{"paper_id":"1703aecf-2b0a-4faf-b1fd-a52f41df3d80","body_text":"How teacher support influences EFL learners’ emotions? 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A Latent Profile Transition Analysis Ruihua Zhang¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6656719/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 This study investigated the longitudinal relationship between teacher support and foreign language learners’ emotional experiences, focusing on enjoyment, anxiety, and boredom. Utilizing a two-wave longitudinal design with a sample of 440 Chinese university students at Wave 1 and 212 at Wave 2, the study employed latent profile analysis to identify emotional subgroups and latent transition analysis to examine changes in emotional profiles over time. Five distinct emotional profiles were identified—Happy, Apprehensive-Happy, Bored, Bored and Anxious, and Moderate—confirming the heterogeneity of learners’ emotion experiences (Hypothesis 1). Significant transitions between these profiles occurred over a six-month period, indicating dynamic changes in learners’ emotional states (Hypothesis 2). Crucially, teacher support significantly predicted emotion profile membership transitions between profiles (Hypothesis 3). Learners who perceived higher levels of teacher support were more likely to transition into, more adaptive emotional profiles characterized by higher enjoyment and lower anxiety or boredom. These findings highlight the pivotal role of teacher support in shaping the emotional development of foreign language learners and underscore the value of adopting longitudinal, person-centered approaches in educational psychology. foreign language emotions teacher support enjoyment anxiety boredom latent profile analysis latent transition analysis longitudinal study 1 Introduction Teacher Support and Foreign Language Emotions Teacher support encompasses the academic, emotional, and motivational resources that educators provide to promote student development and well-being [ 1 ]. Within the domain of foreign language education, teacher support is increasingly recognized as a critical factor in shaping students' affective experiences, collectively known as foreign language emotions, which typically include enjoyment, anxiety, and boredom [ 2 ]. These emotions have been shown to influence learners’ cognitive processes, engagement, and overall language proficiency [ 3 ]. Two prominent theoretical frameworks underlie the study of teacher support and foreign language emotions. The control-value theory of achievement emotions posits that emotions arise from learners’ perceptions of control over academic tasks and the value they attach to these tasks. In foreign language contexts, teacher behaviors such as encouragement and responsiveness enhance students’ perceived control and task value, thereby promoting enjoyment and reducing anxiety [ 3 ][ 4 ]. Complementing this perspective, self-determination theory emphasizes the role of social environments in fulfilling basic psychological needs—autonomy, competence, and relatedness—which are foundational for sustaining positive academic emotions [ 5 ][ 6 ]. Empirical studies have consistently supported the theoretical claim that teacher support contributes positively to foreign language learners' emotional experiences. For instance, teacher emotional support, characterized by warmth, empathy, and responsiveness, has been shown to enhance students’ motivation, academic engagement, and self-efficacy [ 7 ][ 8 ][ 9 ]. Alrabai and Algazzaz (2024), using a quasi-experimental design, demonstrated that teacher emotional support significantly enhanced students’ emotional engagement and satisfaction of basic needs. Similar results have been reported in large-scale studies linking teacher support to increased enjoyment and achievement [ 10 ]. In contrast, the absence of teacher support has been linked to heightened anxiety and emotional disengagement [ 11 ]. Further research suggests that the quality of teacher–student relationships mediates these emotional outcomes. Teacher–student relationships grounded in trust, respect, and emotional closeness serve as affective buffers against classroom stressors [ 12 ][ 13 ]. The attachment theory framework positions teachers as potential attachment figures whose supportive presence can regulate students' academic emotions, particularly for learners from disadvantaged backgrounds [ 14 ]. Despite the consensus that teacher support influences foreign language emotions, most empirical studies are cross-sectional, providing only snapshots of these relationships [ 13 ][ 15 ]. As emotions are inherently dynamic, there is a need to adopt methodological approaches that capture temporal fluctuations and developmental trajectories in emotional experiences, particularly in relation to classroom interactions and teacher behaviors. Person-Centered Approaches to Foreign Language Emotions Traditional variable-centered approaches, such as regression and structural equation modeling, assume sample homogeneity and often obscure within-person emotional diversity. Person-centered approaches like latent profile analysis and latent transition analysis address this limitation by identifying subgroups of learners who experience distinct configurations of emotions [ 16 ]. This is especially relevant in foreign language emotions research, where learners may simultaneously experience contradictory emotions such as enjoyment and anxiety [ 17 ]. Latent profile analysis studies have revealed that learners can be categorized into discrete emotional profiles that reflect their affective dispositions toward foreign language learning. For instance, Feng et al. (2023) identified four foreign language emotion profiles—ranging from high enjoyment to high boredom—among Chinese English as a Foreign Language (EFL) learners [ 18 ]. These profiles corresponded to variations in students’ satisfaction of psychological needs, highlighting the interactive roles of learner characteristics and classroom context. Similarly, Wang et al. (2021) found that students with high self-efficacy were more likely to experience positive emotional states and avoid anxiety and boredom [ 19 ]. In an online learning context, Lee and Chei (2020) identified emotional profiles such as “positive,” “negative,” and “ambivalent,” suggesting that learners' emotional experiences are shaped by instructional variables including teacher interaction and content quality [ 20 ]. Radišić et al. (2024) conducted a comparable study among younger learners, identifying profiles such as “happy”, “bored”, and “anxious”[ 21 ]. These studies emphasize that emotional experiences are not monolithic but emerge from complex interactions between individual dispositions and contextual affordances. The consistent emergence of three to six emotional profiles across studies underscores the utility of person-centered methods in capturing affective heterogeneity. More importantly, these profiles often coalesce around dimensions of teacher support, learner motivation, and perceived task value—key constructs in both control-value theory and self-determination theory [ 3 ][ 5 ]. The Understudied Link Between Teacher Support and Emotional Profile Transitions Although teacher support has been established as a predictor of positive emotions in variable-centered frameworks, its role within person-centered approaches remains underexplored. The interaction between teacher support and emotional profiles has been acknowledged in some studies [ 18 ], but most of these investigations are cross-sectional and static. As a result, they fall short of capturing the dynamic influence of teacher support on emotional development across time. Latent transition analysis offers a promising solution to this limitation. Latent transition analysis models changes in profile membership over time, allowing researchers to track how students move between emotional profiles and identify the predictors of these transitions [ 22 ]. While latent transition analysis has been applied in broader educational contexts to examine boredom and motivation, applications within foreign language emotions research remain rare [ 21 ]. Moreover, given that foreign language emotions are context-sensitive and emerge from interpersonal interactions [ 23 ], it is critical to investigate how supportive teaching facilitates not only emotional states but also emotional change. The use of longitudinal person-centered methods can shed light on whether teacher support merely correlates with existing emotional profiles or actively fosters adaptive emotional growth. Research Gap and Current Study The reviewed literature converges on several key insights. First, teacher support is a robust predictor of foreign language emotions, operating through mechanisms of psychological need satisfaction, emotion regulation, and social support. Second, learners exhibit diverse emotional profiles that reflect complex combinations of enjoyment, anxiety, and boredom—profiles that are influenced by contextual factors, including teacher behaviors. Third, the majority of foreign language emotions studies remain cross-sectional and do not model how teacher support influences transitions between emotional profiles over time. This methodological gap is particularly critical given the dynamic and evolving nature of emotional experiences in foreign language learning [ 24 ]. Without longitudinal data and appropriate modeling techniques, it is difficult to discern whether teacher support promotes lasting emotional growth or merely reflects students' current dispositions. To address these limitations, the present study adopts a two-wave longitudinal design incorporating both latent profile analysis and latent transition analysis. By identifying emotional profiles at two time points and examining how teacher support predicts both initial membership and transitions, the study offers a comprehensive and dynamic view of how supportive instructional environments shape foreign language emotions. 2 The Current Study The present study aims to identify distinct profiles of university students’ foreign language emotions—specifically enjoyment, boredom, and anxiety—and to examine the relationship between these emotional profiles and teacher support over time. Utilizing a two-wave longitudinal design with data collected over a six-month interval, this study employs Latent profile analysis to identify subgroups of learners with similar emotional experiences, and latent transition analysis to track how these profiles shift across time. Crucially, the study investigates the predictive role of teacher support in determining both initial emotional profile membership and transitions between profiles. Drawing on theoretical frameworks such as the control-value theory and self-determination theory, as well as accumulating empirical evidence demonstrating the emotional and motivational benefits of teacher support [ 6 ][ 9 ], the study adopts a person-centered approach to better capture the complexity and temporal dynamics of learners’ affective experiences. The following hypotheses are proposed: H1: Foreign language learners will exhibit distinct subgroups characterized by unique combinations of FLE (enjoyment, boredom, and anxiety). H2: FLE profiles will demonstrate significant transitions over time. H3: Teacher support will significantly predict both initial emotional profile membership and subsequent transitions between profiles across the two waves. 3 Method Participants and procedure This longitudinal study recruited a cohort of 440 Chinese university students (aged 18–23 years) and employed a two-wave data collection design spanning from October 2024 to February 2025. The initial assessment (Wave 1) was conducted in the fifth week of the academic semester, followed by a second wave of data collection approximately six months later. Attention check items were embedded within the survey (e.g., “I have never used a computer”). Participants who provided implausible responses to these items (e.g., selecting “disagree” or “strongly disagree”) were excluded from the analyses. After applying this quality control procedure, the final sample at Wave 1 comprised 397 careful respondings. At Wave 2, 212 participants provided complete data across both waves, constituting the longitudinal sample. The retained sample included 19.8% female students, with a mean age of 19.66 years ( SD = 0.88). The study received ethical approval from the Institutional Review Board of Dalian University of Technology and was conducted in accordance with the ethical standards outlined in the Declaration of Helsinki. Informed consent was obtained electronically from all participants via an online platform, following a detailed explanation of the study’s objectives, procedures, potential risks and benefits, confidentiality assurances, and the voluntary nature of participation. Participants were explicitly informed of their right to withdraw at any point without penalty, and all data were handled in accordance with institutional data protection guidelines. Measure Foreign language emotions were assessed using validated self-report instruments with Likert-type response formats, using the 11-item scale developed by Li et al. (2018) which captures learners’ positive emotional engagement in EFL classrooms (e.g., “In English classes, I have learned many interesting things”) [25]. Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Foreign language anxiety was assessed using the 8-item short-form version of the Foreign Language Classroom Anxiety Scale [26], designed to capture learners’ apprehension and nervousness in language learning contexts (e.g., “Even if I am well prepared for English classes, I still feel anxious”), also rated on a 5-point Likert scale. Foreign language boredom was measured using an 8-item scale developed by Li et al. (2023), which evaluates learners’ disengagement and lack of interest during EFL instruction (e.g., “I find it difficult to concentrate in English classes”), similarly rated on a 5-point scale [27]. Teacher support was measured using the teacher support subscale of the Multidimensional Scale of Perceived Social Support (MSPSS) [28]. This subscale comprises 4 items tailored to the EFL learning context (e.g., “My EFL teacher is around when I am in need”), with responses originally rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (very strongly agree), assessing students’ perceived availability and responsiveness of their English teacher. Analysis strategy Descriptive analyses were conducted to explore the relationships among key variables. Confirmatory factor analyses (CFA) were performed using the lavaan package in R version 4.4.2 to examine the factorial validity of the measurement models [29]. The CFA models employed robust maximum likelihood (MLR) estimation, and missing data were handled using full information maximum likelihood (FIML) procedures [30]. To identify underlying emotional subgroups, latent profile analyses (LPA) were carried out in Mplus 8.5 [31], testing one- through eight-profile solutions across two time points. Each model was estimated using 2,000 random sets of starting values and 100 iterations to ensure solution stability. Model selection was guided by a comprehensive evaluation of statistical fit indices—including AIC, BIC, CAIC, and SABIC—alongside the Bootstrap Likelihood Ratio Test (BLRT), adjusted Lo-Mendell-Rubin tests [32][33], theoretical interpretability, and visual inspection via elbow plots [34][35][36][37][38][39][40]. Latent transition analysis (LTA) was subsequently employed to model changes in profile membership over time, using equal-profile structures and regressing Wave 2 profiles onto Wave 1 profiles to capture stability and transitions. Finally, teacher support was included as a covariate to predict both initial profile classification and transition probabilities between profiles, following procedures outlined by Morin and Litalien (2017) [38]. 