An Investigation of Motivation and Burnout Factors Among Learners

An Investigation of Motivation and Burnout Factors Among Learners

Haslina Hassan¹, Amri Muaz Azmimurad^2*, Nurul Liya Saffura Rostam ³, Wani Nurfahani Mohd Sapuan⁴ , Anis Marjan Azmimurad⁵ , Noor Hanim Rahmat⁶

^1,2,3,4,6 English Department, Academy of Language Studies, University Technology MARA, 40450 Shah Alam, Selangor, Malaysia

⁵Dungun Vocational College, Jalan Paka, 23000 Dungun, Terengganu

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000259

Received: 29 August 2025; Accepted: 02 September 2025; Published: 06 October 2025

ABSTRACT

Motivation and burnout are critical factors that influence students’ academic performance and personal wellbeing, especially in hybrid learning environments that require more autonomy and adaptability. Understanding the relationship between these variables is imminent in ensuring the effectiveness of students’ learning environment. Motivation and burnout has been researched extensively in the literature; however, it is evident that with the implementation of hybrid learning in most institutions, the relationship between these factors within the context of hybrid learning remains underexplored. A quantitative study was conducted using a 5-Likert scale questionnaire distributed to a convenience sample of 75 Engineering students in a public university. The instrument assessed a number of motivational factors in addition to markers of exhaustion and disengagement. Overall, results suggest that students reported high levels of performance driven motivation with moderate signs of burnout and reduced engagement over time. The correlation analysis indicated a positive relationship between motivation levels and burnout indicators. The results reveal that while academic effort is driven by motivation, high performance-focused motivation may also be linked to a higher risk of burnout. Approaches that address psychological needs and encourage sustained involvement should be balanced with accomplishment goals in educational initiatives.

Keywords— Motivation, burnout, exhaustion, disengagement, hybrid learning

INTRODUCTION

The pillar of motivation to learn steam from the combination of intrinsic factors such as personal interest, academic goals and extrinsic motivation such as grades and acknowledgements. Face-to-face learning or also called as physical learning environment helps to elevate motivation from time to time via organized schedules, peer or classmates interaction, real-time feedback and also a sense of belonging [40]. On the other hand, students who are juggling work or family obligations especially value and appreciate the flexibility, autonomy and accessibility that online learning offers. However, these benefits also come with drawbacks, such as heightened loneliness, a lack of accountability, and technostress. The term technostress is not relatively new but it is more relevant to portray the impact of online learning. This term is more popular as ‘Zoom fatigue’ which refers to the emotional depletion and a decline in motivation caused by the mental strain of prolonged videoconferencing or screen time [7]. Depending on how effectively each modality meets their individual and contextual demands, students who experienced both learning modalities may go through fluctuations in their motivation levels.

In both in-person and virtual learning environments, burnout which is typified by emotional tiredness, depersonalization, and a diminished sense of accomplishment, is a critical concern [29]. According to research by [15] and [9] during the pandemic, learners experienced high levels of digital fatigue, stress, and burnout. This happens often as workloads are sometimes heavy and their social support network is weak and challenged [22]. Interestingly, a more recent quantitative study by [19] revealed that emotional weariness and other forms of burnout were far lower in online courses than in-person settings. This implies that although there are stressors associated with online learning, in-person learning may also cause significant emotional strain. This situation may possibly be the result of social constraints, having to travel to the place or even strict scheduling. In relation to the situation, this finding is critical as it suggests online learning is also positively correlated.

When students alternate between or juggle various learning modalities, the relationship between motivation and fatigue is particularly noticeable. Increased intrinsic motivation can serve as a protective factor against burnout, supported by engagement, self-control, and well-designed learning settings [2]. Several studies found that a strong sense of community, which is made possible by fusing in-person engagement with digital flexibility, can significantly improve motivation, lessen the feelings of loneliness and avert burnout in blended or hybrid learning [25],[6]. However, individual differences do matter. Since younger generations are more infused and exposed to technology, they tend to be the first line victims of burnout and fatigue due to excessive screen time. In comparison to older or more established generations, they may be able to navigate the hybrid settings but still experience burnout regardless [30].

