Learning English as a foreign language at the undergraduate level is a complex undertaking that goes beyond linguistic proficiency, requiring sustained motivation, confidence, and effective learning strategies to navigate academic challenges. English is a compulsory subject in the first 2 years of undergraduate study in China (Haidar & Fang, 2019; Tian et al., 2019). Because the English language is based on an Indo-European system, whereas the Chinese language is based on a Sino-Tibetan system, Chinese undergraduates studying English frequently experience difficulties such as communication anxiety and apprehension associated with limited grammatical and lexical knowledge; difficulties in revising, retaining, and retrieving foreign-language vocabulary; and fear of being misunderstood because of imprecise pronunciation (Malik et al., 2021; S. Yang et al., 2022). The ability to bounce back from such challenges is critical for sustaining learners’ motivation, strengthening their resilience, and fostering flourishing in English learning. Within the framework of positive psychology, this adaptive capacity is captured by the construct of academic buoyancy, defined as students’ ability to cope effectively with everyday academic pressures and minor setbacks (Yun et al., 2018).

 

Researchers have identified various psychological factors that contribute to academic buoyancy. Scholars have consistently recognized self-efficacy as a key predictor of academic buoyancy (Crossley, 2024; Martin & Marsh, 2008). Self-efficacy is defined as the belief in one’s capability to organize and execute actions to achieve desired goals (Bandura, 1997). Research has shown that self-efficacy enables students to persist with difficult tasks and approach challenges with confidence, thereby reducing stress and anxiety (Man & Liu, 2025; Olendo et al., 2019). Within the context of second language (L2) acquisition, self-efficacious learners tend to stay motivated, employ adaptive strategies, and persevere when facing difficulties, ultimately contributing to better L2 achievement (Almayez et al., 2025; X. Wang et al., 2021). A key mechanism linking self-efficacy to successful achievement is the facilitation of buoyancy (Yun et al., 2018). However, although the relationship between self-efficacy and academic buoyancy has been recognized in the field of educational psychology, its role in L2 acquisition has been less explored. In particular, within the context of positive psychology, understanding of the connection between self-efficacy and academic buoyancy in explaining individual differences in L2 acquisition is only starting to develop (H. Liu, 2022). Therefore, we formed the following hypothesis:

Hypothesis 1: Self-efficacy will exert a significant direct effect on academic buoyancy among Chinese undergraduates studying English as a foreign language.

 

Self-efficacy provides confidence, but navigating the academic demands of learning English as a foreign language (EFL) also depends on self-regulated learning, which involves a multifaceted process through which learners manage the cognitive, metacognitive, and motivational aspects of learning to achieve their goals (Zimmerman, 2013). According to social cognitive theory, self-efficacy is a key determinant of self-regulated learning (Bandura, 1986). Those who perceive themselves as capable tend to set challenging goals, monitor their progress, adjust their strategies, and persist through challenges (Schunk & DiBenedetto, 2014). Similarly, in the context of L2 acquisition, self-efficacious students adopt effective self-regulatory strategies, such as knowledge rehearsal, goal-oriented monitoring, idea planning, and interest enhancement, which promote academic engagement and persistence (Althewini, 2025), and help them achieve better language learning outcomes (L. S. Teng, 2024; Xu et al., 2022).

 

Self-regulated learning plays an essential role as a protective factor in promoting individuals’ educational growth by fostering resilience (Bostanabad, 2023). Research across educational levels has consistently shown that students with stronger (vs. weaker) self-regulation demonstrate greater academic buoyancy (Bostanabad, 2023; Collie et al., 2024). In L2 acquisition, learners who can self-regulate adapt their learning strategies, monitor their performance, maintain motivation, manage the learning environment and distractions, seek resources and help, and bounce back quickly from setbacks (Park et al., 2025; Yun et al., 2018). Moreover, the use of self-regulation strategies is crucial for promoting L2 proficiency (L. S. Teng & Zhang, 2020; M. F. Teng et al., 2022). This interplay between self-regulated learning and language competence indicates that learners who respond to challenges with resilience employ effective learning strategies, leading to enhanced academic achievement (Park et al., 2025).

