The effects of academic resilience on foreign language anxiety: A structural equation modeling-based multi-group analysis

The effects of academic resilience on foreign language anxiety: A structural equation modeling-based multi-group analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effects of academic resilience on foreign language anxiety: A structural equation modeling-based multi-group analysis Yali Hao, Jinming Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7750527/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Foreign language (FL) anxiety is a significant factor influencing FL achievement. However, few studies have systematically examined the role of academic resilience in shaping FL anxiety. To address this gap, the present study employed a self-report survey of 650 junior high school students to investigate the mediating effect of self-efficacy on the relationship between academic resilience and FL anxiety. Furthermore, the study explored whether urban-rural differences moderate the interrelationships among academic resilience, self-efficacy and FL anxiety. Structural equation modeling-based multi-group analysis revealed that (1) academic resilience negatively predicts FL anxiety; (2) self-efficacy partially mediates the negative effect of academic resilience on FL anxiety; and (3) urban-rural differences moderate the link between academic resilience and self-efficacy, as well as the relationship between self-efficacy and FL anxiety, with both effects being stronger in urban schools. By applying Social Cognitive Theory, this study deepens our understanding of FL anxiety and underscores the critical importance of fostering academic resilience. The findings suggest that when designing FL courses, instructors should strategically promote students' academic resilience and self-efficacy to reduce their FL anxiety. Foreign language anxiety academic resilience self-efficacy urban-rural differences Figures Figure 1 Figure 2 1 Introduction Foreign language (FL) learning presents numerous challenges, with FL anxiety identified as a significant barrier to success [ 1 , 2 ]. Defined as feelings of apprehension and nervousness during FL use [ 3 ], FL anxiety negatively affects various aspects of language learning. Research indicates its detrimental impact on students' academic engagement [ 4 ], confidence [ 5 ], and test performance [ 6 – 8 ], highlighting the urgent need for effective intervention strategies in language education. The existing research has explored various interventions targeting teacher support, learner traits, and classroom environments to address FL anxiety [ 9 – 11 ]. For example, Man, Fang, Chan and Han [ 11 ] highlighted the role of academic support and mutual respect in fostering a supportive classroom environment, which can reduce stress and create favorable conditions for language learning. Similarly, Liu, Li and Yan [ 10 ] demonstrated that perceived emotional and instrumental support from teachers indirectly alleviates FL anxiety by fostering L2 grit, which helps learners develop perseverance and passion for long-term language learning goals. Additionally, Li [ 9 ] emphasized the importance of both internal traits and external conditions, showing that trait emotional intelligence enables learners to manage their emotional responses, thereby reducing anxiety. These studies suggest that supportive teacher behaviors, positive classroom environments, and individual learner traits can effectively reduce FL anxiety. Nevertheless, the role of academic resilience in reducing FL anxiety has received limited attention [ 12 ]. Academic resilience, defined as the ability to achieve educational goals despite adversity [ 13 ], is a critical psychological resource that enables students to adapt positively to challenges and persist toward long-term objectives. By helping learners manage the frustration and stress inherent in FL learning, academic resilience may directly alleviate anxiety levels [ 14 ]. This unexplored area warrants further investigation to identify effective strategies for reducing FL anxiety. Additionally, Social Cognitive Theory [ 15 ] underscores self-efficacy, the belief in one's ability to achieve specific goals, as a crucial mediator among environmental factors (e.g., urban-rural differences), cognitive resources (e.g., academic resilience), and emotional outcomes (e.g., FL anxiety). High self-efficacy increases students' confidence in overcoming language-learning obstacles, thus alleviating FL anxiety from self-doubt. This implies that self-efficacy may be a pivotal link between academic resilience and reduced FL anxiety. Furthermore, urban-rural differences potentially moderate these relationships by affecting access to educational resources and psychological support. Rural students often encounter resource limitations that adversely affect their self-efficacy and academic resilience [ 16 ]. Understanding how these contextual differences impact the interactions among academic resilience, self-efficacy, and FL anxiety is vital for developing interventions tailored to diverse student populations. The present study, which is grounded in a robust theoretical framework, leverages structural equation modeling to elucidate: (1) the direct impact of academic resilience on FL anxiety among junior high school students; (2) the mediating role of self-efficacy in this relationship; and (3) the moderating influence of urban-rural disparities through multi-group analysis. The findings seek to expand the theoretical understanding of the mechanisms underlying FL anxiety reduction, while providing evidence-based strategies for educators to address FL anxiety across diverse classroom contexts. 2 Literature review 2.1 FL anxiety FL anxiety is characterized by learners' self-perceptions, beliefs, emotions, and classroom behaviors, arising from the challenges of learning a foreign language (Elaine K. Horwitz et al., 1986). It is a pivotal factor that impacts both FL learning and teaching, with significant effects on students' academic performance [ 6 , 17 ]. Therefore, mitigating FL anxiety is crucial for enhancing learning outcomes. FL anxiety is shaped by a complex interplay of external and internal factors. Prominent external factors include teacher support, technological support and environmental support. Empirical evidence suggests that teacher-provided academic guidance and fostering mutual respect in the classroom can significantly alleviate learners' language anxiety [ 11 ]. Similarly, technology-based interventions, such as voice-based, video-based, and virtual reality-based oral interaction modes, have proven effective in reducing learners' anxiety, with no significant differences observed among these modalities [ 18 ]. Furthermore, the physical and psychological characteristics of the classroom environment have been identified as key predictors of the degree of language anxiety experienced by students [ 9 ]. In addition to external influences, internal learner-related factors such as language proficiency, emotional intelligence, motivation, and personality traits contribute to FL anxiety. For example, Jin, Dewaele and MacIntyre [ 19 ] showed that reflecting on language progress as a cognitive-emotional strategy reduces FL anxiety by promoting positive psychological processes. This finding is consistent with the findings on trait emotional intelligence [ 9 ], where affect regulation capacities are highlighted as critical for mitigating anxiety. Moreover, Pan and Zhang [ 20 ] identified motivational drivers, self-concept dimensions, and personality traits as key mechanisms illustrating the complex interplay between external support and internal characteristics in shaping FL anxiety. These findings collectively underscore the multifaceted nature of FL anxiety and the dynamic interaction between external resources and internal learner traits. 2.2 Academic resilience Academic resilience refers to the ability to maintain psychological stamina and persist in achieving educational goals despite adversity [ 21 ]. Within the domain of language learning, students with high academic resilience tend to perform well academically even under challenging conditions [ 22 ]. In addition to its impact on academic performance, academic resilience has been shown to reduce stressors and increase engagement in learning contexts [ 23 , 24 ]. By fostering academic resilience, learners can effectively navigate obstacles, pressure, and challenges inherent in academic environments. This empowers students not only to confront difficulties but also to mitigate anxiety through the development of adaptive coping strategies and emotional regulation skills. Empirical research has consistently demonstrated a negative correlation between academic resilience and anxiety across various contexts. For example, Savitsky, Findling, Ereli and Hendel [ 25 ] reported that students who are more resilient report markedly less anxiety in demanding educational settings such as higher education or professional training programs. Similarly, Ragusa, Gonzalez-Bernal, Trigueros, Caggiano, Navarro, Minguez-Minguez, Obregon and Fernandez-Ortega [ 26 ] identified academic resilience as a key predictor of reduced academic anxiety during adolescence because of its protective effects against stressors. Yazdi and Ghanizadeh [ 27 ] extended these findings to virtual learning environments by showing that academic resilience negatively correlates with FL anxiety among university students. Despite these insights, Chinese junior high school students face unique challenges, such as the intense pressure to prepare for high school entrance examinations coupled with their developmental stage, which may alter this relationship [ 28 ]. Furthermore, the specific mechanisms through which academic resilience mitigates FL anxiety in this context remain unclear. Addressing this gap is essential for understanding how academic resilience operates within younger learners under culturally specific pressures [ 29 ]. Thus, we propose the following hypothesis. H1: Academic resilience is negatively related to FL anxiety. 2.3 Self-efficacy Self-efficacy, rooted in Social Cognitive Theory [ 30 ], is a critical factor in mitigating anxiety, particularly in language learning environments where anxiety often disrupts cognitive and emotional processes. Learners with strong self-efficacy beliefs tend to exhibit greater persistence, resilience in the face of setbacks, and a willingness to perform challenging tasks [ 31 ]. Conversely, individuals with low self-efficacy are more inclined to withdraw from learning activities, avoid challenges, and opt for easier tasks, thereby impeding their academic progress. The inverse relationship between self-efficacy and anxiety has been well established across diverse domains. Shen, Ismail, Jeyaraj and Teng [ 32 ] demonstrated that students' confidence in their writing ability inversely correlates with their writing-related anxiety, indicating that higher self-efficacy effectively mitigates task-specific anxiety. Similarly, Hong and Tai [ 33 ] reported that self-efficacy in online learning is inversely associated with online learning-related anxiety and that performance-based self-efficacy alleviates general academic stressors. These studies consistently highlight the inverse relationship between self-efficacy and anxiety in both specific and general learning contexts. Previous research has identified key antecedents of self-efficacy, including grit (Cai et al., 2024; Derakhshan & Fathi, 2024), learning approaches (Chen, Chih-Hung, 2025), peer and technical support (Han & Geng, 2023), and self-regulated learning (Chen & Zhu, 2025; Wang, 2021). For example, self-efficacy has been shown to mediate the relationship between grit and engagement (Derakhshan & Fathi, 2024) and can be enhanced by innovative teaching models such as flipped learning (Chen & Shih, 2025). According to Social Cognitive Theory [ 15 ], academic resilience serves as a critical resource for enhancing self-efficacy. Resilient individuals maintain a positive outlook when facing challenges and persist in their efforts, reinforcing their belief in their own abilities. This persistence leads to successful experiences that further strengthen their self-efficacy. Empirical evidence supports this link. For example, Cabrera-Aguilar, Zevallos-Francia, Morales-Garcia, Ramirez-Coronel, Morales-Garcia, Sairitupa-Sanchez and Morales-Garcia [ 34 ] reported that academic resilience positively influences self-efficacy among nurses by fostering adaptive coping mechanisms and positive outcomes. On the basis of the above discussion, we argue that academic resilience may influence FL anxiety indirectly through its impact on self-efficacy. While individual relationships among academic resilience, self-efficacy, and anxiety have been studied separately, the integrated pathway, particularly within FL contexts, has not been extensively investigated [ 35 ]. Thus, the following hypothesis is proposed. H2: Self-efficacy mediates the relationship between academic resilience and FL anxiety. 