The relationship between chemistry growth mindset and chemistry academic engagement: a multiple mediation model

Abstract

Chemistry academic engagement plays a crucial role in shaping students’ academic performance and long-term motivation for learning. The chemistry growth mindset is regarded as a fundamental psychological construct for fostering such engagement. Although previous studies have provided preliminary evidence for the association between growth mindset and academic engagement, the underlying mechanisms within the context of chemistry education have not been extensively examined. Therefore, this study aims to investigate the multiple mediating pathways involving chemistry adaptability (cognitive-behavioural and affective), chemistry academic buoyancy, and chemistry achievement emotions (enjoyment and anxiety) in the relationship between chemistry growth mindset and chemistry academic engagement. A multiple mediation model was constructed based on prior theories and empirical studies. Data were collected from high school students (N = 1049) using scales to measure the relevant variables. The results indicated that: (1) chemistry growth mindset significantly positively influenced students’ chemistry academic engagement; (2) chemistry cognitive-behavioural adaptability, chemistry affective adaptability, and chemistry academic buoyancy all functioned as significant mediators in this relationship; (3) chemistry enjoyment and chemistry anxiety were identified as positive and negative mediators, respectively; and (4) chemistry growth mindset was found to be indirectly associated with chemistry academic engagement through sequential mediation paths involving adaptability or buoyancy and subsequent emotional responses. This study elucidates the mechanism by which growth mindset shapes academic engagement through adaptability, buoyancy, and achievement emotions, thus contributing to a deeper theoretical understanding of how students’ psychological traits shape their engagement. Finally, the study provides pedagogical implications and suggests avenues for future research based on the findings.

---

Introduction

In the field of chemistry education, student engagement has been widely acknowledged as a key determinant of academic achievement (Miltiadous et al., 2020; Ross et al., 2020; Macnamara and Burgoyne, 2023). High levels of engagement are associated with meaningful learning and academic success (Fredricks et al., 2004; Guzey and Li, 2023; Beymer et al., 2025), whereas low engagement has been consistently associated with lower academic performance and diminished long-term interest in science-related fields (Grabau et al., 2017; Joy et al., 2023). Therefore, fostering and sustaining high levels of engagement in high school chemistry classrooms is not only critical for improving students’ immediate learning outcomes but also has far-reaching implications for their future participation in STEM disciplines. However, maintaining high engagement in high school chemistry remains a considerable challenge. Chemistry is frequently perceived as a cognitively demanding and conceptually complex discipline (Tsaparlis and Papaphotis, 2009; Santos and Mooring, 2022), potentially leading to frustration and reduced student investment in learning activities. In addition, a highly competitive academic environment, such as that created by China's national college entrance examination, can exacerbate pressure and negative emotional experiences, ultimately diminishing students’ willingness to engage and persist in Chemistry academic engagement (Gong and Bergey, 2020). Therefore, it is essential to identify student-level psychological characteristics that may mitigate the impact of these adverse influences and to explore their underlying mechanisms to improve Chemistry academic engagement.

One critical psychological construct influencing student engagement is mindset, which refers to individual beliefs regarding the malleability of personal characteristics (Dweck, 2006). According to self-determination theory (SDT), students endorsing a growth mindset tend to view challenges as opportunities to foster competence development rather than as threats (Ryan and Deci, 2017). Such an orientation is likely to enhance students’ intrinsic motivation for chemistry learning, increase the likelihood of academic adaptability when facing change, foster academic buoyancy in response to everyday challenges, and promote engagement through the experience of positive achievement emotions. Previous studies have independently established that a growth mindset is positively related to academic engagement (Wang et al., 2021; Lou et al., 2022; Sadoughi et al., 2023), that both academic adaptability and academic buoyancy are closely associated with academic engagement (Zarrinabadi et al., 2022; Suharsono and Fatimah, 2024), and that achievement emotions serve as significant predictors of engagement (Ebn-Abbasi et al., 2024; Zhong et al., 2024). A wealth of studies outside chemistry has already mapped the motivational machinery that links mindset to engagement. Growth-oriented students adopt mastery goals, use deeper processing strategies, and rebound from failure through adaptive self-regulation and constructive emotions (Robins et al., 2002; Smiley et al., 2016; Lou et al., 2017). Neuro-cognitive evidence further shows that growth theorists allocate attention to corrective feedback and sustain conceptual encoding after errors, thereby enhancing later performance (Mangels et al., 2006). Person-centred work confirms that mindset co-varies with effort beliefs, achievement goals, and perseverance, forming distinct motivational profiles that predict achievement trajectories (Yu and McLellan, 2020). Moreover, mindset filters contextual cues: students high in growth mindset interpret instructors’ beliefs and classroom norms more favourably, bolstering their sense of belonging and engagement in STEM majors (Williams et al., 2021). Even everyday study habits differ: incremental theorists are more likely to self-test, space practice, and persist when learning feels difficult (Yan et al., 2014).

Chemistry learning presents distinctive challenges, namely the integration of macroscopic phenomena, microscopic particle models, and abstract symbolic notation, which create cognitive barriers less frequently addressed in broader science education research (Johnstone, 1991; Talanquer, 2011; Santos and Arroio, 2016). The translation between macroscopic observations, spatial molecular representations, and abstract symbolic notation imposes significant extraneous cognitive load that may undermine students’ perceived controllability of chemistry tasks; high-stakes laboratory assessments further exacerbate evaluative pressure and subject-specific anxiety, compounding the affective demands of chemistry learning (Milenković et al., 2014; Hussain et al., 2018). These domain-specific cognitive and affective demands may alter the control-value appraisals that are central to achievement emotions, thereby shaping the mediating role of emotions in the relationship between a chemistry growth mindset and academic engagement (Pekrun, 2006; Fink et al., 2018). The present study builds on existing models and extends them into a chemistry context by testing cognitive-behavioural adaptability, affective adaptability, academic buoyancy, and achievement emotions as sequential mediators between growth mindset and academic engagement.

Chemistry growth mindset and chemistry academic engagement

Individuals may hold fundamentally distinct conceptions of ability. Some tend to regard ability as a fixed, innate trait, while others perceive it as malleable and capable of development through sustained effort and experience (Dweck et al., 1995; Dweck and Yeager, 2019). This distinction is reflected in what is known as the implicit theory of intelligence, which describes two primary perspectives: the entity theory, viewing intelligence as fixed and unchangeable, and the incremental theory, viewing intelligence as malleable and capable of growth (Dweck and Leggett, 1988; Dweck et al., 1995). In 2006, Dweck reframed the entity and incremental theories as fixed and growth mindsets, respectively (Dweck, 2006).

Students endorsing a fixed mindset typically perceive intelligence as an innate and immutable trait. They prioritize favourable evaluations of their abilities and adopt performance goals to demonstrate their intelligence and talents. When confronted with failure, they often exhibit helpless responses and heightened anxiety (Dweck and Leggett, 1988; Dweck et al., 1995; Dweck, 2006). In contrast, students with a growth mindset view intelligence as improvable through effort, experience, and practice. They emphasize the process of knowledge mastery and skill development and are more likely to adopt mastery goals. When facing setbacks, they are inclined to attribute failure to insufficient effort, actively seek strategies and resources, and persist in problem-focused coping (Dweck and Leggett, 1988; Mueller and Dweck, 1998; Dweck, 2006; Doron et al., 2009). Numerous empirical studies have confirmed that a growth mindset is a significant predictor of academic engagement. For instance, Wang et al. (2021), in a three-year longitudinal study of adolescents aged 11 to 15, found that students endorsing a malleable view of intelligence exhibited higher levels of engagement in mathematics. Lou et al. (2022) reported that while mindset was not a standalone predictor of academic achievement, students with a growth mindset consistently demonstrated the highest levels of engagement. Similarly, Zeng et al. (2016), in a survey of 1260 Chinese primary and secondary school students, found that endorsement of a growth mindset was positively related to psychological well-being and school engagement.

Current research indicates that the growth mindset may manifest as domain-specific, suggesting that beliefs about mindset in specific academic domains (e.g., programming, biology, or chemistry) differ from general growth mindset beliefs and may serve as stronger predictors of behaviours and achievement within those domains (Dai and Cromley, 2014; Scott and Ghinea, 2014; Wichaidit, 2025). In the context of chemistry education, a chemistry growth mindset refers to students’ domain-specific beliefs regarding their ability to improve in chemistry learning (Santos et al., 2021). Santos et al. (2021) found that students often conceptualize intelligence distinctively within chemistry-specific contexts. For instance, while “intelligence” is typically associated with general knowledge and problem-solving ability, “chemistry intelligence” tends to be associated with specialized skills and content knowledge in chemistry. These findings underscore the importance of examining students’ mindset beliefs within specific disciplinary contexts. Students may hold unique mindset beliefs in the chemistry domain, which could directly impact their chemistry academic engagement. Therefore, given the significant cognitive and affective challenges inherent in chemistry learning, focusing on students’ chemistry growth mindset offers a more refined lens through which to examine high school students’ chemistry academic engagement.

The potential mediating role of chemistry academic adaptability and chemistry academic buoyancy

Academic challenges in chemistry often act as notable impediments to student engagement, as students’ capacity to navigate and perform under such challenges directly shapes their enthusiasm and investment in chemistry learning. In educational psychology, the constructs of academic adaptability and academic buoyancy are commonly used to examine students’ responses to these challenges. Academic adaptability refers to students’ capacity to engage in adaptive adjustments when faced with novel or uncertain academic situations, encompassing both cognitive-behavioural and affective dimensions (Martin et al., 2012, 2013). The cognitive-behavioural component involves flexible cognitive and behavioural shifts in response to changing learning demands, whereas the affective component pertains to the regulation of emotional responses to better manage uncertainty and change. Academic buoyancy, in contrast, refers to students’ capacity to effectively manage routine academic adversities such as low grades, test anxiety, or challenging assignments, which are commonly encountered in school life (Martin and Marsh, 2008a, 2009). The key distinction between these two constructs lies in the types of challenges and coping goals they involve. Academic adaptability emphasizes adjustment to unfamiliar or dynamic situations, whereas academic buoyancy functions as a protective factor that helps students recover from typical academic setbacks (Martin et al., 2013).

