Exploring the Influnece of Sources of Burnout in Learning E-Language Courses

Exploring the Influnece of Sources of Burnout in Learning E-Language Courses

*¹Muhammad Haziq Abd Rashid., ²Amir Lukman Abd Rahman., ³Hairul Azhar Mohamad., ⁴Urai Salam., ⁵Wan Muhammad Fitri Wan Amir Nizam., ⁶Sharifah Syazwa Amierah Syed Khalid., ⁷Noor Hanim Rahmat

^1,2,3,6Akademi Pengajian Bahasa, Universiti Teknologi MARA, Shah Alam, Malaysia

⁴Department of English Education, Tangjungpura University, Pontianak, Indonesia

⁵Akademi Pengajian Bahasa, Universiti Teknologi MARA, Machang, Malaysia

⁶Student Administration, Taylor’s University, Subang Jaya, Malaysia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.90300215

Received: 28 February 2025; Accepted: 11 March 2025; Published: 08 April 2025

ABSTRACT

This study explores the dynamics of motivation, exhaustion, and disengagement among students engaged in e-language courses among language and communication students in a public university in Malaysia. The objectives were to investigate how learners perceive these factors and examine their interplay within this context. Employing a quantitative approach, the research utilised a survey research design with a questionnaire as the primary instrument to collect data from a sample of 138 undergraduate students enrolled in e-language courses. The findings revealed that while students exhibit strong extrinsic motivations driven by grades and recognition, they also experience moderate levels of exhaustion due to academic pressures. Additionally, there was evidence of cognitive disengagement during classes despite overall engagement with course materials. The study’s implications highlight the importance of creating supportive learning environments that address both academic demands and emotional well-being.

Keywords: E-language, Motivation, Burnout, Exhaustion, Disengagement; Intrinsic Motivation, Extrinsic Motivation

INTRODUCTION

Background of Study

The increasing prevalence of digital learning environments has transformed the landscape of education, particularly in language acquisition. As a result, the exploration of factors influencing student experiences in e-language courses has become paramount. This research paper aims to investigate the intricate relationship between burnout and motivation among undergraduate students pursuing language and communication studies, with a specific focus on English language courseware development.

In a mainstream academic experience, the students were found struggling with the demands of e-learning, often feeling overwhelmed and disengaged. This led to the initiation of this study, investigating how these feelings of burnout might affect their motivation to learn e-language courses; specializing in the development of courseware and applications. It is inevitable that students nowadays need to be multidisciplinary and highly adaptive, ensuring that they possess the necessary essential skills in today’s interconnected world. The dual pressures of academic performance and technological adaptation can create a challenging environment for learners, prompting this research to delve deeper into this critical issue.

Burnout is characterised by emotional exhaustion, cynicism, and a sense of reduced accomplishment (Parker et al., 2022). In the context of language learning, particularly in e-learning formats, students may experience unique stressors that contribute to burnout. Factors such as inadequate support systems, high expectations for self-directed learning, and the isolating nature of online education can exacerbate feelings of fatigue and demotivation (Amali et al., 2024). Conversely, motivation—defined as the internal drive that propels individuals to engage in learning activities—plays a crucial role in mitigating these negative effects (Ryan & Deci, 2000). Understanding how various sources of burnout influence motivation can provide valuable insights for educators and policymakers aiming to enhance student learning experiences.

Statement of Problem

In an ideal educational environment, students engaged in e-language courses would experience high levels of motivation and minimal burnout, resulting in effective learning outcomes and a positive academic experience. Such an environment would be characterised by supportive instructional practices, accessible resources, and a curriculum that aligns with students’ needs and interests. However, the reality for many undergraduate students in language and communication programs is starkly different.

Recent studies indicate that the shift to e-learning has introduced significant challenges that contribute to feelings of burnout among students. Factors such as increased workload, lack of face-to-face interaction, and the demands of self-directed learning have been identified as primary sources of stress (Yusof et al., 2023; Stephani, 2022). This burnout not only diminishes students’ enthusiasm for learning but also adversely affects their motivation to engage with course materials. Research has consistently shown that high levels of burnout correlate with low motivation, leading to disengagement and poor academic performance (Parker et al., 2022; Ryan & Deci, 2000).

