A Study of Perception on Students’ Motivation, Burnout and Reasons for Dropout

A Study of Perception on Students’ Motivation, Burnout and Reasons


Background of Study
Motivational beliefs and attitudes play a critical role in student's academic success. "Motivation refers to the reasons individuals engage in a particular behaviour or pursue a particular goal" (Ryan & Deci, 2000). Learners are driven to do well in their studies by two types of motivation: intrinsic and extrinsic. According to Ryan and Deci (2000), intrinsically motivated behaviours, performed out of interest and satisfying the innate psychological needs for competence and autonomy, are the prototype of self-determined behaviour. While extrinsically motivated behaviours are executed because they are instrumental to some separable consequence, they can vary in the extent to which they represent selfdetermination. Despite these differences, motivation affects the learning behaviours of students. Hence, good academic results can be attained when academic motivation generates enthusiasm for and interest in learning. This shows that motivation plays an important role in student academic success.
The issue of student dropouts in universities has shown a worrying trend throughout the globe. For instance, recently, COVID-19 has had greater impacts on students' burnout and dropout rate in most educational institutions. The COVID-19 pandemic has created additional challenges and disruptions to higher education, which could impact dropout rates. For instance, a few studies have examined the impacts of the COVID-19 pandemic on the motivation of university students and its relation to dropout rates (Escudero-Torres et al., 2021;Huang et al., 2021;Lavecchia et al., 2021). In Malaysia, the Minister of Higher Education reported that seventeen thousand six hundred thirteen (17,613) public and private university students dropped out last year (Free Malaysia Today, 2022). Fifteen thousand one hundred sixty-five students (15,165) withdraw from their studies without specific reasons (Free Malaysia Today, 2022). This worrying trend has highlighted why students drop out of university. Indeed, in the past decade, researchers have also begun to use student school motivation to understand better and explain why students drop out of school. Thus, from this perspective, students' decisions to drop out are not just an achievement issue but also a function of their motivation.
On the other hand,  found that burnout is a state of physical, emotional, and mental exhaustion caused by long-term exposure to stressful situations, such as the demands of work and school. This shows that long exposure to the demands of an academic environment will cause burnout among our students. Dropout is when a student discontinues his enrolment in a program of study before its completion, regardless of the reason (Tinto, 1975).
Even though students' motivational beliefs and attitudes seem to play a big role in how well they do in their studies, not much research has been done in Malaysia to investigate how these factors relate to burnout and the decision to stop studying. Hence, the overall goal of this study was to find out how learners' motivation, which has three parts: motivational, expectancy, and affective, relates to burnout and reasons for dropping out, as well as to determine how motivated students are and how they feel about burnout and reasons for dropping out. A learner's success or failure in school depends on how much they want to do well. Learners' motivation to study has been investigated and classified into affective and cognitive domains. The affective domain is concerned with attitudes, values, and interests. Whether a learner has a positive or negative attitude affects how motivated they are to study (Guido, 2013) and has a big effect on how well they do in school (Bakar et al., 2010;Lokman et al., 2022). Zainuddin et al (2021) found that students can be motivated in the values component by internal and external factors such as task value, belief, self-efficacy, autonomy, and relatedness. In another study, the Aldefer theory, which has three parts: existence, relatedness, and growth, was used to determine how motivated learners were. Students were motivated to learn by having clear goals, being confident in their execution of assignments, and achieving good results (Harith et al., 2022). Despite the confidence and clear goals, they do not influence students' fear of sitting for an examination or failing it (Harith et al., 2022). Therefore, to motivate learners, they need to be trained in building confidence and good management skills and equipped with ample resources (Harith et al., 2022).

Causes of Learners' Burnout
Burnout is a state of constant stress in learners brought on by repetitive work or tasks. Burnout can also occur in students who experience excessive academic pressure. It refers to students' stress or mental load when they try to do well in school. This pressure can come from various factors, such as demands for high grades, competition from classmates, heavy workloads, and expectations from parents or teachers .
Pressure in learning can cause students to feel anxious, stressed, and sometimes even mentally exhausted or burned out. This can affect academic performance and the general well-being of students (Norez & Daphne, 2017). As an educator or teacher, it is important to pay attention to the pressures experienced by students and help them manage academic stress and burdens healthily and effectively. This can be done by giving emotional support, giving helpful tools for dealing with stress, and creating a learning environment that is positive and helpful.

