233 © 2024 Conscientia Beam. All Rights Reserved. Translation, cross-cultural adaptation and validation of the MBI-SS questionnaire for Chinese art students Zhang Meng1 Lay Yoon Fah2+ 1Faculty of Social Sciences and Liberal Arts, UCSI University, Kuala Lumpur, Malaysia. Email: 1002162322@ucsiuniversity.edu.my 2Faculty of Psychology and Education, Universiti Malaysia Sabah, Kota Kinabalu; Faculty of Social Sciences and Liberal Arts, UCSI University, Kuala Lumpur & School of Liberal Arts and Sciences, Taylor's University, Kuala Lumpur, Malaysia. Email: layyf@ums.edu.my (+ Corresponding author) ABSTRACT Article History Received: 21 November 2023 Revised: 25 January 2024 Accepted: 1 March 2024 Published: 27 March 2024 Keywords Academic burnout Art students Concurrent validity Exploratory factor analysis Learning Burnout Maslach Burnout inventory. The 16-item Maslach Burnout Inventory-Student Survey (MBI-SS) was translated into simplified Chinese (MBI-SS-C) and tested for reliability and validity among Chinese art college students. A sample of 437 students from a Beijing art college was analyzed. The MBI-SS-C with 16 items shows relatively poor psychological measurement characteristics for items 2 and 7. The remaining 14 items showed good validity and reliability after being removed. Conf irmatory factor analysis conf irmed the structural validity of the 14-item MBI-SS-C. The Cronbach alpha for the MBI-SS-C was 0.854, with the three subscales having Cronbach alpha of 0.898, 0.890 and 0.902. The 14-item MBI-SS-C has a moderate correlation with general self-efficacy (GSE) and high concurrent validity. The 14-item MBI-SS-C is a reliable tool for evaluating burnout in Chinese art college students and is simple and effective. Contribution/Originality: This study evaluated the reliability and validity of the Maslach Burnout Inventory - Student Survey (MBI-SS) in Chinese art college students. It provides a fundamental and pioneering analysis of the status and factors influencing learning burnout among Chinese art college students. 1. INTRODUCTION The US-China trade war, COVID-19 and the Russia-Ukraine conflict have all contributed to supply chain disruptions and inflation that have slowed down global economic growth in recent years(Aharon, Aziz, & Nor, 2023; Chen, Zhang, & Miao, 2023; Diaz, Cunado, & de Gracia, 2023; Lee, Olasehinde-Williams, & Özkan, 2023). College graduates worldwide are facing increasingly severe employment pressure. China is the world's factory and a highly educated country with a vast population; therefore, the global recession has had a major effect on it. This has significantly impacted the employment landscape in China (Habib i, Habibi, & Mohammadi, 2022). It is harder for graduates of art colleges to get employment during tough t imes in society and the economy since they are under more pressure to find work (Stephen, 2021). Individuals must acquire additional knowledge and sk ills to compete in the job market. It is important to address these challenges in order to support their success. Art students often face greater employment and study pressures which can lead to burnout (Drăghici & Cazan, 2022; Liu et al., 2023). Accurately measuring learning burnout in art students is valuable for intervening in cases of burnout. Currently, there is no measurement tool suitable for a simplified Chinese background that is specifically designed to Humanities and Social Sciences Letters 2024 Vol. 12, No. 2, pp. 233-247 ISSN(e): 2312-4318 ISSN(p): 2312-5659 DOI: 10.18488/73.v12i2.3691 © 2024 Conscientia Beam. All Rights Reserved. https://orcid.org/0009-0005-2089-3787 https://orcid.org/0000-0002-5219-6696 file:///C:/Users/abdul%20wahab/Downloads/1002162322@ucsiuniversity.edu.my file:///C:/Users/abdul%20wahab/Downloads/layyf@ums.edu.my https://www.doi.org/10.18488/73.v12i2.3691 Humanities and Social Sciences Letters, 2024, 12(2): 233-247 234 © 2024 Conscientia Beam. All Rights Reserved. measure the learning burnout of Chinese art students. Developing a measurement tool suitable for the Chinese art student population is crucial. Research on Chinese college students' learning burnout has employed the Maslach Burnout Inventory-Student Survey (MBI-SS) developed by Schaufeli, Martinez, Pinto, Salanova, and Bakker (2002). However, it has not been tested whether the questionnaire is valid and re liable for Chinese art students; therefore, the accuracy of the measurement cannot be guaranteed. It is essential to ensure that the results can be compared with those of college students in other countries or majors to measure the learning burnout of Chinese art college students accurately. Professionals prof icient in both Chinese and English aim to translate the MBI-SS questionnaire into simplified Chinese. The survey will be given to Chinese art college students to assess their level of academic burnout. The data collected will be used to perform reliability and validity tests and any items that do not meet the measurement criteria will be eliminated. The outcome will be a dependable and valid measuring scale for learning burnout among Chinese art college students. The aim of this study is to evaluate the reliability and validity of MBI-SS among Chinese art students. The research questions are as follows: 1. To test whether the MBI-SS has the expected three-factor structure, namely exhaustion, learning cynicism and reduced efficacy in Chinese undergraduate art students. 2. Is MBI-SS highly reliable among Chinese college students majoring in art? 3. Check whether MBI-SS and its three subscales have an appropriate relationship with the covariate general self-efficacy (GSE) to demonstrate its effectiveness. 