Despite the growing body of literature addressing the relationship between burnout and motivation across various educational contexts, there remains a significant gap in research specifically targeting language and communication students enrolled in e-language courses focused on courseware and application development. While studies have explored these phenomena in broader educational settings (e.g., healthcare, business), the unique challenges faced by language learners in digital environments have not been thoroughly examined. For instance, previous research has highlighted the importance of motivation in language learning but has often overlooked the specific dynamics of e-learning contexts (Zhang et al., 2023; Dörnyei & Ushioda, 2021).

This existing research gap underscores the need for focused investigations into how sources of burnout specifically impact motivation among language and communication students engaged in e-language courses. Addressing this gap is crucial for developing targeted interventions that can enhance student engagement and success in these increasingly prevalent educational formats.

Objective of the Study  and Research Questions

This study is done to explore the perception of learners on sources of burnout and motivation in learning e-learning courses. Specifically, this study is done to answer the following questions;

• How do learners perceive motivation in learning e-learning courses?
• How do learners perceive their exhaustion in learning e-learning courses?
• How do learners perceive disengagement in learning e-learning courses?
• Is there a relationship between sources of burnout and motivation?

LITERATURE REVIEW

Theoretical Framework

Motivation for learning is a critical construct in educational psychology that influences students’ engagement, persistence, and overall academic success. It encompasses the internal and external factors that stimulate interest and drive individuals to pursue their educational goals. Understanding the dynamics of motivation is essential for educators, as it directly impacts students’ learning experiences and outcomes. Various theories have been developed to explain the complexities of motivation, each offering unique insights into how and why individuals engage in learning activities.

Current literature highlights several key theories that elucidate the mechanisms of motivation in educational settings. Self-Determination Theory (SDT), proposed by Ryan and Deci (2000), posits that motivation is influenced by three fundamental psychological needs: autonomy, competence, and relatedness. This theory suggests that when these needs are satisfied, learners are more likely to be intrinsically motivated, leading to enhanced engagement and performance. Recent studies have supported this notion, indicating that environments fostering autonomy and competence significantly improve student motivation (McEown and Oga-Baldwin, 2019).

Expectancy-Value Theory further contributes to the understanding of motivation by emphasising the role of students’ expectations for success and the perceived value of tasks (Rosenzweig et al., 2019). Research has shown that when students believe they can succeed in a task and find it valuable, their motivation levels increase correspondingly. A study by Wang and Xue (2022) confirmed that high expectancy and value perceptions lead to greater persistence in academic tasks among language learners.

Another significant framework is Goal Orientation Theory, which differentiates between mastery goals (focused on learning and self-improvement) and performance goals (focused on demonstrating ability relative to others). Studies have indicated that mastery-oriented students tend to exhibit higher intrinsic motivation and deeper engagement with learning materials compared to their performance-oriented counterparts (Dörnyei & Ushioda, 2021). This finding underscores the importance of fostering a mastery goal orientation in educational contexts to enhance motivation.

In addition, Social Cognitive Theory emphasises the role of self-efficacy beliefs in motivating learners. Martin’s work highlights that individuals with strong self-efficacy are more likely to take on challenging tasks and persist in the face of difficulties (Martin, 2004). Recent empirical evidence supports this theory, showing a positive correlation between self-efficacy and academic achievement in various disciplines, including language learning (Ma, 2022).

Finally, Incentive Theory posits that external rewards can significantly influence motivation. While intrinsic motivation is crucial for deep learning, extrinsic rewards can also play a role in enhancing engagement, particularly in situations where tasks may not inherently interest students (Irvine, 2018). However, it is essential to apply this theory judiciously; misdirected incentives can lead to counterproductive behaviours such as cheating or disengagement from learning (Cook & Artino, 2016).

