How Classroom Climate Is Associated With Prosocial Behavior in College Students: A Multilevel Moderated Mediation Model of Feedback Expectation and Self-Monitoring

How Classroom Climate Is Associated With Prosocial Behavior in College Students: A Multilevel Moderated Mediation Model of Feedback Expectation and Self-Monitoring | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article How Classroom Climate Is Associated With Prosocial Behavior in College Students: A Multilevel Moderated Mediation Model of Feedback Expectation and Self-Monitoring Senlin Zhou, Liping Qin, Jue Deng, Jia Zhe, Daokui Jiang, Simin Long, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7078875/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Dec, 2025 Read the published version in BMC Psychology → Version 1 posted 10 You are reading this latest preprint version Abstract Prosocial behavior, such as helping, sharing, and cooperating, is essential for young adults’ social and emotional development. While prior research emphasizes individual traits, less is known about how classroom-level contexts interact with personality to shape prosocial tendencies in college settings. This study examined whether a supportive classroom climate is associated with higher prosocial behavior among college students, with prosocial feedback expectations as a mediator and self-monitoring as a moderator. Participants were 888 undergraduates (mean age = 21.3) nested in 36 classrooms across seven Chinese universities. A multilevel moderated mediation model was tested using hierarchical linear modeling, controlling for demographic variables. Results showed that a supportive classroom climate was positively associated with prosocial behavior. This direct association was stronger among students high in self-monitoring. In addition, prosocial feedback expectations mediated the climate–behavior link, and this indirect effect was moderated by self-monitoring: it was stronger for low self-monitors, who were more reliant on expected positive feedback to guide their behavior. These findings suggest that both classroom environments and personality traits shape prosocial development. Interventions in higher education may be more effective if they simultaneously foster positive social climates and consider individual differences in students’ responsiveness to social cues. Classroom climate Prosocial behavior Prosocial feedback expectations Self-monitoring College students Personality traits Moderated mediation Hierarchical linear modeling Figures Figure 1 Figure 2 Figure 3 1. Introduction Prosocial behavior—voluntary actions intended to benefit others such as helping, sharing, and cooperating—constitutes a cornerstone of social adaptation and moral development (Eisenberg & Spinrad, 2014 ). Beyond its moral value, prosocial behavior has been linked to a range of positive developmental outcomes. Meta-analytic and longitudinal research shows that it predicts better peer relationships (Layous et al., 2012 ), higher academic achievement (Caprara et al., 2000 ), lower risk of psychological problems (Flouri & Sarmadi, 2016 ), and greater life satisfaction (Aknin et al., 2018 ). Neuroimaging studies further indicate that engaging in helping behaviors activates brain regions associated with reward and valuation (Martela & Ryan, 2016 ), reinforcing the importance of understanding how prosocial behavior develops. While much research has emphasized individual traits such as empathy and moral reasoning (Carlo et al., 2012 ), increasing attention has turned to the influence of contextual factors. In particular, classroom environments are recognized as critical developmental settings, consistent with ecological systems theory (Bronfenbrenner, 1979 ), which highlights the role of proximal environments in shaping behavior. As students spend substantial time within classroom settings, these environments function as key microsystems influencing behavioral norms (Moos, 1979 ). Among classroom-level variables, classroom climate—defined as students’ shared perceptions of emotional support, cooperation, and behavioral norm clarity—has emerged as a salient factor predicting prosocial engagement (Fraser, 1998 ; Wang et al., 2020 ). Empirical studies demonstrate that supportive classroom climates foster helping behaviors across different age groups (Barr & Higgins-D’Alessandro, 2007, 2009), with classroom interventions enhancing spontaneous prosocial acts by nearly 50% (Wentzel et al., 2017 ). Neuroimaging evidence supports this, showing that perceived support increases activation in the ventromedial prefrontal cortex during prosocial decision-making (Reyes et al., 2012 ; Telzer et al., 2018 ). These findings align with social cognitive theory (Bandura, 1986 ), which emphasizes the role of environmental modeling and reinforcement in learning social behaviors. They are also consistent with normative social influence theory (Cialdini & Goldstein, 2004 ), which posits that individuals conform to perceived group norms in order to gain social approval (Fabes et al. 2006 ). A central mechanism in this process is prosocial feedback expectations—individuals’ beliefs that helping behaviors will elicit social rewards such as approval, inclusion, or reciprocal support (Wentzel, 2009 ; Hui et al., 2020 ). These expectations are shaped by repeated exposure to social feedback patterns and reflect cumulative observational learning (Bandura, 1986 ). According to expectancy-value theory (Eccles & Wigfield, 2002 ), behavior is more likely when individuals both anticipate positive outcomes and value them. Empirical studies confirm that even experimentally induced feedback expectations can enhance helping behavior independent of dispositional empathy (Grant & Gino, 2010 ), and reinforcement learning studies show how repeated social rewards strengthen prosocial tendencies over time (Schultz, 2015 ). However, this pathway may not function uniformly across individuals. Self-monitoring, defined as the tendency to regulate behavior in response to social cues (Snyder, 1974 ), may moderate the effects of classroom climate. High self-monitors are particularly sensitive to social norms and adapt their behavior flexibly to fit situational demands (Oh & Kilduff, 2008 ; Snyder, 1987 ). For these individuals, prosocial behavior may serve as a strategy for social alignment, even without explicit feedback (Flynn et al., 2006 ). In contrast, low self-monitors rely more on internal value systems and need clear reinforcement to sustain prosocial behavior (Grant & Gino, 2010 ). Neurobiological evidence supports this distinction: high self-monitors show enhanced activation in social-cognitive areas (e.g., medial prefrontal cortex, temporoparietal junction) when processing implicit social cues (Falk et al., 2015 ). Building upon this theoretical and empirical foundation, the current study proposes a multilevel moderated mediation model to examine how supportive classroom climate influences prosocial behavior in college students. We hypothesize that this relationship is mediated by prosocial feedback expectations and moderated by self-monitoring. This integrative framework captures the interplay of environmental (classroom climate), cognitive-motivational (feedback expectations), and dispositional (self-monitoring) factors. Specifically, we test the following hypotheses: H1: Classroom climate positively predicts students' prosocial behavior. H2: Prosocial feedback expectations mediate the relationship between classroom climate and prosocial behavior. H3: Self-monitoring moderates the direct effect of classroom climate on prosocial behavior, such that the effect is stronger among individuals high in self-monitoring. H4: The indirect effect of classroom climate on prosocial behavior via feedback expectations is moderated by self-monitoring, with a stronger mediation effect for low self-monitors than for high self-monitors. By synthesizing environmental, motivational, and personality factors, this study is expected to offer a more comprehensive framework for understanding prosocial development in higher education. It may also yield practical insights for the design of targeted interventions, suggesting that climate-focused strategies could be particularly beneficial for students with lower self-monitoring tendencies, whereas norm-sensitive students may respond more effectively to subtle contextual cues embedded in classroom environments. 2. Methods 2.1. Ethics approval This study received ethical approval from the Ethics Committee of Fujian Police Collage and was conducted in accordance with the Declaration of Helsinki. Prior to participation, all individuals were informed about the purpose and procedures of the study, assured of confidentiality and anonymity, and informed that their participation was voluntary with the right to withdraw at any time. Electronic informed consent was obtained from all participants before data collection. 2.2. Participants A cluster random sampling method was used to recruit undergraduate students from seven public universities in China. A total of 917 students were invited, and 888 valid responses were obtained (response rate = 96.83%). The final sample comprised 343 males and 545 females, with a mean age of 20.02 years (SD = 3.13), nested within 36 intact classrooms. After obtaining approval from both the institutions and participants, research assistants distributed web-based questionnaires via Wenjuanxing ( www.wjx.cn ). The survey took approximately 10 minutes to complete. Participation was voluntary, and confidentiality was emphasized. 2.3. Measures 2.3.1. Classroom Climate In the present study, classroom climate was assessed using a revised version of the What Is Happening In this Class? (WIHIC) questionnaire (Fraser et al., 1996 ; Skordi & Fraser, 2019 ). This study selected three representative subscales domains—student cohesiveness relationship(mutual assistance and friendship among students). Teacher support relationship (teachers' care and support for students). Cooperation personal development (the level of collaboration among students in learning tasks) —based on their theoretical relevance to prosocial behavior and established psychometric validity in prior research(Moos, 1974 ; Moos & Trickett, 1987). The revised WIHIC used in the present study consisted of 12 items in total, with 4 items per subscale. The scale demonstrated good internal consistency in the current sample, with a Cronbach’s alpha of 0.77 for the total score. Confirmatory factor analysis supported the proposed three-factor structure (CFI = 0.95, TLI = 0.93, RMSEA = 0.07, SRMR = 0.05), consistent with findings from the original validation studies. A composite score was calculated by averaging all items, with higher scores reflecting more positive perceptions of classroom climate. 2.3.2. Prosocial Feedback Expectations Anticipated reactions to prosocial behavior were measured using the Anticipated Reactions to Prosocial Behavior Scale (Grant & Mayer, 2009 ). This 6-item scale assesses individuals' expectations of social responses following prosocial acts, including anticipated approval, respect, and trust (e.g., "Helping others will make people think I am trustworthy"). Responses were collected on a 5-point Likert scale (1 = "Strongly disagree" to 5 = "Strongly agree"), with higher composite scores indicating more positive expectations. The scale showed good reliability in our sample (Cronbach's alpha = 0.75). Confirmed Factor Analysis supporting its construct validity. Confirmed Factor Analysis supporting its construct validity (CFI = 0.99, TLI = 0.98, RMSEA = 0.05, SRMR = 0.02). 