The experience of the flow state in live music performance
William J. Wrigley and Stephen B. EmmersonGriffith University, Australia
AbstractThis study examined the optimal psychological state of flow in a live music performance context at an Australian tertiary music institution in order to advance understanding of this under-researched experience in music performance and education. The Flow State Scale-2 (FSS-2) was administered to 236 students from five instrument families immediately after their performance examinations. A further aim was to examine the psychometric properties of the FSS-2 in order to determine its suitability for use as a measure of flow in music performance domains. The findings provided the first empirical confirmation of the validity and reliability of the flow model in live music performance. The flow experience was found to be consistent with findings from sport performance and did not vary substantially according to instrument type, year level, or gender. Most students in the sample did not believe they were sufficiently skilled to meet the challenge of the performance and most did not experience it as absorbing or enjoyable. The implications of the findings for the enhancement of teaching and learning methods were examined. Future research directions were discussed, particularly in regards to psychological skills training to help improve the music performance experience.
Keywordsflow state, instrument type, music performance, optimal experience
Although there has been extensive investigation of music performance anxiety (Rae & McCambridge, 2004), the experience of positive performance states, such as the psychological state of flow (Csikszentmihalyi, 1990), has remained almost entirely unexamined in live music performance despite its quite substantial research in similar performance domains such as sport and work (Jackson & Kimiecik, 2008; Jackson & Wrigley, 2004). Its potential utility in the context of music education needs to be empirically examined more thoroughly as it is likely that an optimal state such as flow is highly desirable for musicians to achieve when performing, and it is a state that may lead to improved performance experience and quality.
Csikszentmihalyi (1975) originally conceived of flow from the interviews and questionnaire results of his investigations into the concept of enjoyment. From a range of activities as diverse as surgery, rock climbing, and various artistic pursuits, he found that some level of flow could be experienced in almost any activity in the same way, regardless of age, gender, ethnicity, or socioeconomic status (Csikszentmihalyi, 1993). The flow metaphor best describes the effortless absorption and control experienced by people in their best moments (Csikszentmihalyi, 1997). In this flow state, people experience a narrow field of intense concentration, they forget about personal problems, feel competent and in control, experience a sense of harmony and union with their surroundings, and lose their ordinary sense of time. When these elements are present, people report that they enjoy the activity for its own sake, even if it is dangerous or difficult, and are less concerned about the outcome. As a result, the experience becomes autotelic, that is, they experience a high level of intrinsic enjoyment as a result of doing the task (Csikszentmihalyi, 1975, 1990).
The conceptual framework advanced by Csikszentmihalyi (1975) offers a multidimensional model incorporating nine fundamental dimensions of the state of flow. These involve a balance between the perception of above-average challenges and skills, a merging of the action of the activity and the awareness of engaging in it, the possession of clear goals for the activity, the reception of unambiguous feedback while engaging in the activity, the maintenance of total concentration on the task at hand, the experience of control over what one is doing, a loss of self-consciousness, a transformation in the passing of time, and the experience of the activity being autotelic.
Subsequent research has confirmed these nine flow dimensions (Csikszentmihalyi, 1990, 1993; Jackson, 1996; Jackson & Marsh, 1996; Martin & Cutler, 2002) and their construct valid-ity (Jackson & Marsh, 1996). Studies have also consistently demonstrated the flow model’s ability to significantly predict these dimensions in leisure and sport activities (Jones, Hollenhurst, & Perna, 2003; Jones, Hollenhurst, Perna, & Selin, 2000; Kubey & Csikszentmihalyi, 1990; Russell, 2001).
Frequency and degree of flow
Csikszentmihalyi (1975; Sobel, 1995) conceived flow in terms of degrees existing on a continuum, from light or low flow through to the deep flow involved in activities of high complexity. He considered low flow activities to reflect “microflow” and described these as trivial activities that have a low level of complexity, are structured loosely if at all, and are more automatic activities such as doodling, a daydreaming episode, or smoking a cigarette while engaged in other activities (p. 141). He found that deep flow occurred with complex activities, such as chess, surgery, com-posing, and religious rituals. This conception of flow has been supported in subsequent research, such as a qualitative study of 300 people in elite international sporting organizations and teams from ten different sports codes in six different countries (Gilson, Pratt, Roberts, & Weymes, 2000, p. 256). Their results confirmed that flow can vary on an intensity continuum from low flow to “peak flow” as Csikszentmihalyi had proposed.