In summary, the literature reveals a multifaceted understanding of motivation for learning through various theoretical lenses. While significant progress has been made in identifying factors that enhance student motivation across different educational contexts, there remains a need for further research specifically focused on language and communication students engaged in e-language courses. Addressing this gap will provide valuable insights into optimising motivational strategies tailored to the unique challenges faced by these learners.

Sources of Burnout

Burnout is a psychological condition marked by emotional exhaustion, detachment, and a reduced sense of accomplishment. It often arises from prolonged exposure to stress and is particularly prevalent in academic and professional settings. Identifying the sources of burnout is essential to developing strategies that promote well-being and enhance performance. In educational contexts, burnout can significantly hinder students’ motivation, engagement, and academic success.

Research highlights three primary sources of burnout: individual, interpersonal, and organisational factors. Individual factors include high levels of stress, low academic performance, and physical symptoms like fatigue. Yu et al. (2022) found that personal challenges such as these are strongly linked to burnout among students, particularly during periods of heightened academic pressure. Interpersonal factors, such as a lack of support from peers, teachers, or family members, also play a critical role. Yahiaoui et al. (2022) emphasised that students who feel isolated or unsupported are more likely to experience emotional exhaustion.

Organisational factors further contribute to burnout. Excessive workloads, lack of autonomy, and insufficient recognition are common stressors in both educational and occupational settings (Parker et al., 2022). Hu et al. (2024) identified work pressure as a primary driver of burnout, especially when there is a mismatch between an individual’s abilities and the demands placed upon them. For students in e-learning environments, these challenges are often compounded by the unique pressures of remote learning, such as increased screen time and reduced social interaction (Zhang et al., 2024).

Critically analysing these findings reveals the interconnected nature of burnout’s sources. Addressing individual stressors is important but insufficient without also considering the broader interpersonal and organisational context. For example, fostering supportive relationships among peers and educators can alleviate interpersonal stressors, while reducing workload intensity or improving task alignment can address organisational challenges. A holistic approach is needed to effectively combat burnout in academic settings.

Despite extensive research on burnout across various fields, there is limited focus on language and communication students enrolled in e-language courses. These learners face unique challenges related to developing language courseware and applications in digital environments—factors that may exacerbate feelings of stress and disengagement. This gap underscores the need for targeted research that examines how these specific sources of burnout manifest within this group to inform tailored interventions that promote well-being and academic success.

Past Studies 

Past Studies on Motivation for Learning

Motivation for learning is a critical area of research that examines the factors influencing students’ engagement and success in educational settings. Understanding motivation helps educators design effective teaching strategies that foster student interest and achievement. Recent studies have explored various aspects of motivation, including intrinsic and extrinsic factors, the impact of learning environments, and the role of personal beliefs. This literature review synthesizes findings from several key studies conducted in the span of 20 years, highlighting their methodologies, populations, and implications for educational practice.

One significant study by Davidovitch (2023) aimed to investigate the differences in motivation levels between high school and undergraduate students. Utilising a quantitative research design, the study surveyed 121 participants using a 22-item questionnaire focused on motivation for learning. The results indicated that undergraduate students exhibited higher motivation levels compared to their high school counterparts. Additionally, the study found correlations between motivation and factors such as age and socio-economic status, emphasising the importance of understanding these dynamics during formative educational periods. The implications suggest that enhancing motivation in high school could positively influence students’ future academic pursuits.

Another important contribution came from Yahiaoui et al. (2022), who examined the impact of e-learning systems on student motivation during the COVID-19 pandemic. This study employed a mixed-methods approach, collecting data through surveys and interviews with university students in Algeria. The findings revealed a significant positive correlation between the use of e-learning systems and student motivation, suggesting that well-designed online platforms can enhance engagement and learning outcomes. The study underscores the need for educational institutions to adapt their teaching methods to incorporate effective e-learning strategies.

