







































 

 

 

 

 

The Construction and Application of 

Regional Education Quality Monitoring 

Databases: A Case Study of Suzhou’s 

Education Quality Monitoring

Jian Shen,
1

 Qiang Luo
2 

 
1. Suzhou High School-SIP, Suzhou 215000, Jiangsu, China 

2. Suzhou Education Quality Monitoring Center, Suzhou 215000, Jiangsu, 

China 

Abstract: The development of school education depends on the quality of 
the education provided, and it is a key metric for assessing the 

effectiveness of schools in developing talent. Building specialized, 

intelligent education quality monitoring (EQM) databases is crucial for 
speeding EQM progress in the big data era. This article examines the 

development of regional EQM databases in the areas of operational 
procedure and logical structure based on the idea of data lakes by using 

the development of databases for the EQM data analysis system (DAS) 

in Suzhou City as a case study. The goal of this study is to assist in 
addressing the current issues with regional EQM data processing and 

ensuring EQM’s successful implementation. 

Best Evidence in Chinese Education 2022; 12(2):1613-1628. 

Doi: 10.15354/bece.22.re031 

How to Cite: Shen, J., & Luo, Q. (2022). The construction and application of 

regional education quality monitoring databases: A case study of Suzhou’s education 

quality monitoring. Best Evidence in Chinese Education, 12(2):1613-1628. 

Keywords: Education Quality Monitoring, Data Analysis System, Data Lake, Database 

 

 

 

 

 

 



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No.2, 2022 1614 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

About the Authors: Jian Shen, Suzhou High School-SIP, Suzhou 215000, Jiangsu, China. E-mail: 

james@suzhou.edu.cn  

Qiang Luo, Suzhou Education Quality Monitoring Center, Suzhou 215000, Jiangsu, China. E-mail: 

452384761@qq.com 

Correspondence to: Qiang Luo at Suzhou Education Quality Monitoring Center of China. 

Funding: This study is part of the study “Regional Education Quality Improvement Paths Based on EQM Big Data” 

(E-b/2020/16), funded by Jiangsu’s 13th Five-year Plan for Education Sciences. 

Conflict of Interests: None. 
 

© 2022 Insights Publisher. All rights reserved. 

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the 

Creative Commons Attribution-Non Commercial 4.0 License 

(http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of 

the work without further permission provided the original work is attributed by the Insights Publisher. 

mailto:james@suzhou.edu.cn
mailto:452384761@qq.com
http://www.creativecommons.org/licenses/by-nc/4.0/


Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1615 

Introduction 

ducation quality is crucial to the advancement of school education 

because it is an indicator of school performance, educational devel-

opment, and talent training. Building a system to monitor the quality 

of compulsory education is an essential step in deepening the reform of edu-

cational evaluation in the new era and changing the unscientific orientation 

of educational evaluation. The State Council’s Overall Plan for Deepening 

Educational Evaluation Reform in the New Era, released in October 2020, 

proposes the use of innovative evaluation tools as well as the application of 

artificial intelligence, big data, and other modern information technologies to 

develop a longitudinal evaluation system for the entire process of students’ 

learning in all grades as well as a transversal system to comprehensively 

evaluate the results of moral, intellectual, physical, and social development 

(State Council, 2020). 

A database is a warehouse that organizes, stores, and manages data in 

accordance with a data structure; it can connect and interact with data 

through tools such as data collection, organization, analysis, and visualiza-

tion to provide evidence for scientific research and decision-making process-

es. 

Education quality encompasses, among other things, the outcomes of 

school operation, teacher instruction, student learning, and family education. 

The education quality monitoring (EQM) database collects information 

about participating schools, teachers, students, and parents and stores and 

analyzes it using information technology, computer technology, and analyti-

cal data mining. 

