Teacher-student relationship and academic achievement in China: Evidence from a three-level meta-analysis

Abstract

Past studies of the relation between teacher-student relationship (TSR) and students’ academic achievement (SAA) yielded mixed results, so this study determined the overall link between TSR and SAA, along with their moderators. This three-level meta-analysis of 90 independent effect sizes in 74 empirical studies of 233,961 students showed an overall positive link between TSR and SAA in China (r = .259, 95% CI = [.227; .290]). This relationship was higher in: (a) China's central region (.305) than its eastern (.238) or western regions (.166); (b) senior high school (.345), followed by junior high school (.251), then primary school (.221); (c) English (.302), followed by math (.272), Chinese (.269), and science (.202); and (d) females (B = .507) than males. These results suggest the value of improving teacher-student relationships in policies and practices.

Keywords

three-level meta-analysis positive teacher-student relationships students’ academic achievement moderation analysis

Teachers have the most direct school effect on a student's learning (Meece & Eccles, 2010), as teachers with better relationships with students can: (a) motivate them to study more and learn more (Lawman & Wilson, 2013), and/or (b) engage with them more, understand them better, and adapt instruction to address their needs, thereby helping them learn more (Shepard, 2017). Moreover, unlike egalitarian, individualistic students in Western societies (e.g., United States) who value autonomy, students in hierarchical, collectivist societies like China value teacher advice/suggestions much more and are more motivated by their teachers (Chan & Chan, 2005). As a result, the positive link between teacher-student relationship (TSR) and student academic achievement (SAA) might be larger in China than in Western societies. This meta-analysis of 90 effect sizes in 74 studies of 233,961 students’ TSRs and SAAs in China tests (a) their TSR-SAA link and (b) whether it differs across moderators.

Theoretical framework

We begin by explicating the mechanisms through which students with better TSRs might be associated with better learning, before considering a contrary view. Next, we examine how the TSR-SAA link might differ in China compared to Western countries, and how it might differ across demographics, academic subject, or study measures.

TSR and SAA

Better TSRs can improve SAA via student motivation or adaptive teaching. According to self-determination theory (Ryan & Deci, 2017), students have a human need for relationships with others, such as teachers (relatedness). When a teacher cares about, encourages, and supports a student, they tend to have a better TSR than others, so the student is more likely to be motivated, exert more effort, study more, and learn more (Lawman & Wilson, 2013). Moreover, when such a student faces challenging tasks or setbacks, they are more likely to persevere, especially with teacher encouragement, feedback, and support (Koca, 2016), and ultimately learn more (Nicol & Macfarlane-Dick, 2006). Conversely, negative TSRs can hinder the satisfaction of students’ relatedness needs, and harm their learning outcomes (Reeve & Jang, 2006).

Other researchers concede that a TSR can improve a student's long-term motivation and academic resilience but argue that TSR does not improve short-term academic achievement (Fang et al., 2020). As a result, TSRs are not always linked to greater SAA (Chi & Zhou, 2003).

Also, better TSRs can yield teacher actions that help students learn more, according to formative assessment theory (Bennett, 2011). Teachers with better relationships with their students are more motivated to engage with them more often or for longer time periods (Spilt et al., 2012a). During these interactions with students, teachers learn more about them and understand them better (Shepard, 2017). Using their understanding of their students, teacher can adapt their instruction or assistance to better meet their students’ needs to help them learn more (Bennett, 2011).

On balance, these different theoretical perspectives suggest that better TSRs are linked to higher SAA. Furthermore, a meta-analysis of student-centered TSRs and SAA showed a small, positive link (r = .16; Cornelius-White, 2007). Also, a meta-analysis of affective TSRs and SAA showed a similar, small, positive link (r = .16; Roorda et al., 2011). Hence, we test the following hypothesis.

H-1. Among students, those with better TSRs show higher SAA.

China versus Western countries

TSRs might yield stronger positive effects on SAA in hierarchical, collectivistic China rather than egalitarian, individualistic Western societies. In hierarchical cultures (e.g., China), parents and teachers expect students to value and respect the authority of their teachers as superiors, obey them, and avoid conflicts with them (Chan & Chan, 2005; Hofstede et al., 2010; Jia et al., 2009). With greater teacher authority in China than in Western societies, teachers are often the center of TSRs, and many students view their teachers as role models (Jia et al., 2009). As a result, compared to students in Western societies, Chinese students spend more time with their teachers, enabling them to know one another better, which in turn helps teachers adapt their instruction to these students’ needs and improve their learning (Jia et al., 2009).

Unlike individualistic, Western societies, collectivist ones such as China value group goals over individual goals, so Chinese teachers and students are more attentive to each other's concerns, which raises the value of TSRs (Chiu & Chow, 2011). Indeed, Chinese teachers value positive relationships with students as a pedagogical strategy to promote students’ learning and development (Bear et al., 2014). Moreover, students typically interpret teacher criticisms and suggestions as caring rather than intrusive, unlike many students who value autonomy in individualistic societies (Chiu et al., 2007). Hence, these positive teacher-student interactions drive many Chinese students’ high extrinsic motivation, which is linked to superior learning outcomes (unlike students in Western societies; e.g. Chiu & Chow, 2010; Chiu & Zeng, 2008; d’Ailly, 2003).

Since the 2001 curriculum reform in China, the society has gradually formed a consensus of improving teacher-student relationships (China Ministry of Education, 2001). Such improved relationships can help teachers attend to students’ needs, foster their development, and improve their learning outcomes.

Hence, we hypothesize that the TSR-SAA link is larger in China than in Western countries (r = .16 in past meta-analyses of studies in Western countries, Cornelius-White, 2007; Roorda et al., 2011).

H-2. The TSR-SAA link is higher in China than in Western societies (r > .16).

Moderators

Considering the expected high value of TSRs for SAA, the mixed results of past studies and the small results of meta-analyses (Cornelius-White, 2007; Roorda et al., 2011) suggest substantial differences in this TSR-SAA link across studies. In addition to accounting for small samples of past studies, our meta-analysis also tests whether demographics, academic subject, or study attributes account for differences in the TSR-SAA link.

Region

Potential demographic moderators include region, grade level, and gender. China's eastern region is the richest and has the highest population density, while its western region is the poorest and has the lowest population density (Gaughan et al., 2016; Wang, 2010). Richer regions typically train and hire higher quality teachers (Peng et al., 2014; Qian & Smyth, 2008), and give them more educational resources in its richer schools (Wang, 2010) to support their students with better TSRs, which might help them learn more. Hence, the TSR-SAA link might be stronger in richer regions in China.

H-3a. Richer regions of China have larger TSR-SAA links (eastern > central > western).

Meanwhile, greater population density dilutes people's interactions; as teachers and their students are more likely to meet other people both inside and outside school, they are less likely to interact with each other and know each other (Gaughan et al., 2016; Spilt et al., 2012a). As a result, such teachers with poorer understanding of their students cannot adapt as well to their needs, resulting in poorer learning outcomes (Shepard, 2017). Hence, population density suggests a competing hypothesis.

H-3b. China's regions with lower population density have larger TSR-SAA links (western > central > eastern).

To adjudicate these competing hypotheses of the relative importance of resources or population density, moderation tests in our meta-analysis will help determine whether the TSR-SAA link is greatest in the eastern region of China (rich), its western region (population density), or its central region (intermediate values of resources and population density).