In a different context, Goodman et al. (2011) explored how learning motivation affects academic outcomes among university students. This quantitative study involved a sample of 1,125 students and utilised simple linear regression to analyse data. The results indicated that higher levels of motivation were associated with improved academic performance. This research highlights the critical role that motivation plays in achieving educational success and suggests that fostering intrinsic motivation should be a priority for educators.

Furthermore, a qualitative study by Saeed and Zyngier (2012) investigated the relationship between intrinsic and extrinsic motivation in student engagement. Through interviews with students, the researchers found that while extrinsic motivators could encourage participation, intrinsic motivation was essential for deep learning and genuine engagement. This finding aligns with earlier studies that emphasise the importance of creating meaningful learning experiences that resonate with students’ interests and aspirations.

Finally, Lei and Zhou (2018) conducted a meta-analysis to assess the relationship between motivation and academic achievement across various contexts. Their comprehensive review highlighted inconsistencies in previous research findings regarding the impact of motivation on performance. The analysis revealed that while motivation generally correlates with better academic outcomes, other factors such as teaching quality and learning environment also play crucial roles. This underscores the complexity of motivational influences in education and suggests that a multifaceted approach is necessary for understanding student success.

In summary, these studies collectively emphasise the multifaceted nature of motivation for learning, revealing various factors that influence student engagement and achievement. By examining different populations, methodologies, and contexts, this body of research provides valuable insights for educators seeking to enhance motivational strategies in their teaching practices. Addressing both intrinsic and extrinsic motivators will be essential in fostering an environment conducive to effective learning outcomes.

Past Studies on Sources of Burnout

Learning burnout is a significant issue affecting students across various educational contexts. It is characterised by emotional exhaustion and disengagement, which can severely impact academic performance and overall well-being. Understanding the sources of learning burnout is crucial for developing effective interventions that support students in managing their academic demands. This section reviews recent studies that explore the sources of learning burnout, focusing on the dimensions of exhaustion and disengagement.

One notable study by Pham Thi and Duong (2024) aimed to investigate the prevalence of academic burnout among college students. Utilising a cross-sectional research design, the researchers surveyed 300 undergraduate students using a validated burnout inventory. The findings revealed that over 50% of participants reported experiencing moderate to high levels of academic burnout, with emotional exhaustion being the most prevalent symptom. The study concluded that academic pressure and heavy workloads significantly contribute to feelings of exhaustion, highlighting the need for institutions to address these stressors to improve student well-being.

Another important contribution came from Jagodics et al. (2023), who examined the relationship between school demands and student burnout in a sample of 1,200 secondary school students. This quantitative study employed a structured questionnaire to assess levels of emotional exhaustion and disengagement. The results indicated that high academic demands, coupled with inadequate support from teachers, were strongly associated with increased burnout symptoms. This research emphasises the importance of creating supportive learning environments to mitigate the risk of burnout among students.

In a qualitative study conducted by Iuga and David (2024), the researchers explored how emotional regulation strategies influence burnout among university students. Through in-depth interviews with 40 participants, the study found that ineffective coping mechanisms, such as avoidance and suppression of emotions, exacerbated feelings of exhaustion and disengagement. The findings suggest that enhancing students’ emotional regulation skills could serve as a protective factor against burnout, indicating a potential area for intervention.

Additionally, a study by Klinkenberg et al. (2024) focused on the impact of engagement on burnout among higher education students. Utilising a longitudinal design, the researchers collected data from 500 students over two semesters through surveys measuring engagement levels and burnout symptoms. The study revealed that higher levels of engagement were inversely related to emotional exhaustion and disengagement. This finding underscores the importance of fostering an engaging learning environment to combat burnout effectively.

Lastly, research by Matos and Andrade (2023) investigated the role of psychological resources in mitigating burnout among pre-university students. The study employed a mixed-methods approach, combining quantitative surveys with qualitative interviews involving 250 participants. The results indicated that resilience and perceived control significantly reduced feelings of exhaustion, while stress was found to exacerbate burnout symptoms. This highlights the need for educational institutions to promote resilience-building strategies as part of their support systems for students.