2.4 The urban-rural differences The disparity in educational resources between urban and rural China continues to be an enduring challenge. Urban schools generally benefit from better funding, highly qualified teachers, and advanced technological resources, whereas rural schools face significant constraints such as teacher shortages and limited access to extracurricular opportunities [ 36 ]. These disparities contribute to differences in both initial language proficiency levels and psychological environments for FL learning. Moreover, the social environment is a key determinant of emotional outcomes such as anxiety [ 37 ]. Existing studies have shown significant differences in anxiety levels between urban and rural students. For example, Chen, Huang and Riad [ 38 ] and Wang, Mao, Wei, Liu, Fan, Xu, Wang, Wang, Lou, Lin, Sun, Wang and Wu [ 39 ] indicated that rural students often experience higher levels of overall anxiety, particularly in test-related situations, than urban students do [ 40 ]. However, some studies have reached opposite conclusions. For example, Victor Mbanuzuru, Uwakwe, Sochukwu Anyaoku, Okwudili Ojimba, Chinyere Mbanuzuru, Ezenyeaku, Chukwudinma Obi, Nkiru Okafor and Prosper Okonkwo [ 41 ] reported greater overall anxiety among urban adolescents than among their rural peers did, whereas Parad, Kajale, Vartak and Khadilkar [ 42 ] showed that urban students’ test anxiety significantly undermined their academic achievement. In the context of FL learning, urban students often report higher levels of FL anxiety than rural students do [ 37 ]. These findings suggest that differences in urban and rural environments, particularly in terms of living conditions, educational resources, and social support systems, can directly or indirectly influence students' emotional states and anxiety levels. Additionally, the social environment also plays an important role in shaping self-efficacy development. Zimmerman and Schunk [ 43 ] emphasized that mastery experiences are central to developing self-efficacy; these experiences are particularly salient in contexts of adversity. For rural students who face limited access to technology or teacher support, adversity can create opportunities for intrinsic coping strategies and peer collaboration, enabling them to build stronger self-efficacy through overcoming challenges [ 44 ]. In contrast, urban students benefit from abundant resources but tend to rely more on external validations such as teacher feedback and peer comparisons to develop self-efficacy [ 45 ]. These findings suggest that the relationship between academic resilience and self-efficacy is likely stronger in rural contexts, as adversity plays a more prominent role in shaping mastery experiences. On the basis of these findings regarding how urban-rural disparities influence academic resilience, self-efficacy, and FL anxiety, we propose the following hypotheses. H3: Urban-rural differences moderate the relationship between academic resilience and FL anxiety. H4: Urban-rural differences moderate the relationship between academic resilience and self-efficacy. H5: Urban-rural differences moderate the relationship between self-efficacy and FL anxiety. Drawing on prior work linking academic resilience, self-efficacy and FL anxiety, we propose a research model to explain their relationship in the FL context (Fig. 1 ). 3 Methods 3.1 Data collection A paper-based questionnaire survey was employed in this study to investigate the relationship between academic resilience and FL anxiety. The participants were junior high school students recruited from two junior high schools (one urban and one rural) from March 17th to 28th, 2025. Using random sampling, we recruited participants and distributed 750 questionnaires, ensuring anonymous and voluntary participation. We eliminated data from students who provided invalid responses. After rigorous screening, 650 valid responses were retained, representing an 86.7% valid response rate. Table 1 Demographic characteristics of the research sample Frequency % Gender Males 340 52.3 Females 310 47.7 Age 12 113 17.4 13 188 28.9 14 212 32.6 15 137 21.1 Region Urban 316 48.6 Rural 334 51.4 Total 650 100 The detailed demographic information for the sample is shown in Table 1 . A total of 340 participants were male (52.3%), and 310 participants were female (47.7%). With respect to age, the sample primarily consisted of 113 participants aged 12, 188 aged 13, 212 aged 14, and 137 aged 15. Among the 650 participants, 316 (48.6%) came from urban areas, and 334 (51.4%) were from rural areas. On the basis of the overall descriptive statistical distribution of the sample's demographic characteristics, the proportional distribution of the sample is relatively reasonable and demonstrates a certain degree of representativeness. 3.2 Instruments 3.2.1 The academic resilience scale The academic resilience scale was adapted from Guo and Li [ 46 ]. The 19-item scale consists of three dimensions: ego-resilience, metacognitive-resilience, and social resilience. All the items were rated on a 7-point Likert scale, ranging from 1 ("Strongly disagree") to 7 ("Strongly agree"). The scale demonstrated excellent reliability in the present study, with a Cronbach's alpha of 0.942. A representative item from the academic resilience scale is "I am curious about new knowledge when studying a FL". 3.2.2 The self-efficacy scale The self-efficacy scale utilized in this study was developed by Cai, Zhu and Xing [ 47 ]. The 6 items were adapted to assess students’ confidence in FL learning. The scale uses a 6-point Likert scale ranging from “1 (Strongly disagree)” to “6 (Strongly agree)” and showed high reliability in the present study (α = 0.859). A representative item used to measure self-efficacy is "I believe I will receive an excellent grade in this FL class". 3.2.3The FL anxiety scale The FL anxiety scale was adapted from Elouise Botes, LINDIE VAN DER WESTHUIZEN, JEAN-MARC DEWAELE, PETER MACINTYRE and SAMUEL GREIFF [ 48 ]. The 8 items were modified and used to assess students’ FL anxiety. Rated on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree), the scale showed strong internal consistency in this study (α = 0.856). A representative item measuring FL anxiety is "Even if I am well prepared for language class, I feel anxious about it". 3.3 Data analysis Before the data were analyzed, all scales were standardized to a 5-point scale. The present research follows a three-phase data analysis approach. First, SPSS 27.0 was employed to examine the reliability and validity of the questionnaire. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were subsequently employed. The research model was estimated via the maximum likelihood estimation method. Third, we examined the structural model to test the hypotheses about the constructs’ interrelations. AMOS 28.0 was used to assess measurement invariance and conduct multi-group analysis (MGA). In the assessment of measurement invariance, a range of models, including configural, metric, scalar, and strict, were employed for evaluation purposes. After confirming measurement invariance, MGAs were employed to estimate the structural models for each group. 4 Results 4.1 Common method deviation test Common method deviation was controlled through anonymity, the inclusion of lie detector items, and the use of various scoring strategies. A single-factor approach was used to test for the presence of common method deviation, and an exploratory factor analysis was conducted on all the items of the four research variables. After varimax rotation, four factors with eigenvalues greater than 1 were identified, with the largest accounting for 21.5% of the variance. This value is below the critical 40% threshold [ 49 ], suggesting minimal common-method bias in the questionnaire. 4.2 Descriptive statistics Table 2 presents the means, standard deviations, kurtosis values, and skewness values of the variables for the overall, rural, and urban groups. The mean values ranged from 3.25–3.48. T-test analysis revealed that the urban group scored higher in terms of academic resilience and self-efficacy, whereas the rural group presented higher anxiety levels. These differences were statistically significant. Table 2 Descriptive statistics of the research sample Variables Overall(N = 650) Urban(N = 316) Rural(N = 334) M/SD Kurtosis Skewness M/SD M/SD t P AR 3.48/0.61 0.17 -0.31 3.63/0.60 3.34/0.59 6.80 0.00 SE 3.25/0.71 -0.19 0.03 3.49/0.75 3.03/0.60 8.65 0.00 FLA 3.38/0.73 0.37 -0.57 3.26/0.84 3.50/0.60 -4.14 0.00 AR = Academic Resilience; SE = Self-efficacy; FLA = Foreign Language Anxiety 4.3 Reliability and validity of the measurement model Table 3 shows the standardized factor loadings, composite reliability (CR), average variance extracted (AVE), and Kaiser-Meyer-Olkin (KMO) values. The KMO value for the entire questionnaire is 0.961, surpassing the 0.9 requirement. Convergent validity was evaluated through standardized factor loadings, CR, and AVE. All factor loadings exceeded the critical threshold of 0.5 [ 50 ]. The CR values for all the variables were above 0.8, indicating strong internal reliability [ 51 ]. Teo and Noyes [ 52 ] suggest that an AVE above 0.5 is generally adequate, although some researchers contend that convergent validity is acceptable when the CR exceeds 0.6, even if the AVE is slightly below 0.5 [ 53 – 55 ]. In this study, the AVE for anxiety was 0.434, which was deemed acceptable. Table 3 Reliability and validity of the variables Variables Items Standardized Factor Loadings CR AVE KMO Academic resilience AR1 0.773*** 0.951 0.506 0.961 AR2 0.789*** AR3 0.719*** AR4 0.700*** AR5 0.685*** AR6 0.677*** AR7 0.709*** AR8 0.706*** AR9 0.746*** AR10 0.752*** AR11 0.686*** AR12 0.754*** AR13 0.711*** AR14 0.671*** AR15 0.671*** AR16 0.671*** AR17 0.694*** AR18 0.710*** AR19 0.659*** Self-efficacy SE1 0.778*** 0.861 0.511 SE2 0.788*** SE3 0.752*** SE4 0.702*** SE5 0.559*** SE6 0.684*** FL anxiety FLA1 0.725*** 0.859 0.434 FLA2 0.647*** FLA3 0.662*** FLA4 0.644*** FLA5 0.705*** FLA6 0.699*** FLA7 0.599*** FLA8 0.575*** *** p < 0.001;AVE = Average Variance Extracted; CR = Composite Reliability; KMO = Kaiser-Meyer-Olkin; AR = Academic Resilience; FLA = Foreign Language Anxiety; SE = Self-efficacy The results of the discriminant validity of the measurement model are presented in Table 4 . According to Fornell [ 56 ], the square root of each construct’s AVE (shown on the diagonal) should exceed the correlations between that construct and any other construct. All diagonal values in Table 4 satisfy this requirement, indicating adequate discriminant validity for the scales in this study. Finally, the CFA goodness-of-fit indexes indicated that the measurement model fit the data well: χ2 = 1277.4, df = 489, χ2/df = 2.612 0.9, TLI = 0.908 > 0.9, RMSEA = 0.050 < 0.08 [90% CI: 0.047–0.053], and SRMR = 0.0562 < 0.08. Thus, the measurement model exhibits a strong and satisfactory fit to the data. Table 4 Correlations among academic resilience, self-efficacy and FL anxiety Variables Academic resilience Self-efficacy FL anxiety Academic resilience 0.711 Self-efficacy 0.674** 0.715 FL anxiety -0.470** -0.524** 0.659 ** p < 0.01 4.4 The results of path coefficient A structural model was constructed to test the effect of academic resilience on anxiety levels. The results indicated that all the measured parameters of the structural model fell within acceptable ranges (χ2 = 1233.742, df = 489, χ2/df = 2.523 0.9, TLI = 0.924 > 0.9, PNFI = 0.824 > 0.50, RMSEA = 0.048 < 0.08 [90% CI: 0.045–0.052], and SRMR = 0.0556 < 0.08) [ 57 ], suggesting that the model has a relatively good fit with the sample data. Table 5 Regression analyses of the relationships among academic resilience, self-efficacy and FL anxiety Relationship β S.E. t p Result AR→FLA -0.16 0.075 -2.496 0.013* Supported AR→SE 0.747 0.069 14.233 *** Supported SE→FLA -0.497 0.061 -7.221 *** Supported *** p < 0.001; * p < 0.05; AR = Academic Resilience; FLA = Foreign Language Anxiety; SE = Self-efficacy The structural model was adjusted according to the research hypotheses and presented with the path coefficients marked by standardized regression weights (β values) and t-values to show the relationship between academic resilience and FL anxiety (see Fig. 2 ). The results supported H1 and H2 (see Table 5 ). Specifically, academic resilience negatively predicts FL anxiety (β=-0.16, t=-2.496, p < 0.05), supporting H1. Additionally, academic resilience positively predicted self-efficacy (β = 0.747, t = 14.233, p < 0.001), which in turn negatively predicted FL anxiety (β=-0.497, t=-7.221, p < 0.001), supporting H2. 