matching of skill and challenge depends on one’s perception of such, not necessarily the
objective reality. This also shows the fragile state of flow and how one can move in and out of
the state, as the challenge of a task, or the skill level to do it, might shift and unbalance the
relationship between them. The third required condition for the flow state is the presence of clear
and immediate feedback. This feedback during the activity provides the person a means of
knowing exactly how to proceed with the task. This allows the person to respond and adjust their
actions in real time. When these three conditions are present, as well as the three attributes being
experienced, the state of flow is present.
For an application of the attributes of flow in a music setting, one might revisit the
opening narratives. Narrative 1 shows a music teacher operating in the optimal state of flow
while running a rehearsal, then the state immediately being interrupted by the school bell
schedule. The subsequent narratives 2-4 show examples of the other three states,
control/relaxation, stress/anxiety, and boredom/apathy. One might note the absence of the
attributes of flow in the other narratives.
15
Models of Flow
After discovering the attributes and conditions of flow from his interviews,
Csikszentmihalyi created a model of flow that would be adapted over time. The earliest model of
Csikszentmihalyi’s theory, had a flow channel that was an indication of the optimal state of flow
(see Figure 1.1). This operationally defined flow as the interaction of two parameters: action
opportunities (challenges) and action capabilities (skills) or as, “what there is to do and what one
is capable of doing.” (Csikszentmihalyi, 1983, p. 211) In this early model, flow was considered
to be the convergence of equal action and skill, no matter the level of skill or challenge. A higher
level of skill than challenge would result in boredom, whereas as higher level of challenge than
skill would result in anxiety. Therefore, in this early model, the three flow states were the
optimal state of flow, anxiety, and boredom.
By the time Csikszentmihalyi’s famous book Finding Flow (1997) was released, the
model of flow had been updated to be more nuanced based upon the years of research that he and
others had conducted (Keller & Landhäußer, 2012). The Milan group—a group of researchers
lead by Fausto Massimini at the University of Milan—suggested the development of the
quadrant model, adding a flow state for a total of four, and the experience fluctuation model,
which contained a more detailed version with eight states (Delle Fave, Massimini & Bassi,
2011). These new models included an updated definition of flow as well as other categories for
the convergence of challenges and skills (Nakamura & Csikszentmihalyi, 2014). In both new
models, flow was then defined as the convergence of challenge and skill that was above the
average for the individual. In the quadrant model, it was recognized that a below average
convergence of skill and challenge was actually an apathetic state. The four states in the quadrant
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model were then updated to be apathy (low skill/low challenge), control (high skill/low
challenge), anxiety (low skill/high challenge), and flow (high skill/high challenge).
The experience fluctuation model, also known as the 8-channel (see Figure 1.2) added a
more detailed view of the states with an addition of four new states for a total of eight. The eight
states in this model retain the four states used in the quadrant model but added boredom
(moderate skill/low challenge), worry (low skill/moderate challenge), arousal (moderate
skill/high challenge), and control (high skill/moderate challenge). Furthermore, this model added
a mapping with a series of concentric circles showing increasing and/or decreasing
skill/challenge, which can further signify intensity, depending on distance from the personal
average. Researchers using this conceptual framework for flow must decide between the four and
eight state models based on the design and limitations of their study. This study has selected the
four-state quadrant model as the most suitable.
Figure 1.1
Original Model of Flow
(Taken from Nakamura & Csikszentmihalyi, 2014, p. 248)
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Figure 1.2
8-Channel Model of Flow
(Taken from Nakamura & Csikszentmihalyi, 2014, p. 248)
Positive Benefits of Flow
Csikszentmihalyi’s work was a major influence in the emergent field of what would be
termed “positive psychology”. When Martin Seligman (1999) gave his address to the American
Psychological Association as president of that organization, he gave the field its recognized
name and would start a movement behind it (Pawelski, 2016). This branch of psychology would
grow and even gain its own journal, the Journal of Positive Psychology, which began publishing
in 2006. This field, in which Csikszentmihalyi’s work has and continues to influence, focuses on
studying the lives of people to understand and ultimately improve their experiences and
wellbeing.
Motivation. Flow research has shown the positive benefits of flow on motivation. The
evaluation of experience while doing an activity largely determines how or if someone will
proceed (Csikszentmihalyi, Abuhamdeh, & Nakamura, 2014). Flow has been associated with
commitment and achievement of high school students, showing relationship between quality of
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experience and persistence in activity as a predictor of success in academia, and as a predictor of
self-esteem (Nakamura & Csikszentmihalyi, 2014). According to Nakamura and
Csikszentmihalyi (2014, p. 249), “experiencing flow encourages a person to persist at and return
to an activity because of the experiential rewards it promises, and thereby fosters the growth of
skills over time.” In short, positive experience is related with higher motivation and negative
experience is related with lower motivation. Csikszentmihalyi, Abuhamdeh, and Nakamura
(2014) asserted that although a person’s perceived competence for an activity is important for
intrinsic motivation, the most important factor of intrinsic motivation is found in the epitome of
flow, that is the quality of experience of absorption and interest.
Phenomenological research has also shown how motivation and flow can emerge from
activities. A person may start with little to no motivation for an activity, but when opportunities
become clearer or the skill level of a person rises, the activity can become increasingly
interesting and enjoyable (Csikszentmihalyi, Abuhamdeh, & Nakamura, 2014). In other words,
flow can be found in an activity, raising motivation, if opportunities or skills change. “Flow is
important both because it makes the present instant more enjoyable, and because it builds the
self-confidence that allows us to develop skills and make significant contributions to
humankind.” (Csikszentmihalyi, 1990, p. 42) The flow experience is seen as a force for
individual growth as it expands the skills as well as the goal and interest structure of the
individual (Csikszentmihalyi, Abuhamdeh, & Nakamura, 2014).
State or Trait. There has been some debate in psychological research on whether flow is
a state or a trait. The Oxford Review Encyclopedia of Terms (2021) defines a trait as,
“…something that is part of an individual’s personality and therefore a long term characteristic
of an individual that shows through their behaviour, actions and feelings. It seen [sic] as being a
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characteristic, feature or quality of an individual.” A state is defined as “…a temporary condition
that they are experiencing for a short period of time. After the state has passes [sic] they will
return to another condition.”
Csikszentmihalyi (1990, 2014) explained how some people have a propensity towards
having a high frequency of experience in flow. This personality is referred to as the autotelic
personality. Research studies found that over one third of people rarely or never have
experiences where they lose track of time, whereas about one fifth of people report having such
an experience daily (Csikszentmihalyi, 2014). This research has led some theorists to conclude
that this autotelic personality may have flow more of a trait than as of a state. Rathunde (1996)
studied autotelic families that seem to develop a propensity towards flow by spending more time
in high skill, high challenge activities. Early schooling experiences may also contribute to the
development of an autotelic nature (Nakamura & Csikszentmihalyi, 2014). Baumann (2012)
critiqued Csikszentmihalyi’s development of the idea of the autotelic person as lacking adequate
operationalization. Baumann proposed that the personality traits that must exist for someone to
have an autotelic personality are a high need for achievement and an advanced ability to self-
regulate. To achieve flow, a desire to achieve and an ability to self-regulate are needed, but
personalities without high abilities levels in these areas can still achieve flow. Therefore, the
autotelic personality is usually seen as a propensity towards flow. Most research is conducted on
the assertion that flow is a state and not just a trait. This study takes the majority view on the
autotelic personality and views it as a way of looking at personal predisposition to flow. The
design chosen in this study assumes flow to be a state.
Application of Flow Theory. The concept of flow has been used and applied to many
different settings. Flow research is useful in two types of interventions: “(a) those seeking to
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shape activity structures and environments so that they foster or obstruct it less and (b) those
attempting to assist individuals in finding flow.” (Csikszentmihalyi, Abuhamdeh, & Nakamura,
2014, p. 255) Additionally, the authors stated that educational settings provide some of the most
direct opportunities to research flow. Likewise, Engeser and Schiepe-Tiska (2012) stated that
the field of education continues to be one of the most common and applicable areas to
conduct flow research. Whereas flow research may provide the clinical setting with insight on
how to rehabilitate, the non-clinical settings can provide information on optimization
(Csikszentmihalyi, Abuhamdeh, & Nakamura, 2014). The present study seeks to link these ideas
together for use in examining music teachers in their work actions, whether it be interacting with
students or in their everyday administrative duties running their classes/programs. Once
discovering the flow states that exist for categories of teachers and linking that data with actual
work actions, flow will be used as a lens to diagnose problems areas, as well as areas of strength,
that can in turn help influence teacher education and support.
Organization of the Study
This chapter provided an overview of the study and presents the needed information for
understanding the purpose and direction of the study and its underpinning theories. Chapter 2
presents a literature review that provides information on previous research regarding aspects of
the study, including different approaches for studying flow in different settings and the
establishment of the methods and measures chosen, considerations for the implementation of
technology and experience sampling software, and the establishment of variables from previous
research on the subjective experience of teachers. Chapter 3 explains the chosen methodology for
this study, including discussion of variables, survey design, participants, a study timeline,
adaptations made to the study from experience in the pilot study, data collection, and statistical
21
procedures. Chapter 4 presents the results of the study and is organized around the research
questions. Chapter 5 provides an analysis of the results, makes connections to past research, and
offers suggestions for practice and future research.
22
Chapter 2: Literature Review
Introduction
This chapter presents the different methods of measuring flow encountered both inside
and outside of the field of music teaching and learning. Other methods of measuring flow are
noted to explain any related issues pertaining to the present study and methods that are adopted.
Studies, especially those in music, which provide support to the justification of this study and its
research questions are highlighted. The decisions made for the suitability of using the experience
sampling method to collect flow data using the balance of challenge and skill analyzed with the
quadrant model are explained. Issues relating to the current use of technology in recruiting and
implementing experience sampling studies are noted. Lastly, there is an establishment of issues
related to the demographics and subjective experience of music teachers that are used to create
the initial survey and ultimately, the predictor variables relating to reported flow states and
actions.
Measuring Flow
In addition to the previously discussed interview studies exploring the retrospective
experience of deep flow occurrences that initially led to the development of the theory, flow is
measured in daily experience using various methods. The three common methods used to
measure flow in daily experience are the experience sampling method (ESM), flow
questionnaire, and standardized scales. An overview of the measurements used in flow research
is offered below, with focused attention given to the ESM since it is the chosen measure of flow
for the present study.
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Flow Questionnaire
The flow questionnaire (FQ) was developed by Csikszentmihalyi and Csikszentmihalyi
(1992). FQ was based upon the aspects discovered in their early interview studies on flow. The
questions in the FQ ask the participant to identify and rate their experience while involved in a
particular activity. Section one of the FQ asks whether the participant agrees with quotations
describing the experience of flow. The second section askes a series of yes/no questions designed
to identify whether the person is someone who experiences flow or not. The third section askes
participants to list activities in which they believe they experience flow. Section four askes the
participant to select the activity in which they experience the strongest sense of flow. The final
fifth section presents Likert-like scale questions designed to rate their experience during
particular activities including the challenge of the activity and their skill level. The advantages of
the FQ approach are that it provides a single clear operational definition of flow, it does not
arbitrarily assume that all participants experience flow, it allows for estimating the prevalence of
flow in particular contexts, and it includes measures of the attributes of flow along with the
relative skill/challenge balance (Moneta, 2012). Weaknesses of FQ include the fact that it does
not allow for the exploration of different depths or intensity of flow experience in different
activities and the measurement of skill/challenge balance allows for limited exploration on
its corresponding effect on experience (Moneta, 2012).
Standardized Scale
A standardized Flow Scale Suite was developed by Jackson and Marsh (1996). Various
versions of this instrument have been developed over time and are sold commercially as a
proprietary measure. The Flow Scale Suite available on the website Mind Garden (2022),
contains all their developed scale versions, and includes a long and short version, as well as one
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at the dispositional level and one at a state level. The dispositional scales are meant to measure
the frequency of flow in particular domains such as sports, work, or school. The state flow scales
are meant to measure the extent to which flow is experienced in particular activities such as
running a race, working on a project, or taking a test. Additionally, CORE scales were added,
meant to tap into the phenomenological descriptions of flow. These scales have the advantage of
being standardized, but they have the disadvantage of cost as well as applicability to every
research situation.
Experience Sampling Method
Early Work. The Experience Sampling Method (ESM) was developed in order to track
the everyday lives of people doing work in specific disciplines. The method is designed to
answer questions about what people do, why they do it, and their psychological state over time.
These questions required methods that took time to develop and gain acceptance in the scientific
community. Now the method has become commonly accepted and its use and has evolved
especially because of the possibilities of technology (Larson & Csikszentmihalyi, 2014).
The early beginnings of ESM start with psychologist Kurt Lewin in 1935 (Larson &
Csikszentmihalyi, 2014). Lewin was interested in what he referred to as the “topology of daily
activity.” His theory was that by observing the psychology of people throughout their space in
life, he would be able to examine the reasons why people do what they do and how they think on
a daily basis. Lewin, however, did not have a good technique to track this daily experience, so
researchers following in his footsteps, such as Roger Barker, Herbert Wright, and P.V. Gump,
turned instead to behavioral approaches that in, Larson and Csikszentmihalyi’s (2014, p. 22)
assessment, “had laudable scientific rigor but neglected Lewin’s concern for the intrapsychic
aspects of existence.” Larson and Csikszentmihalyi (2014) explained that the methods employed
25
by these psychologists were useful for studying the public lives of people but had the
disadvantage of disrupting of the phenomena. Observing people in work life was not nearly as
disrupting as attempting to observe someone in the privacy of their personal life, which was
problematic in trying to study outside of work life. Personal diary methods implemented by
researchers such as Bevans, Altshuller, Szalai, and Robinson, were able to provide more
perspective into the private life of people, but likewise suffered depth of understanding because
of their focus on behavior (Altshuller, 1923; Bevans, 1913). Most of this research focused on the
stable, unchanging qualities, until the 1965 study of Bradburn and Caplovitz who sought to study
the effect the Cuban missile crisis had on people’s experience (Bradburn & Caplovitz 1965;
Larson & Csikszentmihalyi, 2014). Studies then began to change focus from stable traits to
examine the contextual and situational items can change subjective experience. This history led
to the development of the ESM.
The Experience Sampling Method (ESM) was developed by Csikszentmihalyi, Larson
and Prescott (1977) at the University of Chicago based on their desire to fully explore the entire
experience of people and how it changes over time (Mehl & Conner, 2012). The first studies
were specifically designed to study Csikszentmihalyi’s theory of flow. They developed the
method to overcome previous challenges and constraints of data gathering. The uniqueness of the
ESM is that it is able to study the entirety of the daily experience, from moment to moment
tracking the changes in experience as a stream of consciousness that link the external conditions
and the mind (Mehl & Conner, 2012). Larson and Csikszentmihalyi (2014) state that the ESM
“obtains information about the private as well as the public parts of people’s lives, it secures data
and both behavioral and intrapsychic aspects of daily activity, and it obtains reports about
people’s experience as it occurs, thereby minimizing the effects of reliance on memory and
26
reconstruction. (p. 23)” The authors recognized at an early stage of the ESM’s development that
although the method has its own problems and limitations, it showed great promise for
answering many of the questions that previous methods fell short of addressing.
During the early practice of ESM by Larson and Csikszentmihalyi (2014), participants
carried pagers that would alert them randomly throughout the day. The participants were
expected to complete the survey as immediately as possible once receiving the signal. The
survey contained open-ended questions that asked what they were doing, where they were, and
who they were doing it with. Other questions were Likert-like ratings that inquired about their
perceptions, as well as cognitive, emotional, and motivational states.
The early goal of the ESM was to gain a snapshot in time that was as comprehensive as
possible. The researchers believed the method had great flexibility and could focus on narrow
aspects (Larson and Csikszentmihalyi, 2014). ESM has since been used to study flow in many
different settings and work environments, including studies of teachers and music performance.
Various approaches to what data are collected and how flow conditions are calculated through
the data have been formulated.
More Recent Approaches. In the 2000’s, researchers embraced technology for
implementation of the ESM with the use of computer-based software with handheld device and
smartphone applications. As the availability of recording devices rose and the price of them
dropped, the use of computer-based ESM rose sharply (Mehl & Conner, 2012). In the early
2000’s researchers had to create ESM software and by 2012, there was wide availability of free
and paid software that was easy to use. In their analysis of the use of ESM, Myin‐Germeys,
Kasanova, Vaessen, Vachon, Kirtley, Viechtbauer, and Reininghaus (2018) stated that almost all,
if not all ESM surveys were making use of smartphone applications.
27
Many options in selecting a smartphone-based survey application exist today. There is an
abundance of proprietary applications that require subscription fees. Many of these, such as
Mosio, ExpiWell, FrontlineSMS, Qualtrics SMS Distributions, and Simple Texting, are marketed
to businesses as a means of customer data collection as well as to researchers for academic
purposes. The cost of these products can be justified based on their (1) easy importation and
exportation of data in various software formats, (2) automatic data collection, (3) easy design of
surveys of various types, (4) automatic replies, (5) management of survey timing, (6) technical
support, and many aspects that make implementation of the survey manageable.
Smartphones have also increased the collection capabilities. The original pagers used in
ESM communicated using radio signal, with limited range of 50 miles (Larson &
Csikszentmihalyi, 2014). Now, cell phone signals reach most of the populated world,
tremendously reducing the issues related to people traveling and/or losing signal. Written
answers have now given way to simple typing and using touch screens to select options.
Furthermore, smartphones allow for the collection of all kinds of multimedia, including pictures,
audio, and video. Location and movement data can be tracked with precision. The use of other
devices connected to the smart phone can provide all kinds of data from a person such as heart
rate, steps taken, and other health related measures. All of this data can be intricately tracked and
placed seamlessly into a digital database, along with exact time and locations. These additional
data capabilities can be analyzed to find additional information valuable to researchers, such as
the amount of time a participant took to respond, the amount of time it took to complete a
measure, and other metrics (Myin‐Germeys et al., 2018).
Given all these factors, it was determined that ESM using smartphone technology would
be the most suitable data collection method for the present study.
