702022 40(4): 70-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Academic burnout among Open Distance e-Learning students during the COVID-19 pandemic Abstract The transition to online learning at a time of intensive efforts to ensure that the academic project continued under the trying conditions brought on by the COVID-19 pandemic placed intense pressure on both staff and students, increasing their workload. The increased workload placed students at a risk of burnout. While most burnout research focuses on the workplace, there is growing recognition that study activities can have a similar impact on students. The study drew on the conceptualisation of various authors on burnout which is conceived as three sub- domains, namely, emotional exhaustion, cynicism and feelings of low accomplishment or inefficacy. This study made use of a cross-sectional survey design. The sample for the study was drawn from students at an Open Distance e-Learning (ODeL) institution in South Africa using a census sampling approach. The findings of this study show relatively low levels of burnout and high levels of study engagement among respondents. This is despite most respondents reporting being employed while studying. Furthermore, the relationship between dropout intention and burnout was weak but significant. Further areas of research in this field could include students from contact institutions, or a focus on postgraduate students who are employed while studying or explore gender differences among students in different fields of study. Keywords: Academic burnout, study engagement, online distance education, COVID-19 teaching transitions, Utrecht Work Engagement Scale for students, Oldenburg burnout inventory for students. 1. Introduction The transition to online learning at various institutions marked a time of intensive efforts to ensure that the academic project continued under the trying conditions brought on by the COVID-19 pandemic (Mishra, Sahoo & Pandey, 2021). Prior to COVID-19, at the institution under study, the conditions for online learning were in place but not widely used and assessment was primarily conducted in brick and mortar facilities. A swift transition to fully online examinations had to take place as the regulations governing the state of lockdown made physical examinations unfeasible. This transition disrupted the academic year and AUTHOR: Dr Angelo Fynn1 AFFILIATION: 1University of South Africa, South Africa DOI: http://dx.doi.org/10.38140/ pie.v40i4.6298 e-ISSN 2519-593X Perspectives in Education 2022 40(4): 70-88 PUBLISHED: 23 December 2022 RECEIVED: 18 May 2021 ACCEPTED: 24 October 2022 Research Article http://dx.doi.org/10.38140/pie.v40i4.6298 https://orcid.org/0000-0002-0480-8926 http://dx.doi.org/10.38140/pie.v40i4.6298 712022 40(4): 71-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Fynn Academic burnout among Open Distance e-Learning students during the COVID-19 pandemic required academics to develop new examinations, assessment plans and tuition plans to accommodate the changes in circumstances. One of the changes made at the institution was a mass shift to continuous online assessment. This change resulted in an increased assessment workload for students where those taking a full academic workload faced the possibility of completing up to 100 assessments (10 per module) over the course of a year (Fynn & Mashile, 2022). Distance and e-Learning (ODeL. The institution under study consists of a large proportion of students who work full- time (40%) and a further 46% who are classified as unemployed, which means that while they are not employed they may still be engaged in seeking income generating opportunities. This trend is not exclusive to the institution under study as the number of students who combine work and study have increased worldwide (Creed et al., 2022). These students not only carry the burden of their academic workload but also have to manage maintaining paid work and the responsibilities of family life ( Jones, Samra & Lucassen, 2021). Developing effective coping mechanisms to manage these multiple, demanding roles can mitigate the impact of burnout among students. These coping mechanisms can be developed through effective and consistent support from the institution. However, Makoe and Nsamba (2019) point out that distance education students, typically considered non-traditional students as is the case in this paper, often receive inadequate support which may cause them to abandon their studies. Online distance education students at the institution under study faced several factors that could put them at risk of academic burnout. As mentioned below, burnout can have detrimental effects on students’ academic performance. Therefore, it was necessary to determine not only the prevalence of burnout symptoms among this population but also their levels of study engagement during the teaching transitions brought on by the Covid-19 pandemic. 2. Burnout Maslach, Schaufeli and Leiter (2001) stated that burnout is a prolonged response to chronic emotional and physical stressors on the job. Burnout is a chronic ongoing reaction to one’s work, which is typically a negatively affective state that is not immediately reversible by taking rest or changing activity (Demerouti et al., 2002) at the same time contributing to the understanding of the development of burnout as a long-term effect of impairing work and job design. Demerouti et al. (2002) further argue that burnout is a chronic mental health impairment characterised by enduring physical, cognitive and emotional deterioration. Burnout has three components, namely emotional exhaustion, cynicism and feelings of low accomplishment or inefficacy (Cheng et al., 2020; Jackson et al., 1998; Leiter & Maslach, 2017; Maslach et al., 2001; Taris, Schreurs & Van Iersel-Van Silfhout, 2001). The exhaustion component represents the individual experience of being overextended and depleted physically and emotionally (Maslach et al., 2001; Maslach & Leiter, 2017). In this phase individuals feel drained, used up without any source of replenishment, and lack the energy to face another day or problem (Maslach & Leiter, 2017; Robins, Roberts & Sarris, 2018). The exhaustion component is the most frequently reported symptom of burnout and is often the first sign that people are having a problem (Maslach & Leiter, 2017). Exhaustion is seen to prompt individuals to distance themselves from the workplace cognitively and emotionally. Cynicism refers to the development of negative tendencies toward work, creating a pessimistic attitude resulting in negative behaviours toward work activities (Tajeri Moghadam, Abbasi & Khoshnodifar, 2020). Depersonalisation, as part of cynicism, is seen as an attempt http://dx.doi.org/10.38140/pie.v40i4.6298 722022 40(4): 72-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Perspectives in Education 2022: 40(4) to put distance between the self and service recipients by ignoring qualities that make them unique individuals and rather perceive recipients as objects of one’s work (Aguayo et al., 2019) as students must cope with a variety of academic, social and personal challenges. If these demands persist, and if there are insufficient resources with which to address them, they will eventually provoke stress. When stress is present for long periods of time, it can lead to academic burnout syndrome, the signs of which are emotional exhaustion, depersonalisation and inadequate personal accomplishment. This paper considers certain sociodemographic factors (age, sex, children, marital status, employment status, degree subject, faculty, academic year). Distancing is such a common and immediate reaction to exhaustion that research has established consistent links between cynicism and exhaustion (Byrne et al., 2013; Maslach et al., 2001; Watts & Robertson, 2011) which is particularly prominent for staff in human service sectors. Burnout reactions have been characterised as the depletion of emotional reserves (emotional exhaustion). The inefficiency or lack of accomplishment component refers to feelings of incompetence and a lack of productivity at work (Maslach et al., 2001; Schwarzer, Schmitz & Tang, 2000). According to Maslach et al. (2001), a workplace with chronic, overwhelming demands is likely to erode an individual’s sense of effectiveness relative to his or her job function. This component may arise as a result of exhaustion, cynicism or both or may develop in parallel, particularly in working conditions where there is a chronic lack of resources. 