 

Likewise, studies have suggested that when learners have strong confidence in their L2 abilities, they are better able to navigate challenges in language learning by adaptively regulating their attention, emotions, and behavior (Yun et al., 2018). This implies that self-regulated learning may serve as an important pathway through which self-efficacy translates into academic buoyancy. According to social cognitive theory (Bandura, 1997), personal, behavioral, and environmental factors all interact as determinants of each other. Within this theory, learners’ self-efficacy beliefs play a central role in motivating adaptive self-regulation, with those individuals who believe (vs. do not believe) in their ability to influence outcomes being more likely to exert effort, persist through difficulties, and recover from setbacks. Furthermore, Rameli and Kosnin (2018) proposed a framework grounded in achievement goal theory, suggesting that mathematics students’ achievement goal orientation shapes their self-regulation processes, which, in turn, influence their academic buoyancy. Together, these studies provide support for positioning self-regulated learning as a central mechanism linking motivational factors to buoyancy in the academic context. However, as most scholars have examined these relationships in isolation, empirical evidence validating this mediating mechanism within the context of self-regulation in learning remains limited. Therefore, we proposed the following hypothesis:

Hypothesis 2: Self-regulated learning will mediate the relationship between self-efficacy and academic buoyancy among Chinese undergraduates studying English as a foreign language.

We obtained ethical approval for our research from the Institutional Review Board of Universiti Teknologi Malaysia (Approval No. UTMREC-2025-163). Before they took part in the study, we informed all respondents of the research objectives and assured them of the confidentiality of their responses. The survey took approximately 10 minutes to complete. Participation was voluntary, and we obtained informed consent from all participants prior to data collection.

 

We employed purposive sampling to recruit participants at Neusoft Institute Guangdong and Guangzhou Xinhua University in Guangdong Province, China. We collected 717 survey forms, of which 96 were excluded because of straight-line responding, resulting in 621 valid responses being retained for analysis (rate of response = 86.61%). The participants were 270 first-year students and 351 second-year students with a mean age of 21.05 years (SD = 1.65, range = 18–24), of whom 302 (48.6%) were men and 319 (51.4%) were women.

To ensure linguistic and conceptual accuracy, the English version of the survey was translated into Chinese via back-translation (Brislin, 1980), with the support of specialists in psychology, education, and language studies. We subsequently conducted a pilot study with 60 students to confirm the validity and reliability of the translated instruments. These students did not participate in the main study.

 

We measured academic buoyancy using the Academic Buoyancy Scale developed by Jahedizadeh et al. (2019). The scale consists of 27 items designed to assess four dimensions: sustainability (seven items, e.g., “I never let a bad mark in language learning affect my confidence”), regularity adaptation (four items, e.g., “I have self-discipline in the process of language learning”), positive personal eligibility (eight items, e.g., “My belief in myself gets me through hard times of language learning”), and positive acceptance of academic life (eight items, e.g., “In learning the language, I’m not worried about the future; rather, I live in the present time and enjoy the moment”). Responses are rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater academic buoyancy. Previous studies have indicated strong internal consistency for the scale, with Cronbach’s alpha values reported as .95 (S. Yang et al., 2022) and .83 (Hedeshi & Ghanizadeh, 2025).

 

We measured self-efficacy using the short version of the Questionnaire of English Self-Efficacy developed by C. Wang and Bai (2017). This version comprises 12 items designed to measure students’ perception of their ability in four language skills: listening (three items, e.g., “I can understand stories told in English”), speaking (three items, e.g., “I can answer my teacher’s questions in English”), reading (three items, e.g., “I can read English short novels”), and writing (three items, e.g., “I can write diaries in English”). Participants rate their ability on a 6-point Likert scale ranging from 1 (I cannot do it at all) to 6 (I can do it), with higher scores reflecting stronger self-efficacy beliefs. Previous studies have indicated strong internal consistency for the scale, with Cronbach’s alpha values reported as .95 (C. Wang & Bai, 2017) and .98 (S. Yang et al., 2022).