Csikszentmihalyi (1990; Jackson & Csikszentmihalyi, 1999) has contended that it is usually not possible to initiate flow intentionally, and that attempts to consciously bring on flow are likely to make the state more unobtainable. He has suggested that it is more likely to happen spontane-ously or by chance from engagement in structured activities, though he has contended that “removing obstacles and providing facilitating conditions will increase its occurrence” (Jackson & Csikszentmihalyi, 1999, p. 138). Also, the results of some studies have suggested that the
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relative importance of the flow dimensions may vary in differing settings. Jackson and colleagues (Jackson, Kimiecik, Ford, & Marsh, 1998; Jackson & Marsh, 1996) found a selective weighting of the flow dimensions in their studies in a variety of sport settings.
Flow state research
Since Csikszentmihalyi (1975) first proposed this concept, a large volume of research investigat-ing the state of flow in a variety of life settings has accumulated. Extensive research activity has examined the flow experience in a variety of sport and exercise activities, a domain that is not dissimilar to instrumental music performance (Jackson & Kimiecik, 2008; Jackson & Wrigley, 2004). Other domains include work environments (Csikszentmihalyi & Lefevre, 1989; Kirk & Brown, 2003), leisure (Csikszentmihalyi, 1975; Csikszentmihalyi & Lefevre, 1989; Ellis, Voekl, & Morris, 1994; Haworth, Jarman, & Lee, 1997; Jones et al., 2003; Kowal & Fortier, 1999), effec-tive teaching (Gunderson, 2003), learning with a hypermedia system (Konradt & Sulz, 2001), the study of statistics in an undergraduate psychology programme (Engeser & Rheinberg, 2008), online consumer experience (Novak, Hoffman, & Duhachek, 2003), and computer-mediated environments (Chen, Wigund, & Nilan, 1999; Novak, et al., 2003; Novak, Hoffman, & Yung, 2000; Webster, Trevino, & Ryan, 1993).
The flow experience has also been studied in the performing arts of dance (Hefferon & Ollis, 2006), acting (Martin & Cutler, 2002), and the creativity of musical compositions (Byrne, MacDonald, & Carlton, 2003; MacDonald, Byrne, & Carlton, 2006). Flow has been investigated in the work of music teachers with their students (Bakker, 2005), brief approaches to assessing task absorption and subjective experience with school musicians have been examined (A. J. Martin & Jackson, 2008), and the dispositional flow associated with music student wellbeing has also been researched (Fritz & Avsec, 2007). O’Neill (1999) used the experience sampling method to measure ratings of challenge and skills with a sample of school music students. Surprisingly, however, there have been very few published empirical investigations of the experience of flow during live music performance. No published studies have specifically examined the flow state model in live, solo performance settings in which flow measures are taken either during or imme-diately after a performance.
The measurement of the flow state
It has been recognized that the measurement of an experiential state such as flow presents the researcher with difficulties in relation to reliability and validity. This often momentary, subjective state is not easy to capture and measure accurately without interrupting the experience or introducing measurement bias (Scollon, Kim-Prieto, & Diener, 2003). The Experience Sampling Method (ESM; or a variant thereof) has been used to measure the flow experience during activi-ties (Scollon et al., 2003) in an effort to reduce the potential bias that can result from the retrospective recall inherent in self-report measures of emotional states (Brewer, Van Raalte, Van Raatle, & Linder, 1991; Dewhurst & Marlborough, 2003; Keuler & Safer, 1998; Smyth & Stone, 2003). ESM requires respondents to record repeated, random samplings of their experi-ence during the activity via questionnaires. Because of the impracticality of this method when measuring flow during pursuits such as sport and exercise activities, Jackson and colleagues developed a psychometrically valid self-report instrument called the Flow State Scale (FSS-2), which provides a more practical and convenient method of flow measurement for activities such
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as sport and music performance (Jackson, 1996; Jackson & Eklund, 2002; Jackson et al., 1998; Jackson, Thomas, Marsh, & Smethurst, 2001; Marsh & Jackson, 1999). This scale goes some way to addressing reliability and validity concerns in regard to self-report bias by its administra-tion as soon as possible after a performance.