3. Academic burnout While there is a substantial amount of research into burnout among working populations, there has been relatively little study of the burnout phenomenon among student populations although there has been an increase in attention on the issue more recently (Asikainen et al., 2020a; Stoeber et al., 2011; Tajeri Moghadam et al., 2020; Vizoso, Arias-Gundín & Rodríguez, 2019) few studies have investigated passion for studying and the role passion for studying plays in student engagement and well-being. The present study investigated the relationships between harmonious and obsessive passion for studying and academic engagement (vigour, dedication and absorption. Studies on burnout among students focus heavily on medical students (Aghajari et al., 2018; Cheng et al., 2020; Chong et al., 2020; Lee, Choi, & Chae Lee, 2017) and there have been relatively fewer studies that focus on general student populations. There is growing recognition, that while students may not typically be formally employed, their studies include mandatory activities, such as submitting assignments, class attendance, etc., that can be considered work (Stoeber et al., 2011; Wei, Wang, & Macdonald, 2015). Research on academic burnout among university students shows that burnout is associated with poor academic performance (Aghajari et al., 2018; Asikainen et al., 2020a; Stoeber et al., 2011). Academic burnout is defined as an experience characterised by feelings of emotional, physical and cognitive exhaustion and an attitude of withdrawal and detachment from one’s studies (Reis, Xanthopoulou & Tsaousis, 2015). In this definition, the demands that students face are likely to produce feelings of exhaustion when they exceed the resources that the student has available to address these demands (Bakker & Demerouti, 2007; Reis et al., 2015; Xanthopoulou et al., 2007). The cynicism sub-domain, in particular, is viewed as detrimental to study engagement and worsens poor study performance. Cynicism is believed to lead to feelings disinterested toward academic work such as assignments, class attendance and assessments (Pouratashi & Zamani, 2018). Wei et al. (2015) stated that cynicism, one of the academic burnout symptoms, is caused by frustration and negative beliefs due to unmet expectations. In other words, cynical attitude among students is a negative belief caused by http://dx.doi.org/10.38140/pie.v40i4.6298 732022 40(4): 73-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Fynn Academic burnout among Open Distance e-Learning students during the COVID-19 pandemic mismatched expectations between what the student expected from college/university life and what they are experiencing (Asikainen et al., 2020a; Wei et al., 2015). Cynicism can hold both positive and negative aspects and, as such, can be viewed as a positive coping mechanism against a world that is less than it should be (Wei et al., 2015). The negative outcomes of cynicism include poor performance and deliberate physical and psychological withdrawal from studies (Wei et al., 2015). Another factor that can influence the prevalence or impact of academic burnout is boundary flexibility where students, particularly working students, are able to shift the boundaries of their work, family and study lives to adapt to changing conditions (Creed et al., 2022). Creed et al. (2022) stated that students who can control the organisation of their work and study activities experience less stress and reduce the potential for burnout. 4. Study engagement Engagement is viewed as a positive, work/study-related, persistent cognitive-affective state that is not focused on a single situation or object (Schaufeli et al., 2002). Engagement, more broadly, consists of three key areas, namely vigour, dedication and absorption (Bakker, Albrecht & Leiter, 2011; Schaufeli et al., 2002; Vizoso et al., 2018). Vigour is characterised by high levels of energy and resilience despite challenges or obstacles and the willingness to invest in one’s work (Bakker et al., 2011; Schaufeli et al., 2002). Dedication is characterised by “a sense of significance, enthusiasm, inspiration, pride, and challenge” (Schaufeli et al., 2002: 456), while absorption is characterised by concentrating fully and being engrossed in one’s work so that time passes by swiftly (Vizoso et al., 2018). Employees with higher levels of engagement are physically healthier, experience more satisfaction of their psychological needs and are more committed than those with low engagement (Borst, Kruyen, & Lako, 2019). Given that study engagement and work engagement are premised on the same construct, namely engagement, it stands to reason that these findings would extend to students as well. Research on study engagement has included examining the role of psychological capital, which refers to the self-efficacy, optimism, hope and resilience of students (Barratt & Duran, 2021; Vîrgă, Pattusamy & Kumar, 2020), student motives for attending university (Hyytinen et al., 2022), the impact of Covid-19 (Salmela-Aro et al., 2022), learning styles (Asikainen et al., 2020b), work-study boundary flexibility (Creed et al., 2022). There is a paucity of research on burnout among students which could inform academic workloads and student wellness initiatives. Given the negative impact that academic burnout could have on performance and health, it is therefore imperative to develop a fuller understanding of the prevalence of academic burnout. 5. Research questions To address the issues raised in the last paragraph above, the following research questions were raised: 1. What is the prevalence of burnout symptoms among distance education students during the transition to fully online learning? 2. Do burnout symptoms predict the level of student engagement? 3. Does burnout predict the likelihood of future dropout as measured by dropout intention? 4. Does study engagement predict the likelihood of future dropout as measured by dropout intention? http://dx.doi.org/10.38140/pie.v40i4.6298 742022 40(4): 74-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Perspectives in Education 2022: 40(4) In the pursuit of answering the above questions, the following hypotheses will be tested: 1. Hypothesis 1: There is a negative relationship between study engagement and dropout intention 2. Hypothesis 2: Study engagement negatively predicts dropout intention 3. Hypothesis 3: There is a positive relationship between burnout and dropout intention 4. Hypothesis 4: Burnout positively predicts dropout intention 6. Method This study adopted a cross-sectional, survey design as it aimed to draw on a cross-section of students from the institution to ascertain the impact of the shift to online learning during the transition to online learning. Cross sectional surveys are flexible, relatively quick to implement, inexpensive, lend themselves to hypothesis testing and allow researchers to conduct studies where information is needed about what is happening currently (Connelly, 2016). 