Importantly, although the conceptual foundations of adaptability and buoyancy differ, both reflect how students respond to challenging academic environments. Empirical studies have consistently demonstrated that both constructs serve as significant positive predictors of academic engagement (Datu and Yang, 2018; Holliman et al., 2018; Huangfu et al., 2024). Together, they comprise a psychological resource system that supports active coping in the academic context through distinct mechanisms: adaptability enables students to adapt to novel academic demands and pedagogical approaches, while buoyancy helps sustain engagement by mitigating academic stress and discouraging emotional withdrawal (Martin and Marsh, 2008a; Martin et al., 2013).

Mindset has been identified as a critical psychological antecedent of both adaptability and buoyancy (Zarrinabadi et al., 2022; Chen et al., 2024). Yeager and Dweck (2012) proposed that individuals’ mindset fundamentally influences how they interpret challenges, failure, and effort. Students holding a growth mindset believe that ability is improvable through effort and strategies, which makes them more likely to demonstrate academic adaptability when facing novel or uncertain learning situations (Martin, 2013; Zarrinabadi et al., 2022). Simultaneously, they are more likely to perceive setbacks as surmountable and temporary, and to demonstrate stronger academic buoyancy when facing recurring challenges such as exam failure or increased workload (Burnette et al., 2013; Lakkavaara et al., 2024). A recent study by Chen et al. (2024) conducted within a Chinese educational context found that growth mindset significantly and positively predicted students’ cognitive-behavioural adaptability, affective adaptability, and academic buoyancy. These three factors further mediated the relationship between mindset and mathematics achievement. In light of these findings, it is plausible that within the domain of chemistry education, students’ chemistry growth mindset may influence both their chemistry adaptability and chemistry academic buoyancy, which may play key mediating roles in facilitating their chemistry academic engagement.

The potential mediating role of chemistry achievement emotions

According to the control-value theory and the broaden-and-build theory of positive emotions, achievement emotions serve as a critical psychological mechanism linking learners’ prior beliefs with their subsequent learning behaviours (Fredrickson, 2004; Pekrun, 2006). Within the context of chemistry education, students’ subjective appraisals of task controllability and value elicit specific achievement-related emotional responses, which in turn influence both the depth and persistence of their chemistry academic engagement. Traditional research in chemistry education has predominantly emphasized negative emotions, particularly anxiety. Chemistry anxiety is typically defined as the fear associated with chemistry content and instruction (Eddy, 2000). Numerous empirical pieces of evidence indicate that elevated anxiety levels can undermine academic functioning by diverting cognitive resources, impairing the use of effective learning strategies, and diminishing classroom focus and academic performance (Pekrun, 2000; Meinhardt and Pekrun, 2003; Pekrun, 2006). However, with the rise of positive psychology, researchers have increasingly turned their attention to the role of positive emotions such as enjoyment in promoting learning engagement (Boddey and de Berg, 2018; Gibbons et al., 2018; Pratt and Raker, 2020). Enjoyment tends to emerge when students perceive high task value and strong controllability (Pekrun, 2006). It broadens learners’ cognitive scope, encourages strategic exploration, and fosters sustained engagement through the accumulation of durable learning resources (Fredrickson, 2004). Recent empirical findings have further confirmed that enjoyment is positively associated with academic engagement, whereas anxiety is a significant negative predictor (Gong and Bergey, 2020; Ebn-Abbasi et al., 2024).

A growth mindset provides learners with a sense of perceived control and a developmental orientation toward learning, making it a key antecedent of achievement emotions. Compared to individuals with a fixed mindset, students with a growth mindset tend to view academic outcomes as improvable through effort and strategy, thus maintaining stronger perceptions of control and adopting mastery-oriented learning goals (Dweck et al., 1995; Yeager and Dweck, 2023; Naibert et al., 2024). This “high control–high value” appraisal pattern predicts greater enjoyment and lower anxiety (Karlen et al., 2021; Zarrinabadi et al., 2024). Taken together, these findings suggest that a growth mindset may enhance academic engagement by increasing enjoyment and reducing anxiety. This mediational pathway has been empirically supported in other academic domains. For example, Ebn-Abbasi et al. (2024) found that in online language learning environments, a growth mindset influenced students’ affective engagement through the mediating role of enjoyment, and impacted all dimensions of engagement through reduced anxiety. Similarly, Zhong et al. (2024) found that both positive and negative emotions partially mediated the relationship between growth language mindset and engagement among English language learners.

The relationships between (a) academic adaptability and buoyancy and (b) achievement emotions

Both academic buoyancy and academic adaptability serve pivotal functions in supporting students through setbacks and novel academic situations. These constructs help students manage academic adversity and foster perceived academic control, which in turn shape their achievement emotions (Pekrun et al., 2007; Yeager and Dweck, 2012; Martin, 2013). Han and Eerdemutu (2025) found that academic buoyancy fosters student engagement through increased positive and reduced negative emotions. Similarly, Zhang et al. (2021), in a longitudinal study of undergraduates, demonstrated that adaptability was a direct predictor of learning engagement and also exerted indirect effects via a sequential mediation involving both positive and negative learning emotions. Drawing on the theoretical and empirical evidence reviewed above, it is reasonable to hypothesize that the influence of a growth mindset on academic engagement is transmitted via multiple interconnected pathways. In addition to its indirect effect on academic engagement via emotions, mindset may also influence emotional experiences by promoting students’ academic adaptability and buoyancy, thereby shaping their engagement. These psychological resources shape students’ emotional responses, resulting in greater chemistry enjoyment and lower chemistry anxiety, and ultimately promoting their academic engagement in chemistry.

The present study

Based on the above literature review, a growth mindset has been shown to significantly influence students’ learning behaviours, emotional experiences, and academic outcomes. While comprehensive frameworks have mapped the motivational pathways linking mindset to engagement in diverse domains (Smiley et al., 2016; Williams et al., 2021), domain-specific investigations in chemistry remain sporadic and have not yet tested sequential mediation among adaptability, buoyancy and emotions. The reviewed theories and empirical evidence suggest a complex and interrelated structure among chemistry growth mindset, academic adaptability, academic buoyancy, and achievement emotions. Building on these findings, we propose a hypothetical model (see Fig. 1) to address the following research questions:

Fig. 1 The hypothesized multiple mediation model.

(1) Does students’ chemistry growth mindset positively predict their chemistry academic engagement?

(2) Do chemistry academic adaptability (including cognitive-behavioural and affective adaptability) and academic buoyancy serve as significant mediators between chemistry growth mindset and chemistry academic engagement?

(3) Do chemistry enjoyment and chemistry anxiety mediate the relationship between chemistry growth mindset and chemistry academic engagement?

(4) Do academic adaptability, academic buoyancy, and achievement emotions (enjoyment and anxiety) form a chained mediation pathway that significantly links chemistry growth mindset to chemistry academic engagement?

Methods

Participants

A total of 1049 high school students (597 males and 452 females) participated from four high schools in central China, where chemistry was selected as an elective subject for the national college entrance examination. Of the total sample, 523 students were in Grade 10 and 526 were in Grade 11. Students in Grade 12 were excluded from the study given their imminent participation in the national college entrance examination. All participants had completed six years of primary science education, which covered foundational chemistry content, and had received at least 1.5 years of formal chemistry instruction at the secondary level, thereby ensuring sufficient familiarity with the subject matter. The data were collected at the beginning of the first semester of 2025 over the course of one week. A paper-based questionnaire was administered to all participants. Upon completion, the responses were manually input into SPSS for subsequent statistical analysis. Ethical approval of this study was approved by the Academic Ethics Committee of Henan Normal University and relevant institutional authorities. Informed consent was obtained from the parents of all student participants. Prior to the survey, researchers clearly explained the purpose of the study and the intended use of the data. Participation was entirely voluntary, and no additional incentives were provided.

Instruments

To measure the variables, we employed previously established instruments and collected evidence supporting the reliability and validity of score interpretations in the present sample. As the original versions of the mindset scale, achievement emotion scale, and academic adaptability scale were developed in English, the items were translated into Chinese to ensure linguistic and contextual appropriateness for Chinese high school students. The translation process was carried out by three postgraduates with advanced English proficiency. Each translator independently translated the original English items into Chinese. Upon completion of the independent translations, the three translators met to compare discrepancies and resolve differences through consensus discussion, resulting in the finalized Chinese version of each questionnaire. Subsequently, seven 10th-grade students and seven 11th-grade students were invited to read the questionnaire items and verbalize their understanding of each item to ensure that the content was clearly understood.

Chemistry growth mindset. Chemistry growth mindset was assessed using the chemistry mindset instrument developed by Santos et al. (2022). The scale comprises seven items rated on a 10-point semantic differential scale (e.g., “My ability to apply chemistry knowledge is something that…”), ranging from 1 (cannot be changed at all) to 10 (can be changed a lot). Higher scores indicate a stronger orientation toward a Chemistry growth mindset.

Chemistry academic adaptability. Chemistry academic adaptability was measured using an adapted version of the adaptability scale developed by Martin et al. (2012). The original instrument includes nine items, with Items 1 to 6 measuring the cognitive-behavioural adaptability and Items 7 to 9 measuring the affective adaptability. A 5-point Likert scale was employed, with response options ranging from 1 (strongly disagree) to 5 (strongly agree), higher numbers representing greater degrees of adaptability. As the original scale did not specify subject context, all items were revised to reflect the context of chemistry learning. For example, the item “I am able to think through a number of possible options to assist me in a new situation” was modified to “I am able to think through a number of possible options to assist me in a new situation during chemistry learning.”