2.3.3. Self-Monitoring Self-monitoring was assessed using the Brief Self-Monitoring Scale (Gangestad & Snyder, 2000 ), an adapted 8-item version of Snyder's (1974) original measure. This concise instrument maintains the theoretical construct while improving practicality, assessing: (1) social appropriateness monitoring, (2) attention to social comparison information, and (3) self-presentation modification ability (sample item: "I would probably make a good actor"). Responses used a 5-point Likert scale (1 = "Strongly disagree" to 5 = "Strongly agree"), with higher scores indicating stronger self-monitoring tendencies. The scale showed good reliability (Cronbach's alpha = 0.78) and confirmed unidimensional structure (CFI = 0.95, TLI = 0.91, RMSEA = 0.09, SRMR = 0.05) in our sample. 2.3.4. Prosocial Behavior The Prosocial Behavior Questionnaire for Adolescents (Eisenberg et al., 2006 ) was administered to assess frequency of prosocial actions. This measure includes 5 items describing specific prosocial behaviors across multiple domains (helping, sharing, cooperating, etc.). Participants rated their typical behavior frequency on a 5-point Likert scale (1 = "Never" to 5 = "Always"), with higher scores indicating greater prosocial engagement. In our sample, the measure demonstrated strong internal consistency (Cronbach's alpha = 0.89). Confirmatory factor analysis supported the hypothesized multidimensional structure (CFI = 0.99, TLI = 0.98, RMSEA = 0.06, SRMR = 0.01). 2.3.5. Control Variables In the subsequent data analysis, this study controlled for individual characteristics such as age and gender. 2.4. Data analysis Data analysis was conducted using R software (version 4.3.0; R Core Team, 2024) with the bruceR package (Bao, 2024 ) to accommodate the nested structure of the data. Our analytical approach proceeded through the following steps: First, we examined data distribution properties and missing patterns. Little's MCAR test was conducted to determine if data were missing completely at random (Little, 1988 ), which would inform our subsequent handling of missing values. Second, intraclass correlation coefficients (ICC1) were calculated to quantify between-classroom variance, providing empirical justification for employing multilevel modeling techniques (Shrout & Fleiss, 1979 ). Third, to establish discriminant validity among constructs, we conducted Confirmatory Factor Analysis (CFA) on all measurement items to examine the factor structure at both individual and classroom levels. Fourth, we assessed common method bias using Harman's single-factor test (Podsakoff et al., 2003 ). Fifth, we incorporated age and gender as control variables at their appropriate analytical levels to account for their potential influence on our hypothesized relationships. Sixth, we specified a “2-1-1” multilevel moderated mediation model to test our hypothesized relationships (Krull & MacKinnon, 2001 ). In this framework, the independent variable (classroom climate) was assessed at the classroom level (Level 2), while the mediator (prosocial feedback expectations), moderator (self-monitoring), and outcome (prosocial behavior) were measured at the student level (Level 1). Finally, analyses were executed using the PROCESS function in the bruceR package with maximum likelihood estimation incorporating robust standard errors. Mediation effects were evaluated using quasi-Bayesian Markov Chain Monte Carlo(MCMC) sampling with 5,000 resamples and 95% confidence intervals. An indirect effect was deemed statistically significant when the confidence interval excluded zero. This modeling approach, depicted in Fig. 1, allowed simultaneous estimation of inter-classroom level (level 2) and inter-individual level (level 1) effects, aligning variables with their theoretical and measurement levels. 3. Results 3.1. Preliminary Analyses Little's MCAR test indicated no evidence that data were not missing completely at random ( χ ² = 12.34, p = .135). Therefore, missing data were handled using full information maximum likelihood (FIML) estimation (Schafer & Graham, 2002 ). Descriptive statistics and Pearson correlations among the main study variables are presented in Table 1 . Classroom climate, prosocial feedback expectations, and prosocial behavior were all positively correlated ( p s < .01), indicating initial support for the hypothesized relationships. Table 1 Descriptive statistics of the variables Variables M SD 1 2 3 4 5 1 Age 20.02 3.13 — 2 Gender — — .12*** — 3 Classroom climate 3.89 0.61 –.15*** –.19*** — 4 Prosocial feedback expectation 3.58 0.50 –.10** –.05 .59*** — 5 Self monitoring 3.37 0.36 .03 .05 .13*** .12*** — 6 Prosocial behavior 4.07 0.55 –.07* –.08* .60*** .43*** .32*** Note. * p < .05. *** p < .001. gender (0 = female, 1 = male). 3.2. Intraclass Correlation Coefficients To determine the appropriateness of multilevel modeling, we examined statistical indicators of within-group reliability and agreement for classroom climate measures. The ICC(1) was 0.136, indicating that 13.6% of the variance in classroom climate perceptions was attributable to differences between classrooms, thereby justifying the use of multilevel modeling (James, 1982 ). The ICC(2) value was 0.730, suggesting acceptable reliability of classroom-level mean scores. Additionally, the median rwg(j) score was 0.82, exceeding the commonly accepted threshold of 0.70 (Bliese, 2000 ), and indicating strong within-group agreement. Together, these indices supported the aggregation of classroom climate to the group level for subsequent analyses. 3.3 Confirmatory Factor Analysis To evaluate the discriminant validity of the study's core constructs, we tested the hypothesized four-factor model against three alternative models using Confirmatory Factor Analysis (CFA): Model 1 (Four-factor model): The baseline model specified classroom climate, prosocial feedback expectations, self-monitoring, and prosocial behavior as four distinct latent variables. Model 2 (Three-factor model): This model combined prosocial feedback expectations and prosocial behavior into one latent factor, while classroom climate and self-monitoring remained separate constructs. Model 3 (Two-factor model): This model combined classroom climate, prosocial feedback expectations, and prosocial behavior into a single factor, with self-monitoring as a distinct construct. Model 4 (One-factor model): All items from the four constructs loaded onto a single latent factor. The results should that the four-factor model yielded the best fit to the data ( χ ²/ df = 3.25, CFI = 0.93, TLI = 0 .91, RMSEA = 0.06, SRMR = 0.056), providing evidence for the discriminant validity of the four constructs. 3.4 Common Method Bias Assessment To assess potential common method bias due to the self-report nature of the data, Harman's single-factor test was conducted. An exploratory factor analysis of all items from the main study variables revealed that the first unrotated factor accounted for 27.4% of the total variance, which is below the commonly used threshold of 40% (Podsakoff et al., 2003 ). This result suggests that common method variance is unlikely to be a serious concern in the present study. 3.5 Multilevel Moderated Mediation Analysis The analysis revealed that classroom climate significantly predicted prosocial feedback expectations ( b = 0.314, SE = 0.09, p < .01), supporting the hypothesized cross-level effect. The interaction between classroom climate and self-monitoring on prosocial behavior was also significant ( b = 0.304, SE = 0 .163, p < .01), indicating that students with higher self-monitoring tendencies were more responsive to classroom climate in forming prosocial behavior. Simple slope analysis showed that the effect of classroom climate on prosocial behavior was stronger at + 1 SD of self-monitoring ( b = 0.401, p < .001) than at − 1 SD ( b = 0.035, p = .73) (see Fig. 2). Furthermore, prosocial feedback expectations significantly predicted prosocial behavior ( b = 0.407, SE = .032, p < .01), while the direct effect of classroom climate on prosocial behavior remained significant ( b = 0.218, SE = .079, p < .01). We conducted a conditional process analysis to examine the indirect effect of classroom climate (X) on prosocial behavior (Y) through prosocial feedback expectations (M) at different levels of self-monitoring (moderator). The results indicated that the indirect effect was significant at all three levels of self-monitoring(interaction coefficient = -0.412, SE = 0.072): low (-1 SD, effect = 0.175, SE = 0.049, p < .001, 95% MCMC CI [0.082, 0.272]), mean (effect = 0.128, SE = 0.036, p < .001, 95% MCMC CI [0.060, 0.201]), and high (+ 1 SD, effect = 0.081, SE = 0.026, p = .002, 95% MCMC CI [0.036, 0.136]). Importantly, the strength of the indirect effect decreased as the level of self-monitoring increased, suggesting that self-monitoring moderates the mediation pathway such that the effect of classroom climate on prosocial behavior through prosocial feedback expectations is stronger for individuals with lower self-monitoring (see Fig. 3). Age and gender were included as control variables at their appropriate analytical levels. All parameter estimates are presented in Table 2 . Table 2 Multilevel Model Estimates for the Direct, Mediating, and Moderating Effects on Prosocial Behavior (1) Prosocial Behavior (2) Prosocial Feedback Expectations (3) Prosocial Behavior b SE b SE b SE Fixed effect Intercept 4.063*** 0.02 3.572*** 0.021 4.078*** 0.019 Gender -0.019 0.042 0.035 0.038 -0.035 0.036 Age -0.009 0.006 -0.014* 0.005 -0.005 0.005 Classroom Climate 0.290*** 0.082 0.314*** 0.09 0.218*** 0.079 Self-monitoring 0.172*** 0.045 0.497*** 0.045 Prosocial Feedback Expectations 0.407*** 0.032 Classroom Climate×Self-monitoring 0.304** 0.163 Self-monitoring×Prosocial Feedback Expectations -0.412*** 0.072 Random effect Var: class (Intercept) 0.002 0.006 0.003 Var: Residual 0.298 0.231 0.214 Marginal R2 0.025 0.045 0.301 Conditional R2 0.031 0.07 0.312 Note. ** p < .01, *** p < .001; gender (0 = female, 1 = male); N = 888 students nested in 36 classrooms; Regression coefficients are unstandardized regression coefficients; Marginal R² reflects the proportion of variance in the dependent variable explained by the fixed effects (independent variables). Conditional R² reflects the proportion of variance in the dependent variable explained by both the fixed effects and random effects together. 