Causes of Learners' Dropout
Dropout is one of the biggest challenges that online educators and administrators face, as student dropout rates in online higher education courses are significantly higher than in conventional courses (Lee & Choi, 2011). However, as we face the online turn, the growing trend in higher education towards transitioning to online teaching, which has recently been exacerbated by the impact of COVID-19, forcing institutions to adopt online delivery overnight, which has caused the problem of dropout, has become extremely important (Naylor & Nyanjom, 2020). Andreas et al (2020) found three main reasons students drop out of higher education without a degree. These are because of the national education system that involves the country's financing policy, the higher education institutions, including the type of institution or teaching quality, and the students themselves, where it can be seen through several aspects such as pre-study determinants, such as secondary school type, and study-related aspects, such as working while studying. The current critical cause of dropouts is a lack of job opportunities, which leads students with financial constraints to withdraw from school and work instead. They work not only for their tuition fees but also to support their families.

Past Studies on Learners' Motivation to Study
Many studies have determined what makes people want to learn, and they have found intrinsic and extrinsic factors. The intrinsic factors studied are interest, relatedness, autonomy, and enjoyment. Intrinsic factors (Deci & Ryan, 2008) motivate learners to do things based on their goals, values, and interests. Where extrinsic factors are awards, scholarships, academic grades, certificates, and future careers. Extrinsic factors focus on finishing tasks and getting results, which Lee et al (2010) say is not likely to keep people motivated in the long run. Both types of motivation make it easier for learners to stay interested in what they are doing. Salgado and Olievira's research from 2021 found that some of the internal and external factors that affect learners' motivation are self-efficacy, stress, workload, family expectations, and employability. These factors, in turn, affect students' academic performance and achievement.
There have been many past studies on learners' motivation and burnout. The study by Madigan and Curran (2021) investigated burnout and academic achievement issues. The research is investigating the extent to which it affects students' academic achievement. It did a meta-analysis of search data from 1981 to 2020. It looked at PsycINFO, PsychArticles, MEDLINE, SPORTDiscus, Education Abstracts, and Education Administration Abstracts. The research focused on 55 articles that fit its criteria. It focused on students from secondary and tertiary institutions. The study found that burnout has three main signs: exhaustion, a cynical attitude, and reduced efficacy, whose effects range from low to high. Madigan et al. (2021) explained that when students are tired, they cannot put in the effort or show interest in revising or finishing their work, and the lack of learning resources also contributes to exhaustion. A cynical view of studying also made the students feel less connected to the classroom, their teachers, and their work. This means that students miss out on important information, do not notice opportunities to get help, and avoid spending time studying. The study found that reduced self-efficacy is the most important sign, a crucial reason students try to avoid situations. The study suggests that the effects of burnout should be made clear to students and teachers and that individual students should be the focus of any intervention and prevention plans.
A study at a Romanian university by Cazan (2015) examined how learning motivation, engagement, and burnout are related. The study adopted the Maslach Burnout Inventory Student Survey, the Utretch Work Engagement Scale, and the Motivated Strategies for Learning Questionnaire, distributed to 202 undergraduate students. Learning motivation is positively related to engagement and negatively related to burnout. The result varies between achieving and non-achieving students, where achieving students are more motivated, engaged in learning activities, and have positive attitudes toward their studies. The opposite effect was found in non-achieving students. Cazan (2015) investigated the difference in study years to identify burnout levels. First-year students with high academic achievement and second-year students with low academic achievement have a higher level of burnout. This is because first-year students struggle to keep up with the academic level amidst their adjustment difficulties with university life. In contrast, second-year students are trying to do better, making them feel more stressed because they have much more work. Higher achievers have higher intrinsic motivation, task value, control of learning beliefs, and academic self-efficacy, while lower achievers have lower test anxiety. This is the opposite of what happens with lower achievers. The study implies that intervention plans must specify the students at risk due to their higher level of burnout.

