The Impact of Perfectionism, Self-Efficacy, Academic Stress, and Workload on Academic Fatigue and Learning Achievement: Indonesian Perspectives

2.1 Perfectionism

Ocampo, Wang, Kiazad, Restubog, and Ashkanasy (2020) define perfectionism as a strong desire for flawlessness, where perfectionists strive for high standards in all areas of life and frequently engage in critical self-evaluation and evaluation of others. This characteristic can be divided into three distinct classifications: adaptive perfectionism, which involves setting high benchmarks while harboring a fear of committing errors; maladaptive perfectionism, which entails an excessive fixation on mistakes and the opinions of others; and socially prescribed perfectionism, which encompasses feeling compelled to meet societal expectations and being overly critical of one’s deficiencies (Hewitt, Mikail, Dang, Kealy, & Flett, 2020). Perfectionism, particularly when it becomes maladaptive, has a substantial role in causing academic tiredness, surpassing other factors (Collin, O’Selmo, & Whitehead, 2020). University students who exhibit adaptive perfectionism generally have higher academic performance, whereas those with maladaptive perfectionism are more susceptible to experiencing academic fatigue (Fisher, Hageman, & West, 2023). Adaptive perfectionists strongly believe in their capacity to accomplish goals and conquer obstacles, while maladaptive perfectionists prioritize evading failure. Research indicates that the fundamental mechanism of maladaptive perfectionism involves an increased sensitivity to failure, which activates stress responses that diminish cognitive and emotional resources (Hewitt et al., 2020). In contrast, adaptive perfectionism supports resilience by framing challenges as opportunities for growth rather than threats, mitigating the onset of academic fatigue (Collin et al., 2020). High levels of self-oriented perfectionism may improve performance driven by intrinsic motivation; however, they can also lead to academic fatigue due to setting unrealistic goals. The interaction between these forms of perfectionism highlights their varying effects on academic fatigue and performance.

H1: Does perfectionism have a positive impact on academic fatigue?

H2: Does perfectionism have a positive impact on academic achievement?

2.2 Self-Efficacy

Self-efficacy refers to an individual’s capacity to assess and effectively carry out the activities required for a specific performance (Schunk, 2023). The social learning theory posits that individuals can effectively manage stress, meet demands, and overcome challenges (Yarberry & Sims, 2021). Bandura highlighted the significant impact of self-efficacy on an individual’s decision-making on rules and activities, their abilities, the level of effort they exert, and their persistence in addressing difficulties or challenges (Arghode, Heminger, & McLean, 2021). Individuals with a strong sense of self-efficacy are more likely to persevere and overcome undertaking challenges. It can concurrently improve the experience and self-efficacy (Dixon, Hawe, & Hamilton, 2020). Self-efficacy functions as a psychological buffer, mitigating the adverse impacts of academic stress and fatigue on students. High self-efficacy enhances control over academic tasks, promotes proactive coping strategies, and diminishes the risk of burnout (Ortan, Simut, & Simut, 2021).

In contrast, low self-efficacy increases feelings of helplessness, worsening fatigue, and disengagement in educational environments. As highlighted in recent studies, technology integration in educational settings has been found to enhance self-efficacy, mainly when supported by solid institutional frameworks (Akram et al., 2022). Technology supports proactive coping strategies, helping students build resilience and confidently manage academic tasks (Al-Adwan et al., 2024). Furthermore, competition can foster consistency in response to stressful circumstances, bolstering an individual’s self-assurance in successfully navigating and surmounting challenging periods (Alaniz et al., 2013). Self-efficacy is a significant determinant linked to academic fatigue. Individuals with a strong sense of self-efficacy are more inclined to opt for demanding occupations that require effort (Ortan et al., 2021). They exhibit extraordinary tenacity and a heightened sense of urgency when faced with several obstacles to accomplish the assigned work. Furthermore, there is an inverse relationship between self-efficacy and anxiety, such that as self-efficacy increases, anxiety decreases. Self-efficacy is widely regarded as a significant determinant of academic stress and fatigue (Qin et al., 2022).

H3: Does self-efficacy have a positive impact on academic fatigue?

