







































 

 

 

 

 

Is the Standardization of Compulsory Education 

School Helpful to Improve Students’ 

Performance? An Empirical Analysis Based on 

Monitoring Data in Province A

Yanli Liu,
1
 Jijun Yao,

1
 Shike Zhou

2
 

 
1. Nanjing Normal University, Nanjing 210024, China 

2. Jiangsu Teaching and Research Office of Primary and Secondary Schools, Nanjing 

210024, China 

 

Abstract. The standardization of compulsory education schools is an 

essential measure to promote education balance and improve education 

quality. However, when the implementation effect of this policy was 
evaluated, the improvement of input factors such as facilities and 

equipments, teaching faculty and funding were only focused on, but with 

less attention was paid to the impact of the standardization on students’ 
academic performance. This kind of imbalance makes it difficult to fully 

grasp the real effects of standardization and its potential problems, 
which may lead to deviations in the direction of standardization con-

struction. Therefore, this study used the monitoring data in province A 

that includes the standardized construction of compulsory education 

schools and academic quality of students to analyze the correlation be-

tween standardization construction and student performance through 
Ordinary Least Square (OLS). Further, a Regression Discontinuity De-

sign (RDD) was used to analyze whether standardization construction is 

the cause of differences in student performance. The results showed that 
there was no significant correlation between standardization construc-

tion and elementary school students’ performance, and negative correla-

tion with middle school students’ performance; no substential causal 
relationship exists between standardization construction and student 

performance. This is majorly due to the emphasis on the conditions of 
school-running was set too much, and then the student performance was 

neglected. Therefore, future evaluation should be gradually changed 

while deepening the standardization construction with more attention 
paid to the connotative development and efforts made to the improve-

ment of the education quality. 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No.1, 2020 592 

Best Evid Chin Edu 2020; 5(1):591-608. 

Doi: 10.15354/bece.20.ar039. 

Keywords: Standardization Construction; Academic Performance of Students; Re-

gression Discontinuity; Adequate Education 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

About the Author: Yanli Liu, School of Education Science, Nanjing Normal University, Nanjing, China, E-mail: 

1609604712@qq.com.  

Shike Zhou, School of Education Science, Nanjing Normal University, Nanjing, China, E-mail: jyszsk@163.com; 

Correspondence to: Jijun Yao, School of Education Science, Nanjing Normal University, Nanjing, China, E-mail: 

yaojijun_njnu@163.com. 

Funding: The study was funded by the National Social Science Foundation of China (Project number AHA160006) 

and the Priority Academic Program for Development of Jiangsu Higher Education Institutions (PAPD). 

Conflict of Interests: None. 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 593 

Introduction 

N 2010, China issued the “Outline of the National Middle- and Long-term Educa-

tion Reform and Development Plan (2010-2020)”, which proposed that “advancing 

the standardization of compulsory education schools and balancing the allocation of 

resources such as teachers, equipment, books, school buildings, etc.” “Establish the na-

tional basic quality standards and monitoring system for compulsory education” (July 

29, 2010) strive to continuously improve the standardization of compulsory education, 

promote education equity, improve education quality, and promote balanced develop-

ment of compulsory education. In this context, local governments have successively 

issued relevant policy documents to vigorously promote the standardization of compul-

sory education schools. Take province A as an example, in 2015, the government of the 

province A released the “Standards for Provincial A Compulsory Education Schools 

(Trial)” (Provincial Government of A, 2015). Subsequently, the Education Department 

of Province A (2016) also developed the “Monitoring Indicators for the Standardization 

of Provincial A Schools for Compulsory Education (Trial)”, through which the schools 

do self-assessment and self-test first, and then county-level and city-level review, and 

finally provincial review that formed the monitoring data of school standardization con-

struction of province A.  

The formulation and implementation of these measures have being greatly 

promoted the standardization construction of compulsory education schools (“standard-

ization construction” for short). “The Compulsory Education School Standardization 

Construction Monitoring Report of Province A in 2017” showed that the standardiza-

tion construction compliance rate has gotten significant improvment and the effect was 

remarkable. Up to 2017, “71.26% of schools in province A have basically reached the 

provincial standard”, and the rate of compliance has increased by 15.3% compared with 

2016; The average compliance rate of compulsory education schools in 13 districts and 

cities is 73.34% on average, 16% higher than that in 2016; in addition, the achievement 

of indicators had also been at a high level. In 2017, “the first-level indicators reached 

over 60%, three first-level indicators were close to 100%”, “60% of the second-level 

indicators reached 80%”, and “46 third-level monitoring points was above 80%” (Edu-

cation Evaluation Institute of Province A, 2018). From the data above, we can see that 

the standardization construction of Province A has reached a high level. 