The rapid development of educational settings, now more-reliant on technology, calls more attention to the impact of this integration. Motivation and burnout are often highlighted as the major impacts from the current adaptation. Institutions all across the globe are using blended models, which requires students to adapt to changing needs, with different degrees of autonomy and a variety of interaction styles [12]. The pandemic-driven online education has resulted in the use of both online and in-person learning which poses more challenges and effects [36]. Both the risk factors for burnout and motivating elements can be greatly impacted by this adaptation. Educators may create learning environments that maintain engagements while reducing emotional and cognitive stress that can impair academic achievements. In the process of creating this learning environment, more inputs have to be taken into account especially from the students themselves [26].

As students are the center of attention in learning environments, their problems and concerns have to be addressed significantly. Their motivation and burnout are crucial in predicting their academic performance, retention, and long-term mental health outcomes. Having high motivation during learning processes translates into deeper learning, high persistence, and strong resilience. On the other hand, these achievements can be clouded by burnout which can lead to disengagement, absenteeism, and even withdrawal from studies [1] The rapid evolution of technology forces learners and educators to always be ready to adapt. More evolution and adaptation results in more problems and concerns [4], [35].

Problem Statement

Both motivation and burnout are two fundamental psychological elements that directly influence the learning process and academic performance [3]. However, the dynamics of these two elements needs more exploration. Past studies have unraveled the path of how both elements affects students in both online and in-person learning settings but the way these elements interacts when students navigate both learning modes needs more understanding [41]. Therefore, it is necessary for research to keep investigating the problems and concerns that the students are facing as the technology is growing.

Despite the successful adaptation of hybrid learning settings in higher education, the current literature offers limited empirical evidence on how motivation and burnout manifest in this context. This also calls for an in-depth investigation involving students who have experienced extended periods of learning processes in both settings. Previous research considers their investigation to be either focusing only on online or in-person learning settings but since hybrid learning is more on the forefront, it creates a gap that needs to be explored further. Addressing this gap is critical to understand more of how motivation and burnout among students manifest in hybrid learning settings.

Research Objectives

This study is done to explore motivation and burnout among students. Specifically, this study is done to answer the following questions;

1. How do learners perceive motivation for learning?
2. How do learners perceive exhaustion in learning?
3. How do learners perceive disengagement in learning
4. Is there a relationship between motivation and burnout sources?

LITERATURE REVIEW

Theoretical Framework

The Self-Determination Theory (SDT), proposed by [34], remains a widely accepted framework for understanding motivation, particularly within hybrid and online learning environments. Rather than relying solely on external rewards, SDT differentiates between motivation that stems from a genuine interest in learning and motivation that is driven by external outcomes, such as grades or societal expectations.

A growing body of research has highlighted the benefits of intrinsic motivation in academic settings. Students who are intrinsically motivated tend to participate more actively, perform better academically, and report higher levels of satisfaction [18], [38]. For instance, [38] observed that students with strong intrinsic motivation were better equipped to handle the challenges of transitioning to online learning. In a related study, [10] demonstrated that intrinsic motivation played a vital role in promoting self-directed learning in hybrid environments.

On the other hand, while extrinsic motivation can be effective in encouraging students to complete tasks, it is often linked to superficial learning strategies, increased anxiety, and lower engagement [14]. [13] noted that students motivated by external rewards in blended learning environments were more likely to feel overwhelmed and less satisfied with their learning. [23] also pointed out that digital incentives, by themselves, do not enhance long-term motivation unless paired with strategies that nurture students’ internal interests.

To better understand how motivation functions, SDT outlines three essential psychological needs which are autonomy, competence, and relatedness [34]. [5] found that when instructors encouraged autonomy and developed meaningful interaction, students in hybrid courses felt more motivated. Similarly, [39] reported that fulfilling the need for competence led to increased student engagement and reduced symptoms of academic burnout. [42] emphasised the value of social connection, showing that a sense of belonging plays a pivotal role in maintaining student motivation in online settings.