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One of the challenges in studying flow with ESM, even with modern technology, is
disruption. A request to answers questions can run the risk of eliminating the phenomenon. If the
person is in the middle of a flow activity, a beeping of a pager or the vibrating of a cell phone
interrupts the very feature being studied. However, Larson and Csikszentmihalyi (2014)
explained how the benefits may very well outweigh the flaws, but that these aspects need to
carefully be analyzed. Although ESM interrupts the person, the short nature of the survey allows
them to return to their activity relatively quickly. In reality, the study of flow throughout every-
day life cannot be examined without some sort of interruption to record data.
Experience sampling allows large amounts of data over time and unlocks the potential of
a longitudinal study. The repeated measure of the experience sampling will allow this study to
collect large amounts of flow data for use in correlational statistics while retaining the least
possible amount of intrusiveness to the participants.
Calculating Flow Using ESM Data and the Quadrant Model
The original concept of how to calculate flow with gathered datasets was designed by
Csikszentmihalyi, Larson and Prescott (1977). Initially flow was thought to only be a balance of
skill and challenge (Csikszentmihalyi, 2002). This was found to be inaccurate when skill and
challenge remained low, which led to the addition of the state of apathy. Upon these discoveries
a new model was developed referred to as the four-quadrant or four-channel method first used by
Csikszentmihalyi and LeFevre in 1989 (See Table 3.1 in Chapter 3). The data used to calculate
flow in this method was based upon the reported numeric value of the difficulty of a task
compared to the person’s skill to do the task. Unlike the previous method, flow was determined
by the comparison of each data point to the average of each person. The optimal experience is
then determined by above average skill and above average challenge. Nakamura and
29
Csikszentmihalyi (2014) contended that flow must be determined by comparison to the personal
average of each participant, as their experience is unique to them. The four quadrants can then be
used by determining high/low in skill and high/low in challenge. The four quadrants then relate
to the four flow states: (1) high skill/high challenge is the optimal state of flow located in the
upper right quadrant, (2) high skill/low challenge being the flow state of control being in the
bottom right quadrant, (3) low skill/high challenge being the flow state of stress located in the
upper left quadrant, and (4) low skill/low challenge being the flow state of boredom located in
the lower left quadrant.
Csikszentmihalyi and Nakamura (1989) pointed out how the 4-channels can be zoomed
in and expanded to 8 or 16 in order to get a finer picture, rather than a more global picture for the
four-quadrant model. These zoomed-in views do not change the four flow states of flow, control,
stress, and boredom, but only add additional sub-categorization such as high stress and low
stress, or high control and low control. Massimini, Csikszentmihalyi, and Carli (1987) used the
8-quadrant model to track flow fluctuation in the experience of patients during their psychiatric
rehabilitation. The data is standardized between -1 and 1, with 0 being the average, in order to
plot the data into each flow state.
There has been some criticism of the quadrant model, or the zoomed-in 8 or 16 channel
version of it, for measuring flow because it does not indicate that flow is actually happening, but
that conditions for possible flow exist. In order to say that the state of flow is occurring, a deeper
study of the conditions of flow would need to occur, such as one of the standardized flow scales
previous discussed in the Flow Scale Suite. These scales however are less suitable to experience
sampling because of their larger size. There then is a trade-off between the availability of more
data points and the precision of the data of flow. Researchers need to make these design
30
decisions carefully based upon the aims of the study. The present study makes use of the original
quadrant model using skill/challenge balance to measure flow states. This decision was made to
gain as many datapoints as possible without being overly intrusive to the participants.
Examples of Flow Measures in the General Literature that Guide the Present Study
The following studies highlight various approaches that researchers have taken in
designing their studies. A review of these studies will be provided in order to establish the
suitability of the chosen methods for the present study.
Researchers have used various means of operationally defining and calculating flow.
Sumaya and Darling (2018) used experience sampling to study flow, procrastination, and
academic performance. The main interest in the study was the authors method of studying flow
through experience sampling. The authors chose to measure flow using solely the ratio between
skill and challenge ratings. Instead of using the quadrant method where data is charted using the
participants average scores, the authors simply counted anything with the rating of 7 or higher on
both ratings as being in flow. This approach departs from the accepted above average
skill/challenge definition and replaces it with an arbitrary assumption that anything above 7
indicates flow. This is collected on a Likert-like scale of 0-9. The 0-9 scale was chosen because
it is the scale that Csikszentmihalyi had employed in many of his experience sampling flow
studies. The authors only used this data for descriptive purposes and not for correlation with
other variables.
Debus et al. (2014) studied flow at work between and within days for the purpose of
assessing if the level of recovery or feeling of being refreshed after work is correlated to the
subsequent days’ flow experiences. The researchers used a shortened flow scale, from the Flow
Scale Suite developed by Jackson and Marsh (1996) that was previously discussed in the chapter,
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to determine occasion level measures, day level measures, as well as measures in the general
survey. The authors used personal averages when determining flow state as developed by
Csikszentmihalyi and LeFevre (1989). The authors then coded each of the different occasions
and plugged them into a multi-level regression model for use with their other non-flow measures.
Their use of the established short flow scale is a strength due to its validation over various
studies but has the downside of having a more lengthily interruption to the participant when
compared to using only skill and difficulty balance, especially when repeating the measure
various times. The continual repetition of this measure would be concerning if chosen by the
present study due to its’ length and considerations with retention of participants throughout the
study.
Neuroscience researchers have also used flow theory to study body activity. For example,
Wise (1995) has published on the connection between specific brain waves and high
performance. Leroy and Cheron (2020) speculated that they could measure a difference in brain
activity when participants were in flow using electroencephalography (EEG) recordings. They
used EEG to measure the brain activity of a tightrope walker and found that their hypothesis was
correct. When comparing flow to a stressed state, they found that the emergence of flow required
transient hypo-frontality and that the basal ganglia was identified only during flow (Leroy &
Cheron, 2020). Although highly interesting and providing credence to the theory of flow, these
medical device approaches to flow are not feasible for the present study.
Csikszentmihalyi and Nakamura (1989) studied the lives of adolescents using the ESM.
In one such study, a particular individual named Ted is the focus of the study in order to show
the possibilities of mapping and reporting flow experience sampling data. In this case, the
authors used an 8-channel model to map his scores and actions. This chart showed the placement
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of Ted’s standardized scores between 1 and -1 for both challenge and skill. The 8-channel
categories were placed on top of this to show which category each activity lay. The authors
described how this same data could easily be modified to be a simplified 4-channel quadrant
model, or a more detailed 16-channel model. This exemplifies the need for each researcher to
make a careful decision which of the models to use. One major factor in deciding this is the
amount of data points collected and whether one is looking to take a more global perspective or a
zoomed-in specific one. The global nature of the present study which seeks to correlate flow
states with many predictor variables makes it expedient to take the more wholistic perspective of
the 4-channel quadrant model. For the present study, an 8-channel or 16- channel model would
provide data too cumbersome for a correlational analysis with an excessive number of variables
and flow states.
Implications for Technologically Mediated ESM Study of Flow
The ways in which current technology has evolved in the ESM approach is worth careful
consideration. Issues with the collection and reporting of data as well as reaching and retaining
participants is noted below with special consideration given to the current study.
Technology in Collecting, Coding, and Analyzing Data
Technology has made the collection and coding of data a much simpler process. Original
paper methods often required a major effort of manual data entry. Files were kept on every
individual involved in the study, which included a sheet of paper for every data point, of which
the original surveys had many different types of questions. All of this had to be manually coded
and entered (Larson & Csikszentmihalyi, 2014). Now the software used to implement phone-
based surveys, can automatically collect the data and place it into a digital database. The design
of the question will determine the ease of the collection. For a simple numeric value, such as a
33
Likert-like scale, this can be done automatically and require no additional effort. Open-ended
questions will likely require analysis and further coding by the researcher.
For the coding of open-ended questions, the researcher needs to make decisions on how
to categorize the information and could enter the corresponding category as nominal data in the
database (Miksza, & Elpus, 2018). Furthermore, the simplified input of ESM data into digital
databases allows for the speedy use of various statistical procedures in statistics software.
Correlational statistics procedures such as ANOVA and regression are commonly used with
ESM. The large amount of data points typically needed for ESM allows the researcher to explore
many levels of complex relationships and is seen as a strength of the method.
As previously discussed, this streamlining of the data collection and analysis was
necessary for the practical implementation of the present study. Trying to collect, organize, and
analyze data manually would be a massive task that would require an excessive number of hours.
Early studies had to overcome these collection and analysis hurdles, but they had the resources to
do so. Luckily for present researchers, researchers and developers have designed the ESM
applications to solve these problems and make the ESM accessible to a much wider circle of
researchers.
Reaching and Retaining ESM Participants
The researcher of any survey design must be thoughtful about how participants are
recruited and retained. This is especially true when the survey design is longitudinal in nature
and requires multiple responses over time. Response rates to the survey, as well as the retention
and/or attrition rate of participants has major implications for the study and the power of its
findings (Miksza & Elpus, 2018). The implementation of the actual survey design could have a
major impact on whether people choose to participate. Internet and smartphone-based surveys
34
may provide ease of use and implementation for some, but they could potentially be viewed quite
negatively by the potential participants and that require the researcher to carefully consider the
best administration for each particular study (Miksza & Elpus, 2018, p. 25).
The first potential barrier to the use of smartphones in ESM is cellphone/smartphone
ownership. The Pew Research Center (2019) found that 96% of people own a cell phone, while
81% own a smartphone. Their research also breaks down the data demographically. Although the
numbers skew towards larger ownership numbers amongst younger people, other subcategories
have significantly different ownership numbers. This shows the need to consider target
participant populations when designing the methodology. College graduates have 98% cellphone
and 91% smartphone ownership, making them a prime candidate for this method, whereas
people without a high school diploma, have ownership numbers of 91% and 66% percent
respectively, making them less than ideal candidates.
Miller et al. (2018) studied the potential barriers of using cell phone-based sampling
methods. College students were selected as the chosen participants because of the evidence
suggesting they would be the most receptive to technological methods. It was found that
requiring participants to download an application saw a reduction in those who chose to
participate by about half. Another of their research questions addressed survey retention. They
found that 86% of people took the first survey, whereas only 30% and 29% took the second and
third surveys. Despite these downsides of lowered participation, Miller et al. (2018) nonetheless
promote the promise of using cell phone applications for survey research.
A major goal of the ESM is to attain quick response times to prompting, with little time
in between the participant’s receipt of the survey and their completion of it. De Bruijn and
Wijnant (2014) found that their participants median response time in phone-based surveys was a
35
much quicker 171 minutes, compared to their email surveys which had a median of 734 minutes.
Miller et al. (2018) found no significant difference in response rate between traditional surveys
and surveys using simple gamification (adding points for completing questions). They also found
that 79% of people found the application survey very easy to use, as well as 80% of people
indicating they prefer to take surveys through an application as opposed to other methods.
Researchers must find a balance in the amount of time required to take each survey as well as the
frequency of the surveys, or participants will become likely to drop out. Typical response rates
for the ESM are between 70-90% although some have been published with lower rates (Fisher &
To, 2012). If designed well, studies using smartphone surveys tend to have an adequate level of
success and ease with participants, especially considering the vast amount of data that is
typically collected.
The present study sought to find the balance between these factors in order to recruit and
retain the largest number of participants. After exploring and testing options, especially during
pilot testing more fully explained in Chapter 3, it was determined that the ESM software
ExpiWell (https://www.expiwell.com/) was the best option. Although requiring participants to
download a smartphone application was likely a barrier for some potential participants who
chose not to complete the study, and the programming and collection of the ESM data would be
overwhelming for one person to perform without software, rendering the study unfeasible for the
researcher. Additionally, once downloaded, the application provides the participant with user-
friendly surveys and automatic alerts that give them the most optimized experience. With the
potential downside of participant loss fully considered, the huge upside of streamlining the
process, which will be further discussed below, made the decision process of choosing to use
ExpiWell simple in all practicality.
36
Flow Research in Music Teaching and Learning
This section of the review of literature summarizes on important flow research conducted
within the field of music teaching and learning. This work gives some background for the
research questions and will help to frame implications for research and practice. In recent years,
researchers in music teaching and learning have applied various methodologies to the study of
flow. The following review will describe studies done on flow in relation to (1) music teaching
practice, (2) music performance, (3) general well-being, (4) motivation, as well as (5) music
listening.
Music Teaching Practice
Keay (2018) wrote a content analysis of flow in music as a doctoral dissertation. The
purpose was to organize and synthesize the generalizable findings of existing research in this
area. Upon reviewing studies with music and flow, Keay (2018, p. 99) concluded that:
…the arena of music teaching and flow provides a wealth of opportunity for future
research suggestions and investigations. Although a subjective experience, flow may be
one of the most tangible experiences noted that celebrates motivation, engagement, and
satisfaction in music learning.
Music experiences of all kinds are natural environments for flow to occur. The
possibilities for studying teaching methods and approaches in the music classroom with flow in
mind are abundant. Teachers can be the object of this study as well as the students. Teachers who
themselves experience flow in their work and facilitate flow in their students are worth studying.
This is a driving force behind the current study.
Gunderson (2003) hypothesized that teachers in flow would be more effective than other
teachers. This dissertation was conducted at Claremont Graduate University with
37
Csikszentmihalyi personally giving advice and direct input to the study, as indicated in the
author’s acknowledgements. This fact gives strong credence to the notion that flow can be used
to track the experience of teachers in like settings. The author hypothesized that effective
teachers would be in the optimal state of flow more often, when compared to less effective
teachers. Furthermore, it was thought that teachers being in flow would better facilitate learning.
Flow was measured by a combination of analyzing video-taped classroom activities and post-
interviews with the teachers. Ten teachers were selected as effective teachers because of teaching
awards that had previously been won and were compared to ten other randomly selected teachers
from the schools. The results showed that the selected effective teachers exhibited the aspects of
flow more often than the randomly selected group. This dissertation is a strong basis for the
present study in supporting the use of studying flow amongst teachers as a measurement of
teaching effectiveness.
Bakker (2005) studied the flow level of music teachers, along with other work-place
measures, as well as flow measures for students to see what aspects were related to flow and if
flow could transfer from the teacher to the student. Schools were recruited through the Royal
Dutch Society for Music Artists. Teachers had an average tenure of 13 years and participants
were an average of 19-years old. Bakker hypothesized that job resources, including autonomy,
performance feedback, social support, and supervisory coaching were factors that would
contribute to flow. Flow was defined in this study as the balance between challenge and skill.
Baker self-developed a Work-related Flow Scale influenced by standardized flow scales
developed by Jackson and Marsh (1996). This one-time questionnaire asked participants to
reflect upon their experience in the past week. Bakker (2008) later explored this measure in order
to show its validity. The researchers used structural equation modeling to analyze the data. The
38
analysis showed that flow states were correlated between the experience of the teacher and the
students. It was also confirmed that having positive job resources and support was related to
flow. These external aspects such as support and autonomy are factors that should be explored
further as variables and give support to the hypothesis in the current study.
Matthews (2003) studied flow of three beginning adult students while taking voice
lessons in their studios. Musical improvisation was employed throughout the lessons. Flow was
identified by analyzing video of lessons using an observational protocol that focused on
anticipation, expansion, extension, self-assignment, self-correction as well as deliberate gesture.
The researcher found that flow occurred consistently higher during improvisational activities
compared to non-improvisational activities. The improvisational activities had a flow rate of
86% while prescriptive activities had a rate of only 26%. Furthermore, flow occurred more while
using the more familiar major scale and Dorian mode. This study does not have direct
application for the present study but provides support for the strength of studying flow amongst
educators and it’s potential to be encouraged in students during teaching and learning.
Music Performance
Wrigley and Emerson (2013) studied the flow state in live music performance. The
authors elected to use the established Flow State Scale-2 (FSS-2) questionnaire as their
measurement (Jackson & Marsh, 1996). The strength of this scale is that it measures each of the
aspects of flow however, the weakness when compared to ESM measurements is that this
measurement was only conducted a relatively small number of times. A total of 236 participants
was measured at the end of each semester immediately following their performance evaluations
two years in a row, resulting in a total of four measurements. This was the first study that found
the FSS-2 as having similar results as other fields of study. They found that most students did not
39
feel like they had a high enough skill level to meet the challenge of the activity. No statistically
significant results were found when examining the sub-categories of students including, gender,
year of study, and instrument family. The authors suggested studying how self-efficacy and
performance anxiety can be addressed in the teaching and learning environment in order to help
students achieve the optimal state of flow. It was further suggested that there should be a focus
on mental skills in order to foster improved self-talk, goal setting, imagery, and arousal control.
The authors noted that biological measures or the ESM may be better suited for future studies of
this type.
Iusca (2015) likewise used the Jackson and Marsh (1996) Flow State Scale-2 to measure
the flow state of undergraduate students immediately after their annual performance exams. A
total of 130 undergraduate students of varying instrument or voice classifications participated.
The flow data were compared with their performance scores as well as demographic information.
ANOVA data showed that higher music performance was associated with flow scores. This
association was found especially significant with string and piano players. The authors suggested
a focus on helping musicians develop strategies that allow them to experience flow more
frequently in order to raise performance levels. Actual implementation strategies to encourage
flow were not discussed.
Fullagar, Knight, and Sovern (2013) tracked the relationship between skill/challenge
balance, flow, and music performance anxiety (MPA) in music student practice. Students (n=27)
from a university music department were asked to complete surveys after practice sessions for
ten weeks. The final survey was taken after students performed at their required jury recital.
Challenge and skill were monitored as well as the challenge/skill balance. The researchers
calculated challenge/skill balance by finding the absolute difference between the numeric value
40
of challenge and skill. Performance anxiety was measured by a single scale item in the
questionnaire asking how much anxiety was felt while playing the musical passage. It was found
that skill/challenge balance was significantly correlated with flow and that the skill level
moderated the relationship between flow and performance anxiety. Furthermore, flow and MPA
were found to be antithetical experiences.