Past Studies on Causes of Learners' Burnout
The study "The external and internal factors of academic burnout," by Lin and Yang (2021), aimed to investigate the external and internal factors contributing to academic burnout among Chinese college students. The study used a self-administered questionnaire to collect data from 389 Chinese college students. The study found that external and internal factors contributed significantly to academic burnout. Examination stress, a heavy workload, and a lack of social support from family and friends are some factors outside of the classroom that contribute to academic burnout. Academic burnout was also strongly linked to being a perfectionist, having anxiety, or having low self-esteem. The study showed that outside and inside factors must be addressed to keep college students from getting tired of school. The study suggested several ways to help students avoid academic burnout, such as improving the learning environment, giving them social support, promoting positive ways to deal with stress, and helping them develop a growth mindset. Overall, the study shows how important it is to deal with outside and inside factors to keep college students from getting tired of school (Lin & Yang, 2021). Many studies have been done to investigate the causes of learners' burnout. In the first case study, the Maslach Burnout Inventory-Student Survey (MBI-SS) and academic achievement records from 424 university students are used to examine the link between academic burnout and achievement among medical students at the University of Kerbala. The study found that academic burnout was negatively correlated with academic achievement. The most common signs of burnout were emotional exhaustion, cynicism, and doubts about how well they did in school (Yahya et al., 2017). The study suggested that academic institutions must pay attention to students' mental health and well-being to improve their academic achievement. According to Thuruthel and Tungol's "Causes and Effects of Burnout on Students' Well-Being: A Review" from 2021, long-term stressors like schoolwork, peer pressure, and financial issues are just a few examples of what can cause burnout. Using a scoping review method, the study chose 23 relevant studies to investigate what causes burnout and how it affects the health and well-being of baccalaureate college students. health, academic performance, and overall well-being. Academic pressure, a high workload, and difficult coursework are some of the most common causes of burnout among students. Unrealistic expectations, such as setting too high standards for oneself, can also contribute to burnout. Students are more likely to burn out if they lack social support, such as feeling alone or unsupported. Financial stress, such as worrying about tuition fees or living expenses, can contribute to burnout. Poor time management skills can lead to students feeling overwhelmed and stressed. The study also talked about the effects of burnout on students' health, such as headaches, tiredness, and trouble sleeping. Burnout can also lead to mental health issues such as anxiety and depression. The study "Student burnout in higher education: A demand-resource model approach" by Jagodics and Szabó (2022) found that high academic demands and low academic resources were significant predictors of burnout among university students. Using a self-administered questionnaire, the study was done with 742 Hungarian college students from different fields. The study examined how academic demands, academic resources, ways to deal with stress, social support, and burnout are related. The study's results showed that academic demands like workload, time pressure, and academic pressure were the most important predictors of burnout among students. The study also found that a lack of academic resources, such as access to learning materials and technology, was a major predictor of burnout. Ultimately, the study shows how important it is to deal with academic demands and academic resources to keep college students from getting burned out. It also talks about how important it is for universities to give students social support and teach them good ways to deal with the stress and demands of college.