Further supporting the relevance of SDT, [37] showed that hybrid courses designed to support autonomy and offer constructive feedback boosted both learners’ confidence and intrinsic interest in the subject matter. Additionally, [28] found that students with stronger self-determination set clearer academic goals and demonstrated better self-regulation. [11] echoed this, observing that autonomy-supportive learning environments enhanced students’ intrinsic motivation, resulting in improved academic outcomes and reduced procrastination.

In summary, understanding student motivation through the lens of SDT offers valuable insights for improving educational experiences, particularly in hybrid and online formats where learners must rely heavily on their own initiative. By nurturing students’ internal motivations and supporting their core psychological needs, educators can lay the groundwork for more meaningful and enduring learning experiences.

Causes and Effects of Burnout in Learning

The causes of academic burnout linger aroud learners’ lives each day, while its impacts could be harmful to learners in a lot of ways. As [20] revealed in their study, students’ gender, academic performance, parents’ educational background, financial matters, life experience, professional interest, and smoking habits are among the causes of academic burnout. Other causes of academic burnout include learners’ ability to self-regulate, build relationships with their teachers and friends, and get their family involved in the school system [27]. [16] discussed in their study the two factors that lead to academic burnout among learners: internal and external factors. They listed several causes under each factor. For instance, neuroticism, depressive symptoms, lack of resilience, and poor sleep quality were listed under internal factors, whilst academic load, high curriculum demands, competition between students, and inadequate social support are parts of external factors.

Consequently, academic burnout has significant effects on learners that require careful attention. In their study, [21] highlighted that academic burnout would lead to poor academic performance across all educational levels. Furthermore, [32] stated that academic burnout affects students’ welfare, academic performance, and mental health status. Academic burnout may also lead to disruptions in academic progression, which would affect young learners and their mental health [24].

Overall, academic burnout is closely related to various dimensions of life, which include personal and environmental factors. Its effects are detrimental not only to learners’ academic performance, also to their mental health.

Past Studies on Motivation and Relationship to Burnout

Researchers have long been intrigued on how students’ motivation relates to academic burnout. This is especially valid for learning environments that require a high level of self-direction and flexibility. The study by [3] investigated this relationship through research involving 221 Malaysian undergraduates who studied during the post-COVID period. The study used established measures in a structured questionnaire to show that student motivation based on personal interest or performance pressure directly affects their exhaustion and disengagement levels. Their findings indicate the role of motivation that helps to prevent students from burnout if it comes from genuine interest, however the extrinsic motivation may increase the risk of burnout when driven primarily to meet external expectations. The authors emphasise the importance of designing learning experiences that sustain students’ internal drive while minimising unnecessary academic strain.

The research of [14] utilised Self-Determination Theory for analysing data from 80,000 students of different educational settings through a large-scale meta-analysis. The study showed that learning environments which support autonomy lead to increased intrinsic motivation which produces better academic engagement and reduced burnout levels. The research shows that student needs for autonomy, competence and connection when supported enable them to sustain their energy and involvement in challenging academic programs.

On a similar note, [20] used the Maslach Burnout Inventory-General Survey to study 22,983 Chinese university students to determine the factors that cause academic burnout. The study revealed that 60 percent of the respondents experienced burnout symptoms with workload and lack of interest in studies being the main causes. These findings demonstrate that stress levels increase dramatically when motivation is weak or when it is not aligned with students’ academic objectives. [16] reviewed some research on college student burnout and concluded that the recurring cause of emotional exhaustion and disengagement of these students are the unclear goals, lack of intrinsic purpose, and also unsupportive learning environments. From the research, some approaches are suggested as they are deemed to be potentially helpful in guiding students to self-teach themselves the self-regulation techniques and how they should be pushed forward in both sustainable and balanced ways in order to prevent excessive stress while still maintaining academic effort.