2.3 Academic Stress

Chyu and Chen (2022) define academic stress as the anxiety and stress caused by academic demands from teachers or parents, including achieving high grades and meeting project deadlines in possibly uncomfortable settings. This stress is exceptionally high in universities due to increased expectations, competition, and significant environmental changes, leading to higher student burnout rates (Graves, Hall, Dias-Karch, Haischer, & Apter, 2021). Differentiates stress into negative “distress” and positive “eustress,” impacting students’ career prospects and well-being (Van Slyke, Clary, & Tazkarji, 2023). Factors contributing to university stress include heavy coursework, challenging exams, part-time jobs, and extracurricular commitments, with prolonged stress leading to burnout (Cho & Hayter, 2020). Fatigue affects students’ ability to learn and manage academic responsibilities, highlighting the need for educational resilience (Chen et al., 2023). Extended academic stress disturbs students’ physiological and psychological equilibrium, increasing cortisol levels, fatigue, and reduced motivation (Yusli et al., 2021). Research highlights the importance of technological support in mitigating stress levels among students, as access to digital learning tools can streamline workloads and enhance time management skills (Akram et al., 2022). This effect is especially significant in emerging economies, where technology is critical to improving learning experiences and reducing stress (Al-Adwan et al., 2024). The differentiation between eustress and distress elucidates how students’ perceptions of stressors influence their outcomes – resulting in either constructive energy for growth or harmful burnout. Recognizing these types of stressors can assist institutions in designing support programs that alleviate distress and utilize eustress for academic and personal development.

H4: Does academic stress have a negative impact on academic fatigue?

2.4 Workload

Thornby, Brazeau, and Chen (2023) noted that intense workloads from studies, assignments, and extracurriculars cause academic fatigue in college students. Such workloads significantly cause academic fatigue, reducing learning achievement (Ortan et al., 2021). Wang and Littlewood (2021) discovered that too many tasks demotivate pupils, and a boring teaching style makes the class more complicated to understand. Although effort is acceptable, excessive extracurricular activity might distract from academics (Mason, Ronconi, Scrimin, & Pazzaglia, 2022). This fatigue is also caused by instructors’ preparation and evaluation time, which can influence their well-being and teaching efficacy (Bardach, Klassen, & Perry, 2022). Chen et al. (2022) emphasized that overloading pupils with courses can cause academic fatigue and exceed physical limits. Technological integration offers potential relief from high workloads, as efficient digital tools enable students to manage time and academic responsibilities better, ultimately helping reduce fatigue (Akram et al., 2022). Studies show that enhanced access to technology, supported by institutional resources, improves student workload management and academic motivation (Al-Adwan et al., 2024). Excessive workloads adversely affect students’ capacity to dedicate adequate time for recovery, resulting in chronic fatigue and reduced cognitive performance. The relationship is heightened when students view tasks as unmanageable or misaligned with their learning abilities (Wang & Littlewood, 2021). Addressing workload challenges requires reducing task volume and enhancing task clarity alongside the implementation of structured support systems to mitigate academic fatigue.

H5: Does workload have a positive impact on academic fatigue?

H6: Does workload have a positive impact on academic achievement?

2.5 Academic Fatigue

Fatigue is characterized by a decline in motivation and challenges in fulfilling work responsibilities, which also extends to the educational domain as a diminished enthusiasm for studying and accomplishing academic assignments (Guo, Liu, Chen, Chai, & Zhao, 2022). Students experiencing complex academic challenges sometimes suffer from stress characterized by mental and physical exhaustion (Salgado & Au-Yong-oliveira, 2021). Academic fatigue detrimentally affects students’ motivation to study and meet academic obligations since demanding expectations frequently undermine their physical and mental preparedness (Juárez & Becton, 2024). Academic fatigue is characterized by a depletion of physical and emotional energy from extended exposure to stressors without sufficient recovery. This condition depletes cognitive resources, resulting in diminished focus, slower information processing, and compromised problem-solving capabilities (Guo et al., 2022).