4 Results D escriptive statistics Table 1 presents the descriptive statistics, internal consistency estimates, and bivariate correlations among the primary variables at both time points. All multi-item measures demonstrated strong internal reliability, with Cronbach’s alpha values ranging from .82 to .96. Gender (1 = male, 2 = female) was weakly but significantly correlated with teacher support at both Wave 1 ( r = –.18, p < .01) and Wave 2 ( r = –.13, p < .05), suggesting that female students reported slightly higher levels of perceived teacher support. Age showed no significant associations with the key study variables. Foreign language enjoyment, anxiety, and boredom demonstrated expected interrelations. Wave 1 enjoyment was positively associated with Wave 1 teacher support ( r = .63, p < .01), and negatively correlated with anxiety ( r = –.51, p < .01) and boredom ( r = –.60, p < .01). Similar patterns emerged at Wave 2, where enjoyment was again negatively correlated with both anxiety ( r = –.43, p < .01) and boredom ( r = –.54, p < .01), and positively associated with teacher support ( r = .54, p < .01). Teacher support was consistently linked to more favorable emotional outcomes across both time points. Specifically, it was positively correlated with enjoyment (Wave 1: r = .63; Wave 2: r = .54, p s < .01), and negatively correlated with anxiety and boredom (Wave 1 anxiety: r = –.31; Wave 2 anxiety: r = –.30; Wave 1 boredom: r = –.45; Wave 2 boredom: r = –.48, p s < .01). These patterns underscore the close association between perceived teacher support and emotional experiences in the foreign language learning context. Table 1 Results of descriptive statistics and bivariate correlations Variable 1 2 3 4 5 6 7 8 9 10 1. Gender (W1) — 2. Age (W1) -.06 — 3. Enjoyment (W1) -.12 -.01 — 4. Anxiety (W1) .12 * -.07 -.51 ** — 5. Boredom (W1) 0 -.03 -.60 ** .60 ** — 6. Teacher Support (W1) -.18 ** 0.02 .63 ** -.31 ** -.45 ** — 7. Enjoyment (W2) -.05 .08 .70 ** -.50 ** -.58 ** .38 ** — 8. Anxiety (W2) 0 -.02 -.46 ** .73 ** .46 ** -.30 ** -.43 ** — 9. Boredom (W2) -.06 -.03 -.66 ** .46 ** .72 ** -.39 ** -.54 ** .56 ** — 10. teacher Support (W2) -.13 * 0.07 .54 ** -.31 ** -.48 ** .55 ** .69 ** -.18 * -.38 ** — Cronbach's Alpha - - 0.90 0.87 0.96 0.91 0.9 0.82 0.96 0.89 Mean 1.25 19.75 3.6 3.06 2.58 3.74 3.47 2.98 2.45 3.63 SD 0.43 1.26 0.67 0.79 0.91 0.76 0.54 0.63 0.82 0.57 Note. * p < .05, ** p < .01 Validity Table 2 showed the results of CFA conducted to evaluate the factorial validity of the latent constructs—foreign language enjoyment, anxiety, boredom, and teacher support—at two time points. Model 1 assessed these constructs at Wave 1, while Model 2 assessed them at Wave 2. Both models demonstrated an acceptable level of model fit. For Model 1, the CFI (0.937) and TLI (0.929) values surpassed the conventional cutoff of 0.90, and RMSEA was 0.063, indicating a satisfactory approximation fit. Although the SRMR value of 0.084 slightly exceeded the ideal threshold of 0.08, it remained within an acceptable range. Model 2 showed comparable results, with a CFI of 0.915, TLI of 0.904, RMSEA of 0.072, and SRMR of 0.099. Taken together, these indices provide robust evidence supporting the factorial validity of the measured constructs across both waves. Table 2 Factorial validity results of the latent constructs Model χ 2 df χ 2 /df CFI TLI RMSEA SRMR Model 1 (Wave1) 1006.05 409 2.46 0.937 0.929 0.063 0.084 Model 2 (Wave2) 987.7 409 2.41 0.915 0.904 0.072 0.099 Note: df = degree of freedom; CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; and SRMR = standardized root mean squared residual. Longitudinal measurement invariance To assess the stability and comparability of the constructs over time, longitudinal measurement invariance tests were conducted for foreign language enjoyment, anxiety, boredom, and teacher support. For enjoyment, the model fit indices supported configural, metric, scalar, and strict invariance, with only minimal decreases in CFI (△CFI ≤ 0.004) and TLI (△TLI ≤ 0.002), and stable RMSEA values (0.056–0.057), indicating a stable factorial structure across waves. Similarly, anxiety exhibited acceptable model fit across all levels of invariance, with △CFI and △TLI changes consistently below the recommended cutoff of 0.01, and RMSEA values increasing slightly from 0.045 to 0.050, supporting the assumption of longitudinal invariance. Boredom demonstrated the strongest invariance evidence among the emotional variables, with CFI values remaining above 0.97 and RMSEA below 0.04 for all models, despite a slightly larger △CFI of 0.007 at the scalar level. In contrast, the longitudinal invariance of teacher support was less robust. While the configural model showed excellent fit (CFI = 0.991, RMSEA = 0.029), subsequent levels of invariance revealed notable declines in model fit, particularly at the strict level (△CFI = 0.029; △TLI = 0.030; SRMR = 0.201), suggesting that strict invariance may not hold for this construct. Overall, the findings indicate that the foreign language emotions achieved acceptable levels of measurement invariance over time, while teacher support only achieved scalar invariance. Table 3 Model Fit indices for analysis of longitudinal measurement invariance Invariance χ 2 (df) CFI △ CFI TLI △ TLI RMSEA SRMR Foreign Language Enjoyment Configural invariance 1426.218 (695) 0.927 – 0.918 – 0.056 0.067 Metric invariance 1467.137 (715) 0.925 -0.002 0.918 0 0.056 0.071 Scalar invariance 1684.682 (735) 0.921 -0.004 0.916 -0.002 0.056 0.087 Strict Invariance 1752.877 (755) 0.917 -0.004 0.914 -0.002 0.057 0.086 Foreign Language Anxiety Configural invariance 177.119 (85) 0.957 – 0.94 – 0.045 0.072 Metric invariance 190.034 (97) 0.954 -0.003 0.939 -0.001 0.046 0.074 Scalar invariance 212.002 (109) 0.950 -0.004 0.936 -0.003 0.048 0.078 Strict Invariance 232.772 (121) 0.948 -0.002 0.934 -0.002 0.05 0.079 Foreign Language Boredom Configural invariance 124.901 (89) 0.990 – 0.987 – 0.027 0.024 Metric invariance 137.008 (97) 0.988 -0.002 0.986 -0.001 0.029 0.026 Scalar invariance 170.983 (105) 0.981 -0.007 0.974 -0.012 0.036 0.034 Strict Invariance 191.562 (113) 0.976 -0.005 0.969 -0.005 0.038 0.036 Teacher Support Configural invariance 14.578 (10) 0.991 — 0.974 — 0.029 0.028 Metric invariance 31.398 (14) 0.985 -0.006 0.970 0.004 0.048 0.153 Scalar invariance 53.264 (18) 0.975 0.010 0.961 0.011 0.061 0.159 Strict Invariance 86.863 (22) 0.946 0.029 0.931 0.030 0.074 0.201 Note: df = degree of freedom; CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; and SRMR = standardized root mean squared residual. Latent profile analyses To uncover distinct subgroups of students characterized by their foreign language emotions, latent profile analysis was conducted using three observed indicators: foreign language enjoyment, anxiety, and boredom. In line with the model selection guidelines proposed by Nylund et al. (2007), a series of models specifying one to eight latent profiles were estimated. Model fit was evaluated using multiple indices, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-size adjusted BIC (aBIC), bootstrap likelihood ratio test (pBLRT), and entropy values [33]. As detailed in Table 4, all models from two to eight profiles yielded significant pBLRT results ( p s < .01) at both Wave 1 and Wave 2, suggesting that each additional profile improved model fit. Nevertheless, the selection of the optimal number of profiles was informed not only by statistical criteria but also by conceptual clarity, classification quality, and the practical consideration of class sizes. Across both waves, the five-profile solution was identified as the most parsimonious and interpretable model. At Wave 1, this model yielded the lowest AIC, BIC, and aBIC values, alongside the highest entropy (.959), indicating good classification precision. The latent classes were well-represented, with proportions ranging from 3% to 43%. At Wave 2, the same five-profile model also exhibited favorable fit indices, acceptable entropy (.834), and class sizes between 2% and 29%, supporting its continued empirical and theoretical adequacy. Table 4 Results of the latent profile analyses Profiles AIC BIC aBIC p BLRT Entropy Group proportion for each profile Latent profile analysis at Wave 1 1-profile 3837.18 3863.4 3844.03 – 1 1 2-profile 3526.33 3569.22 3538.21 < .01 0.699 0.59 / 0.41 3-profile 3351.32 3411.37 3366.39 < .01 0.785 0.61 / 0.34 / 0.05 4-profile 3246.27 3324.34 3266.7 < .01 0.817 0.48 / 0.38 / 0.04 / 0.12 5-profile 3112.08 3207.16 3136.95 < .01 0.959 0.43 / 0.28 / 0.14 / 0.13 / 0.03 6-profile 3096.71 3209.81 3127.33 < .01 0.922 0.37 / 0.28 / 0.14 / 0.12 / 0.03 / 0.06 7-profile 3072.34 3201.46 3106.71 < .01 0.85 0.28 / 0.28 / 0.14 / 0.08 / 0.03 / 0.05 / 0.14 8-profile 3080.21 3226.35 3118.32 < .01 0.781 0.28 / 0.02 / 0.28 / 0.14 / 0.08 / 0.03 / 0.05 / 0.06 Latent profile analysis at Wave 2 1-profile 3462.49 3488.37 3469.04 – 1 1 2-profile 3107.07 3150.27 3118.93 < .01 0.757 0.65 / 0.35 3-profile 2984.77 3045.38 3000.73 < .01 0.733 0.51 / 0.47 / 0.02 4-profile 2715.28 2792.35 2734.31 < .01 0.832 0.42 / 0.28 / 0.24 / 0.06 5-profile 2648.23 2743.31 2673 < .01 0.834 0.29 / 0.28 / 0.27 / 0.13 / 0.02 6-profile 2648.45 2760.55 2677.97 0.129 0.771 0.27 / 0.27 / 0.26 / 0.12 / 0.04 / 0.04 7-profile 2602.96 2732.08 2637.23 < .01 0.822 0.26 / 0.26 / 0.25 / 0.09 / 0.04 / 0.05 / 0.05 8-profile 2607.45 2753.59 2645.56 0.505 0.782 0.26 / 0.02 / 0.26 / 0.14 / 0.08 / 0.03 / 0.05 / 0.06 Latent profile analysis identified five distinct and relatively stable emotional subgroups among foreign language learners across two measurement waves, as summarized in Table 5. Classification of these profiles was based on systematic evaluation of the mean levels of enjoyment, anxiety, and boredom within each class. To enhance theoretical clarity and interpretability, the profiles were labeled following the conventions proposed by Radišić et al. (2024), with labels reflecting the predominant emotional characteristics of each group [21]. The first group, termed Happy, comprised learners reporting the highest levels of enjoyment and the lowest levels of anxiety and boredom at both time points (13% at Wave 1; 28% at Wave 2), indicative of an emotionally engaged and affectively positive profile. The second and most prevalent group at Wave 1 (43%), labeled Apprehensive-Happy, exhibited relatively high enjoyment alongside moderate-to-high anxiety and moderate boredom, suggesting a complex emotional experience in which positive affect co-occurs with emotional strain. The third group, Bored (14% at Wave 1; 13% at Wave 2), was defined by high boredom and low enjoyment, reflecting a disengaged learner profile with limited emotional involvement in language study. The fourth profile, Bored and Anxious, represented the smallest proportion of the sample (3% at Wave 1; 2% at Wave 2) and was characterized by the highest levels of both boredom and anxiety, coupled with the lowest enjoyment. This profile indicates a highly vulnerable group likely to experience negative academic and emotional outcomes. The final group, labeled Moderate (28% at Wave 1; 27% at Wave 2), included students with emotional scores near the sample average, suggesting a relatively balanced yet emotionally neutral pattern. These five profiles reveal substantial heterogeneity in learners’ emotional experiences and are consistent with prior findings on student affect in educational settings. The identification of these distinct emotional patterns provides empirical support for Hypothesis 1. Table 5 Results of the 5-profile analyses Variable (M ± SD) Category 1 Happy Category 2 Apprehensive-Happy Category 3 Bored Category 4 Bored&Anxious Category 5 Moderate Wave 1 Foreign Language Emotions Enjoyment 4.30 ± 0.51 3.80 ± 0.51 3.26 ± 0.51 2.19 ± 0.51 3.36 ± 0.51 Anxiety 2.29 ± 0.62 2.82 ± 0.62 3.66 ± 0.62 4.64 ± 0.62 3.20 ± 0.62 Boredom 1.11 ± 0.20 2.10 ± 0.20 3.80 ± 0.20 4.77 ± 0.20 3.00 ± 0.20 Proportion 0.13 0.43 0.14 0.03 0.28 Wave 2 Foreign Language Emotions Enjoyment 3.91 ± 0.17 3.71 ± 0.17 2.64 ± 0.17 1.06 ± 0.17 3.22 ± 0.17 Anxiety 2.23 ± 0.43 3.24 ± 0.43 3.36 ± 0.43 3.61 ± 0.43 3.25 ± 0.43 Boredom 1.62 ± 0.66 2.47 ± 0.66 3.25 ± 0.66 3.08 ± 0.66 2.90 ± 0.66 Proportion 0.28 0.29 0.13 0.02 0.27 Latent transition analyses To examine the temporal consistency and dynamic shifts in students’ foreign language emotion profiles, LTA was conducted under the assumption of invariance in the number and mean structure of latent classes across two time points. The transition probabilities across the five previously identified profiles—Happy, Apprehensive-Happy, Bored, Bored & Anxious, and Moderate—are presented in Table 6. Diagonal entries reflect the likelihood of individuals remaining in the same profile from Wave 1 to Wave 2, while off-diagonal entries indicate probabilities of shifting to alternative profiles. Overall, the results revealed substantial longitudinal stability in emotional profiles. Learners initially classified as Happy demonstrated a 76.7% probability of remaining in the same category, although 23.3% transitioned to the Apprehensive-Happy group. The Apprehensive-Happy profile exhibited the highest retention rate, with 83.3% of individuals maintaining their classification, and 16.7% transitioning to Moderate. Similarly, the Bored group displayed strong stability, with 89.6% of individuals remaining in the same profile, while small proportions shifted to Happy (8.7%) or Moderate (1.7%). Students in the Bored & Anxious group showed moderately high stability (78.5%), but some transitioned to Moderate (17.4%) or Happy (4.1%). Finally, those classified as Moderate at Wave 1 exhibited an 84.0% probability of profile consistency, though 16.0% transitioned into the more negatively valenced Bored & Anxious category. These findings suggest that while emotional profiles among learners are generally stable over time, a subset of students do experience meaningful changes in their emotional experiences. Accordingly, these results provide empirical support for Hypothesis 2. Table 6 Transition probability of latent transition analyses Transition Probability Wave 2 Happy Apprehensive -Happy Bored Bored &Anxious Moderate Wave 1 Happy 0.767 0.233 0 0 0 Apprehensive -Happy 0 0.833 0 0 0.167 Bored 0.087 0 0.896 0 0.017 Bored&Anxious 0.041 0 0 0.785 0.174 Moderate 0 0 0 0.160 0.840 Effects of teacher support on transitions of emotion profiles To evaluate the predictive role of teacher support in the longitudinal transitions between foreign language emotion profiles, we conducted a series of multinomial logistic regression analyses. In each model, the reference category comprised individuals who remained in the same latent profile from Wave 1 to Wave 2, allowing for the