2. LITERATURE REVIEW The term 'learning burnout' in academic research can be traced back to the study of 'job burnout'. Freudenberger (1974) introduced the concept of burnout while studying the work pressure of service industry personnel. He described burnout as the ongoing exhaustion, melancholy and frustration people feel when others or organisations do not value or appreciate their effort. Later, Maslach and Jackson (1986) began to further study job burnout. He believed that "job burnout is a state of mental exhaustion and emotional exhaustion which refers to a long-term reaction of individuals, including emotional exhaustion, cynicism and a low sense of accomplishment because they cannot effectively cope with various kinds of continuous pressure at work." Researchers who are conducting deeper research are starting to view burnout from the standpoint of education rather than work. Pines (1981) first proposed the concept of student burnout. Salmela-Aro and Tynkkynen (2012) demonstrated that young people in academic fields are more likely to experience burnout than those in the workforce. According to their definition, learning burnout is a situation in which students have a pessimist ic attitude towards their academics and a diminished sense of passion for their studies and school-related activities resulting in a depressed state. The most influential of the many theories on the structure of learning burnout is the three -dimensional theory proposed by Maslach. Maslach and Jackson (1981) developed an important measure of burnout known as the MBI Scale which contains 22 items. The scale mainly evaluates job burnout in three dimensions: emotional exhaustion, depersonalization, and personal achievement. The MBI scale is regularly updated and optimized to meet changing needs. Currently, there are five available versions. The General Survey (MBI-GS) version is intended for individuals who do not work in human service or educational occupations. This includes customer serv ice, manufacturing, corporate management and other common jobs (Maslach, Schaufeli, & Leiter, 2001). A popular modification of the MBI created especially for professionals in the human services field is the Human Serv ices Survey (MBI-HSS). It is suitable for a variety of occupations, including nurses, doctors, social workers and police officers (Maslach, Jackson, & Leiter, 1997). The MBI-HSS is a survey designed for medical personnel to assess their experience in human services (Lheureux, Truchot, Borteyrou, & Rascle, 2017). The MBI-ES is a survey designed for teachers, teaching administrators, teaching support staff and education volunteers (Firth, McIntee, McKeown, & Britton, 1985). The Survey for Students (MBI-SS) is specifically Humanities and Social Sciences Letters, 2024, 12(2): 233-247 235 © 2024 Conscientia Beam. All Rights Reserved. designed for students at universities and colleges (Schaufeli et al., 2002; Yavuz & Dogan, 2014). These MBI-derived scales all have the same three-dimensional structure with similar meanings. Scholars from various countries and cultural contexts have also tested the reliability and validity of the MBI-SS. According to the study by Wickramasinghe, Dissanayake, and Abeywardena (2018), the 15-item Sinhala version of the MBI-SS is a valid and useful instrument for measuring university student burnout. According to Montiel- Company, Subirats-Roig, Flores-Martí, Bellot-Arcís, and Almerich-Silla (2016), the MBI-HSS is feasible, effective and reliable in the measurement of dental student burnout. According to the study, burnout syndrome is highly prevalent among dental students. The authors suggest implementing interventions to reduce burnout levels in students. In 2009, Chinese scholar Hu tested the reliability and validity of Schaufeli's MBI-SS scale among Chinese students. The authors opted to keep these items in the study without altering or removing them despite the fact that decreased factor loading was noted (Hu & Schaufeli, 2009). A study by Morgan, De Bruin, and De Bruin (2014) on South African students showed that the passively worded ineffectiveness scale is more appropriate for the burnout scale's core component than the positively stated personal efficacy scale. Yavuz and Dogan (2014) conducted a study on the reliability and validity of MBI-SS among high school students in Turkey. The results indicated that the MBI-SS had a three-dimensional structure consist ing of fatigue, cynicism and reduced effectiveness and demonstrated very good reliability performance. Faye‐Dumanget, Carré, Le Borgne, and Boudoukha (2017) conducted research similar to this study on the validity and reliability of the MBI-SS scale in a French-speaking context using questionnaire data from 667 French university students. The results indicate that the three-dimensional structure can be maintained and the psychometric properties are satisfactory. Tsubakita and Shimazaki (2016) conducted a survey of 2061 Japanese college students who were also part of the East Asian cultural circle. The MBI-SS scale's initial 3-factor structure did not perform well on Japanese students and required revision. However, the scale can still be used to assess academic burnout among Japanese college students. Jia, Rowlinson, Kvan, Lingard, and Yip (2009); Lee et al. (2010); Moore and Loosemore (2014); Pérez-Mármol and Brown (2019) and Schaufeli et al. (2002) have shown that the factor structure of the MBI-SS scale is preserved across different cultural contexts but some items exhibit poor psychometric properties. This paper aims to test the reliability and validity of the simplified Chinese version of the MBI-SS using a sample of Chinese art students. Appropriate modifications will be made to ensure its reliability and validity. 3. RESEARCH METHODS 3.1. Participants 500 students from a Beijing art institution were chosen at random to participate in a survey for this study. Participation was voluntary and anonymous. 437 out of the 500 students answered the questionnaire obtaining 87.4% response rate. The respondents' ages ranged from 17 to 30 years old with a mean age of 21.86 years (standard deviation = 2.38) throughout the 437 valid samples. Of the respondents, 137 (31.4%) were male and 300 (68.6%) were female. Additionally, 199 (45.5%) were undergraduates and 238 (54.5%) were graduate students. 