Comprehensive evaluation of higher education systems using indicators: PCA and EWM methods

Abstract

The higher education system refers to the organisational structure of higher education institutions and the staff and infrastructure needed to provide postsecondary education. To better develop a country or region’s higher education system, administrators need to have a handle on the current state of the system, which requires regular and realistic assessments of the quality and sustainability of higher education. Thus, this study constructed a quality-sustainability model (QSM) for national higher education. Nine countries with developed higher education and 13 indicators were selected to reference higher education quality and sustainability globally. Principal component analysis (PCA) was used to downgrade these 13 indicators and extract the factor coefficient score matrixes. Of these, four principal components were used for further analysis. Each sub-indicator is assigned weights by the entropy weighting method (EWM) to obtain a quantifiable QSM. The model innovatively includes indicators such as “academic integrity” and is applied experimentally to data from nine countries to analyse the strengths and weaknesses of their higher education systems. The study found that each country’s education system has different strengths, and by comparing and summarising them, the findings can guide the development of future-oriented higher education. This study has made some development recommendations based on the model by combining multidisciplinary theories. The study enriches existing methods for assessing the quality of higher education and identifies the weaknesses and directions for the development of higher education in some developed countries.

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ARTICLE
Comprehensive evaluation of higher education
systems using indicators: PCA and EWM methods
Cong Cao 1,4 ✉, Tianlan Wei1,4, Shengyuan Xu2, Fan Su3& Haiquan Fang3
The higher education system refers to the organisational structure of higher education
institutions and the staff and infrastructure needed to provide postsecondary education. To
better develop a country or region’s higher education system, administrators need to have a
handle on the current state of the system, which requires regular and realistic assessments of
the quality and sustainability of higher education. Thus, this study constructed a quality-
sustainability model (QSM) for national higher education. Nine countries with developed
higher education and 13 indicators were selected to reference higher education quality and
sustainability globally. Principal component analysis (PCA) was used to downgrade these 13
indicators and extract the factor coefficient score matrixes. Of these, four principal compo-
nents were used for further analysis. Each sub-indicator is assigned weights by the entropy
weighting method (EWM) to obtain a quantifiable QSM. The model innovatively includes
indicators such as “academic integrity”and is applied experimentally to data from nine
countries to analyse the strengths and weaknesses of their higher education systems. The
study found that each country’s education system has different strengths, and by comparing
and summarising them, the findings can guide the development of future-oriented higher
education. This study has made some development recommendations based on the model by
combining multidisciplinary theories. The study enriches existing methods for assessing the
quality of higher education and identifies the weaknesses and directions for the development
of higher education in some developed countries.
https://doi.org/10.1057/s41599-023-01938-x OPEN
1School of Management, Zhejiang University of Technology, Hangzhou, China. 2College of Computer Science and Technology, Zhejiang University of
Technology, Hangzhou, China. 3School of Public Administration, Zhejiang University of Technology, Hangzhou, China.
4
These authors contributed equally:
Cong Cao, Tianlan Wei. ✉email: congcao@zjut.edu.cn
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1234567890():,;
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Introduction
For modern countries, quality assessment of the higher
education system is important. The system refers to the
organisational structure of higher education institutions and
the personnel and infrastructure required to educate post-
secondary students. It has value as a progression of primary and
secondary education, not only as an industry in itself but also as
an important source of trained and highly educated citizens in the
country. That is why modern countries are eager to improve the
quality of the higher education system, which requires a clear and
accurate assessment of the system to find a breakthrough in its
development, especially after Covid-19 brought many changes to
the industry. At the 70th session of the United Nations General
Assembly in September 2015, the 2030 Agenda for Sustainable
Development also emphasised the importance of lifelong educa-
tion (UNESCO, 2016). Unfortunately, strong evidence suggests
that countries will get more excellent value in return by investing
their limited financial resources in primary and secondary edu-
cation rather than in universities or technical training (McCowan,
2016), so they prefer to invest in the former.