Based on the experience of countries around the world, the construction of ed-

ucation standardization is an essential step to promote a balanced development of edu-

cation and improve education quality. These achievements made by Province A in 

standardization construction were undoubtedly of great significance. However, in terms 

of specific indicators and construction content, most provinces, including A, currently 

focus on condition improvement in the process of standardization. Although this can 

improve school security, promote education equity, and provide a better learning envi-

ronment, but as far as the ultimate goal of education policy implementation is con-

cerned, it is obvious that it should be the development of students, rather than “input 

factors”. When taking only the input such as facilities, equipment, teachers, and funds 

I 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 594 

as the first evaluation criteria, it will make us depend too much on the conditions for 

running schools, but not on students’ development. However, it is hard to obtain the 

answer directly from the results of such evaluations. 

Since 1980, the United States has promoted a gigantic “education standardiza-

tion movement”. During that process, the transformation of educational evaluation cri-

teria had become the major reform point. Starting with Rose V. Council for A Better 

Education in Kentucky in 1989, a series of education lawsuits were passed and the 

standards for American education investment and accountability had gradually realized 

a change from “fair” to “adequate”. The so-called “adequate education” was definitely 

different from the “fair” one, of which did not simply stop at judging whether the edu-

cational opportunities and resources were being equally allocated (This is exactly the 

starting and end points of most Chinese domestic education policy evaluations), but 

from the perspective of the extent to which the education provided by the education 

system for the educated has realized the development of the educated  to assess the ef-

fectiveness of various educational measures. This is a typical result-oriented educational 

evaluation and resource allocation. However, the “adequate education” largely refers to 

students’ academic performance that judges whether an education policy is effective, 

through looking at the improvement of students’ performance (Yao, 2018; Li & Tao, 

2015)  

The reason for result-oriented education policy evaluation as an essential one is 

majorly due to “it is not enough to only focus on the fairness of resource allocation, it is 

necessary to combine the fair resource allocation to the quality of education, so as to 

achieve a balanced high-quality development” (Li & Zeng, 2002). Regarding China’s 

education practice, studies on the result-oriented evaluation is yet adequate. Taking 

compulsory education school standardization construction as an example, the existing 

studies mainly focus on the discussion of standards, necessity and feasibility, construc-

tion principles, strategies, methodologies, and issues. But it remains unclear whether 

standardization promotes student performance. Today, China has basically realized “let-

ting each child learn”, but the urgent problem is how to realize “making each child learn 

better”. So, the lack of evaluation research and practical exploration of output dimen-

sion is obviously not conducive to the completion of this core task. 

 Ideally, for output-oriented education policy evaluation, a comprehensive as-

sessment of the impact of a policy on student performance, abilities, and attitudes 

should be made. However, most of these indicators are difficult to measure, so most of 

the output-oriented education policy evaluations use student performance as a proxy for 

output. In view of this, we, in the following content, will try to analyze the policy ef-

fects of standardization construction based on student performance. Through this type 

of analysis, we tried to answer whether standardization construction has improved the 

learning output situation represented by academic performance? Furthermore, we ana-

lyzed the differences in standardization construction and whether they met the standards, 

and explore whether it was the cause of academic differences between schools. There-

fore, we first used the Ordinary Least Square (OLS) to study the correlation between the 

degrees of standardization construction, whether or not the standardization construction 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 595 

meets the standards and the academic performance. Secondly, Regression Discontinuity 

Design (RDD) was used to infer causality between both these variables. Finally, the 

research conclusions were analyzed, discussed and related suggestions were put forward. 

Research Design 

Research Framework 

Based on the available data, we took students in grades 3 and 8 in the compulsory edu-

cation stage of province A as the research objects, used the academic performance of 

the students as the dependent variable, and standardize construction work as the inde-

pendent variable. So we examined the impact of standardization construction on student 

development from the output side. 