3.2. Instrument and Procedure Translate the Maslach Burnout Inventory–Student Survey (MBI-SS) used by Schaufeli et al. (2002) in a cross- country study into simplified Chinese. The original English questionnaire for MBI-SS is shown in Table 1. The original questionnaire has 16 items in three dimensions, namely exhaustion, learning, cynicism and reduced efficacy. MBI-SS was translated into simplified Chinese by two professionals who are fluent in both Chinese and Engl ish and have worked at Chinese universities for many years. The two translators worked independently . The results show that the original English version of MBI-SS is very similar to the simplified Chinese version. Chinese college students can benefit from the translated MBI-SS questionnaire items in terms of word expression, function, and culture. The translated MBI-SS questionnaire and the original English questionnaire also had 16 items. It should Humanities and Social Sciences Letters, 2024, 12(2): 233-247 236 © 2024 Conscientia Beam. All Rights Reserved. be noted that the seven items corresponding to the reduced efficacy sub-dimension are reverse scoring items (marked with R). Reverse coding should be done to these items in subsequent data analysis. The Chinese version of the MBI-SS questionnaire uses a 7-point Likert scale where 1 means very inconsistent and 7 means very consistent. The General Self-Efficacy (GSE) questionnaire developed by Zeng, Fung, Li, Hussain, and Yu (2022) was selected as the measurement of the covariate. The GSE questionnaire has been proven to have good reliability and validity for Chinese college students. The research that has already been done includes Charkhabi, Azizi Abarghuei, and Hayati (2013); Lee, Jeon, Lee, and Jeon (2015); Rohmani and Andriani (2021) and Yang, Sun, and Jiang (2022). It is noteworthy that there is a negative relationship between GSE and learning burnout despite the fact that GSE has been shown to have a strong correlation with learning burnout. Therefore, it is reasonable to select GSE as a covariate to test the concurrent validity of the simplified Chinese version of the MBI-SS questionnaire. In this study, the GSE questionnaire uses a 4-point Likert scale with 1 indicating very inconsistent and 4 indicating very consistent. The MBI-SS questionnaire and the GSE questionnaire adopt different scoring methods which can avoid the common method deviation caused by the same questionnaire type in program design (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Table 1. Items in the initial Maslach Burnout inventory–Student survey (MBI-SS). Exhaustion LB2. I feel lonely these days and don't know what to do. LB 5. I feel very tired at the end of a day's study. LB 8. Recently, I often feel exhausted. LB 12. At school, I often feel exhausted. Learning cynicism LB 3. I study so badly that I really want to give up. LB 6. I don't think I know it anyway, so it doesn't matter whether I learn it or not. LB 9. In terms of studying, I don't feel a sense of accomplishment. LB 10. I don't think study is meaningful to me. LB 13. I study with a cynical attitude. Reduced efficacy LB 1. (R) I can devote myself to study energetically. LB 4. (R) I can often achieve my goals. LB 7. (R) When studying, I forget everything around me. LB 11. (R)I can handle the exam well. LB 14. (R) I can effectively solve the problems in my study. LB 15. (R) I can always deal with study problems easily. LB 16. (R) It is easy for me to master what I have learned. Note: The label R indicates that the item is a reverse scoring item. 3.3. Data Analysis The aim of this study was to assess the reliability and validity of the MBI-SS questionnaire among Chinese art students. A total of 437 samples were randomly divided into two groups: Sample 1 (n=218) and sample 2 (n=219). Sample 1 was used for project analysis and exploratory factor analysis while sample 2 was used for confirmatory factor analysis. Descriptive analysis was conducted on all 437 samples. Firstly, a project analysis will be conducted to determine whether to retain different projects. The analysis will use three criteria: the CR value calculated by the critical ratio method should be greater than 3 (Fan, 1952). The relationship between the items and the total score must exceed 0.4 According to (Baker, Gibson, Sufi, Barlow, & Robinson, 2015), there must be a relationship between the items and the total score that is greater than 0.4. Additionally, the scale's Cronbach alpha after removing items is not greater than the scale's overall Cronbach alpha (Pallant, 2016). The number of factors was determined by parallel analysis and the factor structure of the scale was determined by exploratory factor analysis (Çokluk Bökeoğlu & Koçak, 2016). The principal axis factoring method was used to extract factors followed by Humanities and Social Sciences Letters, 2024, 12(2): 233-247 237 © 2024 Conscientia Beam. All Rights Reserved. factor rotation using the promax with the Kaiser normalization method. The resulting factor structure obtained from exploratory factor analysis was then cross validated by confirmatory factor analysis. The model's fitting effect was evaluated using standardized estimation results and a fitting index with a standardized factor loading greater than 0.5 was considered acceptable (Byrne, 2016). The ideal model fit index should satisfy: the chi-square/degrees of freedom ratio 𝜒2/𝑑𝑓 should be between 1 and 5, the goodness of fit index (GFI) ≥ 0.90, the Tucker-Lewis index (TLI)≥0.9, the comparative fit index (CFI)≥0.9, the root means square error of approximation (RMSEA)≤0.08. The study analyzed the convergent validity of the three sub dimensions of learning burnout. Composite Reliability (CR) and Average Variance Extracted (AVE) were used for this purpose. The Fornell-Larcker criterion was applied to assess discriminant validity. CR and AVE were calculated based on the standardized factor loading obtained from confirmatory factor analysis. The study found good convergent and discriminant validity with CR greater than 0.7 and AVE greater than 0.5 (Hair, Black, Babin, & Anderson, 2019). We used the General Self-Efficacy (GSE) questionnaire as a covariate and evaluated the correlation between GSE and MBI-SS using Pearson's correlation coefficient to assess the concurrent validity of the Chinese version of the MBI-SS questionnaire. The analysis was conducted using SPSS 26.0 and AMOS 26.0. 