4. Discussion This study employed multilevel mediation analysis to examine the influence of classroom climate, a contextual factor at the classroom level, on students' prosocial behavior both directly and indirectly via the individual-level mechanism of prosocial feedback expectations. Furthermore, it investigated how students' self-monitoring tendencies moderate both the direct and indirect pathways. The findings demonstrate that classroom climate not only directly promotes students' prosocial behavior but also indirectly enhances it through strengthening their expectations of receiving prosocial feedback. Importantly, self-monitoring tendencies emerged as a critical moderator. By integrating environmental and dispositional factors within a multilevel modeling framework, this research offers a comprehensive perspective on the interplay between environmental and dispositional factors in shaping prosocial development in educational contexts. 4.1. Classroom Climate and Prosocial Behavior Consistent with our hypotheses, the results showed that classroom-level perceptions of a supportive climate positively predicted student-level prosocial behavior. These findings extend prior research by Wentzel et al. ( 2017 ), who reported associations between classroom support and prosocial goals, by identifying a specific psychological mechanism underlying this link. Building upon existing correlational studies linking classroom climate to student outcomes (Wang et al., 2020 ), our study advances this literature by elucidating the multilevel processes that account for these effects. Through multilevel mediation analysis, we found that approximately 13% of the variance in prosocial behavior could be attributed to classroom-level differences, highlighting the substantial contextual influence of classroom environments. This provides empirical support for Bronfenbrenner’s bioecological model (Bronfenbrenner, 1979 ), which emphasizes how proximal microsystems such as classrooms create distinctive developmental contexts shaping individual behavior through ongoing social interactions and feedback processes. From a practical perspective, these findings imply that fostering a supportive prosocial classroom climate benefits not only collective behaviors but also influences students’ internalized social motivations. This insight extends previous universal social-emotional learning approaches (Durlak et al., 2022 ) by emphasizing the importance of highlighting the positive social consequences of prosocial actions in intervention design. Teacher training programs might be enhanced by incorporating explicit strategies to create classroom environments that visibly recognize and reinforce prosocial behaviors, moving beyond generic classroom management techniques (Emmer & Sabornie, 2015 ) toward establishing clear and transparent social contingencies that encourage helping, sharing, and cooperation. 4.2. The Mediating Role of Prosocial Feedback Expectations The present findings indicate that the relationship between classroom climate and prosocial behavior is partially mediated by students’prosocial feedback expectations. Specifically, the supportive classroom environment appears to indirectly influence prosocial behaviors by shaping students’anticipations of positive social responses—such as approval or acceptance—following prosocial acts. This pattern aligns with a classic “2-1-1” multilevel mediation framework (Preacher et al., 2010 ), wherein a macro-level contextual factor (classroom climate) affects an individual-level psychological mechanism (feedback expectations), which in turn influences individual behavior (prosocial actions). Our findings significantly extend existing literature by demonstrating how classroom climate influences prosocial behavior through both direct and indirect psychological pathways. While prior research has primarily examined prosocial behavior as either a function of dispositional traits (Eisenberg et al., 2015) or direct environmental effects (Barr & Higgins-D'Alessandro, 2007 ), we reveal a more complex, interactive process. The partial mediation through prosocial feedback expectations suggests this cognitive mechanism operates alongside other potential pathways (e.g., moral identity formation, Hardy et al., 2015 ; normative conformity, McDonald & Crandall, 2015 ), highlighting the multiply determined nature of prosocial development. Notably, our results advance beyond studies of direct teacher influences (Mikami et al., 2012 ) by identifying an indirect social-cognitive pathway: classroom environments shape behavior by modifying students' anticipated social consequences of helping. This mediation effect provides empirical support for theoretical models emphasizing environment-individual interactions (Bandura, 2002 ), particularly bridging motivational psychology and social behavior research. By showing how environmental factors become internalized as motivational drivers through anticipated social contingencies, we extend social cognitive theory and underscore the centrality of expectancy constructs in understanding prosocial development. Moreover, our results challenge the traditional dichotomy that treats classroom climate as an organizational-level construct separate from prosocial behavior as an individual-level trait. The observed cross-level mediation underscores the interconnectedness of these constructs via social-cognitive processes, aligning with calls for integrated, multilevel approaches to understanding educational diversity and inclusion effects (Schachner et al., 2016 ). Such perspectives encourage researchers and practitioners to consider how contextual and individual factors reciprocally shape prosocial development within complex educational ecosystems. From an applied perspective, these findings suggest that schools and educators could enhance prosocial development by systematically assessing both classroom climate and students’ social-motivational orientations. Such assessments may serve as valuable early indicators of prosocial trajectories, potentially more sensitive than behavioral measures alone. The mediating role of feedback expectations also points to promising intervention targets: fostering positive and consistent social feedback within classrooms could precede and predict increases in prosocial behavior, thus amplifying intervention efficacy. 4.3. The Moderating Role of Self-Monitoring This study found that self-monitoring significantly moderated both the direct and indirect pathways linking classroom climate to prosocial behavior, albeit in distinct and contrasting ways. First, self-monitoring amplified the direct positive effect of classroom climate on prosocial behavior. Specifically, students with higher self-monitoring tendencies exhibited a stronger association between a supportive classroom climate and their prosocial actions. This finding aligns with Snyder’s ( 1974 ) conceptualization of self-monitoring as an individual difference reflecting heightened sensitivity to social cues and contextual demands. High self-monitors are more inclined to adjust their behavior to conform with situational norms, thereby displaying greater responsiveness to the prosocial culture nurtured within a positive classroom environment. This result supports the person × environment interaction framework (Magnusson & Stattin, 1996 ), underscoring that identical environmental conditions may produce divergent behavioral outcomes contingent on individual traits. Conversely, the indirect effect of classroom climate on prosocial behavior through prosocial feedback expectations was attenuated for students high in self-monitoring. This suggests that while high self-monitors exhibit prosocial behavior through direct responsiveness to observable social norms, they may depend less on internalized motivational expectations concerning social feedback. In other words, their prosocial actions likely arise from immediate adaptation to external social cues rather than being mediated by anticipated social reinforcement. By contrast, students with lower self-monitoring tendencies—who are generally less sensitive to situational social signals—may rely more heavily on consciously held beliefs about the social consequences of their behavior (e.g., expectations of praise or acceptance) to motivate prosocial engagement. This pattern elucidates a nuanced differential susceptibility mechanism (Belsky & Pluess, 2009 ), whereby the same environmental context—the classroom climate—exerts influence through distinct psychological pathways depending on students’ self-monitoring levels. High self-monitors tend to engage in prosocial behaviors as a form of contextually driven behavioral adaptation, whereas low self-monitors appear to require more explicit motivational cues grounded in anticipated social feedback. These findings contribute to the understanding of individual differences in social responsiveness and highlight the importance of tailoring educational interventions to accommodate varying dispositional profiles. For instance, climate-based strategies may be particularly effective for norm-sensitive, high self-monitoring students, while interventions emphasizing explicit reinforcement and feedback may better support those lower in self-monitoring. 4.4. Practical Implications The findings of this study offer several practical implications for educators, school administrators, and policymakers aiming to promote prosocial behavior in educational settings. First, schools should regularly and systematically assess classroom climate alongside students’ social-motivational orientations, such as prosocial feedback expectations. Such assessments can help identify early trajectories of prosocial development and provide a scientific basis and timely opportunities for targeted interventions. Second, intervention programs should focus on fostering a positive social feedback environment that explicitly reinforces prosocial behaviors. Emphasizing the positive social consequences of helping others can effectively activate students’ intrinsic prosocial motivation and promote sustained behavioral engagement. Third, teacher training should address individual differences and classroom dynamics by equipping educators to recognize and respond to students with varying levels of self-monitoring (Gest et al., 2014 ). Teachers need to develop strategies for establishing supportive and transparent social reinforcement mechanisms to more effectively encourage prosocial behavior across diverse student groups. Finally, educational policies and institutions should support differentiated and context-sensitive social-emotional learning practices by providing appropriate resources and guidance. Promoting inclusive classroom climates that integrate environmental and individual factors can enhance the overall efficacy and sustainability of prosocial development initiatives. 4.5. Limitations and Future Directions Despite the strengths of a multilevel design, several limitations should be acknowledged. First, the cross-sectional nature of the data restricts our ability to draw causal inferences. Future longitudinal or experimental studies are needed to clarify the temporal sequence of effects and to determine whether changes in classroom climate lead to subsequent shifts in prosocial feedback expectations and prosocial behavior over time. Second, although multilevel modeling appropriately addresses data clustering, all variables were measured through self-reports, which may introduce common method bias. To enhance the validity of findings, future research should incorporate multiple data sources, such as behavioral observations, sociometric nominations, or teacher evaluations, to triangulate prosocial outcomes. Additionally, reliance on students’ perceived classroom climate raises the question of whether objective classroom characteristics or subjective perceptions primarily drive the observed effects—a distinction emphasized by Marsh et al. ( 2012 ) that future work should explicitly examine. 