Based on this, we first studied the correlation between standardization con-

struction and student performance. Specially, we used the standardized construction 

evaluation data of province A to describe the implementation effect of the policy by 

using the two variables: one is the standardized construction scores of each school (con-

tinuous variables) and whether the standardized construction had reached the standard 

(categorical variables). On this basis, OLS was used to analyze the relationship between 

the degree of standardization construction, the achievement of the standard, and student 

performance. 

We need to point out that general regression analysis can help us figure out the 

relationship between independent and dependent variables, but it is difficult to do the 

causal inference. As mentioned earlier, we wanted to understand not only the relation-

ship between standardization construction and student performance, but also whether 

this policy was responsible for differences in performance between schools. To this end, 

we used RDD to analyze whether there was a causal relationship between standardiza-

tion construction and student performance. 

RDD was first proposed by Thistlethwaite and Campbell (1960) in 1960 and it 

is a method for estimating processing effects in non-experimental environments. Using 

this method, they analyzed the impact of scholarships on students’ future academic suc-

cess. Students' scholarships depend on observable test scores. However, between the 

students who just met the award criteria and the students who almost reached the award 

criteria, in addition to whether they received scholarships (differentiated by the score 

standard), other characteristics are in line with the principle of randomness, so they are 

comparable. If the student’s test score was greater than or equal to the cutoff value, then 

a scholarship was awarded to enter the experimental group; otherwise, the student was 

assigned into the control group. If there was a difference in the academic performance 

of the two groups, we could conclude that this difference is caused by the scholarship 

policy. Initially, this evaluation strategy did not receive much attention. However, since 

this method has advantages that other methods do not have in terms of causal inference, 

after the 1990s, more and more studies began to use this method to estimate policy ef-

fects in various backgrounds, making it gradually become an important method of caus-

al inference in social science research (Lee & Lemieux, 2010). In the field of education, 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 596 

some representative research results have also appeared. For example, Lee (2018) took 

Tennessee as an example and used Fuzzy Regression Discontinuity to study whether a 

grade-based bursary policy would affect students' bachelor's degree within six years. Li 

and Konstantopoulos (2016) used 2011 TIMSS data from 14 European countries, and 

used Instrumental Variable method and Regression Discontinuity Design to study the 

impact of class size on the mathematics performance of fourth grade students. Wei 

(2012) studied whether the “No One Child Left behind Act” improved the performance 

of students with disabilities based on RDD. But so far, the research on the use of RDD 

by Chinese scholars is relatively rare ( Chen et al., 2014; Chu & Meng, 2017; Liu et al., 

2016; Wang & Sun, 2015). In particular, within our scope, no researcher has used this 

method to carry out causal relationships between the standardization of compulsory 

education schools and student performance. 

According to the “Monitoring Report on the Standardization Construction of 

Compulsory Education Schools in Province A in 2017”, the schools with more than 80% 

of the monitoring points that have reached the standard are defined as “basically meet-

ing the standardization construction standards” (“achieve the standard” for short) and 

the schools with less than 80% are defined as “not meeting the standardization construc-

tion standard”(“below the standard” for short). This formed a quasi-experimental data 

structure at the policy level and provided the conditions for causal inference using RDD. 

We set the schools with “achieve the standard” as the treatment group, other schools as 

the control, and set the policy rate of reaching standard 80% as the threshold. In this 

way, we can observe whether there is a “cutoff” in the performance of schools near this 

critical value, so as to determine whether the standardized construction of the compul-

sory school as an exogenous shock variable has become the cause of the difference in 

student performance. 

Model Set-up and Variable Description 

Based on the above idea, we first used the OLS to explore the relationship between the 

degrees of standardization construction, whether or not the standardization construction 

meets the standards and student performance. The specific model is set as follows: 

 

                                  (1) 

                          (2) 

 

Among them, xxmeani represents the average score of the test results of stu-

dents in i school; xzhachratei represents the degree of  standardized construction of i 

school; Ti is a categorical variable, which indicates whether the standardized construc-

tion of i school has reached the standard, Ti = 1 represents reached the standard, Ti = 0 

represents not reached; Wi is the control variable, it includes socio-economic variables 

such as the area to which the i school belongs, the per capita GDP of the city where the 

school located, the education demands of the city where the school located, and urban-

rural categories; μi is the residual. 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 597 