4. RESULTS 4.1. Descriptive and Correlation Analysis Table 2 presents the mean, standard deviation and pairwise correlation coefficient of items in the original MBI- SS questionnaire calculated based on all samples (n=437). LB1, LB4, LB7, LB11, LB14, LB15 and LB16 are reverse-scored items that can be found alongside the positive-scored items that are essential for negative correlation. It is necessary to recode the reverse score items to show a positive relationship with the remaining questions before analyzing the data to ensure ease of understanding and accurate calculation. This will result in higher scores indicating more severe learning burnout. We can determine the factor structure of the scale by observing the correlation coefficient matrix. The questions LB2, LB5, LB8 and LB12 in the original MBI-SS questionnaire correspond to sub-dimension exhaustion. The pairwise relationship coefficient between LB5, LB8, and LB12 is high ranging from 0.72 to 0.76. However, the relationship coefficient between LB2 and these three items is low ranging from 0.33 to 0.42. Simultaneously, it was discovered that the relationship coefficient between LB2 and other sub-dimension items was higher. This suggests that fatigue may not be associated with the component corresponding to item LB2 in the MBI-SS questionnaire for the sample of Chinese art college students. This will need to be verified in the subsequent exploratory factor analysis. Table 7 also presents descriptive statistics for age, sex, covariates of the GSE and MBI-SS questionnaires and their sub-dimensions. Humanities and Social Sciences Letters, 2024, 12(2): 233-247 238 © 2024 Conscientia Beam. All Rights Reserved. Table 2. Descriptive analysis and correlation analysis of the items (n = 437). Items Inter item correlations 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 1.00 2 -0.27 1.00 3 -0.10 0.46 1.00 4 0.53 -0.14 -0.11 1.00 5 -0.06 0.33 0.30 -0.10 1.00 6 -0.12 0.38 0.80 -0.08 0.16 1.00 7 0.55 -0.16 0.09 0.62 -0.07 0.08 1.00 8 -0.08 0.42 0.29 -0.15 0.72 0.15 -0.08 1.00 9 -0.15 0.50 0.68 -0.34 0.37 0.66 -0.09 0.36 1.00 10 -0.08 0.40 0.67 -0.18 0.21 0.75 0.01 0.23 0.67 1.00 11 0.53 -0.17 -0.20 0.43 -0.04 -0.19 0.35 -0.02 -0.35 0.02 1.00 12 -0.12 0.38 0.36 -0.10 0.76 0.28 -0.06 0.76 0.40 0.31 0.02 1.00 13 0.00 0.33 0.54 0.10 0.17 0.64 0.14 0.16 0.33 0.58 0.10 0.29 1.00 14 0.55 -0.24 -0.23 0.67 -0.05 -0.15 0.56 -0.09 -0.48 -0.20 0.69 -0.05 0.11 1.00 15 0.43 -0.14 -0.22 0.66 -0.12 -0.16 0.57 -0.08 -0.44 -0.17 0.59 -0.12 0.09 0.82 1.00 16 0.45 -0.09 -0.18 0.50 0.01 -0.14 0.50 0.02 -0.38 -0.13 0.62 -0.02 0.10 0.82 0.81 1.00 Mean 4.86 3.42 2.60 4.54 4.56 2.22 3.91 4.31 2.75 2.13 4.65 4.00 2.28 4.55 4.39 4.40 SD 1.41 1.96 1.58 1.53 1.77 1.45 1.59 1.93 1.90 1.68 1.57 1.86 1.65 1.50 1.50 1.43 Range 1-7 1-7 1-7 1-7 1-7 1-7 1-7 1-7 1-7 1-7 1-7 1-7 1-7 1-7 1-7 1-7 Humanities and Social Sciences Letters, 2024, 12(2): 233-247 239 © 2024 Conscientia Beam. All Rights Reserved. 4.2. Item Analysis Three methods were employed to analyse each item based on sample 1, the critical ratio approach, the relationship between the items and the overall score and Cronbach alpha following the deletion of items. Critical ratio analysis divides the original MBI-SS questionnaire's total score into three groups: high, medium and low based on the 27% and 73% quantiles of the overall score (Fan, 1952). An inter-group difference analysis was conducted between the high and low groups using an independent sample t -test. The critical ratio statistics obtained are shown in the second column of Table 3. It can be observed that all values are greater than 3 and significant at the 0.001 level. Subsequently, a correlation analysis was performed to obtain the correlation coefficient between each item and the total score as shown in the third column of Table 3. The correlation coefficient between item LB7 and the overall score is clearly lower than the crucial value of 0.4 among these correlation values. Additionally, we calculated Cronbach alpha after deleting items (the fourth column in Table 3) and found that the Cronbach alpha if item deleted corresponding to items LB7(R) and LB13 was higher than the Cronbach alpha of the original MBI-SS. Removing these items is likely to improve the overall scale's reliability. Therefore, LB7 should be deleted as it does not meet two criteria. After removing LB7, the Cronbach alpha for all item deleted including LB13 was not greater than the Cronbach alpha of the whole scale. Finally, it is necessary to remove project LB7 during the item analysis stage. Table 3. The result of item analysis (n=218). Items Critical ration (P-value) Relationship between items and total score Cronbach alpha if item deleted LB1(R) 9.129 (P<0.001) 0.561 0.850 LB2 10.653 (P<0.001) 0.630 0.847 LB3 13.356 (P<0.001) 0.689 0.843 LB4(R) 6.347 (P<0.001) 0.535 0.851 LB5 4.373 (P<0.001) 0.471 0.855 LB6 11.846 (P<0.001) 0.634 0.846 LB7(R) 4.612 (P<0.001) 0.383 0.859 LB8 5.315 (P<0.001) 0.486 0.856 LB9 17.502 (P<0.001) 0.785 0.836 LB10 12.150 (P<0.001) 0.646 0.845 LB11(R) 7.168 (P<0.001) 0.468 0.854 LB12 5.616 (P<0.001) 0.542 0.852 LB13 6.525 (P<0.001) 0.411 0.859 LB14(R) 10.284 (P<0.001) 0.659 0.845 LB15(R) 8.475 (P<0.001) 0.625 0.847 LB16(R) 7.474 (P<0.001) 0.547 0.850 Threshold ≥3.000 ≥0.400 0.858 4.3. Exploratory Factor Analysis (EFA) The first step in the process should be the exploratory factor analysis using sample 1. LB7 was removed during the item analysis phase and will not be considered in the exploratory factor analysis. The number of factors that could be retrieved was determined using parallel analysis prior to the exploratory factor analysis(Muijs, 2010). Parallel analysis recommended extracting three components when LB7 was removed from the original MBI-SS questionnaire as shown in Figure 1(A). The principal axis factoring method is used for factor extraction while the Promax with Kaiser Normalization method is used for factor rotation. Table 4 (initial EFA) shows the pattern matrix obtained from exploratory factor analysis where each item is assigned to the factor with the highest loading. It can be observed that LB2 is grouped with LB3, LB6, LB9, LB10 and LB13 while LB5, LB8 and LB12 are grouped with LB2 on the original scale. Therefore, LB2 and LB7 should be excluded from the MBI-SS questionnaire. After their removal, the parallel analysis shown in Figure 1(B) still suggests that only 3 factors should be extracted. Factor extraction was performed using principal axis factoring and factor rotation was done Humanities and Social Sciences Letters, 2024, 12(2): 233-247 240 © 2024 Conscientia Beam. All Rights Reserved. using Promax with Kaiser normal ization. Table 4 (second EFA) displays the pattern matrix indicating that LB1, LB4, LB11, LB14, LB15 and LB16 should be reduced to factor 1, LB3, LB6, LB9, LB10 and LB13 should be reduced to factor 2, LB5, LB8, and LB12 should be summarized as factor 3. According to the sub-dimensions of the original MBI-SS questionnaire, these elements can be termed as follows: reduced efficacy, learning cynicism and exhaustion. Figure 1. Parallel analysis results (A excludes item 7 and B excludes items 2 and 7). The communalities obtained from both exploratory factor analyses were all greater than the critical value of 0.2 (Hair et al., 2019). This indicates that these items had high homogeneity with the scale. Humanities and Social Sciences Letters, 