Past Studies on Causes of Dropout
University dropout has negative societal consequences, such as fewer qualified employees in the workforce (Heublein and Wolter, 2011), as well as personal consequences, such as reduced personal income, lower perceived happiness, higher reported depression, and increased stress (Faas et al., 2018). The issue of university dropouts impacts society because, in the labor force, it contributes to low-qualified employees and will affect personal life since they will get a lower income, have lower perceived happiness, lead to depression, and can increase stress (Faas et al., 2018). Several factors lead to increased number of students dropping out, such as personal factors, skills and abilities, learning experience issues, course factors, financial constraints, and employment attraction. Individuals' readiness to continue their studies is critical to completing their courses. This is linked to the concept of perceived academic control (PAC). Perceived academic control refers to beliefs about one's ability to influence academic achievement outcomes (Perry, 1991). PAC is a domain-specific component of perceived control that significantly impacts motivation and academic achievement. The controlling method was positively associated with motivation to learn, effective study strategies, university adjustment, and mastery orientation (Cassidy and Eachus, 2000;Hall et al., 2006;Perry et al., 2001). In other words, if the students' beliefs and goals for study were justified, they were motivated to focus and strategize their plan to forge ahead. Other causes of dropout include poor academic skills, students' abilities, and prior experience with online courses, which have been linked to individual dropout in the literature. This was most common during the Movement Control Order (MCO). According to Khalil and Ebner (2014), the most significant indicators of the high attrition rate are personal factors such as learners' limited experience and insufficient online skills. In a study conducted by Yamba-Yugsi and Lujan-Mora (2017), factors such as students' previous experience in MOOC courses and their level of satisfaction with the platform's interaction were revealed as playing a key role in the dropout problem from online courses (Bonk et al., 2018;Ghazal et al., 2018). The primary reasons for course dropout were student-centred learning factors. One of the most important factors mentioned was time management skills. Most professors believed many students who dropped out of their classes were time-poor or had poor time management skills. Additionally, a student's secondary school academic background impacts their persistence. Those from the same stream have some knowledge in the field of study and can assist them in their performance. Apart from that, it's also very important to ensure that the students are offered the program they apply for, not the one their parents offer. Some students request dismissal because they are in a program that does not suit them; some continue their studies upon request by their parents. When there is no passion for studying, it's very difficult for them to carry on and survive, which leads to dropouts. Other internal factors that can be used to predict dropouts are the students' preconceptions of course difficulty. Adamopoulos (2013) discovered that course materials and assignments could significantly increase students' completion rates. In contrast, factors such as the difficulty of learning content and the course duration were found to harm students' completion of online courses (Al-Samarraie, 2019). Furthermore, Feng et al (2019); Itani et al (2018) explored the main reasons for student dropouts in the course because certain course factors, such as course design, time, and course difficulty, are among the critical factors contributing to the high student dropout rate Onah et al (2014); Lee and Choi (2011) reviewed online course dropout and argued that course design and institutional support play a significant role in students' decision to drop out. Furthermore, Greene et al (2015) discuss course design, difficulty, commitment, and time. Aside from the course, the continuous assessment (CA) workload was mentioned as a factor in dropout. The issue is the course workload, which includes excessive tasks listed for CA and demands much work to complete. Consider this: if students enroll in 7 to 8 courses per semester, they will have many assessments to complete, burdening them. Learners who do not engage with their CA believe that learning resources are abundant (Xavier and Meneses, 2021). Furthermore, there is a problem with curriculum contents that are difficult and do not correspond to the needs and abilities of students and society. Many research studies have concluded that the medium of instruction may be a leading factor in dropout. For example, in UiTM, the medium of delivery is the English language, and some of the students found it difficult to adapt to the new culture since, at school, they communicate in Bahasa Malaysia. So, some students were shocked and felt it was difficult to learn and study since it took a long time for them to understand what they had learned. Another important aspect of academic success is the student's financial situation. Some students must work to help support their families. And among the top reasons was the attractiveness of the labour market, due to which the students are willing to leave the higher education institution without qualifications to ensure and improve their financial situation. There is a suspicion that intensive student employment contributes to poorer academic achievements, postpones the fulfilment of requirements, and eventually even leads to dropout. As a result, student employment can be interpreted as a risk factor that increases the risk of dropping out by keeping students away from university culture and embedding them in the community (Perna, 2010). When working, they must commit to their job and cannot attend classes because they missed a lot on the syllabus. Since they have no certificate or experience, they won't get a high salary, and sometimes the pay is reserved for family expenses, so they cannot afford to pay their study fees. This may cause dropouts. According to Euro Student VI survey data, 39% of students in Hungary regularly work during the semester, while 14% work periodically during the study period (Masevičiūtė et al., 2018). Earlier research has confirmed that the attractiveness of the labor market causes students who do not receive any financial support to be more likely to work during their university studies and with a higher intensity (Roshchin, and Rudakov, 2015). The results show that the primary motivation for Hungarian students to work is to cover their living expenses. Most of them said that without paid work, they could not afford to be university students, and the same amount of them work for experience. Previous research has also confirmed that the tendency to drop out is higher among students with long-term, intensive employment (Masevičiūtė et al., 2018;Bocsi et al., 2018). In the process of dropping out, it is worth mentioning the combination of factors that may increase the negative impact of employment, such as the family and financial background of the students, their motivation to work, and their relationship to their studies. Previous research by Bocsi et al (2018) showed that students with low, unfavourable status indicators who work because of their financial difficulties and students who find their studies non-marketable are more vulnerable to dropping out. Working reduces the time spent studying and the student's commitment to study. As the attractiveness of the labour market was even more pronounced among dropout students, they believed that their work provided greater financial security and was a 'guarantee' of their future employment, so they chose to withdraw from their studies instead of completing them (Bocsi et al., 2018). Figure 1 shows the conceptual framework of the study. This study investigates the relationship between learner motivation, burnout, and dropout for students learning online and in a hybrid setting. According to Rahmat et al (2022), working remotely, be it working or studying, can be both a blessing and a drawback. On the positive side, learners who study online feel autonomous and competent because they feel they are independently making sense of the learning activities. On the other hand, while the learners initially felt independent, they may eventually feel a lack of engagement and thus feel alone when making learning-related decisions. This study is rooted in Pintrich & De Groot's (1990) concept of learners' motivation. Learners' motivation can be divided into three parts: (a) motivational components; (b) expectancy components; and (c) affective components. Studies have found that when there is a lack of motivation, learners may experience burnout (Campos et al., 2011). There are two types of burnouts: exhaustion and disengagement. In adverse situations, learners may opt to drop out of the course. According to Shula et al (2022), students dropped out due to (i) economic hardship, (ii) e-learning difficulties, or (iii) other factors.  Table 1 below. The survey has four sections. Section A has items on the demographic profile. Section B has 12 items on the learner's motivation, which are divided into the motivational component, which consists of intrinsic goal orientation (4 items), extrinsic goal orientation (3 items), and task value belief (5 items), followed by the expectation component, which consists of the student's perception of self-efficacy (5 items), and control belief for learning (2 items). Section C has eight items on exhaustion and eight on disengagement, which falls under the burnout category. In comparison, Section D has three items on the economic crunch, five on e-learning difficulties, and four on other issues, which are the subcomponents of factors affecting dropout, as shown in Table 1. The collected data was analysed using SPSS version 26. The mean score was utilized to answer the first three research questions, which focused on understanding how learners' motivation influences their learning, their perception of burnout, and the reasons for dropout. Additionally, Pearson correlation analysis examined the relationship between learners' motivation, burnout, and dropout.  Table 2 shows the reliability of the survey. The analysis shows a Cronbach alpha of the variables that are associated with learners' motivation, such as the motivational component (0.859), the expectancy component (0.892), and the affective component (0.835), which are above 0.7. The Cronbach alpha for economic crunch and e-learning difficulties also is above 0.7, thus revealing good reliability of the instrument chosen or used. However, two constructs have a Cronbach Alpha below 0.7, as suggested by Nunnally: exhaustion (0.625) and disengagement (0.684). According to Nunnally and Bernstein (1994), a Cronbach's alpha of 0.6 may be acceptable for pilot studies or in situations where only a few items are on the scale. DeVellis (2012) notes that a Cronbach's alpha below 0.7 may be acceptable if the measured construct is multidimensional or if only a few items are in the scale. Further analysis using SPSS is done to present findings that answer the research questions for this study.   With reference to figures 1-4, Females constituted 73.3%, while males constituted 26.7%. The age group of 20-29 made up most of the respondents. 90% of respondents were from the Social Sciences discipline, and 69.3% were bachelor's degree holders, followed by diploma (24%), and postgraduate (6.5%).

