Measurement and Evaluation of the Modernization Development Level of Higher Education in China: Based on Panel Data Analysis of 31 Provinces from 2012 to 2022

Abstract

The scale and quality of higher education are key indicators of a country’s development level and its potential for future growth. This study utilizes literature analysis and the core functions of higher education institutions to construct an evaluation index system for the modernization level of higher education in China, using data from 2012 to 2022. The results reveal the following: (1) From 2012 to 2022, the modernization level of higher education across China’s 31 provinces generally increased, despite some fluctuations. Beijing consistently maintained the highest level of modernization, while Hainan demonstrated the fastest growth rate. (2) The modernization level of higher education exhibited uneven distribution across the provinces: eleven provinces were at a low level, eight at a moderate level, eight at a relatively high level, and four at a high level. (3) The development level of higher education modernization shows a clear correlation with geographic location and economic development, characterized by a distinct ‘high in the east, low in the west’ pattern. (4) There is a urgent need to enhance the internationalization of higher education development in China. (5) The overall index of higher education development in China displayed a slow decline with fluctuations from 2012 to 2022, reflecting ongoing efforts toward balanced development across the 31 provinces and cities. The most significant disparities in higher education development remain between the western and eastern regions.

1. Introduction

China has highlighted that the level of higher education development is a significant indicator of a country’s overall sustainable development and potential. The modernization of higher education with Chinese characteristics is a crucial component of China’s broader modernization strategy [1,2]. Unlike Western educational modernization, this approach incorporates stronger socialist elements and Chinese cultural values, aiming not only to catch up with but also to surpass Western educational models [3,4,5].

Higher education serves as a key lever for achieving intensive development and acts as a major engine for driving high-quality economic growth [6]. In light of the current contrast between the ’prosperity of explicit indicators‘ and the ’poverty of intrinsic capabilities‘ in higher education [7], the question of how to achieve high-quality development within the framework of socialism with Chinese characteristics in the new era has become a prominent topic for both theorists and practitioners [8,9,10]. Current research predominantly addresses the logic, characteristics, and implementation strategies of higher education modernization in China from a macro perspective. However, most evaluation indicator systems related to ’education modernization‘ emphasize general attributes and fail to establish evaluation dimensions specifically tailored to the context of modernization with Chinese characteristics.

Using ‘higher education’ and ‘modernization’ as search terms and ‘subject/title/keywords’ as search fields in the China National Knowledge Infrastructure (CNKI) database, a total of 6099 papers were retrieved by the end of April 2024. Among these, 2044 were published in core journals. Quantitative visualization analysis reveals a significant increase in research output on ’the modernization of higher education‘ in China over the years. In 1992, only 4 research papers were identified, rising to 147 papers by 2021, and further increasing to 489 papers in 2023. It is projected that by 2024, the number of research papers on ‘the modernization of higher education’ will reach approximately 879. This growth trajectory underscores the increasing scholarly attention and research focus on the modernization of higher education in China, reflecting its growing significance and academic exploration over the decades.

As research on the modernization of Chinese higher education advances, findings increasingly integrate aspects such as the development of top-tier universities, regional economic development, common prosperity, and rural revitalization, among others. This integration also considers changes in both international and domestic environments and regulatory adjustments. The literature predominantly addresses the following three aspects [11,12]:

(1) Quality and Excellence in Higher Education: emphasizing high-quality development and the construction of first-class universities (‘Double First-Class’ initiative).

(2) Governance and Reform: discussing governance structures within universities and broader educational reforms aimed at modernizing governance practices.

(3) Integration with Socio-Economic Development: exploring the role of higher education in promoting regional economic development [13,14], achieving common prosperity, and contributing to rural revitalization.

These themes reflect the comprehensive approach to modernizing Chinese higher education, highlighting its strategic alignment with national development priorities and responding to dynamic global and local contexts [15].

2. Literature Review

2.1. Connotation

Drawing from the ‘China Education Modernization 2035’ policy and the research of scholars such as Wu [16], the modernization of Chinese higher education aligns with advanced international standards, emphasizing both the modernization of ideological concepts and material conditions. This dual focus accelerates the development of both the hardware and software aspects of higher education, ultimately leading to the modernization of individuals and achieving an optimal state.

As higher education enters a stage of connotative development, it must closely align with national development goals and societal needs [17]. Connotative development, in contrast to ‘extensive development’, focuses on advancing educational undertakings by maximizing the potential of existing institutions. Once education has reached a certain scale, the emphasis should shift to connotative development, which involves leveraging existing facilities, equipment, and faculty, rather than establishing new schools and programs, to achieve further expansion and growth. While additional investment in education may be necessary, it is typically less than that required for extensive development. To effectively modernize higher education, efforts should align with national policy directives, particularly those promoting high-quality education. By considering the value goals and strategic tasks outlined in ‘China Education Modernization 2035,’ the core components and connotations of higher education modernization can be identified and summarized [9].

2.1.1. Essence of Higher Education Modernization Is Modernization of Individuals

In contrast to other stages of education, the ultimate goal of higher education development is the comprehensive cultivation of individuals, nurturing people with a sense of innovation and contemporary spirit [18]. The essence of this process is to develop modernized individuals. Through the accumulation of human resources, the modernization of higher education plays a crucial role in achieving true societal modernization.

2.1.2. Prerequisite for Higher Education Modernization Is Ideological Concepts

The modernization of ideological concepts is a crucial prerequisite and foundational basis for the development of higher education modernization. It is essential to prioritize advanced concepts, integrating the principles of quality education, innovative education, lifelong education, and the promotion of comprehensive human development throughout the entire development process.

2.1.3. The Foundation of Higher Education Modernization Is Material Conditions

The advancement of human modernity and modernized ideological concepts relies on the modernization of material infrastructure, encompassing both hardware and software conditions. Higher education modernization must be built on a solid foundation of modernized material resources, such as campus size, library collections, and laboratory facilities. Equally important is the enhancement of faculty quality, academic disciplines, and professional expertise. Only through the integrated modernization of both hardware and software can higher education meet contemporary needs and achieve continuous improvement.