The FSS-2 questionnaire has been used effectively in examining the flow experience during sport performance and has been shown to have acceptable psychometric properties. Several stud-ies have found the reliability of the nine FSS-2 subscales to be acceptable, ranging from .72 to .91 (Jackson & Eklund, 2002; Jackson et al., 1998; Jackson & Marsh, 1996; Jackson et al., 2001; Kowal & Fortier, 1999; Pates & Maynard, 2000; Vlachopoulos, Karageorghis, & Terry, 2000). Research has also shown the FSS-2 to be a valid measure of the flow state (Jackson & Eklund, 2002), including its use with non-English-speaking samples (Kawabata, Mallett, & Jackson, 2008).
Several studies have found that some dimensions of flow may be less universally important than others (Jackson & Wrigley, 2004). Relatively low factor loadings have been found in par-ticular for the transformation of time and loss of self-consciousness factors. Jackson and Eklund (2002) have suggested that a more appropriate definition of flow may be one in which specific components of flow are more or less prominent according to the characteristics of the situation or the individual.
Aims of the study
This study examined the optimal psychological state of flow in a live music performance context with performers in tertiary music education from the five instrument families of strings, piano, brass, woodwind, and voice in order to advance understanding of this under-researched experi-ence in music performance and education. A further aim was to examine the psychometric properties of the FSS-2 in order to determine its suitability as a reliable and valid measure of flow in music performance domains. Specifically, the following research questions were investigated: To what extent do tertiary music students achieve the state of flow in their solo live performance examinations? Within this context, are there differences in the way the flow state is experienced according to instrument type, gender, semester, or year level? Do music students experience the state of flow in similar ways when performing, as reported in other pursuits? Is the FSS-2 a suit-ably valid and reliable instrument for measuring the flow state in music performance?
Method
Participants
A sample of 236 students enrolled in undergraduate and postgraduate music performance programs at a Conservatorium of Music of an Australian university completed the FSS-2. The participants varied in age between 16 and 47 years, with a modal age of 20 years. A majority of the participants were female (65%). The students consisted of string and piano performers (35% and 26% respectively), 18% woodwind, 11% voice and 10% brass performers. Just over half of the sample was in Year 3 or above (31%), with 11% in Year 4, and 15% at postgraduate level. Twenty percent were in Year 1, 19% in Year 2, and 4% in the preparatory year.
Measures
Flow State Scale-2. The measurement of the flow state was undertaken using the Flow State Scale-2 (FSS-2) questionnaire (Jackson & Marsh, 1996). In the present study, the participants were instructed
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to respond to the statements in relation to the examination performance they had just completed. The FSS-2 consists of 36 items, with four items for each of the nine dimensions of flow. Each item is in the form of a statement that represents an element of the flow experience. These are Challenge-Skill Balance (“I was challenged, but I believed my skill would allow me to meet the challenge”); Merging of Action and Awareness (“I made the correct movements without thinking about trying to do so”); Clear Goals (“I knew clearly what I wanted to do”); Unambiguous Feedback (“It was really clear to me how my performance was going”); Total Concentration (“My attention was focused entirely on what I was doing”); Sense of Control (“I had a sense of control over what I was doing”); Loss of Self-consciousness (“I was not concerned with what others may have been thinking of me”); Transformation of Time (“Time seemed to alter – either slowed down or sped up”); and Autotelic Experience (“I really enjoyed the experience”). Respondents indicated the extent to which they agreed with each statement on a 5-point Likert scale from 1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, to 5 = strongly agree. For the purposes of this study, a mean score ranging from 1 to 5 for each subscale was calculated for each participant.
Before its application in this study, the questionnaire was reviewed by two performance exam-iners who were expert music performers and teachers at the institution to confirm its suitability in a music performance setting. It was found to be acceptable and only minimal changes to the wording of two items of the autotelic experience subscale were made to reflect the immediacy and relevance of the music performance.
Biographical information regarding age, gender ,and year level of the performer was recorded.
Procedure
The FSS-2 was administered over two consecutive academic years (2002 and 2003), involving the examination periods of four semesters of study. Almost all the FSS-2 data were collected at the end of the academic year in Semester 2 of both years (94%). Although many participants completed repeated measures of the FSS-2 from more than one semester examination period, only one completed FSS-2 per participant was included in the analysis of the FSS-2 scale –from Semester 2 where possible.
