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Exploring Motivation and Demotivation Factors for Learning

Nurul Nadiah Dewi Faizul Ganapathy,

Izlin Mohamad Ghazali,

Mohd Hafriz Abdul Hamid,

Norazean Sulaiman,

Nurul Syahida Abu Bakar,

Noor Hanim Rahmat

1,2, 6

Akademi Pengajian Bahasa, University Technology MARA, Shah Alam, Malaysia,

Fakulti Pendidikan, University Technology MARA, Puncak Alam, Malaysia,

Akademi Pengajian Bahasa, University Technology MARA Cawangan Terengganu, Terengganu,

Malaysia

STEM Foundation Center, University Malaysia Terengganu, Terengganu, Malaysia

*Corresponding Author

DOI:

https://dx.doi.org/10.47772/IJRISS.2025.924ILEIID00115

Received: 23 September 2025; Accepted: 30 September 2025; Published: 03 November 2025

ABSTRACT

This study explored factors that influenced learners’ motivation and demotivation in learning by focusing on

their relationship and interaction. Motivation was assessed using Pintrich and DeGroot’s (1990) framework,

while burnout was measured via exhaustion and disengagement based on Campos et al.’s (2011) model. Intrinsic

motivation was most strongly linked to students’ interest in understanding course content, whereas extrinsic

motivation was mainly associated with achieving high grades and demonstrating academic performance to

family and peers. Respondents acknowledged the usefulness and importance of the course materials and reported

moderate confidence in their ability to succeed (self-efficacy). They also believed that consistent effort and

appropriate study strategies would enable them to master the course content (control beliefs). While most

students valued the subject matter, some reported concerns about performing worse than their peers, thus

reflecting a moderate level of task-related anxiety. Findings indicated moderate to high physical and emotional

fatigue caused by burnout, with many learners requiring extended recovery after classes. Some learners remained

engaged due to their interest in learning and the ensuing challenges faced, while others participated mechanically

and displayed detachment. Mean scores showed higher motivation than demotivation, suggesting general

motivation despite persisting demotivational factors. The correlation analysis revealed a significant moderate

positive relationship, thus highlighting the coexistence of motivational and demotivational influences. These

findings underscore the need for interventions that enhance self-regulation, sustain engagement, and address

demotivational triggers.

Keywords: motivation in learning, demotivation factors, academic burnout, intrinsic and extrinsic motivation,

motivation theory, exhaustion and disengagement

INTRODUCTION

Research on motivation in learning has received extensive attention, and this reflects its critical and multifaceted

role in ensuring effective learning experiences. Alongside motivation, demotivation has also gained recognition

as a factor that significantly influences learners’ engagement and achievement. The field of motivation and

demotivation research is well-developed, nevertheless, ongoing studies continue to introduce novel methods and

perspectives to more precisely measure these constructs. Recent research indicates that students today exhibit

unique patterns of engagement and burnout, with many reporting feelings of emotional exhaustion and stress

due to ongoing academic pressures (Salmela-Aro & Upadyaya, 2020). A complex relationship between student

motivation, demotivation and academic burnout in modern education has also been established (Syed Husain et

al., 2025; Wan Mohd et al., 2024; Zolkapli et al., 2023).

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Motivation is defined as “the processes that initiate and sustain goal-directed activity” (Schunk & DiBenedetto,

2020, p. 1) and it involves the direction and intensity of behaviour (Dörnyei & Ushioda, 2011). It explains why

individuals commit to an activity, how long they persist and the effort they invest. Conversely, demotivation

refers to a decline or loss of this drive triggered by various internal or external factors that disrupt learning (Gao

& Liu, 2022; Qiu, 2024). Academic burnout is closely linked to demotivation, and manifests as emotional

exhaustion, cynicism and reduced accomplishment (Campos et al., 2011), while serving as a useful framework

for understanding demotivational factors such as exhaustion and disengagement.

Recent studies involving Malaysian pre-university and undergraduate students (Syed Husain et al., 2025; Wan

Mohd et al., 2024; Zolkapli et al., 2023) have shed light on the interplay between motivation and demotivation;

however, findings reveal nuanced patterns that merit further examination. These studies found a significant and

positive relationship between motivational and demotivational factors, suggesting that these elements can coexist

and fluctuate together in complex ways.