2.2. Necessity and Feasibility

Experts and scholars such as Cai et al. (2022) [19], Chen et al. (2023) [20], and Gao (2022) [21] have discussed the relationship between Chinese-style higher education modernization and concepts such as common prosperity, rural revitalization, building a strong education nation, and achieving high-quality economic development. They emphasize that Chinese-style higher education modernization should establish principles that are uniquely ‘Chinese-style’, make rational choices aligned with this concept, and uphold its essential mission [22]. Efforts should focus on serving the needs of national modernization, aligning with the goal of building a strong higher education nation, and reinforcing the core of a high-quality higher education system.

Looking ahead to the development goals and trends for Chinese-style education modernization by 2035, it is essential to focus on high-quality human resource development, optimize the structure of human capital, enhance investment in human capital, promote the modernization of the national education governance system and governance capacity, and generate higher-quality educational outcomes.

2.3. Analysis of the Driving Forces

Based on a comprehensive review of the literature, the development of modernization in higher education in the new era incorporates elements such as ‘high quality’, ‘equity’, ‘social service’, ‘internationalization’, and ‘informatization’ into the process of modernization. From this, three major driving forces that propel the development of modernization in higher education are identified. For details, please refer to Figure 1 [23,24].

The first driving force is the synergistic interaction between higher education and economic development. The second driving force is the collaborative effort among key stakeholders in higher education—including the state, universities, and society—all actively working together to enhance educational quality. The third driving force is the synchronized development of higher education across different regions, characterized by two key aspects: Firstly, at the national level, the government should effectively allocate higher education resources. Secondly, at the provincial level, efforts should be made to align the development of higher education with local economic and social conditions.

2.4. Interactive Mechanism between Higher Education and Economic Development

The interaction between Chinese higher education modernization and high-quality economic development is characterized by mutual reinforcement. Modernized higher education systems act as catalysts for economic transformation, innovation, and sustainable growth. This coupling mechanism manifests in three key aspects [25]:

(1) Direct Impact: internal investment in higher education directly influences economic development.

(2) Indirect Impact: external investment in higher education indirectly affects the economy.

(3) Spillover Effect: talent cultivation and scientific research output in higher education positively impact worker productivity and the broader economy.

Drawing on education economics and human capital theory, we elucidate the coupling mechanism between education and high-quality economic development, highlighting the following specific mechanisms (see Figure 2 for details).

The meso-level mechanism of education in high-quality economic development centers on talent and innovation as intermediary variables. This mechanism encompasses two dimensions: education–talent–high-quality economic development and education–innovation–high-quality economic development. It aligns with the foundational and strategic goals for building a socialist modernized country, as outlined in the 20th National Congress of the Communist Party of China, which emphasizes education, science, technology, and talent as essential pillars for comprehensive socialist modernization.

At the macrolevel, education contributes to high-quality economic development in two primary ways. First, it cultivates a larger and more skilled pool of scientific research talent, driving technological advancements.. Second, it facilitates the acquisition of new technologies through channels such as foreign investment and imports.

A review of relevant domestic and international research reveals that domestic studies primarily focus on two aspects [26,27]. The first involves a literature review, policy analysis, and theoretical research, which help assess existing research results and identify gaps in the current literature The second examines theories related to high-quality development in higher education, intensive development, the modernization of higher education with Chinese characteristics, and regional economic coordination.

Based on available and valid data, a scientific, systematic, and practical multidimensional evaluation system should be developed, accompanied by empirical testing. This includes creating an evaluation indicator system for the modernization of higher education with Chinese characteristics, targeting the year 2035. The mechanisms of this modernization and its relationship with high-quality economic development should be investigated through theoretical, statistical, empirical, and differential analyses [28]. This approach will demonstrate the ‘quantity’ and ‘quality’ premium effects of higher education across the eastern, central, and western regions, addressing the issue of uneven distribution of high-quality higher education resources across provinces and regions in China.

Finally, based on theoretical foundations and empirical data, the relationship between regional higher education development levels and economic development levels should be assessed. Key factors affecting the modernization of higher education with Chinese characteristics should be identified, leading to targeted implementation pathways and policy recommendations. These insights will help guide the future development of higher education and China’s sustainable economic growth.

3. Construction of an Evaluation Index System

Face validity refers to the extent to which survey indicators accurately reflect the subjects being studied. Therefore, developing a comprehensive evaluation index system for the modernization of higher education in China must be based on the actual conditions of Chinese higher education and social realities. This approach ensures that the indicators created are relevant and accurately reflect the goals and challenges of higher education modernization in China.

A literature review reveals that modern higher education development integrates the concepts of ‘high quality’, ‘equity’, ‘social service’, ‘internationalization’, and ‘informatization’. Scholars have identified three main driving forces (see Figure 1) propelling higher education modernization, which have informed the establishment of a comprehensive evaluation index system.

The evaluation index system for higher education modernization in China, as constructed in this paper [29,30], draws on established research and adheres to principles of social adaptability, scientific validity, guidance, representativeness, and objectivity. The system is based on the five major functions of universities—talent cultivation, scientific research, social service, cultural inheritance and innovation, and international exchange and cooperation. Additionally, it considers data continuity, availability, and scientific rigor, providing a comprehensive assessment of the modernization level of higher education in China [31].

As shown in

Table 1. Evaluation indicators of modernization development level of higher education in China.