6.1 Sample The population for this study were all undergraduate and Honours students at the unit of study. The objective of this study is to ascertain the impact of the transition to online learning among students. While it should be acknowledged that Master’s and Doctoral students may also have been exposed to conditions that could lead to burnout, the purpose of this paper was to focus on undergraduate and Honours students. The institution from which the participants were recruited had enrolled approximately 360 971 undergraduate and Honours students in 2020. The sample for this study is 10% of the population, which equates to 36 000 students. 6.2 Data collection Data collection was conducted online through anonymous surveys. Students were sent an anonymous e-mail invitation to participate in the study. The e-mails were sent by the ICT department on behalf of the researcher to preserve anonymity. The platform used for data collection, Qualtrics, has been used in numerous studies based at the institution and meets the security and privacy requirements of the institution. 6.3 Instrument The instruments used in this study are the demographics inventory, Oldenburg Burnout Inventory for students (OLBI-s) and the Utrecht Work Engagement Scale for students (UWES-s). The demographics inventory is self-developed and gathers information related to the age, race, gender, qualification, college, year of study, employment status. The aforementioned variables are known to play a role in burnout and engagement. The OLBI-s was developed in response to conceptual and measurement deficiencies in the Maslach Burnout Inventory (MBI). The MBI is the most widely used instrument for studying burnout. However, the MBI has been criticised for not including impaired cognitive functioning as a symptom of burnout. Furthermore, the depersonalisation and personal accomplishment domain of the MBI was determined to be debatable in the diagnosis of burnout (Sakakibara et al., 2020). Bakker et al. (2004) argued that personal accomplishment shows a weak relationship with the exhaustion and cynicism components of burnout. The OLBI-s, like its http://dx.doi.org/10.38140/pie.v40i4.6298 752022 40(4): 75-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Fynn Academic burnout among Open Distance e-Learning students during the COVID-19 pandemic parent instrument the OLBI, has sixteen items, eight items measuring exhaustion and eight items measuring disengagement (Reis et al., 2015). Each subscale contains four negatively worded items and four positively worded items to provide a balanced outlook for respondents (Reis et al., 2015). Burnout level score ranges are represented in Table 1. Low levels of burnout score less than 44 on the OLBI-s, moderate levels of burnout score between 44-59 and high levels of burnout score greater than 59. Table 1: Burnout score ranges Level Range scores Low <44 Moderate 44-59 High >59 (Oana Tipa, Tudose & Pucarea, 2019) In terms of the OLBI-s subscales, the score ranges are represented in Table 2. For the exhaustion component low level scores are less than 21, moderate scores are between 21-29 and high is greater than 29. Table 2: Burnout score ranges per component Burnout component Level Range scores Exhaustion Low <21 Moderate 21-29 High >29 Disengagement Low <24 Moderate 24-31 High >31 (Oana Tipa et al., 2019) The disengagement scale low level scores are less than 24, moderate scores are between 24-31 and high scores are greater than 31. These norm scores will be used to interpret the findings of this study. The UWES-s operationalises the concept of work engagement into three domains. The first ̶ vigour ̶ refers to high levels of energy, willingness to exert effort and mental resilience in your line of work (Lekutle & Nel, 2012). Dedication refers to strong involvement in your work, a sense of significance about your work and pride in your work (Lekutle & Nel, 2012; Van Den Broeck et al., 2008) the presence of job demands (e.g., work pressure, while absorption refers to difficulty tearing oneself away from work and being unaware of time lapsing due to concentration on your work (Lesener, Gusy & Wolter, 2019) this meta-analytic review uses longitudinal evidence to validate the essential assumptions within the JD-R model. Burnout is generally viewed as the erosion of engagement (Lekutle & Nel, 2012). The instrument consists of fourteen items divided across the three domains described above. The instrument has been tested for validity and reliability in a sample of the South African university population (Mostert et al., 2007) construct equivalence and reliability of adapted versions of the Maslach Burnout Inventory Student Survey (MBI SS). http://dx.doi.org/10.38140/pie.v40i4.6298 762022 40(4): 76-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Perspectives in Education 2022: 40(4) In Table 3 the norm scores for the UWES -9s are provided. The scores are categorised based on the mean score of the scale or subscale and are categorised into five categories. Vigour is classified as very low when it has a score below 2.00, low when it has a score between 2.01-3.25, average when it has a score between 3.26-4.80, high when it has a score between 4.81-5.65 and very high when it has a score above 5.66. Table 3: UWES-s Norm scores Vigour Dedication Absorption Total Score Very low ≤2.00 ≤1.33 ≤1.17 ≤1.77 Low 2.01-3.25 1.34-2.90 1.18-2.33 1.78-2.88 Average 3.26-4.80 2.91-4.70 2.34-4.20 2.89-4.66 High 4.81-5.65 4.71-5.69 4.21-5.33 4.67-5.50 Very high ≥5.66 ≥5.70 ≥5.34 ≥5.51 M 4.01 3.88 3.35 3.74 SD 1.13 1.38 1.32 1.17 SE .01 .01 .01 .01 Range .00-6.00 .00-6.00 .00-6.00 .00-6.00 (Schaufeli & Bakker, 2004) Dedication is classified as very low when it has a score below 1.33, low when it has a score between 1.34 and 2.90, average when it has a score between 2.91 and 4.70, high when it has a score between 4.71 and 5.69 and very high when it has a score above 5.70. Absorption is classified as very low when it has a score below 1.17, low when it has a score between 1.18 and 2.33, average when it has a score between 2.34 and 4.20, high when it has a score between 4.21 and 5.33 and very high when it has a score above 5.34. The total score is classified as very low when it has a score below 1.77, low when it has a score between 1.78 and 2.88, average when it has a score between 2.89 and 4.66, high when it has a score between 4.67 and 5.50 and very high when it has a score above 5.51. These norms will be used to interpret the findings of this study. 6.4 Data analysis Descriptive statistics and measures of central tendency were used to analyse demographic items such as race, age, gender and position. The scores for the UWES-s were calculated by adding the items and dividing by the number of items (Schaufeli & Bakker, 2004). The OLBI-s scores were calculated by adding up the items that result in a total score of between 16 and 64. Scores for the OLBI-s and the UWES-s were analysed using the cut-off values specified by the relevant literature and elaborated on above. This was followed by exploratory factor analysis to determine whether the underlying factor structure fits that of international samples. Linear regression was conducted to establish whether the variables under study predicted dropout intention among students. 6.5 Ethical considerations The study received ethical approval from the institutional College of Human Sciences Research Ethics Workgroup with reference number 90169298_CREC_CHS_2021. Respondent anonymity was guaranteed in the invitation e-mail and implemented by collecting no identifiable information and making use of automated e-mail distribution software run by another department within the same university. The researcher therefore had no access to 3𝑥𝑥 = 12 𝑥𝑥 2𝑦𝑦(𝑥𝑥 + 2𝑦𝑦 − 3𝑥𝑥 + 4𝑦𝑦�) − 3 1. 2𝑦𝑦 × 𝑥𝑥 = 2𝑥𝑥𝑦𝑦 2. 2𝑦𝑦 × 2𝑦𝑦 = 4𝑦𝑦� 3. 