A developed higher education system has value both as an
industry and as a source of trained and educated citizens for the
nation’s economy. It also plays an essential role in global sus-
tainable development (Franco et al., 2019). Measuring the quality
and sustainability of higher education systems is more complex
because primary and secondary education can visually reflect the
quality of education through the level of students. In contrast,
higher education carries the complex parts of research missions,
academic integrity, and transnational exchange. Thus, an effective
higher education assessment system is difficult yet indispensable.
Our job is to find the correct elements to assess the quality and
sustainability of higher education in a country or region, project
it, and improve the quality and sustainability of higher education
by proposing policies.
Several scholars have studied the quality and sustainability of
higher education in some countries in recent years. Moreover,
campus operations, inter-university collaboration, and partner-
ships with government and civil societies are essential factors that
influence higher education sustainability (Wu and Shen, 2016). It
is also influenced by economic and social factors, such as sus-
tainable value-investing donations and community partnership
building (Barlett and Chase, 2004). While not all schools engage
in all of these activities, the sector identifies core sustainability
initiatives in higher education: academic, operational, and
administrative (Owens, 2017).
For this study, we selected a dozen factors relating to the
quality and sustainability of higher education. This was done
through a literature review and an analysis of existing theories.
We collected datasets for nine countries with good higher edu-
cation development by collating data sources, such as the World
Bank’s public database and the Organization for Economic Co-
operation and Development (OECD) database. We used the
entropy valuation method to assign weights to these factors.
Based on this, we constructed a quality-sustainability model
(QSM) for evaluating higher education in different countries.
Using this model and the different factors, with their weight
assignments, we derived two indicators that can be used to
evaluate the quality and sustainability of higher education in a
country. The model was used to assess and rank the quality and
sustainability of higher education in the nine countries. To verify
the model’s generalisability and value in guiding the quality and
sustainability of higher education in a country, we selected some
representative countries and assessed their sub-indicators. A
regression analysis was conducted using historical data relating to
higher education to apply the model’s effects more effectively and
to develop goals and policies for higher education development.
The study showed that the higher education systems of different
countries have varying strengths, which include the developed
countries with more advanced higher education systems. The
development of higher education needs to be comprehensive and
balanced, and if countries share their experiences of development,
they may be able to compensate for their own shortcomings and
guide their sustainable development more effectively.
This study innovatively explores the results of a quantitative
assessment of higher education from the perspectives of eco-
nomics and sociology. While higher education must be promoted,
there is a need to consider supply and demand in the education
and employment markets and to avoid social problems, such as
an imbalanced talent structure. When evaluating education, the
quality of teaching and learning can be considered from several
different perspectives. However, constructing a national or
regional education system requires a macro perspective on each
aspect, and a sudden increase in individual indicators is not
necessarily beneficial to the educational system as a whole. The
construction of a higher education system requires the support of
multidisciplinary theories, and its development needs to encom-
pass many aspects. This study adds the indicator of academic
integrity to traditional higher education assessment methods,
which is represented by the retraction rate of papers. This study
develops popular contemporary higher education assessment
measures and provides new perspectives on evaluating education
systems.
First, the latter section compares the current development of
higher education assessment activities and describes how the 13
indicators needed to construct the QSM have been used to assess
higher education throughout history. Second, we use entropic
assessment methods to assign weights to these factors to construct
the QSM and then use the model and the different factors and
weight assignments to derive the two indicators used to assess the
quality and sustainability of the higher education system. Third,
we compiled data from nine developed countries with mature and
reliable data on higher education development based on
authoritative data, such as the World Bank and OECD databases,
and used the QSM for assessment. Finally, we discuss the model’s
results and the findings of its application to provide suggestions
for the development of higher education.
Literature review
The activity of higher education assessment has multiplied in the
last hundred years and is now very active (Wiethe-Körprich and
Bley, 2017). In recent decades, the number of higher education
institutions has increased significantly, and the evaluation of
higher education has been increasingly studied (Van Mol et al.,
2021). The primary purpose of this activity is to improve the
quality of the higher education system. Currently, the higher
education system in each country is integrated into the national
system, generally funded by the state and serving national needs
(Reymert et al., 2021). Despite the increasing internationalisation
of academic careers, they are still formed in national contexts.