Primary Indicator	Secondary Indicator	Tertiary Indicator	Unit	Attribute
“Input–output” Index (A1)	Input Indicator	Number of Schools	Unit	+
		Number of Faculty and Staff	Persons	+
		Number of Full-time Teachers	Persons	+
		Ratio of Teachers with Doctoral Degrees in Full-time Teaching Staff	%	+
		Number of Teachers with Senior Professional Titles in Full-time Teaching Staff	Persons	+
		Expenditure on Education	Ten Thousand Yuan	+
		General Public Budget for Educational Expenses	Thousand Yuan	+
		Area of School Property Buildings	Square Meters	+
		Number of Books	Volumes	+
	Output Indicator	Undergraduate Enrollment	Persons	+
		Total Enrollment	Persons	+
		Number of Undergraduate Graduates	Persons	+
		Number of Graduate (Postgraduate) Graduates	Persons	+
Equity Index (A2)	Equal Opportunity	Gross Enrollment Ratio in Higher Education	%	+
	Procedural Fairness	Student–Teacher Ratio	%	-
		Per Capita Education Expenditure per Student	Yuan	+
Social Service Index (A3)	Talent Assurance	Average Number of University Students per 100,000 Population	Persons	+
	Research Transformation	Income from Patent Ownership Transfer and Licensing	Ten Thousand Yuan	+
		R&D Expenditure in Universities	Ten Thousand Yuan	+
		Number of R&D Projects in Universities	Item	+
Informatization Index (A4)	Scientific Research	Number of Scientific Papers Published Abroad	Article	+
Internationalization Index (A5)	Infrastructure	Number of Network Multimedia Classrooms	Unit	+
		Number of Computers (Units)/Digital Terminals	Unit	+
	Safety system	Value of Information Technology Equipment Assets	Ten Thousand Yuan	+

Note: “-” indicates a reverse indicator, while “+” indicates a positive indicator.

,This index system comprises five primary indicators: the high-quality ‘input–output’ index of higher education, the equity index of higher education, the social service index of higher education, the internationalization index of higher education, and the informatization index of higher education [30,32]. These primary indicators are further divided into 9 secondary indicators and 33 tertiary indicators, all of which are scientifically and logically constructed.

4. Methodology

4.1. Data Sources

The foundational data primarily come from authoritative sources, including the China Statistical Yearbook, China Education Statistics Yearbook, China Education Finance Statistics Yearbook, China Science and Technology Statistics Yearbook, as well as data from the National Bureau of Statistics, the Ministry of Education, and other reliable sources spanning from 2012 to 2022. These sources ensure the index system maintains high levels of objectivity, reliability, and scientific rigor.

4.2. Processing and Calculation Methods

Data standardization involves indexing statistical data to ensure consistency and comparability. The main methods of data standardization include data normalization and dimensionless processing. For calculating relevant indicators, we use range normalization during data standardization to eliminate dimensional effects, ensuring consistency and operability within the indicator system. For positively oriented indicators, ${\mathrm{Y}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{X}}_{\mathrm{i}\mathrm{j}}-\mathrm{m}\mathrm{i}\mathrm{n}{\mathrm{X}}_{\mathrm{i}\mathrm{j}}}{\mathrm{m}\mathrm{a}\mathrm{x}{\mathrm{X}}_{\mathrm{i}\mathrm{j}}-\mathrm{m}\mathrm{i}\mathrm{n}{\mathrm{X}}_{\mathrm{i}\mathrm{j}}}}$. For negatively oriented indicators, ${\mathrm{Y}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{\mathrm{m}\mathrm{a}\mathrm{x}{\mathrm{X}}_{\mathrm{i}\mathrm{j}}-{\mathrm{X}}_{\mathrm{i}\mathrm{j}}}{\mathrm{m}\mathrm{a}\mathrm{x}{\mathrm{X}}_{\mathrm{i}\mathrm{j}}-\mathrm{m}\mathrm{i}\mathrm{n}{\mathrm{X}}_{\mathrm{i}\mathrm{j}}}}$, where X_ij represents the j-th index of the i-th system [32,33].

4.3. Weight Determination

In accordance with the requirements of China’s modernization process and supported by scholars such as Liu [30] and Wang [34], each indicator is considered equally significant in measuring the level of higher education development. Therefore, equal weights are assigned to each indicator.

4.4. Comprehensive Evaluation Model

The comprehensive evaluation index for the modernization of higher education is calculated using the comprehensive evaluation model. For specific details, see the following:

$$U_1=\sum _{i=1}^n{W}_i{X}_i$$

W_i refers to the weights calculated using the entropy method. X_i refers to the standardized value of the indicators after dimensionless standardization.

Based on references to relevant domestic and international data on the development level of higher education, a set of evaluation criteria for the modernization level in higher education has been established. The comprehensive score of higher education development level corresponds to its respective stage [31,35]. See

Table 2. The evaluation grade standards for the modernization level of higher education development.

Composite Score	Higher Education Development Level	Corresponding Stage
<0.2	Low Level	Starting Stage
0.2–0.3	Intermediate Level	Development Stage
0.3–0.5	High Level	Formation Stage
>0.5	Very High Level	Maturity Stage

for details.

4.5. Theil Index

The Theil Index, originally proposed to measure income disparities between nations, has become a crucial measure of inequality widely applied to assess equity in public health services and the development of higher education [32,33].

This paper uses the Theil Index to measure the regional disparities in the development level of higher education in China. Leveraging the decomposability of the Theil Index, the overall Theil Index T can be divided into Tw (inter-regional differences) and Tb (intra-regional differences).