2𝑦𝑦 × (−3𝑥𝑥) = −6𝑥𝑥𝑦𝑦 … (Teacher L, Second interview, 2021) http://dx.doi.org/10.38140/pie.v40i4.6298 772022 40(4): 77-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Fynn Academic burnout among Open Distance e-Learning students during the COVID-19 pandemic respondent contact information at any point in the study. Respondents were informed of their rights to informed consent and it was emphasised that no negative outcomes would result from withdrawal from the study. 7. Results This section details the results of the survey. The results’ section is divided into demographics, a discussion on the OLBI-s, a discussion on the UWES-s and a section on the hypothesis testing for the study. As mentioned earlier, the sample for the study was 36000 students. A total of 7400 students responded, of which 5400 responses were complete and thus usable for analysis as the instruments are sensitive to missing data. This realises a response rate of 15% which was determined to be sufficient as it was greater than 10% of the sample. 7.1 Demographics of the respondents Approximately 70% of the respondents were African, followed by 18% of respondents who were White, 8% of respondents were Coloured, while 6% were Indian. The majority of respondents (73.4%) were female and 26.1% were male. The mean age of the respondents was 30 years with a minimum of 18 and a maximum of 82. The standard deviation was 8.688. In terms of whether or not they were first generation students, the respondents were almost equally split. The majority of respondents (53%) were not first-generation students (students who are the first in their family to attend university) while 47% were. Approximately 21% of respondents were from the College of Law, followed by 20% who were from the College of Human Sciences. The third largest group (19%) they were from the College of Education and were followed by the College of Economic and Management Sciences at 16%. The College of Accounting Sciences stood at 9.9% and the College of Science, Engineering and Technology at 9% followed by the College of Agricultural and Environmental Sciences at 5%. The majority (54%) of respondents were full-time employed, working 8 hours or more a day. This was followed by 18% who were unemployed but still engaged in job-seeking. Approximately 15% were studying full-time and did not engage in any income generating activities, while 7% were working part-time. Those in occasional employment (5%) were the second smallest group and the smallest group (1%) was registered at two institutions simultaneously. Most respondents (75%) had a module workload of more than five courses. 7.2 Dropout intention of respondents The majority of respondents (45%) indicated that they were unlikely to discontinue their studies in the coming 12 months. Approximately 17% were undecided about whether they would discontinue their studies, while 14% indicated that it was somewhat unlikely that they would discontinue their studies. Approximately 13% indicated that it was somewhat likely that they would discontinue their studies while 11% indicated that this outcome was extremely likely. 7.3 Oldenburg Burnout Inventory for students 7.3.1 Reliability statistics Overall the OLBI-s had a Cronbach alpha of 0.864, suggesting that the items have relatively high internal consistency. Disengagement is calculated by adding the scores of the items indicated in Table 4 with items 3, 6, 8, 9 and 11 reverse scored. The disengagement subscale had a Cronbach alpha score of 0.761, which indicates acceptable levels of internal consistency. http://dx.doi.org/10.38140/pie.v40i4.6298 782022 40(4): 78-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Perspectives in Education 2022: 40(4) Table 4: Disengagement items Question number Item description 1 I always find new and interesting aspects in my studies. 3 It happens more and more often that I talk about my studies in a negative way. 6 Lately, I tend to think less about my academic tasks and do them almost mechanically. 7 I find my studies to be a positive challenge. 9 Over time, one can become disconnected from this type of study. 11 Sometimes I feel sickened by my studies. 13 This is the only field of study that I can imagine myself doing. 15 I feel more and more engaged in my studies. The exhaustion subscale also consisted of eight items and was calculated by adding up the scores of the items in Table 5 with items 2, 4, 8 and 12 reverse scored. The exhaustion subscale had a Cronbach alpha of 0.822, indicating a relatively high level of internal consistency. Table 5: Exhaustion items Question number Item description 2 There are days when I feel tired before I arrive in class or start studying. 4 After a class or after studying, I tend to need more time than in the past to relax and feel better. 5 I can tolerate the pressure of my studies very well. 8 While studying, I often feel emotionally drained. 10 After a class or after studying, I have enough energy for my leisure activities. 12 After a class or after studying, I usually feel worn out and weary. 14 I can usually manage my study-related workload well. 16 When I study, I usually feel energised 7.4 Oldenburg Burnout Inventory for students results The OLBI-s burnout score had a mean of 41.88 with a standard deviation of 7.5, a minimum of 16 and a maximum of 64 out of 64. The disengagement score had a mean of 19.28 with a standard deviation of 4.14, a minimum of 8 and a maximum of 32 out of 32. The exhaustion scale had a mean of 22.59 with a standard deviation of 4.16, a minimum of 8 and a maximum of 32 out of 32. In terms of the prevalence of burnout among students, 51% of students could be classified as having low levels of burnout, while 38% were moderately burned out. Only 1% of students presented high levels of burnout. In terms of the levels of disengagement, 57% of respondents showed low levels of disengagement, 35.1% showed moderate levels and 1% showed high levels. In terms of exhaustion symptoms, 54% of respondents showed low levels of exhaustion, 38% showed moderate levels and 1% showed high levels of exhaustion. http://dx.doi.org/10.38140/pie.v40i4.6298 792022 40(4): 79-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Fynn Academic burnout among Open Distance e-Learning students during the COVID-19 pandemic 7.5 Utrecht Work Engagement Scale for students results 7.5.1 Reliability statistics The UWES-s had a Cronbach alpha of .927, which indicates a very high level of internal consistency. The UWES-s consists of three subscales, each made up of three items. These subscales are vigour, dedication and absorption. Vigour is constructed of the items in Table 6, which are added and then divided by the number of items. The vigour subscale had a Cronbach alpha of .826, which indicates a relatively high level of internal consistency. Table 6: Vigour items Question number Item description 1 When I’m doing my work as a student, I feel bursting with energy. 2 I feel energetic and capable when I’m studying or going to class. 5 When I get up in the morning, I feel like going to class. The dedication subscale is constructed of items in Table 7. The dedication scale had a Cronbach alpha of .809, which indicates a relatively high level of internal consistency. Table 7: Dedication items Question number Item description 3 I am enthusiastic about my studies. 4 My studies inspire me. 7 I am proud of my studies. The absorption subscale is constructed of items in Table 8. The absorption scale had a Cronbach alpha of .800, which indicates a relatively high level of internal consistency. Table 8: Absorption items Question number Item description 6 I feel happy when I am studying intensely. 8 I am immersed in my studies. 9 I get carried away when I am studying. 