5. Conclusions The present study yields three key findings regarding the mechanisms underlying prosocial behavior in educational settings. First, we establish that positive classroom climate serves as a significant contextual predictor of prosocial behavior among college students. Second, our results reveal that this relationship is partially mediated by students' expectations of receiving prosocial feedback. Third, and most notably, we identify a dual moderating role of self-monitoring tendencies in these processes. The direct effect of classroom climate on prosocial behavior proves particularly strong for high self-monitorsy. Conversely, the indirect effect through prosocial feedback expectations emerges as more salient for low self-monitors, reflecting their greater reliance on internalized motivational processes. Declarations Author Contributions : S. Z. developed the research concept studies and drafted the manuscript, L.Q., Y. W., J. D. and D. J. performed testing and data collection; X. X. , C.X. and Y. C. conducted the data analysis. Z. J. edited and supervised the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Hunan Provincial Department of Education Outstanding Youth Project (grant number 22B0937); Hunan Provincial Department of Education Key Teaching Reform Project on Curriculum Ideology and Politics (grant number HNJG-20231401); General Entrusted Project of Hunan Provincial Social Science Achievements Evaluation Committee (grant number XSP2025WTY007). Institutional Review Board Statement : The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Fujian Police College. Informed Consent Statement : Informed consent was obtained from all subjects involved in the study. 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Gangestad, S. W., & Snyder, M. (2000). Self-monitoring: Appraisal and reappraisal. Psychological Bulletin, 126 (4), 530–555. https://doi.org/10.1037/0033-2909.126.4.530 Gest, S. D., Madill, R. A., Zadzora, K. M., Miller, A. M., & Rodkin, P. C. (2014). Teacher management of elementary classroom social dynamics: Associations with changes in student adjustment. Journal of Emotional and Behavioral Disorders, 19 (4), 195–210. https://doi.org/10.1177/1063426610377869 Grant, A. M., & Gino, F. (2010). A little thanks goes a long way: Explaining why gratitude expressions motivate prosocial behavior. Journal of Personality and Social Psychology, 98 (6), 946–955. https://doi.org/10.1037/a0017935 Grant, A. M., & Mayer, D. M. (2009). Good soldiers and good actors: Prosocial and impression management motives as interactive predictors of affiliative citizenship behaviors. Journal of Applied Psychology, 94 (4), 900–912. https://doi.org/10.1037/a0013770 Hardy, S. A., Bean, D. S., & Olsen, J. A. (2015). Moral identity and adolescent prosocial and antisocial behaviors: Interactions with moral disengagement and self-regulation. Journal of youth and adolescence, 44 , 1542–1554. Hui, C. M., Li, X., & Wang, Z. (2020). The role of social feedback expectation in promoting prosocial behavior: An experimental study. Journal of Experimental Social Psychology, 86 , 103924. https://doi.org/10.1016/j.jesp.2019.103924 Layous, K., Nelson, S. K., Oberle, E., Schonert-Reichl, K. A., & Lyubomirsky, S. (2012). Kindness counts: Prompting prosocial behavior in preadolescents boosts peer acceptance and well-being. PLoS ONE, 7 (12), e51380. https://doi.org/10.1371/journal.pone.0051380 Little, R. J.A (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American statistical Association, 83 (404), 1198–1202. James, L. R. (1982). Aggregation bias in estimates of perceptual agreement. Journal of applied psychology, 67 (2), 219. Krull, J. L., & MacKinnon, D. P. (2001). Multilevel modeling of individual and group level mediated effects. Multivariate behavioral research, 36 (2), 249–277. Magnusson, D., & Stattin, H. (1996). Person-context interaction theories . Stockholm: Department of Psychology, University of Stockholm. Marsh, H. W., Lüdtke, O., Nagengast, B., Trautwein, U., Morin, A. J., Abduljabbar, A. S., & Köller, O. (2012). Classroom climate and contextual effects: Conceptual and methodological issues in the evaluation of group-level effects. Educational psychologist, 47 (2), 106-124. Martela, F., & Ryan, R. M. (2016). Prosocial behavior increases well-being: The role of autonomy and competence satisfaction. Journal of Happiness Studies, 17 (3), 1309–1327. https://doi.org/10.1007/s10902-015-9630-3 McDonald, R. I., & Crandall, C. S. (2015). Social norms and social influence. Current Opinion in Behavioral Sciences, 3, 147-151. Mikami, A. Y., Griggs, M. S., Reuland, M. M., & Gregory, A. (2012). Teacher practices as predictors of children's classroom social preference. Journal of School Psychology, 50 (1), 95-111. Moos, R. H. (1979). Evaluating educational environments . Jossey-Bass. Moos, R. H., & Trickett, E. J. (1974). Classroom Environment Scale Manual (2nd ed.). Consulting Psychologists Press. O Oh, I., & Kilduff, G. J. (2008). The role of self-monitoring in prosocial behavior: A meta-analytic review. Personality and Social Psychology Bulletin, 34 (2), 255–266. https://doi.org/10.1177/0146167207311207 Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88 (5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological methods, 15 (3), 209. R Core Team. (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing . Retrieved from https://www.R-project.org/ Reyes, M., Dahl, R. E., & Telzer, E. H. (2012). Neural correlates of prosocial behavior in supportive contexts. Developmental Cognitive Neuroscience, 2 (3), 261–272. https://doi.org/10.1016/j.dcn.2012.01.004 Schachner, M. K., Noack, P., Van de Vijver, F. J. R, & Eckstein, K. (2016). Cultural diversity climate and psychological adjustment at school—Equality and inclusion versus cultural pluralism. Child development, 87 (4), 1175–1191. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7 (2), 147–177. https://doi.org/10.1037/1082-989X.7.2.147 Schultz, W. (2015). Neuronal reward and reinforcement learning. Neuron, 80 (2), 284–298. https://doi.org/10.1016/j.neuron.2013.10.028 Skordi, P., & Fraser, B. J. (2019). Validity and use of the What Is Happening In this Class? (WIHIC) questionnaire in university business statistics classrooms. Learning Environments Research, 22(3), 275–295. https://doi.org/10.1007/s10984-018-09277-4 Snyder, M. (1974). Self-monitoring of expressive behavior. Journal of Personality and Social Psychology, 30 (4), 526–537. https://doi.org/10.1037/h0037039 Snyder, M. (1987). Public appearances, private realities: The psychology of self-monitoring . W. H. Freeman. Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86 (2), 420–428. https://doi.org/10.1037/0033-2909.86.2.420 Telzer, E. H., Masten, C. L., Berkman, E. T., Lieberman, M. D., & Fuligni, A. J. (2018). Neural correlates of prosocial behavior in adolescence. Social Cognitive and Affective Neuroscience, 8 (1), 1–8. https://doi.org/10.1093/scan/nsr077 Wang, M. T., Degol, J. L., Amemiya, J., Parr, A., & Guo, J. (2020). Classroom climate and children’s academic and psychological wellbeing: A systematic review and meta-analysis. Developmental Review, 57 , 100912. https://doi.org/10.1016/j.dr.2020.100912 Wentzel, K. R. (2009). Peers and academic functioning at school. In K. H. Rubin, W. M. Bukowski, & B. Laursen (Eds.), Handbook of peer interactions, relationships, and groups (pp. 531–547). Guilford Press. Wentzel, K. R., Baker, S. A., & Russell, S. L. (2017). Peer relationships and prosocial behavior: A longitudinal analysis. Journal of Applied Developmental Psychology, 51 , 1–10. https://doi.org/10.1016/j.appdev.2017.03.004 Additional Declarations No competing interests reported. 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The Moderating Role of Self-Monitoring in the Relationship between Prosocial Feedback Expectation and Prosocial Behavior\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7078875/v1/e6e0828d7fd450441b3c3774.png"},{"id":97725001,"identity":"208b7a16-8bc7-4ca1-bb66-b913183640db","added_by":"auto","created_at":"2025-12-08 16:14:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1331988,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7078875/v1/d062a121-93f9-41a2-857b-1c6025440985.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Classroom Climate Is Associated With Prosocial Behavior in College Students: A Multilevel Moderated Mediation Model of Feedback Expectation and Self-Monitoring","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eProsocial behavior\u0026mdash;voluntary actions intended to benefit others such as helping, sharing, and cooperating\u0026mdash;constitutes a cornerstone of social adaptation and moral development (Eisenberg \u0026amp; Spinrad, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Beyond its moral value, prosocial behavior has been linked to a range of positive developmental outcomes. Meta-analytic and longitudinal research shows that it predicts better peer relationships (Layous et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), higher academic achievement (Caprara et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), lower risk of psychological problems (Flouri \u0026amp; Sarmadi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and greater life satisfaction (Aknin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Neuroimaging studies further indicate that engaging in helping behaviors activates brain regions associated with reward and valuation (Martela \u0026amp; Ryan, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), reinforcing the importance of understanding how prosocial behavior develops.