The performance examination required students to perform a program of differing styles of classical music of 20 to 45 minutes’ duration depending on the student’s year level. Much of the music required an accompanist (for all instruments except piano). The audience consisted of a panel of two or three examiners and, depending on the performer, a small number of invited friends, family members, and music peers.
In accordance with Jackson et al. (1998), the participants completed the FSS-2 immediately after the completion of their end-of-year or end-of-mid-year performance examination, usually just outside the performance venue. Most (92%) of the flow state questionnaires were completed within 15 minutes of the completion of the performance. The median time of completion was 5 minutes post-performance, with a modal time of 1 minute.
Results
The validity of the FSS-2 scores was examined using confirmatory factor analyses (CFAs) with AMOS 5.0 (Arbuckle, 2003; Arbuckle & Wothke, 1999).
As the FSS-2 scale was externally generated and as the subscales have previously been identi-fied, analyses were limited to comparisons of the single- versus the nine-subscale versions of this instrument. An initial single-factor CFA provided a screening for multivariate outliers, for
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univariate skew, and for univariate and multivariate kurtosis (Mardia’s coefficient of multivari-ate kurtosis). This produced a multivariate kurtosis coefficient of 218.41. Forty-three observa-tions were removed with a coefficient of 51.0 or greater, leaving a sample size of n = 236 and an improved coefficient of 79.56. Only one item, “I knew what I wanted to achieve” (Item 21), was slightly above (2.49) the acceptable level of kurtosis (± 2), and all items were within the acceptable range of skew (± 2).
Single- and nine-factor models
Table 1 displays the fit estimates for both the single- and nine-factor models. The initial single-factor CFA produced a model that did converge but produced very poor fit values. All except one item (“Time seemed to alter – either slowed down or sped up”) achieved significant regression weight estimates. This model provided a convenient comparison with the independent nine-subscale solution.
The nine-factor model exhibited statistically acceptable fit estimates, with the chi-square ratio (1.35) falling within the acceptable threshold. For the population discrepancy measures, the RMR of .05 and RMSEA of .04 fell within acceptable thresholds. The model also showed reason-ably good estimates of fit indices with TLI and CFI values above .95 and marginal fit values with the NFI, RFI, and GFI just below .90. The model exceeded the acceptable threshold of .80 for the AGFI when the complexity of the model was taken into account. Comparison of goodness of fit estimates for the two FSS-2 models (see Table 1) clearly indicated that the nine-subscale solution improved on the single-factor solution, and acceptable measures of fit were obtained. This out-come supported the validity of examining FSS-2 scores in terms of the nine-subscale scores in subsequent analyses.
Table 1. Estimates of goodness of fit for FSS-2 single- and nine-factor models
Estimate of fit Single-factor model Nine-factor model
Note: c2 = chi square; df = degrees of freedom; p = probability; c2/df = Chi-square ratio; AIC = Akaike information criterion; RMR = root mean square residual; RMSEA = root mean square error of approximation; NFI = normed fit index; RFI = relative fit index; TLI = Tucker–Lewis index; CFI = comparative fit index; GFI = goodness of fit index; AGFI = adjusted goodness of fit index.
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All regression weight estimates were significant in this model. The items pertaining to each subscale were all sufficiently substantial predictors, with beta weight values ranging from .56 to .91, which were well above the acceptable threshold of .30. None of the correlations between the subscales (latent variables) appeared to be in danger of being linearly dependent (that is, exceeding .90), ranging between .01 and .81, with most not exceeding .60.
Relationship of nine FSS-2 subscales to the flow state. A second-order CFA that investigated the rela-tionship of the nine flow subscales to the latent variable of the flow state was undertaken. All the subscales predicted the flow state, with all regression weights falling within a significant critical ratio. All beta weight values exceeded .30 except for Transformation of Time (.19), varying between .46 and .85. The subscales Sense of Control, Challenge–Skill Balance, and Autotelic Experience were the strongest contributors and explained the most variance, while the subscale Transformation of Time provided a very weak prediction and explained very little of the variance. This was consist-ent with the findings of previous research (Jackson & Wrigley, 2004; Russell, 2001).