In summary, these studies collectively illustrate the multifaceted nature of learning burnout, particularly its sources related to exhaustion and disengagement. By examining various populations and employing diverse methodologies, this body of literature provides valuable insights into how educational environments can be optimised to reduce burnout among students. Addressing both academic pressures and emotional regulation strategies will be essential in fostering healthier learning experiences.

Conceptual Framework

Figure 1 shows the conceptual framework of the study. This study explores the influence of burnout sources and motivational components in learning e-learning courses. According to Rahmat & Thasrabiab (2024), motivation is important to sustain learning. There are several known sources of motivation. According to (Pintrich,et.al. 1990).  sources of motivation include value, expectancy and affective components.  Value components include learners’ intrinsic goal orientation, extrinsic goal orientation and also their task value beliefs. Next, expectancy components refer to learners’ perception of self-efficacy and their control beliefs for learning. Finally, affective components refer to learners’ emotions towards the learning activities.

Learners can also face burnout when learning gets overwhelming. Among some sources of burnout are exhaustion and disengagement (Campos et.al, 2011), This study also explores the relationship between sources of burnout and motivation among learners.

Figure 1- Conceptual Framework of the Study

Exploring Sources of Burnout and Motivation in learning e-Language Courses

METHODOLOGY

This quantitative study explores motivation factors for learning among undergraduates. A purposive sample of 138 participants responded to the survey. The instrument used is a 5-likert-scale survey and is rooted from Campos,et.al (2011) and Pintrich, et.al (1990)to reveal the variables in table 1 below. The survey has 3 sections. Section A has items on the demographic profile. Section B has 24 items on motivational components. Section C has 16 items on burnout.

Table 1- Distribution of Items in the Survey

Table 21 also shows the reliability of the survey. The analysis shows a Cronbach alpha of .901 for the motivational scale and .714 for burnout sources. The overall external reliability for all 40 items is .878.; thus, revealing a good reliability of the instrument chosen/used. Further analysis using SPSS is done to present findings to answer the research questions for this study.

FINDINGS

Findings for Demographic Profile

The demographic analysis of the respondents among undergraduate language and communication students in a public university, as illustrated in Table 2, highlights a pronounced gender imbalance among the respondents. Females constitute 73% of the sample, while males account for 27%. This substantial predominance of female participants raises important considerations regarding the representativeness of the findings.

Table 3- Percentage for Q2- Age Group

Based on the collected data, the age distribution of the respondents reveals a concentrated demographic profile. Table 3 indicates that the majority of participants, accounting for 59%, fall within the 22-23 years old age bracket. A further 28% of the respondents are aged between 20 and 21 years old. Only 13% of the sample are 24 years old and above, whereas none of the participants are between 18 and 19 years old. This age distribution suggests that the study primarily captures the perspectives and experiences of students in their later undergraduate years, which could be reflective of enrolment patterns in language and communication programs focusing on courseware and application development.

Table 4- Percentage for Q3- Previous Education Level

Analysis of the respondents’ previous education levels reveals a diverse range of academic backgrounds, with a notable concentration among those holding a diploma qualification. As illustrated in Table 4, over half of the sample (56%) completed their prior education with a diploma. STPM holders constitute the next largest group at 19%, followed by Matriculation (13%) and Foundation (12%) graduates. This distribution suggests that the program attracts a significant number of students who have pursued more vocationally-orientated pathways before entering their undergraduate studies in language and communication, potentially influencing their approach to courseware and application development within the curriculum.

Table 5- Percentage for Q4- Prior Experience in Multimedia

Table 5 presents the distribution of respondents based on their prior experience in multimedia. A substantial majority of the sample, accounting for 68%, indicated that they possess previous experience in multimedia. Conversely, 32% reported having no prior engagement in the field. This suggests that a significant proportion of students entering this program already have some foundational knowledge or interest in multimedia-related activities, which may influence their approach to learning and their overall motivation within the course.