4.5 The mediating role of self-efficacy in the relationship between academic resilience and FL anxiety Table 6 Serial mediating paths between academic resilience and FL anxiety β S.E. Bootstrapping Bias-corrected 95% CI Two-tailed significance LLCI ULCI Standardized direct effect AR→FLA -0.188 -0.160 -0.304 -0.10 0.04* Standardized indirect effect AR→SE→FLA -0.436 -0.371 -0.501 -0.262 0.000*** Standardized total effect AR→FLA -0.623 -0.531 -0.600 -0.452 0.000*** *** p < 0.001; * p < 0.05; AR = Academic Resilience; FLA = Foreign Language Anxiety; SE = Self-efficacy The direct and indirect effects of the hypotheses were analyzed in the present study to investigate the mediating effects. On the basis of the above analyses, we performed bias-corrected bootstrapping at a 95% confidence interval with 2000 bootstrap samples [ 58 ] to test the mediation effect. The results presented in Table 6 revealed that the indirect effect of academic resilience (β=-0.436, p < 0.001) on FL anxiety through self-efficacy was significant. 4.6 The moderating role of urban-rural areas in the relationships among academic resilience, self-efficacy and FL anxiety Establishing measurement invariance is essential to ensure the validity and reliability of a given instrument across different groups before conducting multi-group analysis. Table 7 presents the outcomes of these model comparisons. The model comparisons (M1-M2, M2-M3, M3-M4, and M4-M5) all demonstrated ΔCFI values less than 0.01, indicating good model fit [ 59 ]. Thus, the results were deemed acceptable, confirming the measurement invariance. Therefore, a multi-group analysis was conducted to identify whether the relationships differed significantly between rural and urban students. Table 7 Tests of measurement invariance Model X²/df CFI RMSEA SRMR Model △CFI Decision Overall 2.523 0.930 0.048 0.056 - - - Urban 2.107 0.907 0.059 0.061 - - - Rural 1.826 0.909 0.050 0.067 - - - M1: Configural Invariance 1.966 0.908 0.039 0.061 - - - M2: Metric Invariance 1.964 0.905 0.039 0.061 M1 0.003 Accepted M3: Scalar Invariance 1.986 0.903 0.039 0.079 M2 0.002 Accepted M4: Strict Invariance 1.985 0.903 0.039 0.083 M3 0.000 Accepted M5: Partial strict invariance 1.998 0.901 0.039 0.077 M4 0.002 Accepted To examine the regional differences in terms of the path differences, the multi-group analyses were performed. Critical ratio (C.R.) values were used to compare the differences in the structural path coefficients of different regional groups (see Table 8 ). The path coefficients for AR→SE and SE→FLA differed between the urban and rural groups (|C.R.|>1.96, p < 0.01), supporting H4 and H5. The path coefficients for the AR→FLA (|C.R.| 0.05) links did not differ between the urban and rural groups. Thus, H3 was not supported. Notably, the positive impact of academic resilience on self-efficacy is significantly stronger in urban areas, with the difference being highly statistically significant. The anxiety-alleviating effect of self-efficacy is more pronounced in urban populations. While academic resilience has a significant negative effect on anxiety in the rural group (higher academic resilience is associated with lower anxiety), the effect is not statistically significant in the urban population. Table 8 Multi-group analysis with geographic origin as a moderator Standardized path coefficient Hypotheses Urban(N = 316) Rural(N = 334) C.R. Result H3:AR→FLA -0.158 -0.244* 0.598 Reject H4:AR→SE 0.756*** 0.690*** -3.229** Support H5:SE→FLA -0.553*** -0.301** 3.249** Support *** p < 0.001; ** p < 0.01; AR = Academic Resilience; FLA = Foreign Language Anxiety; SE = Self-efficacy 5 Discussion The present study aims to explore the influence of academic resilience on FL anxiety among junior high school students and to examine the impact of geographic origin on FL anxiety. To achieve this goal, a theoretical model for FL anxiety was proposed. The findings of this study reveal that academic resilience reduces FL anxiety, and that self-efficacy mediates the relationship between academic resilience and FL anxiety. Furthermore, the multi-group analysis revealed significant differences in the paths from academic resilience to self-efficacy and from self-efficacy to FL anxiety between urban and rural junior high school students. 5.1 The impact of academic resilience on FL anxiety The findings reveal a significant negative relationship between academic resilience and FL anxiety, supporting H1 that academic resilience mitigates FL anxiety. This result is consistent with prior research [ 27 ]. Moreover, Yazdi’s sample comprised adult distance learners aged 19–40, whereas the present study extends this effect to Chinese junior high school students, a group especially prone to foreign-language-learning anxiety, confirming that resilience’s protective role is applicable across different age groups. Additionally, prior research on resilience and anxiety has focused primarily on general academic anxiety or test anxiety [ 25 , 26 ], whereas the present study narrows the focus to the FL context, thereby answering recent calls for examinations of mechanisms of resilience within specific subject domains. Grounded in Social Cognitive Theory [ 15 ], resilient students are more likely to attribute setbacks in FL learning to controllable factors, such as effort and strategy use, rather than viewing them as fixed personal shortcomings. This attributional style enhances their sense of self-efficacy and disrupts the vicious cycle of anxiety. In essence, resilience lowers anxiety by fostering a perception of control over language-related challenges. Given that academic resilience can be cultivated through targeted interventions, such as growth mindset training or emotion regulation micro-skills, the findings suggest that FL teachers should prioritize strategies beyond merely simplifying tasks. The incorporating of resilience-building activities into daily instruction could serve as a cost-effective and sustainable method to reduce anxiety in FL classrooms. 5.2 Self-efficacy as a mediator between academic resilience and FL anxiety The results show that self-efficacy mediates the relationship between academic resilience and FL anxiety. Specifically, the findings substantiate H2, demonstrating that academic resilience exerts significant indirect effects on FL anxiety through the mediating mechanism of self-efficacy. From the lens of social cognitive theory [ 15 ], self-efficacy operates as a key cognitive filter that translates resilience resources into emotional outcomes. Academic resilience, defined as the capacity to “bounce back” from academic setbacks, first equips learners with mastery experiences, adaptive attributions, and a sense of control [ 60 ]. These experiences, in turn, help foster strong self-efficacy beliefs as students develop confidence in their ability to plan and carry out the actions needed to achieve success in the FL classroom. High self-efficacy then attenuates threat appraisals and reduces state anxiety by promoting problem-focused coping and optimistic outcome expectations, whereas low self-efficacy leaves learners vulnerable to worry, avoidance, and debilitating anxiety. On the one hand, self-efficacy is negatively associated with FL anxiety, which is consistent with prior studies [ 32 , 33 ]. In the Chinese context, most students experience anxiety in FL classes because they are afraid of expressing themselves. However, students who have high self-efficacy can believe that they have the ability to complete the tasks well in FL classes, which can reduce their anxiety. On the other hand, academic resilience is positively associated with self-efficacy, which is consistent with the findings of previous studies [ 34 , 61 ]. Cabrera-Aguilar, Zevallos-Francia, Morales-Garcia, Ramirez-Coronel, Morales-Garcia, Sairitupa-Sanchez and Morales-Garcia [ 34 ] and Wang, Wang and Zhao [ 61 ] demonstrated that academic resilience affords students repeated mastery experiences in overcoming academic setbacks. Bandura [ 15 ] identified such mastery experiences as the most potent source of self-efficacy. When learners transfer these experiences to foreign-language tasks, they develop greater second-language self-efficacy, which in turn reduces anxiety. Thus, academic resilience can indirectly predict FL anxiety negatively through self-efficacy among junior high school students. Therefore, interventions to improve self-efficacy may also effectively reduce anxiety in FL learning. 5.3 Urban-rural differences as moderators of academic resilience, self-efficacy and FL anxiety The findings reveal that urban-rural differences moderate the relationships among academic resilience, self-efficacy, and FL anxiety. Specifically, the results substantiate H4 and H5, showing that urban-rural differences influence the connections between academic resilience and self-efficacy, as well as between self-efficacy and FL anxiety. However, H3 was not validated, indicating that urban-rural differences do not moderate the relationship between academic resilience and FL anxiety. The results show that academic resilience has a more pronounced effect on self-efficacy in urban students than in their rural counterparts. Similarly, the results show that self-efficacy has a more pronounced effect on FL anxiety in urban students than in their rural counterparts. This suggests that urban students may have better access to resources, support systems, and educational opportunities, which enhances their academic resilience. Consequently, they are more likely to translate this resilience into higher self-efficacy beliefs, which in turn reduces their levels of FL anxiety. Conversely, although academic resilience negatively influences FL anxiety among rural students, this effect is not statistically significant. This can be explained by the unique challenges faced by rural students, including limited access to high-quality educational resources, fewer extracurricular opportunities, and reduced exposure to diverse linguistic environments. These factors, which are consistent with the findings of Cuong [ 36 ], hinder the development of academic resilience and self-efficacy in rural contexts. As a result, rural students experience heightened levels of FL anxiety due to their lower self-efficacy and resilience, which is consistent with the findings of Chen, Huang and Riad [ 38 ] and Wang, Mao, Wei, Liu, Fan, Xu, Wang, Wang, Lou, Lin, Sun, Wang and Wu [ 39 ]. Furthermore, the findings underscore the importance of considering contextual factors when designing interventions aimed at reducing FL anxiety. For urban students, enhancing academic resilience through targeted programs can effectively bolster self-efficacy and alleviate anxiety. For rural students, however, a multifaceted approach is necessary to address external barriers such as improving teacher support, increasing access to technology-mediated learning resources, and fostering a supportive classroom environment. 