2024, 12(2): 233-247 241 © 2024 Conscientia Beam. All Rights Reserved. Table 4. Factor loadings and communalities for the learning burnout scale(n=218). Items Initial EFA excluding item 7 Second EFA excluding items 2 and 7 F1 F2 F3 Communalities 1 2 3 Communalities LB1 0.63 0.02 0.07 0.407 0.63 0.01 0.06 0.400 LB2 0.11 0.44 0.26 0.386 - - - - LB3 0.03 0.84 0.07 0.754 0.05 0.82 0.09 0.751 LB4 0.70 -0.07 0.06 0.483 0.70 -0.06 0.07 0.485 LB5 0.01 -0.04 0.83 0.661 0.02 -0.04 0.84 0.683 LB6 -0.03 0.99 -0.16 0.880 -0.02 0.99 -0.13 0.903 LB8 -0.01 -0.06 0.90 0.773 0.00 -0.06 0.88 0.735 LB9 0.29 0.68 0.11 0.706 0.31 0.65 0.13 0.683 LB10 -0.03 0.91 -0.07 0.776 -0.02 0.90 -0.04 0.778 LB11 0.74 -0.04 -0.07 0.537 0.74 -0.04 -0.07 0.537 LB12 -0.05 0.09 0.86 0.803 -0.04 0.10 0.88 0.828 LB13 -0.28 0.72 0.01 0.505 -0.26 0.71 0.03 0.505 LB14 0.95 0.06 -0.06 0.918 0.95 0.05 -0.06 0.914 LB15 0.90 -0.07 0.08 0.790 0.90 -0.06 0.09 0.797 LB16 0.86 0.00 -0.11 0.739 0.86 0.00 -0.10 0.741 Note: Factor; item numbers correspond with numbers in Table 1. Figure 2. Standardized factor loadings and correlation of the final model with reduced items (n=219). 4.4. Confirmatory Factory Analysis (CFA) It is crucial to use a different sample for cross-validation when creating, modifying, or translating a scale when exploratory factor analysis has produced a satisfactory factor structure . Byrne (2016) suggests performing confirmatory factor analysis on sample 2 (n=219) in order to verify the 3-component structure that was discovered through exploratory factor analysis. Figure 2 shows the standardized factor loadings obtained from confirmatory factor analysis and the relationships between the three sub-dimensions. The standardized factor loadings range from 0.51 to 0.95 which is reasonable. The correlation coefficients between the three sub-dimensions range from Exhaustion Learning cynicism Reduced efficacy LB5 LB8 LB12 LB3 LB6 LB9 LB10 LB13 LB1_R LB4_R LB11_R LB14_R LB15_R LB16_R e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12 e13 e14 0.86 0.84 0.90 0.86 0.63 0.90 0.73 0.76 0.51 0.69 0.95 0.89 0.85 0.70 0.15 0.39 0.25 Humanities and Social Sciences Letters, 2024, 12(2): 233-247 242 © 2024 Conscientia Beam. All Rights Reserved. 0.15 to 0.39 indicating a low correlation among them. The fitting index of the confirmatory factor analysis model is presented below: 𝜒2/df=1.401, GFI=0.956, TLI=0.984, CFI=0.987, RMSEA=0.043. These fit indices all meet the criteria given by Kline (2015). 4.5. Convergent or Discriminant Validity The standardized factor loadings obtained from confirmatory factor analysis can be used to analyze the convergent and discriminative validity of the three sub-dimensions of the 14-item MBI-SS questionnaire. Convergent validity was assessed using Composite Reliability (CR) and Average Variance Extracted (AVE). According to Hair et al. (2019), CR should be greater than 0.7 and AVE should be greater than 0.5. Table 5 demonstrates that all three of the sub-dimensions have CR values greater than 0.7 ranging from 0.886 to 0.901. The AVE is greater than 0.5 and ranges from 0.608 to 0.753. The study used the discriminant validity test which was conducted using Fornell and Larcker (1981). According to the criterion, the square root ( √𝐴𝑉𝐸 ) of each subdimension should be higher than the maximum correlation coefficient with any other latent variable. Table 5 shows that the √𝐴𝑉𝐸 of the three subdimensions is greater than their correlation coefficients with other sub - dimensions. In a nutshell, the removal of items LB2 and LB7 resulted in good convergence validity and discriminant validity for the three sub-dimensions of the MBI-SS questionnaire. This suggests that dividing the MBI-SS questionnaire into three sub-dimensions is a reasonable approach. Table 5. The result of the convergent or discriminant validity test (n=219). Sub-dimensions CR AVE Exhaustion Learning cynicism Reduced efficacy Exhaustion 0.901 0.753 0.868 Learning cynicism 0.886 0.611 0.390*** 0.782 Reduced efficacy 0.900 0.608 0.154* 0.246** 0.780 Note: The numbers bolded diagonally are √𝐴𝑉𝐸.* p < 0.050; ** p < 0.010; *** p < 0.001. 4.6. Concurrent Validity A scale that has a significant theoretical relationship with learning burnout must be used as a covariate in order to evaluate the concurrent validity of the 14-item MBI-SS questionnaire among Chinese art college students (Lajunen & Özkan, 2011). In this study, general self-efficacy (GSE) was used as a covariate for MBI-SS. A correlation analysis was conducted between the original MBI-SS questionnaire, the 14-item MBI-SS questionnaire and general self-efficacy (GSE). The results are presented in Table 6. A significant negative relationship was found between the MBI-SS and GSE with correlation coefficients of r=-0.490 (P<0.01) and r=-0.452 (P<0.01) before and after adjustment, respectively. A significant and negative relationship was found between the MBI-SS and GSE with correlation coefficients of r=-0.490 (P<0.01) and r=-0.452 (P<0.01) before and after adjustment respectively. These results indicate a negative relationship and support the validity of the MBI-SS questionnaire. Table 6. Pearson’s correlations between covariates and learning burnout (n=437). Scale/sub-dimensions GSE Sub1-exhaustion -0.182** Sub1-exhaustion (Excluding item 2) -0.125** Sub2-learning cynicism -0.110* Sub3-reduced efficacy -0.661** Sub3-reduced efficacy (Excluding item 7) -0.638** Learning burnout -0.490** Learning burnout (Excluding item 2 and 7) -0.452** Note: **P < 0.01; *P < 0.05. Humanities and Social Sciences Letters, 2024, 12(2): 233-247 243 © 2024 Conscientia Beam. All Rights Reserved. 