7.5.2 UWES-s results The vigour subscale had a mean of 12.73, with a minimum of 3, a maximum of 21 and a standard deviation of 5.05. The dedication subscale had a mean of 15.33, with a minimum of 3, a maximum of 21 and a standard deviation of 4.66. The absorption subscale had a mean of 13.61, with a minimum of 3, a maximum of 21 and a standard deviation of 4.95. Respondents were categorised into five categories ranging from very low to very high for the vigour, dedication, absorption and study engagement variables. This enabled analysis of the trends and distribution of these variables among the population. For vigour, 14% showed very low levels of vigour, 14% showed low levels, 28% showed average levels, 11% showed high levels and 28% showed very high levels of vigour. For dedication, 1% indicated very low levels of dedication, 8% reported low levels, 28% reported average levels, 17% reported high levels and 40% reported very high levels of dedication. In terms of absorption levels, 3% reported http://dx.doi.org/10.38140/pie.v40i4.6298 802022 40(4): 80-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Perspectives in Education 2022: 40(4) very low levels, 8% reported low levels, 25% reported average levels, 18% reported high levels and 33% reported very high levels of absorption. In terms of study engagement as an overall construct, 3% reported very low levels of study engagement, 10% reported low levels, 28% reported average levels, 14% reported high levels and 32% reported very high levels of study engagement. 7.6 Factor analysis Prior to conducting inferential testing of the study hypotheses, it wass necessary to determine whether the factor structures of the instruments align with findings in international studies. Both the OLBI-s and UWES-s met all of the assumptions for factor analysis to take place. 7.6.1 OLBI-s Assumption testing showed that Bartlett’s test of sphericity was significant (χ2 (66) = 19436.792, p < .01) and the Kaiser-Meyer-Olkin Measure of Sampling Adequacy was 0.9, which is above the recommendation of 0.7. Five items had communalities below 0.5, which is considered ideal, and were removed from the factor equation. The factor analysis component matrix shows that the exhaustion and disengagement subscales loaded onto a single factor, burnout, with loadings of .909 each. The factor structure for the OLBI-s therefore corroborates findings from other studies which indicated that the exhaustion and disengagement subscales loaded onto a single factor (Oana Tipa et al., 2019). 7.6.2 UWES-s Assumption testing showed that Bartlett’s test of sphericity was significant (χ2 (28) = 26251.02, p < .01) and the Kaiser-Meyer-Olkin Measure of Sampling Adequacy was 0.9, which is above the recommendation of 0.7. Only one item showed communalities below 0.5 and was removed from the factor analysis equation. The vigour, dedication and absorption scales load onto a single factor, namely study engagement. Vigour had a loading of .926, dedication had a loading of .924 and absorption one of .924. 7.7 Hypothesis testing In this section the outcomes of the hypothesis testing are reported. The section is structured around the four hypotheses. Hypothesis 1: There is a negative relationship between study engagement and dropout intention Table 9 shows the results of the Pearson correlation for the relationship between study engagement and dropout intention. The results show a significant but small negative correlation between study engagement and dropout intention r(5432)= -.229, p<.001. This hypothesis can therefore be retained and the null hypothesis rejected. http://dx.doi.org/10.38140/pie.v40i4.6298 812022 40(4): 81-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Fynn Academic burnout among Open Distance e-Learning students during the COVID-19 pandemic Table 9: Study engagement and dropout intention correlation How likely are you to discontinue your studies in the coming 12 months? Study engagement How likely are you to discontinue your studies in the coming 12 months? Pearson Correlation 1 -.229** Sig. (1-tailed) .000 N 5434 4962 Study engagement Pearson Correlation -.229** 1 Sig. (1-tailed) .000 N 4962 4977 **. Correlation is significant at the 0.01 level (1-tailed). Hypothesis 2: Study engagement negatively predicts dropout intention In Table 10 we have the model summary for the regression equation predicting whether study engagement predicts dropout intention. The R is the correlation between the observed and predicted values of the dependent variable. Table 10 shows a significant but weak correlation (R=0.229, p<.000). The R Square indicates the proportion of variance which can be predicted by the independent variable. In this case R2=0.053, which indicates that the model explains 5% of the variance in dropout intention. Table 10: Model summary for regression equation Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .229 0.053 0.052 1.381 0.053 275.032 1 4960 0.000 Looking at the regression coefficients we see that β=-0.024, t=-16.584, p=0.000, which means that, for every one point increase in study engagement, there is a 2% drop in dropout intention as shown in Table 11. This supports the alternative hypothesis and the null hypothesis is therefore rejected. Table 11: Regression coefficients Model B Unstandardised Coefficients Standardised Coefficients T Sig. Std. Error Beta 1 (Constant) 3.297 0.063 52.044 0.000 Study engagement -0.024 0.001 -0.229 -16.584 0.000 http://dx.doi.org/10.38140/pie.v40i4.6298 822022 40(4): 82-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Perspectives in Education 2022: 40(4) Hypothesis 3: There is a positive relationship between burnout and dropout intention In Table 12 we have the correlation coefficients for the relationship between burnout and dropout intention. It is apparent that there is a significant but small correlation between the two variables r(5432)=0.259, p<0.05, which means we can reject the null hypothesis and the alternative hypothesis is supported. Table 12: Burnout and dropout intention correlation Correlations How likely are you to discontinue your studies in the coming 12 months? Burnout How likely are you to discontinue your studies in the coming 12 months? Pearson Correlation 1 .259** Sig. (1-tailed) .000 N 5434 4914 Burnout Pearson Correlation .259** 1 Sig. (1-tailed) .000 N 4914 4929 **. Correlation is significant at the 0.01 level (1-tailed). Hypothesis 4: Burnout positively predicts dropout intention In Table 13 the model summary for the regression equation between burnout and dropout intention is represented. It is evident that there is a weak correlation between the observed and predicted values of the dependent variable (R=0.259) and only a small amount of variance is explained R2=.067, F(1)=352.16, p<.000, which equates to 6.7% of the variance in dropout intention explained by burnout scores. Table 13: Model summary of regression Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .259 .067 .067 1.362 0.067 352.156 1 4912 0.000 The regression coefficients show that there is significant but small relationship β=.048, t=2.399, p=.000 between burnout and dropout intention where a one point increase in burnout leads to a 0.048 increase in dropout intention as shown in Table 14. The alternative hypothesis is therefore supported and the null hypothesis rejected. http://dx.doi.org/10.38140/pie.v40i4.6298 832022 40(4): 83-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Fynn Academic burnout among Open Distance e-Learning students during the COVID-19 pandemic Table 14: Regression coefficients Model B Unstandardised Coefficients Standardised Coefficients t Sig. Std. Error Beta 1 (Constant) .263 0.110 2.399 0.016 Burnout .048 0.003 0.259 18.766 0.000 8. Discussion This study aimed to investigate the prevalence of burnout symptoms and the level of study engagement among ODeL students during the Covid-19 pandemic. A secondary aim was to establish what relationship, if any, existed between burnout, study engagement and dropout intention. The purpose of this investigation was to proactively identify and support students who were at risk of burnout or study disengagement. The findings of this study provide insights