A larger Theil Index indicates greater disparity in the development of higher education modernization across regions. The calculation will be conducted using Stata 16.0, with the specific formula as follows [33,34]:

$$\mathrm{T}={\displaystyle \frac{1}{\mathrm{n}}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\displaystyle \frac{{\mathrm{y}}_{\mathrm{i}}}{\overline{\mathrm{y}}}}\log \left({\displaystyle \frac{{\mathrm{y}}_{\mathrm{i}}}{\overline{\mathrm{y}}}}\right)$$

(1)

T = Tw + Tb

(2)

$${\mathrm{T}}_{\mathrm{b}}={\sum }_{\mathrm{k}=1}^{\mathrm{k}}{\mathrm{y}}_{\mathrm{k}}\mathrm{l}\mathrm{n}\left({\displaystyle \frac{{\mathrm{y}}_{\mathrm{k}}}{{\mathrm{n}}_{\mathrm{k}/\mathrm{n}}}}\right)$$

(3)

$${\mathrm{T}}_{\mathrm{W}}={\sum }_{\mathrm{k}=1}^{\mathrm{k}}{\mathrm{y}}_{\mathrm{k}}\left(\sum \mathrm{i}\mathsf{\epsilon }\mathrm{g}\mathrm{k}{\displaystyle \frac{{\mathrm{y}}_{\mathrm{i}}}{{\mathrm{y}}_{\mathrm{k}}}}\mathrm{l}\mathrm{n}{\displaystyle \frac{\frac{{\mathrm{y}}_{\mathrm{i}}}{{\mathrm{y}}_{\mathrm{k}}}}{\frac{1}{{\mathrm{n}}_{\mathrm{k}}}}}\right)$$

(4)

In Equation (1), T represents the overall disparity in the modernization level of higher education in China, with values ranging from 0 to 1. A smaller Theil Index indicates less overall disparity in the modernization level of higher education, whereas a larger value signifies greater overall disparity.

Here, i denotes the province, n represents the total number of provinces, y_i is the average level of higher education modernization development for province i, and $\overline{y}$ is the average level of higher education modernization development across all provinces in the country.

In Equation (3), Tb represents the Theil Index for the level of higher education modernization within a region, n_k denotes the number of provinces in region k, and y_k is the level of higher education modernization in region k. A larger Tb value indicates greater imbalance in development within the unified region.

In Equation (4), Tw represents the Theil Index for the level of higher education modernization between regions. For each region, there are g_k provinces in group k (where k = 1, 2, 3, …, K). In this context, y_k represents the average level of higher education modernization development in region k. A larger Tw value indicates greater imbalance in development between different regions.

5. Measurement Results

5.1. Overall Comparative Analysis of Levels

1. The mean value increased from 0.242 in 2012 to 0.332 in 2021 and 0.328 in 2022.

As shown in

Table 3. Overall level of higher education modernization development in China, 2012–2022.

Year	Total Index	“Input–Output” Index	Equity Index	Social Service Index	Informatization Index	Internationalization Index
2012	0.242	0.285	0.317	0.153	0.145	0.308
2013	0.288	0.330	0.326	0.260	0.305	0.308
2014	0.279	0.361	0.296	0.143	0.209	0.319
2015	0.275	0.327	0.307	0.208	0.150	0.329
2016	0.280	0.327	0.335	0.221	0.149	0.325
2017	0.275	0.328	0.340	0.199	0.142	0.325
2018	0.280	0.326	0.373	0.201	0.151	0.336
2019	0.296	0.327	0.382	0.253	0.154	0.348
2020	0.320	0.371	0.397	0.241	0.210	0.348
2021	0.332	0.374	0.421	0.275	0.207	0.358
2022	0.328	0.368	0.395	0.277	0.207	0.312
mean	0.290	0.339	0.354	0.221	0.184	0.329

and Figure 3, data calculations reveal that from 2012 to 2022, the average level of modernization development of higher education in 31 provinces in China increased from 0.242 in 2012 to 0.332 in 2021 and 0.328 in 2022. This represents a significant progression from intermediate to high levels of higher education development, with an average annual growth rate of 3.264%. This growth aligns closely with the national strategy that prioritizes education in China’s modernization process.

2. From 2012 to 2022, the mean values of the five primary indicators are ranked as follows: equity index > “input–output” index > informatization index > social service index > internationalization index.

From 2012 to 2022, the mean values of the five primary indicators in higher education are as follows: high-quality “input–output” index 0.339; equity index of higher education 0.354; social service index of higher education 0.221; internationalization index of higher education 0.184; informatization index of higher education 0.329.

3. The annual average growth rates vary, with an overall average growth rate of 3.264%. Hainan province ranks first with an annual average growth rate of 7.711%.

From 2012 to 2022, the average annual growth rate of the modernization development level of higher education in 31 provinces in China is 3.264%. Twenty-one provinces have annual average growth rates exceeding the national average, ranked from highest to lowest percentage as follows: Hainan (7.711%), Guangdong (6.625%), Guangxi (5.412%), Zhejiang (4.981%), Tibet (4.812%), Chongqing (4.439%), Shandong (4.381%), Anhui (4.308%), Fujian (4.250%), Hunan (4.136%), Yunnan (4.066%), Shaanxi (4.035%), Gansu (3.957%), Jiangxi (3.954%), Inner Mongolia (3.940%), Ningxia (3.845%), Sichuan (3.771%), Shanghai (3.702%), Jiangsu (3.519%), Hebei (3.414%), Henan (3.348%).

For details, refer to Figure 4 illustrating the average annual growth rates of the modernization development level of higher education in 31 provinces in China from 2012 to 2022.

In 2021, Hainan Province aligned with “China’s Education Modernization 2035” and “Hainan Education Modernization 2035” by issuing the “14th Five-Year Plan for Education Modernization in Hainan Province”. During the 13th Five-Year Plan period, Hainan’s education structure continued to optimize, with steady improvements in educational equity and noticeable increases in educational quality.

To enhance the accessibility of higher education in Hainan, efforts will focus on achieving a gross enrollment ratio in higher education of over 60% and ensuring that every 100,000 people have 3000 enrolled in higher education. Concurrently, there will be a concerted effort to advance the development of top-tier disciplines in universities and promote the establishment of high-level local universities. Concentrating higher education resources is crucial for talent cultivation and technological innovation, significantly contributing to regional economic development. The moderate concentration of regional higher education resources has become a pivotal factor influencing regional economic development. Therefore, it is essential to carefully consider the rational allocation of higher education resources in the planning and distribution process.

