The Relationship between Teacher-Students Relationships and Academic Grades Among High School Student: The Role of Perceived Teaching Quality and Student's Mental Health Symptoms

doi:10.21203/rs.3.rs-4871554/v1

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Research Article

The Relationship between Teacher-Students Relationships and Academic Grades Among High School Student: The Role of Perceived Teaching Quality and Student's Mental Health Symptoms

https://doi.org/10.21203/rs.3.rs-4871554/v1

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Objective

This study has examined the indirect role of perceived teaching quality in the relationship between TSRs and academic grades, further considered the moderating effects of students' mental health symptoms.

Methods

Utilizing Structural Equation Modelling, this study collected academic grades at three distinct time points to examine the associations among Teacher-Student Relationships, perceived teaching quality, and mental health symptoms with academic grades.

Results

The findings reveal that perceived teaching quality plays a statistically significant indirect role in the relationship between Teacher-Student Relationships and student academic grades. Additionally, the size of this indirect effect is moderated by students' mental health symptoms.

Conclusion

While Teacher-Student Relationships may not be directly associated with students' academic grades, they are significantly linked to perceived teaching quality, which in turn is closely related to academic grades. The extent of this indirect effect is moderated by students' mental health symptoms, suggesting that the relationship between perceived teaching quality and academic grades is influenced by students' mental health status. These findings empirically indicate that Teacher-Student Relationships are important for both teaching and learning activities, underscoring their essential role in improving educational outcomes.

Moderated Mediation

Structural Equation Modelling

Educational Psychology

Student Well-being

School Environment Dynamics

Quality Education

In high school education, academic grades serve not only as markers of learning and mastery of course content, but also as crucial indicators that can shape future educational and career opportunities, particularly in college admissions (Erbes et al., 2021; Geiser & Santelices, 2007). Academic grades encompass not only academic knowledge but also student effort, participation, and behaviour, making them a multidimensional assessment tool (Bowers, 2019). Extensive research underscores that high school grades are more reliable predictors of college success than standardized test scores, demonstrating a stronger correlation with not just freshman year performance but also longer-term academic outcomes in higher education (Galla et al., 2019; Hodara & Lewis, 2017; Geiser & Santelices, 2007). This evidence suggests that the grades reflect a student's comprehension and skills acquisition, influencing their prospects for university acceptance and subsequent academic achievements. In such situation, it is essential to explore the factors that associated with student's academic grades to better support students' learning outcomes and enhance their readiness for future challenges.

Teacher-student relationships (TSRs) lie the heart of the educational landscape and forming the cornerstone of both student academic success and mental well-being (Quin, 2017; Pianta et al, 2012). Students who experience supportive and engaged relationships with their teachers consistently demonstrate higher levels of academic motivation and perseverance (Wentzel, 2012; Roorda et al., 2011). Positive teacher-student relationships play a crucial role in enhancing academic performance (Goetz et al., 2021; Martin & Collie, 2019; Gehlbach et al., 2016), the support and encouragement from teachers help students overcome challenges and build academic resilience (Martin & Collie, 2019; Wentzel, 2016). The influence of TSRs on academic grades is particularly pronounced at the middle and high school level. Students who experience higher levels of closeness and lower levels of conflict in their relationships with teachers tend to show more favourable outcomes (Vos et al., 2020). Positive TSRs is crucial for promoting student engagement, motivation, and academic success (Mallik, 2023). The effects of positive TSRs evident in both traditional and online learning environments (Akram & Li, 2024). Furthermore, the association between TSRs and achievement grades is often mediated by factors such as student's learning engagement (Quin et al., 2017) and academic psychological capital (Carmona-Halty et al., 2019).

Although extensive literature has documented the association between TSRs and students' academic outcomes, most studies focus on the student's role, such as the indirect role of student engagement in these relationships (Quin et al., 2017; Akram & Li, 2024). However, few of them examine how TSRs might also reflect on the teacher's side, such as impacting teaching quality. This gap highlights the need for a deeper exploration of the teacher's role and an examination of how it is associated with student learning outcomes. Furthermore, given that there is a large body of literature linking mental health symptoms to student academic performance (Bolinski et al., 2020; Evans, 2021), this study will consider its moderating role. Therefore, this study aimed to explore the association mechanism between TSR and academic performance. Specifically, it will examine the indirect role of perceived teaching quality and investigate whether students' mental health symptoms moderate the effect size of the relationship between teaching quality and academic grades.

2.1 The association between Teacher-Student Relationships and academic Grades

TSRs play a pivotal role in promoting positive academic outcomes for students (Taxer et al., 2019; Rooda et al., 2011). Attachment theory serves as the theoretical grounding for the role of TSRs as direct supportive mechanisms in students’ development (Ainsworth, 1989). It proposes that adult-child relationships, such as those with caregivers or teachers, bolster cognitive and self-regulatory abilities. This is partly achieved by giving children a sense of security, facilitating their exploration of the world, and helping establish an “internal working model” for interacting with attachment figures. These interactions are crucial for acquiring information and developing learning skills (Ainsworth, 1978). Therefore, students with positive relationships with their teachers can more effectively use them as a secure base and developmental asset (Pianta, 1999). As a result, these students are likelier to collaborate and engage with the teacher in classroom learning and developmental activities. They are also more inclined to persist in learning opportunities and mastering challenging tasks (Quin et al., 2017). On the other hand, students who lack a feeling of security in their relationship with a teacher may become withdrawn and disengaged from learning (Martin & Collie, 2019), or they might exhibit emotional or behavioural conflicts with the teacher (Baker et al., 2008). Improving teacher-student interactions can substantially enhance student achievement (Allen et al., 2011). Therefore, TSRs in schools can act as a promoting, safeguarding, or risk element for students in developing the essential skills and resources required for their success and well-being in school (Ansari et al., 2020).