Grade level

Grade level's temporal proximity to China's annual national college entrance examination (gaokao, 高考) might moderate the TSR-SAA link. High school seniors (grade 12) in China must take the gaokao for entrance into nearly all higher education institutions (Muthanna & Sang, 2015). Hence, high school seniors and their teachers are much more motivated to optimize SAA to perform well on the gaokao, compared to students and teachers in lower grades (Wise & Kingsbury, 2016). While students with higher SAA are more likely to perform well on the gaokao and enroll in universities with more prestige (Muthanna & Sang, 2015), their teachers (and their school) gain superior reputations (e.g., schools use the high scores on the math component of the gaokao to tout the high quality of their math teachers; Pires, 2019). Hence, we propose that compared to students in lower grades, students in higher grades closer to the gaokao (especially high school seniors) are likely capitalize on their relationships with teachers more, better utilizing their adaptive teaching to their own needs to learn more.

H-4 Among students, those in higher grade levels show stronger, positive TSR-SAA links.

Gender

Compared to boys, girls’ greater empathy (Chen et al., 2014; Christov-Moore et al., 2014; O'brien et al., 2013) and social skills (Salavera et al., 2020) enable them to not only build better relationships with their teachers (Spilt et al., 2012b) but also to better capitalize on them. Teachers often provide more academic support to female students than to male students (Korlat et al., 2021; Lietaert et al., 2015), which female students use for greater SAA, compared to male students (Chi et al., 2018).

H-5 Compared to boys, girls show stronger, positive TSR-SAA links.

Academic subject

Academic subjects that mostly rely on the teacher rather than other sources (student, family, etc.) might show greater TSR effects on SAA (Lehrl et al., 2020). Students can learn many academic subjects from the world around them (e.g., science from observing nature, Chinese from listening to others; Scheele et al., 2010; Taber, 2011). By contrast, students have less access to abstract or foreign academic subjects, such as mathematics and foreign language (e.g., English), which students learn much more easily from teachers in school than without them (Danesi, 2016; Johnson, 2017). Hence, better TSRs might be benefit SAA more in unfamiliar, foreign, abstract academic subjects rather than familiar, socially- or physically-accessible academic subjects. Hence, we propose the following hypothesis.

H-6. The TSR-SAA link is strongest for English, and progressively weaker for mathematics, Chinese, and science.

Study attributes

Study limitations unrelated to the underlying phenomena (TSR measure, SAA measure, publication type, publication year) might also affect the results (and omitting significant study limitation effects can bias the overall effect size, omitted variable bias; Cinelli & Hazlett, 2020). No published study has used an appropriate multilevel factor analysis of the four possible factor structures (single, multiple, hierarchical, nested [bifactor]) of TSR (see full procedure and details in Ahn et al., 2019), and past studies have shown one TSR factor (Adams & Turner, 2014; Ingels et al., 2004), 2 TSR factors (Drugli & Hjemdal, 2013), or 3 TSR factors (Ang et al., 2008, 2020), so our meta-analysis tested whether different TSR measures yielded significant differences in their links to SAA.

Students who view a person favorably are more likely to positively bias assessments of their attributes (halo effect, Forgas & Laham, 2016). Hence, students who perceive a positive relationship with their teacher might want to view their learning interactions positively and self-report high SAA in that academic subject, yielding larger effect sizes compared to teacher assessments (Kuncel et al., 2005). Also, journals are more likely to publish significant findings than non-significant findings, so effect sizes might be larger in journal articles than dissertations (Card, 2015). Lastly, the TSR-SAA link might differ across time (publication year). Hence, our meta-analysis accounts for the effects of such study attributes.

This study

This meta-analysis synthesized the results of past TSR-SAA studies to (a) determine the overall effect size of the TSR-SAA link and (b) test whether this link differed across demographics (region, grade level/age, gender), academic subjects, or study attributes.

Method

Literature search

We retrieved articles published from January 2001 to March 2021 using keywords in these electronic databases: CNKI, Wanfang Data, VIP, Web of Science, Google Scholar, ERIC, PsycINFO, ProQuest dissertations & Theses Global. We searched for articles using keywords for TSRs (teacher-student relationship, teacher attachment, teacher empathy, teacher support, teacher involvement, teacher closeness, attachment, warmth, support, relationship, relatedness, involvement, affection, empathy, trust, sensitivity, responsive, like, liking, care, caring or concern) and for SAA (student academic achievement, student performance, student school results, student learning, student development, student attainment, school results, achievement, performance, learning, development, attainment, success or outcome). When searching in English databases, we added the keywords: China or Chinese. These searches yielded 647 potentially relevant listings for consideration, 403 from Chinese databases and 244 from English databases.

Inclusion

We applied the following inclusion criteria to the retrieved articles: (1) the study examined the link between TSRs and SAA in the Chinese context; (2) the assessment tools and research methods were clearly reported; (3) the participants were students within primary to senior high school; and (4) the study provided sufficiently detailed statistical information for a meta-analysis (sample size and correlations between two variables or a t or F-value that could be transformed into r). Two researchers reviewed the titles, abstracts, and full texts of the articles, and removed duplicate reports and papers that failed the inclusion criteria, yielding 74 articles (see Fig. 1). The actual, included studies were published between 2002 and 2020.

Figure 1.
Flow chart of the searching procedure.

Coding

Two researchers who studied and regularly run meta-analyses separately coded the included articles based on their attributes: author, publication year, sample size, correlation coefficient, publication type (journal vs. dissertation), region in China (central, east, west; see Table 1), grade level (primary school, junior high school, senior high school, and mixed), gender (female %), academic subject (Chinese, English, math, science, mixed), achievement report type (test, self-report, teacher report, and mixed), TSR survey (index vs. question), and TSR questionnaire (see Appendix A). These researchers had an initial agreement of 91%. Then, they discussed their disagreements and agreed on the final codes via consensus.

Table 1.
Region coding details.

Region Province

Central Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henai, Hubei, Hunai

East Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Hong Kong

West Sichuan, Chongqing, Guizhou, Yunnan, Xizang, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Guangxi, Neimeng

Of the primary studies, 74% used achievement tests, 10% relied on students’ self-report, 2% relied on teacher reports, 3% used multiple measures, and 10% did not clearly report their SAA; 58% were journal articles and 42% were dissertations (see Table 2). Students’ self-reported SAA rather than objective test scores are weaknesses in several primary studies. On the other hand, the 90 effect sizes, 74 studies (all were surveys), diversity of publications, seven TSR measures (see Appendix A), geographic regions in China (eastern, central, western), grade levels (primary, junior secondary, secondary), and academic subjects (science, Chinese, math, English) are strengths.

Table 2.
Characteristics of studies included in the meta-analysis.