Reliability and validity of FSS-2 subscales
The means, standard deviations, and Cronbach’s alpha coefficients of the nine-factor model from the present study using the sample employed in the confirmatory factor analyses are out-lined in Table 2. Each of the subscales of the nine-factor model was found to have above-acceptable alpha levels, ranging from .81 to .92, which compared favourably with previous research. (All of the mean subscale scores except for Clear Goals were within the moderate range – neither agree nor disagree). The consistency in the range of these scores across almost all of the subscales suggested that the student performers experienced flow on average, though only at a moderate degree during their performance examination.
Individual responses on each FSS-2 subscale score in the present study were compared with a sample of 747 respondents from a variety of competitive and noncompetitive sport activities
Table 2. Cronbach’s alpha coefficients of FSS-2 nine-factor model, and FSS-2 means and standard deviations from present and previous research
Table 3. Z-scores derived from previous studies’ mean flow subscale scores
FSS-2 subscales Jackson et al. (2001) Jackson & Eklund (2004)
M SD M SD
Challenge-Skill Balance −0.43 1.21 −0.14 1.00Merging of Action & Awareness −0.01 1.06 −0.39 0.99Clear Goals −0.02 0.91 0.05 0.88Unambiguous Feedback −0.38 1.07 −0.15 1.09Total Concentration −0.18 1.16 −0.25 1.10Sense of Control −0.70 1.16 −0.58 0.99Loss of Self-Consciousness −0.26 1.14 −0.90 1.07Transformation of Time 0.47 1.07 −0.09 1.16Autotelic Experience −1.03 1.62 −0.52 1.12
reported by Jackson and Eklund (2004), and with a sample of 236 respondents engaged in competitive sport activities from a study by Jackson et al. (2001). This comparison converted each subscale score for each participant of the present study into z-scores. Each subscale mean in the Jackson et al. and Jackson and Eklund studies was subtracted from each participant’s subscale score in the present study. The deviation scores were then divided by the corresponding standard deviation of the Jackson et al. and Jackson and Eklund studies, thereby providing z-scores. A z-score by definition has a mean score of zero and a standard deviation of one. Scores that are one standard deviation from the mean score have a z-score value of (plus or minus) one.
By convention and by assuming a relatively normal distribution, z-scores with a value of ± 1.96 or greater have no more than 5% probability of being part of the comparison distribution. Therefore, this conversion allows the identification of extreme scores that depart from the norms. The mean z-scores for the present sample (N = 236) for each comparison group of Jackson’s studies are displayed in Table 3. It shows that all subscale z-scores from the present sample fell well below ± 1.96 when compared with either study. This suggested that the present sample’s FSS-2 scores were not significantly dissimilar to those of the comparison groups.
Factors associated with flow
Gender, year level, semester, and instrument family effects on the nine FSS-2 subscale scores were examined by conducting multivariate analyses of variance (MANOVAs). In the following analyses, Levene’s test was found to be nonsignificant, unless reported otherwise. Where it was found to be significant, the Kruskal-Wallis one-way ANOVA test was used.
Gender and year level effects. The preparatory year level was removed from this analysis due to low cell frequencies. Levene’s test for the equality of error variation was nonsignificant for eight of the nine subscales, with the subscale Unambiguous Feedback being significant. No significant main effect for year level (Roy’s largest root, p = .10) was found at the multivariate level, suggesting that the experience of flow was not greatly influenced by advancement through the years of study. No significant gender effect (Roy’s largest root, p = .31) was found at the multivariate level. With all flow subscales a trend was apparent, with males scoring slightly higher than females.1 However, this trend did not reach statistical significance. An interaction effect between gender and year level at the multivariate level lay just outside significance (Roy’s largest root, p = .06).
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Between semester and year differences. To examine whether a student’s flow experience varied between semesters and between years of study, a repeated measures MANOVA examined the difference in subscale scores for those students who had completed the FSS-2 questionnaire in more than one semester examination. No significant differences were found for any of the subscales in any semester or year period, indicating that the students’ flow experience did not significantly vary from one semester examination to the next or over a 12-month period. These results need to be considered with caution as sample sizes for each analysis were not particularly large (between semesters, n = 19–31; between years, n = 45). However, these results, taken together with the nonsignificant effect found for year level, suggested that the students’ experience of flow was not greatly influenced by their advancement in their studies.