The domestic study on Compulsory Education Quality Monitoring 

(CEQM) focuses mostly on two topics. One is to examine its implementation 

measures and support mechanisms, such as the research and development of 

monitoring tools (Wang, 2016), the establishment and operation of monitor-

ing institutions (Zhang, 2010), the application of monitoring results (Yang, 

2019; Guo & Wang, 2016), and IT support for monitoring (Wang, 2016; 

Zhang et al., 2016). The second field of research focuses on EQM’s imple-

menting actors and the development of a monitoring system that includes 

EQM at the national, provincial, municipal, and district levels (Li & Chen, 

2020; Xin & Zhao, 2020; Li et al., 2017; Zhou, 2016). These two interrelated 

topics are the primary focus of CEQM research. However, there is a paucity 

of research on how to leverage big data technology and appropriate theories 

to generate an EQM database and build a data analysis system (DAS) for 

EQM, which has impeded the development of education quality monitoring. 

E 



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1616 

The purpose of this study is to examine the building of the regional 

EQM database using the construction of the EQM database in Suzhou as an 

example in order to address the difficulties in data analysis. 

The Status Quo of EQM Development in Suzhou 

Massive amounts of data are involved in EQM. Suzhou’s CEQM 2021, for 

example, generated more than 67.15 million data records during the data col-

lection stage, each of which is made up of data of various types and struc-

tures. It is necessary to retrieve relevant data from previous years for follow-

up analysis during the data analysis stage. Every year, hundreds of millions 

of raw monitoring data points must be processed. With such a large amount 

of data and complex structures, the planning and construction of an EQM 

database is an urgent task that must be completed. 

The “Compulsory Education Academic Quality Monitoring Project” 

was launched in Suzhou City, Jiangsu Province, in 2015, and the Suzhou 

Education Quality Monitoring Center (hereinafter referred to as the Suzhou 

EQM Center) has been tracking and monitoring junior secondary school stu-

dents ever since. More than 1.3 million students had been tested by 2021, 

more than 180 million pieces of monitoring data  been collected, and more 

than 14,000 monitoring reports been issued (Song & Luo, 2021). The Su-

zhou EQM Center strengthens the application of intelligent technology in 

monitoring practices in response to educational evaluation reform in the arti-

ficial intelligence era and builds a regional EQM database with Suzhou char-

acteristics. It has also established a data analysis system and made incremen-

tal technological advances in standardized data governance, intelligent data 

analysis, and visualized data presentation. 

Suzhou EQM Center created the DAS framework based on its own 

needs (Figure 1), which divides the data analysis procedure into six process-

es involving 12 functional modules. 

The DAS provides data assistance for the data presentation step after 

the data gathering stage. Data import, data cleaning, quality analysis, project 

establishment, calculation analysis, and data push are the six steps that make 

up the data analysis process. 

Based on the aforementioned structure, two additional structural lay-

ers - algorithm support and Data Lake - are added to the data analysis system 

to create a full analysis system design. To be exact, the data lake contains six 

databases, including raw data, virtual hierarchical data, user rights control, 

desensitized project data, algorithm rule resources, and result presentation 

data. The algorithm support layer also contains four algorithm libraries (Fig-

ure 2). 



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1617 

 
Figure 1. Procedure and Architecture of Data Analysis System for Suzhou 

EQM. 

 

 

 

 

 
Figure 2. A Plan of Suzhou EQM Data Analysis Databases. 

 

 

 

The Construction Paths of Regional EQM Databases 

In the new era of big data and data science, having a centralized data archi-

tecture that is consistent with operational processes is critical. This is also 

true in EQM. Good database architecture should be able to grow with the 

monitoring scale and evolve with technological advancements. 

Suzhou EQM Center creates databases for EQM data analysis based 

on the “data lake” concept. The data lake unifies the storage of all organiza-

tional data, including both the original data in the source system and the 

converted data (Campbell, 2017). It has become an important tool for organ-



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1618 

izations wishing to make use of big data. Structured data (relational database 

data), semi-structured data (CSV, XML, JSON, etc.), unstructured data 

(emails, documents, PDFs), and binary data (images, audio, and video) are 

all stored in the data lake, forming a centralized data storage that holds all 

types of data. It aggregates and stores streams from various data sources, 

much like a large lake in nature, and outputs valuable data based on specific 

needs. In terms of monitoring data, the data lake contains not only data from 

various platforms, such as a question bank system, an examination service 

system, a scanning and marking system, and so on, but also a wide range of 

files, such as spreadsheets, scanned images, databases, and so on. It also 

saves process and result data from various data analysis processes. Its inclu-

siveness enables the cross-analysis of diverse data information and the use of 

large capacity and high-speed data pipelines, as well as the management of 

the entire data lifecycle to make data flow processes such as access, storage, 

processing, and application traceable (Figure 3). 