Furthermore, fatigue can create a cycle of disengagement, as students experiencing exhaustion are less inclined to engage actively in learning, which diminishes their academic performance. Nevertheless, specific population groups, such as married medical students, exhibit resilience due to a robust and inspiring personal objective that aids them in effectively managing academic stressors (Kassymova et al., 2023). The impact of interest in one’s subject of study on reducing academic fatigue is still being determined (Milyavskaya, Galla, Inzlicht, & Duckworth, 2021). In addition, academic fatigue has been associated with significant health conditions such as cardiovascular disease and hypertension, as well as depression, reduced academic achievement, and absenteeism (Lau, Chow, Wong, & Lim, 2020; Yahya, Abutiheen, & Al-Haidary, 2021). Students experiencing academic fatigue may display physical and psychological debilitation symptoms, hindering their ability to handle difficulties. Academic fatigue may indicate Behaviors such as cheating, truancy, and excessive gaming, which may suggest that students are trying to avoid these pressures. Moreover, it can change a person’s mindset, such as cynicism and arrogance (Bui, Bui, & Nguyen, 2022).

H7: Does academic fatigue have a positive impact on learning achievement?

2.6 Academic Achievement

Academic achievement encompasses conceptual understanding, critical thinking, creativity, social–emotional factors, independence, and the practical application of knowledge in everyday life, indicating educational success and scientific advancement (Elmi, 2020). The fact serves as a thorough assessment of students’ capacity to acquire and apply knowledge effectively. Active learning entails the acquisition of knowledge, skills, and understanding through engagement, prioritizing intrinsic motivation over reliance on external factors (Lombardi et al., 2021). The relationship between academic achievement and its predictors, including perfectionism, self-efficacy, academic fatigue, and stress, is substantial. Adaptive perfectionism and high self-efficacy improve academic performance by promoting goal-setting, persistence, and resilience (Alaniz et al., 2013; Arghode et al., 2021). These attributes enable students to effectively address academic challenges while sustaining focus and motivation. Maladaptive perfectionism fatigue and stress reduce students’ ability to focus and realize their academic potential. Understanding these dynamics is crucial for developing evidence-based educational strategies that enhance resilience and promote student success. Furthermore, technology adoption in teaching practices, as shown in recent studies, significantly influences student engagement, performance, and the development of digital competencies required for academic achievement (Akram et al., 2022). Understanding these dynamics is critical for designing evidence-based educational strategies to promote resilience, reduce stress, and improve student success.

Learning, a change in behavior due to environmental factors, is fundamental to academic success (Clayton, 2020). Experiential learning emphasizes hands-on engagement and promotes enduring behavioral change through significant experiences (Rogti, 2021). Quantitative, institutional, and qualitative learning approaches support the significance of experience-based education for deep understanding and retention (Wiley et al., 2021). Learning achievement encompasses physical and emotional engagement and cognitive development, highlighting its holistic influence on individual behavior (Claver et al., 2020). This comprehensive approach emphasizes the dual function of education: delivering knowledge and skills while equipping individuals for practical application in real-world contexts. Evaluation methods, including quantitative assessments and qualitative feedback, are essential for capturing the diverse outcomes of learning processes. Research highlights the significance of psychosocial factors – including perfectionism, self-efficacy, academic stress, and workload – in influencing academic burnout and, subsequently, learning outcomes (Alaniz et al., 2013; Arghode et al., 2021; Bardach et al., 2022). These studies demonstrate intricate relationships between these variables and academic performance, highlighting the necessity for a detailed comprehension of their interactions. This research investigates these interactions to guide the development of targeted educational interventions that enhance students’ well-being and academic outcomes.

3.1 Instrumentation

The questionnaire instrument in this research consists of two different parts. The first part collected participant demographics, and the second part included 22 statements from 5 constructs from the study (Qin et al., 2022). The survey by Qin et al. (2022) presented six constructs: perfectionism, self-efficacy, academic stress, workload, and academic burnout, taken from previous research. In this study, the researcher did not use a coping strategy framework. The researcher emphasized learning performance as a substitute for building coping strategies. This study intentionally replaced coping mechanisms with learning performance in the Qin et al. (2022) paradigm. This was done because the current research prioritizes the direct effect of predictor variables like perfectionism, workload, and academic stress on learning achievement over intermediary coping techniques. Practicality is another reason to focus on learning performance, especially in Indonesian higher education, where institutional aims promote academic accomplishments over psychological changes (Kotera et al., 2022). The constructs include Professionalism (four items), Self-Efficacy (4), Academic Stress (3), Workload (4), Academic fatigue (4), and learning achievement (3) (Mehrvarz et al., 2021). Each item uses a Likert scale (“always,” “strongly agree,” to “strongly disagree”) to facilitate institutional acceptance and ease of research. Additional study validation was provided by an expert panel consisting of two instructors, one undergraduate, and two respondents. To increase the credibility of the instrument, the content validity index (CVI) test was used, following the rules for assessing instrument reliability given by Behaghel and Vogt (2007), Hertzog (2008), and Polit and Beck (2008). Our findings were consistently above the 0.8 CVI criterion, verifying the authenticity of statement items and highlighting the importance of expert judgment in evaluating their relevance, clarity, and simplicity.