5.2. Hierarchical Analysis

Table 4. Total scores of higher education modernization development levels in 31 provinces of China, 2012–2022.

Year	Beijing	Tianjin	Hebei	Shanxi	Inner Mongolia	Liaoning	Jilin	Heilongjiang	Shanghai	Jiangsu	Zhejiang
2012	0.858	0.202	0.249	0.151	0.101	0.314	0.208	0.246	0.367	0.542	0.312
2013	0.855	0.233	0.295	0.188	0.136	0.365	0.247	0.317	0.426	0.637	0.376
2014	0.878	0.231	0.294	0.166	0.117	0.356	0.215	0.281	0.449	0.664	0.373
2015	0.877	0.232	0.283	0.166	0.120	0.361	0.230	0.276	0.429	0.607	0.365
2016	0.883	0.233	0.288	0.166	0.119	0.352	0.237	0.279	0.440	0.619	0.382
2017	0.864	0.213	0.286	0.166	0.122	0.329	0.227	0.265	0.428	0.614	0.364
2018	0.864	0.225	0.293	0.172	0.125	0.327	0.222	0.254	0.429	0.621	0.380
2019	0.867	0.246	0.300	0.177	0.127	0.336	0.228	0.263	0.457	0.649	0.414
2020	0.779	0.247	0.332	0.191	0.141	0.347	0.239	0.277	0.481	0.745	0.456
2021	0.873	0.268	0.338	0.207	0.140	0.383	0.262	0.297	0.535	0.750	0.490
2022	0.821	0.260	0.342	0.186	0.140	0.365	0.249	0.278	0.519	0.749	0.496
mean	0.856	0.236	0.300	0.176	0.126	0.349	0.233	0.276	0.451	0.654	0.401
Year	Anhui	Fujian	Jiangxi	Shandong	Henan	Hubei	Hunan	Guangdong	Guangxi	Hainan	Chongqing
2012	0.219	0.194	0.191	0.407	0.312	0.378	0.270	0.389	0.146	0.041	0.179
2013	0.270	0.235	0.247	0.525	0.373	0.440	0.344	0.490	0.201	0.047	0.231
2014	0.266	0.223	0.222	0.462	0.383	0.446	0.324	0.466	0.167	0.046	0.227
2015	0.270	0.221	0.235	0.466	0.371	0.424	0.317	0.473	0.178	0.044	0.214
2016	0.280	0.239	0.231	0.470	0.369	0.430	0.310	0.501	0.184	0.048	0.215
2017	0.265	0.234	0.223	0.464	0.369	0.409	0.298	0.497	0.187	0.056	0.210
2018	0.271	0.244	0.226	0.469	0.394	0.408	0.311	0.520	0.194	0.063	0.222
2019	0.283	0.266	0.242	0.503	0.417	0.438	0.336	0.582	0.211	0.070	0.234
2020	0.316	0.279	0.271	0.558	0.468	0.474	0.384	0.671	0.230	0.076	0.259
2021	0.323	0.286	0.263	0.580	0.412	0.511	0.391	0.682	0.235	0.085	0.268
2022	0.325	0.287	0.269	0.598	0.420	0.500	0.390	0.717	0.229	0.084	0.266
mean	0.281	0.246	0.238	0.500	0.390	0.442	0.334	0.544	0.197	0.060	0.229
Year	Sichuan	Guizhou	Yunnan	Tibet	Shaanxi	Gansu	Qinghai	Ningxia	Xinjiang	mean
2012	0.319	0.122	0.131	0.036	0.318	0.108	0.045	0.049	0.093	0.242
2013	0.374	0.110	0.199	0.043	0.355	0.152	0.046	0.064	0.123	0.288
2014	0.391	0.113	0.151	0.050	0.379	0.130	0.039	0.050	0.091	0.279
2015	0.381	0.127	0.149	0.052	0.359	0.123	0.037	0.055	0.092	0.275
2016	0.382	0.132	0.155	0.048	0.357	0.139	0.044	0.057	0.098	0.280
2017	0.361	0.141	0.153	0.056	0.351	0.143	0.056	0.065	0.100	0.275
2018	0.379	0.160	0.159	0.043	0.361	0.142	0.049	0.061	0.095	0.280
2019	0.406	0.163	0.163	0.045	0.382	0.146	0.053	0.061	0.098	0.296
2020	0.443	0.175	0.188	0.060	0.427	0.154	0.053	0.072	0.113	0.320
2021	0.462	0.159	0.168	0.060	0.485	0.152	0.052	0.068	0.103	0.332
2022	0.453	0.161	0.169	0.051	0.463	0.147	0.055	0.065	0.099	0.328
mean	0.395	0.142	0.162	0.049	0.385	0.140	0.048	0.061	0.101

data reveal significant disparities in the modernization development levels of higher education across the 31 provinces. Beijing exhibits the highest level of modernization development, with an average index of 0.856 from 2012 to 2022, followed by Jiangsu with an average of 0.654. Conversely, Qinghai and Tibet rank at the bottom, with average modernization development indices of 0.048 and 0.049,respectively.

Meanwhile, from 2012 to 2022, there has been uneven development in the modernization of higher education across 31 provinces in China. Based on the average scores and evaluation criteria for the modernization of higher education during this period, we can see the following:

As shown in

Table 5. Levels of modernization development in higher education in 31 provinces of China from 2012 to 2022.

