Academic Flow And Burnout In College Students: An Eight-Month Longitudinal Study

Academic burnout

Burnout is a condition that has primarily been studied in a work setting, but there is increasing evidence that it can also affect students in school and college. Burnout among college students refers to tiredness resulting from academic demands (emotional exhaustion), having a pessimistic sense and lack of interest toward academic tasks (cynicism toward meaning of studying), and feeling of incompetency as a student (inefficacy) (Schaufeli et al., 2002; Salmela-Aro, Kiuru, Leskinen & Nurmi, 2009). Studies show that college students experience substantial levels of burnout (Frajerman et al., 2019; Jacobs & Dodd, 2003; Leupold et al., 2019; Pisarik, 2009). As it occurs with professionals, it is observed that students, who began their studies with enthusiasm, subsequently came to express a sense of disappointment, lack of energy, fatigue, feeling of emptiness or failure, low self-esteem, lack of concentration, and desire to leave their studies (Caballero-Domínguez, Gutiérrez, & Palacio Sañudo, 2015).

Student burnout is important for several reasons. First, academic burnout can have serious physical and psychological consequences for students’ health (Caballero, Bresó, & Gutiérrez, 2015; May, Sanchez-Gonzalez, Brown, Koutnik, & Fincham, 2014). It is positively related to sleep deprivation (Mazurkiewicz et al., 2012), cardiovascular risk factors (May et al., 2014; May, Sanchez-Gonzalez, & Fincham, 2015), depression and Internet disorder (Peterka-Bonetta et al., 2019), and future work burnout (Robins et al., 2017). It is also associated with decreased academic achievement and diminished cognitive functioning (May, Bauer & Fincham, 2015), as well as with unprofessional students' conduct (Dyrbye et al., 2010). It is also a potential indicator of poor emotion regulation in students (Seibert, Bauer, May, & Fincham, 2017). Second, student burnout may also influence their relationships with peers, instructors, and institution. Third, the student burnout may negatively affect the general reputation of the institution for new students with potential consequences for enrolment of students (Neuman, Finaly-Neumann, & Reichel, 1990). Taken all together, academic burnout has serious negative consequences and therefore, studies which are trying to find the protective factors are beginning to emerge (May, Seibert, Sanchez-Gonzalez, & Fincham, 2019).

Flow

Flow has been defined as an optimal experience related to a specific activity when person is totally focused and absorbed in the activity at hand and enjoys it intensely (Csikszentmihalyi, 1975/2000). Nothing else seems to matter in these moments and he/she completely loses track of time. Necessary conditions for flow experience to occur include autonomy, clear rules, feedback on the progress being made and perceived high opportunities for action (challenges) balanced with high personal skills (Csikszentmihalyi, 1975/2000). Flow can emerge in any activity and so far has been researched mainly in leisure activities such as art (Butkovic, Ullén, & Mosing, 2015) or sport (Bakker, Oerlemans, Demerouti, Bruins Slot, & Karamat Ali, 2011) but also at work and educational settings. Although academic activities in school and college are usually obligatory and students have little freedom in regard to these activities, they nevertheless can experience flow while doing them (Rijavec, Ljubin-Golub, & Olčar, 2016; Shernoff & Csikszentmihaly, 2009). There is evidence that experiencing flow in general is related to wellbeing (Asakawa, 2010), and flow in educational settings is related to various positive outcomes such as higher engagement in learning, better academic achievement (Engeser, Rheinberg, Vollmeyer, & Bischoff, 2005; Ljubin-Golub, Rijavec, & Olčar, 2016), higher psychological and physical health (Steele & Fullagar, 2009) and higher well-being (Rijavec et al., 2016; Rijavec, & Ljubin Golub, 2019).

Participants and Procedure

A sample comprised 160 students from University of Zagreb (91.9% females). Participants’ age ranged from 18-43 ( M = 19.6, SD = 2.2). The students participated voluntarily and anonymously in the study during the regular psychology class.

Measures

We used an adapted version of the Swedish Flow Questionnaire (SFPQ; Ullen et al., 2012) for assessing flow in the academic and flow in the leisure domain. The Oldenburg Burnout Inventory-S, a version of the OLBI (OLBI-S; Demerouti et al., 2003) adapted to measure academic burnout was used for measuring academic burnout.

• SFPQ. The original SFPQ is a self-report measure of how frequently the respondent experiences flow in different domains (work, maintenance, leisure) using 7 identical items for each domain. In this study we used leisure domain and also added the academic domain, since study-related activities are the core activities in students’ life. The SFPQ- academic domain was used also in previous study in Croatia (Ljubin-Golub, Rijavec, & Jurčec, 2018). Participants rated the 7 original items on a 5-point Likert scale ranging from 1 ( never ) to 5 ( every day, or almost every day ) in relation to the question “When you do something for your studies, how often does it happen that…. e.g. you feel completely concentrated?”. Flow proneness in academic activities (academic flow) was calculated as the mean item score. They also rated the same 7 items in relation to the question “When you do something in your leisure time, how often does it happen that…. e.g. you feel completely concentrated?” Flow proneness in leisure activities (flow in leisure) was calculated as the mean item score.