estimation of the likelihood of transitioning to a different profile relative to profile stability. Odds ratios (ORs), 95% confidence intervals (CIs), and corresponding p-values were calculated for each transition. ORs greater than 1 indicate that higher levels of teacher support were associated with increased odds of transitioning to a different emotional profile, whereas ORs less than 1 suggest a decreased likelihood of such transitions. Transitions involving 1% or fewer participants were not interpreted due to low frequency and unstable estimates; these are denoted as “a” in Table 7. As shown in Table 7, teacher support significantly predicted several key transitions. Most notably, learners who initially belonged to the Bored profile at Wave 1 were 13.60 times more likely ( p < .05) to transition to the Happy profile at Wave 2 with higher levels of teacher support, compared to those who remained in the Bored profile. However, teacher support was negatively associated with the likelihood of transitioning from the Apprehensive-Happy profile to the Moderate profile (OR = 0.36) and from the Bored & Anxious profile to the Moderate profile (OR = 0.51), although these associations did not reach statistical significance. Due to insufficient sample sizes, transitions involving ≤ 1% of participants were excluded from interpretation. Overall, the results suggest that teacher support plays a facilitative role in promoting positive emotional development in foreign language learning contexts. By enabling learners to transition from negative or mixed emotional profiles to more adaptive ones, teacher support emerges as a significant predictor of emotional change over time. These findings provide empirical support for Hypothesis 3, which posited that teacher support would significantly influence transitions between foreign language emotion profiles. Table 7 Effects of teacher support on transitions of emotion profiles Predictor Profiles Happy W2 Apprehensive -Happy W2 Bored W2 Bored &AnxiousW2 Moderate W2 Peer Support Happy W1 REF 0.67 a a a Apprehensive -Happy W1 a REF a a 0.36 Bored W1 13.60 * a REF a a Bored&Anxious W1 42.60 a a REF 0.51 Moderate W1 a a a 3.68 REF * p < .05; REF was the reference group; Transitions labeled “a” involved ≤1% of participants and were excluded from further analysis. 5 Discussion The present study sought to illuminate the dynamic interplay between teacher support and EFL learners’ emotional experiences through a person-centered, longitudinal approach. By employing LPA and LTA, we not only confirmed the presence of heterogeneous emotional profiles among learners—comprising enjoyment, anxiety, and boredom—but also tracked their transitions over time. Crucially, teacher support emerged as a significant predictor of both initial profile membership and transitions between profiles, corroborating and extending the theoretical premises of the control-value theory and self-determination theory. These findings underscore the powerful role of teacher support in shaping not only static emotional states but also the developmental trajectory of learner emotions in foreign language classrooms. Foreign language emotion profiles and stability Consistent with prior person-centered research [17][21], the study identified five distinct emotional profiles: Happy, Apprehensive-Happy, Bored, Bored & Anxious, and Moderate. These profiles represent multifaceted emotional landscapes that learners navigate during language acquisition. For example, learners in the Bored & Anxious profile reported high levels of disengagement and apprehension, while those in the Happy profile exhibited high enjoyment and minimal negative emotions. The existence of such profiles confirms that learners experience emotions in complex, often co-occurring ways rather than as isolated affective states. Transition probabilities revealed a notable degree of emotional stability, particularly among learners situated in the Happy and Bored profiles, aligning with Elahi Shirvan et al.'s (2020) assertion that affective states can become entrenched over time without external interventions [24]. Nevertheless, meaningful transitions did occur, particularly toward more adaptive emotional configurations. These transitions suggest the possibility of emotional growth and regulatory change—an outcome central to educational aspirations. The role of teacher support in foreign language emotion profiles A primary contribution of the current study is its demonstration that teacher support significantly predicts profile membership transitions over time. Learners who perceived higher levels of teacher support were more likely to begin in adaptive profiles (e.g., Happy) and transition away from maladaptive profiles (e.g., Bored & Anxious) toward more balanced or positive profiles (e.g., Moderate or Happy). These results resonate with existing evidence that teacher support enhances student engagement, motivation, and emotional well-being [9][10]. The findings extend prior research by confirming that teacher support does not merely correlate with positive emotions but plays a developmental role in facilitating emotional transformation. This is theoretically significant, as it situates teacher support not just as a static environmental variable but as a dynamic interpersonal force that supports learners’ emotional regulation, engagement, and resilience. Attachment theory offers a compelling lens through which to interpret these results [14]. Teachers, when perceived as emotionally available and trustworthy, serve as secure bases from which learners can explore challenging linguistic terrain. This emotional security fosters risk-taking, expression, and engagement, all of which are conducive to positive emotional outcomes [41]. As Huang et al. (2024) noted, teacher support promotes emotional attachments and comfort, both of which reduce anxiety and foster enjoyment in language learning [42]. The capacity of teacher support to predict emotional transitions underscores its potential as an intervention target. Learners experiencing anxiety or boredom may not be \"stuck\" in these profiles permanently; with sufficient support—academic, motivational, and emotional—from instructors, movement toward more adaptive profiles is feasible. This aligns with empirical findings from intervention-based research, such as Alrabai and Algazzaz (2024), who reported significant gains in learners’ emotional engagement and satisfaction of basic psychological needs following targeted teacher interventions [6]. In our study, learners transitioning out of negative profiles happened in the presence of strong teacher support. This suggests that teacher support fosters emotional immunity by equipping students with psychological resources such as self-efficacy, coping strategies, and a sense of relatedness [13][42]. Moreover, the observed transitions support the view that learners are not passive recipients of educational experiences but active agents whose emotional development is deeply intertwined with their social context. Teacher support, in this regard, is a relational scaffold that enables learners to reframe difficulties, regulate affective states, and maintain engagement. Theoretical Contributions The study contributes theoretically to the growing literature at the intersection of control-value theory and self-determination theory. The control-value theory posits that learners’ emotions stem from their perceptions of control over and value of academic tasks [3]. Our findings illustrate how teacher support enhances these appraisals, thereby fostering enjoyment and mitigating anxiety and boredom. Self-determination theory further elucidates the role of teacher support in satisfying learners’ psychological needs—autonomy, competence, and relatedness—all of which emerged as relevant in our observed transitions [5]. For instance, learners who moved toward the Happy profile likely benefited from an environment where they felt supported, capable, and connected. Moreover, our findings underscore the value of longitudinal person-centered approaches. Traditional variable-centered analyses may mask the heterogeneity of emotional experiences; by contrast, LTA captures not only individual differences but also developmental shifts, providing a richer understanding of emotion regulation and affective change [35]. 6 Limitations While the current study offers valuable insights, it is not without limitations. First, the reliance on self-report measures may introduce bias related to social desirability. Second, although the two-wave design allows for stronger inferences about temporal change, additional waves could provide a more nuanced understanding of emotional trajectories. Third, future research should explore other classroom variables—such as peer support, instructional style, or feedback quality—that may interact with teacher support in shaping emotional transitions. Moreover, qualitative and ecological momentary assessment methods could further illuminate the processes underlying profile shifts. Investigating how specific teacher behaviors trigger emotional change in real time remains an important direction for future inquiry [24]. 7 Conclusion This study examined how teacher support influences the emotional experiences of English as a foreign language learners over time, using latent profile and latent transition analyses. Five distinct emotional profiles were identified—Happy, Apprehensive-Happy, Bored, Bored and Anxious, and Moderate—each reflecting unique combinations of enjoyment, anxiety, and boredom. Significant transitions occurred across profiles over a six-month interval, indicating that learners’ emotional states are dynamic rather than static. Teacher support significantly predicted profile membership transitions: learners perceiving higher levels of teacher support were more likely to move from negative profiles (e.g., Bored and Anxious) toward more adaptive ones (e.g., Happy), highlighting teacher support as a key factor in promoting emotional development in language learning contexts. Declarations Acknowledgements We would like to grateful all participants who agreed to participate in this study. Authors’ contributions R.H.Z. was involved in the study design, data collection, analysis, and writing of the article. All authors read and approved the final manuscript. Funding This work was supported by Research on the Construction of Ideological and Political Education Model in College English Courses Based on “Henan Education Modernization 2035”, Education Department of Henan Province. Data availability The data that support the study may be available upon request with permission from the researchers who collected the data. Ethics approval and consent to participate The study adhered to the guidelines set forth in the Declaration of Helsinki, was approved by the ethical committee at Dalian University of Technology (DUTSH240409-02). Informed written consent was obtained from all participants. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Wentzel, K. R., Battle, A., Russell, S. L., & Looney, L. B. (2010). Social supports from teachers and peers as predictors of academic and social motivation. Contemporary Educational Psychology, 35 (3), 193–202. https://doi.org/10.1016/j.cedpsych.2010.03.002 Dewaele, J.-M., & MacIntyre, P. D. (2014). The two faces of Janus? Anxiety and enjoyment in the foreign language classroom. Studies in Second Language Learning and Teaching, 4 (2), 237–274. https://doi.org/10.14746/ssllt.2014.4.2.5 Pekrun, R. (2006). 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BMC Psychology, 12 , 124. https://doi.org/10.1186/s40359-024-01602-2 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6656719\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":475674565,\"identity\":\"0d88cde1-be76-4159-a063-9c5a73703034\",\"order_by\":0,\"name\":\"Ruihua Zhang¹\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDCCAwwMEiCaDcK14eFnbyBOiwRUS5qMZM8BIrVAuYdtDG444NfBd/vwwds8NXZ1fOy9Bz983HGeh+EGA+OHjzm4tUieS0u25jmWLMHGcy5ZcuaZ2zyMsxuYJWduw63F4AyPmTRvA7MEm0SOGTNv220eZpkDbMy8eLXwfwNqqYdo+dt2jodNIoGQFh42oJbDEC2MbQd4eAhpkTzDZmw559hxyTaeM8aSvW3JPBI8B5vx+oXvDPPDG29qqvnl23sMP/xss7O3P94MDDo8WrABxgbS1I+CUTAKRsEowAAAwydJGVsOVSsAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Zhongyuan University of Technology\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Ruihua\",\"middleName\":\"\",\"lastName\":\"Zhang¹\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-05-13 14:53:18\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6656719/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6656719/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":87256257,\"identity\":\"e64ca249-0d5f-40bb-a990-3624f244b456\",\"added_by\":\"auto\",\"created_at\":\"2025-07-22 06:01:37\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1109139,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6656719/v1/d2ccd46a-6f64-4010-9ffd-108d5ad1cde8.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"How teacher support influences EFL learners’ emotions? A Latent Profile Transition Analysis\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003eTeacher Support and Foreign Language Emotions\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTeacher support encompasses the academic, emotional, and motivational resources that educators provide to promote student development and well-being [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Within the domain of foreign language education, teacher support is increasingly recognized as a critical factor in shaping students' affective experiences, collectively known as foreign language emotions, which typically include enjoyment, anxiety, and boredom [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. These emotions have been shown to influence learners\\u0026rsquo; cognitive processes, engagement, and overall language proficiency [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTwo prominent theoretical frameworks underlie the study of teacher support and foreign language emotions. The control-value theory of achievement emotions posits that emotions arise from learners\\u0026rsquo; perceptions of control over academic tasks and the value they attach to these tasks. In foreign language contexts, teacher behaviors such as encouragement and responsiveness enhance students\\u0026rsquo; perceived control and task value, thereby promoting enjoyment and reducing anxiety [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Complementing this perspective, self-determination theory emphasizes the role of social environments in fulfilling basic psychological needs\\u0026mdash;autonomy, competence, and relatedness\\u0026mdash;which are foundational for sustaining positive academic emotions [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eEmpirical studies have consistently supported the theoretical claim that teacher support contributes positively to foreign language learners' emotional experiences. For instance, teacher emotional support, characterized by warmth, empathy, and responsiveness, has been shown to enhance students\\u0026rsquo; motivation, academic engagement, and self-efficacy [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Alrabai and Algazzaz (2024), using a quasi-experimental design, demonstrated that teacher emotional support significantly enhanced students\\u0026rsquo; emotional engagement and satisfaction of basic needs. Similar results have been reported in large-scale studies linking teacher support to increased enjoyment and achievement [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. In contrast, the absence of teacher support has been linked to heightened anxiety and emotional disengagement [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFurther research suggests that the quality of teacher\\u0026ndash;student relationships mediates these emotional outcomes. Teacher\\u0026ndash;student relationships grounded in trust, respect, and emotional closeness serve as affective buffers against classroom stressors [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. The attachment theory framework positions teachers as potential attachment figures whose supportive presence can regulate students' academic emotions, particularly for learners from disadvantaged backgrounds [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eDespite the consensus that teacher support influences foreign language emotions, most empirical studies are cross-sectional, providing only snapshots of these relationships [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. As emotions are inherently dynamic, there is a need to adopt methodological approaches that capture temporal fluctuations and developmental trajectories in emotional experiences, particularly in relation to classroom interactions and teacher behaviors.