Findings for Motivation

This section presents data to answer research question 1- How do learners perceive motivation in learning e-learning courses?

In the context of this study, this refers to (A) value components, (B) expectancy components and (C) affective components.

To begin with, (A) value components are measured by (i) intrinsic goal orientation, (ii) extrinsic goal orientation and (iii) task value beliefs.

Table 6- Mean for (i) INTRINSIC GOAL ORIENTATION (4 items)

Table 6 presents the mean scores for items measuring intrinsic goal orientation. The data indicates a general inclination towards intrinsic motivation, with respondents expressing a preference for challenging coursework and a curiosity-driven approach to learning. Notably, “The most satisfying thing for me in this program is trying to understand the content of the courses” and “When I have the opportunity in this class, I choose course assignments that I can learn from even if they don’t guarantee a good grade” received the highest mean scores (3.8), suggesting a strong internal drive to comprehend the material and prioritize learning over grades.

Table 7- Mean for (ii) EXTRINSIC GOAL ORIENTATION (3 items)

Table 7 displays the mean scores for extrinsic goal orientation. The data reveals a heightened emphasis on external rewards and validation. The statement “Getting a good grade in the classes is the most satisfying thing for me right now” garnered the highest mean score (4.5), followed closely by “The most important thing for me right now is improving my overall grade point average, so my main concern in this program is getting a good grade” (4.3). This suggests that students are significantly motivated by achieving high grades and demonstrating their abilities to others, indicating a strong extrinsic orientation.

Table 8- Mean for (ii) TASK VALUE BELIEFS (5 items)

Table 8 outlines the mean scores for items related to task value beliefs. The results indicate that respondents generally perceive the course materials as valuable and useful for their learning. The statement “I think the course material in the courses of this program is useful for me to learn” received a mean score of 4.2, while “It is important for me to learn the course materials in the courses” and “Understanding the subject matter of the courses is very important to me” each garnered a mean score of 4.0. This suggests that students recognize the practical relevance and importance of the subject matter, enhancing their overall motivation.

Next, expectancy components are measured by (i) students’ perception of self-efficacy, and (ii) control beliefs for learning.

Table 9- Mean for (i) STUDENTS ‘PERCEPTION  OF SELF-EFFICACY (5 items)

Table 9 presents the mean scores for items assessing students’ perception of self-efficacy. The data suggests a moderate level of confidence among students regarding their academic abilities. The mean scores for all items ranged from 3.2 to 3.5, indicating that while students generally believe in their capacity to perform well, there may still be some uncertainty or variability in their confidence levels across different aspects of the course. For instance, items such as “I’m confident I can understand the most complex materials presented by the instructors” received slightly lower mean scores (3.2) compared to other self-efficacy items, suggesting that some students may find certain concepts challenging. Overall, the results indicate a reasonable degree of self-efficacy, which is a positive predictor of academic success.

Table 10- Mean for (ii) CONTROL BELIEFS FOR LEARNING (2 items)

Table 10 displays the mean scores for control beliefs for learning. The high mean scores for both items indicate a strong belief among students that their efforts and study habits directly influence their learning outcomes. The statement “If I study in appropriate ways, then I will be able to learn the material in the courses of this program” had a mean of 4.2, while “If I try hard enough, then I will understand the course materials” had a mean of 4.3. This suggests that students believe that with adequate effort and appropriate study strategies, they can effectively master the course materials. This strong sense of control over their learning environment is a significant motivational factor.

Table 11- Mean for((C )AFFECTIVE COMPONENT -reversing (5 items)

Table 11 presents the mean scores for the affective component, which reflects students’ emotional reactions to assessments. The scores are generally low, indicating a relatively low level of anxiety and negative feelings when facing tests. The statement “When I take a test, I think about how poorly I am doing compared with other students” had a mean of 3.0, while “When I take a test, I think about items on other parts of the test I can’t answer” received a mean of 2.8. The lowest mean score was for the item “When I take tests I think of the consequences of failing” (2.5). These results suggest that while some anxiety is present, it is not overwhelming, potentially indicating a healthy approach to assessments or effective coping mechanisms.