Findings for Learners' Motivation
This section presents data to answer the research question 1-How does learners' motivation influence their learning?
Three variables represent learners' motivation: the motivational component, the expectancy component, and the affective component. The motivational component is divided into three subcomponents: intrinsic goal motivation, extrinsic goal motivation, and task value belief, while the expectancy component is divided into students' perceptions of self-efficacy and control belief.

Figure 2-Mean for Intrinsic Goal orientation
Intrinsic goal orientation is a psychological term for how much people are driven by internal goals or desires, like personal growth and overseeing their lives. People with a high intrinsic goal orientation care more about learning and improving than competition or rewards. The respondents have a moderate mean of 3.88, with the statement that the most satisfying thing for them is trying to understand the content of the course. followed by their preference for course material that arouses their curiosity, even if it is difficult (mean = 3.41). Other statements referring to this construct also show a moderate mean.

Figure 3-Mean for Extrinsic Goal orientation
Extrinsic goal orientation is a psychological concept that refers to the degree to which a person is motivated by external factors, such as rewards, recognition, or competition, rather than internal goals or desires. Individuals with a high level of extrinsic goal orientation are often more focused on the outcome or result of a task than the learning or personal development process. Compared to intrinsic goal motivation, the respondent seems motivated by extrinsic motivation. This is shown in a high level of extrinsic motivation. mean such as getting a good grade in the classes is the most satisfying thing (mean=4.41), improving overall grade point average is their main concern to get good grades in the program (4.46) and to do well in the class because it is important to show their ability to family and friends (mean=4.28) (iii) Task Value Beliefs (5 items)

Figure 4: Mean for Task Value Beliefs
Task value belief is when learners see the worth of something, which leads them to put the worth of the activity into action. Learners believe understanding the courses is highly valued, with a mean score of 4.17. They believe that it is important that they learn the course materials because they are useful to them, with a mean score of 4.00. Learners moderately believe that they like the subject matter in the course and can transfer what they have learned in one course into another, with a mean score of 3.74 and 3.53, respectively.