Chemistry academic buoyancy. Chemistry academic buoyancy was assessed using the revised Academic Buoyancy Scale, adapted by Huangfu et al. (2024) based on the original scale developed by Martin and Marsh (2008a, 2008b). The scale consists of four items (e.g., “I’m not going to let a bad grade affect my confidence”), rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), higher numbers representing greater degrees of chemistry academic buoyancy.

Chemistry achievement emotions. Chemistry achievement emotions were assessed using a short version of the Achievement Emotions Questionnaire developed by Bieleke et al. (2021). The original scale covers eight emotions: enjoyment, hope, pride, anger, anxiety, shame, helplessness, and boredom. In this study, only enjoyment and anxiety were selected for measurement, with four items each. A 5-point Likert scale was used, ranging from 1 (strongly disagree) to 5 (strongly agree). As the original instrument was not domain-specific, all items were adapted to reflect the chemistry learning context. For instance, “When my studies are going well, it gives me a rush.” was revised to “When my chemistry studies are going well, it gives me a rush.”

Chemistry academic engagement. Chemistry academic engagement was measured using a revised version of the Engagement Scale initially developed by Schaufeli et al. (2002) and subsequently adapted and translated for the chemistry education context by Shengqi Yuan (2024). The scale consisted of 15 items, such as “I can continue studying for very long periods at a time”, and used a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Higher numbers indicate greater degrees of chemistry academic engagement.

Statistical analysis

All statistical analyses were conducted using SPSS (version 27.0), Mplus (version 8.3), and R (version 4.3.3). First, descriptive statistics (e.g., means, standard deviations, skewness, and kurtosis) and Pearson correlation coefficients were calculated in SPSS for sample to examine the distributional characteristics and preliminary relationships among variables. Following the guidelines proposed by Curran et al. (1996), the data were considered approximately normally distributed if the absolute value of skewness was less than 2 and the absolute value of kurtosis was less than 7.

To validate the instruments, this study conducted confirmatory factor analysis (CFA) using Mplus (version 8.3) for all model fitting procedures. Given its robustness to potential violations of normality and heteroscedasticity commonly observed in educational data, maximum likelihood estimation with robust standard errors (MLR) was used to estimate model parameters (Kline, 2016). Model fit was considered acceptable if the comparative fit index (CFI) and Tucker–Lewis index (TLI) were ≥0.90, and the root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) were ≤0.08 (Hu and Bentler, 1999).

For reliability assessment, although Cronbach's alpha is still widely used in psychometric evaluations, it relies on stringent assumptions that may underestimate the true reliability of a scale (Peters, 2014). In contrast, McDonald's omega requires fewer assumptions and is considered more appropriate for evaluating complex measurement models and psychometric data. As such, it has been increasingly adopted as a more robust indicator of internal consistency (Komperda et al., 2018; McNeish, 2018). Accordingly, McDonald's omega coefficients were estimated using the coefficientalpha package (version 0.7.2) in R (version 4.3.3). An omega value above 0.70 was considered indicative of satisfactory internal consistency (Green and Yang, 2015).

Before testing the hypothesised model, a common method bias test using the unmeasured latent method construct approach was employed to assess potential common method bias (Podsakoff et al., 2003). A baseline model (M1) was specified to include four first-order latent variables. A second model (M2) was constructed by adding a method factor to the baseline model. Model fit indices of M1 and M2 were compared to detect the presence of common method bias. According to the criteria proposed by Wen et al. (2018), a substantial increase in the CFI and TLI (Δ > 0.10) along with a decrease in RMSEA and SRMR (Δ > 0.05) would indicate the presence of serious method bias.

Structural equation modeling was employed to test the hypothesized multiple mediation model, and analyses were conducted in Mplus (version 8.3) using maximum likelihood estimation. The model incorporated multiple mediating pathways linking chemistry growth mindset to chemistry academic engagement via four mediators: chemistry cognitive-behavioural adaptability, chemistry affective adaptability, chemistry academic buoyancy, and chemistry achievement emotions (i.e., chemistry enjoyment and chemistry anxiety). Latent factor scores were used to represent each construct in the SEM analysis (Hershberger, 2005; Taniguchi et al., 2020). Model fit was evaluated using the same indices as in the confirmatory factor analyses. To evaluate the significance and magnitude of indirect effects, a bias-corrected bootstrapping procedure with 10 000 resamples and 95% confidence intervals was implemented (Preacher and Hayes, 2008; Hayes, 2017). Compared to traditional methods such as the Sobel test, bootstrapping provides greater statistical power and more accurate interval estimates (Taylor et al., 2008). An indirect effect was considered statistically significant if the 95% CI did not include zero (Hayes, 2015). Additionally, the total indirect effect across all mediators was computed to capture the overall mediating influence of chemistry growth mindset on chemistry academic engagement. To facilitate the interpretation of the standardized path coefficients in the present study, general guidelines for effect size in education and second language research were adopted. According to Hair and Alamer (2022), standardized path coefficients (β) in the structural model can be interpreted as indicative of weak (0–0.10), modest (0.11–0.30), moderate (0.30–0.50), and strong (>0.50) effects. These thresholds provide a reference for evaluating the practical significance of the relationships among the study variables in this sample.

Suitability for measurement scales

Chemistry growth mindset. To evaluate the structural validity of the chemistry growth mindset scale, confirmatory factor analysis was conducted. The results demonstrated a satisfactory model fit: CFI = 0.976, TLI = 0.961, RMSEA = 0.076 (95% CI [0.061, 0.091]), and SRMR = 0.021. All items exhibited standardized factor loadings ranging from 0.793 to 0.889, which are well above the recommended threshold of 0.40. Furthermore, the chemistry growth mindset scale's omega coefficient was 0.962 (95% CI [0.956, 0.967]), indicating excellent reliability and high internal consistency.

Chemistry academic adaptability. To evaluate the structural validity of the chemistry academic adaptability scale, a CFA analysis was conducted, supporting the two-factor model with acceptable fit indices: CFI = 0.957, TLI = 0.938, RMSEA = 0.070 (95% CI [0.059, 0.080]), and SRMR = 0.041. All items exhibited standardized factor loadings ranging from 0.641 to 0.832. A unidimensional CFA for the cognitive-behavioural adaptability subscale indicated adequate model fit: CFI = 0.986, TLI = 0.971, RMSEA = 0.058 (95% CI [0.039, 0.080]), and SRMR = 0.022. All items exhibited standardized factor loadings ranging from 0.573 to 0.804. The omega coefficient for the cognitive-behavioural adaptability was 0.872 (95% CI [0.855, 0.889]). Due to the limited number of items (n = 3) in the affective adaptability, a unidimensional CFA was not conducted, and reliability was not estimated for this dimension (Naibert et al., 2024).

Chemistry academic buoyancy. The structural validity of the chemistry academic buoyancy scale was examined using CFA, which supported a unidimensional structure with a good model fit: CFI = 0.992, TLI = 0.977, RMSEA = 0.062 (95% CI [0.002, 0.121]), and SRMR = 0.016. All items exhibited standardized factor loadings ranging from 0.700 to 0.826. The scale exhibited good internal consistency, as indicated by a McDonald's omega coefficient of 0.864 (95% CI [0.847, 0.882]).

Chemistry achievement emotions. The initial two-factor CFA demonstrated inadequate fit: CFI = 0.812, TLI = 0.723, RMSEA = 0.162 (95% CI = [0.151, 0.174]), SRMR = 0.078. Inspection of the modification indices (MI) indicated substantial residual covariance between Item 3 (“I am so happy about the progress I made in chemistry that I am motivated to continue studying.”) and Item 4 (“When my chemistry studies are going well, it gives me a rush.”) on the Enjoyment factor (MI = 195.24). Because both items emphasise progress-induced excitement and motivation and employ highly similar phrasing, the large MI was interpreted as evidence of local item dependence attributable to content redundancy rather than to a misspecified latent structure (Cole, 1987). Accordingly, their residual covariance was freed in the model. After this adjustment, the model showed substantial improvement in fit indices: CFI = 0.980, TLI = 0.969, RMSEA = 0.055 (95% CI = [0.042, 0.068]), SRMR = 0.046. All remaining MIs were below 20, and no further modifications were undertaken. All items exhibited standardized factor loadings ranging from 0.497 to 0.892. Separate unidimensional CFA models were subsequently conducted for each subscale. The model fit for chemistry anxiety was satisfactory: CFI = 0.993, TLI = 0.979, RMSEA = 0.055 (95% CI [0.020, 0.096]), and SRMR = 0.014. All items exhibited standardized factor loadings ranging from 0.585 to 0.854. The model fit for chemistry enjoyment was excellent: CFI = 1.000, TLI = 1.002, RMSEA < 0.001 (95% CI [0.000, 0.112]), and SRMR = 0.002. All items exhibited standardized factor loadings ranging from 0.496 to 0.902. Internal consistency reliability, as estimated by McDonald's omega, was 0.846 (95% CI [0.827, 0.865]) for the enjoyment subscale and 0.835 (95% CI [0.815, 0.855]) for the anxiety subscale. Both values exceeded the conventional threshold of 0.70, indicating good reliability.

Chemistry academic engagement. To evaluate the structural validity of the Chemistry academic engagement scale, a CFA analysis was conducted. The results demonstrated a satisfactory model fit: CFI = 0.922, TLI = 0.908, RMSEA = 0.069 (95% CI [0.064, 0.075]), and SRMR = 0.044. All items exhibited standardized factor loadings ranging from 0.497 to 0.785. The internal consistency of the scale was high, as estimated by McDonald's omega = 0.937 (95% CI [0.929, 0.945]).