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003ePerson-Centered Approaches to Foreign Language Emotions\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTraditional variable-centered approaches, such as regression and structural equation modeling, assume sample homogeneity and often obscure within-person emotional diversity. Person-centered approaches like latent profile analysis and latent transition analysis address this limitation by identifying subgroups of learners who experience distinct configurations of emotions [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. This is especially relevant in foreign language emotions research, where learners may simultaneously experience contradictory emotions such as enjoyment and anxiety [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eLatent profile analysis studies have revealed that learners can be categorized into discrete emotional profiles that reflect their affective dispositions toward foreign language learning. For instance, Feng et al. (2023) identified four foreign language emotion profiles\\u0026mdash;ranging from high enjoyment to high boredom\\u0026mdash;among Chinese English as a Foreign Language (EFL) learners [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. These profiles corresponded to variations in students\\u0026rsquo; satisfaction of psychological needs, highlighting the interactive roles of learner characteristics and classroom context. Similarly, Wang et al. (2021) found that students with high self-efficacy were more likely to experience positive emotional states and avoid anxiety and boredom [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn an online learning context, Lee and Chei (2020) identified emotional profiles such as \\u0026ldquo;positive,\\u0026rdquo; \\u0026ldquo;negative,\\u0026rdquo; and \\u0026ldquo;ambivalent,\\u0026rdquo; suggesting that learners' emotional experiences are shaped by instructional variables including teacher interaction and content quality [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Radišić et al. (2024) conducted a comparable study among younger learners, identifying profiles such as \\u0026ldquo;happy\\u0026rdquo;, \\u0026ldquo;bored\\u0026rdquo;, and \\u0026ldquo;anxious\\u0026rdquo;[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. These studies emphasize that emotional experiences are not monolithic but emerge from complex interactions between individual dispositions and contextual affordances.\\u003c/p\\u003e \\u003cp\\u003eThe consistent emergence of three to six emotional profiles across studies underscores the utility of person-centered methods in capturing affective heterogeneity. More importantly, these profiles often coalesce around dimensions of teacher support, learner motivation, and perceived task value\\u0026mdash;key constructs in both control-value theory and self-determination theory [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eThe Understudied Link Between Teacher Support and Emotional Profile Transitions\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eAlthough teacher support has been established as a predictor of positive emotions in variable-centered frameworks, its role within person-centered approaches remains underexplored. The interaction between teacher support and emotional profiles has been acknowledged in some studies [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e], but most of these investigations are cross-sectional and static. As a result, they fall short of capturing the dynamic influence of teacher support on emotional development across time.\\u003c/p\\u003e \\u003cp\\u003eLatent transition analysis offers a promising solution to this limitation. Latent transition analysis models changes in profile membership over time, allowing researchers to track how students move between emotional profiles and identify the predictors of these transitions [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. While latent transition analysis has been applied in broader educational contexts to examine boredom and motivation, applications within foreign language emotions research remain rare [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Moreover, given that foreign language emotions are context-sensitive and emerge from interpersonal interactions [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e], it is critical to investigate how supportive teaching facilitates not only emotional states but also emotional change. The use of longitudinal person-centered methods can shed light on whether teacher support merely correlates with existing emotional profiles or actively fosters adaptive emotional growth.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eResearch Gap and Current Study\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe reviewed literature converges on several key insights. First, teacher support is a robust predictor of foreign language emotions, operating through mechanisms of psychological need satisfaction, emotion regulation, and social support. Second, learners exhibit diverse emotional profiles that reflect complex combinations of enjoyment, anxiety, and boredom\\u0026mdash;profiles that are influenced by contextual factors, including teacher behaviors. Third, the majority of foreign language emotions studies remain cross-sectional and do not model how teacher support influences transitions between emotional profiles over time.\\u003c/p\\u003e \\u003cp\\u003eThis methodological gap is particularly critical given the dynamic and evolving nature of emotional experiences in foreign language learning [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Without longitudinal data and appropriate modeling techniques, it is difficult to discern whether teacher support promotes lasting emotional growth or merely reflects students' current dispositions.\\u003c/p\\u003e \\u003cp\\u003eTo address these limitations, the present study adopts a two-wave longitudinal design incorporating both latent profile analysis and latent transition analysis. By identifying emotional profiles at two time points and examining how teacher support predicts both initial membership and transitions, the study offers a comprehensive and dynamic view of how supportive instructional environments shape foreign language emotions.\\u003c/p\\u003e\"},{\"header\":\"2 The Current Study\",\"content\":\"\\u003cp\\u003eThe present study aims to identify distinct profiles of university students\\u0026rsquo; foreign language emotions\\u0026mdash;specifically enjoyment, boredom, and anxiety\\u0026mdash;and to examine the relationship between these emotional profiles and teacher support over time. Utilizing a two-wave longitudinal design with data collected over a six-month interval, this study employs Latent profile analysis to identify subgroups of learners with similar emotional experiences, and latent transition analysis to track how these profiles shift across time.\\u003c/p\\u003e \\u003cp\\u003eCrucially, the study investigates the predictive role of teacher support in determining both initial emotional profile membership and transitions between profiles. Drawing on theoretical frameworks such as the control-value theory and self-determination theory, as well as accumulating empirical evidence demonstrating the emotional and motivational benefits of teacher support [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e], the study adopts a person-centered approach to better capture the complexity and temporal dynamics of learners\\u0026rsquo; affective experiences. The following hypotheses are proposed:\\u003c/p\\u003e \\u003cp\\u003eH1: Foreign language learners will exhibit distinct subgroups characterized by unique combinations of FLE (enjoyment, boredom, and anxiety).\\u003c/p\\u003e \\u003cp\\u003eH2: FLE profiles will demonstrate significant transitions over time.\\u003c/p\\u003e \\u003cp\\u003eH3: Teacher support will significantly predict both initial emotional profile membership and subsequent transitions between profiles across the two waves.\\u003c/p\\u003e\"},{\"header\":\"3 Method\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eParticipants and procedure\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis longitudinal study recruited a cohort of 440 Chinese university students (aged 18\\u0026ndash;23 years) and employed a two-wave data collection design spanning from October 2024 to February 2025. The initial assessment (Wave 1) was conducted in the fifth week of the academic semester, followed by a second wave of data collection approximately six months later. Attention check items were embedded within the survey (e.g., \\u0026ldquo;I have never used a computer\\u0026rdquo;). Participants who provided implausible responses to these items (e.g., selecting \\u0026ldquo;disagree\\u0026rdquo; or \\u0026ldquo;strongly disagree\\u0026rdquo;) were excluded from the analyses. After applying this quality control procedure, the final sample at Wave 1 comprised 397 careful respondings. At Wave 2, 212 participants provided complete data across both waves, constituting the longitudinal sample. The retained sample included 19.8% female students, with a mean age of 19.66 years (\\u003cem\\u003eSD\\u003c/em\\u003e = 0.88). The study received ethical approval from the Institutional Review Board of Dalian University of Technology and was conducted in accordance with the ethical standards outlined in the Declaration of Helsinki. Informed consent was obtained electronically from all participants via an online platform, following a detailed explanation of the study\\u0026rsquo;s objectives, procedures, potential risks and benefits, confidentiality assurances, and the voluntary nature of participation. Participants were explicitly informed of their right to withdraw at any point without penalty, and all data were handled in accordance with institutional data protection guidelines.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMeasure\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eForeign language emotions were assessed using validated self-report instruments with Likert-type response formats, using the 11-item scale developed by Li et al. (2018) which captures learners\\u0026rsquo; positive emotional engagement in EFL classrooms (e.g., \\u0026ldquo;In English classes, I have learned many interesting things\\u0026rdquo;) [25]. Responses were recorded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Foreign language anxiety was assessed using the 8-item short-form version of the Foreign Language Classroom Anxiety Scale [26], designed to capture learners\\u0026rsquo; apprehension and nervousness in language learning contexts (e.g., \\u0026ldquo;Even if I am well prepared for English classes, I still feel anxious\\u0026rdquo;), also rated on a 5-point Likert scale. Foreign language boredom was measured using an 8-item scale developed by Li et al. (2023), which evaluates learners\\u0026rsquo; disengagement and lack of interest during EFL instruction (e.g., \\u0026ldquo;I find it difficult to concentrate in English classes\\u0026rdquo;), similarly rated on a 5-point scale [27]. Teacher support was measured using the teacher support subscale of the Multidimensional Scale of Perceived Social Support (MSPSS) [28]. This subscale comprises 4 items tailored to the EFL learning context (e.g., \\u0026ldquo;My EFL teacher is around when I am in need\\u0026rdquo;), with responses originally rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (very strongly agree), assessing students\\u0026rsquo; perceived availability and responsiveness of their English teacher.\\u003c/p\\u003e\\n\\u003cp\\u003eAnalysis strategy\\u003c/p\\u003e\\n\\u003cp\\u003eDescriptive analyses were conducted to explore the relationships among key variables. Confirmatory factor analyses (CFA) were performed using the lavaan package in R version 4.4.2 to examine the factorial validity of the measurement models [29]. The CFA models employed robust maximum likelihood (MLR) estimation, and missing data were handled using full information maximum likelihood (FIML) procedures [30]. To identify underlying emotional subgroups, latent profile analyses (LPA) were carried out in Mplus 8.5 [31], testing one- through eight-profile solutions across two time points. Each model was estimated using 2,000 random sets of starting values and 100 iterations to ensure solution stability. Model selection was guided by a comprehensive evaluation of statistical fit indices\\u0026mdash;including AIC, BIC, CAIC, and SABIC\\u0026mdash;alongside the Bootstrap Likelihood Ratio Test (BLRT), adjusted Lo-Mendell-Rubin tests [32][33], theoretical interpretability, and visual inspection via elbow plots [34][35][36][37][38][39][40]. Latent transition analysis (LTA) was subsequently employed to model changes in profile membership over time, using equal-profile structures and regressing Wave 2 profiles onto Wave 1 profiles to capture stability and transitions. Finally, teacher support was included as a covariate to predict both initial profile classification and transition probabilities between profiles, following procedures outlined by Morin and Litalien (2017) [38].