Students spend nearly 15,000 hours in school from age five until they leave (Rutter, 1979). Their relationships with teacher can significantly impact their personal, academic and social development (Pakarinen et al., 2018). TSRs is a valuable resource for educational and developmental progress (Ansari et al., 2020). For teenage students, teachers assume a crucial role in offering valuable support, guidance, and feedback to help students navigate the intricacies of the school environment (Wentzel, 2016). Adolescents rely on the guidance and expertise of their teachers to develop academic skills, set goals, and make informed decisions about their education (Wentzel, 2016; Midgley et al., 2015). Teachers’ support and encouragement help students overcome challenges, build resilience, and develop a positive academic identity (Wentzel, 2016). Which is significantly correlated with a wide range of desirable student outcomes, such as academic achievement, emotional well-being, behaviour and motivation (Gehlbach et al., 2016). Students who experience higher levels of closeness and lower levels of conflict in their relationships with teachers tend to show more favourable outcomes (Vos et al., 2020). These positive relationships are associated with enhanced academic achievement, attendance, and motivation, as well as reduced rates of behavioural problems and dropout (Roorda et al., 2021). Conversely, poor teacher-student relationships can lead to disengagement, particularly among marginalized or trauma-affected students (Martin & Collie, 2019). Therefore, the connection between teachers and students is pivotal element in shaping the overall school environment, with a substantially influencing students’ academic outcomes and well-being (Roorda et al., 2021).

Hypothesis 1: There is a direct relationship between teacher-student relationships and students' academic grades.

2.2 The indirect role of perceived teaching quality

Teaching is widely considered an inherently stressful profession (Johnson et al., 2005). The demanding and continuously challenging nature of the teaching profession frequently results in educators experiencing emotional exhaustion (Donker et al., 2020). Teachers who are emotionally drained may find it challenging to engage and inspire their students effectively (Frenzel et al., 2021). This state may not only lead to reduced job satisfaction (Wolpin et al., 1991), but also affects the individual teacher’s well-being and thus creates a less positive learning environment for students (Milatz et al., 2015). Positive TSRs can boost teachers’ self-efficacy (Mashburn et al., 2006), and which in turn has associations with teaching quality (Burić & Kim, 2020; Holzberger et al., 2013). Teachers who establish strong and positive relationships with their students are motivated to invest additional time and effort into advancing their students’ success (Robinson, 2022).

Teaching quality is a critical factor in promoting student's achievement and educational outcomes (Smimou & Dahl, 2012). The expectations of students—specifically regarding the knowledge they acquire—are crucial for gaining a deeper insight into teaching quality (Smimou & Dahl, 2012). Perceived teaching quality encompasses numerous attributes as conceptualized by students—who may be viewed as consumers, clients, or partners in the educational process (Smimou & Dahl, 2012). Student perceived teaching quality can be argued that true quality in teaching is achieved when student satisfaction is at its peak (Rapert et al., 2004). Perceived teaching quality is positively correlated with student motivation, particularly intrinsic motivation (Dahl & K. Smimou, 2011). For teachers, strong TSRs can safeguard teachers against emotional burnout by enhancing their enjoyment and reducing feelings of anger in the classroom (Taxer et al., 2019). Teachers tend to adopt higher- quality teaching practices when they maintain positive relationships with their students (Li & Wang, 2024). When teachers have strong, positive connections with their students, they are more inclined to engage in teaching practices that require greater effort and complexity but are known to have a high impact on student learning (Li & Wang, 2024).

Positive TSRs enable educators to foster a more fulfilling and effective teaching and learning environment, benefiting teachers and their students (Jennings & Greenberg, 2009). For students, a positive TSR can enhance their learning motivation (Quin et al., 2017), and this relationship is positively related to the improvement of their academic performance (Roorda et al., 2021; Wentzel, 2016). Positive TSRs are associated with increased school engagement and achievement from preschool to high school (Roorda et al., 2011; Quin, 2017), directly and indirectly influence student academic outcomes and classroom motivation (Jowett et al., 2023). Furthermore, the link between academic grades and perceived teaching quality is intuitively logical, suggesting that students who view the quality of instruction favourably are likely to achieve better academic grades. However, this relationship requires empirical validation to establish a definitive connection. This study hypothesis that perceived teaching quality logically serves an indirect role in the relationship between TSRs and students' academic grades.

Hypothesis 2: Students' perceived teaching quality serves as an indirect mediator in the relationship between teacher-student relationships and academic grades.

2.3 The moderation role of mental health symptoms

Mental health symptoms, particularly anxiety, depression, and stress, are prevalent among students in educational settings (Meeks et al., 2021; Othman et al., 2019), these symptoms are negatively associated with academic performance (Leow et al., 2024; Lipson & Eisenberg, 2018), including lower GPAs and higher dropout rates (Eisenberg et al., 2009; Lipson & Eisenberg, 2018). Students with mental health symptoms are more likely to be dissatisfied with their academic experience and have lower academic expectations (Lipson & Eisenberg, 2018). For example, students experiencing high levels of anxiety or depressive symptoms often struggle with concentration, memory, and overall cognitive function, which are critical for learning and academic grades (Eisenberg et al., 2013). Furthermore, anxiety disorders may play a significant role in academic underachievement and increased dropout rates, influencing high school dropout rates (Ameringen et al., 2003; Brumariu et al., 2023). These students are less likely to enter or complete college (Woodward & Fergusson, 2001). Moreover, anxiety symptoms are linked to lower academic self-concept (Brumariu et al., 2023), impaired social functioning, and school refusal (de Lijster et al., 2018). The relationship between mental health symptoms and education outcomes is bidirectional (Esch et al., 2014), high levels of academic stress, which are prevalent among students, are also linked to increased levels of depression and anxiety, further impacting academic outcomes negatively (Deng et al., 2022).