\u003c/p\u003e\u003cp\u003eWhile much research has emphasized individual traits such as empathy and moral reasoning (Carlo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), increasing attention has turned to the influence of contextual factors. In particular, classroom environments are recognized as critical developmental settings, consistent with ecological systems theory (Bronfenbrenner, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), which highlights the role of proximal environments in shaping behavior. As students spend substantial time within classroom settings, these environments function as key microsystems influencing behavioral norms (Moos, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1979\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong classroom-level variables, classroom climate\u0026mdash;defined as students\u0026rsquo; shared perceptions of emotional support, cooperation, and behavioral norm clarity\u0026mdash;has emerged as a salient factor predicting prosocial engagement (Fraser, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Empirical studies demonstrate that supportive classroom climates foster helping behaviors across different age groups (Barr \u0026amp; Higgins-D\u0026rsquo;Alessandro, 2007, 2009), with classroom interventions enhancing spontaneous prosocial acts by nearly 50% (Wentzel et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Neuroimaging evidence supports this, showing that perceived support increases activation in the ventromedial prefrontal cortex during prosocial decision-making (Reyes et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Telzer et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These findings align with social cognitive theory (Bandura, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), which emphasizes the role of environmental modeling and reinforcement in learning social behaviors. They are also consistent with normative social influence theory (Cialdini \u0026amp; Goldstein, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which posits that individuals conform to perceived group norms in order to gain social approval (Fabes et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA central mechanism in this process is prosocial feedback expectations\u0026mdash;individuals\u0026rsquo; beliefs that helping behaviors will elicit social rewards such as approval, inclusion, or reciprocal support (Wentzel, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hui et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These expectations are shaped by repeated exposure to social feedback patterns and reflect cumulative observational learning (Bandura, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). According to expectancy-value theory (Eccles \u0026amp; Wigfield, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), behavior is more likely when individuals both anticipate positive outcomes and value them. Empirical studies confirm that even experimentally induced feedback expectations can enhance helping behavior independent of dispositional empathy (Grant \u0026amp; Gino, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and reinforcement learning studies show how repeated social rewards strengthen prosocial tendencies over time (Schultz, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, this pathway may not function uniformly across individuals. Self-monitoring, defined as the tendency to regulate behavior in response to social cues (Snyder, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1974\u003c/span\u003e), may moderate the effects of classroom climate. High self-monitors are particularly sensitive to social norms and adapt their behavior flexibly to fit situational demands (Oh \u0026amp; Kilduff, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Snyder, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). For these individuals, prosocial behavior may serve as a strategy for social alignment, even without explicit feedback (Flynn et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In contrast, low self-monitors rely more on internal value systems and need clear reinforcement to sustain prosocial behavior (Grant \u0026amp; Gino, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Neurobiological evidence supports this distinction: high self-monitors show enhanced activation in social-cognitive areas (e.g., medial prefrontal cortex, temporoparietal junction) when processing implicit social cues (Falk et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBuilding upon this theoretical and empirical foundation, the current study proposes a multilevel moderated mediation model to examine how supportive classroom climate influences prosocial behavior in college students. We hypothesize that this relationship is mediated by prosocial feedback expectations and moderated by self-monitoring. This integrative framework captures the interplay of environmental (classroom climate), cognitive-motivational (feedback expectations), and dispositional (self-monitoring) factors.\u003c/p\u003e\u003cp\u003eSpecifically, we test the following hypotheses:\u003c/p\u003e\u003cp\u003eH1: Classroom climate positively predicts students' prosocial behavior.\u003c/p\u003e\u003cp\u003eH2: Prosocial feedback expectations mediate the relationship between classroom climate and prosocial behavior.\u003c/p\u003e\u003cp\u003eH3: Self-monitoring moderates the direct effect of classroom climate on prosocial behavior, such that the effect is stronger among individuals high in self-monitoring.\u003c/p\u003e\u003cp\u003eH4: The indirect effect of classroom climate on prosocial behavior via feedback expectations is moderated by self-monitoring, with a stronger mediation effect for low self-monitors than for high self-monitors.\u003c/p\u003e\u003cp\u003eBy synthesizing environmental, motivational, and personality factors, this study is expected to offer a more comprehensive framework for understanding prosocial development in higher education. It may also yield practical insights for the design of targeted interventions, suggesting that climate-focused strategies could be particularly beneficial for students with lower self-monitoring tendencies, whereas norm-sensitive students may respond more effectively to subtle contextual cues embedded in classroom environments.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Ethics approval\u003c/h2\u003e\u003cp\u003e This study received ethical approval from the Ethics Committee of Fujian Police Collage and was conducted in accordance with the Declaration of Helsinki. Prior to participation, all individuals were informed about the purpose and procedures of the study, assured of confidentiality and anonymity, and informed that their participation was voluntary with the right to withdraw at any time. Electronic informed consent was obtained from all participants before data collection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Participants\u003c/h2\u003e\u003cp\u003eA cluster random sampling method was used to recruit undergraduate students from seven public universities in China. A total of 917 students were invited, and 888 valid responses were obtained (response rate\u0026thinsp;=\u0026thinsp;96.83%). The final sample comprised 343 males and 545 females, with a mean age of 20.02 years (SD\u0026thinsp;=\u0026thinsp;3.13), nested within 36 intact classrooms. After obtaining approval from both the institutions and participants, research assistants distributed web-based questionnaires via Wenjuanxing (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.wjx.cn\" target=\"_blank\"\u003ewww.wjx.cn\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.wjx.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The survey took approximately 10 minutes to complete. Participation was voluntary, and confidentiality was emphasized.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Measures\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1. Classroom Climate\u003c/h2\u003e\u003cp\u003eIn the present study, classroom climate was assessed using a revised version of the What Is Happening In this Class? (WIHIC) questionnaire (Fraser et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Skordi \u0026amp; Fraser, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This study selected three representative subscales domains\u0026mdash;student cohesiveness relationship(mutual assistance and friendship among students). Teacher support relationship (teachers' care and support for students). Cooperation personal development (the level of collaboration among students in learning tasks) \u0026mdash;based on their theoretical relevance to prosocial behavior and established psychometric validity in prior research(Moos, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Moos \u0026amp; Trickett, 1987). The revised WIHIC used in the present study consisted of 12 items in total, with 4 items per subscale. The scale demonstrated good internal consistency in the current sample, with a Cronbach\u0026rsquo;s alpha of 0.77 for the total score. Confirmatory factor analysis supported the proposed three-factor structure (CFI\u0026thinsp;=\u0026thinsp;0.95, TLI\u0026thinsp;=\u0026thinsp;0.93, RMSEA\u0026thinsp;=\u0026thinsp;0.07, SRMR\u0026thinsp;=\u0026thinsp;0.05), consistent with findings from the original validation studies. A composite score was calculated by averaging all items, with higher scores reflecting more positive perceptions of classroom climate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2. Prosocial Feedback Expectations\u003c/h2\u003e\u003cp\u003eAnticipated reactions to prosocial behavior were measured using the Anticipated Reactions to Prosocial Behavior Scale (Grant \u0026amp; Mayer, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This 6-item scale assesses individuals' expectations of social responses following prosocial acts, including anticipated approval, respect, and trust (e.g., \"Helping others will make people think I am trustworthy\"). Responses were collected on a 5-point Likert scale (1 = \"Strongly disagree\" to 5 = \"Strongly agree\"), with higher composite scores indicating more positive expectations. The scale showed good reliability in our sample (Cronbach's alpha\u0026thinsp;=\u0026thinsp;0.75). Confirmed Factor Analysis supporting its construct validity. Confirmed Factor Analysis supporting its construct validity (CFI\u0026thinsp;=\u0026thinsp;0.99, TLI\u0026thinsp;=\u0026thinsp;0.98, RMSEA\u0026thinsp;=\u0026thinsp;0.05, SRMR\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3. Self-Monitoring\u003c/h2\u003e\u003cp\u003eSelf-monitoring was assessed using the Brief Self-Monitoring Scale (Gangestad \u0026amp; Snyder, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), an adapted 8-item version of Snyder's (1974) original measure. This concise instrument maintains the theoretical construct while improving practicality, assessing: (1) social appropriateness monitoring, (2) attention to social comparison information, and (3) self-presentation modification ability (sample item: \"I would probably make a good actor\"). Responses used a 5-point Likert scale (1 = \"Strongly disagree\" to 5 = \"Strongly agree\"), with higher scores indicating stronger self-monitoring tendencies. The scale showed good reliability (Cronbach's alpha\u0026thinsp;=\u0026thinsp;0.78) and confirmed unidimensional structure (CFI\u0026thinsp;=\u0026thinsp;0.95, TLI\u0026thinsp;=\u0026thinsp;0.91, RMSEA\u0026thinsp;=\u0026thinsp;0.09, SRMR\u0026thinsp;=\u0026thinsp;0.05) in our sample.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4. Prosocial Behavior\u003c/h2\u003e\u003cp\u003eThe Prosocial Behavior Questionnaire for Adolescents (Eisenberg et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) was administered to assess frequency of prosocial actions. This measure includes 5 items describing specific prosocial behaviors across multiple domains (helping, sharing, cooperating, etc.). Participants rated their typical behavior frequency on a 5-point Likert scale (1 = \"Never\" to 5 = \"Always\"), with higher scores indicating greater prosocial engagement. In our sample, the measure demonstrated strong internal consistency (Cronbach's alpha\u0026thinsp;=\u0026thinsp;0.89). Confirmatory factor analysis supported the hypothesized multidimensional structure (CFI\u0026thinsp;=\u0026thinsp;0.99, TLI\u0026thinsp;=\u0026thinsp;0.98, RMSEA\u0026thinsp;=\u0026thinsp;0.06, SRMR\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.5. Control Variables\u003c/h2\u003e\u003cp\u003eIn the subsequent data analysis, this study controlled for individual characteristics such as age and gender.