\\u003c/p\\u003e\"},{\"header\":\"4 Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eD\\u003c/strong\\u003e\\u003cstrong\\u003eescriptive statistics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1 presents the descriptive statistics, internal consistency estimates, and bivariate correlations among the primary variables at both time points. All multi-item measures demonstrated strong internal reliability, with Cronbach\\u0026rsquo;s alpha values ranging from .82 to .96. Gender (1 = male, 2 = female) was weakly but significantly correlated with teacher support at both Wave 1 (\\u003cem\\u003er\\u0026nbsp;\\u003c/em\\u003e= \\u0026ndash;.18,\\u0026nbsp;\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .01) and Wave 2 (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.13,\\u0026nbsp;\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .05), suggesting that female students reported slightly higher levels of perceived teacher support. Age showed no significant associations with the key study variables. Foreign language enjoyment, anxiety, and boredom demonstrated expected interrelations. Wave 1 enjoyment was positively associated with Wave 1 teacher support (\\u003cem\\u003er\\u003c/em\\u003e = .63,\\u0026nbsp;\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .01), and negatively correlated with anxiety (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.51,\\u0026nbsp;\\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; .01) and boredom (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.60,\\u0026nbsp;\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .01). Similar patterns emerged at Wave 2, where enjoyment was again negatively correlated with both anxiety (\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.43,\\u0026nbsp;\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .01) and boredom (\\u003cem\\u003er\\u0026nbsp;\\u003c/em\\u003e= \\u0026ndash;.54,\\u0026nbsp;\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .01), and positively associated with teacher support (\\u003cem\\u003er\\u003c/em\\u003e = .54,\\u0026nbsp;\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .01). Teacher support was consistently linked to more favorable emotional outcomes across both time points. Specifically, it was positively correlated with enjoyment (Wave 1:\\u0026nbsp;\\u003cem\\u003er\\u0026nbsp;\\u003c/em\\u003e= .63; Wave 2:\\u0026nbsp;\\u003cem\\u003er\\u003c/em\\u003e = .54,\\u0026nbsp;\\u003cem\\u003ep\\u003c/em\\u003es \\u0026lt; .01), and negatively correlated with anxiety and boredom (Wave 1 anxiety:\\u0026nbsp;\\u003cem\\u003er\\u0026nbsp;\\u003c/em\\u003e= \\u0026ndash;.31; Wave 2 anxiety:\\u0026nbsp;\\u003cem\\u003er\\u0026nbsp;\\u003c/em\\u003e= \\u0026ndash;.30; Wave 1 boredom:\\u0026nbsp;\\u003cem\\u003er\\u0026nbsp;\\u003c/em\\u003e= \\u0026ndash;.45; Wave 2 boredom:\\u0026nbsp;\\u003cem\\u003er\\u003c/em\\u003e = \\u0026ndash;.48,\\u0026nbsp;\\u003cem\\u003ep\\u003c/em\\u003es \\u0026lt; .01). These patterns underscore the close association between perceived teacher support and emotional experiences in the foreign language learning context.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1 Results of descriptive statistics and bivariate correlations\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"668\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003e1. Gender (W1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003e2. Age (W1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003e3. Enjoyment (W1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003e4. Anxiety (W1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.12\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.51\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003e5. Boredom (W1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.60\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.60\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003e6. Teacher Support (W1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.18\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.63\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.31\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.45\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003e7. Enjoyment (W2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.70\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.50\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.58\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.38\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003e8. Anxiety (W2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.46\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.73\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.46\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.30\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.43\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003e9. Boredom (W2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.66\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.46\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.72\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.39\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.54\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.56\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003e10. teacher Support (W2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.13\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.54\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.31\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.48\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.55\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e.69\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.18\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-.38\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003eCronbach\\u0026apos;s Alpha\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.87\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.91\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e0.89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003eMean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e1.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e19.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e3.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e3.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e2.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e3.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e3.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e2.98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e2.45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e3.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 176px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSD\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e1.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.91\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 50px;\\\"\\u003e\\n \\u003cp\\u003e0.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"11\\\" style=\\\"width: 668px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNote.\\u0026nbsp;\\u003c/em\\u003e\\u003csup\\u003e*\\u003c/sup\\u003e\\u003cem\\u003e\\u0026nbsp;p\\u003c/em\\u003e \\u0026lt; .05, \\u003csup\\u003e**\\u003c/sup\\u003e \\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; .01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eValidity\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2 showed the results of CFA conducted to evaluate the factorial validity of the latent constructs\\u0026mdash;foreign language enjoyment, anxiety, boredom, and teacher support\\u0026mdash;at two time points. Model 1 assessed these constructs at Wave 1, while Model 2 assessed them at Wave 2. Both models demonstrated an acceptable level of model fit. For Model 1, the CFI (0.937) and TLI (0.929) values surpassed the conventional cutoff of 0.90, and RMSEA was 0.063, indicating a satisfactory approximation fit. Although the SRMR value of 0.084 slightly exceeded the ideal threshold of 0.08, it remained within an acceptable range. Model 2 showed comparable results, with a CFI of 0.915, TLI of 0.904, RMSEA of 0.072, and SRMR of 0.099. Taken together, these indices provide robust evidence supporting the factorial validity of the measured constructs across both waves.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 2 Factorial validity results of the latent constructs\\u003c/p\\u003e\\n\\u003cdiv align=\\\"Left\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"613\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eModel\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026chi;\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003edf\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 43px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026chi;\\u003csup\\u003e2\\u003c/sup\\u003e/df\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCFI\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 58px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTLI\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eRMSEA\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSRMR\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eModel 1 (Wave1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e1006.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e409\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 43px;\\\"\\u003e\\n \\u003cp\\u003e2.46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.937\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 58px;\\\"\\u003e\\n \\u003cp\\u003e0.929\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e0.063\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e0.084\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 245px;\\\"\\u003e\\n \\u003cp\\u003eModel 2 (Wave2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e987.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e409\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 43px;\\\"\\u003e\\n \\u003cp\\u003e2.41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.915\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 58px;\\\"\\u003e\\n \\u003cp\\u003e0.904\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e0.072\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003e0.099\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"8\\\" style=\\\"width: 613px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eNote:\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cem\\u003e\\u0026nbsp;df\\u003c/em\\u003e = degree of freedom; CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; and SRMR = standardized root mean squared residual.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLongitudinal measurement invariance\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo assess the stability and comparability of the constructs over time, longitudinal measurement invariance tests were conducted for foreign language enjoyment, anxiety, boredom, and teacher support. For enjoyment, the model fit indices supported configural, metric, scalar, and strict invariance, with only minimal decreases in CFI (△CFI \\u0026le; 0.004) and TLI (△TLI \\u0026le; 0.002), and stable RMSEA values (0.056\\u0026ndash;0.057), indicating a stable factorial structure across waves. Similarly, anxiety exhibited acceptable model fit across all levels of invariance, with △CFI and △TLI changes consistently below the recommended cutoff of 0.01, and RMSEA values increasing slightly from 0.045 to 0.050, supporting the assumption of longitudinal invariance. Boredom demonstrated the strongest invariance evidence among the emotional variables, with CFI values remaining above 0.97 and RMSEA below 0.04 for all models, despite a slightly larger △CFI of 0.007 at the scalar level. In contrast, the longitudinal invariance of teacher support was less robust. While the configural model showed excellent fit (CFI = 0.991, RMSEA = 0.029), subsequent levels of invariance revealed notable declines in model fit, particularly at the strict level (△CFI = 0.029; △TLI = 0.030; SRMR = 0.201), suggesting that strict invariance may not hold for this construct. Overall, the findings indicate that the foreign language emotions achieved acceptable levels of measurement invariance over time, while teacher support only achieved scalar invariance.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 3 Model Fit indices for analysis of longitudinal measurement invariance\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"102%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eInvariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e\\u0026chi;\\u003csup\\u003e2\\u0026nbsp;\\u003c/sup\\u003e(df)\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCFI\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e△\\u003c/em\\u003e\\u003cem\\u003eCFI\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 7px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTLI\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e△\\u003c/em\\u003e\\u003cem\\u003eTLI\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eRMSEA\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSRMR\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"8\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eForeign Language\\u0026nbsp;Enjoyment\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eConfigural invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e1426.218 (695)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.927\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.918\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.056\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.067\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eMetric invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e1467.137 (715)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.925\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e-0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.918\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.056\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.071\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eScalar invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e1684.682 (735)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.921\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e-0.004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.916\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e-0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.056\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.087\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eStrict Invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e1752.877 (755)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.917\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e-0.004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.914\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e-0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.057\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.086\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"8\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eForeign Language Anxiety\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eConfigural invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e177.119 (85)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.957\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.045\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.072\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eMetric invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e190.034 (97)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.954\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e-0.003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.939\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e-0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.046\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.074\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eScalar invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e212.002 (109)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.950\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e-0.004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.936\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e-0.003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.048\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.078\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eStrict Invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e232.772 (121)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.948\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e-0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.934\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e-0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.079\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"8\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eForeign Language Boredom\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eConfigural invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e124.901 (89)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.990\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.987\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.027\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.024\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eMetric invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e137.008 (97)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.988\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e-0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.986\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e-0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.029\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.026\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eScalar invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e170.983 (105)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.981\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e-0.007\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.974\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e-0.012\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.036\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.034\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eStrict Invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e191.562 (113)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.976\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e-0.005\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.969\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e-0.005\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.038\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.036\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"8\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eTeacher Support\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eConfigural invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e14.578 (10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.991\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.974\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.029\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.028\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eMetric invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e31.398 (14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.985\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e-0.006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.970\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e0.004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.048\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.153\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eScalar invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e53.264 (18)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.975\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.010\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.961\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e0.011\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.061\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.159\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003eStrict Invariance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e86.863 (22)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.946\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.029\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46px;\\\"\\u003e\\n \\u003cp\\u003e0.931\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e0.030\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 67px;\\\"\\u003e\\n \\u003cp\\u003e0.074\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e0.201\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"8\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eNote:\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cem\\u003e\\u0026nbsp;df\\u003c/em\\u003e = degree of freedom; CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square error of approximation; and SRMR = standardized root mean squared residual.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLatent profile analyses\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo uncover distinct subgroups of students characterized by their foreign language emotions, latent profile analysis was conducted using three observed indicators: foreign language enjoyment, anxiety, and boredom. In line with the model selection guidelines proposed by Nylund et al. (2007), a series of models specifying one to eight latent profiles were estimated. Model fit was evaluated using multiple indices, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-size adjusted BIC (aBIC), bootstrap likelihood ratio test (pBLRT), and entropy values [33]. As detailed in Table 4, all models from two to eight profiles yielded significant pBLRT results (\\u003cem\\u003ep\\u003c/em\\u003es \\u0026lt; .01) at both Wave 1 and Wave 2, suggesting that each additional profile improved model fit. Nevertheless, the selection of the optimal number of profiles was informed not only by statistical criteria but also by conceptual clarity, classification quality, and the practical consideration of class sizes. Across both waves, the five-profile solution was identified as the most parsimonious and interpretable model. At Wave 1, this model yielded the lowest AIC, BIC, and aBIC values, alongside the highest entropy (.959), indicating good classification precision. The latent classes were well-represented, with proportions ranging from 3% to 43%. At Wave 2, the same five-profile model also exhibited favorable fit indices, acceptable entropy (.834), and class sizes between 2% and 29%, supporting its continued empirical and theoretical adequacy.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 4 Results of the latent profile analyses\\u003c/p\\u003e\\n\\u003cdiv align=\\\"Left\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"652\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eProfiles\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003eAIC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003eBIC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003eaBIC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003eBLRT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003eEntropy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003eGroup proportion for each profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\" style=\\\"width: 652px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Latent profile analysis at Wave 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e1-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e3837.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e3863.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e3844.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e2-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e3526.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e3569.22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e3538.21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; .01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.699\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.59 / 0.41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e3-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e3351.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e3411.37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e3366.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; .01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.785\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.61 / 0.34 / 0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e4-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e3246.27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e3324.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e3266.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; .01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.817\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.48 / 0.38 / 0.04 / 0.12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e5-profile\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e3112.08\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e3207.16\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e3136.95\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt; .01\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.959\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.43 / 0.28 / 0.14 / 0.13 / 0.03\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e6-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e3096.71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e3209.81\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e3127.33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; .01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.922\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.37 / 0.28 / 0.14 / 0.12 / 0.03 / 0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e7-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e3072.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e3201.46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e3106.71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; .01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.28 / 0.28 / 0.14 / 0.08 / 0.03 / 0.05 / 0.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e8-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e3080.21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e3226.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e3118.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; .01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.781\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.28 / 0.02 / 0.28 / 0.14 / 0.08 / 0.03 / 0.05 / 0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\" style=\\\"width: 652px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Latent profile analysis at Wave 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e1-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e3462.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e3488.37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e3469.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e2-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e3107.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e3150.27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e3118.93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; .01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.757\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.65 / 0.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e3-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e2984.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e3045.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e3000.73\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; .01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.733\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.51 / 0.47 / 0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e4-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e2715.28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e2792.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e2734.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; .01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.832\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.42 / 0.28 / 0.24 / 0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e5-profile\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e2648.23\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e2743.31\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e2673\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026lt; .01\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.834\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e0.29 / 0.28 / 0.27 / 0.13 / 0.02\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e6-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e2648.45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e2760.55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e2677.97\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e0.129\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.771\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.27 / 0.27 / 0.26 / 0.12 / 0.04 / 0.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e7-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e2602.96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e2732.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e2637.23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; .01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.822\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.26 / 0.26 / 0.25 / 0.09 / 0.04 / 0.05 / 0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e8-profile\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e2607.45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e2753.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 74px;\\\"\\u003e\\n \\u003cp\\u003e2645.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e0.505\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.782\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 214px;\\\"\\u003e\\n \\u003cp\\u003e0.26 / 0.02 / 0.26 / 0.14 / 0.08 / 0.03 / 0.05 / 0.06\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eLatent profile analysis identified five distinct and relatively stable emotional subgroups among foreign language learners across two measurement waves, as summarized in Table 5. Classification of these profiles was based on systematic evaluation of the mean levels of enjoyment, anxiety, and boredom within each class. To enhance theoretical clarity and interpretability, the profiles were labeled following the conventions proposed by Radi\\u0026scaron;ić et al. (2024), with labels reflecting the predominant emotional characteristics of each group [21].