Over the past ten years, progress in developmental psychopathology has highlighted the longitudinal relationships among students’ performance in academic, psychological, and social performance (Suldo et al., 2013). Mental health symptoms might significantly alter how students perceive and interact with their learning environment and teachers. For instance, a student experiencing high levels of anxiety may perceive neutral feedback from a teacher as negative or critical, which could affect their academic engagement and outcomes (Kern et al., 2017). Similarly, depression might diminish a student’s response to positive educational stimuli (e.g., teaching methods), thereby reducing the positive effects of teaching quality on academic outcomes. Although previous studies have examined the direct impact of teaching quality on academic achievement, research specifically focusing on how mental health symptoms moderate this relationship remains limited. Additionally, little is known about how students' mental health symptoms affect their perceptions of instructional quality and, subsequently, their academic grades. The importance of considering mental health symptoms as moderators is critical due to their capacity to influence students' cognitive appraisals and emotional responses (Roeser et al., 2000). Therefore, this study aims to explore whether students' mental health symptoms moderate the relationship between their perceived teaching quality and academic outcomes.

Hypothesis 3. Students' mental health symptoms moderate the strength of the relationship between perceived teaching quality and academic grades.

2.4 Current study

Previous studies underscore the importance of TSRs in educational and developmental progress (Vos et al., 2020; Gehlbach et al., 2016), highlighting their significant association with students' academic outcomes (Roorda et al., 2011). Despite this, there is still limited understanding of whether TSRs is associated with students' perceived teaching quality and academic grades. Furthermore, although correlations between mental health and academic performance are well-documented, empirical studies exploring whether students' mental health symptoms moderate the relationship between perceived teaching quality and academic grades remain limited. This study aims to address these gaps by using Structural Equation Modelling (SEM) method to explore the indirect role of perceived teaching quality in the relationship between TSRs and academic outcomes, as well as the moderating role of students' mental health status in the relationship between perceived teaching quality and academic grades. The research framework of this study refers to Figure1:

This framework positions TSRs as a foundational element that is presumed to influence students' perceptions of teaching quality. It suggests that positive and supportive teacher-student interactions might associated with students' perceptions of teaching quality. Within this framework, perceived teaching quality serves an indirect role, indicating that the quality of teaching, as perceived by students, is crucial in translating the benefits of positive TSRs into improved academic performance. Additionally, Student's mental health symptoms are served as a moderator between perceived teaching quality and academic grades. This arrangement implies that the association between perceived teaching quality and academic grades can vary based on the mental health status of the students. This framework underscores the complex interplay between emotional and educational dynamics, highlighting how both teacher-student relationships and individual student factors, such as mental health, might influence educational outcomes.

3.1 Sample size and participants

The sample size for this study was carefully calculated according to SEM guidelines, taking into account the number of latent variables and their corresponding indicators (Hair et al., 2014). Table 1 summarizes the recommendations from Hair et al. (2014) regarding minimal sample sizes necessary for various configurations of latent variables and indicators within SEM analysis.

Table 1

Minimal Sample Size in SEM Analysis.
Sample Size Required	Criteria from The Research Model
100 participants	Consists of no more than five latent variables, each with a minimum of three indicators.
150 participants	Contains no more than seven latent variables, each of which has three or more indications.
300 participants	Includes no more than seven latent variables, each with no more than three indicators.
500 participants	More than seven latent variables, each with fewer than three indicators, make up this variable.

Given that the model in this study includes six latent variables, each with three or more indicators, 500 questionnaires were distributed to high school students in China. Of these, 433 students responded.

3.2 Demographic controls

Given the significant influence of demographic factors on academic outcomes (Sirin, 2005; Rumberger & Palardy, 2005), demographic conditions were meticulously controlled in this study. Demographic variables controlled for included age, household income, geographical background, and family structure. Family income was categorized using dummy variables: below 10,000 annually, between 10,000 and 50,000, and above 50,000. Additionally, geographical background was coded as 1 for rural and 2 for urban settings. Family structure was categorized using dummy variables to indicate living arrangements: living with both biological parents, living with one parent and one stepparent, and other family structures. The socio-demographic background of the study participants, along with these classifications, is detailed in Table 2.

Table 2

The Socio-demographic Background of Study Participants (n = 332)
Category	Subcategory	Details
Participant Demographics	Total Participants	433 teenagers
Age	Below 16 years old	0
	Within 16 to 18 years old	387 (90%)
	Over 18 years old	46 (10%)
Geographical background	Rural	407 (96%)
	Urban	26 (4%)
Family Income(yuan)	Below 10000	175 (40.4%)
	Between 10000 to 50000	212 (48.9%)
	Above 50000	46 (10%)
Family Structure	living with both biological parents	365 (84.2%)
	living with one parent and one stepparent	43 (9.9%)
	Other	25 (5.7%)

Following the application of filters based on these demographic controls—participants above 16 years old, from rural areas, family income below 50,000 yuan, and those living with both biological parents—only 350 datasets met the criteria for this study.