6 Theoretical and practical implications The present research makes several important theoretical and practical contributions to the literature on academic resilience, self-efficacy, and FL anxiety in FL learning. From a theoretical perspective, this study advances the field by extending Social Cognitive Theory to FL anxiety research, revealing the mediating mechanism. It specifically identifies self-efficacy as a mediator between academic resilience and FL anxiety. While Shen, Ismail, Jeyaraj and Teng [ 32 ] applied Social Cognitive Theory to emphasize the role of self-efficacy in reducing writing anxiety, this study adapts this mechanism to the FL context. Moreover, building on Cabrera-Aguilar, Zevallos-Francia, Morales-Garcia, Ramirez-Coronel, Morales-Garcia, Sairitupa-Sanchez and Morales-Garcia [ 34 ], who identified academic resilience as pivotal in shaping self-efficacy among nurses, this research applies a similar framework to junior high school students. From a practical perspective, instructors should develop comprehensive pedagogical strategies that cultivate academic resilience and increase learners’ self-efficacy, which collectively reduce FL anxiety. First, it is essential to create a supportive classroom environment where students feel safe to express themselves without fear of negative evaluation. For example, group work and pair activities can foster a sense of community among students, allowing them to share their experiences and strategies for overcoming challenges[ 62 , 63 ]. Second, educators should integrate growth mindset principles into their teaching practices [ 64 ]. By emphasizing that abilities can be developed through effort and perseverance, teachers can help students view challenges as opportunities for growth rather than insurmountable obstacles. This approach can be reinforced through regular feedback that focuses on improvement and effort rather than solely on outcomes. Third, language learning apps or online platforms can enable students, especially rural students, to engage with the material at their own pace. It fosters a sense of autonomy and control over their learning process. These strategies will not only mitigate FL anxiety but also empower students to become more confident and capable language users in diverse contexts. 7 Limitations Some limitations are included in this study. First, the present study adopted a cross-sectional design, which inherently limits the ability to establish causal relationships between academic resilience and FL anxiety. As Maxwell, Cole and Mitchell [ 65 ] noted, cross-sectional data cannot adequately capture the temporal sequence of processes in multivariate associations, nor can they account for potential bidirectional relationships among variables. To address these limitations, future research should incorporate longitudinal or mixed-method designs to better examine the dynamic effects of academic resilience on FL anxiety. Second, the present study relied exclusively on self-reported measures. Although the approach offers methodological convenience, it may introduce limitations, including potential response bias and restricted perspectives. To strengthen the validity and comprehensiveness of future findings, researchers should incorporate multiple data sources, such as qualitative interviews, to provide a more robust examination of the relationship between academic resilience and FL anxiety. Third, while the quantitative design identified the rural-urban gap in FL anxiety, it failed to elucidate its underlying causes; interviews could reveal the deeper motivations and contextual factors contributing to these differences. 8 Conclusions The present research investigated the relationships among academic resilience, self-efficacy, and FL anxiety among urban and rural junior high school students. The findings showed that academic resilience was negatively related to FL anxiety. Furthermore, this study revealed that self-efficacy plays a mediating role in the relationship between academic resilience and FL anxiety. Additionally, the study revealed that urban-rural differences moderated the relationships between academic resilience and self-efficacy, as well as between self-efficacy and FL anxiety. However, urban-rural differences did not play a moderating role in the relationship between academic resilience and FL anxiety. These findings call for policymakers to allocate resources more evenly and safeguard greater equity in FL education. Abbreviations FL=foreign language AR=academic resilience SE=self-efficacy FLA=foreign language anxiety CR=composite reliability AVE=average variance extracted KMO=Kaiser-Meyer-Olkin C.R.=critical ratio Declarations Ethics approval and consent to participate: This study was conducted in accordance with the Helsinki Declaration and was approved by College of Education, Jiangxi Normal University. Written informed consent was obtained from all participants prior to their conclusion in the study. Consent for publication: All participants consented to the publication of the submission version. Competing Interests: No potential conflict of interest was reported by the authors. Funding: Not applicable. 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15:25:51","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":106415,"visible":true,"origin":"","legend":"","description":"","filename":"TheeffectsofacademicresilienceonforeignlanguageanxietyAstructuralequationmodelingbasedmultigroupanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7750527/v1/2cdcacd202f47aceb00e2002.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effects of academic resilience on foreign language anxiety: A structural equation modeling-based multi-group analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eForeign language (FL) learning presents numerous challenges, with FL anxiety identified as a significant barrier to success [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Defined as feelings of apprehension and nervousness during FL use [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], FL anxiety negatively affects various aspects of language learning. Research indicates its detrimental impact on students' academic engagement [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], confidence [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and test performance [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], highlighting the urgent need for effective intervention strategies in language education.\u003c/p\u003e\u003cp\u003eThe existing research has explored various interventions targeting teacher support, learner traits, and classroom environments to address FL anxiety [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For example, Man, Fang, Chan and Han [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] highlighted the role of academic support and mutual respect in fostering a supportive classroom environment, which can reduce stress and create favorable conditions for language learning. Similarly, Liu, Li and Yan [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] demonstrated that perceived emotional and instrumental support from teachers indirectly alleviates FL anxiety by fostering L2 grit, which helps learners develop perseverance and passion for long-term language learning goals. Additionally, Li [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] emphasized the importance of both internal traits and external conditions, showing that trait emotional intelligence enables learners to manage their emotional responses, thereby reducing anxiety. These studies suggest that supportive teacher behaviors, positive classroom environments, and individual learner traits can effectively reduce FL anxiety. Nevertheless, the role of academic resilience in reducing FL anxiety has received limited attention [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Academic resilience, defined as the ability to achieve educational goals despite adversity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], is a critical psychological resource that enables students to adapt positively to challenges and persist toward long-term objectives. By helping learners manage the frustration and stress inherent in FL learning, academic resilience may directly alleviate anxiety levels [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This unexplored area warrants further investigation to identify effective strategies for reducing FL anxiety.\u003c/p\u003e\u003cp\u003eAdditionally, Social Cognitive Theory [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] underscores self-efficacy, the belief in one's ability to achieve specific goals, as a crucial mediator among environmental factors (e.g., urban-rural differences), cognitive resources (e.g., academic resilience), and emotional outcomes (e.g., FL anxiety). High self-efficacy increases students' confidence in overcoming language-learning obstacles, thus alleviating FL anxiety from self-doubt. This implies that self-efficacy may be a pivotal link between academic resilience and reduced FL anxiety. Furthermore, urban-rural differences potentially moderate these relationships by affecting access to educational resources and psychological support. Rural students often encounter resource limitations that adversely affect their self-efficacy and academic resilience [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Understanding how these contextual differences impact the interactions among academic resilience, self-efficacy, and FL anxiety is vital for developing interventions tailored to diverse student populations.\u003c/p\u003e\u003cp\u003eThe present study, which is grounded in a robust theoretical framework, leverages structural equation modeling to elucidate: (1) the direct impact of academic resilience on FL anxiety among junior high school students; (2) the mediating role of self-efficacy in this relationship; and (3) the moderating influence of urban-rural disparities through multi-group analysis. The findings seek to expand the theoretical understanding of the mechanisms underlying FL anxiety reduction, while providing evidence-based strategies for educators to address FL anxiety across diverse classroom contexts.\u003c/p\u003e"},{"header":"2 Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 FL anxiety\u003c/h2\u003e\u003cp\u003eFL anxiety is characterized by learners' self-perceptions, beliefs, emotions, and classroom behaviors, arising from the challenges of learning a foreign language (Elaine K. Horwitz et al., 1986). It is a pivotal factor that impacts both FL learning and teaching, with significant effects on students' academic performance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, mitigating FL anxiety is crucial for enhancing learning outcomes.\u003c/p\u003e\u003cp\u003eFL anxiety is shaped by a complex interplay of external and internal factors. Prominent external factors include teacher support, technological support and environmental support. Empirical evidence suggests that teacher-provided academic guidance and fostering mutual respect in the classroom can significantly alleviate learners' language anxiety [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly, technology-based interventions, such as voice-based, video-based, and virtual reality-based oral interaction modes, have proven effective in reducing learners' anxiety, with no significant differences observed among these modalities [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, the physical and psychological characteristics of the classroom environment have been identified as key predictors of the degree of language anxiety experienced by students [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition to external influences, internal learner-related factors such as language proficiency, emotional intelligence, motivation, and personality traits contribute to FL anxiety. For example, Jin, Dewaele and MacIntyre [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] showed that reflecting on language progress as a cognitive-emotional strategy reduces FL anxiety by promoting positive psychological processes. This finding is consistent with the findings on trait emotional intelligence [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], where affect regulation capacities are highlighted as critical for mitigating anxiety. Moreover, Pan and Zhang [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] identified motivational drivers, self-concept dimensions, and personality traits as key mechanisms illustrating the complex interplay between external support and internal characteristics in shaping FL anxiety. These findings collectively underscore the multifaceted nature of FL anxiety and the dynamic interaction between external resources and internal learner traits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Academic resilience\u003c/h2\u003e\u003cp\u003eAcademic resilience refers to the ability to maintain psychological stamina and persist in achieving educational goals despite adversity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Within the domain of language learning, students with high academic resilience tend to perform well academically even under challenging conditions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition to its impact on academic performance, academic resilience has been shown to reduce stressors and increase engagement in learning contexts [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. By fostering academic resilience, learners can effectively navigate obstacles, pressure, and challenges inherent in academic environments. This empowers students not only to confront difficulties but also to mitigate anxiety through the development of adaptive coping strategies and emotional regulation skills.