\\u003c/p\\u003e\\n\\u003cp\\u003eThe first group, termed Happy, comprised learners reporting the highest levels of enjoyment and the lowest levels of anxiety and boredom at both time points (13% at Wave 1; 28% at Wave 2), indicative of an emotionally engaged and affectively positive profile. The second and most prevalent group at Wave 1 (43%), labeled Apprehensive-Happy, exhibited relatively high enjoyment alongside moderate-to-high anxiety and moderate boredom, suggesting a complex emotional experience in which positive affect co-occurs with emotional strain. The third group, Bored (14% at Wave 1; 13% at Wave 2), was defined by high boredom and low enjoyment, reflecting a disengaged learner profile with limited emotional involvement in language study. The fourth profile, Bored and Anxious, represented the smallest proportion of the sample (3% at Wave 1; 2% at Wave 2) and was characterized by the highest levels of both boredom and anxiety, coupled with the lowest enjoyment. This profile indicates a highly vulnerable group likely to experience negative academic and emotional outcomes. The final group, labeled Moderate (28% at Wave 1; 27% at Wave 2), included students with emotional scores near the sample average, suggesting a relatively balanced yet emotionally neutral pattern. These five profiles reveal substantial heterogeneity in learners\\u0026rsquo; emotional experiences and are consistent with prior findings on student affect in educational settings. The identification of these distinct emotional patterns provides empirical support for Hypothesis 1.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 5 Results of the 5-profile analyses\\u003c/p\\u003e\\n\\u003cdiv align=\\\"Left\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"598\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 156px;\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003cbr\\u003e\\u0026nbsp;(M \\u0026plusmn; SD)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003eCategory 1\\u003c/p\\u003e\\n \\u003cp\\u003eHappy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eCategory 2\\u003c/p\\u003e\\n \\u003cp\\u003eApprehensive-Happy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 81px;\\\"\\u003e\\n \\u003cp\\u003eCategory 3\\u003c/p\\u003e\\n \\u003cp\\u003eBored\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003eCategory 4\\u003c/p\\u003e\\n \\u003cp\\u003eBored\\u0026amp;Anxious\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003eCategory 5\\u003c/p\\u003e\\n \\u003cp\\u003eModerate\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\" style=\\\"width: 598px;\\\"\\u003e\\n \\u003cp\\u003eWave 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003eForeign\\u003c/p\\u003e\\n \\u003cp\\u003eLanguage\\u003c/p\\u003e\\n \\u003cp\\u003eEmotions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003eEnjoyment\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e4.30 \\u0026plusmn; 0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e3.80 \\u0026plusmn; 0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 81px;\\\"\\u003e\\n \\u003cp\\u003e3.26 \\u0026plusmn; 0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e2.19 \\u0026plusmn; 0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e3.36 \\u0026plusmn; 0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003eAnxiety\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e2.29 \\u0026plusmn; 0.62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e2.82 \\u0026plusmn; 0.62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 81px;\\\"\\u003e\\n \\u003cp\\u003e3.66 \\u0026plusmn; 0.62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e4.64 \\u0026plusmn; 0.62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e3.20 \\u0026plusmn; 0.62\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003eBoredom\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e1.11 \\u0026plusmn; 0.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e2.10 \\u0026plusmn; 0.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 81px;\\\"\\u003e\\n \\u003cp\\u003e3.80 \\u0026plusmn; 0.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e4.77 \\u0026plusmn; 0.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e3.00 \\u0026plusmn; 0.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 156px;\\\"\\u003e\\n \\u003cp\\u003eProportion\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e0.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e0.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 81px;\\\"\\u003e\\n \\u003cp\\u003e0.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e0.28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\" style=\\\"width: 598px;\\\"\\u003e\\n \\u003cp\\u003eWave 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003eForeign\\u003c/p\\u003e\\n \\u003cp\\u003eLanguage\\u003c/p\\u003e\\n \\u003cp\\u003eEmotions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003eEnjoyment\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e3.91 \\u0026plusmn; 0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e3.71 \\u0026plusmn; 0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 81px;\\\"\\u003e\\n \\u003cp\\u003e2.64 \\u0026plusmn; 0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e1.06 \\u0026plusmn; 0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e3.22 \\u0026plusmn; 0.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003eAnxiety\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e2.23 \\u0026plusmn; 0.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e3.24 \\u0026plusmn; 0.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 81px;\\\"\\u003e\\n \\u003cp\\u003e3.36 \\u0026plusmn; 0.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e3.61 \\u0026plusmn; 0.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e3.25 \\u0026plusmn; 0.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003eBoredom\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e1.62 \\u0026plusmn; 0.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e2.47 \\u0026plusmn; 0.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 81px;\\\"\\u003e\\n \\u003cp\\u003e3.25 \\u0026plusmn; 0.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e3.08 \\u0026plusmn; 0.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e2.90 \\u0026plusmn; 0.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 156px;\\\"\\u003e\\n \\u003cp\\u003eProportion\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e0.28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 81px;\\\"\\u003e\\n \\u003cp\\u003e0.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e0.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 87px;\\\"\\u003e\\n \\u003cp\\u003e0.27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLatent transition analyses\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo examine the temporal consistency and dynamic shifts in students\\u0026rsquo; foreign language emotion profiles, LTA was conducted under the assumption of invariance in the number and mean structure of latent classes across two time points. The transition probabilities across the five previously identified profiles\\u0026mdash;Happy, Apprehensive-Happy, Bored, Bored \\u0026amp; Anxious, and Moderate\\u0026mdash;are presented in Table 6. Diagonal entries reflect the likelihood of individuals remaining in the same profile from Wave 1 to Wave 2, while off-diagonal entries indicate probabilities of shifting to alternative profiles.\\u003c/p\\u003e\\n\\u003cp\\u003eOverall, the results revealed substantial longitudinal stability in emotional profiles. Learners initially classified as Happy demonstrated a 76.7% probability of remaining in the same category, although 23.3% transitioned to the Apprehensive-Happy group. The Apprehensive-Happy profile exhibited the highest retention rate, with 83.3% of individuals maintaining their classification, and 16.7% transitioning to Moderate. Similarly, the Bored group displayed strong stability, with 89.6% of individuals remaining in the same profile, while small proportions shifted to Happy (8.7%) or Moderate (1.7%). Students in the Bored \\u0026amp; Anxious group showed moderately high stability (78.5%), but some transitioned to Moderate (17.4%) or Happy (4.1%). Finally, those classified as Moderate at Wave 1 exhibited an 84.0% probability of profile consistency, though 16.0% transitioned into the more negatively valenced Bored \\u0026amp; Anxious category. These findings suggest that while emotional profiles among learners are generally stable over time, a subset of students do experience meaningful changes in their emotional experiences. Accordingly, these results provide empirical support for Hypothesis 2.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 6 Transition probability of latent transition analyses\\u003c/p\\u003e\\n\\u003cdiv align=\\\"Left\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"589\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" rowspan=\\\"2\\\" style=\\\"width: 175px;\\\"\\u003e\\n \\u003cp\\u003eTransition Probability\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"5\\\" style=\\\"width: 414px;\\\"\\u003e\\n \\u003cp\\u003eWave 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003eHappy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 115px;\\\"\\u003e\\n \\u003cp\\u003eApprehensive\\u003c/p\\u003e\\n \\u003cp\\u003e-Happy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003eBored\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eBored\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026amp;Anxious\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eModerate\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"5\\\" style=\\\"width: 64px;\\\"\\u003e\\n \\u003cp\\u003eWave 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003eHappy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.767\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 115px;\\\"\\u003e\\n \\u003cp\\u003e0.233\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003eApprehensive\\u003c/p\\u003e\\n \\u003cp\\u003e-Happy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 115px;\\\"\\u003e\\n \\u003cp\\u003e0.833\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e0.167\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003eBored\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.087\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 115px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e0.896\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e0.017\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003eBored\\u0026amp;Anxious\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0.041\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 115px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.785\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e0.174\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003eModerate\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 63px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 115px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 51px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.160\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e0.840\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEffects of teacher support on transitions of emotion profiles\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo evaluate the predictive role of teacher support in the longitudinal transitions between foreign language emotion profiles, we conducted a series of multinomial logistic regression analyses. In each model, the reference category comprised individuals who remained in the same latent profile from Wave 1 to Wave 2, allowing for the estimation of the likelihood of transitioning to a different profile relative to profile stability. Odds ratios (ORs), 95% confidence intervals (CIs), and corresponding p-values were calculated for each transition. ORs greater than 1 indicate that higher levels of teacher support were associated with increased odds of transitioning to a different emotional profile, whereas ORs less than 1 suggest a decreased likelihood of such transitions. Transitions involving 1% or fewer participants were not interpreted due to low frequency and unstable estimates; these are denoted as \\u0026ldquo;a\\u0026rdquo; in Table 7. As shown in Table 7, teacher support significantly predicted several key transitions. Most notably, learners who initially belonged to the Bored profile at Wave 1 were 13.60 times more likely (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .05) to transition to the Happy profile at Wave 2 with higher levels of teacher support, compared to those who remained in the Bored profile. However, teacher support was negatively associated with the likelihood of transitioning from the Apprehensive-Happy profile to the Moderate profile (OR = 0.36) and from the Bored \\u0026amp; Anxious profile to the Moderate profile (OR = 0.51), although these associations did not reach statistical significance.\\u003c/p\\u003e\\n\\u003cp\\u003eDue to insufficient sample sizes, transitions involving \\u0026le; 1% of participants were excluded from interpretation. Overall, the results suggest that teacher support plays a facilitative role in promoting positive emotional development in foreign language learning contexts. By enabling learners to transition from negative or mixed emotional profiles to more adaptive ones, teacher support emerges as a significant predictor of emotional change over time. These findings provide empirical support for Hypothesis 3, which posited that teacher support would significantly influence transitions between foreign language emotion profiles.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 7 Effects of teacher support on transitions of emotion profiles\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"630\\\" class=\\\"fr-table-selection-hover\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003ePredictor\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 133px;\\\"\\u003e\\n \\u003cp\\u003eProfiles\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003eHappy\\u003c/p\\u003e\\n \\u003cp\\u003eW2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 105px;\\\"\\u003e\\n \\u003cp\\u003eApprehensive\\u003c/p\\u003e\\n \\u003cp\\u003e-Happy W2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003eBored\\u003c/p\\u003e\\n \\u003cp\\u003eW2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003eBored\\u003c/p\\u003e\\n \\u003cp\\u003e\\u0026amp;AnxiousW2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eModerate\\u003c/p\\u003e\\n \\u003cp\\u003eW2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"5\\\" style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003ePeer\\u003c/p\\u003e\\n \\u003cp\\u003eSupport\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 133px;\\\"\\u003e\\n \\u003cp\\u003eHappy W1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003eREF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 105px;\\\"\\u003e\\n \\u003cp\\u003e0.67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 133px;\\\"\\u003e\\n \\u003cp\\u003eApprehensive\\u003c/p\\u003e\\n \\u003cp\\u003e-Happy W1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 105px;\\\"\\u003e\\n \\u003cp\\u003eREF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e0.36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 133px;\\\"\\u003e\\n \\u003cp\\u003eBored W1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003e13.60\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 105px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003eREF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 133px;\\\"\\u003e\\n \\u003cp\\u003eBored\\u0026amp;Anxious W1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003e42.60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 105px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003eREF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 133px;\\\"\\u003e\\n \\u003cp\\u003eModerate W1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 79px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 105px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 73px;\\\"\\u003e\\n \\u003cp\\u003ea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e3.68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eREF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\" style=\\\"width: 630px;\\\"\\u003e\\n \\u003cp\\u003e\\u003csup\\u003e*\\u003c/sup\\u003e\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .05; REF was the reference group; Transitions labeled \\u0026ldquo;a\\u0026rdquo; involved \\u0026le;1% of participants and were excluded from further analysis.