3.3 Procedures

The current study received approval from the Institutional Ethical Committee (IEC) (Approval Code: IEC-2024-FOSSLA-0012). Prior to the main survey, a pre-testing phase was conducted to refine the questionnaire. Data collection assistants are trained in data collection methods to ensure consistency and accuracy. The questionnaire was revised based on feedback from the pre-testing phase and suggestions from research assistants (high school teachers). The final questionnaire was distributed online by research assistants (teachers) working in public high schools in China. In the Chinese educational system, each classroom typically has a teacher known as "ban zhu ren (班主任)," who is responsible for the class and teaches a subject. These teachers spend significant time with students and often have the closest relationships with them. Therefore, students participating in this study were asked to report on their relationships with these head teachers and their perceptions of the teaching quality in the subjects taught by these teachers. Comprehensive informed consent was obtained from all participants and their guardians, ensuring they were fully informed about the study’s objectives, procedures, and potential impacts. Participation was strictly voluntary, and participants were free to withdraw from the study at any time. Upon completing the survey, participants were informed that they could access counselling services if needed.

3.4 Measures

3.4.1 Teacher-Student Relationships

The items of this scale were adapted from the ED School Climate Surveys STUDENT SURVEY, originally developed by the National Centre for Education Statistics at the U.S. Department of Education (U.S. Department of Education, n.d.). This study received permission to adapt the questionnaire from the centre. The original scale consists of five items. To ensure the scale's suitability and good fit for this study, the first and third items was removed from the original questionnaire. Consequently, three items were retained, including "I have a good relationship with my teacher," "My teacher and I respect each other," "My teacher makes me feel good about myself," The revised scale consisted of three items rated on a 5-point scale from 1 (completely disagree) to 5 (completely agree). The results from a confirmatory factor analysis (CFA) demonstrated good fit indices [χ2/df = 4.062, CFI = 0.983, TLI = 0.948, RMSEA (90% CI) = 0.080, SRMR = 0.024].

3.4.2 Perceived teaching quality

The "Perceived Teaching Quality Scale" was also adapted from the ED School Climate Surveys STUDENT SURVEY (U.S. Department of Education, n.d.). The items were modified to better reflect specific learning contexts for each item. The original questionnaire included 10 items, after a CFA to ensure optimal scale fit, only items 4,5,6,7 and 9 remained. Examples of retained items include "I like what we learn in our class," "Our teacher's feedback helps me understand how to improve," and "Our teacher spends time helping us remember what we have learned," These items rated on a 5-point Likert scale from 1 (completely disagree) to 5 (completely agree). The fit indices from the CFA showed satisfactory results with χ2/df = 2.706, CFI = 0.993, TLI = 0.987, RMSEA (90% CI) = 0.073, and SRMR = 0.013, indicating a good fit of the model.

3.4.3 Mental health symptoms

In this study, mental health symptom items were carefully adapted from the well-established and mature measurement instruments of DASS21 (Lovibond & Lovibond, 1995). This scale contains 3 indicators, anxiety, depression and stress. From the anxiety scale, items 7 and 9 were removed due to their poor factor loadings and potential redundancy. Items 3, 13, and 21 from the depression scale, and items 1 and 12 were removed from the stress scale were also deleted. The process of item refinement through CFA ensures that the remaining items provide a reliable and valid measure of each construct, thereby enhancing the overall quality of the research findings. The indices fit for the adapted DASS 21 questionnaire are presented in Table 3.

Table 3

The Indices Fit for The Adapted DASS 21 Questionnaire
	χ2/df	CFI	TLI	RMSEA (90% CI)	SRMR
DE	1.603	0.997	0.990	0.043	0.014
ST	1.071	0.999	0.998	0.015	0.020
AN	1.646	0.992	0.985	0.045	0.021
MHS	2.590	0.936	0.922	0.070	0.044

Table 3 indicates that the second-order construct of mental health symptoms shows a CFI of 0.936 and a TLI of 0.922, both of which are above the acceptability threshold of 0.90, indicating a satisfactory fit. The RMSEA of 0.070 is within the acceptable range. The SRMR value of 0.044, which remains below the threshold of 0.08, indicates a good fit. Additionally, the Chi-Square ratio of the first stage to the second stage is 640.367/641.368 = 0.998, closely approximating the value of 1. This proximity suggests that the second stage can enhance the model's simplicity and reduce overfitting (Kline, 2011). Therefore, align with the principles of simplicity in SEM and fit indices, this study justifies selecting second-order variables as the moderator variables. Therefore, the fit indices for mental health symptoms were: χ2/df = 2.590, CFI = 0.936, TLI = 0.922, RMSEA (90% CI) = 0.070, and SRMR = 0.044. These values indicate a good fit of the model.

3.4.4 Academic grades

In this study, academic grades were obtained from the administrative office records, which included grades from three tests conducted over a six-month period: in the first, third, and sixth months. To account for the stability of students' performance, we calculated the average of these three test scores to represent each student's academic grades. This average grade serves as a quantifiable indicator of academic outcomes. By focusing on test scores from the last six months, the study ensures that the data accurately reflects students’ current understanding and the stability of their academic grades.