\u003c/p\u003e\u003cp\u003eEmpirical research has consistently demonstrated a negative correlation between academic resilience and anxiety across various contexts. For example, Savitsky, Findling, Ereli and Hendel [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] reported that students who are more resilient report markedly less anxiety in demanding educational settings such as higher education or professional training programs. Similarly, Ragusa, Gonzalez-Bernal, Trigueros, Caggiano, Navarro, Minguez-Minguez, Obregon and Fernandez-Ortega [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] identified academic resilience as a key predictor of reduced academic anxiety during adolescence because of its protective effects against stressors. Yazdi and Ghanizadeh [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] extended these findings to virtual learning environments by showing that academic resilience negatively correlates with FL anxiety among university students. Despite these insights, Chinese junior high school students face unique challenges, such as the intense pressure to prepare for high school entrance examinations coupled with their developmental stage, which may alter this relationship [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, the specific mechanisms through which academic resilience mitigates FL anxiety in this context remain unclear. Addressing this gap is essential for understanding how academic resilience operates within younger learners under culturally specific pressures [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Thus, we propose the following hypothesis.\u003c/p\u003e\u003cp\u003eH1: Academic resilience is negatively related to FL anxiety.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Self-efficacy\u003c/h2\u003e\u003cp\u003eSelf-efficacy, rooted in Social Cognitive Theory [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], is a critical factor in mitigating anxiety, particularly in language learning environments where anxiety often disrupts cognitive and emotional processes. Learners with strong self-efficacy beliefs tend to exhibit greater persistence, resilience in the face of setbacks, and a willingness to perform challenging tasks [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Conversely, individuals with low self-efficacy are more inclined to withdraw from learning activities, avoid challenges, and opt for easier tasks, thereby impeding their academic progress. The inverse relationship between self-efficacy and anxiety has been well established across diverse domains. Shen, Ismail, Jeyaraj and Teng [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] demonstrated that students' confidence in their writing ability inversely correlates with their writing-related anxiety, indicating that higher self-efficacy effectively mitigates task-specific anxiety. Similarly, Hong and Tai [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] reported that self-efficacy in online learning is inversely associated with online learning-related anxiety and that performance-based self-efficacy alleviates general academic stressors. These studies consistently highlight the inverse relationship between self-efficacy and anxiety in both specific and general learning contexts.\u003c/p\u003e\u003cp\u003ePrevious research has identified key antecedents of self-efficacy, including grit (Cai et al., 2024; Derakhshan \u0026amp; Fathi, 2024), learning approaches (Chen, Chih-Hung, 2025), peer and technical support (Han \u0026amp; Geng, 2023), and self-regulated learning (Chen \u0026amp; Zhu, 2025; Wang, 2021). For example, self-efficacy has been shown to mediate the relationship between grit and engagement (Derakhshan \u0026amp; Fathi, 2024) and can be enhanced by innovative teaching models such as flipped learning (Chen \u0026amp; Shih, 2025). According to Social Cognitive Theory [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], academic resilience serves as a critical resource for enhancing self-efficacy. Resilient individuals maintain a positive outlook when facing challenges and persist in their efforts, reinforcing their belief in their own abilities. This persistence leads to successful experiences that further strengthen their self-efficacy. Empirical evidence supports this link. For example, Cabrera-Aguilar, Zevallos-Francia, Morales-Garcia, Ramirez-Coronel, Morales-Garcia, Sairitupa-Sanchez and Morales-Garcia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] reported that academic resilience positively influences self-efficacy among nurses by fostering adaptive coping mechanisms and positive outcomes.\u003c/p\u003e\u003cp\u003eOn the basis of the above discussion, we argue that academic resilience may influence FL anxiety indirectly through its impact on self-efficacy. While individual relationships among academic resilience, self-efficacy, and anxiety have been studied separately, the integrated pathway, particularly within FL contexts, has not been extensively investigated [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Thus, the following hypothesis is proposed.\u003c/p\u003e\u003cp\u003eH2: Self-efficacy mediates the relationship between academic resilience and FL anxiety.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 The urban-rural differences\u003c/h2\u003e\u003cp\u003eThe disparity in educational resources between urban and rural China continues to be an enduring challenge. Urban schools generally benefit from better funding, highly qualified teachers, and advanced technological resources, whereas rural schools face significant constraints such as teacher shortages and limited access to extracurricular opportunities [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These disparities contribute to differences in both initial language proficiency levels and psychological environments for FL learning.\u003c/p\u003e\u003cp\u003eMoreover, the social environment is a key determinant of emotional outcomes such as anxiety [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Existing studies have shown significant differences in anxiety levels between urban and rural students. For example, Chen, Huang and Riad [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and Wang, Mao, Wei, Liu, Fan, Xu, Wang, Wang, Lou, Lin, Sun, Wang and Wu [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] indicated that rural students often experience higher levels of overall anxiety, particularly in test-related situations, than urban students do [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, some studies have reached opposite conclusions. For example, Victor Mbanuzuru, Uwakwe, Sochukwu Anyaoku, Okwudili Ojimba, Chinyere Mbanuzuru, Ezenyeaku, Chukwudinma Obi, Nkiru Okafor and Prosper Okonkwo [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] reported greater overall anxiety among urban adolescents than among their rural peers did, whereas Parad, Kajale, Vartak and Khadilkar [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] showed that urban students\u0026rsquo; test anxiety significantly undermined their academic achievement. In the context of FL learning, urban students often report higher levels of FL anxiety than rural students do [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These findings suggest that differences in urban and rural environments, particularly in terms of living conditions, educational resources, and social support systems, can directly or indirectly influence students' emotional states and anxiety levels.\u003c/p\u003e\u003cp\u003eAdditionally, the social environment also plays an important role in shaping self-efficacy development. Zimmerman and Schunk [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] emphasized that mastery experiences are central to developing self-efficacy; these experiences are particularly salient in contexts of adversity. For rural students who face limited access to technology or teacher support, adversity can create opportunities for intrinsic coping strategies and peer collaboration, enabling them to build stronger self-efficacy through overcoming challenges [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In contrast, urban students benefit from abundant resources but tend to rely more on external validations such as teacher feedback and peer comparisons to develop self-efficacy [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These findings suggest that the relationship between academic resilience and self-efficacy is likely stronger in rural contexts, as adversity plays a more prominent role in shaping mastery experiences. On the basis of these findings regarding how urban-rural disparities influence academic resilience, self-efficacy, and FL anxiety, we propose the following hypotheses.\u003c/p\u003e\u003cp\u003eH3: Urban-rural differences moderate the relationship between academic resilience and FL anxiety.\u003c/p\u003e\u003cp\u003eH4: Urban-rural differences moderate the relationship between academic resilience and self-efficacy.\u003c/p\u003e\u003cp\u003eH5: Urban-rural differences moderate the relationship between self-efficacy and FL anxiety.\u003c/p\u003e\u003cp\u003eDrawing on prior work linking academic resilience, self-efficacy and FL anxiety, we propose a research model to explain their relationship in the FL context (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data collection\u003c/h2\u003e\u003cp\u003eA paper-based questionnaire survey was employed in this study to investigate the relationship between academic resilience and FL anxiety. The participants were junior high school students recruited from two junior high schools (one urban and one rural) from March 17th to 28th, 2025. Using random sampling, we recruited participants and distributed 750 questionnaires, ensuring anonymous and voluntary participation. We eliminated data from students who provided invalid responses. After rigorous screening, 650 valid responses were retained, representing an 86.7% valid response rate.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of the research sample\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe detailed demographic information for the sample is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 340 participants were male (52.3%), and 310 participants were female (47.7%). With respect to age, the sample primarily consisted of 113 participants aged 12, 188 aged 13, 212 aged 14, and 137 aged 15. Among the 650 participants, 316 (48.6%) came from urban areas, and 334 (51.4%) were from rural areas. On the basis of the overall descriptive statistical distribution of the sample's demographic characteristics, the proportional distribution of the sample is relatively reasonable and demonstrates a certain degree of representativeness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Instruments\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 The academic resilience scale\u003c/h2\u003e\u003cp\u003eThe academic resilience scale was adapted from Guo and Li [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The 19-item scale consists of three dimensions: ego-resilience, metacognitive-resilience, and social resilience. All the items were rated on a 7-point Likert scale, ranging from 1 (\"Strongly disagree\") to 7 (\"Strongly agree\"). The scale demonstrated excellent reliability in the present study, with a Cronbach's alpha of 0.942. A representative item from the academic resilience scale is \"I am curious about new knowledge when studying a FL\".