• OLBI-S. The English version of the OLBI questionnaire was translated to Croatian and the items were modified in order to relate to study instead of work, followed by back translation procedure. OLBI is a self-report measure of burnout and has 16 items rated on a 4-point scale ranging from 1 ( strongly agree ) to 4 ( strongly disagree ). It was designed to distinguish two dimensions, i.e., exhaustion and disengagement dimension, with each dimension having eight items, out of which four items are positively worded and four items are negatively worded. Pprevious studies demonstrated that the OLBI is a psychometrically sound instrument (Demerouti et al., 2003; Reis, Xanthopoulou, & Tsaousis, 2015). However, in study done in Croatia (Ljubin-Golub, Olčar, & Rijavec, 2014) CFA failed to confirm the original structure and EFA revealed two factors (one factor related to positive and the other related to negative item formulation), so we decided to use a total score as an indicator of students’ burnout level.

Descriptive statistics and correlations

For an initial overview of the data, descriptive statistics, internal consistencies (Cronbach’s alpha), and correlations among the study variables are presented in Table 1 . Generally, students reported moderate levels of flow proneness in the academic domain, relatively high levels of flow proneness in leisure, and somewhat above average levels of burnout.

Bivariate correlations indicated positive correlations between academic flow at T1 and T2 ( r =.53), as well as for flow in leisure ( r = .50) and burnout ( r = .61). Academic flow was significantly negatively correlated with academic burnout at T1 ( r = -.40) and T2 ( r = -.33), and flow in leisure was not correlated with academic burnout neither at T1 ( r = -.15) nor T2 ( r = .00) (Table 1 ).

Values of dependent t tests used to examine differences between the mean values of academic flow, flow in leisure and burnout at Time 1 and Time 2 were not significant: academic flow ( t = 0.252, ns), flow in leisure ( t = 1.274, ns) and academic burnout ( t = -1.136, ns). On average, academic flow, flow in leisure and academic burnout were not different at the beginning and the end of the academic year.

Structural Equation Modeling

Data were checked for multivariate normality and the joint multivariate kurtosis value equalled 10.12, indicating non-normality. Therefore, in the SEM analyses to follow, we adjusted the critical value of the chi-square test and standard errors using the Bollen and Stine (1993) bootstrapping method. To determine whether academic flow and flow in leisure predicted burnout and/or were predicted by burnout, structural equation modeling (SEM) with manifest variables and maximum likelihood estimation was employed using AMOS 24 to test four competitive statistical models (see Table 2 ). Model fit was assessed with a variety of absolute and incremental fit indices as recommended by Hu and Bentler (1999), including Tucker-Lewis index (TLI), the incremental fit index (IFI), the comparative fit index (CFI), the root mean square error of approximation (RMSEA), in addition to the Akaike information criterion (AIC). Lower values of AIC indicate better model fit while for TLI, IFI, and CFI cut-off values of .95 have been used as benchmark for acceptable model fit (see Hu & Bentler, 1999; Marsh, Hau, & Wen, 2004).

• M1 - Stability model

This model implied stable effects of the variables over time. It included autoregressive paths of the variables at Time 1 on themselves at Time 2 (academic flow T1 → academic flow T2, flow in leisure T1 →flow in leisure T2, burnout T1→burnout T2) and served as a baseline model. All variables were allowed to correlate with each other within Time 1 and within Time 2.

• M2 – Direct causal model

Model 2 additionally included crosslagged paths from flow to burnout (academic flow T1 → academic burnout T2, flow in leisure T1 →academic burnout T2).

• M3 – Reverse model

This model included cross-lagged paths from burnout to flow (burnout T1 → academic flow T2, burnout T1 → flow in leisure T2) in order to test whether the relationships between the variables were stronger in a reverse matter.

• M4 – Reciprocal model

Model 4 included cross-lagged paths between flow and burnout. Thus, we tested whether prior flow predicted burnout and whether earlier burnout was related to later flow.

When compared to the stability baseline model, the direct causal model, with cross-lagged effects from flow measured at Time 1 to burnout measured at Time 2, provided better fit to the data (Δχ² = 10.73, Δdf =2, p < .01). Contrary to that, reversed model M3 with cross-lagged effects from burnout measured at Time 1 to flow measured at Time 2 did not differ statistically from the stability model (Δχ²= 2.67, Δdf =2, p = n.s.) and performed worse than the direct model.

Also, the reciprocal model provided a significant better fit to the data than the stability model (Δχ² = 12.86, Δdf =4, p < .05) and the reversed model (Δχ²= 12.86, Δdf = 4, p < .01) but has a comparable fit to the direct model (Δχ² = 12.86, Δdf = 2, p = n.s.). The reciprocal causation model can only be justified when it has a better fit than all three other (Farrell, 1994). As this was not the case, the reciprocal causation model was rejected in favour of the direct causal model.

Standardized path coefficients for the cross-lagged model indicating significant path coefficients are presented in Figure 1 .

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As is evident from Figure 1 , academic flow measured at Time 1 negatively predicted academic burnout measured at Time 2 ( b = -.18, p < .05). Flow in leisure activities, on the other hand, positively predicted academic burnout ( b = .18, p < .01) assessed at Time 2.