into the roles of burnout and study engagement in dropout intention among a diverse student population. The findings of the study show that there are relatively low levels of burnout within the student population with 51.4% of respondents indicating low levels of burnout. A point of concern is the 37.7% who report moderate symptoms of burnout as these symptoms could develop into more severe symptoms. The low levels of burnout symptoms among the respondents are quite surprising given the fact that the majority (54.2%) of respondents are working full-time and are simultaneously engaged in study. When looking at student study loads, the overwhelming majority of respondents (75%) were registered for more than five courses, where five courses would equate to half of the years’ study load , the low levels of burnout symptoms are even more surprising considering that perceived workload is associated with exhaustion (Maslach et al., 2001; Salmela-Aro et al., 2022).This finding contrasts those of Salmela-Aro et al. (2022) who found that distance-study- related demands were associated with lower study engagement and higher burnout earlier in the pandemic. However, they found that as time went on students were more able to manage their daily lives, distance study challenges and the role of these demands in their study demands reduced (Salmela-Aro et al., 2022). A possible explanation for these findings is that the students surveyed in this study are existing online, distance education students who may have developed strategies to effectively manage study, work and family responsibilities. Other factors that may have contributed to this finding is the level of support, whether financial or social and motivation for learning (Hyytinen et al., 2022; Stoeber et al., 2011). The work by Creed et al. (2022) suggests that students who have flexibility in their work or study environments are better off psychologically and are better placed in terms of their studies. These findings suggest that flexible assessment policies, such as continuous assessments or portfolios as opposed to timed examinations at fixed dates, at institutions may play a key role in mediating burnout among students as these elements increase study flexibility. Furthermore, Vizoso, Arias-Gundin and Rodriguez (2019) dispositional optimism, academic burnout and academic performance using structural equation modelling. Data were collected from a sample of 532 Spanish undergraduate students. Participants completed a battery of questionnaires including the LOT-R to assess optimism, CSI for the measurement of (adaptive and maladaptive a coping strategies also highlight the role of adaptive coping skills such as problem solving, adjusting the significance of the demanding situation, social support and expressing emotion in mitigating burnout. These findings are supported by Alves http://dx.doi.org/10.38140/pie.v40i4.6298 842022 40(4): 84-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Perspectives in Education 2022: 40(4) et al. (2022) who stated that the higher the maladaptive coping mechanisms, the higher the dropout intention. These skills are seen as malleable and can be influenced with training and support and therefore could be developed in students through appropriate training. Burnout only explained 6.7% of the variance in dropout intention. This finding suggests that, while burnout plays a role in dropout intention, there are other factors that have a greater impact on student persistence. This is corroborated by earlier research into the factors that impact on student attrition (Howie, 2003; Kuh et al., 2006; Mayet, 2016). Among these factors is the mode of learning, in this case online learning, which typically have higher dropout rates than their contact counterparts (Mishra, et al., 2021). Engagement is viewed as the positive antithesis of burnout and may provide an indicator of student disengagement from study (Maslach et al., 2001). In this study, study engagement levels were also shown to be very high, with 46.3% of respondents indicating either high or very high levels of engagement. This corroborates the finding of the low levels of dropout intention among respondents, with 45% of respondents indicating that it was extremely unlikely that they would discontinue their studies in the next twelve months. These findings corroborate the results from the burnout dimension of the study and the general theory regarding engagement as a protective factor against burnout and as a mediating factor in future success (Abreu Alves et al., 2022; Salanova et al., 2009) this is not the whole story. The current study investigated the additional impact of psychosocial factors (i.e., performance obstacles and facilitators). The high levels of study engagement may be related to the number of interventions and increased engagement with academic staff and peers through online platforms. Study-related resources such as support from peers and instructors support study engagement (Salmela-Aro et al., 2022). Salmela-Aro et al. (2022) also highlighted the relevance of competence, autonomy and relatedness in developing study engagement among distance education students. Support programmes focusing on these key concepts could prove to be decisive in improving and maintaining high levels of study engagement among students. 9. Conclusion While the pandemic, and its concomitant pressures, continues, burnout risk will persist among the student population. Studies like this one are therefore important monitoring tools to ensure that students receive the necessary and timely support from institutions in time. Nevertheless, the findings of this study showed low levels of burnout among ODeL students and significant but weak relationships between burnout and dropout intention. Future studies could extend this research to include students from contact institutions to ascertain whether the mode of learning influences the prevalence of burnout symptoms. The role of social capital was also not examined in this study which may explain the relatively low rates of burnout among respondents. Furthermore, future students could also integrate burnout resilience factors to determine the levels of resilience student populations have. There are several limitations to the study. Firstly, the study sample mostly comprised of working students, which represent a third of the student population at the institution under study. Relatively few unemployed and full-time students responded to the call for participation in this study. The findings of this study therefore cannot be directly generalised to these other groups within the student population as they have a different profile. Finally, the study did not include other factors that have been shown to effect dropout intention in students. This was due to the study focusing exclusively on the impact of burnout and study engagement on dropout intention. http://dx.doi.org/10.38140/pie.v40i4.6298 852022 40(4): 85-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Fynn Academic burnout among Open Distance e-Learning students during the COVID-19 pandemic References Abreu Alves, S., Sinval, J., Lucas Neto, L., Marôco, J., Gonçalves Ferreira, A. & Oliveira, P. 2022. Burnout and dropout intention in medical students: the protective role of academic engagement. BMC Medical Education, 22(1): 1–11. https://doi.org/10.1186/s12909-021-03094-9 Aghajari, Z., Loghmani, L., Ilkhani, M., Talebi, A., Ashktorab, T., Ahmadi, M. & Borhani, F. 2018. The relationship between quality of learning experiences and academic burnout among nursing students of Shahid Beheshti University of medical sciences in 2015. Electronic Journal of General Medicine, 15(6). https://doi.org/10.29333/ejgm/93470 Aguayo, R., Cañadas, G.R., Assbaa-Kaddouri, L., Cañadas-De la Fuente, G.A., Ramírez- Baena, L. & Ortega-Campos, E. 2019. A risk profile of sociodemographic factors in the onset of academic burnout syndrome in a sample of university students. International Journal of Environmental Research and Public Health, 16(5). https://doi.org/10.3390/ijerph16050707 Asikainen, H., Salmela-Aro, K., Parpala, A. & Katajavuori, N. 2020a. Learning profiles and their relation to study-related burnout and academic achievement among university students. Learning and Individual Differences, 78 (March 2019), 101781. https://doi.org/10.1016/j. lindif.2019.101781 Asikainen, H., Salmela-Aro, K., Parpala, A. & Katajavuori, N. 2020b. Learning profiles and their relation to study-related burnout and academic achievement among university students. Learning and Individual Differences, 78. 