Results

To evaluate the potential impact of common method bias, the unmeasured latent method construct approach was employed (Podsakoff et al., 2003). The resulting changes in model fit indices were as follows: ΔCFI = 0.001, ΔTLI = 0.006, ΔRMSEA = 0.002, and ΔSRMR = 0.012. All changes were far below the cut-off values recommended by Wen et al. (2018), indicating that the inclusion of a method factor did not substantially improve model fit. Consequently, the common method bias was not a major concern in this study.

Descriptive statistics and correlational analysis

Means, standard deviations, and correlations of the variables are presented in Table 1. The skewness values ranged from −0.303 to 0.105, and kurtosis values ranged from −0.069 to 0.992, within acceptable thresholds for normal distribution (Curran et al., 1996). In addition, all six variables except chemistry anxiety were positively and significantly correlated with each other, while chemistry anxiety showed significant negative correlations with the remaining six variables.

Table 1 Descriptive statistics and correlation matrix for all variables (n = 1049)

	M	SD	Skewness	Kurtosis	1	2	3	4	5	6	7
Note: *p < 0.05. **p < 0.01. ***p < 0.001.
1 Chemistry growth mindset	5.948	1.751	0.004	−0.069	1
2 Chemistry cognitive-behavioural adaptability	3.195	0.655	0.026	0.725	0.616***	1
3 Chemistry affective adaptability	3.201	0.721	−0.006	0.591	0.499***	0.594***	1
4 Chemistry academic buoyancy	3.278	0.772	−0.031	0.255	0.500***	0.526***	0.670***	1
5 Chemistry enjoyment	3.506	0.743	−0.303	0.265	0.637***	0.695***	0.522***	0.484***	1
6 Chemistry anxiety	3.236	0.817	−0.232	0.089	−0.425***	−0.391***	−0.394***	−0.445***	−0.397***	1
7 Chemistry academic engagement	3.157	0.632	0.105	0.992	0.606***	0.723***	0.569***	0.550***	0.715***	−0.420***	1

---

Mediating model analysis

Based on the correlation results, a structural equation model was constructed using Mplus (version 8.3) to examine the mediating pathways linking chemistry growth mindset to chemistry academic engagement. The model demonstrated good fit to the data: CFI = 0.997, TLI = 0.982, RMSEA = 0.056 (95% CI [0.027, 0.089]), and SRMR = 0.013. Fig. 2 and Table 2 present the standardized path coefficients, which can be interpreted as indicators of effect size for the direct relationships. All direct paths were statistically significant, with the exception of the paths from cognitive-behavioural adaptability to chemistry anxiety, affective adaptability to chemistry anxiety, and academic buoyancy to chemistry enjoyment. As shown in Table 2, the standardized path coefficients have been classified as “weak”, “modest”, “moderate”, and “strong” effects based on Hair and Alamer (2022).

Fig. 2 The mediating model of chemistry mindset in predicting chemistry academic engagement. The dotted line indicates that the path coefficient is not significant. Note: *p < 0.05. **p < 0.01. ***p < 0.001.

Table 2 The direct effects test of the model

Path	Effect	SE	Bootstrap 95% CI		Effect size interpretation
			Lower	Upper
Note: SE = standard error, 95% CI = 95% confidence interval. According to Hair and Alamer (2022), standardized path coefficients (β) of 0–0.10, 0.11–0.30, 0.30–0.50, and >0.50 are interpreted as weak, modest, moderate, and strong effects, respectively, in education research.
Chemistry growth mindset → Chemistry cognitive-behavioural adaptability	0.616	0.024	0.566	0.662	Strong
Chemistry growth mindset → Chemistry affective adaptability	0.499	0.027	0.443	0.550	Moderate
Chemistry growth mindset → Chemistry academic buoyancy	0.500	0.026	0.447	0.549	Moderate
Chemistry growth mindset → Chemistry enjoyment	0.305	0.032	0.241	0.368	Moderate
Chemistry growth mindset → Chemistry anxiety	−0.213	0.043	−0.296	−0.131	Modest
Chemistry growth mindset → Chemistry academic engagement	0.089	0.033	0.026	0.157	Weak
Chemistry cognitive-behavioural adaptability → Chemistry enjoyment	0.434	0.039	0.358	0.509	Moderate
Chemistry cognitive-behavioural adaptability → Chemistry anxiety	−0.089	0.047	−0.182	0.004	—
Chemistry cognitive-behavioural adaptability → Chemistry academic engagement	0.325	0.034	0.256	0.391	Moderate
Chemistry affective adaptability → Chemistry enjoyment	0.077	0.033	0.012	0.142	Weak
Chemistry affective adaptability → Chemistry anxiety	0.072	0.044	−0.155	0.016	—
Chemistry affective adaptability → Chemistry academic engagement	0.070	0.033	0.006	0.135	Weak
Chemistry academic buoyancy → Chemistry enjoyment	0.051	0.033	−0.012	0.117	—
Chemistry academic buoyancy → Chemistry anxiety	−0.244	0.044	−0.330	−0.156	Modest
Chemistry academic buoyancy → Chemistry academic engagement	0.109	0.032	0.048	0.171	Modest
Chemistry enjoyment → Chemistry academic engagement	0.324	0.034	0.258	0.390	Moderate
Chemistry anxiety → Chemistry academic engagement	−0.062	0.027	−0.114	−0.009	Weak

---

Subsequently, a bias-corrected bootstrap procedure with 10 000 resamples and 95% confidence intervals was conducted to evaluate the significance of indirect effects (see Table 3). Chemistry growth mindset exhibited a significant direct effect on chemistry academic engagement (β = 0.088, 95% CI [0.026, 0.157]). The total effect was also significant (β = 0.597, 95% CI [0.498, 0.697]), with a total indirect effect of β = 0.508 (95% CI [0.433, 0.584]), accounting for 85.09% of the total effect. These results indicate that the influence of chemistry growth mindset on engagement was primarily mediated through indirect pathways.

Table 3 Mediating effect test

Path	Effect	SE	Bootstrap 95% CI		Proportion mediated (%)
			Lower	Upper
Note: SE = standard error, 95% CI = 95% confidence interval.
Indirect effects
Chemistry growth mindset → Chemistry cognitive-behavioural adaptability → Chemistry academic engagement	0.200	0.022	0.156	0.246	33.50
Chemistry growth mindset → Chemistry cognitive-behavioural adaptability → Chemistry enjoyment → Chemistry academic engagement	0.087	0.012	0.062	0.111	14.57
Chemistry growth mindset → Chemistry affective adaptability → Chemistry academic engagement	0.035	0.017	0.002	0.067	5.86
Chemistry growth mindset → Chemistry affective adaptability → Chemistry enjoyment → Chemistry academic engagement	0.012	0.006	0.002	0.023	2.01
Chemistry growth mindset → Chemistry academic buoyancy—Chemistry academic engagement	0.054	0.016	0.023	0.086	9.05
Chemistry growth mindset → Chemistry academic buoyancy → Chemistry anxiety → Chemistry academic engagement	0.008	0.004	0.001	0.015	1.34
Chemistry growth mindset → Chemistry enjoyment → Chemistry academic engagement	0.099	0.015	0.070	0.128	16.58
Chemistry growth mindset → Chemistry anxiety → Chemistry academic engagement	0.013	0.006	0.001	0.026	2.18
Total chemistry cognitive-behavioural adaptability	0.287	0.026	0.237	0.337	48.07
Total chemistry affective adaptability	0.047	0.017	0.013	0.082	7.87
Total chemistry academic buoyancy	0.062	0.017	0.030	0.095	10.39
Total indirect	0.508	0.039	0.433	0.584	85.09
Total effects (Chemistry growth mindset → Chemistry academic engagement)	0.597	0.051	0.498	0.697	—

---

A more granular analysis revealed several key indirect effects. Chemistry cognitive-behavioural adaptability played a substantial mediating role (β = 0.200, 95% CI [0.156, 0.246]), accounting for 33.50% of the total effect. In contrast, the indirect effect through chemistry affective adaptability was comparatively smaller (β = 0.035, 95% CI [0.002, 0.067]), contributing 5.86% of the total effect. Chemistry academic buoyancy also yielded a significant indirect effect (β = 0.054, 95% CI [0.023, 0.086]), explaining 9.05% of the total effect. Regarding achievement emotions, chemistry enjoyment significantly mediated the relationship between growth mindset and engagement (β = 0.099, 95% CI [0.070, 0.128]), accounting for 16.58% of the total effect. The indirect effect via chemistry anxiety was also significant but relatively small (β = 0.013, 95% CI [0.001, 0.026]), representing 2.18% of the total effect. Several chained mediation pathways were also identified. The path through chemistry cognitive-behavioural adaptability → chemistry enjoyment was significant (β = 0.087, 95% CI [0.062, 0.111]), accounting for 14.57% of the total effect. The total indirect effect of all cognitive-behavioural adaptability-related pathways was β = 0.287 (95% CI [0.237, 0.337]), representing 48.07% of the total effect. A chained pathway via affective adaptability → chemistry enjoyment also reached significance (β = 0.012, 95% CI [0.002, 0.023]), accounting for 2.01% of the total effect. The total indirect effect associated with chemistry affective adaptability was β = 0.047 (95% CI [0.013, 0.082]), representing 7.87% of the total effect. The total indirect effect associated with chemistry affective adaptability was β = 0.047 (95% CI [0.013, 0.082]), representing 7.87% of the total effect. Meanwhile, a chained pathway through chemistry academic buoyancy → chemistry anxiety → chemistry academic engagement was found to be significant (β = 0.008, 95% CI [0.001, 0.015]), contributing 1.34% to the total effect. The overall indirect effect of all chemistry academic buoyancy-related paths was β = 0.062 (95% CI [0.030, 0.095]), accounting for 10.39% of the total effect.