Author, year N r Publ Region Grade % Girl Subject Report Survey type Survey origin Quality

Chao, 2013-1 1092 .190 Diss Eastern Mixed .43 Chinese Mixed index Other .85

Chao, 2013-2 1092 .200 Diss Eastern Mixed .43 English Mixed index Other .85

Chen & Guo, 2016 1379 .260 Journal N Junior .51 Mixed test question Ouyang .90

Chen et al., 2018 824 .080 Journal Western Mixed .54 Mixed test question Ouyang .90

Chen, 2005-1 270 .300 Journal Eastern Mixed .49 Chinese self question Other .75

Chen, 2005-2 270 .330 Journal Eastern Mixed N Math self question Other .75

Chen, 2005-3 270 .270 Journal Eastern Mixed .52 English self question Other .75

Chen, 2011 519 .170 Diss Central Senior .52 Mixed test index Zhang .85

Chen, 2014 1502 .280 Diss Central Junior .54 Mixed test question Williams .81

Chen, 2015 287 .244 Journal Western Senior .47 English test question Ouyang .60

Chi & Zhou, 2003 227 .160 Journal Central Junior .43 Mixed test question Other .60

Chi et al., 2018 3279 .119 Journal Eastern Mixed .43 Science test index Other .91

Cui et al., 2020-1 14021 .110 Journal Eastern Senior .43 Chinese test question PISA2012 .75

Cui et al., 2020-2 14021 .140 Journal Eastern Senior N Math test question PISA2012 .75

Cui et al., 2020-3 14021 .160 Journal Eastern Senior N English test question PISA2012 .75

Dai & Lin, 2019 36248 .227 Journal N Primary .48 Chinese test question Other .70

Dong, 2015 305 .259 Diss Eastern Primary .44 English test index Zhang .70

Fang et al., 2020 2328 .081 Journal Mixed Junior N Mixed test question Other .80

Fu et al., 2017 3154 .147 Journal Eastern Junior .43 Chinese test question Other .60

Fu, 2018-1 553 .410 Diss Eastern Junior .47 Mixed test question Ouyang .85

Fu, 2018-2 553 .460 Diss Eastern Junior .64 Mixed self question Ouyang .85

Gan, 2013 235 .278 Diss Eastern Senior N Mixed test question Other .60

Gao, 2009 455 .232 Diss Eastern Junior .51 Mixed test question Ouyang .75

Gu, 2018-1 650 .095 Diss Eastern Junior .48 Chinese test question Other .95

Gu, 2018-2 650 .116 Diss Eastern Junior .51 Math test question Other .95

Gu, 2018-3 650 .096 Diss Eastern Junior .49 English test question Other .95

Hong, 2014 621 .169 Diss Central Junior .60 Mixed self index Other .95

Hou, 2018 337 .281 Diss Central Primary .68 Mixed test index Zhang .90

Huang, 2019 891 .177 Diss Central Senior .27 Science N index Zhang .75

Hui & Sun, 2010 760 .290 Journal Eastern Primary .46 Mixed self question Other .80

Jia et al., 2009 706 .060 Journal Eastern Junior .46 Mixed test index Other .90

Jiang & Qi, 2011 289 .203 Journal Central Primary .46 Mixed test question Pianta .75

Jiang, 2018 2324 .378 Diss Eastern Senior .46 Science test question Pianta .80

Kang, 2004 94 .639 Diss Eastern Senior .59 English N question Other .50

Lam et al., 2012 300 .160 Journal Mixed Junior N Mixed teacher question Other .90

Lei et al., 2015 1430 .430 Journal Central Mixed .54 Mixed test question Lei .85

Li & Bai, 2018 331 .167 Journal Western Junior .48 Mixed test question Ouyang .90

Li et al., 2018 614 .090 Journal Western Junior .42 Mixed test question Other .90

Li, 2019 373 .153 Diss Eastern Primary N Mixed test question Ouyang .90

Liao et al., 2011 149 .370 Journal Central Junior .58 Science test index Zhang .70

Lin et al., 2020 1423 .225 Journal Eastern Junior .43 Math test question Other .95

Liu et al., 2019 601 .300 Journal Eastern Primary .43 Mixed test question Ouyang .80

Liu, 2018 690 .122 Diss Eastern Junior .51 Mixed test question Lei .90

Liu, 2019 141 .499 Diss Central Senior .54 English N index Other .75

Lu et al., 2016 594 .230 Journal Mixed Primary .49 Mixed N question Other .80

Lu, 2016 392 .189 Diss Central Primary N Mixed test index Zhang .80

Luo & Ye, 2019 237 .314 Journal N Mixed .52 Science N index Other .55

Ma et al., 2018 11036 .397 Journal N Junior .52 English test question PISA2012 .95

Ni, 2006 72 .506 Journal Eastern Senior .54 English test question Other .60

Ouyang, 2005 364 .157 Diss Western Primary .47 Mixed test question Ouyang .75

Pang, 2009 288 .121 Journal Eastern Junior .43 Mixed test index Other .75

Qiao et al., 2013 331 .433 Journal Eastern Junior .43 Mixed test question Ouyang .95

Ren et al., 2017-1 2584 .200 Journal Central Junior .43 Chinese test index Other .95

Ren et al., 2017-2 2584 .170 Journal Central Junior N Math test index Other .95

Ren et al., 2017-3 2584 .220 Journal Central Junior N English test index Other .95

Song, 2017 577 .172 Diss Eastern Junior .48 Mixed test question Other .90

Song et al., 2015 209 .215 Journal Eastern Primary .44 Mixed test question Pianta .65

Tang et al., 2013 4727 .450 Journal Mixed Senior N Mixed self question Williams .90

Teng et al., 2017 606 .247 Journal Eastern Primary N Mixed test question Ouyang .90

Wang & Wang, 2002 498 .239 Journal Eastern Primary .43 Mixed teacher question Pianta .75

Wang et al., 2018 1150 .170 Journal Mixed Primary .47 Mixed self index Other .85

Wang et al., 2019-1 7106 .220 Journal Western Primary .64 Chinese test question PISA2012 .95

Wang et al., 2019-2 7106 .240 Journal Western Primary N Math test question PISA2012 .95

Wang et al., 2019-3 7106 .150 Journal Western Primary .51 Science test question PISA2012 .95

Wang, 2016 696 .108 Diss Central Primary .48 Mixed test question Lei .90

Wang, 2017 412 .601 Diss Mixed Junior .51 Mixed N index Zhang .85

Wang, 2018 564 .188 Diss Eastern Senior .49 Mixed N question Williams .95

Wei, 2013 116 .541 Diss Central Senior .49 English test index Other .70

Xu & Qi, 2019 42643 .222 Journal N Junior .68 Math test question Other .80

Yan, 2018-1 306 .180 Diss Eastern Junior .46 Chinese test question Pianta .64

Yan, 2018-2 306 .180 Diss Eastern Junior .46 Math test question Pianta .64

Yan, 2018-3 306 .190 Diss Eastern Junior .46 English test question Pianta .64

Yan, 2018-4 306 .170 Diss Eastern Junior .46 Mixed test question Pianta .64

Yang et al., 2016 656 .120 Journal Eastern Primary .59 Mixed test question Lei .90

Yang, 2015 2455 .228 Journal Western Junior N Math Mixed index Other .85

Yi, 2020 308 .229 Diss Eastern Senior .54 Math test index Zhang .95

Zhang & Jiang, 2009 384 .624 Journal Central Junior .42 Mixed test question Pianta .70

Zhang & Li, 2019 259 .151 Journal Western Junior .48 Mixed test question Ouyang .85

Zhang et al., 2018-1 1421 .180 Journal Eastern Junior N Chinese test question Williams .80

Zhang et al., 2018-2 1421 .100 Journal Eastern Junior .58 Math test question Williams .80

Zhang et al., 2020 2213 .335 Journal Central Junior .43 Mixed test question Other .85

Zhang, 2017 284 .250 Diss N Primary .43 Mixed test question Williams .60

Zhang, 2019a 272 .410 Diss Eastern Junior .51 Mixed test question Williams .70

Zhang, 2019b 3012 .250 Diss Eastern Primary .54 Math test question Chai .90

Zhao et al., 2017 400 .193 Journal Eastern Primary .49 Math N question Chai .55

Zhao, 2014 518 .409 Diss Central Junior N Math test index Zhang .85

Zhen, 2018 300 .501 Diss Central Senior .52 Mixed N index Zhang .55

Zhu et al., 2017 12404 .198 Journal Central Junior .52 Mixed test index Other .75

Zhu et al., 2018 294 .350 Journal N Primary .54 English test index Other .85

Zhu, 2017 625 .252 Diss Central Mixed .47 Mixed self index Other .90

Mixed: indicates two or more coding categories.

