During the COVID-19 pandemic in early 2020, there was a significant shift within the field of work where most of the office tasks were performed online. Likewise, education has changed radically with the emergence of e-learning, where teaching is conducted remotely via digital platforms. Students and academicians have been using online platforms for teaching and learning and performing assessment activities for almost three years now since the first Movement Control Order (MCO) by the Government of Malaysia in March 2020 to break the chain of COVID-19.
Online learning is learning experiences in synchronous or asynchronous environments using different devices (e.g., mobile phones, laptops, etc.) with internet access where students from anywhere can independently learn and interact with the instructors and the other students (Singh & Thurman, 2019). Students and academicians need to be in front of their electronic devices for an average of six hours daily to meet the university’s contact hour requirements for the effective delivery of the courses.
Although technology is supposed to facilitate teaching and learning, negative consequences might occur when used excessively. Many studies have reported that students and academicians suffer from depression due to the excessive use of online applications during teaching and learning (Besser et al., 2020; Zeeshan et al., 2020). Depression is one of the signs of mental and emotional fatigue. It happens when our bodies can no longer cope with external pressures and is characterized by persistent sadness and a lack of interest or pleasure in previously rewarding or enjoyable activities. It is the leading cause of disability worldwide and contributes greatly to the global burden of disease, which can dramatically affect a person’s ability to function and live a rewarding life (WHO, 2022).
Therefore, this paper is meant to assess the effect of online teaching after long-term enforcement on the academicians as there is a direct implication of this phenomenon on the quality of life of the academicians and the quality of teaching and learning. The paper aims to identify the unprecedented factors among academicians in one of the biggest universities in the country and investigate their relationship with academicians’ perceived mental and emotional exhaustion.
2. Literature Review
2.1. Online Distance Learning (ODL)
Online education is essential for increasing access and involvement in education for a diverse variety of students, the majority of whom hitherto have been underrepresented at universities (Stone, 2007). More than 85% of countries have closed schools entirely or partially due to the COVID-19 pandemic as of April 10th, 2020, leaving more than 1.6 billion students unschooled (UNICEF et al., 2020). COVID-19 prohibits students and faculties from meeting face-to-face, so educational institutions are exploring online course delivery alternatives (Brown et al., 2020). Teachers and students were prohibited from attending schools and universities, yet education should continue.
Online education is now the main option available. However, the level of preparedness of every member of the educational community demands effective management (Santos et al., 2021). Due to the COVID-19 pandemic in Malaysia, schools and universities must maintain online education and curriculum. Online teaching and learning have altered the landscape of the Malaysian education industry (Karupiah, 2021). However, the unprecedented disruption of worldwide education systems has brought significant effects not only on students but also academicians. Therefore, the present study is meant to determine the factors that might lead to mental and emotional exhaustion among academicians during online distance learning.
Educators face new obstacles due to the COVID-19 epidemic and socioeconomic upheavals. They face occupational health concerns like stress and burnout (Hussien et al., 2022). During the COVID-19 pandemic, they are involved in personal continuing education, course preparation, student counseling, online learning, classroom instruction, workloads, departmental matters, and the pressure to maintain a high education quality. These factors may affect their mental and emotional health. Burnout is the state of mental and emotional weariness. Burnout is also an adverse reaction to prolonged work stress, characterized by high levels of emotional weariness, depersonalization, and low levels of personal accomplishment at work (Maslach et al., 2001).
The factors are identified and discussed based on the existing studies on mental and emotional exhaustion, especially among academicians.