Instrument family differences. Table 4 displays the FSS-2 mean subscale scores and standard deviations for each instrument family. A MANOVA examined the differences in the FSS subscale scores as dependent variables (DVs), according to instrument family as the independent variable (IV). The main effect of instrument family was significant with the FSS-2 scores at the multivariate level (Roy’s largest root, p < .001), with a significant univariate effect with the one subscale of Clear Goals, F(4, 231) = 3.54, p < .01. The Tukey HSD post-hoc criterion for significance indicated that the piano performers achieved a significantly lower score (M = 3.93, SD = 0.49) than their brass (M = 4.36, SD = 0.55) and string (M = 4.22, SD = 0.56) counterparts. Across all instruments, Clear Goals achieved the highest score overall (M = 4.12, SD = 0.57) and Loss of Self-consciousness the lowest (M = 3.12, SD = 0.95). These results indicated that the experience of flow did not vary substantively according to instrument family.
High and low/no flow. In order to deepen the understanding of the flow experience in music educa-tion, each flow subscale response category was compressed to a dichotomous or dummy variable
Table 4. FSS-2 subscale means and standard deviations for each instrument family
Table 5. FSS-2 subscale frequencies and percentages of high and low/no flow groups
FSS-2 subscale (N = 236) High flow Low/No flow
n % n %
Challenge-Skill Balance 99 41.95 137 58.05Merging of Action & Awareness 74 31.36 162 68.64Clear Goals 174 73.73 62 26.27Unambiguous Feedback 131 55.51 105 44.49Total Concentration 92 38.98 144 61.02Sense of Control 60 25.42 176 74.58Loss of Self-Consciousness 55 23.31 181 76.69Transformation of Time 66 27.97 170 72.03Autotelic Experience 97 41.10 139 58.90
of either the presence or absence of that variable. Each FSS-2 subscale was categorized into ‘high flow’ (agree and strongly agree) and ‘low/no flow’ (strongly disagree, disagree, and neither agree nor disagree). Table 5 presents the frequencies and percentages of students who experienced high or low/no flow scores on each flow subscale. These results suggested from a group perspective that the performance examination was not likely to be a very satisfying or rewarding experience for most students. A majority of students were in the low/no flow group for most subscales, except for Clear Goals and Unambiguous Feedback. Almost 70% of students did not have a strong experience of their action merging with their awareness, and 77% struggled with losing self-consciousness.
These results suggested that most students struggled with becoming absorbed in their perfor-mance. Almost 60% of students experienced low amounts of enjoyment (Autotelic Experience), and just over half of the students (58%) did not experience a strong balance between their perceived skills and challenge of the examination.
Differences in the year of data collection. A MANOVA was used to test whether the FSS-2 scores were influenced by the year of data collection. For this analysis, the data were collapsed across Semesters 1 and 2 for each of the years 2002 and 2003. Levene’s test for the equality of error variation was non-significant for all nine subscales. No main effect of Year at the multivariate level was found (Roy’s largest root, p = .34). These results suggested that the flow state was not influenced by the year in which it was experienced.
Discussion
This study examined the optimal state of flow in music performance in order to advance under-standing of this under-researched experience in music performance. The findings provided the first empirical confirmation of the validity and reliability of the flow model in live music perfor-mance as proposed by Csikszentmihalyi (1975) and were consistent with research in other performance domains such as sport activities.
Confirmatory factor analyses suggested that the nine-dimensional model as measured by the FSS-2 best fit the data, with all the subscales predicting the flow state. The FSS-2 subscales achieved acceptable levels of internal reliability ranging from Cronbach’s alpha of .81 to .92, which compared favourably with previous research (Jackson & Eklund, 2002; Vlachopoulos et al.,
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2000). The subscales of Sense of Control, Autotelic Experience, and Challenge-Skill Balance showed the strongest associations and explained the most variance, while the subscale Transformation of Time provided a very weak prediction and explained very little of the variance. These results were consistent with the findings of Jackson and colleagues (Jackson et al., 1998, 2001). Also, the mean scores for the nine FSS-2 subscales compared favourably and showed no statistically significant differences against a general reference group (Jackson & Eklund, 2004) and a sample of participants in competitive sports (Jackson et al., 2001). These results suggested that the students experienced flow in similar ways to participants in other activities.