N: indicates no report.

Three-level meta-analysis

Assessment of study quality

We applied Kmet et al.'s (2004) checklist to assess the quality of quantitative studies. Its 14 criteria include: study aim and problem, participants’ characteristics, study design, study methods, and reporting of study results. For each study, two researchers assessed it along each criterion; a study that fully met it earned 2 points, that partially met it earned 1 point, and that did not meet it earned no points. Inter-rater reliability was 92%. Criteria not applicable to a study design were excluded from the summary score. For each study, all points were summed and divided by its maximum possible score. Quality categories were strong (> .8), good (.7 - .8), adequate (.5 - .7) or limited (< .5). Study quality ranged from .50 to .95 (mean = .79), so all studies were at least of adequate quality and were included in the meta-analysis.

Effect size

Studies that included multiple effect sizes for different measures of academic achievement (e.g., academic subjects such as math vs. science) were separately coded. These 74 studies of 233,961 students yielded 90 effect sizes.

As correlation coefficients are widely used and easy to explain, we used them as indicators of effect sizes (Hedges & Olkin, 2014). The sample sizes of the studies differed substantially, so we applied the Fisher Z-transformation with weights based on their sample sizes to compute the final r and 95% confidence intervals (see equations 1 and 2, Fisher, 1921). Correlation coefficients were first transformed into Fisher's z-values. After conducting the meta-analysis, Fisher's z-values and their confidence intervals were converted back respectively into correlation coefficients and their confidence intervals (Hedges & Olkin, 2014).
$z = .5 \log_{e} [(1 + r) / (1 - r)]$
(1)
$S E_{z} = (1 / n - 3)^{.5}$
(2)

Data analysis

As effect sizes within a study likely resemble one another more than those across studies, they can yield false positives (Assink & Wibbelink, 2016). To address this issue, this study used a three-level meta-analysis to model sampling errors (level 1), within-study errors (level 2) and between-studies errors (level 3, Assink & Wibbelink, 2016). The three-level meta-analysis was performed in R40.0.4, using the functions of metafor, dmetar, and meta packages (Schwarzer et al., 2015; Viechtbauer, 2010).

Unlike a fixed effects model that assumes one true effect size for all studies, a random-effects model assumes different true effect sizes across populations and contexts. As these studies occurred in different regions in China and assessed SAA in different academic subjects, they were likely distinct and heterogeneous, so we used a random effects model, which likely better fit the sampling distribution, allowed effect sizes to vary, and allowed our conclusions to generalize more broadly (Borenstein et al., 2010).

This study used a one-tailed log likelihood ratio test to assess heterogeneity in correlations within and between studies (level 2 and level 3) (Assink & Wibbelink, 2016). If the heterogeneity test was significant, post-hoc contrasts tested whether the groups statistically differed. For each potential moderation effect, studies that did not report the value of the moderating variable were not included in the analysis.

This three-level meta-analysis determined the overall TSR-SAA relation. Then, it tested for moderators across demographics, academic subject, and study attributes.

Evaluation of publication bias

We assessed the risk of publication bias with a funnel chart (Madigan et al., 2016; Wei et al., 2017), Egger's regression (Egger et al., 1997), and Rosenthal's fail-safe N (Rosenthal, 1979). For the funnel chart, effects symmetrically distributed around the overall effect size showed no publication bias. If the intercept of Egger's regression was near 0 and was not significant, the likelihood of publication bias is low (Egger et al., 1997). When the minimum number of additional studies with non-significant results that would render the current meta-analysis results non-significant (fail-safe N) falls below 5k + 10 (k = number of original studies), publication bias is a substantial possibility (Rosenthal, 1979).

Results

These 74 studies of 233,961 students (sample range: 72 … 42,643) yielded 90 effect sizes between TSR and SAA (see Table 2 ). See forest plot in Fig. 2.

Figure 2.
Forest plot for the random-effects model.

Effect size and the homogeneity test

The three-level random-effects model showed a significant positive correlation between TSR and SAA (r = .259, 95% CI = [.227; .290]; see Table 3 ), supporting hypothesis H-1. Furthermore, the overall effect size was larger than those in other countries (r = .160; Cornelius-White, 2007; Roorda et al., 2011), supporting H-2. Most of the variance was across studies (94%), 4% across effect sizes within studies, and 2% from random sampling error (see Table 3 ). The heterogeneity test showed significant heterogeneity at both level 2 and level 3 that moderation variables might explain (see Table 3 ).

Table 3.
Random-effects model of the correlation between positive teacher-student relationships and students’ academic achievement.

k Fisher’ s Z (SE) 95% CI t r [95% CI] Level 1% Level 2 variance Level 3 variance

Level 2% Level 3%

Effect sizes 90 .265 (.017) [.231; .299] 15.553* .259 [.227; .290] 2.079 .018* 4.225 .019* 93.696

*p < .001; **p < .01; p < .05.

Assessment of publication bias

The funnel plot, Egger's regression, and Rosenthal's fail-safe N showed no evidence of publication bias. The funnel plot showed a somewhat symmetric distribution of the 90 effects around the overall effect size (see Fig. 3). Also, Egger's regression showed no significant bias (β₀ = 1.007, t ₍₉₀₎* = 1.393, p = .17), and Rosenthal's fail-safe N far exceeded the minimum threshold (8,935 > 460).

Figure 3.
Funnel plot of effect sizes of the correlation between positive teacher-student relationships and students’ academic achievement.

Moderator analysis

As the heterogeneity test was significant across both effects (level 2) and studies (level 3; see Table 3 ), we tested for moderation effects across demographics (region, grade level, gender), academic subject, and study attributes.

Region, grade level, and gender moderated the TSR-SAA link (see Table 4 and Table 5 ). Region of China moderated the TSR-SAA link (F = 3.722, p < .05). The central region showed the largest effect size (r = .305, 95% CI = [.106 … .481]), followed by the eastern region (.238 [.041 … .418]) and the western region (.166 [.072 … .257]), partially rejecting both H-3a (rich) and H-3b (population density).

Table 4.
Results of moderators for the correlation between positive teacher-student relationships and students’ academic achievement (categorical variables).