Cohen and Bodner (2019) further sought to show the relationship between flow and
performance anxiety in professional musicians. The authors hypothesized that teaching strategies
to encourage flow would help reduce performance anxiety. Flow was measured using a
shortened Dispositional Flow Scale-2 (Jackson & Eklund, 2002). MPA was measured using the
Martin and Jackson (2008) Performance Anxiety Index following a rehearsal time. The study
found that flow and MPA were negatively correlated, with about half of the musicians
experiencing some MPA. They also found that these professional musicians reported high levels
of flow. The notion that helping musicians find flow as a strategy to reduce MPA was supported
by their findings. A moderate correlation between age and flow was found in this study that has
not been found in previous studies. This study did not discuss the actual strategies for
encouraging flow but suggested that they exist amongst these professional musicians.
Flow in Well-being/Motivation
Fritz and Avsec (2007) did a comparison of flow states and subjective well-being. The
authors used the Dispositional Flow Scale (Jackson & Eklund, 2002) to measure the experience
of 84 musicians while rehearsing performing solo and with a group. The scores of flow were
compared to results on two different psychological state measures: the Positive Affect Negative
Affect Schedule (PANAS) developed by Diener et al. (1985) and the Satisfaction with Life Scale
(SWLS) developed by Watson, Clark, and Tellegen (1988). They found that aspects of flow were
41
related to measures of subjective well-being. Skill-challenge balance was found to be a strong
predictor of both positive and negative affect. The authors concluded that flow is more correlated
with emotional aspects rather than cognitive aspects of subjective well-being.
Torres Delgado (2017) sought the relationships between flow and intrinsic and extrinsic
motivation in music teachers as well as teachers of other subjects. The author hypothesized that
music teachers would have higher intrinsic motivation and rates of optimal flow states when
compared with teachers from other areas. A motivational measure was used along with a Spanish
version of the FSS-2 (Jackson & Marsh, 1996). The study was based on 738 participants from
various teaching backgrounds. The results showed that teachers of all subjects had high rates of
being intrinsically motivated without extrinsic rewards being present. Furthermore, the author
concluded that the optimal experience of flow was related to having high intrinsic motivation.
No statistical differences were found between music teachers and non-music teachers.
Valenzuela, Codina, and Pestana (2018) did a comparison of scores on measures of self-
determination theory (SDT), psychological needs satisfaction (PNS), as well as flow. SDT and
PNS were used in a hierarchical regression model as a predictor of flow conditions in instrument
practice among 109 conservatoire students. An adapted dispositional flow scale, used as a one-
time survey was used to measure flow. Conservatory students showed high levels of flow
conditions as well as intrinsic motivation and perceived competence. Perceived competence was
found as the strongest predictor of flow, which strongly supported Csikszentmihalyi’s notion that
flow is the relative balance of both high skill and high challenge. Autonomous motivation was
also a strong predictor of the optimal flow state. The authors encouraged conservatoire teachers
to develop students in their perceived competence in order to develop flow.
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Music Listening
Lamont (2011) studied the experience of college students while listening to music.
College students (n=46) studying psychology were asked to recall their most vivid music
listening experiences, focusing on Seligman’s (2002) positive psychology framework based on
pleasure, engagement, and meaning. The measure was a series of open-ended questions about the
participants experience with music based on the work of Gabrielsson and Lindström (1995).
Most of the music listening events described were positive and were experienced with other
people in a live music setting. Lamont determined that many different styles of music were able
to bring about great feelings of happiness as described by the participants. The importance of
considering the person, their context, and the music when analyzing music listening was
supported by the findings. It was found that music had an ability to evoke a state of authentic
happiness that may be further attributed to Csikszentmihalyi’s theory of flow.
Diaz (2013) examined flow in relation to music listening using a previously unused
method of measuring flow. Using a Continuous Response Digital Interface (CRDI) (Gregory,
1989), participants were measured for flow variables in real time while listening to music.
Additionally, a researcher-designed traditional questionnaire was given. The use of CRDI was
intended to be an extension of the ESM. It is unclear how successful this method was in relation
to ESM, especially when considering the data points being reduced and the scope of the
measurement being particularly short and narrow. Due to the narrow scope, it was unclear when
flow was actually occurring. The measurement itself could interrupt flow, so the operational
definition of when flow was occurring was ambiguous.
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Subjective Experience of Teachers
This section of the review of literature focuses on establishing areas that research has
already identified as important issues for music teachers. These established issues centering
around teachers’ subjective experience, background information, and feelings for support and
preparedness are used as predictor variables for each of the four flow states in the quadrant
model in the present study. They are also correlated with the corresponding reported actions
being taken by the participants. The literature reviewed examined the issues of praxis
shock/survival mode, teacher career development, student issues and classroom management,
administrative issues, and the urban/suburban teacher experience. This section helps establish
and discuss the predictor variables for the present study that provide data needed to answer
whether there are correlations between them and reported flow states and actions.
Praxis Shock/Survival Mode
The experience of teachers in the first phase of their career has been referred to as praxis
shock or survival mode. Ballantyne (2007) did a qualitative study with 15 Australian music
teachers. The interview questions revolved around experience with their pre-service education as
well as their experience at work. Teachers interviewed ranged from one to four years of
experience in secondary schools. Ballantyne’s (2007) major finding was that these beginning
music teachers were experiencing praxis shock. The author defined praxis shock as the state that
a teacher experiences when their preparation and expectations for the job is different than reality.
The teachers experiencing praxis shock felt that they were left to sink or swim. It was found that
there are two main factors to praxis shock: physical and professional isolation within the school
and high workload and multiple responsibilities associated with the extra-curricular music
program (Ballantyne, 2007).
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The problem of isolation amongst music teachers is well documented. In a literature
review on beginning music teacher experience, Conway (2015) documented evidence that
pointed to isolation being a common issue amongst all teachers, but that it is heightened amongst
music teachers for various reasons. Both Conway (2015) and Ballantyne (2007) presented the
attributes that make music teacher isolation a major problem. First of all, music teachers are
often isolated from their colleagues. This may be because they are the only music teacher present
at their location. In certain contexts, they may be the only music teacher of a particular specialty
in a large area (Sindberg, 2011). There are often little to no opportunities to engage with other
teachers in their subject or specialty. Furthermore, due to the noise levels and department
separations, music buildings or classrooms are often physically isolated from the rest of campus,
making it difficult to interact with anyone other than the students, let alone a colleague who can
be helpful in their context. In addition to this physical isolation, there is also an emotional and
intellectual isolation that occurs (Conway, 2015). These teachers rarely have the opportunity to
“talk shop” with others. These music teachers are drained from a lack of ability to talk about
matters related to their teaching with people who are qualified in that area.
Related to the feelings of isolation is the lack of support. Although most beginning
teachers are usually required to go through some sort of induction support program, there was
great dissatisfaction with them (Conway et al., 2006). Although these induction support
programs place a high value on identified issues of need, such as classroom management and
instructional strategies, music teachers identifed them as unhelpful because they are inconsistent
in addressing their needs (Conway et al., 2006). One problem in the induction programs is that
they tended not to start until the school year was in full swing, while the music teachers were
often working since summer, leaving a gap in needed support. There was also a frequency of
45
required extra-curricular duties conflicting with the induction times, making it so they were
unable to attend. Furthermore, Conway et al. (2006) found that music teachers perceived their
needs as distinctly different from other teachers. The induction programs largely failed to address
these needs, resulting in the dissatisfaction from the music teachers. Conway (2001, 2012) also
found that as music teachers progressed and became successful, half of the participants
apparently had a tendency to forget their past struggles and see this music-specific support as not
as important, although they had strongly advocated for it years before when they were beginning
teachers.
The other main factor contributing to praxis shock is high workload and multiple
responsibilities. Ballantyne (2007) found that music teachers often described themselves as
having to be a “one-man band” where they were required to do far more than the average
teacher. In addition to their normal teaching schedule, they were expected to perform various
other duties, such as running an entire extra-curricular program, performing at various venues,
and promoting the school and the music program. The combination of all these duties, as well as
the expectation to do them all to a high level, came from the administration and/or the
communities (Ballantyne, 2007). There were also indications that schools often value the extra-
curricular activities of music programs more than they do the educational classroom activities.
Various music teachers indicated that to fulfill these expectations meant that music teachers were
going to burn-out. Even school administrators acknowledge the huge expectations placed upon
music teachers, regarding classroom numbers, course assignments, performance expectations,
etc., however, there seemed to be a culture that viewed this as requiring no attention because it is
viewed as just the way it is (Conway et al., 2006). Music teachers were also unsatisfied with their
lack of financial payment for their extra duties, although it was not clear whether additional
46
compensation would change their perception or experience at all (Ballantyne, 2007). What was
clear, however, was that beginning music teachers did not feel prepared for the size and scope of
responsibilities for which they would be given, and it contributed to their feelings of praxis
shock.
Music teachers often struggle with the additional responsibilities related to program
administration. Conway (2001, 2012) found that music teachers would like a focus on
administrative duties in their teacher induction programs. Music teachers often find themselves
in a position of spending large amounts of time fundraising, working with booster clubs,
managing budgets and finances, managing staff, and event organization, for which they may
have little to no training and/or experience handling. Miksza, Roeder, and Biggs (2010) found
that many experienced band teachers reported issues in dealing with administrative duties, class
scheduling, event and facilities management, fundraising and budgets, staff oversight, and
paperwork that has nothing to do with teaching.
Teacher Career Development
Kington, Reed, and Sammons (2014) did a qualitative study of teachers across different
phases of their career. They found that teachers define their effectiveness in the classroom in
different ways as they progress through their career. The researchers define three career phases:
early-career (0–7 years), mid-career (8–23 years) and late career (24 + years). The majority of
teachers in their study felt that enthusiasm and motivation diminished over the course of a
teaching career. Teachers also seemed to judge each other on attributes rather than simply years
of experience. Their participants also associated effectiveness with high values of motivation,
commitment, self-efficacy and well-being. Kington, Reed, and Sammons (2014, p. 551) stated
that when dealing with areas of struggle in their practice, “underpinning these areas of tension is
47
the apparent conflict effective teachers have between effective professional practice and their
emotional effectiveness.” This research indicated that the teachers’ experience and emotional
development as a teacher over the course of their career can be an indicator of their success and
effectiveness in the classroom.
Eros (2011) researched the development of teachers across the span of their career. He
noted how teachers are sometimes lumped into the overly simplistic categories of either pre-
service or in-service. The in-service label was insufficient for describing the entirety of a career.
The career cycle of a teacher needed to be recognized and he pointed out the attributes of what
he referred to as the second stage of teaching. There was a recognition that after a number of
years, estimates lie somewhere between four to ten years, where the initial phase is left behind.
The initial phase was well described by Ballantyne’s (2007) studies on praxis shock. Eros (2011)
laid out the arrival of the second stage as a stabilization, becoming professional, competency
building, and expertise.
Based on his findings, Eros also speculated that the career cycle of a teacher must be
understood in order to support the changing needs of teachers throughout their careers. This may
be important for policy and professional development. It is likely that a 2
nd
year teacher has very
different needs than someone towards the end of their career. “Similarly, a potential problem for
second-stage teachers is the risk of being ignored by administrators. Years of teaching
experience does not indicate that a teacher no longer requires professional development.” Eros
(2011, p. 69) Second stage teachers are also at risk at falling between the cracks because the
focus is elsewhere. Eros maintained that the retention stakes for second stage teachers is high
because of the risk of losing their years of experience to high attrition rates. These teachers must
not be ignored or dismissed as having already “arrived” and in need of no further attention.
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Hargreaves et al. (2007) studied music teachers as they developed their identity and
attitudes about being a classroom music teacher. The findings suggested that music teachers may
have greater difficulty developing an identity as a teacher because of their identity as a musician
first. This conflict between identity as a musician and that of a teacher may lead to increased
struggle in their practice and satisfaction with their job. Pre-service music teachers were given a
Musical Careers Questionnaire that gathered information about self-efficacy in music and in
teaching. Although there were not statistically significant results found from this data, the results
suggested that students with a music education concentration may have a much clearer
propensity to identify as a teacher than the musicians with other concentration areas.
Student Issues and Classroom Management
The issue of classroom management, or student discipline, is well known in education.
There is strong evidence to show that music teachers likewise have struggles with classroom
management and that they experience unique issues in relation to other teachers (Conway, 2015).
Beginning music teachers and/or student teachers are frequently seen as needing more skill in
their classroom management (Hester, 2013). Teachout (1997) found that experienced teachers
rated maximizing time on task and managing student behavior as two of the most important
skills that a music teacher needs.
Miksza, Roeder, and Biggs (2010) likewise found in a survey of experienced band
teachers that classroom management and student success were the most important factors for
new music teachers. Music classrooms are usually designed differently than traditional
classrooms. The class sizes are often larger than traditional classes and students are asked to
make noise—presenting a dynamic that music teachers perceive as unique to traditional
classroom settings. Also, the role of the teacher as well as the students are usually different.
49
There is often a focus on the group dynamic and performance rather than an individual
performance common to traditional classrooms.
Simon (2012) examined what factors might predict classroom management challenges.
The results of a regression analysis were that the variables of teacher gender, teaching area
emphasis, overall teaching experience, classroom management support, music-specific training,
overall classroom management self-efficacy, and the context variable of ability were all
predictors of classroom management challenges. This is consisted with research that has shown
music teachers commonly reporting the need for music specific classroom management support.
Working with special needs students in the music classroom also provides unique
challenges. There is much research that supports music teachers often feeling unprepared to
teach students with special needs, leading to further problems regarding classroom management
and student behavior. Gooding, Hudson, and Yinger (2013) found that the only factor for feeling
prepared to work with special needs students in music classrooms was the amount of coursework
teachers had taken that specifically addressed teaching special needs students in music. The
researchers also found that the Orff approach led to greater feelings of preparedness amongst
primary music teachers.
Vanweelden and Whipple (2007) determined that hands-on experience in working with
special needs students increased music teachers’ positive attitudes towards working with special
needs populations. Music teachers typically sharde a positive view of mainstreaming special
needs populations but showed a more negative view of this when considering bringing them into
their own classrooms. Thus, a lack of experience and knowledge about working with special
needs populations seemed apparent. Salvador (2010) surveyed universities on their course
requirements for degrees in music education. She found that a course in teaching music to special
50
populations was required in 29.6% of those responding. Other results showed 38.9% had an
available but not required course, and 59.8% reported integrating the subject throughout other
courses. These numbers provide evidence that the topic is likely insufficiently addressed in many
programs and further explains why many music teachers feel unprepared to manage classroom
and student issues regarding students with special needs.
Administrative Issues
Administrators have a major impact on the schools with which they work. These
professionals are the people that make many of the decisions that directly affect teachers. They
are responsible, at least partially, for creating the culture, environment, and expectations within
the culture of teaching. In discussing school culture, Conway (2012) noted:
The finding that with music teacher experience comes an understanding of how schools
work supports the notion of the importance of this landscape for music teachers. It also
supports the idea that much of what is needed in order for music teachers to experience
success is learned on the job as there are no methods courses for understanding the
macro- and micro-political landscapes of schools. (p. 72)
Music teachers are in a position such that their job expectations can be changed
significantly by the expectations of the administration. An administration may be completely
hands-off on how things are handled, or they may be entirely prescriptive. Additionally,
administrative focus and/or personnel may change frequently adding to the stress on teachers.
It is not surprising that music teachers have cited administrative support as the top factor
that makes them satisfied with their work. Brown (2015) found, in a regression analysis of music
teachers, that administrative support was a predictor of job satisfaction. Brown also found in a
survey of music teachers that 85% listed their administration as the most important item of job
51
satisfaction. Bryant (2012) likewise found that administrative support, along with student
participation, and salary were statistically significant predictors of teacher job satisfaction. Trace
(2016) found that principal support and trust were moderately related to teacher satisfaction in
correlation measures. There is clear evidence to support the notion that a music teachers’
satisfaction with their administration may have a strong impact on their professional happiness.
Urban and Suburban Teacher Experience
There is also strong evidence to support that teachers working in different demographic
and socioeconomic backgrounds have different experiences. Fitzpatrick (2012) surveyed the
experience of urban and suburban music teachers in the Chicago area and found that there were
many differences in their experience. In this study, suburban participants had higher levels of
educational attainment, taught a greater variety of courses, and were slightly older with more
corresponding teaching experience than urban teachers. Urban participants were more diverse
and more likely to be female; urban music teachers also reported an alarming number of negative
aspects about their job including: (1) less job satisfaction on a job satisfaction index, (2) less
satisfaction about their teaching context, (3) less satisfied feelings about the rewards of their job,
(4) an unmanageable number of students, (5) less parental support, (6) less gains from
professional development, and (7) too much time spent on classroom management (Fitzpatrick,
2012).
There is evidence that aspects of praxis shock, such as isolation and workload, may be
typically greater in urban settings (Sindberg, 2011). In turn, Shaw (2018) made several
suggestions for addressing the unique needs of urban music teachers, including: professional
development tailored to urban settings, collaborative communities of practice, mentoring, and
school-university partnerships.
52
Teachers’ decisions to teach in an urban context also provides insight into the differences
of the urban and suburban settings. Eros (2018) interviewed teachers on why they chose to teach
in an urban setting. The findings showed that teachers felt a need to be of service to others,
wanted to experience cultural differences, and curiosity about the city and being a part of the
city. Participants stated wanting to help people of color and problematized this outlook as
wanting to be a savior (Eros, 2018). Eros (2018) reasoned that trust and respect are necessities in
urban settings and having a socio-cultural awareness was key to this.
Music teachers speaking about a special mission when going to an urban setting may be
evidence that the suburban setting is seen as the prime destination for a music teacher. Docker
(2012) found the reasons music teachers chose to leave urban teaching positions included: poor
salary, student discipline issues, lack of job security, inadequate time with students, overcrowded
classes, poor facilities, lack of security, too much time spent on testing, and lack of
administrative support. On the other hand, Docker (2012) found teachers that reported being
likely to stay in their position were predicted by the teacher’s job satisfaction, perception of labor
issues, and the teacher’s race. The location of a music teacher’s job may have an impact on job
satisfaction.