2.2. Work-Life Balance (WLB)
Academicians face numerous daily struggles to maintain a work-life balance (WLB) and a sense of fulfillment because they are responsible for guiding the nation’s youth, who will build the country in the future (Azam & Zafrul, 2021) and separating work and personal life is complicated. WLB is frequently discussed and sometimes challenging to be achieved in the education sector (Wong & Ko, 2009). Academicians, like other working populations, are affected by the COVID-19 pandemic. COVID-19 experiences revealed that working from home could be problematic for individuals, particularly women. Due to domestic commitments and childcare obligations, female employees’ work hours are regularly interrupted (Hjálmsdóttir & Bjarnadóttir, 2021). A balance between work and personal life will help academicians avoid depression and burnout (Malik & Allam, 2021; Mulyani et al., 2021).
2.3. Lack of Support
Due to constrained working conditions during the COVID-19 pandemic, academicians must continue working from home using existing or new technologies. The environmental benefits of office space have been replaced by technological benefits (Waizenegger et al., 2020). However, the lack of support from employers, co-workers, and family members during the COVID-19 pandemic to employees working from home has led to mental and emotional exhaustion. Otero-López et al. (2008) and Salahshour and Esmaeili (2021) mentioned in their studies that burnout among academicians is due to a lack of social support, daily life stresses with work and family life, and excessive working hours. Employees were shown to benefit significantly from employers’ emotional support (Talukder et al., 2018).
2.4. Students’ Lack of Engagement
Online learning is learning in a virtual environment where communication is facilitated largely via technology (Moore-Adams et al., 2016). Recently, numerous researchers have performed studies to address the concern associated with online teaching and learning during COVID-19. However, the vast majority of these studies have focused on the issues faced by students while disregarding the issues faced by teachers (Joshi et al., 2020). The lack of interaction and student participation is one of the primary concerns of educators during online lectures (Damary et al., 2017). Lack of student engagement or feedback during online classes has led to academicians’ loss of motivation in teaching and increased mental and emotional exhaustion (Pellerone, 2021).
2.5. Lecturer’s Lack of Expertise
Brooks (2021) mentioned that educators’ technology proficiency and technical expertise are positively connected with their intrinsic motivation to apply online learning. The teacher’s proficiency influences teachers’ motivation to employ new technology and pedagogical strategies (Li, 2021). The emergence of COVID-19 has significantly altered university teaching through digital transformation. The transition from traditional face-to-face teaching to virtual classrooms had proceeded without the required adaption steps when the pandemic happened. The sudden transition to online teaching affects lecturers’ mental load, stress, and increased emotional exhaustion. Consequently, lecturers will experience burnout due to their lack of digital abilities and technical vulnerability (Arora & Srinivasan, 2020; Fernández-Suárez et al., 2021; Gratz & Looney, 2020).
2.6. Students’ Lack of Learning Facilities
Online education is a viable alternative in the current era of advanced technology and communication (Schneider & Council, 2021). During the COVID-19 pandemic, students and teachers must transition from traditional to online schooling (Fahruddin et al., 2022). Students faced many challenges regarding learning facilities (Humphrey & Wiles, 2021), including access to the required technology (Ferdig et al., 2020), exasperation with modes of learning (Aristovnik et al., 2021; Ye et al., 2020), and dealing with various unforeseen and compounded personal and familial challenges (Dhawan, 2020). Students’ technical issues, including internet access, limited bandwidth, and acquiring the technical characteristics of synchronous online learning technologies, make teaching more exhausting for the lecturers’ (Peper et al., 2021).
2.7. Students’ Sharing Attitude
With the rapid shift in online teaching and learning, there has been a concern raised regarding academic misconduct. During online learning, the sharing of teaching materials has become a practice among students. This can be seen as an academic form of cheating. Academic cheating can be categorized as information transfer between individuals, aiding tools, exploiting weakness, and copying answers or information (Anderman et al., 2009; Chala, 2021; Cynthia, 2022). Institutions of higher education and educators lack knowledge of how students use internet-based capabilities in ways that are not morally sound (Bailey & Withers, 2018). According to a study, the transition to online learning during the pandemic increases students’ likelihood of academic dishonesty (Maryon et al., 2022). This will affect mental and emotional exhaustion among academicians.