These studies show that the type and quality of motivation are crucial in either preventing or amplifying the burnout symptoms among students. They also highlight the pivotal role of learning environments that support autonomy and meaningful engagement for the students’ welfare. While previous research offers valuable insights on this matter, most of it has concentrated on traditional academic settings. With the rise of hybrid learning which requires students to be more self-directed and adaptable, it is essential to revisit these findings in order to confirm whether the same dynamics apply or if new challenges arise that impact the motivation-burnout dynamics.

Conceptual Framework of the Study

There are many pushing factors that motivate students to be interested in learning. According to [33], students are motivated by factors such as attention, relevance, confidence and satisfaction. According to [31], some motivational factors are value, expectancy and affective components. To begin with, some students are fueled by their value components. These can be aspects of their study goals that made it personally meaningful to them. Next, some students are motivated by expectancy components where they choose the course of study based on what they expect the result will be later in life. Lastly, affective components are said to play an important role for students to sustain learning. This involves students’ feelings, emotions and their attitudes towards the learning tasks.

The workload for students these days can sometimes be overwhelming for them. While a small number would give up; many persevered and strive to complete their studies. According to [8], students can face exhaustion with the studies or feel disengaged with increased learning tasks. Both exhaustion and disengagement can cause learners to face burnout.

Figure 1 below shows the conceptual framework of the study. This study explores if there is a relationship between motivation and exhaustion. It also explores if there is a relationship between motivation and disengagement.

Figure 1: Conceptual Framework of the Study

Relationship between Motivation and Burnout Sources

METHODOLOGY

This quantitative study is done to explore the relationship between motivation and burnout sources. A convenient sample of 75 participants responded to the survey. The instrument used is a 5 Likert-scale survey.  Table 1 below shows the categories used for the Likert scale; 1 is for Never, 2 is for Rarely, 3 is for Sometimes, 4 is for Very Often and 5 is for Always.

Table 1: Likert Scale Use

Table 2 shows the distribution of items in the survey. This study replicates motivational scale from [31] and burnout scales by [8] for burnout to reveal the variables in the table below. Section B has 24 items on a motivational scale. Section c has 8 items for exhaustion and 8 items for disengagement.

Table 2: Distribution of Items in the Survey

Table 2 also shows the reliability of the survey. The analysis shows a Cronbach alpha of .906 for motivational scale, .779 for exhaustion and .806 for disengagement. The overall Cronbach Alpha for all 40 items is .923; thus, revealing a good reliability of the instrument chosen. Further analysis using SPSS is done to present findings to answer the research questions for this study.

FINDINGS AND DISCUSSION

Demographic Analysis

Table 3: Percentage for Demographic Profile

Table 3 reveals the demographic profiles for the participants in this study. This reveals predominantly male respondents (71%) and only 29% female. The majority of the participants fall within 26 – 28 years of age (88%) and the rest are 1% aged 17 – 19 years old, 3% aged 20 – 22 years, and 8% aged 23 – 25 years old. Civil Engineering consisted of the most of the participants with 61%, followed by Mechanical Engineering (61%) and Chemical Engineering (8%).

Findings for Motivation

This section presents data to answer research question 1- How do learners perceive motivation for learning? In the context of this study, motivation is measured by (a) value components, (b) expectancy components and (ii) affective components.

Value Component

Value components are then categorised into (i) intrinsic goal orientation, (ii) extrinsic goal orientation and (iii) task value beliefs.

Table 4: Mean for Intrinsic Goal Orientation

Table 4 displays the mean for Intrinsic Goal Orientation. Item MSVCQ3 shows the highest mean (mean=4.0, SD=0.8), followed by MSVCQ2 (mean=3.8, SD=0.8), and MSVCQ1 (mean=3.6, SD=0.8). Item MSVCQ4 reports the lowest mean (mean=3.4, SD= 1.0). The findings show that students are enjoying the process of understanding the content of the courses. This situation is also reflected when students prefer to be challenged with challenging materials and class work that can spark their learning process. Despite this, students weigh more on grades rather than learning opportunities, reflecting on their goal to perform better academically.