The present study extended past research using mainly correlational design (i.e., Rijavec et al., 2017) to investigate the flow–burnout relationship by using a full cross-lagged design with two measurement points examining college students over an eight-month period during their first year of studying. Four competing models were tested using structural equation modeling. It was found that flow predicted longitudinal changes in academic burnout with academic flow and flow in leisure having opposite effects. Whereas academic flow predicted longitudinal decreases in students’ academic burnout, flow in leisure predicted longitudinal increases in academic burnout. These findings suggest that academic flow and academic burnout are not merely correlates, but academic flow appears to be a protective factor against academic burnout. On the other hand, flow in leisure might be a factor contributing to the development of academic burnout.

The protective role of academic flow on academic burnout is in line with previous cross-sectional studies that associated academic flow with higher levels of well-being and lower levels of psychological distress and burnout (Rijavec et al., 2016; Rijavec et al., 2017; Steele & Fullagar, 2009; for review see Rijavec & Ljubin Golub, 2019).

However, negative influence of flow in leisure activities on students’ academic wellbeing as indexed by increasing academic burnout is more difficult to explain. Previous cross-sectional research reported that flow in leisure activities had no significant relationship with academic burnout in college students (Rijavec et al., 2017), and had inconsistent relationship with well-being indexes, either positive (Rijavec et al., 2017) or no relationship (Rijavec et al., 2016). Leisure activities are for the majority of students the most flow inducing activities (Massimini & Carli, 1988; Rijavec et al., 2017; Rijavec, Ljubin-Golub, & Olčar, 2016). Students may experience flow in leisure time while reading, sport training, watching a movie, playing games, dancing, watching TV, playing an instrument, listening to music, etc. (Bakker, Ljubin Golub, & Rijavec, 2017). Since flow is very enjoyable state people may develop a tendency to repeat activities that are flow producing, thus interfering with academic duties. That may result in spending more and more time in these activities with less time left for obligatory and important academic activities. In other words, it may be that flow in leisure activities (for example in video games or social media) may cause procrastination in academic activities, thus leading to being overwhelmed by study finally due to extremely short deadlines, which in turn can contribute to increased academic burnout.

Besides leading to spend great amount of time in leisure activities and thus having less time for obligatory activities, experiencing flow in some of these activities is found to be associated with negative outcomes, such as developing problematic use or even addiction toward a flow inducing activity (e.g., Marino, Gini, Vieno, & Spada, 2018). Namely, some of these leisure activities may have been flow addictive (such as video games or social networks). For example, one study showed that the significant positive association found between Facebook flow and Facebook Addiction Disorder was positively moderated by the intensity of Facebook use, indicating that the link between Facebook flow and Facebook addiction may be the result of experiencing flow while using Facebook (Brailovskaia, Rohmann, Bierhoff, & Margraf, 2018). In addition, these types of activities consume time without resulting in increased feelings of achievement and self-worth as in other activities in free time.

On the other hand, involvement in other types of leisure activities has different effect on wellbeing and burnout. The involvement in extracurricular activities (extra academic activities such as volunteer/community services, research and teaching activities, reading activities, arts and cultural activities) either is not associated with the burnout level, or may even counteract burnout, especially in college men (Jacobs & Dodd, 2003) and in those leading and organizing extracurricular activities (Almalki, Almojali, Alothman, Masuadi, & Alaqeel, 2017). Extracurricular involvement promotes sense of accomplishment and thus does not lead to emotional exhaustion but play a protective role against low personal accomplishment, one aspect of burnout (Jacobs & Dodd, 2003). Similary, involvement in healthy lifestyle and especially physical activity has been found to be an extremely strong factor to reduce burnout (Cecil, McHale, Hart, & Laidlaw, 2014). Thus, it seems that the type and quality of leisure activity is of crucial value. However, in this study we had no information about the type of flow inducing leisure activities neither the amount of time students spend in these activities.

The other interesting finding was that there were no significant statistical differences between flow and burnout measures at the start and at the end of the academic year. This contradicts the usual belief that burnout is greatest at the end of the academic year. However, it may be that burnout increases along with the years of studying (Salmela-Aro & Read, 2017) and that the period of eight months was too short for burnout to increase significantly.

The current study has a number of limitations. First, the sample was relatively small, dominantly female and included students from one faculty. Thus, future studies should aim to recruit larger and more heterogeneous samples, since empirical studies have shown that the prevalence of academic burnout varies considerably depending on the university degree in question (Altannir et al., 2019; Barboza & Beresin, 2007; Boni et al., 2018; Caballero, Bresó, & Gutiérrez, 2015; Tomaschewski-Barlem et al., 2014). In addition, the types of leisure activities in which students experience flow should be examined. Also, future studies should longitudinally explore burnout along with years of studying, i.e. over a prolonged period of time. Since previous research proposed existence of burnout subtypes (Montero-Marin, Piva Demarzo, Stapinski, Gili, García-Campayo, 2014), it should be also interesting to investigate the role of academic flow in development of specific burnout components and burnout subtypes.