Table 2 presents descriptive information about the demographic characteristics of the participants in this study. Demographic data reveals that the students were categorized by age, with 73 (33%) under 20 and 145 (67%) over 20. In addition, the individuals were classified based on their gender, with 80 (37%) being male and 138 (63%) being female. According to the semester rate, there were 47 students (22%) in Semester 1–2, 101 students (46%) in Semester 3–4, 31 students (14%) in Semester 5–6, and 39 students (18%) in Seventh and Upper Semester. This distribution helps explain academic stress and effort across a student’s career.

Studies show that cognitive demands, time management issues, and graduation-related obligations influence academic stress. Early-semester students must learn time management, academic adjustments, and study tactics. This first year might be challenging as students balance academic and personal obligations (Marcenaro-Gutierrez et al., 2023). For mid-level students, core themes, projects, and exams require additional effort and time, especially in Semesters 3–4. Advanced classes and extracurriculars steepen students’ learning curves, raising academic stress (Marcenaro-Gutierrez et al., 2023). However, in semesters 5–6 and beyond, students commonly experience graduation-related pressures. The last academic phases involve more significant work on projects, internships, and career preparation. Weariness and stress may emerge from balancing academic obligations with post-graduation preparations such as job placements and advanced study applications (Thakkar et al., 2020; Rosli, 2022). Understand these demographic and academic differences because they may moderate how perfectionism, self-efficacy, and workload affect academic outcomes. This study ensures a comprehensive understanding of academic experiences across diverse student backgrounds and stages, capturing the full spectrum of academic stress and performance factors.

The analysis process is outlined in a sequential workflow, beginning with confirmatory composite analysis (CCA) steps followed by the Structural Model Assessment. Each step is designed to ensure the model's reliability, validity, and predictive power, as described below. The analysis begins with CCA, which evaluates Indicator Significance to establish measurement item relevance. Indicator Reliability is then assessed to ensure each item accurately represents its construct. Construct Reliability checks internal consistency, then Convergent Validity checks that each construct captures enough variance from its indications. To ensure concept distinction, Discriminant Validity is tested.

After the CCA, the Structural Model Assessment starts with a Multicollinearity Check to eliminate predictor variable overlap. Next, path coefficients and hypothesis testing will be analyzed to determine the strength and relevance of variable associations. The Predictive Ability (R ²) test evaluates the degree to which independent variables explain each dependent variable. Next, Effect Size (f ²) is determined to assess the impact of each predictor. Finally, Predictive Relevance (Q ²) is evaluated to verify the model’s ability to predict outcomes beyond the sample.

3.2 Data Analysis

PLS-SEM was chosen for its robust predictive capabilities. Innovative PLS software was used for data analysis and hypothesis evaluation; following Hair et al. (2011), PLS-SEM was used to model the factors influencing academic fatigue and student achievement. The study’s instruments were validated using Smart PLS to strengthen the research design. This validation process ensures accurate measurement of the intended variables. Initially, unprocessed CSV data was imported into Excel. After inputting the raw data, the data analysis phase followed the designated protocol.

Figure 1 shows the correlations between Perfectionism, Self-Efficacy, Academic Stress, Workload, Academic Fatigue, and Learning Achievement. Many indicators (yellow boxes) measure each construct, with numbers adjacent to arrows demonstrating how well each indicator represents its construct. Values near to 1 imply strong representation. Arrows between the key structures (blue circles) reflect path coefficients or effect strength. Perfectionism reduces academic weariness (−0.377), while Academic Stress increases it (0.409). Increased effort increases Academic Fatigue (0.255) and Learning Achievement (0.261). The blue circles show R-squared values for each construct’s variance explained by the model (e.g., 29.2% for Academic Fatigue and 44.5% for Learning Achievement). The model shows that Academic Fatigue negatively impacts Learning Achievement (−0.366), although Workload positively impacts it. This structure clarifies direct and indirect construct interactions in the investigation.