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\"},{\"header\":\"5 Discussion\",\"content\":\"\\u003cp\\u003eThe present study sought to illuminate the dynamic interplay between teacher support and EFL learners\\u0026rsquo; emotional experiences through a person-centered, longitudinal approach. By employing LPA and LTA, we not only confirmed the presence of heterogeneous emotional profiles among learners\\u0026mdash;comprising enjoyment, anxiety, and boredom\\u0026mdash;but also tracked their transitions over time. Crucially, teacher support emerged as a significant predictor of both initial profile membership and transitions between profiles, corroborating and extending the theoretical premises of the control-value theory and self-determination theory. These findings underscore the powerful role of teacher support in shaping not only static emotional states but also the developmental trajectory of learner emotions in foreign language classrooms.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eForeign language emotion profiles and stability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConsistent with prior person-centered research [17][21], the study identified five distinct emotional profiles: Happy, Apprehensive-Happy, Bored, Bored \\u0026amp; Anxious, and Moderate. These profiles represent multifaceted emotional landscapes that learners navigate during language acquisition. For example, learners in the Bored \\u0026amp; Anxious profile reported high levels of disengagement and apprehension, while those in the Happy profile exhibited high enjoyment and minimal negative emotions. The existence of such profiles confirms that learners experience emotions in complex, often co-occurring ways rather than as isolated affective states.\\u003c/p\\u003e\\n\\u003cp\\u003eTransition probabilities revealed a notable degree of emotional stability, particularly among learners situated in the Happy and Bored profiles, aligning with Elahi Shirvan et al.\\u0026apos;s (2020) assertion that affective states can become entrenched over time without external interventions [24]. Nevertheless, meaningful transitions did occur, particularly toward more adaptive emotional configurations. These transitions suggest the possibility of emotional growth and regulatory change\\u0026mdash;an outcome central to educational aspirations.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eThe role of teacher support in foreign language emotion profiles\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA primary contribution of the current study is its demonstration that teacher support significantly predicts profile membership transitions over time. Learners who perceived higher levels of teacher support were more likely to begin in adaptive profiles (e.g., Happy) and transition away from maladaptive profiles (e.g., Bored \\u0026amp; Anxious) toward more balanced or positive profiles (e.g., Moderate or Happy). These results resonate with existing evidence that teacher support enhances student engagement, motivation, and emotional well-being [9][10].\\u003c/p\\u003e\\n\\u003cp\\u003eThe findings extend prior research by confirming that teacher support does not merely correlate with positive emotions but plays a developmental role in facilitating emotional transformation. This is theoretically significant, as it situates teacher support not just as a static environmental variable but as a dynamic interpersonal force that supports learners\\u0026rsquo; emotional regulation, engagement, and resilience. Attachment theory offers a compelling lens through which to interpret these results [14]. Teachers, when perceived as emotionally available and trustworthy, serve as secure bases from which learners can explore challenging linguistic terrain. This emotional security fosters risk-taking, expression, and engagement, all of which are conducive to positive emotional outcomes [41]. As Huang et al. (2024) noted, teacher support promotes emotional attachments and comfort, both of which reduce anxiety and foster enjoyment in language learning [42].\\u003c/p\\u003e\\n\\u003cp\\u003eThe capacity of teacher support to predict emotional transitions underscores its potential as an intervention target. Learners experiencing anxiety or boredom may not be \\u0026quot;stuck\\u0026quot; in these profiles permanently; with sufficient support\\u0026mdash;academic, motivational, and emotional\\u0026mdash;from instructors, movement toward more adaptive profiles is feasible. This aligns with empirical findings from intervention-based research, such as Alrabai and Algazzaz (2024), who reported significant gains in learners\\u0026rsquo; emotional engagement and satisfaction of basic psychological needs following targeted teacher interventions [6]. In our study, learners transitioning out of negative profiles happened in the presence of strong teacher support. This suggests that teacher support fosters emotional immunity by equipping students with psychological resources such as self-efficacy, coping strategies, and a sense of relatedness [13][42].\\u003c/p\\u003e\\n\\u003cp\\u003eMoreover, the observed transitions support the view that learners are not passive recipients of educational experiences but active agents whose emotional development is deeply intertwined with their social context. Teacher support, in this regard, is a relational scaffold that enables learners to reframe difficulties, regulate affective states, and maintain engagement.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTheoretical Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study contributes theoretically to the growing literature at the intersection of control-value theory and self-determination theory. The control-value theory posits that learners\\u0026rsquo; emotions stem from their perceptions of control over and value of academic tasks [3]. Our findings illustrate how teacher support enhances these appraisals, thereby fostering enjoyment and mitigating anxiety and boredom. Self-determination theory further elucidates the role of teacher support in satisfying learners\\u0026rsquo; psychological needs\\u0026mdash;autonomy, competence, and relatedness\\u0026mdash;all of which emerged as relevant in our observed transitions [5]. For instance, learners who moved toward the Happy profile likely benefited from an environment where they felt supported, capable, and connected. Moreover, our findings underscore the value of longitudinal person-centered approaches. Traditional variable-centered analyses may mask the heterogeneity of emotional experiences; by contrast, LTA captures not only individual differences but also developmental shifts, providing a richer understanding of emotion regulation and affective change [35].\\u003cstrong\\u003e\\u003cbr\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\"},{\"header\":\"6 Limitations\",\"content\":\"\\u003cp\\u003eWhile the current study offers valuable insights, it is not without limitations. First, the reliance on self-report measures may introduce bias related to social desirability. Second, although the two-wave design allows for stronger inferences about temporal change, additional waves could provide a more nuanced understanding of emotional trajectories. Third, future research should explore other classroom variables—such as peer support, instructional style, or feedback quality—that may interact with teacher support in shaping emotional transitions.\\u003c/p\\u003e\\n\\u003cp\\u003eMoreover, qualitative and ecological momentary assessment methods could further illuminate the processes underlying profile shifts. Investigating how specific teacher behaviors trigger emotional change in real time remains an important direction for future inquiry [24].\\u003c/p\\u003e\"},{\"header\":\"7 Conclusion\",\"content\":\"\\u003cp\\u003eThis study examined how teacher support influences the emotional experiences of English as a foreign language learners over time, using latent profile and latent transition analyses. Five distinct emotional profiles were identified—Happy, Apprehensive-Happy, Bored, Bored and Anxious, and Moderate—each reflecting unique combinations of enjoyment, anxiety, and boredom. Significant transitions occurred across profiles over a six-month interval, indicating that learners’ emotional states are dynamic rather than static. Teacher support significantly predicted profile membership transitions: learners perceiving higher levels of teacher support were more likely to move from negative profiles (e.g., Bored and Anxious) toward more adaptive ones (e.g., Happy), highlighting teacher support as a key factor in promoting emotional development in language learning contexts.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to grateful all participants who agreed to participate in this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eR.H.Z. was involved in the study design, data collection, analysis, and writing of the article. All authors read and approved the final manuscript.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by Research on the Construction of Ideological and Political Education Model in College English Courses Based on \\u0026ldquo;Henan Education Modernization 2035\\u0026rdquo;, Education Department of Henan Province.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data that support the study may be available upon request with permission from the researchers who collected the data.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study adhered to the guidelines set forth in the Declaration of Helsinki, was approved by the ethical committee at Dalian University of Technology (DUTSH240409-02). Informed written consent was obtained from all participants.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eWentzel, K. R., Battle, A., Russell, S. L., \\u0026amp; Looney, L. B. (2010). Social supports from teachers and peers as predictors of academic and social motivation. \\u003cem\\u003eContemporary Educational Psychology, 35\\u003c/em\\u003e(3), 193\\u0026ndash;202. https://doi.org/10.1016/j.cedpsych.2010.03.002\\u003c/li\\u003e\\n \\u003cli\\u003eDewaele, J.-M., \\u0026amp; MacIntyre, P. D. (2014). The two faces of Janus? 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(1998\\u0026ndash;2017). \\u003cem\\u003eMplus user\\u0026rsquo;s guide\\u0026nbsp;\\u003c/em\\u003e(8th ed.). Muth\\u0026eacute;n \\u0026amp; Muth\\u0026eacute;n.\\u003c/li\\u003e\\n \\u003cli\\u003eLo, Y., Mendell, N. R., \\u0026amp; Rubin, D. B. (2001). Testing the number of components in a normal mixture. \\u003cem\\u003eBiometrika, 88\\u003c/em\\u003e(3), 767\\u0026ndash;778. https://doi.org/10.1093/biomet/88.3.767\\u003c/li\\u003e\\n \\u003cli\\u003eNylund, K. L., Asparouhov, T., \\u0026amp; Muth\\u0026eacute;n, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. \\u003cem\\u003eStructural Equation Modeling: A Multidisciplinary Journal, 14\\u003c/em\\u003e(4), 535\\u0026ndash;569. https://doi.org/10.1080/10705510701575396\\u003c/li\\u003e\\n \\u003cli\\u003eMarsh, H. W., L\\u0026uuml;dtke, O., Trautwein, U., \\u0026amp; Morin, A. J. S. (2009). 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Webnote: Longitudinal tests of profile similarity and latent transition analyses. \\u003cem\\u003eSubstantive Methodological Synergy Research Laboratory\\u003c/em\\u003e. http://smslabstats.weebly.com/\\u003c/li\\u003e\\n \\u003cli\\u003eMuth\\u0026eacute;n, B. (2003). Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003). \\u003cem\\u003ePsychological Methods, 8\\u003c/em\\u003e(3), 369\\u0026ndash;377. https://doi.org/10.1037/1082-989X.8.3.369\\u003c/li\\u003e\\n \\u003cli\\u003ePeugh, J., \\u0026amp; Fan, X. (2013). Modeling unobserved heterogeneity using latent profile analysis: A Monte Carlo simulation.\\u003cem\\u003e\\u0026nbsp;Structural Equation Modeling: A Multidisciplinary Journal, 20\\u003c/em\\u003e(4), 616\\u0026ndash;639. https://doi.org/10.1080/10705511.2013.824780\\u003c/li\\u003e\\n \\u003cli\\u003eMa, L., Li, B., \\u0026amp; Liu, J. (2025). Unraveling the dynamics of teacher-student relationships, emotions, and socioeconomic status in shaping subjective well-being among FL learners. \\u003cem\\u003eInternational Journal of Educational Research, 131\\u003c/em\\u003e, 102581. https://doi.org/10.1016/j.ijer.2025.102581\\u003c/li\\u003e\\n \\u003cli\\u003eHuang, L., Al-Rashidi, A. H., \\u0026amp; Bayat, S. (2024). Teacher support in language learning: A picture of the effects on language progress, academic immunity, and academic enjoyment. \\u003cem\\u003eBMC Psychology, 12\\u003c/em\\u003e, 124. https://doi.org/10.1186/s40359-024-01602-2\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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},\"keywords\":\"foreign language emotions, teacher support, enjoyment, anxiety, boredom, latent profile analysis, latent transition analysis, longitudinal study\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6656719/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6656719/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study investigated the longitudinal relationship between teacher support and foreign language learners\\u0026rsquo; emotional experiences, focusing on enjoyment, anxiety, and boredom. Utilizing a two-wave longitudinal design with a sample of 440 Chinese university students at Wave 1 and 212 at Wave 2, the study employed latent profile analysis to identify emotional subgroups and latent transition analysis to examine changes in emotional profiles over time. Five distinct emotional profiles were identified\\u0026mdash;Happy, Apprehensive-Happy, Bored, Bored and Anxious, and Moderate\\u0026mdash;confirming the heterogeneity of learners\\u0026rsquo; emotion experiences (Hypothesis 1). Significant transitions between these profiles occurred over a six-month period, indicating dynamic changes in learners\\u0026rsquo; emotional states (Hypothesis 2). Crucially, teacher support significantly predicted emotion profile membership transitions between profiles (Hypothesis 3). Learners who perceived higher levels of teacher support were more likely to transition into, more adaptive emotional profiles characterized by higher enjoyment and lower anxiety or boredom. These findings highlight the pivotal role of teacher support in shaping the emotional development of foreign language learners and underscore the value of adopting longitudinal, person-centered approaches in educational psychology.\\u003c/p\\u003e\",\"manuscriptTitle\":\"How teacher support influences EFL learners’ emotions? 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