On this basis, we set the basic standards (that is, the rate exceeds or equal to 

80%) defined in the standardization construction assessment of Province A as cutoff, 

and use Sharp Regression Discontinuity to infer causality. The specific study design is: 

according to whether each school’s degree of standardization construction exceeds 80%, 

the following formula can be obtained: 

 

    
                    
                    

    (3) 

 

Where Ti is treatment variable, xzhachratei is assignment variable. If the degree 

of standardization construction xzhachratei ≥ 80% of i school, then Ti is equal to 1; oth-

erwise, Ti is 0. Whether the school enters the treatment group or the control group de-

pends entirely on whether the degree of standardization construction xzhachratei ex-

ceeds 80%. According to this, the school are randomly grouped in the smallest neigh-

borhood [80% - ε, 80% + ε] near the cutoff to obtain quasi-experimental design , So, the 

local average treatment effect (LATE) near xzhachrate = 80% can be estimated: 

 

                              

                                            

    
              

                 

    
              

                  

(4) 

Set the Regression Discontinuity model as follows: 

 

                                                                

（-h ≤ xzhachratei－x0 ≤ h）                                                                               (5) 

 

Among them: xxmeani  represents the average score of the test results of stu-

dents in i school; xzhachratei indicates the degree of standardized construction of i 

school; x0 indicates the cutoff value, which is 80%; xzhachratei-x0 is central treatment 

for the degree of standardization construction; Ti indicates whether the standardized 

construction of i school has reached the standard with 80% as the cutoff, 1 means that 

achieve standard, and 0 means that not up to standard; Zi is the control variable, which 

includes the area where i school belongs, the per capita GDP of the city in which the 

school is located, education needs, and urban-rural categories; The interaction term 

β3Ti·(xzhachratei-x0) is to allow regressions on both sides of the cutoff to have different 

slopes. 

Data Source and Variable Description 

The data used in this study mainly came from the monitoring data of standardization 

construction of compulsory education schools in Province A, the test data of academic 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 598 

quality of compulsory education students, and the statistical yearbooks of various cities 

in Province A. 

Among them, the monitoring indicators of standardization construction of 

province A in 2017 were composed of 8 first level indicators (school setting, campus 

construction, educational equipment, teacher team, education and teaching, school man-

agement, quality evaluation, and funding guarantee), 40 second level indicators and 69 

monitoring points. The elementary schools (including teaching center), middle schools 

and nine-year schools with compulsory education in 13 cities and 116 counties (cities 

and districts) in Province A were estimated. 

The academic quality monitoring of compulsory education students in Province 

A was an independent test of the academic quality of fourth and ninth grade students in 

Province A. The test content was the academic level of Chinese and mathematics of the 

third grade students, and the academic level of Chinese, mathematics, English and sci-

ence of the eighth grade students. In 2016, the Province A adopted a two-stage stratified 

sampling method and sampled 1,771 elementary schools. Among them, the number of 

students sampled was 141,516 in Chinese, and 141,460 in math. In middle school, Chi-

nese and Mathematics had 992 schools were sampled, and 82,358 and 82,319 students 

were chosen respectively; English and Science had 883 schools were sampled, 78,246 

and 78,685 students were chosen, respectively. 

The statistical yearbook data of 13 cities in Province A were from the websites 

of statistical bureau of each city. It was for obtaining the annual data of economic de-

velopment and education demand of each district and city. 

On this basis, we conducted data processing according to the research design. 

Given the research focus was on the relationship between standardized construction and 

students’ academic performance, so we matched the school standardized construction 

data and student academic performance data. In the end, the data of 1,619 elementary 

schools and 880 middle schools were used. 

Among them, the academic performance of elementary school was measured 

by the average of Chinese and math performance of each school; the academic perfor-

mance of middle school was measured by the average of Chinese, math, English and 

science performance of each school. The calculation of the degree of the school’s 

standardized construction was divided by the number of compliance monitoring points 

divided by the actual number of monitoring points. To make the estimation more accu-

rate, control variables were added. Under the decentralized and county-based education 

management system in China, the development of local education was related to the 

local socio-economic level, so the per capita GDP index of the city where the school 

was located represented the level of local economic development. In addition, the de-

mand for education was also one of the factors affecting the standardization of schools. 