Summary of Reviewed Literature
After establishing the theory, history, and practice of flow research grounded in the
positive psychology movement in the initial chapter, this review of literature sought to establish
the applications of flow research most suitable for this study. Exploration of the different
measures of flow gave credence to the suitability of using the quadrant model with the ESM
approach in answering the research questions of this study. An analysis of the ESM was done to
determine the best design and use of technology for implementing the study, ultimately leading
53
to the use of ExpiWell. The exploration of flow research done within the field of music teaching
and learning revealed that the ESM has had limited utilization and studies have not used methods
that could be directly adopted for this study. Therefore, the quadrant model used by
Csikszentmihalyi and the Milan group, which has seen much use outside of the field of music
teaching and learning, was found be the best fit to answer the proposed research questions.
Previous research on music teacher issues, such as praxis shock, isolation, career
development, student issues, classroom management, urban/suburban teacher experience, and
administrative issues established aspects of subjective teacher experience and satisfaction, as
well as demographic information items that will be used as variables for statistical correlation
with music teacher flow states and corresponding actions reported during experience sampling.
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Chapter 3: Design
Introduction
This chapter explains the method, data collection, and analysis. As suggested by the
purpose and research questions, experience sampling data on flow states and corresponding
professional actions were collected from an experience sampling of music teachers and
correlated with demographic information and measures of satisfaction and preparedness from an
initial survey. This helped to determine which variables were related to each of the four flow
states as well as which flow states are correlated with which actions. Results from this study are
expected to be applied to the general music teacher population for informing music teacher
education and support. After a restatement of research questions and hypotheses, aspects of the
design, implementation, and analysis will be discussed including the lessons learned from the
pilot study.
Research Questions and Hypothesis
The following are the research questions for the study:
1. What are the trends in the subjective experience of music teachers when professional
actions are correlated with flow states?
2. Are there notable trends in the subjective experience of music teachers based on (a) years
of experience, (b) type of music classes taught, (c) means of teaching transmission, and
(d) measures of job satisfaction and preparedness?
3. Do music teachers who experience the optimal state of flow more frequently report more
job satisfaction and success?
It was hypothesized that meaningful correlations would be found between subgroups of
music teachers and their reported flow states and corresponding act. It was expected that trends
55
already found in previous studies, such as struggles of beginning teachers, difficulties with
working with special needs students, years of experience corresponding with more control and
success, isolation of music teachers, would likely be found again in this study. Furthermore, it
was expected that positive high skill flow states (optimal state of flow and control/relaxation
state) would correlate positively with higher satisfaction levels on items such as student
population, administrative support, parental support, pay, and teacher preparation programs.
Likewise, it was expected that negative low skill flow states (boredom and stress) would
correlate with low satisfaction scores on the same satisfaction measures. Also, the correlation of
flow states and actions would show if there are specified teaching actions that need to be
specially addressed in teacher training and support.
Research Methodology
A quantitative approach was chosen to help find answers to the research questions. Much
previous research has been conducted on flow using Csikszentmihalyi’s experience sampling
method. Most previous quantitative research done on flow have used descriptive procedures.
This study fits into the trend of this previous research while extending the commonly used
methods by examining relationships with multivariate procedures. Multiple linear regression was
chosen as the most suitable statistical method due to its flexibility in finding relationships
between multiple variables. This approach provides a wider view of data allowing for correlation
that has more systematic study across a wider sampling spectrum. The use of this approach with
flow ESM data has been suggested by many scholars as one of the future directions for flow
research (Hektner, Schmidt, & Csikszentmihalyi, 2007). A qualitative method could be used for
a much more personal exploration of the phenomenon of deep flow but would not allow for the
multivariate approach that is sought in this study. Other methods such as a one-time instrument
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of flow, could be used, but would provide far less data and would not be as likely to capture the
full experience of music teachers across their busy schedule. The repeated measures of the
experience sampling are more likely to collect data on all the various and important
responsibilities/actions of a music teacher that do not necessarily happen on a regular basis
during their normal daily schedule.
Software Choice. After researching the logistics of running an experience sampling
study, it became apparent that software would be needed. Multiple software companies have
developed programs specifically for this purpose. After researching capabilities of software as
well as pricing, it was clear that ExpiWell was a prime candidate. The company allowed for a
software trial period, so an initial pilot study was run using myself and colleagues as the
participants. The software effectively programmed the survey as well as the scheduling. The
survey’s design platform was somewhat cumbersome and not always user-friendly, but became
manageable after some experimentation with the different features. It gave multiple options of
how best to format and design the look of each question (Figure 3.1). A logic function was of
use, letting the survey be designed in a way that routes the questions differently depending on the
participants response. This was useful because the survey was designed to end if the participant
indicated they were not engaged in a work activity. Scheduling of the survey had a calendar view
that allowed many options, including allowing for a randomized feature within a block of time
(Figure 3.2). After some practice, it was not difficult to adjust amount of time allowed before a
survey timed out. All of these were crucial features for the study. Furthermore, data collection
and reporting were simple to use. All data was collected and automatically reported in
downloadable spreadsheets. The only barrier to using ExpiWell was the price, but when I was
57
informed my department at the university had already purchased a subscription and that I was
able to use, it was a clear choice.
It should be noted that during implementation, there were some reported issues with the
software that did not occur during the pilot work. One participant indicated that they were not
getting alerts for the experience sampling. It was unclear whether this was due to the software, or
settings on the participant’s phone. Nonetheless, this is an indicator that, at least for one
respondent, participation was lowered due to issues with using the software. It is unclear how
many additional participants may have had issues that went unreported, or whether it became a
barrier that led to their non-participation. Future research using these methods need to carefully
consider the implementation issues from this software, if they can be improved, or if there are
better software options.
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Figure 3.2
ExpiWell Scheauling
Figure 3.1
ExpiWell Survey Programming
59
Study Timeline
Internal Review Board approval from the University of Southern California was given on
February 10, 2021. The pilot study was conducted from February 16-20, 2021. After minor
revision of the study and solving of logistical and analytical items, formal recruitment started in
February 2022 through personal contacts as well as through the National Association for Music
Education Survey Assistance program (see participants and their recruitment below). The initial
survey opened to participants March 1, 2022. The 2-week experience sampling was conducted
March 10-23, 2022. At this point, all data was collected and coding and analysis began.
Pilot Study
The pilot study was conducted using a two-week free trial of ExpiWell software with
seven trusted music teacher colleagues with whom the researcher has personal and professional
contact. The participants were made known of the troubleshooting and calibrating nature of the
pilot study. Their feedback, questions, and suggestions were communicated to the researcher
throughout the process and are presented in detail below. This pilot study was a shortened four-
day version of the experience sampling survey conducted from February 16-20, 2021. The initial
survey was made available five days before the experience sampling portion to be completed at
any time before the end of the study. This process was very helpful in optimizing various aspects
of the study, as well as giving the researcher an opportunity to learn the software programming
and receive actual sample data.
Adaptations Made from Pilot Study. Learning the programming of ExpiWell took some
time and there were necessary adjustments made throughout to make the study as clear as
possible. A series of questions and suggestions about the clarity or quality of questions in the
initial survey poured in once participants started taking it. The following summarizes the changes
60
made based on the pilot study feedback (see Appendix A for the survey questions included in the
final ExpiWell implementation).
1. It was suggested that the “age” input question be changed from an exact number to a
selection of age categories due to the discomfort some may feel about sharing their
exact age. This question was modified to include age categories by decade.
2. Asking for the participants phone number also made some of the pilot subjects feel
uncomfortable. This question was originally included before selection of using
ExpiWell in case SMS text messages were needed. The messaging communication
available through ExpiWell, or provided email, made this information unnecessary so
the question was eliminated.
3. Several people commented on their confusion over the 0-9 scale. This scale was used
because it was the same scale Csikszentmihalyi used in many of his studies. It seemed
apparent that the participants preferred a scale out of 10, likely due to familiarity. It
was decided to use a 0-10 scale, to allow for a response of no ability/difficulty while
still having the familiarity of an “out of 10” scale. The descriptors of (0 = none, 10 =
very high) was included to avoid any confusion.
4. A couple of small wording choice issues were also adjusted from suggestions, such as
changing the word “tell” to “share.” The question asking participants to “describe
their behavioral action” was slightly reworded and a couple of examples were
provided due to some confusion over the intent of the question.
5. The last change dealt with how participants reported their experience sampling
actions. Coding of reported actions proved to be challenging, and hard to interpret.
Having too many codes caused statistical problems with having too many variables.
61
After much deliberation and analysis, it was decided that a total of eight codes, four
musical and four non-musical would be used. Additionally, a question asking the
participant to select a coded category themselves was added to the experience
sampling questions adjacent to the original open-ended question.
Need for Software. Learning to use the ExpiWell software was crucial. At the beginning
on the pilot study, there was a small error in the program scheduling of the experience sampling
that was discovered and fixed within the first few hours of implementation. One question was
also formatted incorrectly and showed an error message. Both issues were simply a matter of
experience with use of the software and were quickly discovered and edited without further
error. Additionally, learning how to input the data collected through ExpiWell into appropriate
regression options within the IBM SPSS Statistics (IBM Corp., 2020) software was a learning
process.
Having actual data to work with collected from the pilot study through the ExpiWell
proved to be invaluable. First, the choice to use ExpiWell was confirmed. Trying to coordinate
experience sampling messaging schedules with scores of participants on any platform not
specifically designed for ESM would be an absolute logistical nightmare. Likewise, trying to
collect and organize the data would likewise be a ridiculously monumental task. Although
research showed there might be a likely drop in participation due to the barrier of requiring
participants to download an app, it was undoubtedly necessary to realistically implement this
study as an individual researcher.
The second major discovery from working with the data from the pilot study was a data
reduction problem when trying to implement the planned statistical method. It was discovered
that using the coded flow state as the dependent variable did not work in the regression model
62
without modifying how it was entered because of it being a repeated measure. The original
design using multinominal regression had to be rethought. The solution to the problem was to
enter the experience sampling data on flow state in a single column for each of the four flow
states as a tally representing the number of times in a particular state. In a similar manner, each
experience sampling reported activity received a column with a tally of the number of times that
activity was reported while in each state. This change in how flow was entered in the spreadsheet
necessitated four different regression models, one for each flow state. This also changed the
regression model for this aspect of the study to multiple linear regression, as the entered value
would then be a continuous variable and not a coded variable like previously designed. It was
determined that this solution retained its integrity to the research questions and design of the
study as the flow states were still used as the dependent variable and allowed for correlation of
the intended variables.
There were not enough participants in the pilot study for there to be any initial
correlational discoveries. This gave me information about the need to recruit enough participants
to have statistically significant results. Additionally, it caused more mindfulness to keep coded
variables limited to a manageable number.
Participants and Their Recruitment
The participants for the main study were all music teachers of primary and secondary
grades, of any location. Participants were recruited through the National Association for Music
Education (NAfME) Survey Research Assistance program (National Association for Music
Education, 2022). It seemed ideal to have a large variety of types of teachers across grade levels,
elementary, middle school, and high school and to have teachers of various musical specialties
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such as choral, band, orchestra, general music, or other music classrooms. Variety of music
teachers was also preferred regarding teacher background, location, age, experience level, and
education.
It was decided that the request for research participants be sent out to all k-12 NAfME
members, which included approximately 30,000 music teachers. The proposal to NAfME for
research assistance was approved on February 14, 2022. The mass email was scheduled and sent
March 1, 2022, with the same email being sent as a follow-up on March 9, 2022. The service for
research assistance cost $250, which included the email, follow-up email, a rush order, and was
paid for entirely by me. This informational and participant recruitment email can be viewed in
Appendix C. This email provided information about the purpose of the study as well as the
actions with which participants need to be involved. Links as well as information on how to
download the experience sampling application software ExpiWell were also provided. The
researcher provided a raffle incentive of ten different $20 Amazon gift cards to the possible
participants in order to encourage response rate. Additionally, the researcher sent out emails to
colleagues and did a series of posts looking for participants on personal social media accounts.
The same information was provided through this means of recruitment.
Despite recruiting efforts, only 52 people downloaded the ExpiWell application and
signed up for the experience sampling. Of these 52 participants, two never completed any
surveys, two did not complete the initial survey, but did complete experience sampling questions,
while eight participants completed the initial survey, but did not complete experience sampling
questions. The need for both sets of data for statistical analysis required these participants to be
removed from the study, leaving 40 participants with usable data for analysis. The only
exception to the inclusion of data from participants that had incomplete data sets was the
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inclusion of participant answers to the initial survey final open-ended response question “is there
anything else you would like to share?”
Initial Steps in Data Collection
To take part in the study, participants needed to download the ExpiWell smartphone
application and enter the study code. Links for application download and the participation code
were provided in all recruitment efforts. Once participants decided to enroll in the study, the first
step was for them to complete the initial survey. The participants had immediate access to the
initial survey upon signing up for the study. This initial study collected demographic information
as well as measures on satisfaction and preparedness that would be used as variables in
correlational analysis. There was a period of a week and a half while the recruitment efforts were
taking place. Participants were signing up throughout this period. Once the recruitment effort had
ended, they were notified through the ExpiWell app messaging that the 2-week period of
experience sampling would begin and run from March 10-23, 2022. The next step was for
participants to engage in the 2-week period of experience sampling. During the 2-week period,
participants would receive an alert three times per day from the smartphone ExpiWell application
to complete the questions. These alerts occurred randomly during a morning, afternoon, and
evening block of time. This information collected would give data on flow states while engaged
in specified actions. The demographic, satisfaction, and preparedness variables would then be
correlated with flow states and actions.
Variables
All items listed in the initial survey and experience sampling survey in Appendix A are
independent variables, except for the coded measure of flow state. The coded flow states from
65
the quadrant model (see Table 3.1) were tallied for the number of times each participant was in
each flow state and used as the dependent variable in the regression models. Each corresponding
flow action was coded into 8 different categories. The number of variables used from the initial
survey in the regression models had to be reduced due to the lower than anticipated number of
participants who took part in the study. The projected number of variables was 20+, although it
needed to be adjusted to fit statistical guidelines for appropriate calculations. Any of the
variables not included in the regression were instead used as descriptive data only. The ESM data
collected, coded flow state and work actions, are the crucial elements of the regression model
used for analysis.
Table 3.1
Quadrant Model
(Adapted from Nakamura & Csikszentmihalyi, 2014, p. 248)
Survey Design and Data Collection
A single time initial survey was conducted followed by an extended experience sampling
survey at intervals of time during the two weeks of data collection. All surveys, initial survey and
High Challenge
2. Stress/Anxiety
4. Optimal State of Flow
Low Challenge
1. Boredom/Apathy
3. Control/Relaxation
Low Skill High Skill
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experience sampling, were completed using ExpiWell software. Participants were asked to
download the smartphone app and enter the participation code for the study. The initial survey
focused on demographic information of the teachers as well as one-time indications of teacher
experience identified as likely predictors in research. The demographic portion of the initial
survey contained simple entry answers, such as a single numeric value or a selection from
various options as listed below. Participants were also asked to rate their satisfaction levels of
multiple items and provided an opportunity to share any additional information they would like
in an open-ended question.
The ESM survey is a digital adaptation of Csikszentmihalyi experience sampling flow
questionnaire that focuses specifically on the skill and challenge ratings. ExpiWell was used to
conduct the experience sampling surveys. The experience sampling service was tested during
pilot testing to determine the suitability of the service.
Each experience sampling survey began with an initial question asking if work-related
activity was being conducted when the message was received. If the participant responded “No”
the experience survey ended immediately, as only work-related activity was of interest for the
purposes of this study. If answered “Yes”, four further questions were asked: a description of
what one was doing at a designated time with a corresponding selected category as well as two
Likert-like ratings of the flow measurement: challenge and skill rating.
The experience sampling survey was randomly sent 3 times per day for a two-week
period in March during the times of 8:00am and 8:00pm. ExpiWell was programmed to send an
experience sampling alert once randomly during each block of time. The morning survey was
sent between 8:00 am-11:59 a.m., afternoon between 12:00 p.m.-3:59 p.m., and the evening
between 4:00 p.m.-8:00 p.m. Once the survey was sent during each block of time, the participant
67
had a 3-hour block of time to answer before the survey timed out and became unavailable. This
affordance of time allowed the participants some latitude in using the software reporting so as to
maximize the comfort in reporting during busy professional activities. This resulted in a total of
42 possible data points per participant throughout the experience sample collection.
Data Collection
The coding of data was done on an individual participant basis, classifying each
skill/challenge data point on an above/below average basis into a code for each of the four states.
For example, above average for challenge and skill was coded as “4” indicating the optimal flow
state. Below average challenge and above average skill was coded as “3” in the
control/relaxation state. Above average challenge and below average skill was coded as “2” in
the stress/anxiety state. Below average challenge and skill were coded as “1” in the
boredom/apathy state. This data was then entered as a tally count for the number of times each
participant was in a particular flow state in a spreadsheet column for each flow state. A perusal
of the spreadsheet would then read the number of times each participant was in each individual
flow state 1-4. The reported participant work-related actions from the ESM were also coded and
then entered as a tally of the number of times each action was reported, while in each flow state.
There were eight action items, four categorized as musical (conducting rehearsal/performance,
teaching concept(s), planning/preparing instruction, and evaluating student work), and four
categorized as non-musical (school administrative duties, fundraising activities, event
planning/coordinating, dealing with student issues/behavior). The spread-sheet had a column for
each flow state and its corresponding action. Each of the four flow states had eight actions for a
total of thirty-two columns representing flow state and corresponding action (i.e., ESM Flow
68
State 4 Action 1 – ESM Flow State 4 Action 8). A sample of this data in spreadsheet form can be
seen in Figure 3.2. Demographic and categorical information from the initial survey will be
coded to identify subgroups of teachers. Since the Likert-like scales contain 11 options (0-10),
they were entered into the regression as continuous variables as suggested by Johnson and
Creech (1983). The flow state being entered as a continuous dependent variable allows for the
use of multiple linear regression (Miksza & Elpus, 2018).
Table 3.2
Data Collection Tally of Flow States 1-4 and Actions 1-8
Statistical Procedures
The main procedure used in the correlational analysis was multiple linear regression.