The logical structure design that stresses the security, integrity, and 

efficiency of all databases is also a crucial issue since the database must be 

constructed to support the monitoring procedure, making the design based on 

data analysis procedures the most significant component. The location of 

various storage types can only be determined by a database architecture that 

takes into account the two factors mentioned above. This architecture can 

also guarantee that data is safeguarded, effectively stored, and accurately 

processed. 

Database Design Based on Analytical Processes 

Separation of Raw Data and Project Data 

Importing different types of data from various platforms is frequently neces-

sary for EQM data analysis. Examples include participant, school, and re-

gional information from the examination system; monitoring tool infor-

mation, dimensional information, scoring instructions, and other data from 

the question bank system; and original response records, scoring records, 

scanned images, and other data from the scanning and marking system. The 

raw databases for EQM are made up of all this data. 

Different logical principles must be called upon and applied to the 

raw data in accordance with the demands of monitoring programs. In the 

Regional School Quality Analysis Project, data must be retrieved on a re-

gional basis, and in the Private School Project, data must be based on the 

classification of private schools for children of migrant workers or non-

migrant workers. As an example, it is necessary to include the monitoring 



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1619 

 
Figure 3. A Schematic Diagram of the Data Lake of Suzhou EQM Data Analy-

sis System. 

 

 

 

 

 
Figure 4. A Schematic Diagram of Raw Databases and Data Analysis Projects 

of Suzhou EQM 2021. 

 

 

 

data of the same subject for consecutive years in the tracking analysis. 

Therefore, the raw database and the project database should be maintained 

separately in the data lake to meet the various calling logics of various data 

analysis projects (Figure 4). 



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1620 

Virtual Stratification in a Project-Based Database 

The data is frequently converged and displayed by various school attributes 

in various data analysis initiatives. For instance, a three-level structure of 

“Suzhou city, district, and school” is necessary for calculation and report 

creation in the regional analysis project, whereas a three-level structure of 

“District, Group, and School” is necessary in the educational group project. 

A distinct virtual stratification module is added to the system func-

tion module to construct a different database for each project based on the 

logic of virtual stratification to satisfy the needs of the project. According to 

the specifications of the data analysis project, a three-tier structure based on 

school attributes can be created, as illustrated in Figure 5, supporting a range 

of monitoring reports. 

Database Design Based on Logical Structure 

Hierarchical Management of Classified Databases 

The database faces more internal security concerns because of its growing 

value and accessibility. For instance, unlawful overstepping operations and 

hostile infiltration result in the theft and disclosure of confidential infor-

mation, but it is unable to adequately trace and audit the events that caused 

these events. Strengthening data security management is especially crucial 

for databases that contain a lot of personal data. Information leakage must be 

prevented at all costs, in addition to rapid improvements and effective vul-

nerability management. Database security protection techniques that are 

flexible and targeted are the most effective way to do this. 

A crucial security measure for databases is database access control, 

and Suzhou EQM Center now mostly uses role-based access control (RBAC). 

Users and operation rights are intertwined with the idea of roles. To achieve 

hierarchical management, various roles are first developed in accordance 

with the organizational functions, each of which corresponds to a distinct 

level of operation privileges. RBAC must assign the user’s account a role 

and associated rights in accordance with the information in the role rights 

database, in addition to verifying the user’s identity and password when they 

log in to the system (Figure 6). The application system can be modified to 

meet the new access control requirements in the case of a functional shift in 

the organization by merely reassigning permissions to the roles. 

Suzhou EQM Center has also implemented four different types of se-

curity protection measures for the database. In order to prevent operational 

paralysis in advance and ensure the ongoing availability of operational sys- 



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1621 

 
Figure 5. A Schematic Diagram of the Data Virtual Stratification Logic in Data 

Analysis Projects. 