Discussion and conclusions

The relationship between chemistry growth mindset and chemistry academic engagement

The present study identified a significant and positive association of chemistry growth mindset on high school students’ chemistry academic engagement (β = 0.088). This finding aligns with the broader literature on mindset theory (Zeng et al., 2016; Wang et al., 2021; Lou et al., 2022). Students who regard chemistry ability as malleable through effort and strategic learning tend to participate more actively in chemistry tasks. Furthermore, this result provides empirical support for previous studies conducted in other domains such as mathematics and language, which have emphasized the domain-specific nature of mindset and its influence on student engagement (Sadoughi et al., 2023; Tormon et al., 2023; Zhong et al., 2024). By confirming the relevance of domain-specific mindset in chemistry, this study extends the applicability of mindset theory into the context of chemistry education. Notably, the direct effect of growth mindset on academic engagement was considerably smaller than the total indirect effect (β = 0.508), suggesting that the influence of growth mindset on students’ engagement may operate mainly through intermediate psychological processes.

The mediating roles of chemistry academic adaptability and chemistry academic buoyancy

The study found that chemistry academic adaptability and chemistry academic buoyancy statistically mediated the relationship between chemistry growth mindset and chemistry academic engagement. Consistent with prior empirical evidence, these findings underscore the central role of students’ adaptive capacity to navigate novel or uncertain learning contexts and their ability to rebound from everyday academic stressors in linking growth mindset to engagement (Chen et al., 2024; Suharsono and Fatimah, 2024). Notably, chemistry cognitive-behavioural adaptability emerged as the most robust mediator, with a single indirect effect of β = 0.200, accounting for 33.50% of the total effect. Affective adaptability and academic buoyancy likewise displayed significant indirect associations, although their contributions were comparatively smaller, at 5.86% and 9.05%, respectively.

Cognitive-behavioural adaptability may help students’ ability to flexibly adjust their learning strategies and action plans when confronted with complex, unfamiliar, or cognitively demanding chemistry tasks (Martin et al., 2012, 2013). In contrast, affective adaptability reflects students’ capacity to recognize, interpret, and regulate their emotional responses in challenging or uncertain situations. Students who endorse a growth mindset tend to show greater cognitive flexibility, are more open to trying alternative approaches, and possess stronger metacognitive skills for monitoring their learning progress (Martin et al., 2013; Zarrinabadi et al., 2022). Academic buoyancy, on the other hand, pertains specifically to students’ everyday resilience in the face of common academic adversities (Martin and Marsh, 2008a, 2009). Believing that chemistry ability can be improved through effort and strategic learning is associated with higher academic self-efficacy and perceived control, which may help students cope more effectively with challenges such as low test scores or increasing task demands (Dweck and Leggett, 1988; Yeager and Dweck, 2012). From the perspective of self-determination theory, both adaptability and buoyancy contribute to fulfilling students’ psychological needs for autonomy and competence, thereby fostering greater intrinsic motivation and academic engagement (Ryan and Deci, 2017).

Furthermore, the total indirect effect associated with cognitive-behavioural adaptability alone accounted for nearly one-third of the total effect, emphasizing its critical role in the context of high school chemistry, which requires abstract reasoning, procedural competence, and experimental application (Tsaparlis and Papaphotis, 2009; Martin et al., 2017). Although the effects of affective adaptability and academic buoyancy were comparatively smaller, they nonetheless contributed meaningfully to academic engagement by supporting emotional regulation and buffering anxiety. Given the frequent presence of evaluative pressure and experimental uncertainty in chemistry learning, the capacity for emotional regulation and academic resilience constitutes represents a crucial psychological resource to for sustaining engagement (Martin and Marsh, 2008a; Martin et al., 2017).

The mediating roles of chemistry achievement emotions

This study further revealed that chemistry enjoyment and anxiety statistically mediated the relationship between chemistry growth mindset and academic engagement. Students with a chemistry growth mindset were more likely to experience higher levels of enjoyment and lower levels of anxiety during chemistry learning, and these emotions were respectively associated with higher and lower academic engagement. These findings align with previous studies indicating the positive predictive value of enjoyment in Chinese chemistry classrooms (Gong and Bergey, 2020) and the detrimental role of anxiety in online learning environments (Ebn-Abbasi et al., 2024). Notably, the mediating effect of chemistry enjoyment was substantially stronger than that of chemistry anxiety, suggesting that the broaden-and-build function of positive emotions may play a more prominent influence on sustaining and enhancing student engagement in the context of chemistry education (Fredrickson, 2004).

According to the control-value theory, achievement emotions are proposed to be shaped by their subjective perceptions of task value and control over learning outcomes (Pekrun et al., 2006). Students who endorse a chemistry growth mindset are more likely to interpret academic challenges as opportunities for growth, attach greater value to chemistry learning tasks, and perceive themselves as capable of influencing learning outcomes through effort and strategy (Dweck, 2006). These appraisals tend to elicit positive emotional experiences while mitigating negative ones (Gong and Bergey, 2020). Enjoyment has been shown to expand attentional focus, enhance cognitive flexibility, and motivate exploratory learning behaviours, thereby sustaining engagement across academic tasks (Fredrickson, 2001, 2004). Empirically, Reschly et al. (2008) demonstrated that positive achievement emotions promote academic engagement indirectly by facilitating effective coping strategies, lending support to the broaden-and-build theory in educational settings. In contrast, anxiety impairs engagement by narrowing attention, depleting cognitive resources, and reducing the effectiveness of cognitive strategies. High levels of anxiety may also increase avoidance behaviours, particularly when students anticipate failure or uncertainty (Pekrun, 2000; Meinhardt and Pekrun, 2003). According to Gross's (2015) Process Model of Emotion Regulation, a growth mindset may function as a positive cognitive appraisal framework that encourages adaptive regulation strategies such as cognitive reappraisal, rather than maladaptive strategies like suppression or avoidance, particularly in response to academic threats (e.g., examinations, failure).

The chain mediating effect of (a) academic adaptability and buoyancy and (b) achievement emotions

An important fruit of our study is that this study identified a significant chained mediation pathway involving academic adaptability (including cognitive-behavioural and affective components) and academic buoyancy, which influenced chemistry academic engagement through achievement emotions (enjoyment and anxiety). These findings extend prior work concerning the dynamic interplay among motivational beliefs, psychological resources, and emotional processes in learning (Zhang et al., 2021; Han and Eerdemutu, 2025). The results indicate that academic adaptability, academic buoyancy, and achievement emotions not only function as independent mediators but also operate collectively through chained pathways to account for the observed the relationship between growth mindset and academic engagement. Students with a chemistry growth mindset are more likely to develop internal motivation and a strong sense of self-efficacy, which facilitates their use of proactive and flexible strategies, adaptive emotion regulation, and the ability to rebound from daily academic setbacks (Dweck, 2006; Martin and Marsh, 2008a; Martin et al., 2013). These psychological resources may help students cope with academic challenges both directly and indirectly by shaping their emotional experiences, thereby supporting sustained classroom engagement over time (Zhang et al., 2021; Han and Eerdemutu, 2025).

Interestingly, this study did not observe significant negative association from either cognitive-behavioural or affective adaptability to chemistry anxiety. This finding diverges from some prior research and may be explained by the distinction that adaptability primarily aids in managing novel or changing academic contexts, while anxiety often arises when students perceive tasks as excessively difficult or the outcomes to be beyond their control (Eddy, 2000; Pekrun et al., 2006). Furthermore, anxiety in high school chemistry may stem from contextual factors such as examination systems, sociocultural expectations, or perfectionistic tendencies—factors that adaptability alone may not sufficiently address (Arana and Furlan, 2016; Landsman et al., 2023).

Similarly, academic buoyancy did not significantly association chemistry enjoyment. One possible explanation may be that buoyancy is more closely associated with buffering the adverse effects of failure rather than directly fostering positive emotions (Martin and Marsh, 2005, 2008a). According to the control-value theory, enjoyment tends to arise in “high-value–high-control” contexts and is closely tied to students’ perceived competence and performance outcomes (Pekrun, 2006). In contrast, academic buoyancy places greater emphasis on persistence than on success. As a result, students may endure challenges rather than thrive in them, which may reduce the likelihood of experiencing spontaneous enjoyment.

In conclusion, the multiple mediation model suggests that the influence of chemistry growth mindset on academic engagement is predominantly exerted through indirect pathways, with cognitive-behavioural adaptability emerging as the strongest mediator. The combined roles of adaptability, buoyancy, and achievement emotions point to a dynamic, multifaceted psychological pattern through which growth mindset may support sustained engagement in demanding science-learning environments.

Implication for practice

The findings of this study offer practical implications for chemistry educators aiming to enhance students’ academic engagement. Our findings pointed out that chemistry growth mindset possessed a beneficial influence on students’ chemistry academic engagement. Thus, fostering students’ chemistry growth mindset represents a viable approach to enhancing their chemistry academic engagement. Teachers should actively foster students' chemistry growth mindset by emphasizing that chemistry intelligence and abilities can be developed through effort and effective strategies. To be specific, it is advised that teachers should prioritize students' perspectives, acknowledge their diligent efforts in chemistry, and emphasize process-oriented learning, thereby fostering a belief that overcoming difficult tasks is more valuable than easily succeeding at simple ones (Zarrinabadi et al., 2022).

Furthermore, our findings revealed the crucial mediating roles of chemistry cognitive-behavioural adaptability and affective adaptability in the relationship between growth mindset and academic engagement. This implies that nurturing these skills contributes to translating a student's growth mindset into active engagement. For instance, teachers can explicitly teach students how to adapt to novel or difficult problems by reframing setbacks, adjusting study strategies, or managing unexpected outcomes (Zimmerman, 2008; Martin et al., 2013). To bolster academic buoyancy, educators can help students build confidence and coordination in handling everyday academic pressures, such as by providing structured support for complex assignments and teaching effective time-management and goal-setting skills (Martin and Marsh, 2009).