 

We assessed self-regulation using the Academic Self-Regulation Scale developed by Magno (2010). This scale consists of 50 items across seven subconstructs: memory strategy (13 items, e.g., “I make a summary of my readings”), goal setting (five items, e.g., “I plan the things I have to do in a week”), self-evaluation (11 items, e.g., “I evaluate my accomplishments at the end of each study session”), seeking assistance (eight items, e.g., “I call a classmate about the homework that I missed”), environmental structuring (four items, e.g., “I isolate myself from unnecessarily noisy places”), learning responsibility (five items, e.g., “I prioritize my schoolwork over other activities”), and organizing (four items, e.g., “I highlight important concepts and information I find in my readings”). Each item is rated on a 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree), with higher scores reflecting stronger self-regulation. Previous studies have indicated strong internal consistency for the scale, with Cronbach’s alpha values reported as .86 (Johny et al., 2012) and .95 (Malaga & Oducado, 2021).

We employed SPSS 30.0 and SmartPLS 4.0 for data analysis, summarizing participants’ demographic characteristics with descriptive statistics, and calculating Pearson correlation coefficients to examine the associations among variables. To evaluate potential common method bias, we applied Harman’s single-factor test to the self-reported data, following the procedure outlined by Podsakoff et al. (2003). In accordance with established partial least squares structural equation modeling (PLS-SEM) guidelines (Hair et al., 2017), the analysis followed a two-step approach. In the first step we assessed the measurement model in terms of indicator reliability, internal consistency, convergent validity, and discriminant validity. The second step involved evaluating the structural model to test the hypothesized relationships and determine the predictive power of the model.

As we collected the data through self-reports, we could not rule out the potential for common method bias. To minimize this risk, we assured participants of anonymity and confidentiality and informed them that their responses would be used solely for research purposes. Harman’s single-factor test produced 14 unrotated factors with eigenvalues greater than 1. The first factor accounted for 27.93% of the total variance, which is well below the 40% threshold recommended by Podsakoff et al. (2003). These results suggest that common method bias was not a major concern in this study.

Table 1 shows the descriptive statistics and intercorrelations among academic buoyancy, self-efficacy, and self-regulated learning. All variables were significantly and positively correlated. Academic buoyancy was moderately associated with self-efficacy and self-regulated learning, and self-efficacy also showed a significant positive correlation with self-regulated learning. These associations underscore the potential pathways through which self-efficacy and self-regulatory strategies may enhance students’ academic buoyancy, providing a solid foundation for further exploration of mediation effects within this model.

Table 2 sets out the results of the measurement model analysis conducted using SmartPLS 4.0. We established item reliability by retaining indicators with factor loadings of .70 or higher (Hair et al., 2017). Convergent validity was assessed by calculating average variance extracted values, with a threshold of .50 or above indicating adequacy (Fornell & Larcker, 1981; Hair et al., 2017). We assessed internal consistency reliability using Cronbach’s alpha and composite reliability. According to Hair et al. (2017), composite reliability values between .60 and .70 are acceptable for exploratory research, and values ranging from .70 to .90 are considered satisfactory in more advanced research contexts. According to these criteria, we retained 18 items for the Academic Buoyancy Scale, 12 items for the Questionnaire of English Self-Efficacy, and 32 items for the Academic Self-Regulation Scale. Additionally, all variance inflation factor values were below the threshold of 5, indicating that multicollinearity was not a concern in the model (Hair et al., 2017).

We evaluated discriminant validity for lower order constructs using the heterotrait–monotrait (HTMT) ratio, following the recommendation of Henseler et al. (2015). According to the criteria proposed by Clark and Watson (2016) and Kline (2023), HTMT values exceeding .85 may indicate a lack of discriminant validity. As shown in Table 3, all HTMT values were below the .85 threshold, indicating acceptable discriminant validity among the lower order constructs.