 

 

 

 

 
Figure 6. The Process of Account Authentication. 

 

 

 

tems, it must be able to monitor the database operation state in real time and 

provide early warning when the condition is abnormal. The second is to be 

able to evaluate the database system’s risk, including weak password detec-

tion, system vulnerability, configuration risk, etc., to identify user and sys-

tem access behavior patterns to the data and produce access rules with vari-

ous levels of strength. Thirdly, it must be able to keep track of data activities 

in real time, create data access models, assess access risks promptly, find and 

prevent unauthorized access, and encrypt sensitive data to improve data se-



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1622 

curity protection. The fourth is to use audit logs to carry out thorough user 

access behavior monitoring. Finding the risk source at the outset and follow-

ing the individual responsible for the risk become required actions to close 

the security gap once the security risk has materialized. Using the audit log 

query tool, this issue can be fixed. 

Desensitization and Recovery of Sensitive Data 

For the purpose of scheduling exams, information on the monitoring subjects, 

such as student, teacher, and other personnel personal identifying infor-

mation, must be gathered during the EQM process. A desensitization and 

recovery module should be set up in the system to centrally desensitize, en-

code, and save sensitive information in basic data in order to guarantee data 

security. 

It is also necessary to establish an administrator position for the de-

sensitization module, whose primary responsibility is to grant permissions to 

data in the desensitization and restoration module, because data analysis is a 

collaborative task that requires the participation of numerous people. This 

functional module and any associated forms and fields are not accessible to 

other roles in the system. 

The coding rules for the desensitization and recovery of each datum 

are mostly stored in the desensitization rule algorithm library in the Suzhou 

EQM databases. These guidelines are used to desensitize the sensitive data-

base used for data collection in order to obtain code-version basic data. To 

create the analysis result database with real names, the calculation and analy-

sis module recovers the code-version result database. In order to offer data 

support for later data presentation and report creation, the desensitized code-

version result database and the recovered name-version result database are 

finally pushed to the data presentation link through a data interface (see Fig-

ure 7). 

Establishment of an Algorithm Rule Repository 

The term “algorithm rule repository” primarily refers to the storage of data 

analysis algorithms as a new type of data asset and the formulation of perti-

nent specification processes in accordance with the guidelines and specifica-

tions of data asset management. 

By building unique algorithm repositories for each functional module, 

the data analysis system controls them. Data production, data processing, 

data analysis, data preservation, data access, and data reuse are the six nodes 

that make up the loop structure model that the UK Data Achieve (UKDA) 

uses to characterize the cycle of research data (UK Data Service, n. d.). Su 



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1623 

 
Figure 7. A Schematic Diagram of Data Flow in the Desensitization and Re-

covery Module. 

 

 

 

 

 
Figure 8. A Schematic Diagram of the Algorithm Repository Application Pro-

cess and the Algorithm Asset Lifecycle. 

 

 

 

zhou EQM Center splits the life cycle of data analysis algorithms into four 

stages based on this model: planning and definition, condensing and storing, 

choice and use, and updating and iteration (Figure 8). To meet the unique 



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1624 

and creative demands of data analysis for various monitoring projects, appli-

cable analysis algorithms can be chosen from the algorithm library during 

the process of specific application for various monitoring projects. Addition-

ally, new data analysis algorithms, various indicators, and charts can be cus-

tomized in accordance with various analysis models. 

The algorithm rule repository contains a cleaning rule algorithm li-

brary, a quality analysis index algorithm library, a desensitization rule library, 

and a data analysis algorithm library, which provide algorithm support for 

the four processes of data cleaning, quality analysis, data desensitization, and 

calculation of analysis, respectively. Each algorithm library has amassed a 

specific number of algorithms as a technological reserve and is regularly 

generating new algorithms as the EQM project advances. Taking the clean-

ing rule library as an example, to adapt to the needs of diverse projects, it has 

so far accumulated 106 cleaning methods for a variety of situations, such as 

missing tests, contradictory alternatives, contradictory logic between ques-

tions, invalid answers, etc. Also, using the data analysis algorithms library as 

an illustration, there are multiple algorithms for a single indicator of “percen-

tile grade,” as well as algorithms that encapsulate multidimensional charac-

teristics of the educational ecology, such as the balance of education, ecolog-

ical health, etc. 