\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 The self-efficacy scale\u003c/h2\u003e\u003cp\u003eThe self-efficacy scale utilized in this study was developed by Cai, Zhu and Xing [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The 6 items were adapted to assess students\u0026rsquo; confidence in FL learning. The scale uses a 6-point Likert scale ranging from \u0026ldquo;1 (Strongly disagree)\u0026rdquo; to \u0026ldquo;6 (Strongly agree)\u0026rdquo; and showed high reliability in the present study (α\u0026thinsp;=\u0026thinsp;0.859). A representative item used to measure self-efficacy is \"I believe I will receive an excellent grade in this FL class\".\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3The FL anxiety scale\u003c/h2\u003e\u003cp\u003eThe FL anxiety scale was adapted from Elouise Botes, LINDIE VAN DER WESTHUIZEN, JEAN-MARC DEWAELE, PETER MACINTYRE and SAMUEL GREIFF [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The 8 items were modified and used to assess students\u0026rsquo; FL anxiety. Rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree), the scale showed strong internal consistency in this study (α\u0026thinsp;=\u0026thinsp;0.856). A representative item measuring FL anxiety is \"Even if I am well prepared for language class, I feel anxious about it\".\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Data analysis\u003c/h2\u003e\u003cp\u003eBefore the data were analyzed, all scales were standardized to a 5-point scale. The present research follows a three-phase data analysis approach. First, SPSS 27.0 was employed to examine the reliability and validity of the questionnaire. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were subsequently employed. The research model was estimated via the maximum likelihood estimation method. Third, we examined the structural model to test the hypotheses about the constructs\u0026rsquo; interrelations. AMOS 28.0 was used to assess measurement invariance and conduct multi-group analysis (MGA). In the assessment of measurement invariance, a range of models, including configural, metric, scalar, and strict, were employed for evaluation purposes. After confirming measurement invariance, MGAs were employed to estimate the structural models for each group.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Common method deviation test\u003c/h2\u003e\u003cp\u003eCommon method deviation was controlled through anonymity, the inclusion of lie detector items, and the use of various scoring strategies. A single-factor approach was used to test for the presence of common method deviation, and an exploratory factor analysis was conducted on all the items of the four research variables. After varimax rotation, four factors with eigenvalues greater than 1 were identified, with the largest accounting for 21.5% of the variance. This value is below the critical 40% threshold [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], suggesting minimal common-method bias in the questionnaire.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Descriptive statistics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the means, standard deviations, kurtosis values, and skewness values of the variables for the overall, rural, and urban groups. The mean values ranged from 3.25\u0026ndash;3.48. T-test analysis revealed that the urban group scored higher in terms of academic resilience and self-efficacy, whereas the rural group presented higher anxiety levels. These differences were statistically significant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of the research sample\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eOverall(N\u0026thinsp;=\u0026thinsp;650)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUrban(N\u0026thinsp;=\u0026thinsp;316)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRural(N\u0026thinsp;=\u0026thinsp;334)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM/SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM/SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eM/SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.48/0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.63/0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.34/0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.25/0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.49/0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.03/0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFLA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.38/0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.26/0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.50/0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-4.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eAR\u0026thinsp;=\u0026thinsp;Academic Resilience; SE\u0026thinsp;=\u0026thinsp;Self-efficacy; FLA\u0026thinsp;=\u0026thinsp;Foreign Language Anxiety\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Reliability and validity of the measurement model\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the standardized factor loadings, composite reliability (CR), average variance extracted (AVE), and Kaiser-Meyer-Olkin (KMO) values. The KMO value for the entire questionnaire is 0.961, surpassing the 0.9 requirement. Convergent validity was evaluated through standardized factor loadings, CR, and AVE. All factor loadings exceeded the critical threshold of 0.5 [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The CR values for all the variables were above 0.8, indicating strong internal reliability [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Teo and Noyes [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] suggest that an AVE above 0.5 is generally adequate, although some researchers contend that convergent validity is acceptable when the CR exceeds 0.6, even if the AVE is slightly below 0.5 [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In this study, the AVE for anxiety was 0.434, which was deemed acceptable.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReliability and validity of the variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandardized Factor Loadings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKMO\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"18\" rowspan=\"19\"\u003e\u003cp\u003eAcademic resilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.773***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"18\" rowspan=\"19\"\u003e\u003cp\u003e0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"18\" rowspan=\"19\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"32\" rowspan=\"33\"\u003e\u003cp\u003e0.961\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.789***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.719***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.700***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.685***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.677***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.709***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.706***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.746***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.752***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.686***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.754***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.711***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.671***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.671***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.671***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.694***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.710***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAR19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.659***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eSelf-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.778***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSE2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.788***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSE3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.752***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSE4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.702***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSE5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.559***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSE6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.684***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eFL anxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFLA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.725***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFLA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.647***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFLA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.662***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFLA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.644***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFLA5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.705***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFLA6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.699***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFLA7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.599***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFLA8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.575***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001;AVE\u0026thinsp;=\u0026thinsp;Average Variance Extracted; CR\u0026thinsp;=\u0026thinsp;Composite Reliability; KMO\u0026thinsp;=\u0026thinsp;Kaiser-Meyer-Olkin; AR\u0026thinsp;=\u0026thinsp;Academic Resilience; FLA\u0026thinsp;=\u0026thinsp;Foreign Language Anxiety; SE\u0026thinsp;=\u0026thinsp;Self-efficacy\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of the discriminant validity of the measurement model are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. According to Fornell [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], the square root of each construct\u0026rsquo;s AVE (shown on the diagonal) should exceed the correlations between that construct and any other construct. All diagonal values in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e satisfy this requirement, indicating adequate discriminant validity for the scales in this study.\u003c/p\u003e\u003cp\u003eFinally, the CFA goodness-of-fit indexes indicated that the measurement model fit the data well: χ2\u0026thinsp;=\u0026thinsp;1277.4, df\u0026thinsp;=\u0026thinsp;489, χ2/df\u0026thinsp;=\u0026thinsp;2.612\u0026thinsp;\u0026lt;\u0026thinsp;3, CFI\u0026thinsp;=\u0026thinsp;0.915\u0026thinsp;\u0026gt;\u0026thinsp;0.9, TLI\u0026thinsp;=\u0026thinsp;0.908\u0026thinsp;\u0026gt;\u0026thinsp;0.9, RMSEA\u0026thinsp;=\u0026thinsp;0.050\u0026thinsp;\u0026lt;\u0026thinsp;0.08 [90% CI: 0.047\u0026ndash;0.053], and SRMR\u0026thinsp;=\u0026thinsp;0.0562\u0026thinsp;\u0026lt;\u0026thinsp;0.08. Thus, the measurement model exhibits a strong and satisfactory fit to the data.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelations among academic resilience, self-efficacy and FL anxiety\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcademic resilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelf-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFL anxiety\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcademic resilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.711\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.674**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.715\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFL anxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.470**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.524**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.659\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e**\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.4 The results of path coefficient\u003c/h2\u003e\u003cp\u003eA structural model was constructed to test the effect of academic resilience on anxiety levels. The results indicated that all the measured parameters of the structural model fell within acceptable ranges (χ2\u0026thinsp;=\u0026thinsp;1233.742, df\u0026thinsp;=\u0026thinsp;489, χ2/df\u0026thinsp;=\u0026thinsp;2.523\u0026thinsp;\u0026lt;\u0026thinsp;3, CFI\u0026thinsp;=\u0026thinsp;0.930\u0026thinsp;\u0026gt;\u0026thinsp;0.9, TLI\u0026thinsp;=\u0026thinsp;0.924\u0026thinsp;\u0026gt;\u0026thinsp;0.9, PNFI\u0026thinsp;=\u0026thinsp;0.824\u0026thinsp;\u0026gt;\u0026thinsp;0.50, RMSEA\u0026thinsp;=\u0026thinsp;0.048\u0026thinsp;\u0026lt;\u0026thinsp;0.08 [90% CI: 0.045\u0026ndash;0.052], and SRMR\u0026thinsp;=\u0026thinsp;0.0556\u0026thinsp;\u0026lt;\u0026thinsp;0.08) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], suggesting that the model has a relatively good fit with the sample data.