LITERATURE REVIEW

Motivation Theory in Learning

Motivation is a critical driver of students’ academic engagement and success. Foundational theories offer

different but complementary perspectives on how motivation is formed and sustained. Maslow’s (1943)

Hierarchy of Needs situates learning within the broader framework of human needs, which suggests that higher-

order learning goals can only be pursued once basic physiological and psychological needs are met. Deci and

Ryan’s (1985) Self-Determination Theory distinguishes between intrinsic motivation driven by personal interest

and enjoyment, and extrinsic motivation shaped by rewards, recognition or external pressures. Bandura’s (1977)

concept of self-efficacy highlights learners’ beliefs in their ability to perform successfully, which strongly

influences persistence and resilience in the face of challenges.

Pintrich and De Groot (1990) provided an integrative model that conceptualizes motivation in three interrelated

components:

• Expectancy – learners’ beliefs about their capacity to succeed that incorporates self-efficacy and control

beliefs.

• Value – both intrinsic and extrinsic goal orientations that reflect the perceived importance and usefulness

of learning tasks.

• Affective – emotional responses that can enhance or inhibit learning, such as enjoyment, anxiety, or

boredom.

Empirical evidence was found to support the relevance of these components. González-Arias et al. (2025) found

that satisfying basic psychological needs promotes intrinsic motivation, which in turn improves academic

performance. Burke et al. (2024) reported that intrinsic goal orientations, including a love of learning and

experiencing “flow”, were strongly linked to achievement, while extrinsic drivers, such as grades and family

support, also played a role. Wang et al. (2024) showed that interest in course content and supportive learning

environments enhance expectancy and value beliefs, which then influences achievement outcomes.

The affective dimension has also been recognized as integral to motivation. Hamzah et al. (2022) observed that

supportive teacher-peer relationships can create engagement, whereas negative interactions could diminish it.

Similarly, González-Arias et al. (2025) found that positive emotions enhance motivation, while negative

emotions impede learning. Collectively, these studies indicate that motivation is shaped not only by cognitive

beliefs and goal orientations but also by the learner’s emotional experiences and surrounding context.

Sources of Burnout among Students

Burnout, originally conceptualized in occupational settings, has been adapted to education in order to describe

the psychological exhaustion students experience from sustained academic demands (Maslach & Jackson, 1981).

Models commonly used in educational contexts include the Maslach Burnout Inventory (MBI), which assesses

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emotional exhaustion, cynicism and reduced accomplishment; School Burnout Inventory (SBI), which measures

exhaustion, cynicism towards the school, and feelings of inadequacy (Salmela-Aro et al., 2009); Schaufeli et

al.’s (2002) model, which emphasizes study-related exhaustion, cynicism and inefficacy; and the Copenhagen

Burnout Inventory (CBI), which identifies personal, study-related and interaction-related burnout (Kristensen et

al., 2005).

Research has consistently identified several contributing factors, such as excessive workload, time pressure, lack

of autonomy, insufficient recognition, poor relationship with peers or teachers, perceived unfairness in

assessment, and emotional fatigue (Maslach & Leiter, 1997; Jacobs & Dodd, 2003; Pines & Aronson, 1988).

While burnout is often associated with disengagement, it can also occur alongside high motivation, especially in

high-pressure academic environments. This coexistence of motivation and demotivation suggests the need for

an integrated approach to studying both phenomena.

Previous Studies on the Relationship between Motivation and the Causes of Burnout

International research suggests that intrinsic motivation often correlates with higher academic satisfaction,

despite stress and workload. One study found that intrinsic motivation in medical undergraduates was linked to

a stronger sense of personal accomplishment (Felaza et al., 2020).

Local Malaysian studies reflect similar dynamics. Syed Husain et al. (2025) investigated the relationship between

student motivation and burnout, and found that while students were motivated by intrinsic and extrinsic factors,

they frequently experienced physical and emotional exhaustion. Wan Mohd et al. (2024) reported that low self-

esteem and poor learning environments demotivated learners, thus, contributing to stress and burnout, especially

under exam pressure.