Figure 5-Mean for Students' Perception of Self-Efficacy
Learners' self-efficacy showed a lower overall mean score, where the highest is 3.64, and students believe they will receive higher grades. In contrast, students are confident in producing excellent assignments, with a mean score of 3.54, and believe they can do well despite the course difficulty, with a mean score of 3.52. A lower mean score of 3.43 shows that students believe they can master skills taught in class. The low mean score of 3.29 shows that students believe they understand the most complex materials presented in class.

Figure 6-Mean for Control Beliefs for Learning
Control belief is the extent to which individuals believe that they can control events in their lives. In the context of students, "control belief" refers to the extent to which they believe they have control over their academic outcomes, such as their grades, test scores, and overall academic performance. Therefore, the highest mean data shows students will understand the course materials if they put more effort (4.09), followed by the appropriate ways to learn the course materials (4.03).

Figure 7-Mean for Affective Component
The affective component of learning refers to the emotional and motivational aspects of the learning process. It encompasses the attitudes, values, beliefs, and feelings students bring to their learning experiences. The affective component is essential to learning, as it can greatly influence students' engagement, motivation, and success. The mean value data showed that most students believe they can take the test (3.03) and answer test questions confidently (2.86). Low mean data value also shows that students do not think about the consequences of failing (2.70), upset (2.66), and heart beating (2.59) when sitting for the examination.

Findings for Burnout
This section presents data to answer the research question, 2-How do learners perceive burnout?

Figure 8-Mean for Exhaustion
Learners' perceived burnout showed an average overall mean score, where the highest is 3.94, and students need more time to relax and feel better after class finishes. whereas sometimes, students feel tired even before the class starts, with a mean score of 3.87. A lower mean score of 3.38 shows that students always feel worn out and weary after class. However, the low mean score of 3.35 shows that although they feel burnout, the students can still manage their work very well.

Figure 9-Mean for Disengagement
The highest is 3.64, where students accept their study process as a positive challenge. Despite this, the response shows that the students always explore new and interesting aspects in their study to make them perform, with a mean score of 3.53. A lower mean score of 3.02 shows that the students sometimes think less during classes and attend classes almost mechanically to fulfill their attendance. The lowest mean score of 2.98 shows that even though they felt burnout, the students didn't talk negatively about their difficulty in studying.

Findings for Dropout
This section presents data to answer research question 3-How do learners perceive the reasons for dropout?
Learners' perceptions of the reasons for dropout can vary and often include personal, academic, and environmental factors. Personal factors can be health, financial, or motivationrelated; academic factors may include difficulty with coursework; and environmental factors may involve a lack of social support or conflicts. It's important to note that learners' perceptions may not align with objective reasons but understanding them can help develop strategies for improving retention and supporting learners' success.
Factors Affecting Driving Dropout (i) Economic Crunch Figure 10-Mean for Economic Crunch Figure 9 shows the inability to pay fees as mean = 3.09; students who dropped out and could not afford the cost of living as mean = 3.28, and students who dropped out could not afford to buy computers/devices for their online learning as mean = 3.21. This is logical since UiTM is the most affordable university in Malaysia and has many information technologies and educational device support services for its students.
(ii) E-Learning Difficulties

Figure11-Mean for E-Learning Difficulties
Online learning difficulties have a relatively low mean score as a reason for dropout. The most significant concern regarding e-learning difficulties is that it can make students feel isolated from their teachers (mean = 3.00), followed by difficulties contacting the administrator (mean=2.98). This shows that online learning can be a reason for dropout, but it is not necessarily the only or primary reason. Learners may drop out of online courses or programs for various reasons. However, online learning difficulties, such as social isolation, can contribute to learners' decisions to drop out.
(iii) Other Issues Some issues appear to be more focused on the perception of dropping out of a program, particularly regarding student health, including mental health. Students who dropped out due to mental health concerns had the highest mean score in the variable (3.73), followed by those who left due to physical health issues (3.47). Additionally, students who dropped out often cited family issues (3.33) and peer problems (3.19) as contributing factors. Mental health and general issues can be reasons for dropping out of educational programs. Learners who experience mental health challenges such as anxiety, depression, or stress may find it difficult to cope with their coursework demands or manage their daily responsibilities. Mental health issues can also impact learners' motivation, concentration, and ability to participate in their program.