We conceptualized academic buoyancy, self-efficacy, and self-regulated learning as higher order constructs, each comprising multiple lower order constructs. To ensure the validity and reliability of these higher order constructs, we conducted a comprehensive evaluation of the measurement model, including assessments of indicator reliability, internal consistency, convergent validity, and discriminant validity. All item loadings exceeded the recommended threshold of .70, indicating satisfactory indicator reliability (Sarstedt et al., 2019). We confirmed internal consistency through both Cronbach’s alpha and composite reliability, with all values exceeding the established benchmark of .70 (Hair et al., 2017), suggesting adequate reliability (Sarstedt et al., 2019). As can be seen in Table 4, the average variance extracted for each higher order construct exceeded the recommended threshold of .50 (Hair et al., 2017), indicating good convergent validity. We assessed discriminant validity using the HTMT ratio of correlations, and all values were below the .85 criterion (see Table 5), providing strong evidence of discriminant validity across the higher order constructs (Hair et al., 2017).

Hypothesis testing in this study followed the PLS-SEM procedure for assessing the significance of path coefficients, as outlined by Hair et al. (2017). We used SmartPLS 4.0, applying a bootstrapping analysis with 5,000 resamples, in line with the guidelines provided by Becker et al. (2022).

 

Figure 1 shows the direct and indirect paths within the structural model, and Table 6 shows the results of hypothesis testing. Hypothesis 1, in which we proposed a positive association between self-efficacy and academic buoyancy, was fully supported. Hypothesis 2 proposed that self-regulated learning would mediate the relationship between self-efficacy and academic buoyancy. The bootstrapping analysis revealed a significant indirect effect, thus supporting the hypothesis. Given that both the direct and indirect effects were significant, self-regulated learning partially mediated the relationship between self-efficacy and academic buoyancy.

We evaluated the explanatory power of the model by analyzing the coefficient of determination (R²). Applying the PLS algorithm in SmartPLS 4.0, the calculated values for the coefficient of determination all surpassed the recommended minimum threshold of .10 suggested by Falk and Miller (1992). In particular, the R² for self-regulated learning was .123 and that for academic buoyancy was .354.

 

Following the PLS-Predict procedure proposed by Shmueli et al. (2019), we assessed Q² values for both manifest variables (MV or indicators) and latent variables (LV or constructs). A Q² value greater than zero indicates that the model exhibits predictive relevance and should be further evaluated by comparing its performance against a linear benchmark model, typically using root-mean-squared error (RMSE) or mean absolute error (MAE). While RMSE is commonly used to assess predictive accuracy, MAE is preferred when prediction errors are skewed (Hair et al., 2017; Shmueli et al., 2019). As noted by Hair et al. (2017), linear model prediction errors are available only for MVs, not LVs. The predictive power of a model is considered strong when all PLS linear model differences are negative, moderate when the majority are negative, and weak when only a few are negative. As shown in Table 7, the Q² values for all MVs and LVs were greater than zero, indicating satisfactory predictive relevance. Furthermore, the PLS model outperformed the linear model in most cases based on both RMSE and MAE, supporting its moderate predictive capability in line with the criteria suggested by Shmueli et al. (2019).

In this study we examined the relationship between self-efficacy and academic buoyancy among Chinese EFL undergraduates, with a particular focus on the mediating role of self-regulated learning. As one of the first investigations to address this relationship in the context of Chinese EFL undergraduates, the study offers insights into the interplay between motivational and self-regulatory processes in L2 learning. The results revealed that self-efficacy was significantly associated with academic buoyancy through the partial mediation of self-regulated learning. These results highlight the critical role of self-regulated learning as a mechanism through which motivational beliefs contribute to students’ ability to cope effectively with academic challenges in L2 learning.