Several studies on learning English as a second language (ESL) in Malaysia further illuminated this interplay.

Azhari et al. (2023) identified a moderate positive correlation between motivation and burnout, whereby

motivated learners still showed signs of exhaustion and disengagement under high academic pressure. Zolkapli

et al. (2024) extended this finding by quantifying moderate to strong correlations between burnout and

motivational subcomponents, with value (r = 0.333), expectancy (r = 0.341), and affective (r = 0.855)

highlighting test anxiety and maladaptive perfectionism as key risk factors.

Bandura’s (1997) theory of self-efficacy offers a foundational lens that indicates a strong belief in one's

capabilities tends to support persistence and manage stress. Honicke and Broadbent (2016) and Schunk and

DiBenedetto (2020) reinforced this view by stating that self-efficacy is a crucial predictor of academic

performance and long-term engagement.

In reference to extrinsic motivation, Koenka et al. (2021) and de Bruin et al. (2024) found that performance-

oriented goals, like GPA and recognition, fuelled short-term persistence, although it could lead to superficial

learning if intrinsic motivation is absent. Similarly, Rahman et al. (2024) noted that Malaysian students often

equate academic success with GPA and external validation.

Task value beliefs, which refers to learners perceiving content as useful, enjoyable and relevant, have also been

shown to encourage deeper engagement. Lauermann et al. (2023), Shehzad et al. (2024) and Phan et al. (2025)

found that higher task value supports persistence, although Eccles and Wigfield (2020) and Hulleman and

Harackiewicz (2021) cautioned that value must be supported by competence and suitable educational contexts.

Affective factors, like anxiety and cognitive interference, have been strongly linked to learning setbacks. Rahmat

(2024), Amaruddin et al. (2023) and Khaira et al. (2024) documented the emotional and physiological toll of test

anxiety. Conversely, Barattucci et al. (2022) and Ismail et al. (2023) showed that mindfulness and emotional

regulation-based interventions significantly reduced anxiety and improve performance.

Burnout and exhaustion are recurring themes. Rahmat (2023) and Ibrahim et al. (2024) observed students

grappling with chronic fatigue and limited recovery time, thus echoing the burnout models of Schaufeli et al.

(2002) and Salmela-Aro & Upadyaya (2014) that link sustained pressure to engagement decline. Li et al. (2021),

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along with Barattucci et al. (2022) and Ismail et al. (2023) affirmed that resilience-building and coping strategies

can buffer burnout’s impact.

Lastly, the co-occurrence of motivation and demotivation, which is evident in mechanical attendance or negative

talk despite high engagement, has been framed by Schaufeli et al. (2002), Salmela-Aro et al. (2016) and

Upadyaya & Salmela-Aro (2020), by emphasizing the protective role of peer support, mindfulness and reflection

in sustaining motivation.

Research Gap and Questions

The reviewed literature highlighted several gaps. First, motivation and demotivation were often studied

separately and this limits the understanding of how they coexist in learners’ experiences. Second, few studies

had directly compared the average levels of motivation and demotivation in the same population, which could

reveal whether one predominates or both are present at high levels. Third, studies, in the Malaysian context, had

examined motivation and burnout individually but had seldom measured them together using the same

framework. Finally, while the affective component of motivation is acknowledged, its interaction with

expectancy and value beliefs in shaping demotivation has received limited attention.

This present study addressed these gaps by examining motivation and demotivation together among Malaysian

undergraduates using validated measures to allow comparison of both their mean levels and analysis of their

interrelationship. The study also investigated how emotional factors interact with expectancy and value beliefs

to influence engagement and disengagement.

Conceptual Framework of the Study

Staying motivated is important for students in institutions of higher learning. Some of the reasons why students

stay motivated is that they feel confident with the learning tasks and they gain satisfaction in the learning

outcome (Rahmat et al., 2021). Pintrich and DeGroot (1990) listed three main components of motivation. First

is the value component and this refers to learners’ intrinsic and extrinsic goal orientation, as well as learners’

task value beliefs. Next, the expectancy component refers to students’ perception of self-efficacy and also control

beliefs for learning.

When it comes to learning motivation, even the most motivated students may sometimes become demotivated.