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression analyses of the relationships among academic resilience, self-efficacy and FL anxiety\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationship\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR\u0026rarr;FLA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR\u0026rarr;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSE\u0026rarr;FLA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; AR\u0026thinsp;=\u0026thinsp;Academic Resilience; FLA\u0026thinsp;=\u0026thinsp;Foreign Language Anxiety; SE\u0026thinsp;=\u0026thinsp;Self-efficacy\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe structural model was adjusted according to the research hypotheses and presented with the path coefficients marked by standardized regression weights (β values) and t-values to show the relationship between academic resilience and FL anxiety (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results supported H1 and H2 (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Specifically, academic resilience negatively predicts FL anxiety (β=-0.16, t=-2.496, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), supporting H1. Additionally, academic resilience positively predicted self-efficacy (β\u0026thinsp;=\u0026thinsp;0.747, t\u0026thinsp;=\u0026thinsp;14.233, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which in turn negatively predicted FL anxiety (β=-0.497, t=-7.221, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H2.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.5 The mediating role of self-efficacy in the relationship between academic resilience and FL anxiety\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSerial mediating paths between academic resilience and FL anxiety\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eBootstrapping\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eBias-corrected 95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTwo-tailed significance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLLCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eULCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandardized direct effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR\u0026rarr;FLA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandardized indirect effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR\u0026rarr;SE\u0026rarr;FLA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandardized total effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR\u0026rarr;FLA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; AR\u0026thinsp;=\u0026thinsp;Academic Resilience; FLA\u0026thinsp;=\u0026thinsp;Foreign Language Anxiety; SE\u0026thinsp;=\u0026thinsp;Self-efficacy\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe direct and indirect effects of the hypotheses were analyzed in the present study to investigate the mediating effects. On the basis of the above analyses, we performed bias-corrected bootstrapping at a 95% confidence interval with 2000 bootstrap samples [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] to test the mediation effect. The results presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e revealed that the indirect effect of academic resilience (β=-0.436, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on FL anxiety through self-efficacy was significant.\u003c/p\u003e\u003cp\u003e4.6 The moderating role of urban-rural areas in the relationships among academic resilience, self-efficacy and FL anxiety\u003c/p\u003e\u003cp\u003eEstablishing measurement invariance is essential to ensure the validity and reliability of a given instrument across different groups before conducting multi-group analysis. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the outcomes of these model comparisons. The model comparisons (M1-M2, M2-M3, M3-M4, and M4-M5) all demonstrated ΔCFI values less than 0.01, indicating good model fit [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Thus, the results were deemed acceptable, confirming the measurement invariance. Therefore, a multi-group analysis was conducted to identify whether the relationships differed significantly between rural and urban students.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTests of measurement invariance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX\u0026sup2;/df\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e△CFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDecision\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1: Configural Invariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2: Metric Invariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAccepted\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM3: Scalar Invariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAccepted\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM4: Strict Invariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eM3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAccepted\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM5: Partial strict invariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eM4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAccepted\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo examine the regional differences in terms of the path differences, the multi-group analyses were performed. Critical ratio (C.R.) values were used to compare the differences in the structural path coefficients of different regional groups (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The path coefficients for AR\u0026rarr;SE and SE\u0026rarr;FLA differed between the urban and rural groups (|C.R.|\u0026gt;1.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), supporting H4 and H5. The path coefficients for the AR\u0026rarr;FLA (|C.R.|\u0026lt;1.96, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) links did not differ between the urban and rural groups. Thus, H3 was not supported. Notably, the positive impact of academic resilience on self-efficacy is significantly stronger in urban areas, with the difference being highly statistically significant. The anxiety-alleviating effect of self-efficacy is more pronounced in urban populations. While academic resilience has a significant negative effect on anxiety in the rural group (higher academic resilience is associated with lower anxiety), the effect is not statistically significant in the urban population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulti-group analysis with geographic origin as a moderator\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eStandardized path coefficient\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypotheses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban(N\u0026thinsp;=\u0026thinsp;316)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRural(N\u0026thinsp;=\u0026thinsp;334)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC.R.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eResult\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3:AR\u0026rarr;FLA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.244*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReject\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4:AR\u0026rarr;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.756***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.690***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.229**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5:SE\u0026rarr;FLA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.553***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.301**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.249**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; AR\u0026thinsp;=\u0026thinsp;Academic Resilience; FLA\u0026thinsp;=\u0026thinsp;Foreign Language Anxiety; SE\u0026thinsp;=\u0026thinsp;Self-efficacy\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eThe present study aims to explore the influence of academic resilience on FL anxiety among junior high school students and to examine the impact of geographic origin on FL anxiety. To achieve this goal, a theoretical model for FL anxiety was proposed. The findings of this study reveal that academic resilience reduces FL anxiety, and that self-efficacy mediates the relationship between academic resilience and FL anxiety. Furthermore, the multi-group analysis revealed significant differences in the paths from academic resilience to self-efficacy and from self-efficacy to FL anxiety between urban and rural junior high school students.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e5.1 The impact of academic resilience on FL anxiety\u003c/h2\u003e\u003cp\u003eThe findings reveal a significant negative relationship between academic resilience and FL anxiety, supporting H1 that academic resilience mitigates FL anxiety. This result is consistent with prior research [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, Yazdi\u0026rsquo;s sample comprised adult distance learners aged 19\u0026ndash;40, whereas the present study extends this effect to Chinese junior high school students, a group especially prone to foreign-language-learning anxiety, confirming that resilience\u0026rsquo;s protective role is applicable across different age groups. Additionally, prior research on resilience and anxiety has focused primarily on general academic anxiety or test anxiety [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], whereas the present study narrows the focus to the FL context, thereby answering recent calls for examinations of mechanisms of resilience within specific subject domains.\u003c/p\u003e\u003cp\u003eGrounded in Social Cognitive Theory [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], resilient students are more likely to attribute setbacks in FL learning to controllable factors, such as effort and strategy use, rather than viewing them as fixed personal shortcomings. This attributional style enhances their sense of self-efficacy and disrupts the vicious cycle of anxiety. In essence, resilience lowers anxiety by fostering a perception of control over language-related challenges. Given that academic resilience can be cultivated through targeted interventions, such as growth mindset training or emotion regulation micro-skills, the findings suggest that FL teachers should prioritize strategies beyond merely simplifying tasks. The incorporating of resilience-building activities into daily instruction could serve as a cost-effective and sustainable method to reduce anxiety in FL classrooms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Self-efficacy as a mediator between academic resilience and FL anxiety\u003c/h2\u003e\u003cp\u003eThe results show that self-efficacy mediates the relationship between academic resilience and FL anxiety. Specifically, the findings substantiate H2, demonstrating that academic resilience exerts significant indirect effects on FL anxiety through the mediating mechanism of self-efficacy. From the lens of social cognitive theory [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], self-efficacy operates as a key cognitive filter that translates resilience resources into emotional outcomes. Academic resilience, defined as the capacity to \u0026ldquo;bounce back\u0026rdquo; from academic setbacks, first equips learners with mastery experiences, adaptive attributions, and a sense of control [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These experiences, in turn, help foster strong self-efficacy beliefs as students develop confidence in their ability to plan and carry out the actions needed to achieve success in the FL classroom. High self-efficacy then attenuates threat appraisals and reduces state anxiety by promoting problem-focused coping and optimistic outcome expectations, whereas low self-efficacy leaves learners vulnerable to worry, avoidance, and debilitating anxiety.