 

We found that self-efficacy was a significant predictor of academic buoyancy, aligning with social cognitive theory (Bandura, 2016), according to which self-efficacy is regarded as the foundation of human aspirations, motivation, and accomplishments. Our finding reinforces previous evidence that students with high, compared to low, self-efficacy tend to exhibit greater academic buoyancy (Anderson et al., 2020; Martin et al., 2010). Specifically, in the context of L2 acquisition, highly self-efficacious learners are more likely to devote more time and greater effort to language learning (S. Yang et al., 2022), interpret difficulties as opportunities for personal growth rather than threats to their performance (S. Yang & Wang, 2022), bounce back from setbacks (Li, 2022; D. Yang, 2023), and, ultimately, be more successful in learning the foreign language (Yun et al., 2018). Moreover, by fostering a sense of personal agency, self-efficacy enables learners to navigate uncertainties, manage stress, and recover from depression (Lu & Rameli, 2025; Sharma & Kumra, 2022).

 

In addition, we found that self-regulated learning partially mediated the relationship between self-efficacy and academic buoyancy, as both the direct and indirect effects were statistically significant and meaningful (Hair et al., 2017). This highlights the pivotal role of regulatory processes in transforming motivational factors into academic resilience in the context of L2 acquisition. Consistent with Bandura’s (2016) social cognitive theory, self-efficacy influences the quality of human functioning by shaping cognitive, motivational, emotional, and decisional processes. Specifically, learners’ ability to regulate their learning actively largely depends on how they perceive their own capabilities. Our finding resonates with the results of Bai and Wang (2023), who suggested that students’ use of strategies such as goal setting, effort regulation, and self-monitoring was shaped by their self-efficacy and growth mindset. Similarly, L. S. Teng (2024) found that self-efficacy strongly predicted learners’ metacognitive, cognitive, and motivational regulation strategies in EFL writing. Scholars have suggested that self-efficacy fosters the use of self-regulatory strategies (Chen et al., 2022), which enhances academic engagement and persistence (Althewini, 2025), thereby further strengthening self-efficacy (Theobald, 2021). These findings underscore the reciprocal relationship between self-efficacy and self-regulation, with the two constructs reinforcing each other over time (Ning & Downing, 2010).

 

Furthermore, we found a significant positive association between self-regulated learning and academic buoyancy, which is in line with previous research. Empirical evidence has consistently suggested that students who demonstrate higher levels of self-regulated learning tend to exhibit stronger academic buoyancy across a range of educational contexts, including in middle school (Fried & Chapman, 2012; Vinter, 2019), high school (Deng et al., 2023; Ragusa et al., 2023), and university settings (Khojasteh et al., 2022). In particular, self-regulated learners are better equipped with cognitive and motivational strategies that support effective learning methods, efficient time management, and continual self-monitoring of progress (Khojasteh et al., 2022; Seli & Dembo, 2019). These abilities help learners manage challenges in the academic context and strengthen their capacity for academic buoyancy (Yun et al., 2018).

This study advances understanding of academic buoyancy among L2 learners by revealing the partial mediating role of self-regulated learning between self-efficacy and academic buoyancy. The findings underscore the importance of integrating motivational and regulatory strategies in EFL research and instruction. By extending previous research on Chinese EFL undergraduates, this study has elucidated how self-efficacy and self-regulated learning jointly support EFL students in navigating academic challenges. On a practical level, the results provide valuable insights for educators in creating learning environments that foster strategic learning behaviors and enhance learners’ resilience in the face of challenges.

This study has several limitations. First, the use of a cross-sectional research design restricts drawing causal inference, so that further longitudinal research is needed. Second, reliance on self-reported data may introduce response bias. Researchers could address this in future studies by employing mixed-method designs or collecting data from multiple sources, such as teachers or peers. Finally, the use of purposive sampling may limit the generalizability of the findings. To improve external validity, future studies could employ probability sampling across diverse institutions and cultural contexts in other EFL settings.

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