3.5 Data clean

A fundamental step in this process is determining the extent of missing data to decide on an appropriate handling method. Mplus provides complicated mechanisms for managing missing data, primarily through the Maximum Likelihood (ML) estimation method for continuous data and Maximum Likelihood estimation with Robust standard errors (MLR) for dealing with non-normality and heteroscedasticity (Muthén & Muthén, 2017). The ML method operates under the assumption that the missing data are Missing at Random (MAR), which allows the analysis to proceed without manually imputing missing values, thus retaining all available data for the most statistically robust analysis (Enders, 2010). However, when the amount of missing data in a dataset or a specific questionnaire is excessive, it may distort the validity of the analysis. In this study, any questionnaire with more than 20% for each section missing data was removed (Wolf et al., 2013). In this study, no outlier values were detected. After removing cases with over 20% missing data (Wolf et al., 2013), 332 datasets were retained and advanced to the next phase of analysis. Of the valid responses, 46% were from males and 54% from females. The average age of participants was approximately 18 years, with a typical age variance of about 2 years.

3.6 Data analyses

This study SEM to examine the relationships between variables and test hypotheses. SEM allows for a comprehensive examination of multiple variables simultaneously (Kline, 2023), enabling a deeper understanding of how TSRs, perceived teaching quality, mental health symptoms associated with academic grades. Mplus 8.3 software was employed for this study analysis (Muthén & Muthén, 2017). ML, MLR, bias correct bootstrapping and percentile techniques were applied in the structural models. The model fit was evaluated using several metrics, such as the ratio of chi-square value to degrees of freedom, the comparative fit index (CFI), the Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean residual (SRMR). Typically, the ratio of chi-square to degrees of freedom is less than 5, CFI and TLI values equal to or greater than 0.90, along with RMSEA and SRMR values of 0.08 or lower, indicate an acceptable fit (Marsh et al., 2004; Baron & Kenny, 1986). This comprehensive approach allowed for a detailed examination of direct, indirect, mediating, and moderating effects within the relationship under study.

4.1 Measurement model

Before moving on to hypothesis testing with the structural model, it is essential to confirm the constructs’ internal consistency reliability, convergent validity, and discriminant validity of the constructs (Anderson & Gerbing, 1988). This step is critical in SEM as it lays the foundation for credible hypothesis testing. The process of refinement was based on various parameters such as factor loadings (Estimates), standard errors (SE), Z-scores, P-values, and R-squared values. These parameters were instrumental in determining the strength and significance of the relationship between observed variables and their respective latent constructs. The reliability aspect is presented in Table 4.

Table 4

Reliability and Convergence Calibration Scale
Dim	Item	Parameters of significance test				Item Reliability	Composite Reliability	Convergence Validity
Dim	Item	Estimate	S.E.	Z	P Value	R square	CR	AVE
TSR	TSR2	0.738	0.032	23.019	***	0.544	0.823	0.608
	TSR4	0.826	0.029	28.320	***	0.683
	TSR5	0.772	0.031	25.142	***	0.597
PTQ	PTQ4	0.723	0.028	25.457	***	0.523	0.925	0.712
	PTQ5	0.825	0.020	42.066	***	0.681
	PTQ6	0.954	0.009	106.039	***	0.910
	PTQ7	0.889	0.014	64.417	***	0.790
	PTQ9	0.810	0.021	38.420	***	0.656
MHS	AN	0.724	0.037	19.661	***	0.524	0.803	0.505
	DE	0.701	0.038	18.355	***	0.491
	AT	0.697	0.038	18.161	***	0.486
Note. TSR = Teacher-Students Relationship, PTQ = Perceived Teaching Quality, MHS = Mental Health Symptoms. ⁎⁎⁎ p<0.001.

According to Hair et al. (2019), it is essential to ensure that individual items serve as reliable indicators of their respective dimensions. Within this study, all dimensions have Composite Reliability (CR) values surpassing 0.7, signalling robust reliability for every scale. All the individual item reliability (R²) values surpassing 0.36, indicate that the items appropriately represent their respective sizes. Additionally, each variable's Average Variance Extracted (AVE) values exceed 0.5, indicating that most of the variance (over 50%) can be attributed to the variable itself, rather than to measurement errors, thus highlighting commendable convergent validity (Fornell & Larcker, 1981). The Z-value is used to test hypotheses about the parameter being zero or having no effect. A large absolute value of the Z-value indicates that the estimate is significantly different from zero relative to its standard error, suggesting a statistically significant result (Kline, 2023).

To further understand the constructs within this study, we conducted an in-depth analysis focusing on convergent calibration and differential calibration. Convergent calibration assesses whether different items measuring the same construct agree, which is essential for establishing the reliability and validity of the constructs. Differential calibration examines the extent to which these constructs differ from one another, thereby establishing the discriminant validity of the measurement model. Table 5 presents the results of these analyses.

Table 5

Convergent Calibration and Differential Calibration Table
Dim	Item Reliability	Composite Reliability	Convergent Validity	Discriminate Validity
Dim	ST. Loading	CR	AVE	TSR	TPQ	MHS
TSRs	0.738–0.826	0.823	0.608	0.780
PTQ	0.723–0.954	0.925	0.712	0.737	0.844
MHS	0.697–0.724	0.803	0.505	0.304	0.310	0.711
Note. TSRs = Teacher-Students Relationship, PTQ = Perceived Teaching Quality, MHS = Mental Health Symptoms. CR = Composite Reliability, AVE = Average Variance Extracted.

The results from Table 5 indicate that each dimension effectively explains its respective items. Discriminant validity was confirmed through the comparison of the square roots of the AVEs with inter-construct correlations, demonstrating that each construct is distinct and well-differentiated from the others. The square roots of the AVEs for TSRs (0.780), PTQ (0.844), and MHS (0.711) were greater than their respective correlations with each other, confirming clear discriminant validity. Therefore, all variables meet the acceptability criteria, affirming the strong convergent validity of the model.