2.8. Lecturers’ Excessive Teaching Loads
Today, the academic work landscape is defined by rising expectations regarding workload, publication pressure, large-scale organizational change, and government budget reductions. Academicians need more time to plan, prepare and implement course designs for students because of the drastic shift to online teaching during the COVID-19 pandemic. The sudden shift in the modes of teaching and working from home has led to an increasing strain (D’Souza et al., 2022). Excessive paperwork and teaching children simultaneously deprive teachers of the necessary relaxation, which may lead to stress (Tehseen & Ul Hadi, 2015). According to Jendle and Wallnäs (2017), nine out of ten educators felt their workload was excessive, which leads to burnout, and diminished physical and emotional energy (Jomuad et al., 2021).
2.9. Lecturers’ Teaching Preparation
The COVID-19 pandemic has prompted the education community to adopt online education to continue teaching. Online teaching involves a specialized skill set, when and how to use synchronous, asynchronous, or both teaching styles, lesson planning, feedback gathering, and reflection on lessons taught (Martin et al., 2019). The preparations that academicians need to make for online teaching and the increasing job workloads tend to be overwhelming (Hussein et al., 2020). Regarding the lecturer’s online teaching preparation, lecturers are burdened with the technological complexity which pertains to the increased challenges created by work-related technologies, which lecturers must spend time and effort to learn and master (Ma et al., 2021). An overburdened lecturer with high job demands will suffer from mental and emotional exhaustion.
2.10. Lecturers’ Teaching Preferences
Amid the COVID-19 pandemic, e-learning tools have played a significant role in aiding universities and colleges to promote student learning during the closure of schools and institutions (Subedi et al., 2020). Microsoft Teams, Google Classroom, Canvas, and Blackboard are examples of online platforms with unified communication and collaboration tools. The system facilitates the creation of educational courses, training, and skill development programs (Petrie, 2020). In Malaysia, Zoom, Webex, Microsoft Teams, and Google Meet have been widely used to promote Online Distance Learning (ODL) and Work from Home (WFH) during this pandemic (Chung et al., 2020). Educators’ challenge is to improve their skills in generating such resources, locating and customizing them for better usage in academic settings. However, the increasing use of video conferencing creates a problem for educators. Several individuals have reported experiencing Zoom fatigue or mental and physical exhaustion.
In conducting this research, qualitative and quantitative approaches were used to make sure that the causes and consequences of the issues could be identified. Generally, the total population of lecturers in the whole university system is 18,000 members. In the first phase, an interview was conducted with the identified lecturers. Their respective faculty deans chose them. The inputs from the interview session were used to develop the research instrument in the quantitative approach phase of the study.
The questionnaire consisted of three (3) sections, which comprise demographic profile (6 items), mental and emotional exhaustion (13 items), and factors that contribute to mental and emotional exhaustion (46 items). The items were measured using a 5-point Likert scale ranging from Never, Rare, Sometimes, Often, and Always. The complete questionnaire was then given to the panels of experts for validation before it was distributed during the pilot study. After the pilot study, the questionnaire was further improved so that it is ready to be used in the actual study.
The questionnaire was distributed to all 18,000 lecturers via the SurveyMonkey application. The responses with incomplete data were discarded to ensure the findings’ quality can represent the truthfulness of the scenario in online teaching and learning. Only the completed responses were considered in the analysis. After the lapse of one week and a reminder sent, only 275 academicians responded, indicating a response rate of 1.5%. A total of 64 responses were discarded due to high missing values. The remaining 211 responses were used for subsequent analyses.
3.1. Respondent’s Profile
Table 1 shows the distribution of data according to respondents’ demographic characteristics. Of 211 responses, 33 represented male respondents, while female respondents answered 178. Regarding age distribution, 88 respondents were between 40 and 55 years old, followed by those aged 35 to 40 with 58 respondents. A total of 40 respondents were between 30 and 35 years old, and others represented different age categories.