Finally, the mediating roles of chemistry enjoyment and anxiety highlighted in our study suggest that teachers should create classroom environments that enhance positive emotional experiences and reduce negative emotions in chemistry learning. To increase enjoyment and reduce anxiety, chemistry instruction could adopt inquiry-based lab activities, incorporate cooperative learning tasks, and apply formative assessment practices (Ural, 2016). Teachers’ proactive emotional support and sensitivity towards students’ affective states can help build a psychologically safe classroom atmosphere, thus promoting sustained academic engagement in chemistry (Black and Deci, 2000).

Limitations and future directions

Although this study constructed and validated a multiple mediation model in which chemistry growth mindset influenced students’ chemistry academic engagement. However, several limitations remain that warrant further attention in future research.

The sample was drawn exclusively from high school students in central China, an educational context shaped by a high-stakes examination culture and strong societal expectations. These contextual factors may have intensified the observed relationships between mindset, psychological resources, and academic engagement. Moreover, the associations tested in the model may be moderated by cultural values. To evaluate the generalizability and contextual robustness of the findings, future research should include more diverse samples across regions and cultural settings. Besides, the study employed a cross-sectional design based on a single-wave survey. While this design allows for the examination of indirect pathways, it limits the ability to draw causal inferences. Longitudinal research tracking students’ psychological profiles over multiple academic periods is recommended to capture the developmental dynamics of the proposed model. Moreover, the data were obtained primarily through self-report questionnaires, which may introduce social desirability and self-reporting biases. To enhance the validity and credibility of the findings, future research should draw on multiple data sources such as teacher assessments, classroom observations, and records of learning behaviours. In addition, due to the chemistry affective adaptability subscale consisting of only three items, unidimensional confirmatory factor analysis could not be conducted, precluding the assessment of its reliability. Therefore, interpretations involving this subscale should be made with caution.

Notably, this study found that neither chemistry cognitive-behavioural adaptability nor chemistry affective adaptability significantly predicted levels of chemistry anxiety. Likewise, chemistry academic buoyancy did not significantly predict chemistry enjoyment. These findings challenge key assumptions within control-value theory and positive psychology, which assume that psychological resources facilitate emotional regulation. Such results suggest the need to further refine current understandings of the underlying mechanisms of psychological variables. These insights provide useful directions for future research. For instance, future studies could examine potential moderating variables such as students’ learning motivation, goal orientation, or academic self-efficacy to explore the boundary conditions and underlying mechanisms that may account for the observed non-significant pathways. Such investigations would offer a more nuanced and comprehensive understanding of how psychological resources interact with emotional processes in shaping students’ engagement in chemistry learning.

Conflicts of interest

There are no conflicts to declare.

Data availability

Due to ethical concerns and privacy issues, the raw data supporting the conclusions of this manuscript will not be made publicly available. The data contain sensitive information that could compromise the privacy of research participants. However, the anonymized data that underpin the main findings of this study are available from the first author, Yurong Liu, upon reasonable request. Researchers wishing to access the data will be required to sign a data access agreement that stipulates how the data can be used and ensures that the confidentiality of the participants is maintained. For further details on the specifics of the data and conditions for access, please contact Yurong Liu (E-mail: liuyur66@163.com).

Acknowledgements

This work was sponsored by the Henan Province Basic Education Teacher Development Research Innovation Team Project: ‘Relying on “U-G-S-T-S” Learning Community to Promote Professional Development of Chemistry Teachers (2022,02)’.