Figure 1

PLS algorithm processing results.

3.3 CCA

Step 1: Determine indicator significance using standardized loading (≥0.708) and t-statistics (>±1.96) for a two-tail test at a 5% significance level (Hair, Hult, Ringle, & Sarstedt, 2022). Confidence intervals can also assess loadings’ significance and practical value (Leguina, 2015; Shmueli et al., 2019). SmartPLS 3.2.9 was utilized for this analysis, with results in Table 3 and Figure 1. Step 2: Assess indicator reliability by squaring their loadings, a process known as reliability indicator (Hair et al., 2020). Step 3: Evaluate construct reliability through Cronbach’s alpha (α) and composite reliability (CR), requiring values above 0.70. Excessively high reliability (>0.95) suggests redundancy (Hubona, Schuberth, & Henseler, 2021). Hair et al. (2020) state that high CR values indicate redundancy. Still, it is legitimate if the concept measures a well-defined trait like academic fatigue, which involves closely related aspects. The high CR reinforces the Academic Fatigue construct’s internal consistency, capturing crucial student fatigue factors. Step 4: Check convergent validity using the average variance extracted (AVE) with a threshold of 0.5. The study shows AVE values for “self-efficacy” and “academic fatigue” at 0.664 and 0.851, respectively, exceeding the criterion (Henseler, Ringle, & Sarstedt, 2015). Step 5: Assess discriminant validity using the Heterotrait-Monotrait ratio (HTMT) with values below 0.900 indicating significant validity. Smart PLS 3.2.9 employs the Fornell–Larcker Criterion and HTMT for this assessment (Henseler, 2015; Sarstedt, Radomir, Moisescu, & Ringle, 2022). All HTMT values in Table 5 meet this standard.

The study assessed discriminant validity using Fornell-Larcker and cross-loading criteria, as shown in Table 4. Values outside the diagonal in the table indicate associations between variables, while diagonal values represent the square root of AVE, indicating each variable’s variation. Higher AVE values suggest more substantial discriminant validity, with the square root value of AVE for each variable exceeding its correlation with other variables (Henseler, 2015). Additionally, the findings of the HTMT ratio approach are presented in Table 5.

Experts recommend the HTMT ratio for discriminant validity, as cross-loading and Fornell–Larcker criteria may not be sensitive enough. HTMT compares correlations between different structures and within the same structure, with a value below 0.9 indicating good discriminant validity. The study’s HTMT values showed academic fatigue (0.478), perfectionism (0.601), academic achievement (0.641), self-efficacy (0.772), and academic stress (0.871) to workload; perfectionism (0.095), learning achievements (0.561), self-efficacy (0.304), and academic fatigue (0.474) with academic fatigue; and learning achievements (0.476), self-efficacy (0.735), and academic stress (0.728) concerning perfectionism. Additionally, self-efficacy and academic achievement had correlation coefficients of 0.528 and 0.689, respectively. However, accurately assessing discriminant validity requires using multiple methods within the specific study context, including HTMT, cross-loading, and Fornell-Larcker criteria.

3.4 Structural Model Assessment

In Step 1, the study focused on evaluating multicollinearity in structural models, which is crucial for the integrity of regression analysis. Multicollinearity, which affects the beta coefficient’s value and direction, was assessed using variance inflation factor (VIF) values. VIF values below 5.0 suggest an absence of multicollinearity. The study also considered bivariate correlations, with values over 0.50 indicating potential multicollinearity issues. Results, shown in Table 3, revealed that all VIF values were below 5.0, confirming no significant multicollinearity in the study (Figure 2) (Habibi, Riady, Samed Al-Adwan, & Awni Albelbisi, 2023).

Figure 2

Bootstrapping processing results.