According to Campos et al. (2011), students sometimes get overwhelmed with learning tasks and become

exhausted. At the same time, some students who face non-academic related problems may be stressed out with

classes. Some may strive for academic excellence and end up being over-worked and feel disengaged. These are

the main sources of burnout among students. Figure 1 shows the conceptual framework of the study. This study

explored factors responsible for motivation and burnout, as well as to determine whether there is a relationship

between motivation and demotivation among learners.

Figure 1-Conceptual Framework of the StudyMotivation and Demotivation Factors for Learners

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METHODOLOGY

This quantitative study aimed to explore motivation and demotivation factors involved in learning. The survey,

which used an instrument with a 5-point Likert-scale, involved a convenient sample of 114 participants survey.

Table 1 shows the range of the scales used in the Likert scale, with 1 for Never, 2 for Rarely, 3 for Sometimes,

4 for Very Often and 5 for Always.

Table 1- Likert Scale Use

Never

Rarely

Sometimes

Very Often

Always

Table 2 shows the distribution of items in the survey. This study replicated items for motivation from Pintrich

and DeGroot (1990) and items for burnout from Campos et al., (2011) to reveal the variables in the Table below.

Section B has 24 items on motivation and Section C has 16 items on demotivation.

Table 2- Distribution of Items in the Survey

Table 2 also shows the reliability index for the survey, with a Cronbach’s alpha of .901 for motivation and .703

for demotivation. The overall Cronbach’s alpha value for all 16 items is .881 and this indicates a good level of

reliability for the chosen instrument (Jackson, 2015). Further analysis using SPSS was carried out to present

findings to address the research questions for this study.

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FINDINGS

Demographic Analysis

Table 3- Demographic Profile

Table 3 shows that most participants were female (64%), with males making up 36%. Most were between 18

and 29 years old (78%), followed by 17% aged 30–39, and 5% aged 40–49. The majority were degree students

(77%), while 23% were diploma students. Slightly more participants studied part-time (55%) compared to full-

time (45%). Overall, the group comprised mostly young females pursuing a degree-level program, with an almost

even split between part-time and full-time learners.

Descriptive Statistics

FINDINGS for Motivation

This section presents data addressing the first research question: How do learners perceive their motivation for

learning? In the context of this study, motivation was measured by using Value Components, Expectancy

Components, and Affective Components.

Value Component

This study’s value components were measured based on (a) intrinsic goal orientation, (b) extrinsic goal

orientation and (c) task value beliefs.

Intrinsic Goal Orientation (4 Items)

Figure 2: Mean and SD for Intrinsic Goal orientation

Based on Figure 2, four items were utilised to determine the mean scores for respondents’ intrinsic goal

orientation. The highest mean score was recorded for Item 3, which states that students’ most satisfying outcome

is the ability to understand the content of the course (M = 4.1, SD = 0.9). The second highest mean score was

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linked to Item 2 (M = 3.7, SD = 0.8), which highlights students’ preference for course materials that arouse

curiosity, even if they are difficult to learn. Meanwhile, two items shared the same lowest mean score of 3.5. The

first, Item 1 (M = 3.5, SD = 0.8), reflects students’ preference for a challenging form of classwork that allows

them to learn new things. Likewise, Item 4 (M = 3.5, SD = 1.1) indicates that students recognise the importance

of choosing course assignments that enhance their learning, even if those assignments do not always lead to high

grades.

Extrinsic Goal Orientation (3 Items)

Figure 3: Mean and SD for Extrinsic Goal orientation

Figure 3 illustrates three items that measured respondents’ extrinsic goal orientation. Two items recorded the

highest mean score of 4.5. Item 1 (M = 4.5, SD = 0.8) indicates students’ perception that obtaining good grades

in class is the most satisfying outcome, while Item 2 (M = 4.5, SD = 0.7) highlights students’ recognition of the

importance of scoring a good grade to improve their overall grade point average. Finally, Item 3 (M = 4.2, SD =

0.9), which has the second highest mean score, emphasises students’ motivation to perform well academically

in order to demonstrate their academic ability to family, friends and others.