Findings for Relationship Between Learners' Motivation with Burnout and Dropout
This section presents data to answer research question 4. Is there a relationship between learners' motivation and burnout and dropout? This section aims to understand if a learner's motivation predicts dropout or burnout. To assess this objective, the data is analysed using correlation analysis. Correlation is a measure of the relationship between two variables that describes the strength and direction of the linear relationship between those two variables. The analysis was done through SPSS to identify relationships between learners' motivation, burnout, and reasons for dropout. The procedure used in the analysis is the Pearson productmoment correlation coefficient (r). Person r is a designed interval or ratio, and the continuous variable in the case of this study is the Likert scale of the construct investigated.
The objective of this study is to see if there is a correlation between the learner's motivation constancy as depicted by the motivational component (depicted by intrinsic goal orientation, extrinsic goal orientation, and task value belief), expectancy component, and affective component with burnout (exhaustion and disengagement), and with factors affecting dropout (depicted by economic crunch, e-learning difficulties, and other issues), therefore hypotheses are created for it. The hypotheses are as follows.
Hypothesis 1a: There is a significant relationship between the motivational component and exhaustion. Hypothesis 1b: There is a significant relationship between the motivational component and disengagement. Hypothesis 1c: There is a significant relationship between the motivational components and the economic crunch. Hypothesis 1d. There is a significant relationship between the motivational components and e-learning difficulties. Hypothesis 1e: There is a significant relationship between the motivational components and other issues. Hypothesis 2a: There is a significant relationship between the expectancy component and exhaustion. Hypothesis 2b: There is a significant relationship between the expectancy component and disengagement. Hypothesis 2c: There is a significant relationship between the expectancy components and the economic crunch. Hypothesis 2d. There is a significant relationship between the expectancy components and elearning difficulties. Hypothesis 2e. There is a significant relationship between the expectancy components and other issues. Hypothesis 3a. There is a significant relationship between the affective component and exhaustion.
Hypothesis 3b. There is a significant relationship between the affective component and disengagement. Hypothesis 3c. There is a significant relationship between the affective components and Economic Crunch. Hypothesis 3d. There is a significant relationship between the affective components and elearning difficulties. Hypothesis 3e. There is a significant relationship between the affective components and other issues.
The composite mean (table 3) scores of learners' motivation, dropout, and burnout were analysed using SPSS to determine if there is a significant association. The correlations were examined for the mean scores. Before executing the Pearson correlation (r), a normality test using the PP Plot was conducted to verify that the relationships between the dependent and independent variables were linear, individually, and globally and that the residual variable was approximately normally distributed. The data met all assumptions for a Pearson analysis, including continuous variables, no significant outliers, and no strong correlation among dependent variables (multicollinearity). Results were presented in three tables: Table 3 for a composite mean score, skewness, and kurtosis, and Table 4 for Pearson correlation. The data's skewness and kurtosis fell within an acceptable range of −1 to +1 and −2 to +2, respectively, which is considered excellent (Hair et al., 2022, p. 66). Table 3 Composite Mean Score **. Correlation is significant at the 0.01 level (2-tailed).
Jackson (2015) stated that a coefficient is considered significant at the.05 level, and a positive correlation is measured on a scale from 0.1 to 1.0. A weak positive correlation falls within the range of 0.1 to 0.3, a moderate positive correlation ranges from 0.3 to 0.5, and a strong positive correlation range from 0.5 to 1.0. In line with this, the data analysis indicates a positive relationship between the motivational component and exhaustion, disengagement, e-learning difficulties, and other issues, as evidenced by the correlation coefficients. The strength of the relationship falls within the moderate-to-moderately strong range, with r ranging from 0.273 to 0.345. The p-values of 0.000 indicate that these relationships are statistically significant at the conventional level of significance (α = 0.05), except for the relationship between the motivational component and the economic crunch, which was insignificant (r = 0.084, P = 0.000). These findings support Hypotheses 1a, 1b, 1d, and 1e, which predicted a significant positive relationship between the motivational component and burnout and reasons for dropout. The results suggest that more motivated individuals may also experience higher levels of exhaustion, disengagement, e-learning difficulties, and other issues, which can contribute to burnout and dropout, as shown in Table 4.
The data analysis also revealed no significant relationship between the expectancy and affective components and the economic crunch or other issues. The correlation coefficients for the expectancy component and economic crunch (r =.021, p = 0.749) and with other issues (r = 0.129, p = 0.051) indicate a very weak positive relationship, which is not statistically significant at the conventional level of significance (α = 0.05). Similarly, for the affective component, the correlation coefficients with the economic crunch (r =.021, p = 0.749) and other issues (r = 0.007, p = 0.917) indicate a very weak positive relationship, which is also not statistically significant. These findings suggest that the data did not support Hypotheses 1c, 2c, 3c, 1e, 2e, and 3e, which predicted a significant relationship between the expectancy and affective component and the economic crunch and other issues.
On the other hand, the data analysis showed that both the expectancy and affective components are significantly related to exhaustion, disengagement, and e-learning difficulties. Still, the strength of the association is weak. This means that although there is evidence of a positive relationship between these variables, the correlation coefficients are relatively low. These findings also support Hypotheses 2a, 2b, 2d, 3a, 3b, and 3d, which predicted a significant relationship between the expectancy and affective components with exhaustion, disengagement, and e-learning difficulties.