Table 5: Mean for Extrinsic Goal Orientation

Table 5 reports the mean for Extrinsic Goal Orientation. Items MSEGQ1 and MSEGQ2 show the highest mean (mean=4.5, SD=0.7) and (mean=4.5, SD=0.6) respectively.  Item MSEGQ3 reports the lowest mean (mean=4.3, SD= 0.9). The findings show that students are more concerned about their academic performance, which reflects their enthusiasm in learning. This also follows their intrinsic motivation which also aims to perform better academically. Apart from this, showing their ability to other people are not as important as getting good grades.

Table 6: Mean for Task Value Beliefs

Table 6 presents the mean scores for Task Value Beliefs. MSTVQ5 achieved the highest mean score (mean=4.3, SD= 0.7), followed by MSTVQ2 (mean= 4.0, SD = 0.8) and MSTVQ3 (mean= 3.9, SD= 0.8). Both MSTVQ1 and MSTVQ4 recorded the lowest mean (mean=3.7, SD=0.8) and (mean=3.7, SD=0.9).  These results indicate that students place the greatest value on understanding the subject matter, view course materials as important and useful and show relatively lower levels of agreement regarding the transferability of knowledge and enjoyment of the subject matter.

Expectancy Component

Expectancy components are categorized into (i) students’ perception of self-efficacy and (ii) control beliefs for learning.

Table 7: Mean for Students’ Perception of Self-Efficacy

Table 7 shows that ECSEQ1 recorded the highest mean score (mean= 3.5, SD= 0.9), while ECSEQ2 reported the lowest mean (m= 3.2, SD=0.9). ECSEQ3, ECSEQ4 and ECSEQ5 recorded similar mean scores of 3.4  with slight difference in SD. The findings demonstrated that students had slightly higher confidence in achieving excellent grades than in understanding the most complex materials presented in the courses.

Table 8: Mean for Control Beliefs for Learning

Table 8 shows the mean for control beliefs for learning. Both items share the same mean of 4.3 Item 1 (mean=4.3, SD=0.8) reported students’ perception that if they studied in appropriate ways, they would be able to learn the material in the course. Next, item 2 (mean=4.3, SD= 0.8) states that if they tried hard enough, they would understand the course materials.

Affective Component           

Table 9: Mean for Affective Components

Table 9 displays the mean for affective components of academic burnout. Item ACQ2 shows the highest mean (mean=3.6, SD=1.0), while item ACQ4 falls in the middle range (mean=3.0, SD=1.2), and item ACQ3 reports the lowest mean (mean=2.8, SD=1.3). The findings indicate that the most significant affective component among students is their concerns over questions that they couldn’t answer. Feelings of unease or emotional distress while taking exams were reported at a moderate level, whereas thinking about the consequences of failing emerged as the least dominant affective factor.

Findings for Exhaustion

This section presents data to answer research question 2: How do learners perceive exhaustion in learning?

Table 10: Mean for Exhaustion

Table 10 shows the mean scores for learners’ perceived exhaustion in learning. The highest mean is for EQ1

“There are days when I feel tired before the day begins” (mean = 3.8, SD = 0.9), followed by EQ2 “After classes, I tend to need more time than in the past in order to relax and feel better” (mean = 3.6, SD = 1.0) and EQ4 “During classes, I often feel emotionally drained” (mean = 3.6, SD = 0.9). The lowest mean is for EQ6 “After classes, I usually feel energized” (mean = 3.1, SD = 0.9). Other items such as EQ3 “I can tolerate the pressure of my studies very well” (mean = 3.4, SD = 0.8), EQ5 “After classes, I have enough energy for my leisure activities” (mean = 3.3, SD = 0.8), EQ7 “After my classes, I usually feel worn out and weary” (mean = 3.3, SD = 0.9), AND EQ8 “Usually, I can manage the amount of my work well” (mean = 3.4, SD = 0.8) fall in the moderate range.