Therefore, we divided the number of students in school in each prefecture-level city in 

the Statistical Yearbook of Province A by the number of permanent residents (calculated 

separately for elementary and middle schools) to represent the educational needs of the 

city where the school is located. In addition, urban-rural disparities and regional differ-

ences (central, southern, and northern province A) might also affect student perfor-



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 599 

mance, so the urban-rural categories and regions to which schools belong were used as 

control variables in the model. 

The specific variable settings are shown in Table 1. There was a large differ-

ence in the test results of elementary and middle schools in different schools. The min-

imum test score for elementary school was only 342 points, and the highest score is 663 

points. The degree of the standardization construction of elementary schools was be-

tween 58.1%-100.0%, and the average rate was 82.4%. The level of middle school 

standardization was between 58.1%-98.4%, and the average rate was 84.8%. In addition, 

there were large differences in the level of economic development and education de-

mand of different regions. 

Results 

Correlation analysis based on OLS 

First, the relationship between school standardization and student performance was ana-

lyzed by OLS. Through the White test, we found that there was a problem of 

heteroscedasticity in the regression equation. Therefore, weighted least squares regres-

sion (WLS) was used for heteroscedasticity correction. 

Models (1) and (2) showed that after controlling the school’s area and urban-

rural category, the education demand of the city, and the per capita GDP of the region 

where the school is located, the degree of standardization of elementary school con-

struction and whether it meets the standard or not was negatively related to the average 

student’s performance, but not show statistical significance. The regression results of 

model (1) showed that under the control of related variables, for every 1% increase in 

the level of standardization, the grade of elementary school students decreased by 0.113 

points, but it was not statistically significant. The regression results of model (2) 

showed that the difference between achieve the standard or not of standardization con-

struction in elementary schools and the achievement of students was 1.802 points, but it 

was not significant. 

Models (3) and (4) showed that the degree of standardization construction in 

the middle school and whether the standardization construction meets the standards or 

not was significantly negatively related to student performance. Model (3) found that 

the degree of standardization construction increased by 1%, and the score of middle 

school students reduced by 1.225 points. Model (4) showed that compared with below 

the standard schools, the average score of students in middle schools that reached the 

standard was 15.779 points lower. 

In terms of controlling variables, the economic level of the city where the ele-

mentary school is located had a significant positive impact on student performance, and 

there was a positive but not significant impact on the educational demands and student 

performance in the middle school stage. The coefficient of the region to which the 

school belongs indicated that the performance of the Central province A was higher 

than those of the Northern Province A. Students in urban areas were significantly better 

than rural students. 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 600 

 

 

 

Causal Inference Based on RDD 

Based on the previous design, we used the standard of 80% as cutoff to analyze whether 

there is a causal relationship between standardization construction and student academic 

performance. 

Figures 1 and 2 are the RDD data of the degrees of standardization construc-

tion and students’ academic performance after centralized processing. The academic 

performance of elementary and middle school students had a slight fluctuation at 80% 

of the standardization construction indicating that no significant causal relationship ex-

ists between the construction of standardization and the differences in student perfor-

mance at various schools. 

In order to accurately estimate the causal relationship between them, we per-

formed further analysis. For RDD, the choice of bandwidth is critical. We chose the 

optimal bandwidth based on minimizing the Mean Square Error of the two regression 

functions at the cutoff proposed by Imbens & Kalyanaraman (2012). We used a rectan-

gular kernel and half and twice the optimal bandwidth for causality tests. 

Table 3 shows that under different bandwidth settings, there is no significant 

causal relationship between the standardized construction of elementary and middle 

schools and student performance. In order to ensure the robustness of the discontinuity 

regression results, we also tested the continuity of the conditional density of the group- 

Table 1. Variable Descriptive Statistics. 

Variable Variable Meaning Mean SD Min Max 

xxmean Test score 
Elementary School 503.524 57.054 341.750 663.014 

Middle School 495.080 47.461 301.453 649.278 

T i 
Reach the standard or not of 
standardization construction 

Elementary School 0.676 0.468 0 1 

Middle School 0.773 0.419 0 1 

xzhachrate 
The degree of standardization 
construction (%) 

Elementary School 82.442 6.898 58.060 100 

Middle School 84.800 6.922 58.060 98.390 

cityGDP 
Per capita GDP by city (10,000 
CNY) 

Based on Elementary 
School sample 

9.932 3.227 4.831 14.556 

Based on Middle 
School sample 

9.867 3.238 4.831 14.556 

qy1 Central Province A 
Elementary School 0.200 0.400 

0 1 
Middle School 0.206 0.404 

qy2 Southern Province A 
Elementary School 0.460 0.499 

0 1 
Middle School 0.444 0.497 

cx1 City 
Elementary School 0.525 0.500 

0 1 
Middle School 0.557 0.497 

cx2 County town 
Elementary School 0.343 0.475 

0 1 
Middle School 0.362 0.481 

edudemand 
Education demand of 13 cities in 
province A (%) 

Elementary School 6.216 2.021 4.480 10.390 

Middle School 2.383 0.460 1.800 3.460 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

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Figure 1. Regression Discontinuity (Elementary School). 