4.7. Reliability Test Table 7 presents the Cronbach alpha values for the MBI-SS questionnaire including its sub-dimensions before and after adjustment. The results show that after removing items LB2 and LB7, the overall Cronbach alpha of the MBI-SS questionnaire decreased from 0.864 to 0.854 with no significant change. However, the Cronbach alpha of the exhaustion sub-dimension increased from 0.834 to 0.898 while the reduced efficacy sub-dimension remained basically unchanged with a Cronbach's alpha of 0.902, down from 0.907. The removal of items LB2 and LB7 has a minimal impact on the reliability of the learning burnout scale. The main change is an increase in the reliability of sub-dimension exhaustion. It is important to note that the MBI-SS questionnaire and its sub-dimensions had good reliability both before and after the adjustment (Cronbach alpha > 0.7). Furthermore, the Cronbach alpha of the scale used to measure the covariate variable GSE was 0.921 indicating high reliability. Table 7. Reliability test and descriptive of all study variables (n=437). Initial variable or scale Mean SD Range Cronbach alpha Age 21.86 2.38 17.0-30.0 - Gender 1.69 0.46 1.0-2.0 - GSE 2.86 0.53 1.5-4.0 0.921 Sub1-exhaustion 4.07 1.54 1.0-7.0 0.834 Sub 2-learning cynicism 2.40 1.38 1.0-7.0 0.890 Sub 3-reduced efficacy 3.53 1.20 1.0-7.0 0.907 Learning burnout 3.31 0.95 1.0-5.3 0.864 Learning burnout (Excluding item 2 and 7) 3.25 0.96 1.0-5.6 0.854 Subscales identified during exploratory factor analysis Reduced sub1exhaustion (Excluding item 2) 4.29 1.69 1.0-7.0 0.898 Reduced sub3-reduced efficacy (Excluding item 7) 3.43 1.22 1.0-7.0 0.902 5. DISCUSSION This study examines the suitability of the MBI-SS questionnaire for Chinese art students. Item number seven, which has no correlation and the content, "I ignore everything around me when studying," was eliminated throughout the item analysis process. The intended meaning of this item is that when studying, one can fully concentrate on their studies allowing them to forget about their surroundings. However, the Chinese translation of this item has a low correlation with the meaning expressed on the whole scale. The correlation between LB7 and the total score of MBI-SS is only 0.383 which is less than the critical value of 0.4. During the exploratory factor analysis, LB2 (which describes feeling empty and unsure of what to do) was removed. Although LB2 should correspond to the exhaustion sub-dimension in the original MBI-SS questionnaire, it was found to correspond to learning cynicism in the analysis. However, as LB2 describes a basic state of mind rather than learning cynicism, it was excluded from the analysis. Finally, the exploratory factor analysis y ielded a solution that extracted 3 factors while preserving 14 items. The structure of the 3-factor 14-item model is like that of the 3-factor 16-item model proposed by Schaufeli et al. (2002). The revised MBI-SS questionnaire removes only LB2 in the exhaustion sub-dimension and LB7 in the reduced efficacy sub-dimension, respectively without changing the factor structure. All of the communalities were found to be greater than 0.4 in the final exploratory factor analysis model. This indicates that different items have large commonalities. A distinct sample conf irmed the stability and generalizability of the factor structure obtained from the exploratory factor analysis using confirmatory factor analysis. The results of confirmatory factor analysis showed that the standardized factor loadings ranged from 0.51 to 0.95 with no lower loadings observed. Based on standardized load values, the convergence validity indices for Composite Reliability (CR) and Average Variance Extracted (AVE) are both greater than the recommended values of 0.5 and 0.7, respectively. This suggests that the MBI-SS questionnaire's modified simplified Chinese version has strong convergent validity. The discriminant validity between the three sub-dimensions is evaluated using the Fornell-Larcker criterion. This demonstrates that Humanities and Social Sciences Letters, 2024, 12(2): 233-247 244 © 2024 Conscientia Beam. All Rights Reserved. learning burnout manifests itself in different ways such as reduced efficacy, learning cynicism, and exhaustion. The MBI-SS questionnaire and GSE had a moderately negative correlation according to concurrent validity analysis that used GSE as a covariate variable. This suggests the validity of the MBI-SS questionnaire. These results suggest that the original English version of the MBI-SS questionnaire may measure the learning burnout of Chinese art students accurately and effectively with just modest alterations (i.e., the elimination of items 2 and 7). 6. CONCLUSION AND LIMITATIONS 6.1. Conclusion The 14-item MBI-SS-C maintained the same 3-factor structure as the original MBI-SS after eliminating two questions with weak psychometric qualities. All psychometric statistics met the recommended values. It is worth noting that the cultural, educational and social backgrounds of the study population selected in this study may differ from those of the original MBI-SS questionnaire which resulted in two items showing unexpected and unqualified measurement characteristics. After removing the two items, the reliability and validity indexes of the adjusted MBI- SS-C questionnaire performed well. Therefore, the adjusted MBI-SS-C questionnaire in this study can be used to measure the learning burnout of Chinese art college students and can be compared with the resu lts of other empirical studies using the original MBI-SS questionnaire provided that these two problematic items are not considered in data processing. In a nutshell, this study provides empirical evidence supporting the use of the MBI- SS questionnaire to measure learning burnout in Chinese art students. It also contributes to the empirical basis for the cross-cultural universality of the MBI-SS. The language used is clear, objective and value-neutral with a formal register and precise word choice. The text adheres to conventional structure and formatting features with consistent citation and footnote style. The grammar, spelling and punctuation are correct. No changes in content have been made. The study's f indings are similar to those of Japanese scholars. Tsubakita and Shimazaki (2016) also reported similar results. Additionally, the initial MBI-SS scale's psychometric characterist ics were found to be unsatisfactory. The modified scale's CFA model fit index performed well even after removing a few items. This paper offers a revised version of the MBI-SS-C scale that eliminates two items in comparison to Schaufeli's scale which was first presented by Chinese scholars (Hu & Schaufeli, 2009). The multiple indexes of the adapted MBI-SS-C, including factor loading, structural validity, convergent validity, discriminative validity and reliability, all exceed the critical values recommended by Hair et al. (2019). 