4.2 Structural model

After ensuring the reliability and validity of the measurement model, the next critical step in the SEM analysis is the assessment of the structural model. This phase is pivotal as it tests the hypothesized relationships between constructs and evaluates the overall explanatory power of the theoretical model. However, during the application of the Latent Moderated Structural (LMS) method with interaction examination in Mplus, users will observe the absence of conventional fit indices such as chi-square, TLI, and CFI, because these usual fit indices are predicated on assumptions of normality that do not apply to the LMS approach. What is provided is information criteria metrics: Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The methods to evaluate the model fit is analysing a basic model that includes the moderator and outlines its direct effects, that is the paths leading to the dependent variable without incorporating any latent interaction terms (Sardeshmukh & Vandenberg, 2016).

This initial baseline model is assessed using the ML estimation method. Subsequently, information criteria such as AIC and BIC can be used to compare the more complex initial model with the initial baseline model. Generally, a well-fitting baseline model is preferable before adding interaction terms (Burnham & Anderson, 2002; Yang, 2005). If the baseline model fits poorly, adding interactions might be ineffective. Conversely, if the baseline model fits well, then can process to next step to check how adding the interaction term affects the information criteria. In practical applications, AIC is often favoured over BIC for model comparisons (Burnham & Anderson, 2002; Yang, 2005). BIC tends to overly penalize models with more parameters due to certain unrealistic assumptions. AIC is considered asymptotically optimal in identifying the model with the smallest mean square error, especially under the assumption of an infinite-dimensional true population model, which is not the case with BIC (Yang, 2005). Therefore, in this study, we will utilize AIC for comparisons. The baseline model fit indices present in Table 6:

Table 6

The baseline model fit indices
Index	Criteria	Baseline Model
CH-SQR(χ2)	SMALLER IS BETTER	103.793
DF	LARGER IS BETTER	61
CHI-SQR/DF	3>NORM CHI-SQR>1	1.701
CFI	>.90	0.980
TLI	>.90	0.975
RMSEA	<.08	0.047
SRMR	<.08	0.044
Note: CH-SQR = Chi-Square, DF = Degrees of Freedom, CFI = Comparative Fit Index, TLI = Tucker-Lewis Index, RMSEA = Root Mean Square Error of Approximation, SRMR = Standardized Root Mean Square Residual.

For the model fit indices, given that the chi-square χ2 value tends to increase as the sample size increases (Bagozzi & Yi, 1988), it is useful when evaluating model fit to use the χ2 in conjunction with degrees of freedom. A ratio of χ2 to degrees of freedom (df) that does not exceed three is generally considered acceptable (Chin & Todd, 1995). Therefore, this baseline model exhibited a satisfactory fit [χ2/df = 1.701, CFI = 0.980, TLI = 0.975, RMSEA = 0.047, SRMR = 0.044]. Then, this study proceeds to the next step: comparing with the complex model (including interaction effect) of the AIC. See Table 7.

Table 7

AIC comparation of baseline model and interaction model
Index	Criteria	Baseline Model	Complex Model
AIC	LOWER IS BETTER	9342.752	9340.880
Note: AIC = Akaike Information Criterion.

Results indicate that the AIC value is lower for the complex model compared to the baseline model. The lower AIC value supports the conclusion that the complex model is more effective at explaining variations in the data than the baseline model, indicating enhanced model adequacy without introducing unnecessary complexity. This improvement underscores the added explanatory power of including interaction terms, thereby enhancing the model without excessive complexity. Therefore, this study can proceed to hypothesis testing.

4. 3 Hypothesis testing

4.3.1 Indirect effects testing

The concept of indirect testing is instrumental in elucidating the mechanisms that underlie the relationships between variables by introducing a mediating factor (Baron & Kenny, 1986). This approach serves to uncover the processes through which independent variables influence dependent variables via one or more mediators. MacKinnon and colleagues (MacKinnon et al., 2002; MacKinnon et al., 2004) review various strategies to gauge the extent and significance of indirect effects (MacKinnon et al., 2002; MacKinnon et al., 2004). These researchers explored various strategies to assess the magnitude and importance of indirect effects. The consensus among methodologists is that the product of coefficients, specifically the product term a1b1, is a robust measure for quantifying the indirect effect in mediation analysis. This term represents the product of the path coefficients from the independent variable to the mediator (a1) and from the mediator to the dependent variable (b1), effectively capturing the mediated effect. MacKinnon and colleagues argue that among the several statistical strategies available, the product of coefficients approach is likely the most accurate for determining the significance and confidence intervals of these indirect effects in simple mediation models (MacKinnon et al., 2004). Figure 2 presents the concept of a simple mediation model.

This study employed the product of coefficients approach, bias-corrected bootstrapping, and percentile methods for testing indirect effects. Bootstrapping is a resampling technique used to estimate the distribution of a statistic by sampling with replacement from the original data. It provides a way to assess the stability and variability of the indirect effects without relying on assumptions of normality (Hayes, 2009). Since perceived teaching quality was hypothesized to play an indirect role in the relationship between TSRs and students' academic grades, this phase of the study examined the indirect role without considering the moderating effect of students' mental health symptoms. Table 8 presents the results of the indirect tests.