Composite Score	Level of Higher Education Development (Number of Provinces)	Provinces
<0.2	Low Level (11 provinces)	Shanxi (0.176), Inner Mongolia (0.126), Guangxi (0.197), Hainan (0.06), Guizhou (0.142), Yunnan (0.162), Tibet (0.049), Gansu (0.140), Qinghai (0.048), Ningxia (0.061), Xinjiang (0.101)
0.2–0.3	Medium Level (8 provinces)	Tianjin (0.236), Hebei (0.299), Jilin (0.233), Heilongjiang (0.276), Anhui (0.281), Fujian (0.246), Jiangxi (0.238), Chongqing (0.229)
0.3–0.5	High Level (8 provinces)	Liaoning (0.349), Shanghai (0.451), Zhejiang (0.401), Henan (0.390), Hubei (0.442), Hunan (0.334), Sichuan (0.395), Shaanxi (0.385)
>0.5	Very High Level (4 provinces)	Beijing (0.856), Jiangsu (0.654), Shandong (0.500), Guangdong (0.544)

, 11 provinces have a comprehensive average score of less than 0.2, indicating they are at a low level of modernization.

Eight provinces have a comprehensive average score between 0.2 and 0.3, indicating a moderate level of modernization.

Another eight provinces have a comprehensive average score between 0.3 and 0.5, indicating a higher level of modernization.

The remaining four provinces, namely Beijing, Jiangsu, Shandong, and Guangdong, are classified as having a high level of development in higher education.

5.3. Regional Analysis

Based on Figure 5, the “Hierarchical map of higher education modernization development levels in 31 provinces of China from 2012 to 2022”, and

Table 6. Levels of modernization development of higher education in eastern, central, and western China, 2012–2022.

Region	High Level	Relatively High Level	Moderate Level	Low Level
Eastern Region (10 provinces)	(4 provinces) Beijing (0.856), Jiangsu (0.654), Shandong (0.500), Guangdong (0.544)	(3 provinces) Shanghai (0.451), Zhejiang (0.401), Fujian (0.246)	(2 provinces) Tianjin (0.236), Hebei (0.299)	(1 province) Hainan (0.07)
Central Region (6 provinces))		(3 provinces) Henan (0.390), Hubei (0.442), Hunan (0.334)	(2 provinces) Anhui (0.281), Jiangxi (0.238)	(1 province) Shanxi (0.176)
Western Region (12 provinces)		(2 provinces) Sichuan (0.395), Shaanxi (0.385)	(1 province) Chongqing (0.229)	(9 provinces) Guangxi (0.197), Guizhou (0.142), Yunnan (0.162), Tibet (0.049), Gansu (0.140), Qinghai (0.048), Ningxia (0.061), Xinjiang (0.101), Inner Mongolia (0.126)
Northeast (3 provinces)		(1 province) Liaoning (0.349)	(2 provinces) Jilin (0.233), Heilongjiang (0.276)

Note: Regional division standard: National Bureau of Statistics.

, “Levels of higher education modernization development in eastern, central, and western regions of China from 2012 to 2022”, the level of modernization in higher education shows a close correlation with geographical location and economic development levels, highlighting a distinct “east high, west low” characteristic.

In the eastern region, encompassing ten provinces, four provinces are at a high level, three at a relatively high level, and two at a moderate level. Provinces at or above the moderate level constitute 90% of the region. The central region, comprising six provinces, shows five provinces at or above a moderate level, accounting for 83.33%. In contrast, the western region, with 12 provinces, has only 3 provinces at or above a moderate level, making up 25% of the region.

Among the three northeastern provinces, Liaoning is at a relatively high level, while Heilongjiang and Jilin are at a moderate level. These data highlight how modernizing higher education supports high-quality economic development across regions. The concurrent advancement of regional economic and social progress nurtures higher education, providing essential resources for its sustainable development. This synergy fosters a conducive environment where regional economy and higher education complement each other, facilitating coordinated development.

5.4. Comparison Analysis

5.4.1. Comparison of “Input–Output” Indicators

After calculating the average value and ranking of five first-level indicators of the development level of higher education in 31 provinces and cities of China from 2012 to 2022, refer to

Table 7. Average values and rankings of five primary indicators of higher education development levels in China, 2012–2022.

31 Provinces	“Input–Output” Index		Equity Index		Social Service Index		Informatization Index		Internationalization Index
	Average	Ranking	Average	Ranking	Average	Ranking	Average	Ranking	Average	Ranking
Beijing	0.755	2	0.918	1	0.958	1	0.994	1	0.825	2
Tianjin	0.217	23	0.384	12	0.266	9	0.151	13	0.212	21
Hebei	0.417	11	0.375	13	0.141	20	0.086	19	0.367	13
Shanxi	0.238	21	0.204	27	0.111	23	0.05	23	0.194	22
Inner Mongolia	0.15	26	0.23	22	0.084	28	0.019	27	0.156	25
Liaoning	0.411	12	0.403	9	0.257	11	0.223	10	0.398	12
Jilin	0.247	20	0.339	14	0.219	13	0.133	15	0.233	20
Heilongjiang	0.302	16	0.491	5	0.197	14	0.166	12	0.29	16
Shanghai	0.385	13	0.649	3	0.487	2	0.439	3	0.476	9
Jiangsu	0.762	1	0.697	2	0.421	3	0.502	2	0.865	1
Zhejiang	0.434	9	0.569	4	0.3	6	0.245	8	0.516	7
Anhui	0.376	14	0.216	25	0.184	15	0.135	14	0.334	14
Fujian	0.284	17	0.401	10	0.178	16	0.092	18	0.289	17
Jiangxi	0.328	15	0.259	21	0.132	21	0.062	21	0.296	15
Shandong	0.661	4	0.422	8	0.311	5	0.252	7	0.624	4
Henan	0.583	5	0.311	17	0.149	18	0.115	16	0.529	5
Hubei	0.556	6	0.455	6	0.275	8	0.28	6	0.525	6
Hunan	0.427	10	0.313	16	0.224	12	0.179	11	0.399	11
Guangdong	0.683	3	0.454	7	0.396	4	0.308	4	0.662	3
Guangxi	0.256	19	0.208	26	0.146	19	0.041	24	0.237	19
Hainan	0.051	28	0.163	29	0.073	30	0.011	28	0.039	29
Chongqing	0.264	18	0.317	15	0.173	17	0.111	17	0.274	18
Sichuan	0.51	7	0.298	20	0.265	10	0.228	9	0.497	8
Guizhou	0.186	24	0.171	28	0.099	25	0.021	25	0.174	24
Yunnan	0.228	22	0.113	31	0.112	22	0.052	22	0.181	23
Tibet	0.001	31	0.305	19	0.088	26	0.001	31	0	31
Shaanxi	0.453	8	0.399	11	0.283	7	0.295	5	0.431	10
Gansu	0.163	25	0.224	23	0.11	24	0.066	20	0.126	26
Qinghai	0.012	30	0.309	18	0.054	31	0.005	30	0.009	30
Ningxia	0.032	29	0.222	24	0.084	27	0.005	29	0.046	28
Xinjiang	0.123	27	0.144	30	0.078	29	0.021	26	0.113	27

and Figure 6, we can see the following:

From 2012 to 2022, analyzing the “input–output” indicators of high-quality higher education across 31 provinces in China, the annual average is 0.339. The annual averages of 14 provinces exceed the national average, listed from highest to lowest as follows: Jiangsu (0.762), Beijing (0.755), Guangdong (0.683), Shandong (0.661), Henan (0.583), Hubei (0.556), Sichuan (0.510), Shaanxi (0.453), Zhejiang (0.434), Hunan (0.427), Hebei (0.417), Liaoning (0.411), Shanghai (0.385), and Anhui (0.376).

5.4.2. Comparison of High-Quality Higher Education Equity Indicators

As shown in Figure 7, from 2012 to 2022, the average equity index of high-quality higher education across 31 provinces in China is 0.353. Thirteen provinces have annual averages exceeding the national average, listed from highest to lowest as follows: Beijing (0.918), Jiangsu (0.697), Shanghai (0.649), Zhejiang (0.569), Heilongjiang (0.491), Hubei (0.455), Guangdong (0.454), Shandong (0.422), Liaoning (0.403), Fujian (0.401), Shaanxi (0.399), Tianjin (0.384), and Hebei (0.375).

5.4.3. Comparison of Social Service Indicators

As shown in Figure 8, from 2012 to 2022, the average social service indicator of higher education in 31 provinces in China is 0.221. Twelve provinces have annual averages exceeding the national average, ranked from highest to lowest as follows: Beijing (0.958), Shanghai (0.487), Jiangsu (0.421), Guangdong (0.396), Shandong (0.311), Zhejiang (0.300), Shaanxi (0.283), Hubei (0.275), Tianjin (0.266), Sichuan (0.265), Liaoning (0.257), and Hunan (0.224).

5.4.4. Comparison of Internationalization Indicators

As shown in Figure 9, from 2012 to 2022, the average internationalization indicator for higher education in 31 provinces in China is 0.171. Eleven provinces have annual averages exceeding the national average, ranked from highest to lowest as follows: Beijing (0.994), Jiangsu (0.502), Shanghai (0.439), Guangdong (0.308), Shaanxi (0.295), Hubei (0.280), Shandong (0.252), Zhejiang (0.245), Sichuan (0.228), and Liaoning (0.223).

5.4.5. Comparison of Informatization Indicators

As shown in Figure 10, from 2012 to 2022, the average informatization indicator for higher education in 31 provinces in China is 0.333. Fourteen provinces have annual averages exceeding the national average, ranked from highest to lowest as follows: Jiangsu (0.865), Beijing (0.825), Guangdong (0.662), Shandong (0.624), Henan (0.529), Hubei (0.525), Zhejiang (0.516), Sichuan (0.497), Shanghai (0.476), Shaanxi (0.431), Hunan (0.399), Liaoning (0.398), Hebei (0.367), and Anhui (0.334).

6. Discussion

Based on the literature analysis and the five major functions of higher education institutions, this study constructs an evaluation indicator system for the modernization level of higher education in China using data from 2012 to 2022. The system aims to empirically assess the “quality” and “quantity” of higher education modernization with Chinese characteristics [35,36]. To support the sustainable development of higher education and the construction of a strong education system in China, this study aims to foster a scenario of synchronous and coordinated development between high-quality economic growth and higher education [37,38]. Additionally, it reflects the effectiveness of the national plan to revitalize higher education in the central and western regions [39]. The current data analysis shows the following:

6.1. Comparative Analysis

From the above analysis, the following is evident:

(1) Beijing consistently ranks first or second in both the overall level of development in higher education modernization and the five primary indicators: “input-–output” index of high-quality higher education, equity index of higher education, social service index of higher education, internationalization index of higher education, and informatization index of higher education. Jiangsu closely follows, indicating that Beijing and Jiangsu, through continuous deepening of higher education reform, are accelerating the construction of a high-level university system with local characteristics, Chinese attributes, and world-class standards. They are establishing a solid foundation and reaching successive milestones in higher education development, thereby creating a new pattern and positioning. This approach aims to effectively leverage high-quality higher education in support of China’s modernization efforts.

(2) Provinces such as Gansu, Qinghai, Ningxia, Xinjiang, Tibet, Guizhou, Yunnan, and others consistently rank at the bottom in both the overall level of development in higher education modernization and the five primary indicators. This underscores that these provinces are experiencing a relative lag in the development of higher education modernization. Factors contributing to this include inherent natural constraints such as environmental conditions and geographical challenges, coupled with limited economic development advantages. Consequently, the level of modernization in higher education in these provinces remains comparatively low. As of 2022, Ningxia, Qinghai, and Tibet have a total of 20, 12, and 7 higher education institutions offering undergraduate and specialized programs, respectively, which is significantly fewer compared to other provinces.

6.2. Overall Disparity and Regional Disparities Analysis

Based on the calculation results of the Theil Index, as shown in

Table 8. Overall disparity and regional disparities in higher education development level in China from 2012 to 2022.