References

1. Arana F. G. and Furlan L., (2016), Groups of perfectionists, test anxiety, and pre-exam coping in Argentine students, Pers. Individ. Differ., 90, 169–173 DOI:10.1016/j.paid.2015.11.001.
2. Beymer P. N., Schell M. J., Alberts K. M., Phun V., Rosenberg J. M. and Schmidt J. A., (2025), Students’ situational engagement profiles in formal and informal science learning environments, J. Res. Sci. Teach., 62(6), 1522–1545 DOI:10.1002/tea.22017.
3. Bieleke M., Gogol K., Goetz T., Daniels L. and Pekrun R., (2021), The AEQ-S: A short version of the Achievement Emotions Questionnaire, Contemp. Educ. Psychol., 65, 101940 DOI:10.1016/j.cedpsych.2020.101940.
4. Black A. E. and Deci E. L., (2000), The effects of instructors’ autonomy support and students’ autonomous motivation on learning organic chemistry: a self-determination theory perspective, Sci. Educ., 84(6), 740–756 DOI:10.1002/1098-237X(200011)84:6[double bond splayed right]740::AID-SCE4[double bond splayed left]3.0.CO;2-3.
5. Boddey K. and de Berg K., (2018), A framework for understanding student nurses’ experience of chemistry as part of a health science course, Chem. Educ. Res. Pract., 19(2), 597–616 10.1039/C7RP00217C.
6. Burnette J. L., O’Boyle E. H., VanEpps E. M., Pollack J. M. and Finkel E. J., (2013), Mind-sets matter: a meta-analytic review of implicit theories and self-regulation, Psychol. Bull., 139(3), 655–701 DOI:10.1037/a0029531.
7. Chen M., Mok I. A. C., Cao Y., Wijaya T. T. and Ning Y., (2024), Effect of Growth Mindset on Mathematics Achievement Among Chinese Junior High School Students: The Mediating Roles of Academic Buoyancy and Adaptability, Behav. Sci., 14(12), 1134 DOI:10.3390/bs14121134.
8. Cole D. A., (1987), Utility of confirmatory factor analysis in test validation research, J. Consul. Clin. Psychol., 55(4), 584–594 DOI:10.1037/0022-006X.55.4.584.
9. Curran P. J., West S. G. and Finch J. F., (1996), The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis, Psychol. Methods, 1(1), 16–29 DOI:10.1037/1082-989X.1.1.16.
10. Dai T. and Cromley J. G., (2014), Changes in implicit theories of ability in biology and dropout from STEM majors: a latent growth curve approach, Contemp. Educ. Psychol., 39(3), 233–247 DOI:10.1016/j.cedpsych.2014.06.003.
11. Datu J. A. D. and Yang W., (2018), Psychometric Validity and Gender Invariance of the Academic Buoyancy Scale in the Philippines: A Construct Validation Approach, J. Psychoeduc. Assess., 36(3), 278–283 DOI:10.1177/0734282916674423.
12. Doron J., Stephan Y., Boiché J. and Scanff C. L., (2009), Coping with examinations: Exploring relationships between students’ coping strategies, implicit theories of ability, and perceived control, Brit. J. Educ. Psychol., 79(3), 515–528 DOI:10.1348/978185409X402580.
13. Dweck C. S., (2006), Mindset: The New Psychology of Success, Ballantine Books Trade Pbk. Ed. edition, Ballantine Books.
14. Dweck C. S., Chiu C. and Hong Y., (1995), Implicit Theories and Their Role in Judgments and Reactions: A Word From Two Perspectives, Psychol. Inq., 6(4), 267–285 DOI:10.1207/s15327965pli0604_1.
15. Dweck C. S. and Leggett E. L., (1988), A social-cognitive approach to motivation and personality, Psychol. Rev., 95(2), 256–273 DOI:10.1037/0033-295X.95.2.256.
16. Dweck C. S. and Yeager D. S., (2019), Mindsets: A View From Two Eras, Perspect. Psychol. Sci., 14(3), 481–496 DOI:10.1177/1745691618804166.
17. Ebn-Abbasi F., Fattahi N., Sayyahi M. J. and Nushi M., (2024), Language learners’ mindset and their academic engagement in online classrooms: the mediating role of achievement emotions, Asia Pac. Educ. Rev., 25(1), 73–85 DOI:10.1007/s12564-023-09901-w.
18. Eddy R. M., (2000), Chemophobia in the College Classroom: Extent, Sources, and Student Characteristics, J. Chem. Educ., 77(4), 514 DOI:10.1021/ed077p514.
19. Fink A., Cahill M. J., McDaniel M. A., Hoffman A. and Frey R. F., (2018), Improving general chemistry performance through a growth mindset intervention: selective effects on underrepresented minorities, Chem. Educ. Res. Pract., 19(3), 783–806 10.1039/C7RP00244K.
20. Fredricks J. A., Blumenfeld P. C. and Paris A. H., (2004), School Engagement: Potential of the Concept, State of the Evidence, Rev. Educ. Res., 74(1), 59–109 DOI:10.3102/00346543074001059.
21. Fredrickson B. L., (2001), The role of positive emotions in positive psychology: the broaden-and-build theory of positive emotions, Am. Psychol., 56(3), 218–226 DOI:10.1037/0003-066X.56.3.218.
22. Fredrickson B. L., (2004), The broaden–and–build theory of positive emotions, Philos. Trans. R. Soc., B, 359(1449), 1367–1377 DOI:10.1098/rstb.2004.1512.
23. Gibbons R. E., Xu X., Villafañe S. M. and Raker J. R., (2018), Testing a reciprocal causation model between anxiety, enjoyment and academic performance in postsecondary organic chemistry, Educ. Psychol., 38(6), 838–856 DOI:10.1080/01443410.2018.1447649.
24. Gong X. and Bergey B. W., (2020), The dimensions and functions of students’ achievement emotions in Chinese chemistry classrooms, Int. J. Sci. Educ., 42(5), 835–856 DOI:10.1080/09500693.2020.1734684.
25. Grabau L. J., Ma X., Grabau L. J., Ma X., Grabau L. J. and Ma X., (2017), Science engagement and science achievement in the context of science instruction: a multilevel analysis of U.S. students and schools, Int. J. Sci. Educ., 39(8), 1045–1068 DOI:10.1080/09500693.2017.1313468.
26. Green S. B. and Yang Y., (2015), Evaluation of Dimensionality in the Assessment of Internal Consistency Reliability: Coefficient Alpha and Omega Coefficients, Educ. Meas., 34(4), 14–20 DOI:10.1111/emip.12100.
27. Gross J. J., (2015), Emotion Regulation: Current Status and Future Prospects, Psychol. Inq., 26(1), 1–26 DOI:10.1080/1047840X.2014.940781.
28. Guzey S. S. and Li W., (2023), Engagement and Science Achievement in the Context of Integrated STEM Education: A Longitudinal Study, J. Sci. Educ. Technol., 32(2), 168–180 DOI:10.1007/s10956-022-10023-y.
29. Hair J. and Alamer A., (2022), Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example, Res. Methods Appl. Linguist., 1(3), 100027 DOI:10.1016/j.rmal.2022.100027.
30. Han S. and Eerdemutu L., (2025), Buoyancy and Achievement in Japanese Language Learning: The Serial Mediation of Emotions and Engagement, Asia Pac. Educ. Res., 34(4), 1483–1493 DOI:10.1007/s40299-024-00959-7.
31. Hayes A. F., (2015), An Index and Test of Linear Moderated Mediation, Multivariate Behav. Res., 50(1), 1–22 DOI:10.1080/00273171.2014.962683.
32. Hayes A. F., (2017), Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, 2nd edn, Guilford Publications.
33. Hershberger S. L., (2005), Factor Score Estimation, Encyclopedia of Statistics in Behavioral Science, John Wiley & Sons, Ltd, pp. 636–644 DOI:10.1002/0470013192.bsa726.
34. Holliman A. J., Martin A. J. and Collie R. J., (2018), Adaptability, engagement, and degree completion: a longitudinal investigation of university students, Educ. Psychol., 38(6), 785–799 DOI:10.1080/01443410.2018.1426835.
35. Hu L. and Bentler P. M., (1999), Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Modeling, 6(1), 1–55 DOI:10.1080/10705519909540118.
36. Huangfu Q., Huang W., He Q., Luo S. and Chen Q., (2024), The relationship between self-handicapping in chemistry and chemistry academic engagement: a moderated mediation model investigation, Chem. Educ. Res. Pract., 25(3), 920–933 10.1039/D3RP00332A.
37. Hussain S., Shah A. A., Syeda Z. F. and Sarwar M., (2018), Self-Assessment of Students’ Anxiety during High Stake Laboratory Work Examinations, J. Educ. Educ. Dev., 5(2), 146–161.
38. Johnstone A. H., (1991), Why is science difficult to learn? Things are seldom what they seem, Comput. Assist. Learn., 7(2), 75–83 DOI:10.1111/j.1365-2729.1991.tb00230.x.
39. Joy A., Mathews C. J., Zhao M., Law F., McGuire L. and Hoffman A. J., et al., (2023), Interest, Mindsets and Engagement: Longitudinal Relations in Science Orientations for Adolescents in Informal Science Programs, J. Youth Adolesc., 52(5), 1088–1099 DOI:10.1007/s10964-023-01734-5.
40. Karlen Y., Hirt C. N., Liska A. and Stebner F., (2021), Mindsets and Self-Concepts About Self-Regulated Learning: Their Relationships With Emotions, Strategy Knowledge, and Academic Achievement, Front. Psychol., 12, 661142 DOI:10.3389/fpsyg.2021.661142.
41. Kline R. B., (2016), Principles and practice of structural equation modeling, 4th edn, Guilford Press.
42. Komperda R., Pentecost T. C. and Barbera J., (2018), Moving beyond Alpha: A Primer on Alternative Sources of Single-Administration Reliability Evidence for Quantitative Chemistry Education Research, J. Chem. Educ., 95(9), 1477–1491 DOI:10.1021/acs.jchemed.8b00220.
43. Lakkavaara A., Upadyaya K., Tang X. and Salmela-Aro K., (2024), The role of stress mindset and academic buoyancy in school burnout in middle adolescence, Eur. J. Dev. Psychol., 21(6), 847–864 DOI:10.1080/17405629.2024.2382398.
44. Landsman M., Escamilla G. and Matyas J., (2023), Test anxiety and perfectionism, J. Stud. Res., 12(3), 1–13 DOI:10.47611/jsrhs.v12i3.4839.
45. Lou N. M., Chaffee K. E. and Noels K. A., (2022), Growth, Fixed, And Mixed Mindsets: Mindset System Profiles In Foreign Language Learners And Their Role In Engagement And Achievement, Stud. Second Lang. Acquis., 44(3), 607–632 DOI:10.1017/S0272263121000401.
46. Lou N. M., Masuda T. and Li L. M. W., (2017), Decremental mindsets and prevention-focused motivation: an extended framework of implicit theories of intelligence, Learn. Individual Differences, 59, 96–106 DOI:10.1016/j.lindif.2017.08.007.
47. Macnamara B. N. and Burgoyne A. P., (2023), Do growth mindset interventions impact students’ academic achievement? A systematic review and meta-analysis with recommendations for best practices, Psychol. Bull., 149(3–4), 133–173 DOI:10.1037/bul0000352.
48. Mangels J. A., Butterfield B., Lamb J., Good C., Dweck C. S. and Mangels J. A., et al., (2006), Why do beliefs about intelligence influence learning success? A social cognitive neuroscience model, Soc. Cogn. Affect Neurosci., 1(2), 75–86 DOI:10.1093/scan/nsl013.
49. Martin A. J., (2013), Academic buoyancy and academic resilience: Exploring ‘everyday’ and ‘classic’ resilience in the face of academic adversity, Sch. Psychol. Int., 34(5), 488–500 DOI:10.1177/0143034312472759.
50. Martin A. and Marsh H., (2005), Motivating Boys and Motivating Girls: Does Teacher Gender Really Make a Difference? Aust. J. Educ., 49(3), 320–334 DOI:10.1177/000494410504900308.
51. Martin A. J. and Marsh H. W., (2008a), Academic buoyancy: Towards an understanding of students’ everyday academic resilience, J. Sch. Psychol., 46(1), 53–83 DOI:10.1016/j.jsp.2007.01.002.
52. Martin A. J. and Marsh H. W., (2008b), Workplace and Academic Buoyancy, J. Psychoeduc. Assess., 26(2), 168–184 DOI:10.1177/0734282907313767.
53. Martin A. J. and Marsh H. W., (2009), Academic resilience and academic buoyancy: multidimensional and hierarchical conceptual framing of causes, correlates and cognate constructs, Oxf. Rev. Educ., 35(3), 353–370 DOI:10.1080/03054980902934639.
54. Martin A. J., Nejad H., Colmar S. and Liem G. A. D., (2012), Adaptability: Conceptual and Empirical Perspectives on Responses to Change, Novelty and Uncertainty, Aust. J. Guid. Couns., 22(1), 58–81 DOI:10.1017/jgc.2012.8.