Table 8

Indirect and Direct Effects Testing Results with Bootstrap Confidence Intervals
Path Description	Product of Coefficients				Bootstrap1000 Times 95% CI
Path Description	Product of Coefficients				Bias Correct		Percentile
	Estimate	S.E.	Est./S.E.	P-Value	Lower2.5%	upper2.5%	Lower2.5%	upper2.5%
TSRs-PTQ (a1)	0.465	0.051	9.202	0.000	0.362	0.558	0.366	0.565
PTQ -AG(b1)	0.688	0.214	3.218	0.001	1.096	0.295	1.107	0.302
a1b1	1.248	0.398	3.135	0.002	0.557	2.207	0.552	0.140
TSRs- AG	0.031	0.362	0.086	0.931	− 0.695	0.729	-0.695	0.729
Note: TSRs = Teacher-Student Relationships, PTQ = Perceived Teaching Quality, a1b1 = a1*b1, AG = Academic Grades.

Table 8 provides detailed results for both indirect and direct effects testing. This analysis employs bootstrap 1000 times with 95% confidence intervals to ensure robustness in the estimates. The results indicate that TSRs are strongly associated with students' perceived teaching quality. The path from TSRs to perceived teaching quality (TSRs-PTQ) shows a significant positive association (P < 0.001), with confidence intervals that exclude zero, suggesting that improvements in TSRs are associated with higher perceived teaching quality. Similarly, higher perceived teaching quality in turn is associated with better academic grades (PTQ-AG), as evidenced by confidence intervals that exclude zero and a p-value of 0.001. Furthermore, the results from the product of coefficients method (a1b1) indicate that perceived teaching quality plays a significant indirect role in the relationship between TSRs and academic grades, as evidenced by a p-value of 0.002 and confidence intervals that exclude zero. Meanwhile, the results indicate that TSRs are not directly associated with students’ academic grades, as evidenced by a p-value of 0.931 and confidence intervals that include zero. Therefore, the results confirm that perceived teaching quality plays a substantial indirect role between TSRs and academic grades. Therefore, this study will proceed to the next step: examining the moderated mediation effects, which includes the moderating effect of students' mental health symptoms on the relationship between perceived teaching quality and academic grades.

4.3.2 Moderated mediation testing

The moderated mediation analysis examines the indirect role of perceived teaching quality in the relationship between TSRs and academic grades, investigating whether this indirect effect is moderated by students' mental health symptoms. Specifically, this study focuses on the moderating effect of mental health symptoms on the relationship between perceived teaching quality and academic grades. The regression weights and hypothesis testing results are summarized in Table 9, which details the relationships between TSRs, perceived teaching quality, mental health symptoms, and academic grades.

Table 9

Research model regression weight and hypothesis result
DV	IV	Estimate	S.E.	Est./S.E.	P-value	Hypothesis
PTQ	TSRs	1.240	0.375	3.306	0.001	Support
AG	TSRs	0.137	0.358	0.346	0.730	Not Support
AG	TPQ	0.639	0.220	2.912	0.004	Support
AG	MHS	-0.025	0.276	-0.091	0.927	Not Support
AG	MHS*PTQ	-0.565	0.240	-2.354	0.019	Support
Note: TSRs = Teacher-Student Relationships, PTQ = Perceived Teaching Quality, AG = Academic Grades.

Table 9 presents the regression weights and hypothesis test results from the moderated mediation analysis. The results reveal statistically significant evidence of the indirect role of perceived teaching quality and the moderating effect of students' mental health symptoms. An increase in TSRs is associated with an increase in perceptions of teaching quality (Estimate = 1.240, p = 0.001). Furthermore, an improvement in perceived teaching quality is associated with an increase in students' academic grades (Estimate = 0.639, p = 0.004). Theoretically, this suggests that for every one-unit increase in TSRs, there is a corresponding increase of 1.240 units in perceived teaching quality, and each one-unit increase in perceived teaching quality is potentially associated with an increase in academic grades by 0.639 units. Notably, the relationship between teaching quality and academic grades is moderated by students' mental health symptoms (depression, anxiety, and stress). A one-unit increase in mental health symptoms reduces the positive association between perceived teaching quality and academic grades by 0.565 units (Estimate = -0.565, p = 0.019). These findings support the hypothesis that perceived teaching quality plays an indirect role in the relationship between TSRs and students' academic grades. Additionally, students' mental health symptoms moderate the relationship between perceived teaching quality and academic grades. The pathway weight effects further corroborate these results (see Fig. 3).

Figure 3 offers a detailed examination of pathway weight effects. The large coefficients and small standard errors indicate statistically significant relationships. The path coefficient from TSRs to perceived teaching quality is 1.161, with a standard error of 0.115, suggesting a strong and statistically significant relationship between these variables. The path coefficient from perceived teaching quality to students' academic grades is 0.639, with a standard error of 0.220, also indicating a statistically significant association between perceived teaching quality and academic performance. However, the path coefficient from TSRs to academic grades is 0.137 with a standard error of 0.358, indicating that there is no statistically significant direct association between these two variables. The path coefficient for the interaction of perceived teaching quality and mental health symptoms is -0.565 with a standard error of 0.240, indicating a statistically significant and negatively moderating role of mental health symptoms.