 

 

 

 

 

Figure 2. Regression Discontinuity (Middle School). 

 

 

 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

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Table 2. OLS Regression Results of Compulsory Education 
School Students’ Performance and the Degree of Standardiza-
tion. 

 
Elementary School Middle School 

(1) (2) (3) (4) 

The degree of standardization con-
struction (%) 

-0.113  -1.225***  

(-0.636)  (-5.807)  

Reach the standard or not of standardi-
zation construction 

 -1.802  -15.779*** 

 (-0.724)  (-4.374) 

Per capita GDP by city (10,000 CNY) 
2.928* 6.129*** 0.562 1.737 

(1.792) (3.134) (0.291) (0.813) 

Educational demand of the city where 
the school is located (%) 

-0.364 -0.471 8.789 8.527 

(-0.307) (-0.383) (1.518) (1.432) 

School Location 

Central Province A 
44.796*** 41.883*** 43.286*** 41.001*** 

(5.175) (4.865) (5.388) (4.990) 

Southern Province 
A 

-12.517 -34.746** 35.986** 24.875 

(-0.967) (-2.535) (2.464) (1.621) 

Type of School 
Area 

City 
27.568*** 29.380*** 17.730*** 15.556*** 

(6.275) (7.562) (2.942) (2.728) 

County/Town 
-1.204 -1.568 9.087 7.543 

(-0.270) (-0.389) (1.537) (1.365) 

Constant Term 
467.430*** 439.001*** 534.434*** 438.892*** 

(21.346) (26.616) (18.670) (19.640) 

Number of Samples 1,619 1,619 880 880 

F 43.667 53.652 23.708 22.813
 

Adj. R
2
 0.156 0.186 0.153 0.148 

Note: The value of t in parentheses; ***p , **p , and *p indicate that the levels of significance of 1%, 5%, and 
10%, respectively. 

Table 3. Regression Discontinuity Results of Compulsory Educa-
tion School Student Performance and Reach the Standard or not 
of Standardization Construction. 
Achieve the standard or 
not of standardization 
construction 

Elementary 
School 

±3.541 ±7.083 ±14.165 

-6.146 -11.164 8.661 7.619 8.158 5.845 

(-0.551) (-0.963) (1.190) (1.005) (1.583) (1.083) 

Middle 
School 

±3.76 ±7.53 ±15.06 

12.216 11.222 4.217 5.549 2.026 1.815 

(0.754) (0.670) (0.456) (0.571) (0.278) (0.238) 

Add control 
variables or 
not 

Y N Y N Y N 

Note: Z value in parentheses; ***p, **p, and *p indicate that the levels of significance of 1%, 5%, and 10%, re-
spectively. 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 603 

 

ing variables and control variables at the cutoff point. The results showed that there is 

no obvious change at the cutoff between the assignment variable and the control varia-

ble, which was in accordance to the continuity assumption. 

In summary, through RDD analysis, we did not find a clear causal relationship 

between standardization construction and student performance, that is, the level of 

standardization construction is not the cause of differences in student performance. 

Discussion and Reflection 

Based on our analyses, from the monitoring indicators of the standardization construc-

tion of compulsory education schools, in the dimensions involved in the indicators, es-

pecially in terms of hardware security, Province A had basically completed the task of 

balanced development, and the standardization construction work had reached a higher 

level. In other words, from the input side, the effectiveness of the standard construction 

was satisfactory. 