Summary of Findings and Discussions
According to the study's findings, students in the Faculty of Administrative Science and Policy Studies learning behaviour is influenced by their motivation to learn. The mean scores for all the affective, expectancy, and motivational components are moderate to high. The findings are consistent with those of other studies (Lokman et al., 2021;Zainuddin et al., 2021), which found that despite their difficulties in their studies, students can be motivated by internal and external factors. Additionally, it was discovered that students were motivated to learn because they were very confident in their abilities to complete the tasks they were given, even when they were taking an exam. Harith et al (2022) study, which demonstrates that students have higher task value beliefs, self-efficacy, and control beliefs towards their learning behaviour, was consistent with these findings. Therefore, it is advised that the faculty give moral support and study assistance to the students to support their motivation so that they can manage their academic pressures and cope well with the academic expectations at the university.
According to research on how students perceive burnout, students at the Faculty of Administrative Science and Policy Studies had moderate to severe levels of academic burnout, characterized primarily by exhaustion and disengagement. The findings are consistent with Lim and Yang (2021); Jagodics and Szabó (2022), who found that external and internal factors like academic workload and demands, time constraints, academic resources, academic pressures, and social support contributed significantly to academic burnout. However, the study shows that the Faculty of Administrative Science and Policy Studies' students perceived their burnout positively and completed and coped with their tasks. Thus, to reduce the students' burnout, this suggests coaching is necessary to support the students with effective study skills and time management training to help them manage their burnout effectively.
In determining how learners perceive the reasons for dropout. The study's findings show a low to moderate range of mean scores as reasons for dropout. The most significant concern was e-learning difficulties, mainly because online learning isolates students from teachers. As a result, this contributed to dropout, and the most significant factors found in the study were the economic crunch, e-learning difficulties, and other issues, the physical and mental health problems. These factors were consistent with Faas et al (2018); Yamba-Yugsi and Lujan-Mora (2017) studies. Hence, it is recommended that the faculty tackle the students' challenges by increasing the teaching and learning support and facilities, financial support, health and counselling support, and close academic advisor monitoring to prevent the students from falling into high burnout.
Finally, the study indicates a positive relationship between all motivational components and various outcomes related to burnout and dropout. Interestingly, in this study, the Faculty of Administrative Science and Policy Studies' students found that the expectancy and affective components are associated with exhaustion, disengagement, and e-learning difficulties but not with the economic crunch and other issues. This highlights the strong learning motivation among the students, which caused burnout and dropout due to their difficulties with online learning. The results are consistent with previous studies that show, among others, personal factors affecting student abilities, course workload (Xavier and Meneses, 2021), and online learning difficulties (for instance, difficult experiences in online learning (Bonk et al., 2018;Onah et al., 2014 andAl-Samarraie, 2019) have resulted in a high attrition rate among the university students. Hence, this suggests that if the students' motivational beliefs and goals are justified, they need to be supported throughout their learning process to be equipped with effective study skills and abilities in ICT skills to enable them to focus on attaining academic success.

Implications of Study and Suggestions for Future Research
In summary, the analysis results indicate a positive relationship between the motivational component and various outcomes related to burnout and dropout. This suggests that interventions addressing motivational factors could help alleviate these issues. The study also found that the expectancy and affective components are associated with exhaustion, disengagement, and e-learning difficulties but not with economic crunch and other issues. However, it is important to acknowledge that other factors may influence the economic crunch and other issues not measured in this study. Therefore, further research is necessary to fully understand the relationships between these variables and other outcomes related to burnout and dropout. Overall, the findings of this study highlight the need to address burnout and dropout in the context of e-learning and to consider the impact of motivational, expectancy, and affective factors in developing interventions to mitigate these issues.