Findings for Exhaustion

This section presents data to answer research question 2- How do learners perceive exhaustion in learning e-learning courses

Table 12- Mean for (i) EXHAUSTION

Table 12 presents the mean scores for items related to exhaustion among the respondents. The data indicates a notable level of reported exhaustion, particularly concerning feelings of fatigue both before and after classes. Statements EQ1 (“There are days when I feel tired before the day begins”) and EQ2 (“After classes, I tend to need more time than in the past in order to relax and feel better”) both received a mean score of 3.9. This suggests that students frequently experience a lack of energy, possibly due to demanding workloads and the sustained focus required in e-learning environments.

Conversely, the statement EQ6 (“After classes, I usually feel energized”) received the lowest mean score of 2.8, indicating that students generally do not feel energized after their classes. Additionally, EQ4 (“During classes, I often feel emotionally drained”) also had a lower mean score of 3.1, highlighting that students often feel emotionally drained during classes. The overall scores point to a potential issue with fatigue and burnout among students, which could impact their learning outcomes and well-being. Further investigation into strategies for managing workload and promoting self-care may be warranted.

Findings for Disengagement

This section presents data to answer research question 3- How do learners perceive disengagement in learning e-learning courses.

Table 13- Mean for (ii) DISENGAGEMENT

The findings from Table 13 reveal a nuanced perception of disengagement among learners in e-learning courses, with some items indicating ongoing interest while others suggest potential detachment. The highest mean scores were observed for DQ1 (“I always find new and interesting aspects in my study”) and DQ4 (“I find my studies to be positively challenging”), both at 3.7, reflecting students’ recognition of value and intrigue in their coursework. In contrast, the lowest mean score was for DQ2 (“It happens more and more often that I talk about my studies in a negative way”), which received a score of 2.9, suggesting that negative discussions about their studies are not overwhelmingly common. Notably, DQ3 (“Lately, I tend to think less during classes and attend classes almost mechanically”) scored 3.2, indicating moments of cognitive disengagement, while DQ5 (“Over time, students can become disconnected from this type of routine”) at 3.6 highlights concerns about the risk of becoming disengaged over time due to repetitive routines. Overall, these findings suggest that while many students maintain a degree of engagement, there are significant indicators of potential disengagement that should be addressed to enhance their learning experience.

Findings for the Relationship between sources of burnout and motivation

This section presents data to answer research question 4- Is there a relationship between sources of burnout and motivation?

To determine if there is a significant association in the mean scores between sources of burnout and motivation, data is analysed using SPSS for correlations. Results are presented separately in tables 3, 4, 5 and 6 below. In the context of this study, the two sources of burnout are exhaustion and disengagement.

Table 14- Correlation between Motivation and Exhaustion

Table 14 shows there is an association between motivation and exhaustion. Correlation analysis shows that there is a weak significant association between motivation and exhaustion (r=.230**) and (p=.000). According to Jackson (2015), the coefficient is significant at the .05 level and a positive correlation is measured on a 0.1 to 1.0 scale. A weak positive correlation would be in the range of 0.1 to 0.3, a moderate positive correlation from 0.3 to 0.5, and a strong positive correlation from 0.5 to 1.0. This means that there is also a weak positive relationship between motivation and exhaustion.

Table 15-Correlation between Motivation and Disengagement

Table 14 shows there is an association between motivation and disengagement. Correlation analysis shows that there is a weakly significant association between motivation and disengagement (r=.260**) and (p=.000). According to Jackson (2015), the coefficient is significant at the .05 level and positive correlation is measured on a 0.1 to 1.0 scale. A weak positive correlation would be in the range of 0.1 to 0.3, a moderate positive correlation from 0.3 to 0.5, and a strong positive correlation from 0.5 to 1.0. This means that there is also a weak positive relationship between motivation and disengagement.