Moderators k Fisher's z [95% CI] r [95% CI] F Level 2 Level 3

Demographics

Region 77 3.722* .001 .016

Central 22 .315[.106, .525]* .305 [.106; .481]

Eastern 45 .243 [.041, .445] .238 [.041; .418]

Western 10 .167 [.072, .263]*** .166 [.072; .257]

Grade level 80 3.641 * .001 .018

Senior 16 .360 [.280, .440] *** .345 [.273; .414]

Junior 41 .257 [.082, .432] * .251 [.082; .407]

Primary 23 .225 [.042, .407] * .221 [.042; .386]

Academic subject

Academic subject 44 5.782 .000 .015

English 14 .311 [.199 .424] * .302 [.196; .400]

Math 14 .280 [.173, .386] .272 [.171; .368]

Chinese 10 .276 [.169, .383] .269 [.167; .365]

Science 6 .205 [.144, .265] *** .202 [.143; .259]

Study attributes

Report type 78 1.009 .001 .015

Self 9 .303 [.181, .425] .294 [.179, .401]

Test 67 .244 [.209, .279]*** .240 [.206, .272]

TEACHER 2 .204 [-.024, .432] .201[-.024, .407]

Relationship survey 90 .400 .001 .019

index 27 .281 [.167, .395] .274 [.165, .376]

question 63 .258 [.216, .299]*** .252 [.213, .290]

Questionnaire 52 .621 .001 .022

Chai 2 .227 [-.120, .573] .223 [-.119, .518]

Lei 4 .205 [-.084, .494] .202 [-.084, .458]

Ouyang 13 .248 [.008, .488] .243 [.008, .453]

Pianta 9 .330 [.062, .598] .318 [.062, .535]

PISA2012 7 .253 [-.052, .557] .247 [-.052, .506]

Williams 7 .298 [.031, .564] .289 [.031, .511]

Zhang 10 .338 [.236, .440]*** .326 [.231, .414]

Publication type 90 1.708 .001 .019

Dissertation 38 .292 [.179, .404] .284 [.177; .384]

Journal 52 .246 [.202, .290]* .242 [.200; .283]

*p < .001; **p < .01; p < .05.

Table 5.
Results of moderators for the correlation between positive teacher-student relationships and students’ academic achievement (continuous variables).

Moderators k Intercept* [95% CI] B [95% CI] F Level 2 Level 3

Demographics

Female % 74 .266 [.229; .303]* .507 [.285; .729]* 20.754*** .000 .020

Study attributes

Publication year 90 .265 [.231, .299]* -.006 [-.014; .002] 2.394 .001 0.018

*p < .001; **p < .01; p < .05.

Grade level moderated the TSR-SAA link (F = 3.641, p < .05). Senior high school showed the largest effect size (r = .345 [.273 … .414]), followed by junior high school (.251 [.082 … .407]) and primary school (.221 [.042 … .386]), supporting H-4.

Gender moderated the TSR-SAA link (F = 20.754, p < .001). The TSR-SAA link was larger among females than males (B = .507), supporting H-5.

Academic subject moderated the TSR-SAA link (F = 5.782, p < .01). English showed the highest TSR-SAA effect size (r = .302 [.196 … .400]), followed by mathematics (.272 [.171 … .368]), Chinese (.269 [.167 … .365]) and science (.202 [.143 … .259]), supporting H-6.

None of the study attributes (SAA measure, TSR measure, publication type [journal article vs. dissertation] or publication year) moderated the TSR-SAA link (see Table 4 and Table 5 ).

Discussion

This meta-analysis of 90 effect sizes in 74 studies showed a substantial positive link between TSRs and SAA. Furthermore, this TSR-SAA link was moderated by region, grade level, gender, and academic subject.

Positive TSR-SAA link

The positive TSR-SAA link (r = .259) among mainland Chinese students aligns with both self-determination theory (Ryan & Deci, 2017) and formative assessment theory (Bennett, 2011). Although causal conclusions cannot be drawn, this positive TSR-SAA link supports the view that a TSR (in which a teacher cares about, encourages and supports a student) helps satisfy a student's need for relatedness (Ryan & Deci, 2017) and raises his/her motivation. As a result, such students exert more effort, study more (Lawman & Wilson, 2013) and persevere longer on tasks despite challenges or setbacks (Koca, 2016)—ultimately learning more (Nicol & Macfarlane-Dick, 2006).

This positive TSR-SAA link also supports the view that better TSRs can yield teacher actions that help students learn more (Bennett, 2011). Specifically, TSR-SAA link aligns with the view that a TSR motivates teachers to engage more with a student (Spilt et al., 2012a), understand them better (Shepard, 2017), and use this understanding to adapt instruction/assistance to address this student's needs to aid more learning (Bennett, 2011).

Likewise, the positive TSR-SAA link rejects the view that TSR supports only long-term academic resilience, not short-term SAA. Future studies can determine the extent to which the student motivation/effort or teacher motivation/adaptation mechanisms account for the TSR-SAA link.

TSR-SAA link in China versus Western countries

The larger TSR-SAA link in China than in Western countries (r: .259 > .160; Cornelius-White, 2007; Roorda et al., 2011) supports the view of teacher-student relationship differences in hierarchical, collectivistic China versus egalitarian, individualistic Western societies (Chan & Chan, 2005; Jia et al., 2009; Hofstede et al., 2010). Specifically, this result supports the view that compared to Western societies, students in China respect teacher authority more, view them as role models more, and spend more time with them; as a result, these teachers know their students better and adapt their assistance more to their needs to improve their learning (Jia et al., 2009). Moreover, students in China are more likely than students in Western societies to interpret teacher criticisms and suggestions as caring rather than intrusive, thereby driving their high extrinsic motivation, which is linked to superior learning outcomes in China (but not in Western societies; e.g. Chiu & Chow, 2010; Chiu & Zeng, 2008; d’Ailly, 2003). Future studies can determine the extent to which the hierarchy/teacher adaptation or collectivist/student motivation mechanisms account for differences in TSR-SAA links across countries.

Moderators

The positive TSR-SAA link differed across demographics (regions in China, grade levels) and academic subjects. The positive TSR-SAA link was strongest in the central region of China, which partially rejects both hypotheses H-3a (rich) and H-3b (population density). Future fine-grained studies can determine whether: (a) both economic resources and population density aid TSRs (in which case, the central region of China has beneficial, intermediate or threshold quantities of both), (b) central regions in China have a completely different advantage over its eastern and western regions, or (c) this result is a statistical anomaly.

Students in higher grade levels in China showed larger TSR-SAA links. This result supports the view that the greater temporal proximity of higher grade level students to the gaokao motivates them and their teachers to exert more effort to enhance SAA (Wise & Kingsbury, 2016). Specifically, it is consistent with the claim that students in higher grade levels (especially high school seniors) benefit more from engaging with their teachers to study more effectively for higher SAA, ultimately to gain acceptance into higher prestige universities (Muthanna & Sang, 2015); likewise, teachers’ (and their schools’) reputations benefit from higher SAA (Pires, 2019).

The positive TSR-SAA link was larger among females than males. This result supports research suggesting that compared to boys, girls have greater empathy (Chen et al., 2014; Christov-Moore et al., 2014; O’brien et al., 2013) and social skills (Salavera et al., 2020), which they use to build better relationships with their teachers (Spilt et al., 2012b), obtain more academic support from them (Korlat et al., 2021; Lietaert et al., 2015), and capitalize on it for greater SAA (Chi et al., 2018).