Practical Application of EQM Databases in Suzhou 

Although it only makes up a small portion of Suzhou’s overall EQM process, 

the development of databases is a key technology that underpins high-quality 

monitoring. Through these databases, the effective and reliable operation of 

each functional module of the Suzhou EQM data analysis system is ensured 

(Figure 1). This increases the effectiveness of the analysis and evaluation of 

monitoring data and satisfies the specific requirements of data analysis for 

the various Suzhou monitoring projects. 

Using various quality analysis algorithms from the algorithm rule re-

source library, the monitoring tool quality analysis module in the data analy-

sis system, for instance, provides quantitative indicators for analyzing test 

questions, improving tool quality, and improving teaching. It is based on 

classical measurement theory and structural equation modeling. As well as 

indicator algorithms used to verify the validity and reliability of each dimen-

sion of the pertinent factor instruments, there are algorithms for indicators of 

disciplinary tools, such as reliability, difficulty, differentiation, percentage of 

score bands, and ability value of score points. During the whole EQM 2021 

process, this functional module offers more than 100 high-quality analysis 

reports for the monitoring tools at all grade levels and for all subjects, 



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1625 

 
Figure 9. Screenshots of Some Functions of the Monitoring Tool Quality 

Analysis Module. 

 

 

 

 

 
Figure 10. Screenshots of the Functions of Calculation and Analysis Related 

Modules. 

 

 

 

providing a data foundation for the development and accumulation of moni-

toring tools (Figure 9). 

In the “calculation and analysis” process, for instance, the virtual 

stratification database is utilized to ensure that the data required for each pro-

ject can be reliably accessed in order to execute numerous projects simulta-

neously. Through the algorithms of various data analyses in the algorithm 

rule repository, a series of quality assurance measures, such as “double-track 

parallelism and double-blinded comparison; seamless docking and errorless 

flow; sampling verification and reverse verification,” are realized in the 

analysis. Using these databases and functional modules, the data processing 

procedure has been standardized, the effectiveness of data analysis has been 

enhanced, and the precision of data calculation has been ensured. During the 



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1626 

 
Figure 11. Screenshots of the Functions of Each System at the Data Presenta-

tion Stage. 

 

 

 

data analysis in 2021, the Suzhou EQM Center completed the basic data pro-

cessing in just two weeks with high efficiency and high quality and, on this 

basis, completed the projects of basic data of junior secondary schools, basic 

data of elementary schools, educational group research, and private school 

education quality, as well as a total of more than 3,600 reports of various 

monitoring results, which ensured the smooth implementation of Suzhou’s 

education reform (Figure 10). 

Not only does the database play a crucial role in the data analysis 

process, but it also ensures the secure and efficient flow of data inside each 

system throughout the whole EQM process. By setting data requirements 

during the data gathering phase, the “Data Standardization” module com-

pletes the integration of data from disparate systems. Moreover, the database 

supplies data sources for subsequent data presentation, which are handled by 

the “Data Push Control” module and delivered on demand to the batch report 

generating system, the data chart integration system, the big data display sys-

tem, and other platforms (Figure 11). 

Conclusion 

The development of databases for the data analysis system is a lengthy pro-

cess. With the extensive and in-depth promotion of EQM, we will be con-



Shen & Luo. (Jiangsu). Build Up Regional Education Quality Monitoring Databases. 

BECE, Vol.12, No. 2, 2022 1627 

fronted with new issues and challenges, necessitating ongoing innovation 

and the study of new technologies in our research. In the era of big data, we 

believe that the building and maintenance of databases demands not only a 

reorganization of data structure but also a modernization of big data mentali-

ty and data asset management procedures. Together with our contemporaries 

in the field of EQM, we anticipate seizing these new chances and conquering 

these new obstacles. 
 

 

 

 

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Received: 15 September 2022 

Revised: 05 October 2022 

Accepted: 01 November 2022 

 

 

 

 

 


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