CONCLUSION

Summary of Findings and Discussions

This study aimed to explore how learners perceive motivation, exhaustion, and disengagement in e-learning courses. The findings indicate that motivation is influenced by both intrinsic and extrinsic factors, with students showing a preference for intrinsic goals but also being significantly driven by external rewards such as grades. For instance, the data revealed high mean scores for items related to task value beliefs and extrinsic goal orientation. Regarding exhaustion, students reported moderate levels of fatigue before and after classes, suggesting that e-learning environments can be demanding due to sustained focus requirements. Lastly, perceptions of disengagement were mixed; while many students found their studies challenging and engaging, there were signs of cognitive disengagement during classes.

The findings of this study provide valuable insights into how learners perceive motivation, exhaustion, and disengagement in e-learning courses. The data indicates that students are motivated by both intrinsic and extrinsic factors, with a notable emphasis on task value beliefs and extrinsic goal orientation. This aligns with previous research highlighting the importance of perceived usefulness and external rewards in enhancing motivation (Stephani et al., 2023; Berestova et al., 2022; Elshareif & Mohamed, 2021). For instance, studies have shown that e-learning systems can significantly impact student motivation by providing flexible learning environments that cater to individual needs (Stephani & Riatun, 2023; Bosch & Spinath, 2023; Abou El-Seoud et al., 2014).

Regarding exhaustion, the results suggest moderate levels of fatigue among students. This is consistent with broader literature indicating that academic pressures contribute to burnout among learners (Yahiaoui et al., 2022). The COVID-19 pandemic has further exacerbated these challenges by increasing reliance on e-learning platforms (Elshareif & Mohamed, 2021), which can sometimes lead to feelings of isolation or disconnection from traditional classroom dynamics.

Disengagement patterns observed in this study reflect mixed perceptions among students. While many find their studies engaging due to challenging content and positive experiences with course materials, there are signs of cognitive disengagement during classes. Similar trends have been noted in other contexts where lack of social interaction or mismatched expectations between course content and student needs can lead to decreased motivation (Meşe & Sevilen, 2021).

Pedagogical Implications and Suggestions for Future Research

The findings of this study have significant implications for educators and policymakers seeking to enhance student engagement and mitigate burnout in e-learning environments. One key takeaway is the importance of fostering intrinsic motivation by making learning meaningful and relevant to students’ interests. This can be achieved through curricula that emphasize real-world applications and encourage autonomy, allowing students to explore topics at their own pace (Ryan & Deci, 2000). Additionally, educators should prioritise creating supportive learning environments that address both academic pressures and emotional well-being. Strategies such as peer mentoring programs or stress management workshops can help alleviate feelings of exhaustion and disengagement among learners.

Future studies could build upon these findings by exploring how different instructional strategies impact motivation and burnout across various disciplines beyond language learning. For instance, research might investigate the effectiveness of gamification or collaborative learning models in enhancing engagement while reducing stressors associated with e-learning platforms (Hamari et al., 2014). Another area for investigation is the role of technology itself—examining whether certain features or tools within e-learning systems contribute more significantly to student satisfaction or dissatisfaction. Furthermore, longitudinal studies could provide deeper insights into how these dynamics evolve over time, offering a clearer understanding of long-term impacts on student outcomes.

In conclusion, this study has shed light on the complex interplay between motivation, exhaustion, and disengagement among students in e-learning environments. The findings underscore the need for educators to create learning experiences that not only engage students intellectually but also support their emotional well-being. As researchers continue to explore these dynamics, it is hoped that future studies will build upon these insights to develop more effective strategies for fostering resilience and motivation among learners. Ultimately, the aspiration is that by understanding and addressing these challenges, educators can help create a more inclusive and supportive educational landscape where all students can thrive—both academically and personally—in an increasingly digital world.

ACKNOWLEDGMENT

The researchers want to thank the students and all stakeholders involved for volunteering to participate in this study.

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