The positive TSR-SAA links were largest in English, and progressively smaller in math, Chinese, and science. These results align with the view that learning less accessible, foreign, or abstract academic subjects relies more on the teacher (and TSR) than other sources (student, family, etc.; Lehrl et al., 2020): (a) least accessible, foreign language (English, Johnson, 2017), (b) abstract, low accessibility mathematics (Danesi, 2016), (c) accessible Chinese via family and friends (Scheele et al., 2010), and (d) accessible science via physical experiences and observations in the natural world (Taber, 2011).

As past studies did not use multilevel factor analyses of the four possible factor structures (single, multiple, hierarchical, nested [bifactor]) of TSR (Ahn et al., 2019), they showed conflicting results (one factor: Adams & Turner, 2014; Ingels et al., 2004; two factors: Drugli & Hjemdal, 2013; three factors: Ang et al., 2008, 2020), so our meta-analysis tested whether different TSR measures showed significantly different links to SAA. The non-significant moderation effect of TSR measure showed that its different measures do not significantly affect its estimated link with SAA. Hence, researchers studying the link between TSR and SAA can use any of these TSR measures.

Implications

These meta-analysis results have theory, policy, and practice implications. The results not only support the theoretical relationship of a significant positive link between TSR and SAA but also elaborates it by showing its differences across cultures, region, grade level, and academic subjects. Hence, comprehensive theories of TSRs should model these attributes.

Governments and schools can fund and implement policies to enhance TSRs. For instance, a school program that matched a student with a teacher to develop a better relationship yielded higher grades for these students (Murray & Malmgren, 2005). Furthermore, extracurricular activities with both teachers and students tend to improve TSRs (Claessens et al., 2017). Also, allowing students to evaluate teachers yielded positive student views of their classroom teachers, but assigning higher performing teachers with higher performing students yielded negative* student views of their classroom teachers (Barile et al., 2012).

Regarding practice, both students and teachers can make more efforts to build better relationships, which also yield personal gains. Students can make individual efforts to build a better relationship with at least one teacher for both mutual understanding as well as better learning outcomes. Likewise, teachers can spend more time with students (e.g., during lunch time) both to help students and to improve their own teaching.

Limitations and future research

This study's geographic scope, sampling, and heterogeneity limit its generalizability. As the primary studies were only from mainland China, these results do not necessarily generalize outside of its borders. Also, none of the primary studies used representative sampling, so these results do not proportionally reflect the student population of China. Also, the moderation effects show substantial differences across demographics and academic subject.

This meta-analysis had several limitations. First, several primary studies used subjective reports of SAA rather than objective test scores, so future primary studies can measure SAA with psychometrically valid, standardized tests. Second, characteristics such as students’ socioeconomic status or teachers’ working experience were not reported in the primary studies and could not be tested as moderators, so future primary studies can collect and report these data for future meta-analyses. Third, most of the articles were cross-sectional studies. Hence, the causality between TSRs and SAA could not be tested. Future studies can collect longitudinal data to examine the correlation between TSRs and SAA across time.

Conclusion

This meta-analysis examined whether positive teacher-student relationship was linked to student achievement in mainland China. Our three-level meta-analysis of 90 effect sizes from 74 studies of 233,961 students showed a significant positive link between teacher-student relationship and student achievement. Furthermore, this effect size in China is larger than its counterparts of studies in mostly Western societies.

This link between teacher-student relationship and student achievement differed across demographics (region, grade level, gender), academic subject. Studies in China's central region showed the largest links, followed by those in its eastern region, with the lowest in the western regions. For students temporally closer to taking the national college entrance exam, this link was stronger (senior high school > junior high school > primary school). This link was larger among females than males. Moreover, this link was stronger for less accessible, abstract, academic subjects (foreign language [English] > math > Chinese > science).

Region	Province
Central	Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henai, Hubei, Hunai
East	Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Hong Kong
West	Sichuan, Chongqing, Guizhou, Yunnan, Xizang, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Guangxi, Neimeng