However, many previous studies found that the improvement of school running 

conditions may not necessarily lead to the improvement of education quality. For ex-

ample, the Coleman Report found that investment in educational resources was not the 

biggest cause of differences in student performance (Coleman et al., 1966). In addition, 

Hanushek at Stanford University analyzed 377 related studies and found that no strong 

correlation exists between student performance and school resources. Simple resource 

policies had little effect on improving student performance (Hanushek, 1997). Similarly, 

studies from China also reached these conclusions. For example, Hu (2007) used the 

data of rural public elementary and middle schools in western China through regression 

analysis and found that most of the physical and financial resources invested in elemen-

tary and middle schools did not significantly improve student performance. Hou and 

Shen (2014) used the 2009 Shanghai PISA test data for middle school students and 

found that there was no significant correlation between the quality of school education 

resources and student performance. 

As thus, these findings show further that when we do policy assessment, we 

cannot look at the “inputs” only, but also the “outputs”. Although we do not think that 

output-oriented evaluation is a better performance evaluation method than input-

oriented one; on the contrary, we firmly believe that in the process of standardization, 

improvement and balance of guarantee conditions are the top priority of this work. 

However, we also believe that while most of the current similar evaluations focus on 

input performance, the review of output performance is a necessary link to ensure that 

the evaluation conclusions are comprehensive, scientific and robust. 

In fact, our analysis also showed that when we conducted output-oriented poli-

cy performance evaluation, we oftentimes got different results from the input-oriented 

evaluation. In this study, although the construction results on the input side was impres-

sive; many problems were revealed on the output side. OLS regression results showed 

that no significant correlation exists between standardized construction policies and 

elementary school students ‘performance; but there was a negative correlation with 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 604 

middle school students’ performance, and the average score of school that was 15.78 

points lower than those who were below the standard. The RDD analysis also found that 

there was insufficient evidence that standardization construction is the cause of differ-

ences in student performance. 

Our conclusions are obviously different from the results of input-oriented per-

formance evaluation. We are more concerned about what exactly caused this difference. 

So, we further analyzed the monitoring data of standardized construction, and per-

formed variance analysis on 62 quantifiable detection points according to achieve the 

standard group and below the standard group to determine whether the students’ per-

formance in each group was significantly different under different indicators.
1 

The re-

sults showed a significant difference between the two groups in the number of school 

tracks, the area per student, sports area per student, the provision of network multimedia 

classrooms per student, the setting of reading rooms, electronic reading rooms, and the 

number of stored books in libraries, etc. Taking the “number of school tracks” as an 

example, the ANOVA results show that the average score of the students in the schools 

that meet the standards is 13.01 points lower. According to the requirements in the 

standardized monitoring indicators, “complete elementary school is below 6 tracks”. 

Only schools with less than or equal to 6 classes in one grade can be included in the 

compliance group. However, in the course of our investigation, we found that most of 

the rural elementary schools in Province A meet this condition, and in urban elementary 

schools, especially many “quality elementary schools” are often difficult to find. There-

fore, it is impossible to reach the target on this indicator. However, the quality of teach-

ing in such schools is often much higher than the average level, which in general raises 

the student performance of schools that do not meet this target. 

At the level of middle school, indicators such as the number of school tracks, 

the average building area per student, the number of books per student, the number of 

new books per student per year, the student-computer ratio, and the ratio of middle 

school teachers with undergraduate or higher education levels were significantly differ-

ent between achieve and the below the standard. Taking the “student-computer ratio” as 

an example, those who were achieve-the-standard scored 14.31 points lower than the 

contrast ones. According to the requirements of the monitoring indicators, “the ratio of 

the number of students in the school to the number of students using computers in the 

network environment must reach 8:1 in middle schools.” High-quality schools often 

have difficulty in meeting this requirement due to the large scale. The low scores of 

resource indicators per student are often the main reason why the “high-quality schools” 

with higher grades have difficulty meeting the standards. 

From our analyses, in many indicators, the phenomenon of “below the standard” 

group has better performance, which reveals exactly everyone's thirst for quality educa-

tion resources. The relative shortage of quality education resources has led to a shortage 

of “good schools”. The expansion of the scale of running a school will reduce the sup-

ply of resources per student, which will inevitably lead to lower-than-expected results at 

the output end. This divergence between the actual quality of running a school and the 

evaluation results at the input side reminds us once again that we need to reflect on the 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 605 

setting of such education policy evaluation indicators, so as to use a more comprehen-

sive perspective to define and evaluate the implementation of education policies.  