101781. https://doi.org/10.1016/J. LINDIF.2019.101781 Bakker, A.B., Albrecht, S.L., & Leiter, M.P. 2011. Key questions regarding work engagement. European Journal of Work and Organizational Psychology, 20(1): 4-28. https://doi.org/10.10 80/1359432X.2010.485352 Bakker, A.B. & Demerouti, E. 2007. The Job Demands-Resources model: State of the art. Journal of Managerial Psychology, 22(3): 309-328. https://doi.org/10.1108/02683940710733115 Bakker, A.B., Demerouti, E., & Verbeke, W. 2004. Using the job demands-resources model to predict burnout and performance. Human Resource Management, 43(1): 83-104. https://doi. org/10.1002/hrm.20004 Barratt, J.M. & Duran, F. 2021. Does psychological capital and social support impact engagement and burnout in online distance learning students? The Internet and Higher Education, 51: 100821. https://doi.org/10.1016/J.IHEDUC.2021.100821 Borst, R.T., Kruyen, P.M. & Lako, C.J. 2019. Exploring the Job Demands–Resources Model of Work Engagement in Government: Bringing in a Psychological Perspective. Review of Public Personnel Administration, 39(3): 372-397. https://doi.org/10.1177/0734371X17729870 Byrne, M., Chughtai, A., Flood, B., Murphy, E. & Willis, P. 2013. Burnout among accounting and finance academics in Ireland. International Journal of Educational Management, 27(2): 127-142. https://doi.org/10.1108/09513541311297513 Cheng, J., Zhao, Y.Y., Wang, J. & Sun, Y.H. 2020. Academic burnout and depression of Chinese medical students in the pre-clinical years: the buffering hypothesis of resilience and social support. Psychology, Health and Medicine, 25(9): 1094-1105. https://doi.org/10.1080/1 3548506.2019.1709651 http://dx.doi.org/10.38140/pie.v40i4.6298 https://doi.org/10.1186/s12909-021-03094-9 https://doi.org/10.29333/ejgm/93470 https://doi.org/10.3390/ijerph16050707 https://doi.org/10.1016/j.lindif.2019.101781 https://doi.org/10.1016/j.lindif.2019.101781 https://doi.org/10.1016/J.LINDIF.2019.101781 https://doi.org/10.1016/J.LINDIF.2019.101781 https://doi.org/10.1080/1359432X.2010.485352 https://doi.org/10.1080/1359432X.2010.485352 https://doi.org/10.1108/02683940710733115 https://doi.org/10.1002/hrm.20004 https://doi.org/10.1002/hrm.20004 https://doi.org/10.1016/J.IHEDUC.2021.100821 https://doi.org/10.1177/0734371X17729870 https://doi.org/10.1108/09513541311297513 https://doi.org/10.1080/13548506.2019.1709651 https://doi.org/10.1080/13548506.2019.1709651 862022 40(4): 86-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Perspectives in Education 2022: 40(4) Chong, D.Y.K., Tam, B., Yau, S.Y. & Wong, A.Y.L. 2020. Learning to prescribe and instruct exercise in physiotherapy education through authentic continuous assessment and rubrics. BMC Medical Education, 20(1): 1-11. https://doi.org/10.1186/s12909-020-02163-9 Connelly, L. 2016. Cross-Sectional Survey Research. Medsurg Nursing, 25(5): 369–370. Creed, P.A., Hood, M., Brough, P., Bialocerkowski, A., Machin, M.A., Winterbotham, S. & Eastgate, L. 2022. Student work-study boundary flexibility and relationships with burnout and study engagement. Journal of Education and Work, 35(3): 256-271. https://doi.org/10.1080/1 3639080.2022.2048250 Demerouti, E., Bakker, A. B., Nachreiner, F. & Ebbinghaus, M. 2002. From mental strain to burnout. European Journal of Work and Organizational Psychology, 11(4): 423-441. https:// doi.org/10.1080/13594320244000274 Fynn, A. & Mashile, E.O. 2022. Continuous Online Assessment at a South African Open Distance and e-Learning Institution. Frontiers in Education, 7(March): 1-13. https://doi. org/10.3389/feduc.2022.791271 Howie, S.J. 2003. Language and other background factors affecting secondary pupils’ performance in Mathematics in South Africa. African Journal of Research in Mathematics, Science and Technology Education, 7(1): 1-20. https://doi.org/10.1080/10288457.2003.107 40545 Hyytinen, H., Tuononen, T., Nevgi, A. & Toom, A. 2022. The first-year students’ motives for attending university studies and study-related burnout in relation to academic achievement. Learning and Individual Differences, 97(May): 102165. https://doi.org/10.1016/j. lindif.2022.102165 Jackson, S.E., Schwab, R.L. & Schuler, R.S. 1998. Toward An Understanding of the Burnout Phenomenon. Journal of Applied Psychology, 71(4): 630-640. https://doi. org/10.1037/0021-9010.71.4.630 Jones, E., Samra, R. & Lucassen, M. 2021. Key challenges and opportunities around wellbeing for distance learning students: the online law school experience. Open Learning: The Journal of Open, Distance and e-Learning. https://doi.org/10.1080/02680513.2021.1906639 Kuh, G.D., Kinzie, J., Buckley, J.A., Bridges, B.K. & Hayek, J.C. 2006. What matters to student success: a review of the literature. Commissioned Report for the National Symposium on Postsecondary Student Success: Spearheading a Dialog on Student Success (Issue July). Available at https://bit.ly/3VAomAY [Accessed 28 November 2022]. Lee, S.J., Choi, Y.J. & Chae, H. 2017. The effects of personality traits on academic burnout in Korean medical students. Integrative Medicine Research, 6(2): 207-213. https://doi. org/10.1016/j.imr.2017.03.005 Leiter, M.P. & Maslach, C. 2017. Burnout and engagement: Contributions to a new vision. Burnout Research, 5: 55-57. https://doi.org/10.1016/j.burn.2017.04.003 Lekutle, M. & Nel, J.A. 2012. Psychometric evaluation of the utrecht work engagement scale (UWES) and oldenburg burnout inventory (OLBI) within a cement factory. Journal of Psychology in Africa, 22(4): 641-647. https://doi.org/10.1080/14330237.2012.10820580 Lesener, T., Gusy, B. & Wolter, C. 2019. The job demands-resources model: A meta-analytic review of longitudinal studies. Work and Stress, 33(1): 76-103. https://doi.org/10.1080/02678 373.2018.1529065 http://dx.doi.org/10.38140/pie.v40i4.6298 https://doi.org/10.1186/s12909-020-02163-9 https://doi.org/10.1080/13639080.2022.2048250 https://doi.org/10.1080/13639080.2022.2048250 https://doi.org/10.1080/13594320244000274 https://doi.org/10.1080/13594320244000274 https://doi.org/10.3389/feduc.2022.791271 https://doi.org/10.3389/feduc.2022.791271 https://doi.org/10.1080/10288457.2003.10740545 https://doi.org/10.1080/10288457.2003.10740545 https://doi.org/10.1016/j.lindif.2022.102165 https://doi.org/10.1016/j.lindif.2022.102165 https://doi.org/10.1037/0021-9010.71.4.630 https://doi.org/10.1037/0021-9010.71.4.630 https://doi.org/10.1080/02680513.2021.1906639 https://bit.ly/3VAomAY https://doi.org/10.1016/j.imr.2017.03.005 https://doi.org/10.1016/j.imr.2017.03.005 https://doi.org/10.1016/j.burn.2017.04.003 https://doi.org/10.1080/14330237.2012.10820580 https://doi.org/10.1080/02678373.2018.1529065 https://doi.org/10.1080/02678373.2018.1529065 872022 40(4): 87-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Fynn Academic burnout among Open Distance e-Learning students during the COVID-19 pandemic Makoe, M. & Nsamba, A. 2019. The gap between student perceptions and expectations of quality support services at the University of South Africa. American Journal of Distance Education, 33(2): 132-141. https://doi.org/10.1080/08923647.2019.1583028 Maslach, C. & Leiter, M.P. 2017. New insights into burnout and health care: Strategies for improving civility and alleviating burnout. Medical Teacher, 39(2): 160-163. https://doi.org/10. 