6.2. Limitations and Future Research Directions This study confirms that the MBI-SS-C scale retains the 3-factor structure of the original MBI-SS scale in Chinese art college students. However, the sample of this study is limited to one Art University in Be ijing which may not be representative of all art universities in China. The sample sources in this paper are limited to a specific group of art universities in China due to the uncontrollable time and financial costs of random sampling. As a result, the empirical results obtained may lack universality. Furthermore, it is important to note that the simplified Chinese version of the MBI-SS questionnaire is a d irect translation of the English version. As a result, it may not include all the necessary items to accurately reflect the learning burnout experienced by Chinese art students. It is worth mentioning that this study did not introduce any additional items to the MBI-SS questionnaire or expand the sub-dimensions of the scale. In this study, the MBI-SS questionnaire was adapted for Chinese art college students and its reliability and validity were verif ied. However, a new scale was not developed to comprehensively reflect learning burnout according to the cultural background of Chinese college students. Humanities and Social Sciences Letters, 2024, 12(2): 233-247 245 © 2024 Conscientia Beam. All Rights Reserved. Funding: This study received no specific financial support. Institutional Review Board Statement: The Ethical Committee of the Beijing City University, COUNTRY has granted approval for this study on 25 April 2023 (Ref. No. BCU202304250013). Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects of the investigation have been omitted, and that any differences from the study as planned have been clarified. This study followed all writing ethics. Competing Interests: The authors declare that they have no competing interests. Authors’ Contributions: Conceptualization, investigation, methodology, formal analysis, and review and editing, Z.M.; original draft preparation, supervision, and funding acquisition, L.Y.F. All authors have read and agreed to the published version of the manuscript. REFERENCES Aharon, D. Y., Aziz, M. I. A., & Nor, S. M. (2023). Cross-country study of the linkages between COVID-19, oil prices, and inflation in the G7 countries. Finance Research Letters, 57, 104172. https://doi.org/10.1016/j.frl.2023.104172 Baker, S. R., Gibson, B. J., Sufi, F., Barlow, A. P. S., & Robinson, P. G. (2015). 9 - The dentine hypersensitivity experience questionnaire (DHEQ): A longitudinal validation study. In P. G. Robinson (Ed.), Dentine hypersensitivity. In (pp. 141– 154): Academic Press. https://doi.org/10.1016/B978-0-12-801631-2.00009-9. Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). New York: Routledge. Charkhabi, M., Azizi Abarghuei, M., & Hayati, D. (2013). The association of academic burnout with self-efficacy and quality of learning experience among Iranian students. Springerplus, 2(1), 1-5. https://doi.org/10.1186/2193-1801-2-677 Chen, Y., Zhang, S., & Miao, J. (2023). The negative effects of the US-China trade war on innovation: Evidence from the Chinese ICT industry. Technovation, 123, 102734. https://doi.org/10.1016/j.technovation.2023.102734 Çokluk Bökeoğlu, Ö., & Koçak, D. (2016). Using Horn's parallel analysis method in exploratory factor analysis for determining the number of factors. Educational Sciences-Theory & Practice, 16(2), 537–551. Diaz, E. M., Cunado, J., & de Gracia, F. P. (2023). Commodity price shocks, supply chain disruptions and US inflation. Finance Research Letters, 58, 104495. https://doi.org/10.1016/j.frl.2023.104495 Drăghici, G.-L., & Cazan, A.-M. (2022). Burnout and maladjustment among employed students. Frontiers in Psychology, 13, 825588. https://doi.org/10.3389/fpsyg.2022.825588 Fan, C. T. (1952). Note on construction of an item analysis table for the high-low-27-per-cent group method. ETS Research Bulletin Series, 1952(2), 1–12. https://doi.org/10.1002/j.2333-8504.1952.tb00227.x Faye‐Dumanget, C., Carré, J., Le Borgne, M., & Boudoukha, P. A. H. (2017). French validation of the M aslach B urnout I nventory‐S tudent S urvey (MBI‐SS). Journal of Evaluation in Clinical Practice, 23(6), 1247-1251. https://doi.org/10.1111/jep.12771 Firth, H., McIntee, J., McKeown, P., & Britton, P. G. (1985). Maslach burnout inventory: Factor structure and norms for British nursing staff. Psychological Reports, 57(1), 147-150. https://doi.org/10.2466/pr0.1985.57.1.147 Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error . Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312 Freudenberger, H. J. (1974). Staff burn‐out. Journal of Social Issues, 30(1), 159–165. https://doi.org/10.1111/j.1540- 4560.1974.tb00706.x Habibi, Z., Habibi, H., & Mohammadi, M. A. (2022). The potential impact of COVID-19 on the Chinese GDP, trade, and economy. Economies, 10(4), 73. https://doi.org/10.3390/economies10040073 Hair, J., Black, W., Babin, B., & Anderson, R. (2019). Multivariate data analysis. Hampshire, United Kingdom: Cengage Learning. Hu, Q., & Schaufeli, W. B. (2009). The factorial validity of the Maslach burnout inventory–student survey in China. Psychological Reports, 105(2), 394-408. https://doi.org/10.2466/PR0.105.2.394-408 Jia, Y. A., Rowlinson, S., Kvan, T., Lingard, H. C., & Yip, B. (2009). Burnout among Hong Kong Chinese architecture students: The paradoxical effect of Confucian conformity values. Construction Management and Economics, 27(3), 287-298. https://doi.org/10.1080/01446190902736296 Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). New York: Guilford Press. https://doi.org/10.1016/j.frl.2023.104172 https://doi.org/10.1016/B978-0-12-801631-2.00009-9 https://doi.org/10.1186/2193-1801-2-677 https://doi.org/10.1016/j.technovation.2023.102734 https://doi.org/10.1016/j.frl.2023.104495 https://doi.org/10.3389/fpsyg.2022.825588 https://doi.org/10.1002/j.2333-8504.1952.tb00227.x https://doi.org/10.1111/jep.12771 https://doi.org/10.2466/pr0.1985.57.1.147 https://doi.org/10.2307/3151312 https://doi.org/10.1111/j.1540-4560.1974.tb00706.x https://doi.org/10.1111/j.1540-4560.1974.tb00706.x https://doi.org/10.3390/economies10040073 https://doi.org/10.2466/PR0.105.2.394-408 https://doi.org/10.1080/01446190902736296 Humanities and Social Sciences Letters, 2024, 12(2): 233-247 246 © 2024 Conscientia Beam. All Rights Reserved. Lajunen, T., & Özkan, T. (2011). Chapter 4—self-report instruments and methods. In B. E. Porter (Ed.), Handbook of traffic psychology. In (pp. 43–59): Academic Press. https://doi.org/10.1016/B978-0-12-381984-0.10004-9. Lee, C.