Regression was chosen as the optimal statistical method for this study because of its versatility in
exploring relationships between multiple variables. As Miksza and Elpus (2018) explained,
regression is extremely useful in exploring whether or not variables are related to each other as
well as providing estimates to the directionality and degree of the relationships. The large
amount of data and the multiple variables in this study made regression the sensible method of
analysis. The collected ESM data provided Likert-like scale data points on the challenge/skill of
69
actions that were coded into the four different flow states consistent with the flow quadrant
model (Moneta, 2012). A regression model was run using the initial survey data and the flow
state tally as the dependent variable in order to show correlations between flow state and
demographic/satisfaction data. This was done a total of four times, changing the dependent
variable to the tally of each of the four flow states. Lastly a single regression model was run with
flow state and corresponding action tallies using the total number of flow state tallies as the
dependent variable in order to show which flow actions correlated with which flow states.
Validity. Models of regression were explored to find the most suitable model for the data
as interaction effects were discovered. Statistical significance was set at p < .05. The original
minimum participant goal was set at 100. If this were reached the experience sampling would
have produced a possible 4,200 skill/challenge coded data points (100 participants x 42 data
points each). This is in addition to the data in the initial survey which contained 24 possible data
points for each participant. This amount of data allowed for high confidence in the results of the
statistical modeling.
Various sample size minimum suggestions have been given by various authors.
VanVoorhis and Morgan (2007) made the rule of thumb suggestion of a minimum of 50 per
variable. Harris (2001) suggests the common rule of thumb minimum sample size of 10 per
variable, while Vittinghoff and McCulloch (2007) suggested a relaxation of the minimum sample
size to 5 per variable as sufficient. VanVoorhis and Morgan (2007) also made an argument that
using a statistical measure of power may be a better way to determine needed sample size as rule
of thumbs are not necessarily accurate. The goal was to maintain the minimum rule of thumb of
5 participants per variable and will complete a statistical power measure guard against statistical
error.
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Although participant recruitment fell below the intended goal, the strong point of the data
was the number of repeated measures per participant. The use of data from 40 participants
allowed for a total of 1,680 possible experience sampling data points. In the end, 1,180
experience sampling data points were collected. Of the 1,180 data points, 673 indicated the
participant was not engaged in a work activity and no further data was collected, while 507
indicated they were doing a work-related activity and a full set of experience sampling data was
collected. Therefore, the participant response rate to experience sampling questions was over
70%. In actuality, this percentage is even higher, given that some participants signed up for the
study after the two-week window had already started, lowering the actual overall possible
number, and raising the percentage of response. There was an average of 29.5 experience
sampling data points collected per person, with an average of 12.675 full experience sampling
data points describing active work actions and correlating flow states. The reduction of variables
based on actual number of participants ended up being reduced to the usable amount of 4.44
variables per participant, which is on the verge of the 5:1 guideline and reaching the guiding
principle of “as many as possible.”
When using the regression models, I was assured that the following assumptions were
accurate: validity, additivity and linearity, independence of errors/independent observations,
equal variance of errors, and normality of errors (Miksza & Elpus, 2018). Procedures were used
to test the suitability of the data for the linear model.
The internal validity of this study was strong due to the repeated nature of experience
sampling. A one-time survey has limitations as it is only a singular snapshot of experience,
whereas the experience sampling provides an ecological view of a two-week period of time
(Hektner, Schmidt, & Csikszentmihalyi, 2007). The immediacy of the questions leads to a much
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higher possibility of accurate recall of experience. In this study, the questions timing-out and
became unavailable after a short period of time gets as close to guaranteeing this as possible. The
randomness of the scheduled questions throughout different parts of the day made it more likely
that different experiences were captured and included in the data, rather than risking a one-time
outlier. There is a possibility of participants forgetting to answer, being too busy to answer, or
for whatever reason deciding not to answer, but the number of questions asked compensated for
this as much as possible. Reflexivity bias during experience sampling has been found to be low,
with the repeated schedule of self-reporting even being reported as therapeutic among some
participants (Hektner, Schmidt, & Csikszentmihalyi, 2007). There is strong evidence to believe
this study has strong internal validity.
External validity was considered strong for this study. The inclusion of all different types
of participants from different backgrounds and demographics was a strong point. The self-
selection of participants lowers the strength as there may be some self-selection bias of
participants who chose or chose not to participate. This unfortunately was unavoidable, and it
would be impossible to collect this kind of data without this downside. The open-ended
questions at the end of the initial survey provided a much more qualitative view into the thoughts
and feelings of participants, which can help to give insight into any possible selection bias. This
study had many meaningful responses that did give strong insight. The experience sampling
measurement of skill/challenge balance was developed by Csikszentmihalyi and has been used
for many decades in research studies giving strong support to its validity.
Reliability. Cronbach’s alpha was calculated as a common measure of reliability. This
was calculated at 0.549 for the regression of the initial survey data and flow states. Cronbach’s
alpha was 0.659 for the correlation of flow states and actions. Sijtsma (2009) strongly argued
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that Cronbach’s alpha, although commonly used, is not an accurate measure of internal reliability
and has only limited usefulness. Tavakol and Dennick (2011, p. 54) explained that “alpha does
not simply measure test homogeneity or unidimensionality as test reliability is a function of test
length. A longer test increases the reliability of a test regardless of whether the test is
homogenous or not.” Length of test and repeated measures has previously been discussed as a
strength of this study. Taber (2017) further argued that the often-used Cronbach’s limit of
acceptableness at 0.7 is arbitrary and that even proponents of this marker admit that studies with
an alpha below 0.7 may be reliably used. Taber also described how a high alpha may be an
indication of redundancy or other problems. Outside of the common journal publishing standard
of greater than 0.7, some authors have listed the range of 0.64–0.85 as “adequate”, 0.58–0.97 as
“satisfactory”, and 0.45–0.98 as “acceptable” or “sufficient” (Taber, 2017). Given these
guidelines and debate behind Cronbach's alpha, this study falls within the acceptable range of
reliability given the calculated Cronbach's alpha.
Summary
It was hypothesized that correlations would be found between the initial survey variables
and the flow state and corresponding actions. The best way to address the research questions was
determined to be linear regression models correlating these variables to each of the flow states
using the quadrant model as well as a model correlating flow state actions with flow states.
These correlations were expected to have meaningful applications to teacher training and
support. To logistically implement the complex nature of experience sampling, the software
ExpiWell was chosen. The pilot study helped refine the study and provided support for the
decisions made using the software. Although the goal for number of participants was lower than
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anticipated, the realistically maximum possible amount given the circumstances was achieved
and provided acceptable statistical parameters for analysis.
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Chapter 4: Results
Introduction
The research questions for this study focused on three issues: (1) trends in the subjective
experience of music teachers when correlated with flow states during professional activities, (2)
notable trends in the subjective experience of music teachers when viewed in terms of years of
experience, type of music classes taught, means of teaching transmission, and measures of job
satisfaction and preparedness, (3) and whether music teachers who experience the optimal state
of flow more frequently report more job satisfaction and success.
The three issues relate directly to the central research questions for data collected. This
chapter is comprised of four primary sections: (1) demographics and descriptive statistics, (2) the
approach to regression analysis, (3) results and (4) summary of answers to the research
questions.
After a full descriptive reporting of the demographic data, the approach to data analysis
and connection to the research questions is explained in detail. Tables are provided to aid in the
interpretation of the results. The results section shows the answers to the research questions and
presents them by showing the correlations found in each of the four flow states, each represented
by an individual regression model. A fifth and final regression model is presented correlating
reported flow states and professional activities. The results of each regression model will be
shown in a table followed by written explanation. After these data are presented, a summary of
the answers to each research question is provided. A full analysis and exploration of the
implication of results will be given in Chapter 5.
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Demographics and Descriptive Statistics
Table 4.1 shows the demographic descriptive statistics for the study. A total of 40
participants were included in the study. The following represents the data collected from the
initial survey and shows the wide range of backgrounds and subjective experience as reported
from these 40 participants recruited from across the United States. The demographic data shows
a reasonably diverse cross-section of the population and is considered to be representative of the
community at large.
Table 4.1
Participant Demographic Information
Age # of Participants Gender # of Participants
18-24
25-34
35-44
45-54
55-64
1
18
9
9
3
Male
Female
Non-Binary
15
25
0
School Location Highest Education
Urban
Suburban
Rural
10
20
10
Bachelors
Some Graduate Work
Masters
Doctoral
2
4
33
1
Teaching Level Courses Taught
Elementary
Middle School
High School
18
15
23
Band
Choir
Orchestra
General Music
Other
18
12
4
23
12
The participants had an average of 14.1 years teaching experience, with the minimum
being 2 years, and the maximum being 31. These teachers had spent an average of 7.8 years in
the current teaching position, with a minimum of 1 year, and a maximum of 27 years. A total of
39 participants were teaching in-person classes while 1 was teaching in a hybrid format. In
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addition to their traditional band, choir, orchestra, and general music courses, they also reported
teaching private piano, private violin, modern band, class piano, music theory, AP music theory,
handbells, music appreciation, and the non-music courses of theater arts, computers, and public
speaking. Of the total participants, 25 listed previous experience teaching at the elementary level,
26 at the middle school level, and 24 at the high school level. Additionally, 26 of the participants
had previous experience teaching band, 22 teaching choir, 12 teaching orchestra, 31 teaching
general music, and 8 teaching other courses. Other courses previously taught by the participants
included private lessons, piano, music history, music theory, composition and film, guitar, jazz
choir, music technology, and world drumming.
Table 4.2 shows the average rating for all the initial survey questions pertaining to
measures of satisfaction and preparedness. All questions are reported here for descriptive
purposes. The table is meant to show the overall levels of satisfaction and preparedness and does
not yet begin to answer the research questions.
77
Table 4.2
Initial Survey Questions 14-27 with Average Rating
Question # Question Text Average Rating
Q14 What is your level of preparedness to work with students
with special needs? (0 = None, 10 = Very High)
6.3
Q15 What is your level of preparedness to teach online?
(0 = None, 10 = Very High)
6.5
Q16 What is your level of satisfaction with your current
administrative duties? (0 = None, 10 = Very High)
7.1
Q17 What is your level of satisfaction with your student
population? (0 = None, 10 = Very High)
7.2
Q18 What is your level of satisfaction with your parental and
community support? (0 = None, 10 = Very High)
5.9
Q19 What is your level of satisfaction with planning time?
(0 = None, 10 = Very High)
6.6
Q20 What is your level of satisfaction with teaching schedule?
(0 = None, 10 = Very High)
6.8
Q21 What is your level of satisfaction with your administration?
(0 = None, 10 = Very High)
6.9
Q22 What is your level of satisfaction with teacher preparation
program? (0 = None, 10 = Very High)
6.6
Q23 What is your level of satisfaction with the location of your
school? (0 = None, 10 = Very High)
8.0
Q24 What is your level of satisfaction with pay?
(0 = None, 10 = Very High)
6.7
Q25 What is your level of satisfaction with your current position?
(0 = None, 10 = Very High)
7.8
Q26 What level of isolation do you feel in your current position?
(0 = None, 10 = Very High)
5.8
Q27 How likely are you to leave your current position in the near
future? (0 = None, 10 = Very High)
4.0
Approach to Data Analysis
A reduction of variables from the initial survey was needed to achieve acceptable
statistical parameters. There were not enough participants to include all the demographic
variables such as subject, grade level, or location, so the results are indicative of all music
teachers in a singular category. A separate regression model was performed for each of the four
78
flow states in the flow quadrant model (Table 4.3), serving as the dependent variable whose
variance was explained. The quadrant model uses the reported skill/challenge balance to
categorize a high skill/high challenge as the optimal state of flow, a high skill/low challenge as
control/relaxation, a low skill/high challenge as stress anxiety, and a low skill/low difficulty as
boredom/apathy.
Table 4.3
Quadrant Model with Flow State Number Codes
(Adapted from Nakamura & Csikszentmihalyi, 2014, p. 248)
The versatility of multiple linear regression allowed for the correlation of the many
variables. The survey questions acting as independent variables were used to predict the
dependent variable of flow state. The results show the direction and degree of the correlations
with each variable. Flow states were categorized by coding the data into the corresponding
number from Table 4.3. Each participant had many data points of challenge/skill with a
corresponding action from ESM. The average numeric value for challenge and skill was then
calculated for each participant and each ESM datapoint coded based on above or below personal
average. The tally of times in each flow state became the dependent variable for which the
survey questions in Table 4.4 predict as the independent variables.
High
Challenge
(Above
Avg.)
Flow State 2
Stress/Anxiety
Flow State 4
Optimal State of Flow
Low
Challenge
(Below Avg.)
Flow State 1
Boredom/Apathy
Flow State 3
Control/Relaxation
Low Skill (Below Avg.) High Skill (Above Avg.)
79
Table 4.4
Eight Selected Survey Questions Serving as Predictor Independent Variables
Survey Question
Codes
Survey Text
Q13 Number of years in current position: (numeric entry)
Q17 What is your level of satisfaction with your student population?
(0 = None, 10 = Very High)
Q18 What is your level of satisfaction with your parental and community
support? (0 = None, 10 = Very High)
Q19 What is your level of satisfaction with planning time?
(0 = None, 10 = Very High)
Q21 What is your level of satisfaction with your administration?
(0 = None, 10 = Very High)
Q22 What is your level of satisfaction with teacher preparation program?
(0 = None, 10 = Very High)
Q24 What is your level of satisfaction with pay? (0 = None, 10 = Very High)
Q26 What level of isolation do you feel in your current position?
(0 = None, 10 = Very High)
Additionally, a fifth regression model was performed using the reported professional
actions (Table 4.5) and corresponding flow states as predictor variables to a total of number of
actions, to show which flow states are correlated with each action. Tables 4.3-4.5 are provided
for interpretation of the following regression models that will provide correlational data
answering the research questions.
Table 4.5
Action Codes and their Descriptions
Action Codes Descriptive Action
Action 1 Conducting rehearsal/performance
Action 2 Teaching concept(s)
Action 3 Planning/Preparing instruction
Action 4 Evaluating student work
Action 5 School administrative duties
Action 6 Fundraising activities
Action 7 Event planning/coordinating
Action 8 Dealing with student issues/behavior
80
Results
Assumptions for the suitability of multiple regression were addressed by the following
measures. There was acceptable independence of residuals, as assessed by Durbin-Watson
statistics of 2.161 for Flow State 4, 2.392 for Flow State 3, 2.085 for Flow State 2, 2.192 for
Flow State 1, and 1.583 for Flow States and Corresponding Actions (Draper & Smith, 2008).
There was linearity as visually assessed by partial regression scatterplots. Homoscedasticity was
assessed by visual inspection of scatterplots of studentized residuals versus unstandardized
predicted values. No evidence of multicollinearity was found when assessed by tolerance values
greater than 0.1 (Hair et al., 2014). There were no studentized deleted residuals greater than ±3
standard deviations and no leverage values greater than 0.2 (Huber, 1981). There were also no
values for Cook’s distance above 1 (Cook & Weisberg, 1982). The assumption of normality was
assessed using P-P Plots. All assumptions indicating suitability for multiple regression were met.
The flow quadrant model of flow states (Table 4.3) is the guiding principle for analyzing
the regression models. There was a regression model for each of the four flow states. The
questions from the initial survey listed in Table 4.4 were the predictor, independent variables for
each of the flow states (dependent variable). The following four regression models, presented in
Tables 4.6-4.9, show the regression data for a particular flow state in that model. These
correlations provided evidence to answer research questions 2 and 3. Data addressed research
question 2 by showing notable trends in the subjective experience of music teachers based on
years of experience and measures of job satisfaction and preparedness. Data addressed research
question 3 by showing whether music teachers who experience the optimal state of flow more
frequently report more job satisfaction and success. Research question 1 was addressed in the
final fifth regression model shown in Table 4.10, which displays regression data between the
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flow states as independent predictor variables and corresponding professional actions as the
dependent variable in this model (Table 4.5).
Table 4.6
Regression Model: Flow State 4: The Optimal State of Flow as Dependent Variable
Flow State
4
B 95% CI for B
SE B β R
2
ΔR²
p
LL UL
Model .421 .271
Q13 .059 -.098 .217 .077 .114 .448
Q17 .698 .099 1.298 .294 .432 .024*
Q18 -.603 -1.206 -.001 .295 -.415 .050*
Q19 -.249 -.679 .181 .211 -.196 .247
Q21 -.007 -.552 .537 .267 -.005 .979
Q22 .134 -.387 .655 .255 .086 .604
Q24 .531 .051 1.010 .235 .383 .031*
Q26 .570 .136 1.004 .213 .412 .012*
This regression model correlated the listed initial survey question variables (Table 4.4)
with the optimal state of flow (above average skill/above average difficulty, see Table 4.3). The
coefficient of multiple determination (R
2
) was 42.1% with an adjusted R² of 27.1%, a relatively
small size effect according to Cohen (1988). The eight items statistically significantly predicted
Flow State 4, F(8, 31) = 2.816, p = .018, adj. R² = .271. The inclusion of the eight of the
independent variables in this regression model accounted for 27.1% of the variance found in the
dependent variable of the optimal flow state. Four variables added significantly statistically to
the prediction, p < .05. The significance found suggests that the inclusion of the eight
independent variables was a better predictor of Flow State 4 than the mean model alone.
Regression coefficients and standard errors can be found in the above table. There were four
statistically significant correlations. Question 17, “What is your level of satisfaction with your
student population?” was correlated at a level of .024, indicating that higher levels of satisfaction
82
with student population was correlated with the optimal state of flow. Question 18, “What is
your level of satisfaction with your parental and community support?” was unexpectedly
negatively correlated at a level of .050, showing that lower levels of satisfaction with parental
and community support was correlated with the optimal state of flow. Question 24, “What is
your level of satisfaction with pay?” was correlated at the level of .031, indicating that higher
satisfaction with pay was correlated with the optimal state of flow. Lastly, Question 26, “What
level of isolation do you feel in your current position?” was significant at the level of .012,
indicating the somewhat surprising result that higher levels of isolation were correlated with the
optimal state of flow. No other variables reached a significant level.