Findings for Disengagement

This section presents data to answer research question 3: How do learners perceive disengagement in learning?

Table 11: Mean for Disengagement

Table 11 shows the mean of learners’ perceived disengagement in learning. The highest means are for DQ1 “I always find new and interesting aspects in my study” (mean = 3.5, SD = 0.8), DQ4 “I find my studies to be positively challenging” (mean = 3.5, SD = 0.8), and DQ5 “Over time, students can become disconnected from this type of routine” (mean = 3.5, SD = 0.9). The lowest mean and standard deviation is item DQ7 “I feel more and more engaged in my studies” with mean 3.0 and standard deviation 0.9.

Findings for Relationship between Motivation and Burnout Sources

This section presents data to answer research question 4: Is there a relationship between motivation and burnout sources? To determine if there is a significant association in the mean scores between motivation and burnout sources, data is anlaysed using SPSS for correlations. Results are presented separately in Table 4 and 5 below.

Table 12: Correlation between Motivation and Exhaustion

**Correlation is significant at the level 0.01 (2-tailed)

Table 12 shows there is an association between motivation and exhaustion. Correlation analysis shows that there is a high significant association between motivation and exhaustion (r=.464**) and (p=.000). According to [17], coefficient is significant at the .05 level and positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and strong positive correlation from 0.5 to 1.0. This means that there is also a strong positive relationship between motivation and exhaustion.

Table 13: Correlation between Motivation and Disengagement

**Correlation is significant at the level 0.01(2-tailed)

Table 13 shows there is an association between motivation and disengagement. Correlation analysis shows that there is a high significant association between motivation and disengagement (r=.483**) and (p=.000). According to [17], coefficient is significant at the .05 level and positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and strong positive correlation from 0.5 to 1.0. This means that there is also a strong positive relationship between motivation and disengagement.

RECOMMENDATION & CONCLUSION

This section will provide the conclusion and discussion of the studies by relating the results of the study to the findings that have been discussed in the literature. This study investigates the learners’ perceptions of motivation, burnout, and disengagement as well as its relationship between motivation and burnout in a hybrid learning environment.

RQ1: How do learners perceive motivation for learning?

The findings of this study revealed that extrinsic motivation was the strongest motivational driver, with the highest means recorded for the importance of achieving good grades as seen in Figure 3 in the previous chapter. Albeit it was slightly less dominant, intrinsic motivation was also present particularly in the students’ enjoyment of understanding course material and content. Affective components related to test anxiety were moderate, with the greatest concern being unanswered test questions. These findings are in line with the research by [34], and [14], which indicated that both intrinsic and extrinsic motivations are influential, although extrinsic factors typically prevail in systems focused on grades. This observation is also supported by [13], who pointed out that while external rewards can enhance performance, they may also lead to increased stress. Simultaneously, the presence of intrinsic motivation reinforces the arguments made by [38] and [5], who connected autonomy and meaningful learning with greater engagement.

RQ2: How do learners perceive exhaustion in learning?

The results showed that the highest exhaustion levels were reported for feeling tired before the day begins and needing extended relaxation after classes as seen from EQ1 and EQ2 of Figure 8. This suggests that both mental and physical exhaustion were prevalent even when students handled their responsibilities reasonably well. The results align with [20] and [16], who recognized that both internal factors (such as insufficient sleep quality and low resilience) and external factors (like excessive academic demands) contribute to burnout. The moderate level of tolerance for academic pressure observed in this study supports the findings of [21], which associated exhaustion with a decrease in academic performance.

RQ3: How do learners perceive disengagement in learning

Based on the data that has been collected, it is clear that students still found aspects of their studies interesting and challenging, yet feelings of reduced engagement overtime and mechanical attendance were evident as seen in Figure 8. The lowest mean scores showed weaker academic engagement over time. This aligns with [27], who found that regular academic pressures and high expectations can encourage a sense of detachment. Comparable observations were made by [32], who highlighted that disengagement is a fundamental aspect of academic burnout.