55. Martin A. J., Nejad H. G., Colmar S. and Liem G. A. D., (2013), Adaptability: How students’ responses to uncertainty and novelty predict their academic and non-academic outcomes, J. Educ. Psychol., 105(3), 728–746 DOI:10.1037/a0032794.
56. Martin A. J., Yu K., Ginns P., Papworth B., Martin A. J. and Yu K., et al., (2017), Young people's academic buoyancy and adaptability: a cross-cultural comparison of China with North America and the United Kingdom, Educ. Psychol., 37(8), 930–946 DOI:10.1080/01443410.2016.1202904.
57. McNeish D., (2018), Thanks coefficient alpha, we’ll take it from here, Psychol. Methods, 23(3), 412–433 DOI:10.1037/met0000144.
58. Meinhardt J. and Pekrun R., (2003), Attentional resource allocation to emotional events: an ERP study, Cogn. Emotion, 17(3), 477–500 DOI:10.1080/02699930244000039.
59. Milenković D. D., Segedinac M. D., Hrin T. N., Cvjetićanin S. M., Milenković D. D. and Segedinac M. D., et al., (2014), Cognitive Load at Different Levels of Chemistry Representations/Kognitivno opterećenje na različitim razinama kemijskih prikaza, Croatian J. Educ., 16(3), 699–722 DOI:10.15516/cje.v16i3.528.
60. Miltiadous A., Callahan D. L. and Schultz M., (2020), Exploring Engagement as a Predictor of Success in the Transition to Online Learning in First Year Chemistry, J. Chem. Educ., 97(9), 2494–2501 DOI:10.1021/acs.jchemed.0c00794.
61. Mueller C. M. and Dweck C. S., (1998), Praise for intelligence can undermine children's motivation and performance, J. Personal. Soc. Psychol., 75(1), 33–52 DOI:10.1037/0022-3514.75.1.33.
62. Naibert N., Mooring S. R. and Barbera J., (2024), Investigating the Relations between Students’ Chemistry Mindset, Self-Efficacy, and Goal Orientation in General and Organic Chemistry Lecture Courses, J. Chem. Educ., 101(2), 270–282 DOI:10.1021/acs.jchemed.3c00929.
63. Pekrun R., (2000), A Social-Cognitive, Control-Value Theory of Achievement Emotions, Adv. Psychol., 131, 143–163, https://linkinghub.elsevier.com/retrieve/pii/S0166411500800102.
64. Pekrun R., (2006), The Control-Value Theory of Achievement Emotions: Assumptions, Corollaries, and Implications for Educational Research and Practice, Educ. Psychol. Rev., 18(4), 315–341 DOI:10.1007/s10648-006-9029-9.
65. Pekrun R., Elliot A. J. and Maier M. A., (2006), Achievement goals and discrete achievement emotions: a theoretical model and prospective test, J. Educ. Psychol., 98(3), 583–597 DOI:10.1037/0022-0663.98.3.583.
66. Pekrun R., Frenzel A. C., Goetz T. and Perry R. P., (2007), The Control-Value Theory of Achievement Emotions: An Integrative Approach to Emotions in Education, in Emotion in Education, Schutz P. A. and Pekrun R. (ed.), Educational Psychology, ch. 2 Academic Press, pp. 13–36.
67. Peters G.-J. Y., (2014), The alpha and the omega of scale reliability and validity: why and how to abandon Cronbach's alpha and the route towards more comprehensive assessment of scale quality, Eur. Health Psychol., 16(2), 56–69 DOI:10.31234/osf.io/h47fv.
68. Podsakoff P. M., MacKenzie S. B., Lee J.-Y. and Podsakoff N. P., (2003), Common method biases in behavioral research: a critical review of the literature and recommended remedies, J. Appl. Psychol., 88(5), 879–903 DOI:10.1037/0021-9010.88.5.879.
69. Pratt J. M. and Raker J. R., (2020), Exploring student affective experiences in inorganic chemistry courses: Understanding student anxiety and enjoyment, Advances in Teaching Inorganic Chemistry: Classroom Innovations and Faculty Development, ACS Symposium Series, vol. 1, American Chemical Society, pp. 117–129 DOI:10.1021/bk-2020-1370.ch010.
70. Preacher K. J. and Hayes A. F., (2008), Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models, Behav. Res. Methods, 40(3), 879–891 DOI:10.3758/BRM.40.3.879.
71. Reschly A. L., Huebner E. S., Appleton J. J., Antaramian S., Reschly A. L. and Huebner E. S., et al., (2008), Engagement as flourishing: the contribution of positive emotions and coping to adolescents’ engagement at school and with learning, Psychol. Sch., 45(5), 419–431 DOI:10.1002/pits.20306.
72. Robins R. W., Pals J. L., Robins R. W. and Pals J. L., (2002), Implicit Self-Theories in the Academic Domain: Implications for Goal Orientation, Attributions, Affect, and Self-Esteem Change, Self Identity, 1(4), 313–336 DOI:10.1080/15298860290106805.
73. Ross J., Guerra E. and Gonzalez-Ramos S., (2020), Linking a hierarchy of attitude effect to student engagement and chemistry achievement, Chem. Educ. Res. Pract., 21(1), 357–370 10.1039/C9RP00171A.
74. Ryan R. M. and Deci E. L., (2017), Self-determination theory: Basic psychological needs in motivation, development, and wellness, The Guilford Press DOI:10.1521/978.14625/28806.
75. Sadoughi M., Hejazi S. Y. and Lou N. M., (2023), How do growth mindsets contribute to academic engagement in L2 classes? The mediating and moderating roles of the L2 motivational self system, Soc. Psychol. Educ., 26(1), 241–261 DOI:10.1007/s11218-022-09744-0.
76. Santos V. C. and Arroio A., (2016), The representational levels: Influences and contributions to research in chemical education, J. Turk. Sci. Ed., 13(1), 3–18 DOI:10.12973/tused.10153a.
77. Santos D. L., Barbera J. and Mooring S. R., (2022), Development of the Chemistry Mindset Instrument (CheMI) for use with introductory undergraduate chemistry students, Chem. Educ. Res. Pract., 23(3), 742–757 10.1039/D2RP00102K.
78. Santos D. L., Gallo H., Barbera J. and Mooring S. R., (2021), Student perspectives on chemistry intelligence and their implications for measuring chemistry-specific mindset, Chem. Educ. Res. Pract., 22(4), 905–922 10.1039/D1RP00092F.
79. Santos D. L. and Mooring S. R., (2022), Characterizing Mindset-Related Challenges in Undergraduate Chemistry Courses, J. Chem. Educ., 99(8), 2853–2863 DOI:10.1021/acs.jchemed.2c00270.
80. Schaufeli W. B., Salanova M., González-romá V. and Bakker A. B., (2002), The Measurement of Engagement and Burnout: A Two Sample Confirmatory Factor Analytic Approach, J. Happiness Stud., 3(1), 71–92 DOI:10.1023/A:1015630930326.
81. Scott M. J. and Ghinea G., (2014), On the Domain-Specificity of Mindsets: The Relationship Between Aptitude Beliefs and Programming Practice, IEEE Trans. Educ., 57(3), 169–174 DOI:10.1109/TE.2013.2288700.
82. Shengqi Y., (2024), The Relationship Between Chemistry Self-concept and Chemistry Learning Engagement During High School students, Southwest University, CNKI DOI:10.27684/d.cnki.gxndx.2024.002473.
83. Smiley P. A., Buttitta K. V., Chung S. Y., Dubon V. X. and Chang L. K., (2016), Mediation models of implicit theories and achievement goals predict planning and withdrawal after failure, Motiv. Emot., 40(6), 878–894 DOI:10.1007/s11031-016-9575-5.
84. Suharsono Y. and Fatimah S., (2024), Growth Mindset in Higher Education: Exploring Academic Buoyancy's Mediating Effect on Students’ Academic Engagement and Psychological Well-being, KnE Soc. Sci., 9(5), 375–390 DOI:10.18502/kss.v9i5.15189.
85. Talanquer V., (2011), Macro, Submicro, and Symbolic: the many faces of the chemistry “triplet”, Int. J. Sci. Educ., 33(2), 179–195 DOI:10.1080/09500690903386435.
86. Taniguchi E., Dailey R. M., Taniguchi E., Dailey R. M., Taniguchi E. and Dailey R. M., (2020), Parental Confirmation and Emerging Adult Children's Body Image: Self-Concept and Social Competence as Mediators, Commun. Res., 47(3), 373–401 DOI:10.1177/0093650218777575.
87. Taylor A. B., MacKinnon D. P. and Tein J.-Y., (2008), Tests of the Three-Path Mediated Effect, Org. Res. Methods, 11(2), 241–269 DOI:10.1177/1094428107300344.
88. Tormon R., Lindsay B. L., Paul R. M., Boyce M. A. and Johnston K., (2023), Predicting academic performance in first-year engineering students: the role of stress, resiliency, student engagement, and growth mindset, Learn. Individ. Differ., 108, 102383 DOI:10.1016/j.lindif.2023.102383.
89. Tsaparlis G. and Papaphotis G., (2009), High-school Students’ Conceptual Difficulties and Attempts at Conceptual Change: the case of basic quantum chemical concepts, Int. J. Sci. Educ., 31(7), 895–930https://www.tandfonline.com/doi/full/10.1080/09500690801891908.
90. Ural E., (2016), The effect of guided-inquiry laboratory experiments on science education students’ chemistry laboratory attitudes, anxiety and achievement, J. Educ. Train. Stud., 4(4), 217–227.
91. Wang M., Zepeda C. D., Qin X., Del Toro J. and Binning K. R., (2021), More Than Growth Mindset: Individual and Interactive Links Among Socioeconomically Disadvantaged Adolescents’ Ability Mindsets, Metacognitive Skills, and Math Engagement, Child Dev., 92(5), e957–e976 DOI:10.1111/cdev.13560.
92. Wen Z., Huang B. and Tang D., (2018), Preliminary Work for Modeling Questionnaire Data, Psychol. Sci., 41(1), 204–210 DOI:10.16719/j.cnki.1671-6981.20180130.
93. Wichaidit P. R., (2025), Understanding growth mindset and chemistry mindsets of high-achieving students and the impact of influential language on learning motivation, Chem. Educ. Res. Pract., 26(2), 420–444 10.1039/D4RP00218K.
94. Williams C. L., Hirschi Q., Hulleman C. S. and Roksa J., (2021), Belonging in STEM: Growth Mindset as a Filter of Contextual Cues, Int. J. Com. WB, 4(4), 467–503 DOI:10.1007/s42413-021-00111-z.
95. Yan V. X., Thai K.-P. and Bjork R. A., (2014), Habits and beliefs that guide self-regulated learning: Do they vary with mindset? J. Appl. Res. Memory Cogn., 3(3), 140–152 DOI:10.1037/h0101799.
96. Yeager D. S. and Dweck C. S., (2012), Mindsets That Promote Resilience: When Students Believe That Personal Characteristics Can Be Developed, Educ. Psychol., 47(4), 302–314 DOI:10.1080/00461520.2012.722805.
97. Yeager D. S. and Dweck C. S., (2023), Mindsets and adolescent mental health, Nat. Mental Health, 1(2), 79–81 DOI:10.1038/s44220-022-00009-5.
98. Yu J. and McLellan R., (2020), Same mindset, different goals and motivational frameworks: profiles of mindset-based meaning systems, Contemp. Educ. Psychol., 62, 101901 DOI:10.1016/j.cedpsych.2020.101901.
99. Zarrinabadi N., Arandian P. and Yaghoubinejad H., (2024), Investigating the mediating role of positive and negative beliefs about competition and competitive orientation in the relationship between language mindsets and emotions, System, 125, 103395 DOI:10.1016/j.system.2024.103395.
100. Zarrinabadi N., Rezazadeh M., Karimi M. and Lou N. M., (2022), Why do growth mindsets make you feel better about learning and your selves? The mediating role of adaptability, Innov. Lang. Learn. Teach., 16(3), 249–264 DOI:10.1080/17501229.2021.1962888.
101. Zeng G., Hou H. and Peng K., (2016), Effect of growth mindset on school engagement and psychological well-being of Chinese primary and middle school students: the mediating role of resilience, Front. Psychol., 7, 01873 DOI:10.3389/fpsyg.2016.01873.
102. Zhang K., Wu S., Xu Y., Cao W., Goetz T. and Parks-Stamm E. J., (2021), Adaptability Promotes Student Engagement Under COVID-19: The Multiple Mediating Effects of Academic Emotion, Front. Psychol., 11, 633265 DOI:10.3389/fpsyg.2020.633265.
103. Zhong S., Wang Y. and Wu W., (2024), Exploring the Mediating Role of Emotions Between Growth Language Mindset and Engagement Among EFL Learners, Asia Pac. Educ. Res., 33(5), 1037–1049 DOI:10.1007/s40299-023-00771-9.
104. Zimmerman B. J., (2008), Investigating Self-Regulation and Motivation: Historical Background, Methodological Developments, and Future Prospects, Am. Educ. Res. J., 45(1), 166–183 DOI:10.3102/0002831207312909.

---