-C., Olasehinde-Williams, G., & Özkan, O. (2023). Geopolitical oil price uncertainty transmission into core inflation: Evidence from two of the biggest global players. Energy Economics, 126, 106983. https://doi.org/10.1016/j.eneco.2023.106983 Lee, J., Puig, A., Kim, Y. B., Shin, H., Lee, J. H., & Lee, S. M. (2010). Academic Burnout profiles in Korean adolescents. Stress and Health, 26(5), 404-416. https://doi.org/10.1002/smi.1312 Lee, S. H., Jeon, W. T., Lee, S. H., & Jeon, W. T. (2015). The relationship between academic self-efficacy and academic Burnout in medical students. Korean Journal of Medical Education, 27(1), 27–35. https://doi.org/10.3946/kjme.2015.27.1.27 Lheureux, F., Truchot, D., Borteyrou, X., & Rascle, N. (2017). The maslach burnout inventory – Human services survey (mbi- hss): Factor structure, wording effect and psychometric qualities of known problematic items. Le Travail Humain, 80(2), 161-186. https://doi.org/10.3917/th.802.0161 Liu, Z., Xie, Y., Sun, Z., Liu, D., Yin, H., & Shi, L. (2023). Factors associated with academic burnout and its prevalence among university students: A cross-sectional study. BMC Medical Education, 23(1), 317. https://doi.org/10.1186/s12909-023- 04316-y Maslach, C., & Jackson, S. E. (1981). The measurement of experienced Burnout. Journal of Organizational Behavior, 2(2), 99–113. https://doi.org/10.1002/job.4030020205 Maslach, C., & Jackson, S. E. (1986). Maslach Burnout inventory: Manual (2nd ed.). Palo Alto, CA: Consulting Psychologists Press. Maslach, C., Jackson, S. E., & Leiter, M. P. (1997). Maslach Burnout inventory. In: Evaluating stress: A book of resources. In (3rd ed., pp. 191-218). Lanham: Scarecrow Education. Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job Burnout. Annual Review of Psychology, 52(1), 397–422. https://doi.org/10.1146/annurev.psych.52.1.397 Montiel-Company, J. M., Subirats-Roig, C., Flores-Martí, P., Bellot-Arcís, C., & Almerich-Silla, J. M. (2016). Validation of the Maslach Burnout inventory-human services survey for estimating burnout in dental students. Journal of Dental Education, 80(11), 1368-1375. https://doi.org/10.1002/j.0022-0337.2016.80.11.tb06222.x Moore, P., & Loosemore, M. (2014). Burnout of undergraduate construction management students in Australia. Construction Management and Economics, 32(11), 1066-1077. https://doi.org/10.1080/01446193.2014.966734 Morgan, B., De Bruin, G. P., & De Bruin, K. (2014). Operationalizing Burnout in the Maslach Burnout inventory–student survey: Personal efficacy versus personal inefficacy. South African Journal of Psychology, 44(2), 216-227. https://doi.org/10.1177/0081246314528834 Muijs, D. (2010). Doing quantitative research in education with SPSS. In (3rd ed., pp. 156). London: Sage Publications. Pallant, J. (2016). SPSS survival manual: A step by step guide to data analysis using SPSS program (6th ed.). London, UK: McGraw- Hill Education. Pérez-Mármol, J. M., & Brown, T. (2019). An examination of the structural validity of the Maslach Burnout inventory-student survey (MBI-SS) using the rasch measurement model. Health Professions Education, 5(3), 259-274. https://doi.org/10.1016/j.hpe.2018.05.004 Pines. (1981). Burnout: From tedium to personal growth . New york: Free Press. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 Rohmani, N., & Andriani, R. (2021). Correlation between academic self-efficacy and burnout originating from distance learning among nursing students in Indonesia during the coronavirus disease 2019 pandemic. Journal of Educational Evaluation for Health Professions, 18, 1-9. https://doi.org/10.3352/jeehp.2021.18.9 Salmela-Aro, K., & Tynkkynen, L. (2012). Gendered pathways in school Burnout among adolescents. Journal of Adolescence, 35(4), 929–939. https://doi.org/10.1016/j.adolescence.2012.01.001 https://doi.org/10.1016/B978-0-12-381984-0.10004-9 https://doi.org/10.1016/j.eneco.2023.106983 https://doi.org/10.1002/smi.1312 https://doi.org/10.3946/kjme.2015.27.1.27 https://doi.org/10.3917/th.802.0161 https://doi.org/10.1186/s12909-023-04316-y https://doi.org/10.1186/s12909-023-04316-y https://doi.org/10.1002/job.4030020205 https://doi.org/10.1146/annurev.psych.52.1.397 https://doi.org/10.1002/j.0022-0337.2016.80.11.tb06222.x https://doi.org/10.1080/01446193.2014.966734 https://doi.org/10.1177/0081246314528834 https://doi.org/10.1016/j.hpe.2018.05.004 https://doi.org/10.1037/0021-9010.88.5.879 https://doi.org/10.3352/jeehp.2021.18.9 https://doi.org/10.1016/j.adolescence.2012.01.001 Humanities and Social Sciences Letters, 2024, 12(2): 233-247 247 © 2024 Conscientia Beam. All Rights Reserved. Schaufeli, W. B., Martinez, I. M., Pinto, A. M., Salanova, M., & Bakker, A. B. (2002). Burnout and engagement in university students: A cross-national study. Journal of Cross-Cultural Psychology, 33(5), 464-481. https://doi.org/10.1177/0022022102033005003 Stephen, A. (2021). Art education in crisis under COVID. Art Monthly Australasia, 327, 100–105. Tsubakita, T., & Shimazaki, K. (2016). Constructing the J apanese version of the Maslach Burnout inventory–student survey: confirmatory factor analysis. Japan Journal of Nursing Science, 13(1), 183-188. https://doi.org/10.1111/jjns.12082 Wickramasinghe, N. D., Dissanayake, D. S., & Abeywardena, G. S. (2018) . Validity and reliability of the Maslach Burnout inventory-student survey in Sri Lanka. BMC Psychology, 6(1), 1-10. https://doi.org/10.1016/j.ijpsycho.2018.07.334 Yang, G., Sun, W., & Jiang, R. (2022). Interrelationship amongst university student perceived learning Burnout, academic self- efficacy, and teacher emotional support in China’s English online learning context. Frontiers in Psychology, 13, 829193. https://doi.org/10.3389/fpsyg.2022.829193 Yavuz, G., & Dogan, N. (2014). Maslach Burnout inventory-student survey (MBI-SS): A validity study. Procedia-Social and Behavioral Sciences, 116, 2453-2457. https://doi.org/10.1016/j.sbspro.2014.01.590 Zeng, G., Fung, S., Li, J., Hussain, N., & Yu, P. (2022). Evaluating the psychometric properties and factor structure of the general self-efficacy scale in China. Current Psychology, 41(6), 3970–3980. https://doi.org/10.1007/s12144-020-00924-9 Views and opinions expressed in this article are the views and opinions of the author(s), Humanities and Social Sciences Letters shall not be responsible or answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content. https://doi.org/10.1177/0022022102033005003 https://doi.org/10.1111/jjns.12082 https://doi.org/10.1016/j.ijpsycho.2018.07.334 https://doi.org/10.3389/fpsyg.2022.829193 https://doi.org/10.1016/j.sbspro.2014.01.590 https://doi.org/10.1007/s12144-020-00924-9