RQ4: Is there a relationship between motivation and burnout sources?

Table 4 and Table 5 revealed that the correlations analysis indicated a moderate to strong positive relationship between motivation and both exhaustion and disengagement. This suggests that higher motivation may potentially contribute to certain burnout symptoms particularly in performance-driven environments. These results align with the findings of [3] and [41], who pointed out that in hybrid learning environments, motivated students may still encounter burnout if their psychological needs for autonomy, competence, and relatedness are not adequately satisfied.

Implications and Recommendations for Future Research

This section will provide the implications and conclusions that can be drawn based on the findings of this study for the benefit of future researchers.

Theoretical and Conceptual Implications

The findings support the concepts of Self-Determination Theory (SDT). This theory shows that the type of motivation, whether intrinsic or extrinsic, affects how engaged learners feel and their overall well-being. According to SDT, autonomy, competence, and relatedness are essential for long-term motivation [34]. The results show high performance-driven motivation, but also moderate signs of burnout. This suggests that while external performance pressures can boost academic effort, they may drain students’ energy and reduce long-term engagement if not balanced with intrinsic interests.

The data also corroborate previous studies which demonstrate that intrinsic motivation together with autonomy-supportive learning environments decrease emotional fatigue and prevent disengagement [14], [39]. In addition, the study substantiates [20] and [16] by demonstrating that misaligned motivation and unclear learning goals lead to exhaustion, thereby affirming SDT’s theoretical model as an effective framework for understanding motivation-burnout relationships in hybrid learning environments.

Moreover, the research findings demonstrate that SDT provides an effective framework to study student experiences in hybrid learning environments because these settings need students to self-regulate and adapt more than traditional educational environments. Similarly, the study reinforces the necessity to create hybrid courses that fulfill psychological needs for autonomy and competence and connection while reducing external pressures through its positive relationship between performance-focused motivation and burnout. Taken together, the findings indicate a need to enhance SDT applications in hybrid learning because students need more sophisticated instructional approaches to manage internal drive against environmental demands.

Pedagogical Implications

The findings of the study offer new perspectives to educators in formulating new teaching approaches for learners. Academic burnout does not only impede learning, but it also affects learners’ quality of life entirely. Based on the findings of the study, it is imperative that aligning teaching approaches according to learners’ needs should be considered to ensure effective language teaching and learning.  Learners’ motivation is a crucial factor in mitigating academic burnout among learners. Thus, teaching approaches should focus on increasing learners’ motivation in learning. This effort includes paving the way for learners to find something new and positive in their learning and providing full support in helping them understand the content and obtain good results. In general, taking into account the motivational causes of burnout should assist educators in addressing the challenges and concerns that contribute to academic burnout among learners.

Practical Implications

The findings may serve as practical reference for universities and curriculum developers for reviewing and improving current syllabi to enhance teaching and learning in higher institutions. One of the practical suggestions to be considered is offering training and support to both students and lecturers, so that they can seamlessly adapt to the blended learning environment and giving a channel where they can discuss their concerns and opinions. Furthermore, introducing a buddy system for social and moral support would be beneficial for students in need. This would ensure students get all the necessary support they need and remain on track, especially when they are not usually around their classmates in a blended learning setting.  After all, these interventions could promote a more supportive and effective learning environment. This leads to better student engagement and academic outcomes.

Suggestions for Future Research

For future research, this study may be essential as a reference in understanding the influence of motivation on learners’ academic burnout. Although the study shows comprehensive results covering the topic, it can be expanded to other faculties, programmes, and areas of study. Including a larger and more diverse sample in future research would also improve the generalizability of the findings. The findings from various groups will provide new perspectives and dimensions to the topic. Furthermore, future research can include a qualitative study to enable data triangulation, which would enhance the depth of the current study with the support of qualitative perspectives.

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