In Step 2, the study examined the size and significance of path coefficients to test hypothetical relationships between variables like perfectionism, self-efficacy, academic stress, and workload and their effects on academic fatigue and learning achievement. Path coefficients, which range from +1 to −1, indicate the strength of prediction, with values closer to 0 showing weaker predictive power and values closer to ±1 indicating more substantial predictive power. The study employed bootstrapping (500 samples) to evaluate these relationships. The results, detailed in Table 6, showed significant p-values for all seven hypotheses, including Perfectionism → Academic fatigue (p = 0.000), Perfectionism → Learning achievement (p = 0.000), Self-efficacy → Academic fatigue (p = 0.000), Academic stress → Academic fatigue (p = 0.000), Workload → Academic fatigue (p = 0.020), and Workload → Learning achievement (p = 0.003), Academic Fatigue → Learning Achievement (p = 0.000). This indicated significant relationships between these variables in the structural model.

Step 3, the study used the R ² metric, similar to a double regression model, to evaluate the predictive ability of the structural model. The R ² metric, also known as the coefficient of determination, measures the predictive power of endogenous constructs within the model. It’s important to understand that R ² values are specific to the data samples used and should not be extrapolated to the entire population (Rigdon, 1996). The R ² value ranges from 0, indicating no predictive power, to 1, indicating complete predictability, although achieving a value of 1 is rare. Researchers should compare R ² values with similar studies to gauge their model’s efficacy. Customizable R ² values, which adjust for sample size and number of predictors, are also used in some fields (Hair, 2021). R ² values of 0.75, 0.50, and 0.25 indicate high, moderate, and low levels of predictive efficacy, respectively (Sarstedt et al., 2022). Hair et al. (2021) suggest that R ² values of 0.67, 0.33, and 0.19 broadly indicate strong, moderate, and weak levels of predictive power. In this study, the results in Table 7 showed that the test moderately determines the learning achievement variable, whereas the test weakly determines academic fatigue. The finding implies that the learning performance variables have moderate explanatory power for their variability, but the academic fatigue variable shows weak explanatory power.

In Step 4, the study focused on evaluating the effect size, which measures the predictive capability of each independent variable in the structural model. The process was done by removing a predictor variable and observing the change in the R ² value, a process automated by SmartPLS. The difference in R ² values with and without the predictor determines the predictor’s significance. Effect sizes are denoted as F2 and categorized as small (0.02–0.15), medium (0.15–0.35), and significant (above 0.35) (Cohen, 1988). This categorization helps in understanding the predictive measure within the sample. Table 8 indicates that most variables negatively influence Academic Fatigue and Learning Achievement. Self-efficacy exerts a minimal impact, but perfectionism, academic stress, and workload demonstrate relatively minor effects. The sole moderate effect is from Academic Fatigue on Learning Achievement (0.193), signifying a minor impact on educational attainment.

Step 5: The third metric used to evaluate the prediction is the value of Q ², also known as blindfolding (Geisser, 1974; Stone, 1974). Some scholars regard this metric as an assessment of predictive power beyond the sample, and so far, it is. However, this metric is not a model prediction metric as strong as PLSpredict, as described in the next step. When interpreting Q ², values greater than zero have meaning, while values below 0 indicate a lack of prediction relevance. In addition, Q ² values greater than 0.25 and 0.50 represent the PLS-SEM model’s medium and sizeable predictional relevance. Redundant cross-validation (Q ²) or Q-square test is used to evaluate the predictive significance of the model. If the Q ² value is >0. the model has accurate prediction capabilities for a particular variable. Conversely, if the value of Q ² is <0. it shows that a model does not have a significant prediction value (Hair, Hollingsworth, Randolph, & Chong, 2017) This study shows measurements using cross-validated redundancy (Q ²) in Table 9. The results show whether Q ² results in this study are academic fatigue (0.244) and learning achievement (0.348).

4.1 Practical Implications

This study offers various recommendations for education and teacher-training colleges to reduce academic fatigue and improve student success. First, programs that address maladaptive perfectionism can help students set realistic academic goals and handle self-criticism, lowering fatigue. Institutions can offer goal-setting, self-compassion, and positive reinforcement programs and counseling. Peer mentoring, resilience-building seminars, and skills training can help students increase self-efficacy and manage academic problems. Faculty may reduce academic stress by assigning straightforward, reasonable tasks and setting realistic deadlines. Time management tools and flexible deadlines can improve academic conditions. Resilience training on stress management and emotional regulation can help students handle academic tiredness. Finally, providing counseling services, wellness check-ins, and online resources will foster student resilience and stress management. Educational institutions can improve student well-being and academic balance by taking these steps.