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data analysis\u003c/h2\u003e\u003cp\u003eData analysis was conducted using R software (version 4.3.0; R Core Team, 2024) with the bruceR package (Bao, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to accommodate the nested structure of the data. Our analytical approach proceeded through the following steps: First, we examined data distribution properties and missing patterns. Little's MCAR test was conducted to determine if data were missing completely at random (Little, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), which would inform our subsequent handling of missing values. Second, intraclass correlation coefficients (ICC1) were calculated to quantify between-classroom variance, providing empirical justification for employing multilevel modeling techniques (Shrout \u0026amp; Fleiss, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Third, to establish discriminant validity among constructs, we conducted Confirmatory Factor Analysis (CFA) on all measurement items to examine the factor structure at both individual and classroom levels. Fourth, we assessed common method bias using Harman's single-factor test (Podsakoff et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Fifth, we incorporated age and gender as control variables at their appropriate analytical levels to account for their potential influence on our hypothesized relationships. Sixth, we specified a \u0026ldquo;2-1-1\u0026rdquo; multilevel moderated mediation model to test our hypothesized relationships (Krull \u0026amp; MacKinnon, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In this framework, the independent variable (classroom climate) was assessed at the classroom level (Level 2), while the mediator (prosocial feedback expectations), moderator (self-monitoring), and outcome (prosocial behavior) were measured at the student level (Level 1). Finally, analyses were executed using the PROCESS function in the bruceR package with maximum likelihood estimation incorporating robust standard errors. Mediation effects were evaluated using quasi-Bayesian Markov Chain Monte Carlo(MCMC) sampling with 5,000 resamples and 95% confidence intervals. An indirect effect was deemed statistically significant when the confidence interval excluded zero.\u003c/p\u003e\u003cp\u003eThis modeling approach, depicted in Fig.\u0026nbsp;1, allowed simultaneous estimation of inter-classroom level (level 2) and inter-individual level (level 1) effects, aligning variables with their theoretical and measurement levels.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Preliminary Analyses\u003c/h2\u003e\u003cp\u003eLittle's MCAR test indicated no evidence that data were not missing completely at random (\u003cem\u003eχ\u003c/em\u003e\u0026sup2; = 12.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.135). Therefore, missing data were handled using full information maximum likelihood (FIML) estimation (Schafer \u0026amp; Graham, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Descriptive statistics and Pearson correlations among the main study variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Classroom climate, prosocial feedback expectations, and prosocial behavior were all positively correlated (\u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026lt;\u0026thinsp;.01), indicating initial support for the hypothesized relationships.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of the variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 Gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.12***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3 Classroom climate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;.15***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;.19***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4 Prosocial feedback expectation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;.10**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.59***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 Self monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.13***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.12***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6 Prosocial behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;.07*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;.08*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.60***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.43***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.32***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05. *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. gender (0\u0026thinsp;=\u0026thinsp;female, 1\u0026thinsp;=\u0026thinsp;male).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Intraclass Correlation Coefficients\u003c/h2\u003e\u003cp\u003eTo determine the appropriateness of multilevel modeling, we examined statistical indicators of within-group reliability and agreement for classroom climate measures. The ICC(1) was 0.136, indicating that 13.6% of the variance in classroom climate perceptions was attributable to differences between classrooms, thereby justifying the use of multilevel modeling (James, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). The ICC(2) value was 0.730, suggesting acceptable reliability of classroom-level mean scores. Additionally, the median rwg(j) score was 0.82, exceeding the commonly accepted threshold of 0.70 (Bliese, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), and indicating strong within-group agreement. Together, these indices supported the aggregation of classroom climate to the group level for subsequent analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Confirmatory Factor Analysis\u003c/h2\u003e\u003cp\u003eTo evaluate the discriminant validity of the study's core constructs, we tested the hypothesized four-factor model against three alternative models using Confirmatory Factor Analysis (CFA): Model 1 (Four-factor model): The baseline model specified classroom climate, prosocial feedback expectations, self-monitoring, and prosocial behavior as four distinct latent variables. Model 2 (Three-factor model): This model combined prosocial feedback expectations and prosocial behavior into one latent factor, while classroom climate and self-monitoring remained separate constructs. Model 3 (Two-factor model): This model combined classroom climate, prosocial feedback expectations, and prosocial behavior into a single factor, with self-monitoring as a distinct construct. Model 4 (One-factor model): All items from the four constructs loaded onto a single latent factor. The results should that the four-factor model yielded the best fit to the data (\u003cem\u003eχ\u003c/em\u003e\u0026sup2;/\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.25, CFI\u0026thinsp;=\u0026thinsp;0.93, TLI\u0026thinsp;=\u0026thinsp;0 .91, RMSEA\u0026thinsp;=\u0026thinsp;0.06, SRMR\u0026thinsp;=\u0026thinsp;0.056), providing evidence for the discriminant validity of the four constructs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Common Method Bias Assessment\u003c/h2\u003e\u003cp\u003eTo assess potential common method bias due to the self-report nature of the data, Harman's single-factor test was conducted. An exploratory factor analysis of all items from the main study variables revealed that the first unrotated factor accounted for 27.4% of the total variance, which is below the commonly used threshold of 40% (Podsakoff et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This result suggests that common method variance is unlikely to be a serious concern in the present study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Multilevel Moderated Mediation Analysis\u003c/h2\u003e\u003cp\u003eThe analysis revealed that classroom climate significantly predicted prosocial feedback expectations (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.314, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01), supporting the hypothesized cross-level effect. The interaction between classroom climate and self-monitoring on prosocial behavior was also significant (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.304, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0 .163, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01), indicating that students with higher self-monitoring tendencies were more responsive to classroom climate in forming prosocial behavior. Simple slope analysis showed that the effect of classroom climate on prosocial behavior was stronger at +\u0026thinsp;1 SD of self-monitoring (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.401, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) than at \u0026minus;\u0026thinsp;1 SD (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.73) (see Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eFurthermore, prosocial feedback expectations significantly predicted prosocial behavior (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.407, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.032, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01), while the direct effect of classroom climate on prosocial behavior remained significant (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.218, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.079, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). We conducted a conditional process analysis to examine the indirect effect of classroom climate (X) on prosocial behavior (Y) through prosocial feedback expectations (M) at different levels of self-monitoring (moderator). The results indicated that the indirect effect was significant at all three levels of self-monitoring(interaction coefficient = -0.412, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.072): low (-1 SD, effect\u0026thinsp;=\u0026thinsp;0.175, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, 95% MCMC CI [0.082, 0.272]), mean (effect\u0026thinsp;=\u0026thinsp;0.128, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, 95% MCMC CI [0.060, 0.201]), and high (+\u0026thinsp;1 SD, effect\u0026thinsp;=\u0026thinsp;0.081, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002, 95% MCMC CI [0.036, 0.136]).\u003c/p\u003e\u003cp\u003eImportantly, the strength of the indirect effect decreased as the level of self-monitoring increased, suggesting that self-monitoring moderates the mediation pathway such that the effect of classroom climate on prosocial behavior through prosocial feedback expectations is stronger for individuals with lower self-monitoring (see Fig.