The first finding of this study is that perceived teaching quality plays a statistically significant indirect role in the relationship between TSRs and students' academic grades. Although TSRs do not directly associated with students' academic grades, they are strongly linked to how students perceive teaching quality, which in turn, substantially associated with their academic grades. This underscores the crucial role of TSRs in shaping the educational environment and indirectly influencing learning outcomes. Previous research has established that TSRs is significantly associated with students' learning engagement and then influence their learning outcomes, positive relationships within the classroom for fostering an effective learning environment (Rooda et al., 2011). This study builds on these insights by revealing another dimension of TSRs' potential impact: positive interactions between teachers and students not only associated with students' learning attitudes and efficiency but may also associated with the quality of teachers' instructional practices. This research highlights the phenomenon that TSRs are significant not only for students but also for teachers. While teaching is often viewed by educators as a professional role, it can be influenced by the quality of interactions and relationships with student. For teachers, these relationships can be just as vital, potentially enhancing their job satisfaction and the quality of their work. Thus, TSRs are important not only for the educational outcomes of students but also for the professional well-being and effectiveness of teachers

Another key finding from this study is the potential moderating role of students' mental health symptoms on the relationship between perceived teaching quality and academic grades. For instance, even if teachers deliver high-quality instruction, students struggling with emotional issues, or experiencing negative emotions, might not fully perceive or benefit from these educational efforts, which could subsequently diminish their academic performance. According to Cognitive Theory (Neisser, 2014), mental health issues can impair cognitive capacities, emotional stability, and engagement in academic activities—all of which are crucial elements for successful learning. Mental health challenges can disrupt cognitive processes such as attention, memory, and problem-solving, all essential for effective learning and academic performance. This theory explains how such disruptions can hinder students' ability to absorb new information, apply knowledge, and participate actively in the learning process. The empirical results of this study further support the idea that mental health status might associated with student's learning outcomes. By utilizing the SEM method to explore the complex process of education and learning, this study shows that students' academic performance may be affected by multiple factors, especially their psychological state and teaching quality play an important role. The school implements a TSR improvement system, which can theoretically improve teaching quality and thereby improve students' academic performance. These improvements in academic performance may in turn reduce students’ mental health problems, creating a virtuous cycle.

This study has examined the indirect role of perceived teaching quality in the relationship between TSRs and academic outcomes, further considered the moderating effects of students' mental health symptoms. It reveals that while TSRs may not be directly associated with students' academic grades, it is significantly linked to perceived teaching quality, which in turn is critically related to academic grades. This supports our second hypothesis that perceived teaching quality plays a significant indirect role in the relationship between TSRs and students' academic grades. Furthermore, the findings highlight the importance of students' mental health symptoms as a moderating factor in the relationship between perceived teaching quality and academic grades, corroborating our third hypothesis. Consequently, TSRs are crucial as they establish the foundation for quality teaching practices and student engagement, which are strongly associated with academic grades.

Measuring perceived teaching quality through anonymous student feedback mechanism and conducting comprehensive well-being programs for students are recommended strategies to enhance teaching quality and improve students' learning outcomes. Such interventions have the potential to create responsive and supportive learning environments that improve teaching quality and promote student academic achievement. This study employs SEM to empirically demonstrate the critical roles of TSRs and students' mental health in influencing teaching quality and academic grades, and it examines the mechanisms behind this influence. This approach empirically enriches educational psychology theory and strategic practices. However, since the questionnaire survey relies on self-reporting by students, it might be affected by students’ subjective feelings. Future studies can consider incorporating teachers' reports to provide a more objective assessment of teaching quality.

Conflict of Interest Statement:

The authors declare that there is no conflict of interest regarding the publication of this paper. This research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical Approval:

This study was conducted in accordance with the ethical standards of the Institutional Research Board. This study was approved by the Research Ethics Committee of UCSI University Malaysia (Approval Code: IEC-2024-FOSSLA-0012). We confirm that all studies were conducted in compliance with relevant guidelines/regulations. The research respects anonymity and confidentiality.

Data Availability Statement:

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Contributions and Funding Statement:

Dr. Xiao Hui Chen spearheaded the conceptualization of the study, crafted the research methodology, and led the drafting of the manuscript. Professor Richard Peter Bailey provided critical revisions and enriched the manuscript with substantial intellectual content. Dr. Nadia contributed significantly to the analysis of the data. Dr. Hong Bin Xie assisted with data analysis. Each author has actively participated in the research process and approved the final version of the manuscript. All authors have committed to being accountable for all aspects of the work. This study did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.

Acknowledgments:

We extend our heartfelt thanks to the school administrators, teachers, and students who participated in this study. Their cooperation and openness were instrumental in facilitating the research process and enriching the data collected. We are particularly grateful to the administrative staff at the school for their vital role in coordinating the study. Additionally, we owe immense gratitude to our research teams for their invaluable insights and constructive feedback throughout various stages of this research. Their thoughtful suggestions and critiques have substantially improved the quality of our work. Special thanks are due to Professor Richard Peter Bailey and Dr. Nadia for their technical support in data management and analysis, which has been a cornerstone in the successful execution of our study. Lastly, we would like to acknowledge the unwavering patience and support of our families and friends, whose encouragement and understanding have been a constant source of strength throughout the research process.

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No competing interests reported.

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The Relationship between Teacher-Students Relationships and Academic Grades Among High School Student: The Role of Perceived Teaching Quality and Student's Mental Health Symptoms

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Abstract

Objective

Methods

Results

Conclusion

Figures

1. Introduction

2. Theoretical framework and hypotheses development

2.1 The association between Teacher-Student Relationships and academic Grades

2.2 The indirect role of perceived teaching quality

2.3 The moderation role of mental health symptoms

2.4 Current study

3. Methodology

3.1 Sample size and participants

3.2 Demographic controls

3.3 Procedures

3.4 Measures

3.4.1 Teacher-Student Relationships

3.4.2 Perceived teaching quality

3.4.3 Mental health symptoms

3.4.4 Academic grades

3.5 Data clean

3.6 Data analyses

4. Results

4.1 Measurement model

4.2 Structural model

4. 3 Hypothesis testing

4.3.1 Indirect effects testing

4.3.2 Moderated mediation testing

5. Discussion

6. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1