Table 4.7
Regression Model: Flow State 3: Control/Relaxation as Dependent Variable
Flow State
3
B 95% CI for B
SE B β R
2
ΔR²
p
LL UL
Model .321 .146
Q13 .145 -.018 .308 .080 .291 .080
Q17 .318 -.303 .940 .305 .206 .304
Q18 -.080 -.704 .544 .306 -.058 .795
Q19 .269 -.176 .715 .219 .221 .227
Q21 .195 -.369 .759 .277 .138 .486
Q22 .542 .002 1.081 .265 .363 .049*
Q24 -.201 -.697 .296 .243 -.151 .416
Q26 .335 -.114 .784 .220 .253 .139
The above regression model shows the results correlating the listed initial survey question
variables (Table 4.4) with the control/relaxation flow state (above average skill/below average
difficulty, see Table 4.3). R
2
was 32.1% with an adjusted R² of 14.6%, indicating a small size
effect according to Cohen (1988). The eight items did not statistically significantly predict Flow
State 3, F(8, 31) = 1.831, p = .109, adj. R² = .146. The inclusion of all eight variables was
responsible for 14.6% of the variance with the flow state of control/relaxation. Although the
83
model was not significant, there was a small effect, and one variable added significantly
statistically to the prediction at p < .05. Regression coefficients and standard errors can be found
in the above table. There was one statistically significant correlation. Question 22, “What is your
level of satisfaction with teacher preparation program?” was significant at the level of .049,
indicating that higher satisfaction with teacher preparation programs was correlated with the flow
state of control/relaxation.
Table 4.8
Regression Model: Flow State 2: Stress/Anxiety
Flow State
2
B 95% CI for B
SE B β R
2
ΔR²
p
LL UL
Model .154 -.064
Q13 .028 -.089 .145 .057 .087 .632
Q17 .404 -.043 .850 .219 .406 .075
Q18 -.304 -.752 .144 .220 -.340 .176
Q19 -.057 -.377 .263 .157 -.072 .720
Q21 .222 -.183 .627 .199 .245 .272
Q22 .277 -.111 .664 .190 .288 .155
Q24 -.017 -.374 .339 .175 -.020 .922
Q26 .239 -.084 .561 .158 .280 .142
The preceding regression model shows the correlations between the flow state of
stress/anxiety (low skill/high difficulty, see Table 4.3) and the listed initial survey variables
(Table 4.4). R
2
was 15.4% with an adjusted R² of -6.4%, indicating little to no size effect
according to Cohen (1988). The inclusion of all eight variables accounted for -6.4% of the
variance with the flow state of stress/anxiety. This indicates that the inclusion of the independent
variables in this regression model was not better than the mean model at predicting the
dependent variable of stress/anxiety state. The eight items did not statistically significantly
predict Flow State 2, F(8, 31) = .706, p = .684, adj. R² = -.64. Regression coefficients and
standard errors can be found in the above table. There were no statistically significant findings
84
between this flow state and the variables. This indicates that there were no significant
relationships between the second flow state and the variables.
Table 4.9
Regression Model: Flow State 1: Boredom/Apathy as Dependent Variable
Flow State
1
B 95% CI for B
SE B β R
2
ΔR²
p
LL UL
Model .217 .015
Q13 -.009 -.079 .061 .034 -.048 .784
Q17 .042 -.224 .309 .131 .069 .747
Q18 -.120 -.388 .147 .131 -.217 .366
Q19 -.105 -.297 .086 .094 -.217 .269
Q21 -.048 -.290 .194 .119 -.086 .687
Q22 .002 -.230 .233 .113 .003 .989
Q24 .065 -.148 .278 .104 .122 .540
Q26 .149 -.044 .342 .094 .282 .125
This last regression model of the four flow states shows the correlations between the flow
state of boredom/apathy (low skill, low difficulty, see Table 4.3) with the listed variables from
the initial survey (Table 4.4). R
2
was 21.7% with an adjusted R² of 1.5%, which indicates a small
size effect according to Cohen (1988). The inclusion of all eight independent variables in this
model accounted for 1.5% of the variance with the flow state of boredom/apathy. The eight items
did not statistically significantly predict Flow State 1, F(8, 31) = 1.073, p = .407, adj. R² = .015.
The mean regression model was just as effective at predicting the dependent variable of
boredom/apathy state as the model included the eight independent variables. Regression
coefficients and standard errors can be found in the above table. There were no statistically
significant correlations. This indicates that there were no significant relationships found between
the first flow state and the variables.
85
Table 4.10
Regression Model: Flow States and Corresponding Actions as Dependent Variable
Flow State &
Action
B 95% CI for B
SE B β R
2
ΔR²
p
Model .997 .986
Flow1 Action1 1.810 .075 3.545 .752 .125 .043*
Flow1 Action2 .956 -.107 2.019 .461 .073 .072
Flow1 Action3 .516 -.849 1.880 .592 .035 .409
Flow1 Action4 .883 -1.846 3.612 1.183 .045 .477
Flow1 Action5 1.237 -.093 2.567 .577 .099 .064
Flow1 Action6 -.684 -5.591 4.222 2.128 -.020 .756
Flow1 Action7 1.261 -.750 3.271 .872 .141 .186
Flow1 Action8 1.400 -4.085 6.884 2.378 .050 .572
Flow2 Action1 1.239 .395 2.084 .366 .214 .010*
Flow2 Action2 1.455 .363 2.548 .474 .191 .015*
Flow2 Action3 .860 -.564 2.284 .618 .096 .201
Flow2 Action4 -.393 -6.704 5.918 2.737 -.008 .889
Flow2 Action5 .657 -1.125 2.439 .773 .032 .420
Flow2 Action6 -.546 -10.185 9.093 4.180 -.012 .899
Flow2 Action7 .702 -.861 2.265 .678 .053 .331
Flow2 Action8 .406 -2.596 3.409 1.302 .021 .763
Flow3 Action1 .885 -.323 2.093 .524 .265 .130
Flow3 Action2 .995 -.725 2.715 .746 .104 .219
Flow3 Action3 1.003 .247 1.758 .328 .163 .016*
Flow3 Action4 1.950 -.276 4.177 .965 .138 .078
Flow3 Action5 .563 -.146 1.271 .307 .104 .104
Flow3 Action6 1.122 -3.258 5.503 1.900 .024 .571
Flow3 Action7 .982 -.659 2.622 .712 .083 .205
Flow3 Action8 1.204 -1.509 3.917 1.177 .035 .336
Flow4 Action1 .986 .387 1.584 .259 .285 .005*
Flow4 Action2 .942 .556 1.328 .167 .239 <.001*
Flow4 Action3 1.339 .484 2.194 .371 .156 .007*
Flow4 Action4 .934 -.236 2.103 .507 .098 .103
Flow4 Action5 .768 -11.710 13.246 5.411 .039 .891
Flow4 Action7 .078 -1.901 2.058 .858 .006 .930
Flow4 Action8 2.059 -11.850 15.968 6.032 .096 .742
The final regression model shows the correlations between the reported flow states (Table
4.3) and their corresponding actions (Table 4.5). No data was collected from flow state 4 action
6, so this variable was omitted. R
2
was 99.7% with an adjusted R² of 98.6%, which indicates a
large size effect according to Cohen (1988). Inclusion of all independent variables accounted for
86
98.6% of the variance in predicting the flow action. The 31 items did statistically significantly
predict Flow State Actions, F(31, 8) = 87.608, p < .001, adj. R² = .986. The significance found
suggests that the regression model including the independent variables was highly successful at
predicting the independent variable of flow action when compared to the mean model. Seven
variables added significantly statistically to the prediction, p < .05. Regression coefficients and
standard errors can be found in the above table. There were multiple statistically significant
findings in this model. Flow state 1 and action 1 were significant at the level of .043. This
indicates that music teachers were likely to be in the boredom/apathy state of flow while
conducting a rehearsal/performance. Flow state 2 and action 1 were significant at the level of
.010, indicating that the state of stress/anxiety was likely to be occurring while teachers were
conducting a rehearsal/performance. Flow state 2 and action 2 were statistically significant at the
level of .015, showing that the state of stress/anxiety was likely to be occurring while teachers
were teaching concepts. Flow state 3 and action 3 were statistically significant at the level of
.016, signifying that the flow state of control/relaxation was likely to be occurring while teachers
were planning/preparing instruction. Flow state 4 and action 1 were significant at the .005 level,
showing that teachers were likely to be in the optimal flow state while conducting a
rehearsal/performance. Flow state 4 and action 2 were statistically significant at the level of
<.001 indicating that teachers were likely to be in the optimal flow state while teaching concepts.
Finally, flow state 4 and action 3 were statistically significant at the level of .007, indicating that
teachers were likely to be in the optimal state of flow while planning/preparing instruction. No
other statistically significant results were found.
87
Summary of Results
The data shows that pertaining to research question 1, there were trends in the subjective
experience of music teachers when correlating professional activities with flow states. These
trends were: (1) the optimal state of flow was significantly correlated with the actions of
conducting rehearsal/performance, teaching concept(s), and planning and preparing instruction,
(2) the state of control/relaxation was significantly correlated with action of planning and
preparing instruction, (3) the state of stress/anxiety was significantly correlated with the actions
of conducting rehearsal/performance, teaching concept(s), and (4) the state of boredom/apathy
was significantly correlated with the action of conducting rehearsal/performance.
Results regarding research question 2 show that there were notable trends in the
subjective experience of music teachers based on measures of job satisfaction and preparedness.
The results show that: (1) music teachers were more likely to be in the optimal state of flow if
they had higher satisfaction with their student population, higher satisfaction with their pay,
higher levels of isolation, and lower satisfaction with their parent and community support. (2)
Music teachers were significantly more likely to be in the state of control/relaxation if they were
more satisfied with their teacher preparation program.
The data showed that research question 3 had mixed support for whether music teachers
who experience the optimal state of flow more frequently report more job satisfaction and
success. As discussed in the previous section, teachers who reported being in the optimal state of
flow were correlated with satisfaction with their student population and their pay. However, the
optimal state was also correlated with higher levels of isolation and lower satisfaction with
parent and community support. Therefore, there is some support as well as some conflicting
88
results for teachers who experience the optimal state of flow being more satisfied in aspects of
their work.
Summary
The results of the study provided many descriptive statistics that showed the make-up of
the participants that are likely representative of the population. The initial survey also yielded
scores on measures of satisfaction and preparedness that were provided for additional descriptive
analysis. In answering researching question 1, there were 7 different significant correlations
found between reported flow states and actions. There were not enough participants to fully
explore every aspect of question 2, which left out type of music classes taught and means of
teaching transmission. There were no significant results found with years of experience,
however, correlations were found with measures of job satisfaction regarding student population
and pay. Furthermore, in exploring question 3, the correlations found between higher satisfaction
with student population and higher satisfaction with pay suggests that teachers with these
feelings do experience the optimal state of flow more. There were some surprising results, such
as the negative correlation with satisfaction on parental support and the optimal state of flow, as
well as the higher levels of isolation likewise being correlated with the optimal state. Many of
these results have special significance for teacher support and training. All these relationships
and their implications will be explored in Chapter 5.
89
Chapter 5: Conclusion and Implications
Introduction
Flow, the theory of optimal performance (Csikszentmihalyi, 1990, 1997, 2015), was the
lens through which music teacher experience was analyzed. The related literature on music
teacher training, support, satisfaction, and retention showed the need for further research
regarding music teacher experience to inform practice (see Chapters 1 and 2) in addressing these
issues. This study sought to find meaningful relationships between demographic information,
measures of satisfaction and preparedness, and flow states and their corresponding actions. These
relationships were measured by collecting initial survey data as well as an experience sampling
of data used to calculate flow along with the corresponding professional action. Models of
multiple regression were used in this study to find significant correlational relationships with a
sampling of music teachers. Results showed many connections between music teacher reported
flow state, teaching actions, and measures of satisfaction and preparedness. This chapter: (1)
summarizes the findings and places them in context with past research and (2) offers a set of
implications for more informed practice and continued research.
Overall Summary of Results
A summary of the statistically significant results presented in chapter 4 follows, together
with ties to past research. The four flow states in the quadrant model can be reviewed in Table
4.3, initial survey variables of satisfaction and preparedness in Table 4.4, and experience
sampling actions in Table 4.5. Among the many findings, flow state 4 (the optimal state of flow)
was correlated with higher satisfaction with student population, higher satisfaction with pay,
higher levels of isolation, and lower satisfaction with parent or community support. Flow state 3
(control/relaxation) was correlated with higher satisfaction with teacher preparation programs.
90
There were no significant results with flow state 2 (stress/anxiety) or flow state 1
(boredom/apathy) on measures of satisfaction or preparedness from the initial survey. There
were also multiple correlations found when correlating flow states with corresponding
professional actions. Action 1 (conducting rehearsal/performance) was correlated with flow state
4 (the optimal state of flow), flow state 2 (stress/anxiety), and flow state 1 (boredom/apathy).
Action 2 (teaching concepts) was correlated with flow state 4 (the optimal state of flow) and flow
state 2 (stress/anxiety). Action 3 (planning/preparing instruction) was correlated with flow state 3
(control/relaxation) and flow state 4 (the optimal state of flow). There were no statistically
significant findings with actions 4-8, evaluating student work, school administrative duties,
fundraising activities, event planning/coordinating, dealing with student issues/behavior.
Discussion of Findings
The focus of the discussion of findings will revolve around results pertaining to each of
the research questions. In each section, the research question and related hypotheses will be
reviewed. Subsequently, results to each research question will be presented in detail including
points related to previous research and related literature. In connection to the results presented
for each aspect of the research questions, suggestions will be made on how to further extend
research on each of these specific points throughout the discussion. A final overview and
discussion of recommendations from the study as a whole will be presented at the end of the
chapter.
Research Question 1
The first research question, “What are the trends in the subjective experience of music
teachers when correlating professional activities with flow states?” was explored using
regression techniques to correlate reported professional actions with flow state during experience
91
sampling. It was hypothesized that optimal flow states would correlate highly with actions that
teachers typically enjoy, such as leading a rehearsal or performance. It was expected that flow
states involving stress would be correlated with actions found in praxis shock/survival mode
research, such as administrative responsibilities associated with running extra-curricular music
programs or dealing with student behavior problems (Ballantyne, 2007; Conway, 2015). The
results of the statistical correlations had some support for the hypothesis, but also had results that
were somewhat unexpected.
The inclusion of each of the categories of actions as independent variables was based on
research established in Chapter 2 as well as personal experience about the job actions taken by
music teachers. The regression model predicting professional action turned out to be highly
effective, though there were some correlations that were expected, but not found, such as stress
with administrative duties (Conway, 2001; Miksza, Roeder, & Biggs, 2010). Nonetheless, the
effect size of this model strongly suggested the inclusion of all the independent variables. It is
possible that these correlational measures were affected by low samples size, so the following
speculation about causality must be applied with caution as further research is still needed to
provide support for these ideas. Furthermore, there was certainly an effect of the pandemic on
the study, which is explored more in the qualitative feedback section, that may have produced
results that could be different from what would be seen if the study was performed during a
different time period.
Correlation with Action: Conducting Rehearsal/Performance. Action 1,
rehearsing/performing, was significantly correlated with three different flow states:
boredom/apathy, stress/anxiety, and the optimal state of flow. This was likely significant with
three different states because of the frequency of this action, with most teachers involved in this
92
activity for a large portion of their professional activities. It is interesting that this action was
associated with both positive and negative flow states. When comparing results with previous
research one might conceive that the differences could be explained by different levels of
teaching experience and teacher career development such as described by Kington, Reed, and
Sammons (2014). Eros (2011) likewise discusses the phases of teachers’ careers and how they
stabilize in building competency, professionalism, and overall expertise as they enter the second
phase of their teaching career. Ballantyne’s (2007) work with beginning teachers going into
praxis shock further supports this notion. Perhaps beginning teachers had much more stress and
anxiety in their rehearsals and performances, while mid and late career teachers had higher levels
of the optimal experience of flow. It is possible that mid or late career teachers might even get
bored or apathetic in rehearsal if it is overly repetitive or simplistic. This study did not involve
enough participants to separate participants into different sample groups to provide clearer
answers to these questions. Future research with a much larger number of participants is needed
in order to categorize teachers into these subgroups and collect data for comparison.
Other possible explanations exist. For example, it is conceivable that even a single
teacher could experience multiple flow states while conducting rehearsals or performances. The
fluidity of flow states means that a single teacher could experience all of the flow states during
the same day, or even during the same class. Perhaps the different flow states could be explained
by the ability level of the class. A beginning level class may cause boredom or apathy for an
advanced teacher, while at the same time a beginning teacher may be in flow because their lower
skill level matches the difficulty. Likewise, an advanced teacher could be in flow with an
advanced group because of the higher level of musicality, while a lower skilled teacher feels
stress because it is above their skill level. Further exploration of this is needed to ascertain if
93
music skill sets, such as rehearsal methods or conducting are issues influencing feelings of
preparedness.
Another explanation altogether could revolve around student behavior. If a student or
students have behavior problems in a class, no matter the musical skill level, it could put any
teacher into a state of stress. Furthermore, the skill level that would matter in this case would be
less musical skill, and more so classroom management skill (Teachout, 1997). Classroom
management skills, and more specifically classroom management skills for musical classrooms,
are typically seen as lacking in many beginning teachers and could once again be the more likely
explanation (Hester, 2013; Miksza, Roeder, & Biggs, 2010). Conway (2015) further supports the
belief that music teachers have unique struggles with classroom management when compared to
other teachers. Simon (2012) found many predictors of classroom management success including
music-specific training, self-efficacy and overall experience that could all be explanations in
different circumstances. Further research could explore the explanations from individual music
teachers on why they are feeling particular states of flow while conducting rehearsal or
performing. This would likely necessitate more qualitative study or a much more “zoomed in”
view of data rather than the current broad approach.
Another student issue that could provide stress is working with students with special
needs. Vanweelden and Whipple (2007) found that although most music teachers have a positive
view towards working with special needs students, many music teachers feel apprehensive about
having them in their own classrooms. Many teachers have experienced situations where they
have students included in their classrooms when it is debatable whether it is the best setting, or
even a proper setting, for the student. Often these situations are a mixed bag of positives and
negatives. What is certain is that these types of situations bring unique challenges that can be
94
difficult to handle. Feeling stress about how to manage a classroom with special needs students
could single-handedly change the flow state for a teacher who is inexperienced or does not know
what to do. Further research could look specifically at this question and try to isolate this
variable amongst music teachers.