Results indicated that the students’ experience of flow did not substantially fluctuate accord-ing to particular performance variables. No significant gender differences were found, although results showed a slight trend toward males scoring higher than females. The lack of significant gender differences was consistent with the results of other studies in theatre (Martin & Cutler, 2002) and sport activities (Jackson & Wrigley, 2004). Apart from the subscale Clear Goals, the flow experience did not substantively vary between instrument families. Also, no significant year level or semester differences were found, suggesting that flow scores in the assessment perfor-mance context did not significantly vary the further students advanced in their studies. This finding is somewhat surprising, since increased flow experiences could be expected as a result of the students’ progression in intensive study. Taken together, these results may have reflected a dispositional flow experience under examination conditions, in which the students’ experience resulted more from their typical, internal psychological response to assessment contexts in general than from other variables.
Further research is needed to clarify these responses, and in particular to investigate whether students are likely to improve their experience of flow under assessment performance conditions involving a large live audience that reflects a concert setting rather than an examination in front of a panel of judges as in the present study, which for many students may be a judgmental and intimidating experience that inhibits the flow experience.
Also, the retrospective self-report measurement of flow used in the study may have biased the students’ estimation of their experience (Brewer et al., 1991). For example, a negative perception of the quality of their examination performance may have influenced the recall of their perfor-mance experience. Biological measurements or experience sampling methods may be more effective ways to measure the flow state. However, if these were employed in empirical designs, the degree of intrusiveness in the examination process and the maintenance of sufficient levels of ecological validity would also need to be addressed.
The findings of the study have significant implications for the improvement of teaching and learning outcomes. From a group perspective, the results demonstrated that the majority of students did not achieve a high optimal psychological state in their performance examination. An inspection of student frequencies of high flow subscale scores showed that for all subscales except Clear Goals and Unambiguous Feedback, the majority of students experienced an absence or low levels of flow. Most students achieved low flow scores with the subscale Loss of Self-Consciousness and Merging of Action and Awareness, which suggested that they encountered difficulties in becoming absorbed in the performance. Most did not believe they were sufficiently skilled to meet the challenge of the performance and most did not greatly enjoy the experience of the performance examination. These results indicated that the majority of students were likely to have found their performance examination a less than satisfying, absorbing, or reward-ing experience.
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These results suggested that the performance examinations for most students were not likely to have fostered or reinforced effective levels of self-efficacy and confidence, and they may have inhibited substantive teaching and learning benefits for most students. Future research is needed to clarify the relationship and impact of correlates of the flow state, such as performance anxiety and self-efficacy, as well as the influence of different assessment performance contexts. The flow state has been likened to a eudaimonic experience of wellbeing, in which human growth and potential are emphasized (Jackson & Wrigley, 2004; Ryan & Deci, 2001). Focus on such positive states can offer significant psychological benefits that may enhance teaching and learning out-comes. Jackson and Csikszentmihalyi (1999) have suggested that while the flow state cannot be taught or made to happen, improving the pre-conditions for flow can increase its likelihood. Fostering a flow experience in assessment performance contexts could contribute to increased confidence and self-efficacy by reinforcing the students’ performance experience as an absorb-ing state, free of self-consciousness, that results in an outcome of enjoyment and satisfaction. A clearer understanding of these variables is likely to provide a deeper understanding of the students’ optimal experiences in assessment performance contexts, and as a result could assist in improving teaching and learning outcomes.
It may be that the inclusion of teaching and learning methods in the curriculum that enhance the antecedents of flow in music performance – such as mental skills training at each year level to foster improved self-talk, goal setting, imagery, and arousal control skills – can increase the likelihood of students experiencing improved states of flow in assessment performance settings as they advance in their studies. As a result, significant value could be added to the curriculum in terms of the teaching and learning of performance skills. Research is needed to investigate whether this is the case and, if so, the most suitable psychological skills training methods for improving students’ flow experiences in performance assessment. In the light of the results of this study in which the loss of self-consciousness subscale achieved the lowest scores in all instrument families, with high levels being attained by only 23% of students, a particular train-ing focus may be to assist in improving the students’ capacity to lose self-consciousness in performance. Enhancement of such capacities may have the potential not only for enhanced teaching and learning outcomes but for a more rewarding and enjoyable performance experience.
Note
1. A Kruskal-Wallis one-way ANOVA was nonsignificant for gender, c2 (1, n = 236) = 1.00, p = .32, and nonsignificant for year level, c2 (4, n = 226) = 2.90, p = .57 on the subscale Unambiguous Feedback.
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