Table 1: Respondent’s Profile
The 211 respondents represented various faculties, with the highest number coming from the Faculty of Business and Management (36 responses). The Academy of Linguistic Studies followed it with 22 responses, Architecture, Planning and Surveying (19 responses), the College of Engineering Studies (18 responses), and Computer and Mathematical Sciences (18 responses).
Most respondents taught bachelor’s degree programs at their respective faculties (110 responses), followed by diploma programs (82). Most respondents (110) taught between 15 and 20 hours per week, followed by those teaching 10 to 15 hours per week (72 respondents). A total of 141 respondents did not hold any administration positions at their respective faculties.
3.2 Factor Analysis
Table 2 shows the result of factor analysis for the dependent variables. A total of 13 items were used to measure mental and emotional exhaustion among lecturers. Two factors were extracted using a principal component factor analysis with varimax rotation, explaining 72.85% of the variance. The first factor contains eight items representing mental exhaustion, with factor loadings ranging from 0.687 to 0.828. The second factor contains five items assessing emotional exhaustion, and the factor loadings range from 0.768 to 0.861. The KMO value of 0.947 shows sampling adequacy with sufficient correlation matrix among the items. The MSA values support sampling adequacy, ranging from 0.915 to 0.969.
Table 2: Results of Factor Analysis for the Dependent Variables
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Table 3 shows the result of factor analysis for the independent variables. The second-factor analysis with varimax rotation was performed to examine the factor structure of 46 items used to assess various factors that lead to mental and emotional exhaustion. The results indicate a 9-factor structure of the measure, explaining 67.46% of the variance. The KMO value of 0.879 indicates sampling adequacy, with Bartlett’s test showing a significant value. The MSA values ranged from 0.538 to 0.953, showing sampling adequacy for each item.
Table 3: Results of Factor Analysis for the Independent Variables
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
The extracted nine factors represent work-life conflict (13 items), lack of support (10 items), students’ lack of engagement (8 items), lecturers’ lack of expertise (3 items), students’ lack of learning facilities (3 items), students’ sharing attitude (2 items), lecturers’ excessive leaching loads (3 items), lecturers’ lack of teaching preparation (2 items), and lecturers’ teaching preference (2 items).
3.3. Correlation Analysis
Table 4 shows the result of correlation and reliability analyses. From the table, the items used to measure each variable have high Cronbach’s alpha coefficients (as shown in the parentheses along the diagonal), ranging from .584 for lecturers’ teaching preference to .950 for students’ sharing attitude. The results of correlation analysis show that seven out of nine factors have a significant correlation with mental exhaustion, whereas six out of nine factors have a significant correlation with emotional exhaustion. The significant factors are work-life conflict, lack of support, students lack engagement, lecturers’ lack of expertise, students’ lack of learning facilities, lecturers’ excessive teaching loads (nonsignificant for emotional exhaustion), and lecturers’ teaching preparation. The non-significant factors for both types of exhaustion are students’ sharing attitudes and lecturers’ teaching preferences.
Table 4: Correlation and Reliability Analysis
**Correlation is significant at the 0.01 level (1-tailed). *Correlation is significant at the 0.05 level (1-tailed). N = 211.
3.4. Multiple Regression Analysis
Table 5 shows the result of multiple regression analyses. A series of multiple regression analyses were performed to determine the factors contributing to mental and emotional exhaustion among academicians. The first regression model shows that the nine independent variables explain 66.8% of the variance in mental exhaustion, and the second regression model presents 65.2% of the explained variance in emotional exhaustion. Both models are significant, with an F value of 18.035 (p < 0.01) and 16.545 (p < 0.01), respectively. Durbin Watson’s values of 1.787 and 1.999 show that both models are free from the issue of autocorrelation.