Task Value Beliefs (5 Items)

Figure 4- Mean and SD for Task Value Beliefs

Figure 4 presents mean scores for five items under the task value beliefs based on respondents’ perceptions. The

highest mean score was recorded for respondents’ belief that understanding the various courses’ subject matter

is very important (M = 4.4, SD = 0.7). This is closely followed by the perception that the course material is

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useful for learning (M = 4.2, SD = 0.8). Meanwhile, similar mean scores were observed for the importance of

learning the course materials and liking the subject matter (M = 4.1, SD = 0.8). Finally, the lowest mean value

was regarding the ability to transfer learning between different courses in the same programme (M = 3.8, SD =

0.8).

Expectancy

Expectancy was measured based on students’ perception of self-efficacy, and control beliefs for learning.

Students’ Perception Of Self-Efficacy (5 Items)

Figure 5- Mean and SD for Students’ Perception of Self-Efficacy

Based on Figure 5, there are five items under students’ perception of self-efficacy. Three highest mean scores

are associated with the belief in receiving excellent grades in class, confidence in performing excellently in the

assignments and tests related to the programme, and the belief to do well in class despite the difficulty of the

course, teachers and individual skills (M = 3.8, SD = 0.9). The next item shows the second highest mean score,

which is the confidence in understanding the most complex materials shared by course instructors (M = 3.7, SD

= 0.9). Finally, respondents’ certainty in mastering skills taught in class recorded the lowest mean score (M =

3.6, SD = 0.9).

Control Beliefs For Learning (2 Items)

Figure 6- Mean and SD for Control Beliefs for Learning

Figure 6 shows the mean for control beliefs for learning. The higher mean is for Item 2 (mean=4.4, SD=0,7),

which states that learners can understand the course materials if they try hard enough. Whereas, Item 1 (mean-

4.1, SD=0.7) states that if students studied in appropriate ways, they would be able to learn the course materials.

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Affective

Figure 7- Mean and SD for Affective Components

Figure 7 shows that the most prominent concern during tests was comparing performance with others (M = 3.3),

which is an indication of notable social-comparison anxiety. Worry about unanswered items (M = 3.0) and fear

of failure (M = 2.9) were also evident. Overall, the results point to moderate test anxiety, with cognitive factors

more pronounced than emotional or physiological symptoms.

FINDINGS FOR DEMOTIVATION

This section presents data to address research question 2: How do learners perceive demotivation factors in their

learning? This study measured demotivation based on two aspects of burnout, namely Exhaustion and

Disengagement.

Exhaustion

Figure 8- Mean and SD for Exhaustion

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Figure 8 shows that students most frequently reported needing more time than before to recover after classes (M

= 3.8, SD = 0.9) and feeling tired before the day begins (M = 3.7, SD = 1.0), pointing to persistent fatigue.

Although many felt that they were able to manage their workload (M = 3.6, SD = 0.8), post-class exhaustion

remained common. These results highlight moderate to high burnout, with physical fatigue as the dominant

symptom.

Disengagement

Figure 9- Mean and SD for Disengagement

Figure 9 reveals that most students strongly agreed with finding new and engaging aspects in their studies (M =

3.9, SD = 0.8) and perceiving their academic tasks as positively challenging (M = 3.8, SD = 0.8). The lowest

score was for attending mechanically-based classes (M = 2.8, SD = 1.1), indicating that disengagement of this

kind is relatively uncommon. Overall, the results suggest that students generally experience their studies to be

stimulating and intellectually rewarding.

FINDINGS FOR MOTIVATION VS DEMOTIVATION

This section presents data for addressing research question 3: How do the means for motivation and demotivation

differ?

Table 4-Comparison of the Mean for Motivation and Demotivation

Based on Table 4, comparison of the mean values for motivation and demotivation shows that students reported

a higher overall level of motivation (M = 3.8) compared to demotivation (M = 3.4). This indicates that, on

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average, students are more driven and positively inclined towards their studies than they are discouraged or

disengaged. The higher mean for motivation suggests that positive attitudes and enthusiasm for learning are

more dominant among the participants than feelings of disinterest or lack of drive although the difference

between the two constructs is moderate.