Qualitative methods might be best suited to look in depth at this question. Gooding,
Hudson, and Yinger, (2013) found that the only factor leading to music teachers feeling prepared
for working with special needs students was the amount of coursework taken specifically
addressing working with these students. Salvador (2010) surveyed universities and found that
most universities reported integrating this issue into their coursework rather than having stand-
alone courses. Further research could look to answer if integration is more effective than stand-
alone courses or if there is even sufficient time spent on this issue in teacher preparation
programs.
Correlations with Action: Teaching Concept(s). Another correlation was that the
second flow state of stress/anxiety was also likely to be occurring while teaching concept(s). The
same action was also significantly correlated with the optimal flow state. Explanations for these
correlations are much like the above explanations regarding conducting rehearsal or
performances. This could likewise be attributed to experience or level of career development,
classroom management, or student behavior issues as discussed in the previous section. Once
again, previous research would indicate that beginning teachers are more likely to struggle with
these aspects than more experienced teachers (Conway; 2015; Hester, 2013; Miksza, Roeder, &
Biggs, 2010). However, there are multiple possible explanations for these occurrences. First,
teaching concepts may simply be a generally less engaging activity than being involved in
performing or rehearsing music, and therefore less likely to meet the criteria needed be in the
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optimal flow state. Music itself seems to be an optimal activity for entering into optimal flow
states, hence it being one of the original actions observed and studied by Csikszentmihalyi
(2014) and flow researchers and why so many have done music specific research on flow. When
teaching concepts, such as music theory, the subject is operating more in an abstract and
theoretical realm rather than the more participatory nature of music performance. In instances
like these, there is more relation to a class like mathematics than to a musical activity. Likely
every music teacher in existence would expect higher rates of flow in their music performance
course than a mathematics course, but to each their own.
The stress/anxiety state being correlated with teaching concepts might also be an
indication of a lack of preparation for teaching in this area. University teacher preparation
programs may not be focusing enough on preparing future teachers to teach the conceptual
aspects of music, such as music theory. Although university students undoubtably take
coursework in music theory, it is unclear how many of them receive coursework on how to teach
the subject. Shulman’s (1986) concept of pedagogical content knowledge would be key in this
area. That is to say, teachers cannot just have content knowledge, or teaching knowledge, but
need specific knowledge of how to teach the unique content of music theory. Experienced
teachers would be more likely to have a developed pedagogical content knowledge simply from
their years of experience. However, experience still does not mean that a teacher is teaching
effectively. Further research is needed to see if this is an area that is lacking in teacher
preparation.
Correlations with Action: Planning/Preparing Instruction. The action of
planning/preparing instruction was significantly related with both the flow states of
control/relaxation and the optimal state of flow. A likely reason for this is because this is an area
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that music teachers are largely comfortable, no matter their career development phase or level of
experience. This is an indication that teacher preparation and support programs are doing a
sufficient job of preparing music teachers to plan instruction.
One of the common complaints of teacher preparation or induction programs is that they
are not helpful for music teachers because of their focus on non-musical settings/issues (Conway,
2001; 2012; Conway et al., 2006). Miksza, Roeder, and Biggs, (2010) also report dissatisfaction
among band teachers about their lack preparation for many aspects of their jobs. However, in the
case of preparing music teachers for preparing instruction, this does not seem to be a pervasive
problematic issue.
Another factor in preparing instruction being associated with high positive flow states is
that it is usually an individual activity. Although students are considered in planning, the action
of preparation itself is unincumbered by the issues of classroom management and dealing with
challenging students. The elimination of these common difficult group dynamics makes more
positive flow states more likely to occur because there is no outside interference, and the flow
parameters are more easily met. Additionally, in preparing instruction, teachers are likely
involved in musical activities for which they have high skill levels.
Also, wherever their teaching identity lies, whether it be on being a teacher, a musician,
an activist, or other areas described by Chua and Welch (2021), they are likely able to fit into
that identity while preparing instruction. Identity struggles between musician and teacher
identified by Hargreaves et al. (2007) do not seem to be problematic while music teachers are
engaged in preparation. All these factors make it understandable why preparing for instruction
would be seen as an area which is correlated with high positive flow states.
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Research Question 2
There are many results to discuss regarding the second research question, “Are there
notable trends in the subjective experience of music teachers based on (a) years of experience,
(b) type of music classes taught, (c) means of teaching transmission, and (d) measures of job
satisfaction and preparedness?”
Aspects of research question 2 were not included in the analysis. (a) Years of experience
was included with the question pertaining to number of years in their current position. (b) Type
of music classes taught was not possible to study because of limitations with sample size. (c)
Modes of teaching transmission was eliminated because all participants except one reported
teaching in person. (d), measures of job satisfaction and preparedness was included in the form
of multiple questions from the initial survey (see Table 4.4).
It is important to note that each of the eight independent variables included had a strong
research basis established in Chapter 2 and further discussed with each correlation below. Each
of the regression models using these independent variables had different levels of significance at
predicting each of the dependent variables of flow state. The models were meant to test
significance, but not further test the predictability of the model. They were successful at
predicting the optimal state of flow as well as the state of control/relaxation, although to a
statistically small degree. They were not successful at predicting the stress/anxiety state or the
boredom/apathy state. All eight variables were included in each of the four regression models
because of the strength of the research basis supporting them as known issues for music teachers.
Though they did not achieve statistically significant results with each model, it is important to
state that non-significance is still important and indicated a lack of relationship between the
variables. It is also important to state that just because statistically significant results were not
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found in this study, it does not mean that these variables are not related in any way. Additionally,
low sample size could have affected the results in this study and the following speculation about
causality should be applied with restraint as further research is needed to provide support to these
ideas. Finally, it is not possible to say how much the pandemic may have changed the results of
this study and further research is needed to see if the results stand firm under other conditions.
Correlation with Years of Experience. Years of experience were included in the
regression models correlating with flow states as “number of years in current position.” It was
hypothesized that beginning teachers would show higher rates of stress/anxiety because of the
praxis shock/survival mode that likely existed in the initial phase of their career (Ballantyne,
2007; Conway, 2015; Eros, 2011; Kington, Reed, & Sammons, 2014). However, there were no
statistically significant results for this question. This could be simply that not many initial career-
phase participants were included in the study. The fact that someone who is in praxis shock and
feeling overwhelmed would probably be unlikely to take on something extra such as being a
participant in a study is understandable and shows an unavoidable selection bias. To study this
issue further, qualitative methods may be more suitable.
There was one result that was close to statistically significant at the level of .080,
correlating the flow state of control/relaxation with more years in their current position. Perhaps
if there were more participants, this would have achieved a statistically significant level. If so, it
would further the notion that years of experience would lead to more comfort and control in
one’s teaching experience.
Correlation with Satisfaction with Student Population. Question 17, “What is your
level of satisfaction with your student population?” highly correlated the optimal state of flow
with more satisfaction with student population. This correlation is exactly what was expected
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from previous research. There is much evidence that shows student behavior as being a common
cause of stress for teachers (Conway, 2015; Miksza, Roeder, & Biggs, 2010; Teachout,1997).
Gardner (2010) found that two of the three top three problematic issues that music teachers
report are students coming to school unprepared to learn and student apathy. Likewise, Docker
(2012) found that one of the reasons music teachers leave urban school settings is because of
student discipline issues. Furthermore, Bryant (2012) found that student participation was one of
the most important factors for teacher job satisfaction. Therefore, it is not surprising that music
teachers reporting higher levels of satisfaction with their students would lead to higher rates of
the optimal state, as problems in this area would most likely obstruct the optimal state.
Correlation with Parental/Community Support. Unlike the result with student
population satisfaction, parental and community support went against the grain of expectations.
In this case, satisfaction with parental and community support was negatively correlated with the
optimal state of flow. Previous research shows that lack of support from parents and
communities is another common stressor for music teachers. Fitzpatrick (2012) showed that
urban teachers with low job satisfaction had common complaints about lack of parental support.
The additional duties related to running an extra-curricular program, such as fundraising, booster
clubs, and related events are another common reported stressor for music teachers (Ballantyne,
2007; Conway, 2001; 2012; Miksza, Roeder, & Biggs, 2010). Presumably such extra duties are
related to parental and community support, and a lack of support would make things more
stressful for teachers. The fact that results from this study show higher rates of flow with less
parental and community satisfaction, although statistically significant, could just be an outlier.
However, Ballantyne (2007) points towards an interesting explanation in her discussion on how
performing these extra-curricular duties at a high level is a pressure that often comes from the
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community. In this case, having a community that is very involved and has high expectations for
the music teacher could also be a major stressor, and lead to lower rates of the optimal state.
Having very involved parents and community might seem desirable, but it can come with
a downside. Every music teacher has likely experienced or heard horror stories about overly
controlling and domineering booster clubs that can create all kinds of financial and interpersonal
conflicts. Considering possible downsides of parental and community involvement such as this,
it is conceivable that less involvement also means less pressure. In this sense, a lack of
involvement could lead to more time in the optimal state, especially if there are other types of
positive factors present, such as satisfaction with the student population. The issue of satisfaction
with community and parental support is complicated and could be viewed from different angles.
Further research could focus on the aspect of community expectations and pressure and how this
relates to satisfaction to further clarify these results. It could also look specifically at why
teachers are satisfied or dissatisfied with this type of support. The exploration of whether parent
or community over-involvement is a source of dissatisfaction could be studied. Socio-economic
status could also be studied in relation to parent involvement to further explain the dynamics and
interactions between parents, community, and the music teacher. Clearer answers to these areas
of further exploration are needed to inform music teacher education on how to best prepare
music teachers to optimally interact with parents and communities.
Correlation with Isolation. Further unexpected results were reached with Question 26,
pertaining to the level of isolation one feels in their current position. Conventional wisdom
would lead one to believe that isolation is a negative factor that would lead to problems and
stress. This notion is also supported by previous research. Conway (2015) discusses the often-
reported feeling of isolation amongst teachers and how it is likely an even larger problem
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amongst music teachers. Ballantyne (2007) further discusses how isolation is a common
compounding factor for praxis shock. Sindberg (2011) explains how isolation is even more of a
problem for music teachers in urban settings. However, this study found the opposite from the
expected result. The optimal state of flow was correlated with higher levels of isolation.
The first possibility is that once again that this could just be an outlier result. The other
possibility, especially considering the similarly unexpected result with parental support, is there
is emerging support for a more nuanced interpretation of the data. The issue of isolation seems to
have always been problematized, with the apparent solution of ending or lessening the isolation.
As described by the previously cited authors, isolation is a complex problem involving multiple
types and causes from colleagues and support networks. The cause of most of these issues of
isolation are unlikely to really change much in practicality. Perhaps less effort should be spent on
trying to end the isolation and more time should be spent preparing teachers for the inevitability
of it. This study shows that there are teachers thriving despite of their high levels of isolation.
Upon consideration, there are multiple aspects that could explain why music teachers
would have high levels of the optimal state of flow while feeling high levels of isolation. First of
all, the nature of isolation itself could be the explanation. The removal of outside influences,
pressures, and opinions could allow the teacher to focus more on the task at hand, without
disruption from outside concerns and distractions, which would conceivably allow for the flow
parameters to happen more easily. This would remove one layer of barriers to flow but
necessitate that the teacher be operating at high level in their everyday teaching activities. The
bulk of the teaching activities involves interacting with the students. Therefore, the correlations
with the optimal state of flow, high satisfaction with student population, low satisfaction with
parental and community support, and high levels of isolation, paints a picture of a teacher who
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enjoys working with their students and is unincumbered by outside pressures. When the setting is
seen in this light, it is logical that teachers would actually be in conditions that could lead to high
flow rates. Further research could explore how much the notion of pressure and outside influence
effect the state of flow for music teachers. Although this study collected data on related
satisfaction items, such as parents/community and administration, it did not explore why teachers
hold these views. Teachers are likely satisfied or dissatisfied for different reasons and this needs
to be explored more fully to find the emergent trends.
Correlation with Satisfaction with Pay. The measure of satisfaction with pay achieved
an expected result. Satisfaction with pay was correlated with the optimal state of flow.
Dissatisfaction with pay could be a stress that permeates work and home life. Ballantyne (2007)
found that teachers were often unsatisfied with their lack of financial payment for all their extra
duty work. Bryant (2012) found that salary was a significant predictor of teacher job satisfaction.
Likewise, Docker (2012) found that poor salary was a commonly reported reason for teachers to
leave urban schools. It is not surprising then that satisfaction with their pay would correlate with
higher levels of flow in this case. The removal of financial stress gives the teachers the energy to
focus elsewhere. The level of adequate pay for teachers is certainly controversial but should
nonetheless be explored in all localities to ensure teachers can focus on their practice and not be
consumed by an inadequate financial situation.
Correlation with Satisfaction for Teacher Preparation Programs. The final statistical
correlation was found with the flow state of control/relaxation. This correlated control/relaxation
with the measure on satisfaction with teacher preparation programs. It was expected that teachers
with high satisfaction with their programs would have more positive rates of flow, and that low
satisfaction would have more negative flow states. The correlation of the control and relaxation
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state with higher satisfaction rates was as hypothesized. Teacher preparation programs
undoubtably have an extremely important job in training music teachers for a complex career.
Ballantyne’s (2007) definition of praxis shock is when teachers experience with their preparation
and expectations for the job are different from reality. Unfortunately, this very occurrence is a
common complaint with teacher preparation programs (Conway et al., 2006). Furthermore,
music teachers frequently complained that teacher preparation programs often focused on non-
music subjects and were taught by non-music faculty. These disconnects led to high levels of
dissatisfaction and feelings of unpreparedness. Simon (2012) found that music specific training
was a predictor of success with classroom management for music teachers. Therefore, music
teachers having higher rates of control/relaxation when they are more satisfied with their teacher
preparation programs fits right into the vein of previous research and further highlights the
importance of these programs.
Research Question 3
The third research question was, “Do music teachers who experience the optimal state of
flow more frequently report more job satisfaction and success?” It was hypothesized that positive
participant views about their satisfaction and preparedness would lead to higher levels of flow.
Each of the items included in the initial survey and used as predictor variables had a previous
base of research for this notion. Each of these statistically significant variables were discussed
individually in the analysis of research question 2. This section will summarize the picture that is
created by their combined implications. The following should be interpreted as an emerging view
based on this studies results, which had limitations including smaller sample size. Further
research is needed to more fully support the ideas presented.
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The factors that were correlated with flow were high satisfaction with student population,
high satisfaction with pay, low satisfaction with parental and community support, and high levels
of isolation. The result of teachers reporting higher rates of flow being correlated with
satisfaction with their student population is exactly as expected. Teachers certainly spend much
of their time interacting with their students in their job activities, so a satisfaction or
dissatisfaction with them is logically either an encouragement or encumbrance to being in the
optimal state. Research has also shown that dissatisfaction with students and their behavior is
also a factor in why teachers decide to leave their positions (Docker, 2012). Teachers who are
satisfied with their pay reporting higher levels of the optimal state is also an expected result.
Lack of sufficient pay was a complaint of teachers experiencing praxis shock (Ballantyne, 2007).
Bryant (2012) likewise found that satisfaction with salary was a predictor of job satisfaction.
Once again, it is understandable that a feeling of insufficient finances is a problematic distraction
that could permeate work and personal life. This would undoubtedly be an inhibitor to the
optimal state of flow. Satisfaction with financial situation would lift this potential burden and
allow focus in one’s work life.
The negative correlation of satisfaction with parental and community support with the
optimal state flow was initially a surprising result. Likewise, it was not expected that higher rates
of isolation would be associated with more occurrence of the optimal state of flow. Certainly, it
would not be recommended to attempt to achieve more isolation and dissatisfaction with parental
and community support, however a wholistic view of these issues in combination paints a
plausible picture. The portrait of a music teacher most likely to be in the optimal state of flow is
one that enjoys working with their students and is not troubled by outside factors that might
interrupt their work.
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Qualitative Responses
The final question in the initial survey asked, “Is there anything else you would like to
share about your teaching experience?” This was the only open-ended question in the study and
was meant to allow participants to share any qualitative information that they deemed important.
Thirty of participants chose to provide answers to this question. The verbatim answers for each
of these participants can be found in Appendix B. Emergent issues from these participant
comments are analyzed below.
The COVID-19 pandemic was a major conflating factor that was unavoidable for this
study. This study was conducted in a completely unique time in history. Although the lockdowns
existing at the height of the pandemic were largely ended by early 2022 when this study was
completed, the effects of the pandemic lingered. Music teachers were practicing in a time that
was ambiguously somewhat post-COVID, and actual parameters and practice varied greatly due
to differences in local policies and personal perceptions of reality. Some refer to this period as
the COVID hangover. The effects of all of this on education is being studied and will almost
certainly have long lasting effects. The National Education Association published an article by
Walker (2022) shortly after this study was conducted. This article reported a “five-alarm crisis”
of 55% teachers saying they are likely to retire or leave the profession early and refers to what
teachers were currently experiencing as “beyond burnout.” What is certain is that many of these
frustrations were clearly communicate in the open-ended question asking participants to share
anything else they would like.
Of the 30 responses to this question, 10 of them had a direct or implied reference to the
negative effects of COVID. Many of the responses centered around frustrations with students.
Responses involving negative aspects with students involved, “student motivation is down,” “joy
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in teaching seems to have been lost. Rather it is not reciprocated from the students at the level I
am used to and it’s draining,” “kids are bums. Little respect. Life is hard,” “students are the
worst behaved and are the least responsible they have ever been,” and “it is a struggle to get the
students mind set back to accepting challenges before giving up on themselves.” Several
comments revolved around a personal struggle due to COVID and how this period of time has
been extraordinarily difficult. Comments included, “this year is difficult,” “Due to covid, my
numbers are terrible. I suspect a lot of what I am dissatisfied with will get better once the
program takes off again,” “this has been the most difficult year of my teaching career,” “All this
would be more positive if not for COVID,” and “I’m happy to be back in person again but am
feeling an unusual amount of stress and dissatisfaction than I’ve ever felt before.” Three of the
teachers discussed leaving their position because of their dissatisfaction.