\u0026nbsp;3). Age and gender were included as control variables at their appropriate analytical levels. All parameter estimates are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultilevel Model Estimates for the Direct, Mediating, and Moderating Effects on Prosocial Behavior\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e(1)\u0026nbsp;Prosocial Behavior\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e(2) Prosocial Feedback Expectations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e(3) Prosocial Behavior\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.063***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.572***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.078***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.014*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClassroom Climate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.290***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.314***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.218***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.172***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.497***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProsocial Feedback Expectations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.407***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClassroom Climate\u0026times;Self-monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.304**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-monitoring\u0026times;Prosocial Feedback Expectations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.412***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRandom effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVar: class (Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVar: Residual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarginal R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConditional R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote. ** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;.001; gender (0\u0026thinsp;=\u0026thinsp;female, 1\u0026thinsp;=\u0026thinsp;male); N\u0026thinsp;=\u0026thinsp;888 students nested in 36 classrooms; Regression coefficients are unstandardized regression coefficients; Marginal R\u0026sup2; reflects the proportion of variance in the dependent variable explained by the fixed effects (independent variables). Conditional R\u0026sup2; reflects the proportion of variance in the dependent variable explained by both the fixed effects and random effects together.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study employed multilevel mediation analysis to examine the influence of classroom climate, a contextual factor at the classroom level, on students' prosocial behavior both directly and indirectly via the individual-level mechanism of prosocial feedback expectations. Furthermore, it investigated how students' self-monitoring tendencies moderate both the direct and indirect pathways. The findings demonstrate that classroom climate not only directly promotes students' prosocial behavior but also indirectly enhances it through strengthening their expectations of receiving prosocial feedback. Importantly, self-monitoring tendencies emerged as a critical moderator. By integrating environmental and dispositional factors within a multilevel modeling framework, this research offers a comprehensive perspective on the interplay between environmental and dispositional factors in shaping prosocial development in educational contexts.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Classroom Climate and Prosocial Behavior\u003c/h2\u003e\u003cp\u003eConsistent with our hypotheses, the results showed that classroom-level perceptions of a supportive climate positively predicted student-level prosocial behavior. These findings extend prior research by Wentzel et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who reported associations between classroom support and prosocial goals, by identifying a specific psychological mechanism underlying this link. Building upon existing correlational studies linking classroom climate to student outcomes (Wang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), our study advances this literature by elucidating the multilevel processes that account for these effects.\u003c/p\u003e\u003cp\u003eThrough multilevel mediation analysis, we found that approximately 13% of the variance in prosocial behavior could be attributed to classroom-level differences, highlighting the substantial contextual influence of classroom environments. This provides empirical support for Bronfenbrenner\u0026rsquo;s bioecological model (Bronfenbrenner, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), which emphasizes how proximal microsystems such as classrooms create distinctive developmental contexts shaping individual behavior through ongoing social interactions and feedback processes.\u003c/p\u003e\u003cp\u003eFrom a practical perspective, these findings imply that fostering a supportive prosocial classroom climate benefits not only collective behaviors but also influences students\u0026rsquo; internalized social motivations. This insight extends previous universal social-emotional learning approaches (Durlak et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) by emphasizing the importance of highlighting the positive social consequences of prosocial actions in intervention design. Teacher training programs might be enhanced by incorporating explicit strategies to create classroom environments that visibly recognize and reinforce prosocial behaviors, moving beyond generic classroom management techniques (Emmer \u0026amp; Sabornie, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) toward establishing clear and transparent social contingencies that encourage helping, sharing, and cooperation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2. The Mediating Role of Prosocial Feedback Expectations\u003c/h2\u003e\u003cp\u003eThe present findings indicate that the relationship between classroom climate and prosocial behavior is partially mediated by students\u0026rsquo;prosocial feedback expectations. Specifically, the supportive classroom environment appears to indirectly influence prosocial behaviors by shaping students\u0026rsquo;anticipations of positive social responses\u0026mdash;such as approval or acceptance\u0026mdash;following prosocial acts. This pattern aligns with a classic \u0026ldquo;2-1-1\u0026rdquo; multilevel mediation framework (Preacher et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), wherein a macro-level contextual factor (classroom climate) affects an individual-level psychological mechanism (feedback expectations), which in turn influences individual behavior (prosocial actions).\u003c/p\u003e\u003cp\u003eOur findings significantly extend existing literature by demonstrating how classroom climate influences prosocial behavior through both direct and indirect psychological pathways. While prior research has primarily examined prosocial behavior as either a function of dispositional traits (Eisenberg et al., 2015) or direct environmental effects (Barr \u0026amp; Higgins-D'Alessandro, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), we reveal a more complex, interactive process. The partial mediation through prosocial feedback expectations suggests this cognitive mechanism operates alongside other potential pathways (e.g., moral identity formation, Hardy et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; normative conformity, McDonald \u0026amp; Crandall, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), highlighting the multiply determined nature of prosocial development. Notably, our results advance beyond studies of direct teacher influences (Mikami et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) by identifying an indirect social-cognitive pathway: classroom environments shape behavior by modifying students' anticipated social consequences of helping. This mediation effect provides empirical support for theoretical models emphasizing environment-individual interactions (Bandura, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), particularly bridging motivational psychology and social behavior research. By showing how environmental factors become internalized as motivational drivers through anticipated social contingencies, we extend social cognitive theory and underscore the centrality of expectancy constructs in understanding prosocial development.\u003c/p\u003e\u003cp\u003eMoreover, our results challenge the traditional dichotomy that treats classroom climate as an organizational-level construct separate from prosocial behavior as an individual-level trait. The observed cross-level mediation underscores the interconnectedness of these constructs via social-cognitive processes, aligning with calls for integrated, multilevel approaches to understanding educational diversity and inclusion effects (Schachner et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such perspectives encourage researchers and practitioners to consider how contextual and individual factors reciprocally shape prosocial development within complex educational ecosystems.\u003c/p\u003e\u003cp\u003eFrom an applied perspective, these findings suggest that schools and educators could enhance prosocial development by systematically assessing both classroom climate and students\u0026rsquo; social-motivational orientations. Such assessments may serve as valuable early indicators of prosocial trajectories, potentially more sensitive than behavioral measures alone. The mediating role of feedback expectations also points to promising intervention targets: fostering positive and consistent social feedback within classrooms could precede and predict increases in prosocial behavior, thus amplifying intervention efficacy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.3. The Moderating Role of Self-Monitoring\u003c/h2\u003e\u003cp\u003eThis study found that self-monitoring significantly moderated both the direct and indirect pathways linking classroom climate to prosocial behavior, albeit in distinct and contrasting ways. First, self-monitoring amplified the direct positive effect of classroom climate on prosocial behavior. Specifically, students with higher self-monitoring tendencies exhibited a stronger association between a supportive classroom climate and their prosocial actions. This finding aligns with Snyder\u0026rsquo;s (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) conceptualization of self-monitoring as an individual difference reflecting heightened sensitivity to social cues and contextual demands. High self-monitors are more inclined to adjust their behavior to conform with situational norms, thereby displaying greater responsiveness to the prosocial culture nurtured within a positive classroom environment. This result supports the person \u0026times; environment interaction framework (Magnusson \u0026amp; Stattin, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), underscoring that identical environmental conditions may produce divergent behavioral outcomes contingent on individual traits.\u003c/p\u003e\u003cp\u003eConversely, the indirect effect of classroom climate on prosocial behavior through prosocial feedback expectations was attenuated for students high in self-monitoring. This suggests that while high self-monitors exhibit prosocial behavior through direct responsiveness to observable social norms, they may depend less on internalized motivational expectations concerning social feedback. In other words, their prosocial actions likely arise from immediate adaptation to external social cues rather than being mediated by anticipated social reinforcement. By contrast, students with lower self-monitoring tendencies\u0026mdash;who are generally less sensitive to situational social signals\u0026mdash;may rely more heavily on consciously held beliefs about the social consequences of their behavior (e.g., expectations of praise or acceptance) to motivate prosocial engagement.