Exploratory Statistics

Findings On The Relationship Between Motivation And Demotivation In Learning.

This section presents data for addressing research question 4: Is there a relationship between motivation and

demotivation in learning?

Data were analysed using SPSS to determine correlations and a significant association in the mean scores

between motivation and demotivation in learning. Results are presented separately in Table 5.

Table 5 - Correlation between Motivation and Demotivation in Learning

Table 5 shows that there is an association between motivation and demotivation factors in learning. The

correlation analysis shows that there is a high significant association between motivation and demotivation

factors in learning (r=.338**) and (p=.000). According to Jackson (2015), coefficient is significant at the .05

level and a positive correlation is between 0.1 to 1.0 on the 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 demotivation factors in

learning.

CONCLUSION

Summary of Findings and Discussions

This study explored learners’ motivation and burnout by focusing on the relationship between motivational and

demotivational factors in learning.

Intrinsic Motivation

Learners reported high satisfaction when engaging with challenging content that stimulates curiosity (Syed

Husain et al., 2025; Wan Mohd et al., 2024), which is consistent with findings that intrinsic motivation enhances

personal accomplishment (Felaza et al., 2020). They valued activities that encourage discovery and assignments

that enhance understanding, even without guaranteed high grades. However, even motivated learners can burnout

under pressure, especially when anxiety and perfectionism are present (Azhari et al., 2023; Zolkapli et al., 2024).

Extrinsic Motivation

High grades, improved GPA, and social recognition are key motivators (Syed Husain et al., 2025; Wan Mohd et

al., 2024; Koenka et al., 2021). Performance-oriented goals drive persistence but may encourage surface learning

if intrinsic engagement is lacking (Ryan & Deci, 2020; Koenka et al., 2021; de Bruin et al., 2024). Malaysian

students often equate success with GPA and family/peer validation (Rahman et al., 2024). These findings

highlight that extrinsic incentives support short-term achievement but require intrinsic engagement for

meaningful learning.

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Task Value Beliefs

Learners value content that is relevant, enjoyable and transferable. Perceived utility builds persistence (Wan

Mohd et al., 2024; Shehzad et al., 2024; Lauermann et al., 2023). However, task value alone is insufficient for

sustained engagement, whereas, competence support and conducive learning conditions are essential (Hulleman

& Harackiewicz, 2021; Eccles & Wigfield, 2020).

Expectancy Components

Strong self-efficacy was observed for completing tasks, understanding materials and achieving high grades.

Confidence promotes persistence and achievement but is moderated by anxiety, task value and self-regulation

(Bandura, 1997; Schunk & DiBenedetto, 2020; Honicke & Broadbent, 2016; Putwain et al., 2021). Control

beliefs also positively influenced engagement, particularly when coupled with high task value and low anxiety,

though their effectiveness decreased under limited support or undervalued tasks (Li et al., 2023; Putwain et al.,

2021; Liem et al., 2021).

Affective Component – Motivation vs. Demotivation

Social anxiety and cognitive interference were common, and this undermined performance and confidence. Test

anxiety manifested emotionally and physiologically, thus confirming that preparedness alone does not prevent

stress (Rahmat, 2024; Amaruddin et al., 2023; Khaira et al., 2024). Interventions, such as emotional regulation

training and mindfulness, had effectively reduced anxiety and improved outcomes (Barattucci et al., 2022; Ismail

et al., 2023).

Burnout and Exhaustion

Students experienced substantial physical and emotional exhaustion, with chronic fatigue and limited recovery

post-class. Burnout reduces engagement and performance despite workload management (Schaufeli et al., 2002;

Salmela-Aro & Upadyaya, 2014; Ibrahim et al., 2024; Rahmat, 2023), while resilience, coping strategies and

mindfulness-based interventions can buffer these effects (Li et al., 2021; Ismail et al., 2023; Barattucci et al.,

2022).

Motivation–Demotivation Interaction and Disengagement

Learners reported high engagement and intellectual stimulation but showed early signs of disengagement,

including mechanical attendance or negative talk about studies. This gradual disengagement aligns with burnout

frameworks (Schaufeli et al., 2002; Salmela-Aro et al., 2016). Preventive strategies, such as peer support,

mindfulness and reflective practices, help maintain academic commitment (Upadyaya & Salmela-Aro, 2020;

Ismail et al., 2023).