\u003c/p\u003e\u003cp\u003eOn the one hand, self-efficacy is negatively associated with FL anxiety, which is consistent with prior studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In the Chinese context, most students experience anxiety in FL classes because they are afraid of expressing themselves. However, students who have high self-efficacy can believe that they have the ability to complete the tasks well in FL classes, which can reduce their anxiety. On the other hand, academic resilience is positively associated with self-efficacy, which is consistent with the findings of previous studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Cabrera-Aguilar, Zevallos-Francia, Morales-Garcia, Ramirez-Coronel, Morales-Garcia, Sairitupa-Sanchez and Morales-Garcia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and Wang, Wang and Zhao [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] demonstrated that academic resilience affords students repeated mastery experiences in overcoming academic setbacks. Bandura [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] identified such mastery experiences as the most potent source of self-efficacy. When learners transfer these experiences to foreign-language tasks, they develop greater second-language self-efficacy, which in turn reduces anxiety. Thus, academic resilience can indirectly predict FL anxiety negatively through self-efficacy among junior high school students. Therefore, interventions to improve self-efficacy may also effectively reduce anxiety in FL learning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Urban-rural differences as moderators of academic resilience, self-efficacy and FL anxiety\u003c/h2\u003e\u003cp\u003eThe findings reveal that urban-rural differences moderate the relationships among academic resilience, self-efficacy, and FL anxiety. Specifically, the results substantiate H4 and H5, showing that urban-rural differences influence the connections between academic resilience and self-efficacy, as well as between self-efficacy and FL anxiety. However, H3 was not validated, indicating that urban-rural differences do not moderate the relationship between academic resilience and FL anxiety.\u003c/p\u003e\u003cp\u003eThe results show that academic resilience has a more pronounced effect on self-efficacy in urban students than in their rural counterparts. Similarly, the results show that self-efficacy has a more pronounced effect on FL anxiety in urban students than in their rural counterparts. This suggests that urban students may have better access to resources, support systems, and educational opportunities, which enhances their academic resilience. Consequently, they are more likely to translate this resilience into higher self-efficacy beliefs, which in turn reduces their levels of FL anxiety. Conversely, although academic resilience negatively influences FL anxiety among rural students, this effect is not statistically significant. This can be explained by the unique challenges faced by rural students, including limited access to high-quality educational resources, fewer extracurricular opportunities, and reduced exposure to diverse linguistic environments. These factors, which are consistent with the findings of Cuong [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], hinder the development of academic resilience and self-efficacy in rural contexts. As a result, rural students experience heightened levels of FL anxiety due to their lower self-efficacy and resilience, which is consistent with the findings of Chen, Huang and Riad [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and Wang, Mao, Wei, Liu, Fan, Xu, Wang, Wang, Lou, Lin, Sun, Wang and Wu [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, the findings underscore the importance of considering contextual factors when designing interventions aimed at reducing FL anxiety. For urban students, enhancing academic resilience through targeted programs can effectively bolster self-efficacy and alleviate anxiety. For rural students, however, a multifaceted approach is necessary to address external barriers such as improving teacher support, increasing access to technology-mediated learning resources, and fostering a supportive classroom environment.\u003c/p\u003e\u003c/div\u003e"},{"header":"6 Theoretical and practical implications","content":"\u003cp\u003eThe present research makes several important theoretical and practical contributions to the literature on academic resilience, self-efficacy, and FL anxiety in FL learning.\u003c/p\u003e\u003cp\u003eFrom a theoretical perspective, this study advances the field by extending Social Cognitive Theory to FL anxiety research, revealing the mediating mechanism. It specifically identifies self-efficacy as a mediator between academic resilience and FL anxiety. While Shen, Ismail, Jeyaraj and Teng [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] applied Social Cognitive Theory to emphasize the role of self-efficacy in reducing writing anxiety, this study adapts this mechanism to the FL context. Moreover, building on Cabrera-Aguilar, Zevallos-Francia, Morales-Garcia, Ramirez-Coronel, Morales-Garcia, Sairitupa-Sanchez and Morales-Garcia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], who identified academic resilience as pivotal in shaping self-efficacy among nurses, this research applies a similar framework to junior high school students.\u003c/p\u003e\u003cp\u003eFrom a practical perspective, instructors should develop comprehensive pedagogical strategies that cultivate academic resilience and increase learners\u0026rsquo; self-efficacy, which collectively reduce FL anxiety. First, it is essential to create a supportive classroom environment where students feel safe to express themselves without fear of negative evaluation. For example, group work and pair activities can foster a sense of community among students, allowing them to share their experiences and strategies for overcoming challenges[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Second, educators should integrate growth mindset principles into their teaching practices [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. By emphasizing that abilities can be developed through effort and perseverance, teachers can help students view challenges as opportunities for growth rather than insurmountable obstacles. This approach can be reinforced through regular feedback that focuses on improvement and effort rather than solely on outcomes. Third, language learning apps or online platforms can enable students, especially rural students, to engage with the material at their own pace. It fosters a sense of autonomy and control over their learning process. These strategies will not only mitigate FL anxiety but also empower students to become more confident and capable language users in diverse contexts.\u003c/p\u003e"},{"header":"7 Limitations","content":"\u003cp\u003eSome limitations are included in this study. First, the present study adopted a cross-sectional design, which inherently limits the ability to establish causal relationships between academic resilience and FL anxiety. As Maxwell, Cole and Mitchell [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] noted, cross-sectional data cannot adequately capture the temporal sequence of processes in multivariate associations, nor can they account for potential bidirectional relationships among variables. To address these limitations, future research should incorporate longitudinal or mixed-method designs to better examine the dynamic effects of academic resilience on FL anxiety. Second, the present study relied exclusively on self-reported measures. Although the approach offers methodological convenience, it may introduce limitations, including potential response bias and restricted perspectives. To strengthen the validity and comprehensiveness of future findings, researchers should incorporate multiple data sources, such as qualitative interviews, to provide a more robust examination of the relationship between academic resilience and FL anxiety. Third, while the quantitative design identified the rural-urban gap in FL anxiety, it failed to elucidate its underlying causes; interviews could reveal the deeper motivations and contextual factors contributing to these differences.\u003c/p\u003e"},{"header":"8 Conclusions","content":"\u003cp\u003eThe present research investigated the relationships among academic resilience, self-efficacy, and FL anxiety among urban and rural junior high school students. The findings showed that academic resilience was negatively related to FL anxiety. Furthermore, this study revealed that self-efficacy plays a mediating role in the relationship between academic resilience and FL anxiety. Additionally, the study revealed that urban-rural differences moderated the relationships between academic resilience and self-efficacy, as well as between self-efficacy and FL anxiety. However, urban-rural differences did not play a moderating role in the relationship between academic resilience and FL anxiety. These findings call for policymakers to allocate resources more evenly and safeguard greater equity in FL education.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFL=foreign language\u003c/p\u003e\n\u003cp\u003eAR=academic resilience\u003c/p\u003e\n\u003cp\u003eSE=self-efficacy\u003c/p\u003e\n\u003cp\u003eFLA=foreign language anxiety\u003c/p\u003e\n\u003cp\u003eCR=composite reliability\u003c/p\u003e\n\u003cp\u003eAVE=average variance extracted\u003c/p\u003e\n\u003cp\u003eKMO=Kaiser-Meyer-Olkin\u003c/p\u003e\n\u003cp\u003eC.R.=critical ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate:\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in accordance with the Helsinki Declaration and was approved by College of Education, Jiangxi Normal University. Written informed consent was obtained from all participants prior to their conclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants consented to the publication of the submission version.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eDesign of the study:Yali, Hao;Jinming, Sundata collection:Yali, Haoanalysis and interpretation of data:Yali, Haomanuscript preparation:Yali, Hao;Jinming, Sunmanuscript revision:Yali, Hao;Jinming, Sun\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlshumaimeri, Y.A., and Alhumud, A.M.: \u0026lsquo;EFL Students\u0026rsquo; Perceptions of the Effectiveness of Virtual Classrooms in Enhancing Communication Skills\u0026rsquo;, English Language Teaching, 2021, 14, (11)\u003c/li\u003e\n\u003cli\u003eYang, Y.-F., Hsieh, W.-M., Wong, W.-K., Hong, Y.-C., and Lai, S.-C.: \u0026lsquo;Reducing students\u0026rsquo; foreign language anxiety to improve English vocabulary learning in an online simulation game\u0026rsquo;, Computer Assisted Language Learning, 2022, 37, (3), pp. 410-432\u003c/li\u003e\n\u003cli\u003eElaine K. 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However, few studies have systematically examined the role of academic resilience in shaping FL anxiety. To address this gap, the present study employed a self-report survey of 650 junior high school students to investigate the mediating effect of self-efficacy on the relationship between academic resilience and FL anxiety. Furthermore, the study explored whether urban-rural differences moderate the interrelationships among academic resilience, self-efficacy and FL anxiety. Structural equation modeling-based multi-group analysis revealed that (1) academic resilience negatively predicts FL anxiety; (2) self-efficacy partially mediates the negative effect of academic resilience on FL anxiety; and (3) urban-rural differences moderate the link between academic resilience and self-efficacy, as well as the relationship between self-efficacy and FL anxiety, with both effects being stronger in urban schools. By applying Social Cognitive Theory, this study deepens our understanding of FL anxiety and underscores the critical importance of fostering academic resilience. The findings suggest that when designing FL courses, instructors should strategically promote students' academic resilience and self-efficacy to reduce their FL anxiety.\u003c/p\u003e","manuscriptTitle":"The effects of academic resilience on foreign language anxiety: A structural equation modeling-based multi-group analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 15:25:46","doi":"10.21203/rs.3.rs-7750527/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70177af5-6320-45be-ba46-324a4bd47151","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T09:54:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 15:25:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7750527","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7750527","identity":"rs-7750527","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}