1080/0142159X.2016.1248918 Maslach, C., Schaufeli, W.B. & Leiter, M.P. 2001. Job burnout. Annual Review of Psychology, 52: 397-422. https://doi.org/10.1146/annurev.psych.52.1.397 Mayet, R. 2016. Supporting At-Risk Learners at a Comprehensive University in South Africa. 4(2): 1-12. https://doi.org/10.18820/jsaa.v4i2.2 McAlpine, L., Skakni, I. & Pyhältö, K. 2020. PhD experience (and progress) is more than work: life-work relations and reducing exhaustion (and cynicism). Studies in Higher Education, 47(3): 1-15. https://doi.org/10.1080/03075079.2020.1744128 Mishra, S., Sahoo, S. & Pandey, S. 2021. Research trends in online distance learning during the COVID-19 pandemic. https://doi.org/10.1080/01587919.2021.1986373 Mostert, K., Pienaar, J., Gauche, C. & Jackson, L. 2007. Burnout and engagement in university students: A psychometric analysis of the MBI-SS and UWES-S. South African Journal of Higher Education, 21(1): 147-162. https://doi.org/10.4314/sajhe.v21i1.25608 Oana Tipa, R., Tudose, C. & Pucarea, V.L. 2019. Measuring Burnout Among Psychiatric Residents Using the Oldenburg Burnout Inventory (OLBI) Instrument. Journal of Medicine and Life, 12(4): 354-360. https://doi.org/10.25122/jml-2019-0089 Pouratashi, M. & Zamani, A. 2018. Agricultural students’ academic burnout: the influence of employment challenges. Journal of Education and Work, 31(4): 409-417. https://doi.org/10.1 080/13639080.2018.1513637 Reis, D., Xanthopoulou, D. & Tsaousis, I. 2015. Measuring job and academic burnout with the Oldenburg Burnout Inventory (OLBI): Factorial invariance across samples and countries. Burnout Research, 2(1): 8-18. https://doi.org/10.1016/j.burn.2014.11.001 Robins, T.G., Roberts, R.M. & Sarris, A. 2018. The role of student burnout in predicting future burnout: exploring the transition from university to the workplace. Higher Education Research and Development, 37(1): 115-130. https://doi.org/10.1080/07294360.2017.1344827 Sakakibara, K., Shimazu, A., Toyama, H. & Schaufeli, W.B. 2020. Validation of the Japanese Version of the Burnout Assessment Tool. Frontiers in Psychology, 11(August): 1-15. https:// doi.org/10.3389/fpsyg.2020.01819 Salanova, M., Schaufeli, W.B., Martínez, I. & Bresó, E. 2009. How obstacles and facilitators predict academic performance: The mediating role of study burnout and engagement. Anxiety, Stress and Coping, 23(1): 53-70. https://doi.org/10.1080/10615800802609965 Salmela-Aro, K., Upadyaya, K., Ronkainen, I. & Hietajärvi, L. 2022. Study Burnout and Engagement During COVID-19 Among University Students: The Role of Demands, Resources, and Psychological Needs. Journal of Happiness Studies. https://doi.org/10.1007/ s10902-022-00518-1 http://dx.doi.org/10.38140/pie.v40i4.6298 https://doi.org/10.1080/08923647.2019.1583028 https://doi.org/10.1080/0142159X.2016.1248918 https://doi.org/10.1080/0142159X.2016.1248918 https://doi.org/10.1146/annurev.psych.52.1.397 https://doi.org/10.18820/jsaa.v4i2.2 https://doi.org/10.1080/03075079.2020.1744128 https://doi.org/10.1080/01587919.2021.1986373 https://doi.org/10.4314/sajhe.v21i1.25608 https://doi.org/10.25122/jml-2019-0089 https://doi.org/10.1080/13639080.2018.1513637 https://doi.org/10.1080/13639080.2018.1513637 https://doi.org/10.1016/j.burn.2014.11.001 https://doi.org/10.1080/07294360.2017.1344827 https://doi.org/10.3389/fpsyg.2020.01819 https://doi.org/10.3389/fpsyg.2020.01819 https://doi.org/10.1080/10615800802609965 https://doi.org/10.1007/s10902-022-00518-1 https://doi.org/10.1007/s10902-022-00518-1 882022 40(4): 88-88 http://dx.doi.org/10.38140/pie.v40i4.6298 Perspectives in Education 2022: 40(4) Schaufeli, W.B. & Bakker, A. 2004. Utrecht work engagement scale Preliminary Manual Version 1.1. In Utrecht work engagement scale Preliminary Manual Version 1.1 (Issue December). https://doi.org/10.1037/t01350-000 Schaufeli, W.B., Martínez, I.M., Pinto, A.M., Salanova, M. & Barker, A.B. 2002. Burnout and engagement in university students a cross-national study. Journal of Cross-Cultural Psychology, 33(5): 464-481. https://doi.org/10.1177/0022022102033005003 Schwarzer, R., Schmitz, G. S. & Tang, C. 2000. Teacher burnout in Hong Kong and Germany: A cross-cultural validation of the Maslach burnout inventory. Anxiety, Stress and Coping, 13(3): 309–326. https://doi.org/10.1080/10615800008549268 Stoeber, J., Childs, J.H., Hayward, J.A. & Feast, A.R. 2011. Passion and motivation for studying: Predicting academic engagement and burnout in university students. Educational Psychology, 31(4): 513-528. https://doi.org/10.1080/01443410.2011.570251 Tajeri Moghadam, M., Abbasi, E. & Khoshnodifar, Z. 2020. Students’ academic burnout in Iranian agricultural higher education system: the mediating role of achievement motivation. Heliyon, 6(9): e04960. https://doi.org/10.1016/j.heliyon.2020.e04960 Taris, T.W., Schreurs, P.J.G. & Van Iersel-Van Silfhout, I.J. 2001. Job stress, job strain, and psychological withdrawal among Dutch university staff: Towards a dual-process model for the effects of occupational stress. Work and Stress, 15(4): 283-296. https://doi. org/10.1080/02678370110084049 Van Den Broeck, A., Vansteenkiste, M., De Witte, H. & Lens, W. 2008. Explaining the relationships between job characteristics, burnout, and engagement: The role of basic psychological need satisfaction. Work and Stress, 22(3): 277-294. https://doi.org/10.1080/02678370802393672 Vîrgă, D., Pattusamy, M. & Kumar, D.P. 2020. How psychological capital is related to academic performance, burnout, and boredom? The mediating role of study engagement. Current Psychology, 41: 6731-6743. https://doi.org/10.1007/s12144-020-01162-9 Vizoso, C., Arias-Gundín, O. & Rodríguez, C. 2019. Exploring coping and optimism as predictors of academic burnout and performance among university students. Educational Psychology, 39(6): 768-783. https://doi.org/10.1080/01443410.2018.1545996 Vizoso, C., Rodríguez, C. & Arias-Gundín, O. 2018. Coping, academic engagement and performance in university students. Higher Education Research and Development, 37(7): 1515-1529. https://doi.org/10.1080/07294360.2018.1504006 Watts, J. & Robertson, N. 2011. Burnout in university teaching staff: A systematic literature review. Educational Research, 53(1): 33-50. https://doi.org/10.1080/00131881.2011.552235 Wei, X., Wang, R. & Macdonald, E. 2015. Exploring the relations between student cynicism and student burnout. Psychological Reports, 117(1): 103-115. https://doi.org/10.2466/14.11. PR0.117c14z6 Xanthopoulou, D., Bakker, A.B., Demerouti, E. & Schaufeli, W.B. 2007. The role of personal resources in the job demands-resources model. International Journal of Stress Management, 14(2): 121-141. https://doi.org/10.1037/1072-5245.14.2.121 http://dx.doi.org/10.38140/pie.v40i4.6298 https://doi.org/10.1037/t01350-000 https://doi.org/10.1177/0022022102033005003 https://doi.org/10.1080/10615800008549268 https://doi.org/10.1080/01443410.2011.570251 https://doi.org/10.1016/j.heliyon.2020.e04960 https://doi.org/10.1080/02678370110084049 https://doi.org/10.1080/02678370110084049 https://doi.org/10.1080/02678370802393672 https://doi.org/10.1007/s12144-020-01162-9 https://doi.org/10.1080/01443410.2018.1545996 https://doi.org/10.1080/07294360.2018.1504006 https://doi.org/10.1080/00131881.2011.552235 https://doi.org/10.2466/14.11.PR0.117c14z6 https://doi.org/10.2466/14.11.PR0.117c14z6 https://doi.org/10.1037/1072-5245.14.2.121 _Hlk117243069 _Hlk71013087 _Hlk71013129 _Hlk71013285 _Hlk63868742 _Hlk107997569 _Hlk71013621 _Hlk71013639 _Hlk71013658 _Hlk71013690 _Ref83987302 _Ref83987594 _Ref83982115 _Hlk71013746 _Hlk83817249 _Ref83818704 _Ref83819446 _Ref83893970 _Ref83894164 _Ref83894622 _Ref107998203 _Ref107998254 _Ref107998318 _Ref84008543 _Ref85443070 _Ref107998506 _Ref84008742