Author, year	N	r	Publ	Region	Grade	% Girl	Subject	Report	Survey type	Survey origin	Quality
Chao, 2013-1	1092	.190	Diss	Eastern	Mixed	.43	Chinese	Mixed	index	Other	.85
Chao, 2013-2	1092	.200	Diss	Eastern	Mixed	.43	English	Mixed	index	Other	.85
Chen & Guo, 2016	1379	.260	Journal	N	Junior	.51	Mixed	test	question	Ouyang	.90
Chen et al., 2018	824	.080	Journal	Western	Mixed	.54	Mixed	test	question	Ouyang	.90
Chen, 2005-1	270	.300	Journal	Eastern	Mixed	.49	Chinese	self	question	Other	.75
Chen, 2005-2	270	.330	Journal	Eastern	Mixed	N	Math	self	question	Other	.75
Chen, 2005-3	270	.270	Journal	Eastern	Mixed	.52	English	self	question	Other	.75
Chen, 2011	519	.170	Diss	Central	Senior	.52	Mixed	test	index	Zhang	.85
Chen, 2014	1502	.280	Diss	Central	Junior	.54	Mixed	test	question	Williams	.81
Chen, 2015	287	.244	Journal	Western	Senior	.47	English	test	question	Ouyang	.60
Chi & Zhou, 2003	227	.160	Journal	Central	Junior	.43	Mixed	test	question	Other	.60
Chi et al., 2018	3279	.119	Journal	Eastern	Mixed	.43	Science	test	index	Other	.91
Cui et al., 2020-1	14021	.110	Journal	Eastern	Senior	.43	Chinese	test	question	PISA2012	.75
Cui et al., 2020-2	14021	.140	Journal	Eastern	Senior	N	Math	test	question	PISA2012	.75
Cui et al., 2020-3	14021	.160	Journal	Eastern	Senior	N	English	test	question	PISA2012	.75
Dai & Lin, 2019	36248	.227	Journal	N	Primary	.48	Chinese	test	question	Other	.70
Dong, 2015	305	.259	Diss	Eastern	Primary	.44	English	test	index	Zhang	.70
Fang et al., 2020	2328	.081	Journal	Mixed	Junior	N	Mixed	test	question	Other	.80
Fu et al., 2017	3154	.147	Journal	Eastern	Junior	.43	Chinese	test	question	Other	.60
Fu, 2018-1	553	.410	Diss	Eastern	Junior	.47	Mixed	test	question	Ouyang	.85
Fu, 2018-2	553	.460	Diss	Eastern	Junior	.64	Mixed	self	question	Ouyang	.85
Gan, 2013	235	.278	Diss	Eastern	Senior	N	Mixed	test	question	Other	.60
Gao, 2009	455	.232	Diss	Eastern	Junior	.51	Mixed	test	question	Ouyang	.75
Gu, 2018-1	650	.095	Diss	Eastern	Junior	.48	Chinese	test	question	Other	.95
Gu, 2018-2	650	.116	Diss	Eastern	Junior	.51	Math	test	question	Other	.95
Gu, 2018-3	650	.096	Diss	Eastern	Junior	.49	English	test	question	Other	.95
Hong, 2014	621	.169	Diss	Central	Junior	.60	Mixed	self	index	Other	.95
Hou, 2018	337	.281	Diss	Central	Primary	.68	Mixed	test	index	Zhang	.90
Huang, 2019	891	.177	Diss	Central	Senior	.27	Science	N	index	Zhang	.75
Hui & Sun, 2010	760	.290	Journal	Eastern	Primary	.46	Mixed	self	question	Other	.80
Jia et al., 2009	706	.060	Journal	Eastern	Junior	.46	Mixed	test	index	Other	.90
Jiang & Qi, 2011	289	.203	Journal	Central	Primary	.46	Mixed	test	question	Pianta	.75
Jiang, 2018	2324	.378	Diss	Eastern	Senior	.46	Science	test	question	Pianta	.80
Kang, 2004	94	.639	Diss	Eastern	Senior	.59	English	N	question	Other	.50
Lam et al., 2012	300	.160	Journal	Mixed	Junior	N	Mixed	teacher	question	Other	.90
Lei et al., 2015	1430	.430	Journal	Central	Mixed	.54	Mixed	test	question	Lei	.85
Li & Bai, 2018	331	.167	Journal	Western	Junior	.48	Mixed	test	question	Ouyang	.90
Li et al., 2018	614	.090	Journal	Western	Junior	.42	Mixed	test	question	Other	.90
Li, 2019	373	.153	Diss	Eastern	Primary	N	Mixed	test	question	Ouyang	.90
Liao et al., 2011	149	.370	Journal	Central	Junior	.58	Science	test	index	Zhang	.70
Lin et al., 2020	1423	.225	Journal	Eastern	Junior	.43	Math	test	question	Other	.95
Liu et al., 2019	601	.300	Journal	Eastern	Primary	.43	Mixed	test	question	Ouyang	.80
Liu, 2018	690	.122	Diss	Eastern	Junior	.51	Mixed	test	question	Lei	.90
Liu, 2019	141	.499	Diss	Central	Senior	.54	English	N	index	Other	.75
Lu et al., 2016	594	.230	Journal	Mixed	Primary	.49	Mixed	N	question	Other	.80
Lu, 2016	392	.189	Diss	Central	Primary	N	Mixed	test	index	Zhang	.80
Luo & Ye, 2019	237	.314	Journal	N	Mixed	.52	Science	N	index	Other	.55
Ma et al., 2018	11036	.397	Journal	N	Junior	.52	English	test	question	PISA2012	.95
Ni, 2006	72	.506	Journal	Eastern	Senior	.54	English	test	question	Other	.60
Ouyang, 2005	364	.157	Diss	Western	Primary	.47	Mixed	test	question	Ouyang	.75
Pang, 2009	288	.121	Journal	Eastern	Junior	.43	Mixed	test	index	Other	.75
Qiao et al., 2013	331	.433	Journal	Eastern	Junior	.43	Mixed	test	question	Ouyang	.95
Ren et al., 2017-1	2584	.200	Journal	Central	Junior	.43	Chinese	test	index	Other	.95
Ren et al., 2017-2	2584	.170	Journal	Central	Junior	N	Math	test	index	Other	.95
Ren et al., 2017-3	2584	.220	Journal	Central	Junior	N	English	test	index	Other	.95
Song, 2017	577	.172	Diss	Eastern	Junior	.48	Mixed	test	question	Other	.90
Song et al., 2015	209	.215	Journal	Eastern	Primary	.44	Mixed	test	question	Pianta	.65
Tang et al., 2013	4727	.450	Journal	Mixed	Senior	N	Mixed	self	question	Williams	.90
Teng et al., 2017	606	.247	Journal	Eastern	Primary	N	Mixed	test	question	Ouyang	.90
Wang & Wang, 2002	498	.239	Journal	Eastern	Primary	.43	Mixed	teacher	question	Pianta	.75
Wang et al., 2018	1150	.170	Journal	Mixed	Primary	.47	Mixed	self	index	Other	.85
Wang et al., 2019-1	7106	.220	Journal	Western	Primary	.64	Chinese	test	question	PISA2012	.95
Wang et al., 2019-2	7106	.240	Journal	Western	Primary	N	Math	test	question	PISA2012	.95
Wang et al., 2019-3	7106	.150	Journal	Western	Primary	.51	Science	test	question	PISA2012	.95
Wang, 2016	696	.108	Diss	Central	Primary	.48	Mixed	test	question	Lei	.90
Wang, 2017	412	.601	Diss	Mixed	Junior	.51	Mixed	N	index	Zhang	.85
Wang, 2018	564	.188	Diss	Eastern	Senior	.49	Mixed	N	question	Williams	.95
Wei, 2013	116	.541	Diss	Central	Senior	.49	English	test	index	Other	.70
Xu & Qi, 2019	42643	.222	Journal	N	Junior	.68	Math	test	question	Other	.80
Yan, 2018-1	306	.180	Diss	Eastern	Junior	.46	Chinese	test	question	Pianta	.64
Yan, 2018-2	306	.180	Diss	Eastern	Junior	.46	Math	test	question	Pianta	.64
Yan, 2018-3	306	.190	Diss	Eastern	Junior	.46	English	test	question	Pianta	.64
Yan, 2018-4	306	.170	Diss	Eastern	Junior	.46	Mixed	test	question	Pianta	.64
Yang et al., 2016	656	.120	Journal	Eastern	Primary	.59	Mixed	test	question	Lei	.90
Yang, 2015	2455	.228	Journal	Western	Junior	N	Math	Mixed	index	Other	.85
Yi, 2020	308	.229	Diss	Eastern	Senior	.54	Math	test	index	Zhang	.95
Zhang & Jiang, 2009	384	.624	Journal	Central	Junior	.42	Mixed	test	question	Pianta	.70
Zhang & Li, 2019	259	.151	Journal	Western	Junior	.48	Mixed	test	question	Ouyang	.85
Zhang et al., 2018-1	1421	.180	Journal	Eastern	Junior	N	Chinese	test	question	Williams	.80
Zhang et al., 2018-2	1421	.100	Journal	Eastern	Junior	.58	Math	test	question	Williams	.80
Zhang et al., 2020	2213	.335	Journal	Central	Junior	.43	Mixed	test	question	Other	.85
Zhang, 2017	284	.250	Diss	N	Primary	.43	Mixed	test	question	Williams	.60
Zhang, 2019a	272	.410	Diss	Eastern	Junior	.51	Mixed	test	question	Williams	.70
Zhang, 2019b	3012	.250	Diss	Eastern	Primary	.54	Math	test	question	Chai	.90
Zhao et al., 2017	400	.193	Journal	Eastern	Primary	.49	Math	N	question	Chai	.55
Zhao, 2014	518	.409	Diss	Central	Junior	N	Math	test	index	Zhang	.85
Zhen, 2018	300	.501	Diss	Central	Senior	.52	Mixed	N	index	Zhang	.55
Zhu et al., 2017	12404	.198	Journal	Central	Junior	.52	Mixed	test	index	Other	.75
Zhu et al., 2018	294	.350	Journal	N	Primary	.54	English	test	index	Other	.85
Zhu, 2017	625	.252	Diss	Central	Mixed	.47	Mixed	self	index	Other	.90

	k	Fisher’ s Z (SE)	95% CI	t	r [95% CI]	Level 1%	Level 2 variance	Level 3 variance
Effect sizes	90	.265 (.017)	[.231; .299]	15.553***	.259 [.227; .290]	2.079	.018*** 4.225	.019*** 93.696