Year	Eastern	Central	Western	Northeast	Mean
2012	0.459	0.289	0.709	0.143	0.492
2013	0.461	0.286	0.703	0.142	0.49
2014	0.458	0.287	0.714	0.144	0.494
2015	0.46	0.286	0.71	0.143	0.492
2016	0.457	0.287	0.707	0.144	0.491
2017	0.455	0.288	0.696	0.145	0.486
2018	0.453	0.287	0.702	0.147	0.488
2019	0.451	0.287	0.704	0.148	0.488
2020	0.453	0.285	0.698	0.149	0.486
2021	0.45	0.288	0.707	0.147	0.489
2022	0.448	0.288	0.709	0.149	0.489

, it can be observed that (1) from 2012 to 2022, the overall Theil Index of China’s higher education development level has shown a relatively stable trend, with slight fluctuations and a slow downward trajectory. This trend suggests that as China progresses in modernization and urban–rural integration, the balanced development of higher education across the 31 provinces continues to advance.

(2) From 2012 to 2022, the disparity in higher education development levels between regions in China was most pronounced between the western and eastern regions. This polarization in higher education development is more evident compared to the northeastern and central regions. The western region, comprising 12 provinces, faces significant disparities in economic development, education funding, and the quality of teaching staff. In contrast, the eastern region, spanning 10 provinces from north to south, exhibits notable differences in both the distribution of higher education resources and levels of economic development. These regional disparities in educational development levels have a profound impact. Comparatively, the Northeast and Central regions show narrower gaps in educational development levels between them.

7. Conclusions

1. The development of higher education in China still requires improvement, particularly in provinces of the central and western regions. From 2012 to 2022, China’s higher education development has made steady progress and is now at a relatively advanced stage. However, the overall development level still falls short of optimal. In absolute terms [40,41], Beijing ranks the highest, while Hainan has the fastest growth rate. Additionally, the regional disparity in higher education development across China has gradually decreased during this period.

2. The level of modernization in higher education development across the 31 provinces and regions in China shows significant imbalance [42,43]. From 2012 to 2022, the modernization level of higher education development across China’s 31 provinces and regions has been unevenly distributed. Eleven provinces fall into the low-level category, eight into the medium-level, eight into the relatively high-level, and four into the high-level category. Additionally, the most significant disparities within regions are observed between the western and eastern regions [44].

3. The level of modernization in higher education development is closely correlated with geographic location and economic development, characterized by a distinct “higher in the east, lower in the west” pattern [45,46].

In the eastern regions, four out of ten provinces are at a high level, three at a relatively high level, and two at a medium level, with 90% of provinces at or above the medium level. In the central region, five out of six provinces are at or above the medium level, representing 83.33%. In contrast, in the western region, only 3 out of 12 provinces are at or above the medium level, accounting for just 25%.

4. There is a critical need to enhance the level of “higher education internationalization” in the development of Chinese higher education.

Based on data from the five primary indicators, the average level of higher education internationalization in China from 2012 to 2022 is 0.177. Among the 20 provinces and municipalities analyzed, their averages fall below this mean value. Moving forward, China should prioritize enhancing higher education internationalization, encompassing curriculum, research, and administrative aspects. Efforts should concentrate on talent development, scientific research, infrastructure for internationalization, and fostering a supportive environment [47]. This includes establishing regulations, operational systems, mechanisms, and cultivating an international atmosphere. Implementation of specific plans for international education is essential to continually elevate the level of internationalization in higher education.

5. The Total Development of Higher Education (TAI) index for China shows a gradual decline with fluctuations, indicating ongoing progress towards balanced development of higher education across the 31 provinces.

The greatest disparities in higher education development within China are observed between the western and eastern regions. Modernizing higher education aims to achieve human modernization, rooted in modern ideas and supported by advancements in material conditions [48,49]. As higher education transitions into nuanced development, it must align with national goals and strategic directions to meet societal needs. Exploring the essence and elements of higher education modernization should adhere to national policy guidance, with a specific focus on achieving high-quality higher education.

Based on the above research findings, the following recommendations are proposed to further enhance the high-quality development of higher education in China [49]: (1) There are still significant disparities in the development levels of higher education among the eastern, central, and western regions. First and foremost, at the national level, the government should effectively allocate higher education resources to narrow the gap between the central and western regions and the eastern region. In particular, there is a noticeable disparity in economic development levels, education funding, and the allocation of teaching resources among the 12 provinces in the western region. (2) The factors constraining higher education development vary across provinces. Each province should address its own weaknesses in disciplinary fields and regional layout. (3) The eastern region should leverage its leading position by sharing innovative concepts, promoting resource spillovers, and engaging in joint scientific and technological innovations. This approach will deepen collaborative interactions among the eastern, central, and western regions of higher education, effectively enabling the strong to support the weak.

8. Limitations and Future Recommendations

This study has several limitations. Firstly, ensuring the comprehensiveness of the evaluation indicator system for higher education modernization poses a challenge. The modernization of higher education in China encompasses a broad range of components and indicators, making it difficult to accurately measure with appropriate indicators and data [44,45]. The evaluation system aims to encompass all elements necessary for the high-quality development of higher education and the goal of establishing a nation with a strong education system [46]. It should provide a systematic description and overall assessment of constructing a high-quality higher education system in China, addressing both explicit and implicit features, as well as hard and soft indicators, and qualitative and quantitative aspects.

Currently, most evaluation indicator systems are dominated by objective indicators. Future efforts should focus on incorporating subjective indicators that assess cultural aspects, values, and institutional practices [48]. This will facilitate a more comprehensive and systematic evaluation of China’s high-quality higher education system.

As empirical tools develop, indicator data enrich, and the higher education system itself evolves; hence, research on evaluating the development level of higher education in China has become increasingly prominent. While this study offers a potential pathway for achieving the modernization of higher education with Chinese characteristics, it is limited by the available data and scope. Notably, the study did not include an empirical analysis of the causes behind regional differences in higher education development levels or an international comparison. These areas represent important avenues for future research.