Summary

Overall, learners demonstrated strong intrinsic and extrinsic motivation, high self-efficacy and task value

recognition. However, affective challenges, such as social anxiety, test stress and burnout, do coexist and subtly

affect engagement. These findings underscore the need for interventions that support psychological well-being,

competence and resilience in order to sustain meaningful learning.

Implications and Suggestions for Future Research

Theoretical and Conceptual Implications

This study adopted Pintrich and De Groot’s (1990) framework for examining motivation through expectancy

(self-efficacy and control beliefs), value (intrinsic and extrinsic goal orientation) and affective (emotional

responses to learning) components, alongside two central demotivation constructs, as suggested by Campos et

al. (2011), namely cognitive disengagement and emotional exhaustion. These models played a crucial role in

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capturing the positive and negative forces that shape students’ engagement, as the data revealed that motivation

and demotivation co-existed in the same learners.

Based on the framework, intrinsic motivation in this study was strongly tied to students’ desire to understand

course content and meaningfully apply it, while extrinsic motivation centred on achieving high grades and

meeting family or peer expectations. Expectancy beliefs were reflected in students’ moderate confidence (self-

efficacy) and the belief that effective study strategies could lead to mastery (control beliefs). However, the

affective component highlighted a notable presence of anxiety, mainly about performing worse than peers, thus

affirming the value of including emotional responses in the model.

Campos et al.’s (2011) demotivation dimensions aligned well with the burnout findings. Emotional exhaustion

emerged as the most salient demotivator, with students reporting physical and mental fatigue that required

extended recovery time. Cognitive disengagement was evident in mechanical participation and detachment from

learning tasks, although some students maintained interest and challenge-seeking despite the fatigue.

The coexistence of high motivation and notable demotivation underscores the need for teaching strategies that

address both aspects simultaneously. According to the SRL perspective embedded in Pintrich’s model,

interventions should develop goal-setting, time management and sustained focus strategies to help learners

maintain performance despite fatigue. Emotion regulation support is equally important for mitigating the anxiety

and exhaustion revealed in this study. Technology, especially mobile platforms with planning and reflection

prompts, can strengthen self-regulatory habits, but long-term integration is needed to build lasting skills.

Overall, the theoretical framework had successfully captured the interplay between motivation and demotivation,

which allowed this study to comprehensively address the four research questions. However, the findings suggest

that future adaptations of the framework should provide a more balanced perspective to the affective dimension,

as emotional states appear to influence both motivational and demotivational processes more strongly than

anticipated.

Suggestions for Future Research

Longitudinal designs are needed to track changes in SRL, motivation and burnout throughout the semesters.

Most contemporary studies are cross-sectional and offer only a snapshot of learners’ experiences (Trautner &

Pinquart, 2025). Following the same learners over time can reveal when changes occur, how these processes

influence each other, and when interventions will be most impactful.

Technology-based support should also be tested over longer periods. Mobile SRL support is effective and easy

to use but should be tested over time for its impact on reducing demotivation and fatigue (Alshammari &

Alkhabara, 2025).

Pedagogical agents, or virtual characters in digital learning environments that guide, support and interact with

learners, can increase self-efficacy and interest. However, shifting intrinsic motivation may require meaningful

and continuous use supported by strong design (Gladstone et al., 2025). Research can explore how to integrate

these agents more effectively.

The affective dimension should be expanded. Emotion regulation, grit and self-compassion are linked to lower

demotivation and better learning experiences (Zhang, 2025). Future studies can test these skills in different

subjects, delivery modes and cultural contexts, as well as examine how they interact with SRL training in order

to reduce burnout.

Overall, studies should explore the short-term effects of these strategies and also their long-term impact on

learner profiles and contexts. This will help identify approaches that build lasting self-regulation and motivation.

ACKNOWLEDGEMENTS

This paper represents a joint effort of all the authors, and we gratefully acknowledge each other’s contribution

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to the development, analysis and writing of this article. We also like to thank the reviewers for their constructive

feedback that helped improve the final version of the paper.

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