Table 5: Results of Multiple Regression Analysis
**Significant at the 0.01 level (1-tailed). *Significant at the 0.05 level (1-tailed). N = 211.
The analysis results show that two factors significantly influence academicians’ mental and emotional exhaustion. They are work-life conflict and lack of support. Seven other factors are not significant in influencing mental and emotional exhaustion. They are students’ lack of engagement, lecturers’ lack of expertise, learning facilities, students’ sharing attitudes, lecturers’ excessive teaching loads, lecturers’ lack of teaching preparation, and lecturers’ teaching preferences.
This study aims to determine the causes of mental and emotional exhaustion among academicians during online distance learning. Mental and emotional exhaustion are critical as they affect the academician’s performance. Both mental and emotional exhaustion can leave someone feeling detached, unmotivated, apathetic, and trapped (Ghoreyshi & Tahririan, 2021). Their challenges might seem impossible to overcome, and they might feel too drained to keep trying. Exploring the factors influencing their mental and emotional exhaustion during online distance learning is essential to promote academicians’ well-being, sustainability, and performance. According to the findings, only two significant independent variables are work-life conflict and lack of support. Work-life conflict and lack of support among academicians can lead to mental and emotional exhaustion if they are not well-managed.
Work-life conflict exists due to the inability of academicians to balance their work and family lives (Rosa, 2022). It becomes more difficult when academicians have to work at home, including conducting online classes. They must wisely allocate time to attending to family matters and meeting work requirements. Some suggestions are provided to reduce work-life conflict. First, there must be a designated and well-equipped workspace at home where the academicians can concentrate on their work without interference. Second, there must be a common understanding between spouses to take a turn in looking after their family needs so that each has time to focus on their work responsibilities. Third, academicians should enhance their ability to apply emotional regulation techniques (Kim & Kim, 2021), including suppression of emotion, cognitive reappraisal, acceptance, and problem-solving. This can be done through reading and practising.
Academicians need mental, emotional, and physical support from the individuals around them to avoid mental and emotional exhaustion (Jaremka et al., 2020). First, they need adequate support from their immediate boss and colleagues, especially when performing the assigned tasks at home. The boss must be considerate in determining the number of tasks, the deadlines, and the assistance required (Astuty & Udin, 2020). The colleagues must assist in accomplishing the tasks when needed. Second, although the academicians work from home, the top management cannot assume they are available around the clock. Meetings must be properly organized to reduce the excessive burden on academicians. Third, the top management must regularly communicate with the academicians, especially during hard times. This is meant to ensure the academicians receive clear work instructions and adequate support. This practice will ease their mental and emotional strains.
Regarding the research implications of the present study, validation of the findings is required. The present study was conducted during the COVID-19 pandemic, and the situation now is different as we have surpassed the most difficult period of the pandemic. Second, the present study was based on the qualitative approach to determine the themes and items for the instrument development. Since there were a lot of issues pertaining to online distance learning that led to mental and emotional exhaustion, the items for the present study are quite rich. Future research should minimize the items so that they become more practical and manageable. Third, the present study was conducted in a public university where the nature of the work of the academicians is quite different from those in private universities. A replication of the study is required but in a different context.
Online distance learning during the spread of the COVID-19 pandemic has created undesirable situations for academicians that might lead to mental and emotional exhaustion. Therefore, the present study was undertaken to identify the factors that lead to this unhealthy consequence among academicians and develop suitable items to measure these factors. Using 211 samples collected via the online survey application, the present study found that work-life conflict and lack of management support are the prevalent factors that lead to academicians’ mental and emotional exhaustion. Several suggestions are provided to reduce the possibility of mental and emotional exhaustion, including creating and managing a home workstation, having a common understanding between spouses, and equipping them with emotional regulation expertise. Furthermore, the management should be more supportive in attending to the academicians’ well-being during the pandemic. A reliable communication system must be established to ease the mental and emotional burden of the academicians.
This research is funded through the Professor Research Grant Scheme (600-RMC/GPP 5/3 (007/2021)) awarded by Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.
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