\u003c/p\u003e\u003cp\u003eThis pattern elucidates a nuanced differential susceptibility mechanism (Belsky \u0026amp; Pluess, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), whereby the same environmental context\u0026mdash;the classroom climate\u0026mdash;exerts influence through distinct psychological pathways depending on students\u0026rsquo; self-monitoring levels. High self-monitors tend to engage in prosocial behaviors as a form of contextually driven behavioral adaptation, whereas low self-monitors appear to require more explicit motivational cues grounded in anticipated social feedback. These findings contribute to the understanding of individual differences in social responsiveness and highlight the importance of tailoring educational interventions to accommodate varying dispositional profiles. For instance, climate-based strategies may be particularly effective for norm-sensitive, high self-monitoring students, while interventions emphasizing explicit reinforcement and feedback may better support those lower in self-monitoring.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Practical Implications\u003c/h2\u003e\u003cp\u003eThe findings of this study offer several practical implications for educators, school administrators, and policymakers aiming to promote prosocial behavior in educational settings. First, schools should regularly and systematically assess classroom climate alongside students\u0026rsquo; social-motivational orientations, such as prosocial feedback expectations. Such assessments can help identify early trajectories of prosocial development and provide a scientific basis and timely opportunities for targeted interventions. Second, intervention programs should focus on fostering a positive social feedback environment that explicitly reinforces prosocial behaviors. Emphasizing the positive social consequences of helping others can effectively activate students\u0026rsquo; intrinsic prosocial motivation and promote sustained behavioral engagement. Third, teacher training should address individual differences and classroom dynamics by equipping educators to recognize and respond to students with varying levels of self-monitoring (Gest et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Teachers need to develop strategies for establishing supportive and transparent social reinforcement mechanisms to more effectively encourage prosocial behavior across diverse student groups. Finally, educational policies and institutions should support differentiated and context-sensitive social-emotional learning practices by providing appropriate resources and guidance. Promoting inclusive classroom climates that integrate environmental and individual factors can enhance the overall efficacy and sustainability of prosocial development initiatives.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Limitations and Future Directions\u003c/h2\u003e\u003cp\u003eDespite the strengths of a multilevel design, several limitations should be acknowledged. First, the cross-sectional nature of the data restricts our ability to draw causal inferences. Future longitudinal or experimental studies are needed to clarify the temporal sequence of effects and to determine whether changes in classroom climate lead to subsequent shifts in prosocial feedback expectations and prosocial behavior over time. Second, although multilevel modeling appropriately addresses data clustering, all variables were measured through self-reports, which may introduce common method bias. To enhance the validity of findings, future research should incorporate multiple data sources, such as behavioral observations, sociometric nominations, or teacher evaluations, to triangulate prosocial outcomes. Additionally, reliance on students\u0026rsquo; perceived classroom climate raises the question of whether objective classroom characteristics or subjective perceptions primarily drive the observed effects\u0026mdash;a distinction emphasized by Marsh et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) that future work should explicitly examine.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe present study yields three key findings regarding the mechanisms underlying prosocial behavior in educational settings. First, we establish that positive classroom climate serves as a significant contextual predictor of prosocial behavior among college students. Second, our results reveal that this relationship is partially mediated by students' expectations of receiving prosocial feedback. Third, and most notably, we identify a dual moderating role of self-monitoring tendencies in these processes. The direct effect of classroom climate on prosocial behavior proves particularly strong for high self-monitorsy. Conversely, the indirect effect through prosocial feedback expectations emerges as more salient for low self-monitors, reflecting their greater reliance on internalized motivational processes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: S. Z. developed the research concept studies and drafted the manuscript, \u0026nbsp;L.Q., Y. W., J. D. and D. J. performed testing and data collection; X. X. , C.X. and Y. C. conducted the data analysis. Z. J. edited and supervised the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Hunan Provincial Department of Education Outstanding Youth Project (grant number 22B0937); Hunan Provincial Department of Education Key Teaching Reform Project on Curriculum Ideology and Politics (grant number HNJG-20231401); General Entrusted Project of Hunan Provincial Social Science Achievements Evaluation Committee (grant number XSP2025WTY007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Fujian Police College.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e: Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: The data presented in this study are available on request from the corresponding author. The data are not publicly available duo to privacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e: The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAknin, L. B., Hamlin, J. K., \u0026amp; Dunn, E. W. (2018). Prosocial behavior increases well-being in humans. \u003cem\u003eCurrent Directions in Psychological Science, 27\u003c/em\u003e(2), 139\u0026ndash;144. https://doi.org/10.1177/0963721417749657\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBandura, A. (1986). \u003cem\u003eSocial foundations of thought and action: A social cognitive theory\u003c/em\u003e. Prentice-Hall.\u003c/li\u003e\n \u003cli\u003eBandura, A. (2002). Social cognitive theory in cultural context.\u003cem\u003e\u0026nbsp;Applied psychology, 51\u003c/em\u003e(2), 269\u0026ndash;290.\u003c/li\u003e\n \u003cli\u003eBao, HWS. (2024). bruceR: Broadly Useful Convenient and Efficient R Functions. 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H. Rubin, W. M. Bukowski, \u0026amp; B. Laursen (Eds.),\u003cem\u003e\u0026nbsp;Handbook of peer interactions, relationships, and groups\u0026nbsp;\u003c/em\u003e(pp. 531\u0026ndash;547). Guilford Press.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWentzel, K. R., Baker, S. A., \u0026amp; Russell, S. L. (2017). Peer relationships and prosocial behavior: A longitudinal analysis. \u003cem\u003eJournal of Applied Developmental Psychology, 51\u003c/em\u003e, 1\u0026ndash;10. https://doi.org/10.1016/j.appdev.2017.03.004\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Classroom climate, Prosocial behavior, Prosocial feedback expectations, Self-monitoring, College students, Personality traits, Moderated mediation, Hierarchical linear modeling","lastPublishedDoi":"10.21203/rs.3.rs-7078875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7078875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProsocial behavior, such as helping, sharing, and cooperating, is essential for young adults\u0026rsquo; social and emotional development. While prior research emphasizes individual traits, less is known about how classroom-level contexts interact with personality to shape prosocial tendencies in college settings. This study examined whether a supportive classroom climate is associated with higher prosocial behavior among college students, with prosocial feedback expectations as a mediator and self-monitoring as a moderator. Participants were 888 undergraduates (mean age\u0026thinsp;=\u0026thinsp;21.3) nested in 36 classrooms across seven Chinese universities. A multilevel moderated mediation model was tested using hierarchical linear modeling, controlling for demographic variables. Results showed that a supportive classroom climate was positively associated with prosocial behavior. This direct association was stronger among students high in self-monitoring. In addition, prosocial feedback expectations mediated the climate\u0026ndash;behavior link, and this indirect effect was moderated by self-monitoring: it was stronger for low self-monitors, who were more reliant on expected positive feedback to guide their behavior. These findings suggest that both classroom environments and personality traits shape prosocial development. Interventions in higher education may be more effective if they simultaneously foster positive social climates and consider individual differences in students\u0026rsquo; responsiveness to social cues.\u003c/p\u003e","manuscriptTitle":"How Classroom Climate Is Associated With Prosocial Behavior in College Students: A Multilevel Moderated Mediation Model of Feedback Expectation and Self-Monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-05 18:14:38","doi":"10.21203/rs.3.rs-7078875/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-29T07:54:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T03:11:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T15:12:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250880979246379882718168277142199385577","date":"2025-09-09T15:05:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118258300891201141709953936974581232920","date":"2025-09-03T08:36:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-29T08:22:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-23T15:55:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T00:38:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-18T00:37:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-07-09T02:06:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce92075d-aeb2-4131-b1f8-4ce36e07306f","owner":[],"postedDate":"September 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:13:41+00:00","versionOfRecord":{"articleIdentity":"rs-7078875","link":"https://doi.org/10.1186/s40359-025-03775-w","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2025-12-03 15:57:51","publishedOnDateReadable":"December 3rd, 2025"},"versionCreatedAt":"2025-09-05 18:14:38","video":"","vorDoi":"10.1186/s40359-025-03775-w","vorDoiUrl":"https://doi.org/10.1186/s40359-025-03775-w","workflowStages":[]},"version":"v1","identity":"rs-7078875","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7078875","identity":"rs-7078875","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}