Conclusions and Suggestions 

We can draw the conclusions based on our analyses: from the perspective of input, the 

standardized construction of compulsory education schools in Province A had achieved 

gratifying results, but from the perspective of output, such kind of input had not signifi-

cantly affected student performance. No sufficient evidence indicates that standardiza-

tion construction is the cause of differences in student performance. 

We need to point out that this conclusion does not mean we deny the value and 

significance of resource input. After all, resources are the bottom line guarantee, and 

our conclusion shows that the current standardization construction still has much room 

for improvement in terms of how to achieve balance between input and sufficient output. 

In particular, we suggest: 

First, in the direction of standardization construction, we must achieve a transi-

tion from equal input to sufficient results. As pointed out by existing research, “Quanti-

ty and hardware standards are the appearance of standardized school construction, and 

quality and connotation standards are the essence of standardized school construction; 

therefore, the quality of school running must always be the first place for standardized 

evaluation” (Zhang & Zhang, 2017). Currently, the desire for higher quality education 

and the contradiction between the imbalance and inadequate supply of quality education 

resources are the main conflict in the process of educational reform and development. 

As mentioned earlier, since the 1980s in the United States, the principle of education 

supply had gradually changed from fairness to adequateness, and its core idea was to 

allow each child to achieve the maximum possible development under the conditions of 

certain resources (Xue, 2011). This is also of value to China’s current standardization 

construction. 

Second, in the focus of construction and the selection of performance evalua-

tion indicators, to avoid “seeing things but not seeing people,” we must coordinate the 

relationship between the unified requirements and the development of the school's char-

acteristics. This requires that standardized construction is to increase the evaluation of 

output quality while ensuring the necessary facilities and equipment, teachers, curricu-

lum resources and funding for the development of each school. In order to promote 

hardware investment and condition improvement to truly serve the development of stu-

dents, and thus effectively improve the quality of education, strive to achieve the 

growth and development of each student. In this process, it is necessary to avoid setting 

all indicators in a balanced and stable manner. It is necessary to allow the development 

of school characteristics and the substitution and coordination between different evalua-

tion dimensions. It is not necessary to be confined to the full realization of the indica-

tors, as long as the construction of the school can better achieve the development of 

students, it should be regarded as completing the task of “standard construction”. 

Third, strengthen scientific research and set scientific and rational evaluation 

indicators for standardization construction. An important finding of our study is that 



Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 606 

simply focusing on the standardization of the input side does not promote the improve-

ment of student output. The reason for this is largely due to the fact that in the setting of 

standardized construction indicators, the relationship between “input” and “output” has 

not been thoroughly explored. Many indicators are often designed based on experience 

rather than scientific evidence. In the process of evidence-based reform, European and 

the USA require solid empirical evidence as the basis for making major policy decisions. 

In the “Every Child Success Act”, the United States not only continues the requirements 

of evidence-based decision-making in the “No One Child Left Behind” act, but also 

makes clear the strength of various types of evidence (Slavin, 2017). From this point, 

China has a lot of room for improvement. In the process of future standardization, more 

emphases should be placed on the rationality and scientificity of the index setting, and 

each index should be fully demonstrated to make the evaluation play a better role in 

finding problems and guiding reform. 

 

 

 

 

Note: 

1. Due to space limitations, we did not report the results of variance analysis of 62 indica-

tors. For complete information, please contact Yao, the corresponding author of this 

article. Contact email: yaojijun_ njnu@163.com. 

 

 

 

 

How to Cite: Liu, Y., Yao, J., Zhou, S. (2020) Is the standardization of compulsory education school 

helpful to improve students’ performance? An empirical analysis based on monitoring data in prov-

ince a. Best Evid Chin Edu, 5(1):591-608. Doi: 10.15354/bece.20.ar039. 

 

 

 

 

 

 

 

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Received: 27 February 2020 

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Liu et al. Standardization of Compulsory Education School and Students’ Performance. 

Vol.5, No. 1, 2020 608 

The Chinese version of this article has been published Educational Academic Monthly 2019, (05): 

87-95.The English version has been authorized for being publication in BECE by the author(s) and 

the Chinese journal. 

 

刘燕丽, 姚继军, 周世科.义务教育学校标准化建设改善了学业成绩吗?: 基于 A 省域内监测数
据的实证分析. 教育学术月刊, 2019(05):87-95. 


	Review-YanliLiu-BECE_title_30April2020
	Review-YanliLiu-BECE_Maintext30April2020