Despite the negative feelings, there was also a common sense that the degree of these
difficulties was temporary. One participant explained, “COVID exacerbated and also brought to
light many of the issues in our schools; some of these issues interrupt the flow of teaching but
they're honestly just problems in general.” Another participant suggested that the study be
repeated in 5 years to see the difference between the COVID effects and when things are
presumably normalized again.
Several other issues related to past research came up in these comments. Four of the
teachers listed complaints about lack of support from administration. Two of the teachers had
complaints about low pay.
The responses were not all negative. In fact, 10 of the responses shared how much they
are enjoying their positions. With a relative balance between positive and negative found in these
teacher responses, the question remains whether this ratio would be true for the general
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population. An unavoidable selection bias exists in this study in asking for volunteer participants.
It is impossible to state in fact whether more participants joined the study because they wanted to
share their frustrations, or whether more chose not to participate because they were feeling
stressed and overwhelmed. However, with approximately 30,000 possible participants contacted,
and only 40 people fully participating, it seems to strongly indicate that many more participants
were lost due to teacher burnout.
Further Recommendations for Informed Practice and Continued Research
There were multiple trends found that inform music teacher training and support. There
are also several confirmations of previous research and new implications that provide nuance and
detail to previously researched subjects. Although further research is needed to explain and find
further support for the results of this study, there are some clear indicators that should influence
future research and practice.
Work with Students. A clear result from the correlations with flow states and actions is
that music teachers spend a large majority of their time working with students and it was during
these actions that teachers most often experienced both positive and negative flow states. This
further supports the research that shows music teachers need music specific training in their
teacher preparation programs (Simon, 2012). Ideally universities should find a way for music
teacher certification to happen inside of the school of music. If it must be housed in the school of
education, there needs to be a strong partnership integrating the school of music faculty and
curriculum. Too much research shows the unique needs of music teachers for universities to
continue grouping them in with all other teachers and providing no music specific instruction.
Isolation. Isolation is common amongst music teachers, and this is realistically unlikely
to change. Trying to build and find support networks for music teachers is a wise solution,
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however, should not be the only solution. Even if these networks exist, they are likely to address
the issue only to a degree, and not actually solve the problem. Perhaps the most important
solution to isolation is to prepare music teachers for it. Additionally, there are some positives to
isolation, such as a removal of outside distractions and a focus on one’s work. During isolation
self-sufficiency is key and music teachers need to develop it. The only way to achieve this is to
first acknowledge the fact that music teachers will be isolated, and then have a shared focus on
developing the skills needed to be self-sufficient in the teacher preparation programs. These
programs must prepare teachers for every aspect of the job, and not just the teaching aspect.
Preservice teachers must learn how to be self-sufficient in their teaching as well as all their extra-
curricular duties including, fundraising, event planning/organizing, etc. Research on career
resilience could be of use for further informing practice in teacher preparation programs (Mishra
& McDonald, 2017). There has been much research on career resilience conducted in the
teaching profession, but little has been done specifically looking at music teachers. Study of the
many aspects of career resilience as complied in the work of Mishra and McDonald (2017)
showed the many personal factors (personal characteristics/traits, skills, attitudes,
behaviors/habits, and career history) as well as contextual factors (supportive workplace, job
characteristics, and supportive family) that are the building blocks to job satisfaction and
success. Previous research on music teacher struggles would indicate that there are likely aspects
that are unique to music teachers, or at least they are uniquely applied. Research on career
resilience with music teachers is an area of need and a focus future researchers should strongly
consider.
Furthermore, school districts should look for better ways to support their music teachers.
The body of research shows the desire of music teachers for music specific training, and the
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desire for subject specific support does not end after teacher training. The recent passage of
Proposition 29 in California, which earmarked a large amount funds for art and music education,
brings about thoughts of structures than can best support music teachers. Many school districts
need people with expertise in music that can help coordinate, administer, and implement music
programs. Focus tends to drift towards supporting new teachers, which is much needed, but the
support needs of veteran music teachers tend to be forgotten. The brings forth the idea of a music
coordinator, or an administrative support position of similar type, that could be able to support
all the music teachers in the district. Positions such as this could make a positive impact on the
problematic aspects of isolation. Perhaps research needs to be done involving those who
currently have the relatively rare position of music coordinator to identify how to best create
positions such as these in districts that are gaining an influx of funding and will soon be able to
afford such positions.
Teacher Preparation. A topic underscoring confirmation of previous research pertains
to the crucial importance of teacher preparation and support programs. It was found that
satisfaction with these programs leads to music teachers being more likely to be in positive flow
states. Additionally, the result of teachers being more likely to be in positive flow states while
planning/preparing instruction seems to indicate that this is an area where teacher preparation
programs are generally doing a satisfactory or exemplary job. Further research can continue to
explore the details of what specific areas of focus are needed in these programs. Suggested areas
of focus for teacher preparation and support programs found in this study were music specific
training and the ability to deal with isolation.
Ultimately, there needs to be an honest evaluation of teacher preparation programs in
every institution. Complaints and problems reported by beginning music teachers have persisted
110
for far too long without much change in the community at large. Although there are shining
examples of innovation, pervasive change that directly addresses these issues in practice is
overdue. New teachers showing up on the job and entering praxis shock because they had no
idea what the career they chose entailed is simply not acceptable and needs to awaken every
teacher educator to action. This means that many university programs need to change their
curriculum and professors need to change their methods. There also needs to be integration and
partnerships with schools and teachers in practice so preservice teachers can get as much real
time experience as possible. Disconnects between theory and practice, because programs provide
little to no actual real-life experience, need to be ended. Professors need to engage with the
current reality of practice in the schools and adjust accordingly. Administrators who allow the
status quo need to stand up and show leadership. It must not be acceptable at any level in our
profession, top to bottom, to fail our preservice teachers. Our profession is too important for the
masses and students in teacher preparation programs have spent too much time, money, and
energy for there to be significant numbers of music teachers leaving the profession because of
inadequate preparation.
Better Teacher Recruitment. In relation to a focus on building self-reliance in teacher
preparation programs, this aspect should be used in recruiting music teachers. Prospective
students to university music education and/or teacher certification programs should be clearly
informed about the isolated nature of music teaching. Of course, recruiters are concerned with
numbers, but one should not have to resort to used car salesmen tactics. The issue of isolation
has many positives, depending on personal disposition. Many music teachers have very high
degrees of freedom to do as they choose in their classrooms. Being a “lone-wolf” who can
choose curriculum and run their programs as they wish is highly desirable for some. Of course,
111
not all positions are like this, and the differences between them should be discussed so that
prospective students can choose their career direction based on realistic expectations. Informing
prospective students about the reality of being a music teacher increases the likelihood of gaining
people who are going into the profession for the right reasons with the right expectations.
Conclusion
This application of the theory of flow in the practice of music teachers produced
meaningful results that might help to highlight the narratives that began Chapter 1. The
experience sampling method was effective in tracking reported music teacher flow states along
with their corresponding professional actions. This study found that music teachers experienced
both positive and negative flow states while conducting rehearsals/performances and while
teaching concepts with their students. It was also found that preparing for instruction seems to be
a strength for most music teachers which lead to higher positive flow states. Results indicate that
predictors for high levels of positive flow states are feelings of satisfaction with their student
population, satisfaction with pay, and satisfaction with their teacher preparation programs.
Surprisingly, high rates of positive flow states were also correlated with high levels of isolation
and dissatisfaction with parental and community support. This indicates a more nuanced view of
the problem of music teacher isolation, where perhaps more focus should be put on preparing
teachers to succeed in positions where isolation is unavoidable. To help with this, a focus and
application of career resiliency research is suggested to help prepare music teachers.
The qualitative measure showed that music teachers still are struggling with previously
well-known issues including lack of support from administration, insufficient pay, stress with
workload/extra duties, student behavior, and classroom management. Furthermore, the COVID-
112
19 pandemic undoubtably had influence in the results of this study. Much research on the effects
of the pandemic is now being conducted and soon to be published. Lessons learned from these
studies may well have further implications for this study, or likewise, this study be used to
inform research conclusions and directions. Ultimately what is clear is that teacher preparation
and support programs need further change as suggested by this study to address the reported
pervasive issues that continue to be problematic in music teacher education.
113
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Appendix A
Initial Survey
Contact Information
Name
Email
Demographic Information
Age:
Selection: 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75+
Gender:
Selection: Male, Female, Non-binary
School location:
Selection: Urban, Suburban, Rural
Level of education
Selection: Bachelor’s Degree, Some Graduate Coursework, Master’s Degree, Doctoral
Degree
Description of current teaching position
Grade Level (Select all that apply): Elementary, Middle School, High School
Courses taught (Select all that apply): Band, Choir, Orchestra, General Music, Other:
Description of Current Teaching Mode:
Selection: In-person, Online, Hybrid
Description of previous teaching experience:
Previous Grade Levels Taught (Select all that apply): Elementary, Middle School, High
School
Courses taught (Select all that apply): Band, Choir, Orchestra, General Music, Other:
Number of years teaching:
Numeric entry
Number of years in current position:
Numeric entry
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Likert-like Questions All Questions have a scale of 0-10.
What is your level of preparedness to work with students with special needs?
What is your level of preparedness to teach online?
What is your level of satisfaction with your current administrative duties?
What is your level of satisfaction with your student population?
What is your level of satisfaction with your parental and community support?
What is your level of satisfaction with planning time?
What is your level of satisfaction with teaching schedule?
What is your level of satisfaction with your administration?
What is your level of satisfaction with teacher preparation program?
What is your level of satisfaction with the location of your school?
What is your level of satisfaction with pay?
What is your level of satisfaction with your current position?
What level of isolation do you feel in your current position?
How likely are you to leave your current position in the near future?
Open-ended Question
Is there anything else you would like to share about your teaching experience?
Experience Sampling Survey
Were you doing anything work related at the time you received this survey?
Yes or No (If No, survey ends immediately)
Select the category of the behavioral action you were doing at the time you received this
question:
Selection: Musical: Conducting rehearsal/performance, Teaching concept(s),
Planning/Preparing instruction, Evaluating student work; Non-musical: School
administrative duties, Fundraising activities, Event planning/coordinating, Dealing with
student issues/behavior
Describe the behavioral activity you were doing at the time you received this question:
Open-ended response
Rate the difficulty of this activity: 0-10 selection
Rate your skill to do this this activity: 0-10 selection
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Appendix B
Participant Reponses to Open-ended Question
Is there anything else you would like to share about your teaching experience?
This year is difficult. Numbers way down because of Covid. Likewise ability level is down.
Student motivation is down. It is frustrating and I am considering a move to post-secondary to
find more joy in teaching that seems to have been lost. Rather it is not reciprocated from the
students at the level I am used to and it’s draining.
If I weren’t so deeply invested/close to my retirement, I think I would seriously consider
changing career paths. Teaching during CoVid has definitely taken a toll on me as a teacher.
It is very hard - very little pay for what we do. Kids are bums. Little respect. Life is hard.
I still feel like there’s a lot to learn about behavior, child development and psychology, and
motivation.
I teach lower elementary music classes now but taught several different things at my previous
school. Piano lessons make up the majority of my day.
My job is fulfilling but I sometimes wish there was a second music teacher to plan with. It is
overwhelming at times to plan for seven different grade levels.
I have been assigned to one school after teaching at three. This is the first year I have ever had
my own room and it has been the best year for teaching so far. I have had hard years, but the
administration and resources I have now are better than I have had in my 11 years prior.
I am getting ready to pursue a PhD program in Music and Human Learning in the fall but it is
not due to any issues in my current position.
Due to covid, my numbers are terrible. I suspect a lot of what I am dissatisfied with with get
better once the program takes off again. this would be an interesting experiment to do again in
about five years to see if COVID recovery and rebuild changes your findings.
creating a new music program at high school that previously did not have one
I currently have a student teacher
I have been at numerous schools where there wasn't sufficient support for my program. This is
the first time being at a school where the community AND the administration are avid
supporters of my program.
I’m actually really enjoying my position. I started this orchestra program 6 years ago and was
really thankful for it. My program has been growing and slowly getting help from other people.
This has been the most difficult year of my teaching career. Students are the worst behaved and
are the least responsible they have ever been. Expectations on teachers are high and even with
supportive admin, it is difficult.
I've been teaching for 10 years five at my current position where I am the only music teacher. I
have struggled over the past 4 years to get support for the overload of classes which I am
125
teaching at my current position. and I currently have a principal who is gone most days of the
week and it is a very frustrating situation for the entire staff.
Good to be back after a 2 year break
It is a struggle to get the students mind set back to accepting challenges before giving up on
themselves.
I had a 10 year break in between full-time teaching positions to raise my family. I also have 2
masters-one in music and 1 in admin. Also currently a candidate for National Board
Certification.
I retired early from teaching and I returned to teaching after 4 years of working in the business
world. I’m ecstatic to be back.
I am a bit biased since I just found out I will not be rehired by my district for next year.
All this would be more positive if not for Covid. Also, there is a great need for a continuous k-
12 music program. My school is missing a feeder.
Only wish I had more time with ensembles each week.
I have administration experience from last year, and have returned to just teaching this year. It
has been difficult
It has been a roller coaster. I am .8 FTE teach 20 sections and have 3 duties a day.... almost all
music teachers are looking to leave. the average difference between the .8 and 1,0 FTE is
$11,000+
Last year was rough teaching choral music via zoom. I’m happy to be back in person again but
am feeling an unusual amount of stress and dissatisfaction than I’ve ever felt before.
I am pleased and excited to be in my current position.
I have enjoyed it but ready for next challenge.
I am happy with my position.
COVID exacerbated and also brought to light many of the issues in our schools; some of these
issues interrupt the flow of teaching but they're honestly just problems in general. I love my
job: I feel I'm making a positive difference in my students' lives and I'm fortunate to work on a
great team of music teachers so I feel supported when things are bad or frustrating.
I really enjoy it.
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Appendix C
Email Subject Line: Survey of Music Teacher Experience and Flow
Dear K-12 music teacher,
I am looking for K-12 music teachers of all musical categories to volunteer their participation in
an experience sampling survey. This study is being conducted as a component of my doctoral
dissertation at the University of Southern California, under the supervision of my faculty advisor
Dr. Peter Webster. Participants who complete the study will be entered into a drawing for a $20
Amazon gift card. 10 winners will be chosen.
This study titled “Flow States as a Predictor of Subjective Experience and Job Satisfaction of
Music Teachers” will track the experience of music teachers during their work-related activities
over a two-week period. This experience will be analyzed using the theory of optimal experience
called “flow.” We hope to learn what groups of music teachers have experiences that correlate
with particular music teacher attributes and actions and will be used to inform music teacher
training and support. Whether you are a 1
st
year teacher or seasoned veteran, your involvement
can help show areas of need for our profession and inform music teacher training and support.
This study will be conducted on an experience sampling phone application called ExpiWell. The
study will consist of an initial demographic and informational survey that will take
approximately 5-10 minutes to complete. The next part of the survey consists of receiving an
application notification linking to a short answer survey about your work actions that were taking
place when the notification was received. This tracking survey will be sent 3 times a day for a
two-week period, and each will take approximately less than one minute to complete. All surveys
notifications will happen between 8:00AM and 8:00PM. It is intended that you answer these
short questions discussing what you were doing when the alert was received as soon as possible
and will time out after 3 hours.
In order to participate in the study, the application ExpiWell must be downloaded to your
smartphone. You may search for the app by title or click the corresponding links for Apple
or Android.
The ExpiWell Experience code: c25ad must be entered into the app to connect to the study.
For more information on the study, including IRB info, click here. Please also feel free to contact
me or my faculty advisor Dr. Peter Webster at peterweb@usc.edu. Thank you for your time and
participation!
Michael J. de Vries
University of Southern California
DMA Candidate
mdevries@usc.edu
Abstract (if available)
Abstract
Measures of job satisfaction and preparedness from an initial survey with music teachers (n=40) were used to predict flow states using Csikszentmihalyi’s theory of flow. Flow state and corresponding professional action data were collected through experience sampling. Multiple linear regression was used to show relationships between demographic information, satisfaction, and preparedness measures with each flow state from the quadrant model. Correlation was also found between reported flow state and corresponding professional actions. It was found that the optimal state of flow was correlated with higher satisfaction with student population and pay, higher levels of isolation, and lower levels of satisfaction with parental and community support. The state of control/relaxation was correlated with satisfaction with teacher preparation program. Additionally, the professional action of conducting rehearsal/performance was associated with the optimal flow state, stress/anxiety, and boredom/apathy. The action of teaching concept(s) was correlated with the optimal state of flow and the state of stress/anxiety. The action of planning/preparing instruction was correlated with the optimal state of flow and the state of control/relaxation. Implications from this study were used to provide suggestions for further research and change in the practice of training and supporting music teachers.
Linked assets
University of Southern California Dissertations and Theses
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Asset Metadata
Creator
de Vries, Michael John
(author)
Core Title
Subjective experience, job satisfaction, and professional actions of music teachers as a predictor of flow state
School
Thornton School of Music
Degree
Doctor of Musical Arts
Degree Program
Music Teaching and Learning
Degree Conferral Date
2023-05
Publication Date
01/20/2023
Defense Date
01/10/2023
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
flow,job preparedness,music teachers,Music Teaching and Learning,OAI-PMH Harvest,subjective experience
Format
theses
(aat)
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Ilari, Beatriz (
committee chair
), Copolla, William (
committee member
), Mattio, Candice (
committee member
), Webster, Peter (
committee member
)
Creator Email
devries.musiced@gmail.com,mdevries@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-oUC112718703
Unique identifier
UC112718703
Identifier
etd-deVriesMic-11429.pdf (filename)
Legacy Identifier
etd-deVriesMic-11429
Document Type
Dissertation
Format
theses (aat)
Rights
de Vries, Michael John
Internet Media Type
application/pdf
Type
texts
Source
20230126-usctheses-batch-1003
(batch),
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Repository Email
cisadmin@lib.usc.edu
Tags
flow
job preparedness
music teachers
subjective experience