Moderators	k	Fisher's z [95% CI]	r [95% CI]	F	Level 2	Level 3
Demographics
Region	77			3.722*	.001	.016
Central	22	.315[.106, .525]*	.305 [.106; .481]
Eastern	45	.243 [.041, .445]	.238 [.041; .418]
Western	10	.167 [.072, .263]***	.166 [.072; .257]
Grade level	80			3.641 *	.001	.018
Senior	16	.360 [.280, .440] ***	.345 [.273; .414]
Junior	41	.257 [.082, .432] *	.251 [.082; .407]
Primary	23	.225 [.042, .407] *	.221 [.042; .386]
Academic subject
Academic subject	44			5.782 **	.000	.015
English	14	.311 [.199 .424] ***	.302 [.196; .400]
Math	14	.280 [.173, .386] **	.272 [.171; .368]
Chinese	10	.276 [.169, .383] **	.269 [.167; .365]
Science	6	.205 [.144, .265] ***	.202 [.143; .259]
Study attributes
Report type	78			1.009	.001	.015
Self	9	.303 [.181, .425]	.294 [.179, .401]
Test	67	.244 [.209, .279]***	.240 [.206, .272]
TEACHER	2	.204 [-.024, .432]	.201[-.024, .407]
Relationship survey	90			.400	.001	.019
index	27	.281 [.167, .395]	.274 [.165, .376]
question	63	.258 [.216, .299]***	.252 [.213, .290]
Questionnaire	52			.621	.001	.022
Chai	2	.227 [-.120, .573]	.223 [-.119, .518]
Lei	4	.205 [-.084, .494]	.202 [-.084, .458]
Ouyang	13	.248 [.008, .488]	.243 [.008, .453]
Pianta	9	.330 [.062, .598]	.318 [.062, .535]
PISA2012	7	.253 [-.052, .557]	.247 [-.052, .506]
Williams	7	.298 [.031, .564]	.289 [.031, .511]
Zhang	10	.338 [.236, .440]***	.326 [.231, .414]
Publication type	90			1.708	.001	.019
Dissertation	38	.292 [.179, .404]	.284 [.177; .384]
Journal	52	.246 [.202, .290]***	.242 [.200; .283]

Moderators	k	Intercept [95% CI]	B [95% CI]	F	Level 2	Level 3
Demographics
Female %	74	.266 [.229; .303]***	.507 [.285; .729]***	20.754***	.000	.020
Study attributes
Publication year	90	.265 [.231, .299]***	-.006 [-.014; .002]	2.394	.001	0.018

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Social Science Fund of China (Education) (Grant No. BHA220124).

ORCID iDs

Hao Lei

Tongwei Xie

Author biographies

Hao Lei is a Professor in the Institute of Curriculum and Instruction at East China Normal University, China. His research has concentrated on educational assessment, teachers’ education, and students’ learning and development.

Xijing Wang is a Master student in the Institute of Curriculum and Instruction at East China Normal University, China. Her research has concentrated on educational assessment.

Ming Ming Chiu is Chair professor of Analytics and Diversity in the Special Education and Counseling Department and Director of the Assessment Research Centre at The Education University of Hong Kong. His research interests include learning analytics, group processes, inequality, corruption, and online sexual predators.

Mingfeng Du is a assistant researcher of the Institute of Schooling Reform and Development, East China Normal University, China. His research has concentrated on educational policy.

Tongwei Xie is a assistant researcher National Institutes of Educational Policy Research, East China Normal University, China. His research has concentrated on educational policy.

Appendix A: TSR surveys

TSR were reported by students in collected articles. A total of seven questionnaires have been widely used in China. We analyzed the moderating effect of different questionnaires and found that they do not moderate the TSR-SAA link.

Students’ perceived teachers’ supporting (Ouyang, 2005) has three subscales: (a) learning support (9 items; internal consistency reliability = .84; eg., When I can’t answer a question teachers often repeat the explanation of the question to me.); (b) emotional support (2 items; internal consistency reliability = .73; eg., Teachers often encourage me in my study and life.); and (c) ability support (4 items; internal consistency reliability = .79; e.g., Teachers often recommend me to participate in various activities or competitions.) The Likert scale ranges from 1 (Strongly disagree) to 6 (Strongly agree).

Perceived mathematics teacher support (Chai & Gong, 2013) has three subscales: (a) affective support (7 items; Cronbach's α = .89; eg., The math teacher likes me.); (b) autonomy support (5 items; Cronbach's α = .75; eg., When solving math problems, the math teacher allows us to come up with solutions that are different from the standard answers.); and (c) cognitive support (5 items; Cronbach's α = .76; eg., The math teacher often extend what we learn.) The Likert scale ranges from 1 (Strongly disagree) to 5 (Strongly agree).

Teachers’ caring behavior (Lei et al., 2015) has three subscales: (a) consciousness (7 items; Cronbach's α = .86; eg., The teacher can complete the classroom teaching seriously.); (b) supportiveness (6 items; Cronbach's α = .80; eg., The teacher is able to give detailed answers to my questions.); and (c) tolerance (5 items; Cronbach's α = .76; eg., When my opinion is in conflict with the teacher's opinion, the teacher will respect my view.) The Likert scale ranges from 1 (Strongly disagree) to 5 (Strongly agree).

Student-Teacher Relationship Scale (Yan, 2018) was adapted from Pianta. It has four subscales: (a) intimacy (7 items; eg., My relationship with my teacher is intimate and warm.); (b) support (4 items; eg., As long as I make progress, the teacher will praise me.); (c) satisfaction (5 items; eg., I hope to improve my relationship with my teacher.); and (d) conflict (7 items; eg., The teacher and I always seem to be fighting each other.). Cronbach's α coefficients of the four dimensions range from 0.71 to 0.87. The Likert scale ranges from 1 (Strongly disagree) to 5 (Strongly agree).

The teacher-student relationship scale in PISA 2012 (Organization for Economic Co-operation, and Development [OECD]) has 5 items (Cronbach's α = .81; eg., I get on well with English teacher) rated on a 4-point Likert-type scale.

Teacher autonomy support (Williams & Deci, 1996) has 15 items (Cronbach's α = .96; eg., My instructor tried to understand how I saw things before suggesting a new way to do something.) rated on a 5-point Likert-type scale.

Students’ perception of teacher expectancy (Zhang, 2009) has three subscales: (a) teacher's support (5 items; Cronbach's α = .83; eg., Teachers often care about me.); (b) teacher-student interaction (6 items; Cronbach's α = .77; eg., The teacher often asks me to answer questions in class.); and (c) feedback of student's performance (4 items; Cronbach's α = .71; eg., I don't think the teacher marked my homework very carefully.). The Likert scale ranges from 1 (Strongly disagree) to 5 (Strongly agree).

Achievement

For measures of academic achievement, there are three report types (test, self-report, teacher report). For self-report and teacher report, except Lam et al. (2012) and Tang et al. (2013), the other articles used a single item to measure academic achievement and Likert scales range from 1 (Strongly disagree) to 4 or 5 (Strongly agree).

The academic performance of the students (Lam et al., 2012) was reported by teachers, and has three items (students in their class were “good at school work,” had “good performance on tests,” and did “well on assignments.” The coefficient α of the three item-scores was .93,). The Likert scale ranges from 1 (Strongly disagree) to 5 (Strongly agree).

Senior high school students’ academic development scale (Tang et al., 2013) has five subscales (Coefficient is 0. 92): time management, learning and test-taking skills, learning from friends, school planning, and being a responsible learner. The Likert scale ranges from 1 (Strongly disagree) to 5 (Strongly agree).

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