Furthermore, national research systems differ in their research
priorities and evaluation systems. Universities also have different
levels of control over resources (Sivertsen, 2017).
Evaluation activities began many years ago and can be traced
(Guba and Lincoln, 1981). In the mid-1960s, assessment began to
develop as a scientificfield in the United Kingdom and the United
States (Worthen and Sanders, 1987). Assessment activities are
widely applied and generally defined as recognising, clarifying,
and implementing essential criteria to define an object’s value
according to these criteria (Fitzpatrick et al., 2004). Using the
same set of criteria or models for different regional higher
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education systems facilitates the identification of differences and
the search for relative strengths and weaknesses. Through
assessment activities, we can explore the direction of higher
education development. This paper compiles the methods and
indicators commonly used in existing studies for analysis to
determine whether a higher education system is of high quality.
As higher education continues to change and assessment activ-
ities evolve, a number of methods and important metrics are
commonly used in this area, including feedback, formative eva-
luation, and peer evaluation (Leihy and Salazar, 2017). These
methods have good generalisability and can be applied to assess
the quality of teaching and learning in higher education in var-
ious contexts as well as primary and secondary education.
However, to some extent, these three methods ignore the aca-
demic, research-based nature of higher education.
The advent of the era of big data has brought more feasibility to
assessment activities, and the rational use of data and assessment
methods can better ensure the independence of assessment
methods, enhance research reliability, and reduce randomness.
When assessing higher education, scholars often go back to first
hypothesising the impact of certain factors on the quality of
higher education or the relationship between certain scholarly
output data and higher education. These data are then examined
through analytical methods such as Bayesian back-propagation
(Gao and Yu, 2021), entropy weight-TOPSIS, and logistic model
(Zhang et al., 2021). Other methods are often applied; for
example, Yang et al. (2018) constructed a two-stage network
model and used Luenberger indicators to analyse the productivity
and evolution of research universities in China. Thanassoulis
et al. (2017) examined the role of student assessment in higher
education evaluation, using a combination of the analytical
hierarchy process (AHP) and data envelopment analysis (DEA)
to help teachers understand the direction for improving teaching
and learning activities. In addition, many scholars have developed
studies of higher education systems using structural equation
modelling (SEM), which can be used to test alternative models,
reliability, validity, theoretical support models, and data screen-
ing. Green (2016) examined the existing literature using SEM to
study higher education and found that this model is often used to
test alternative models, reliability, validity, theoretical support
models, and data screening in higher education research models.
Big data and data mining techniques have also been applied to
higher education evaluations. Data science research methods,
including time-varying clustering sampling algorithms, data
mining, and big data grey relational decision-making algorithms,
have all been applied to data research related to higher education
and have been used to find the development of methodologies
and the promotion of better-quality teaching and learning (Feng,
2021; Liu and Song, 2021).
After an evaluation, the quality of the research method or
model needs to be tested, and this process focuses on the relia-
bility and validity of the evaluation. Especially in recent years, big
data technologies have often been applied to higher education’s
monitoring and quality analysis. We must use effective data
analysis methods to evaluate the relevance of indicators, the
applicability of methods, and the representativeness of evaluation
subjects in the study to grasp the quality of the evaluation. Xu
et al. (2022) evaluated the level of sustainable development of
Japanese higher education using factor analysis and principal
component analysis. Subsequently, structural validity tests were
used to test the rationality of the model, quantitatively assessing
the effectiveness of the policy and its impact on reality.
As higher education assessment activities have evolved through
the data era, academics have gradually identified many indicators
that can assess the quality of higher education (Gupta et al.,
2015), such as the employment rate of graduates, the number of
papers, and the gender ratio. Therefore, it is important to con-
sider these classical elements, which remain important measures
of the quality of higher education, when studying new changes
that may impact higher education. Considering the important
role of higher education in academic research and the now
common incidents of academic misconduct, we have considered
indicators such as the number of patents per capita, paper
retraction rate, and self-citation rate, which are rarely discussed in
historical studies along with those traditional indicators, when
organising the assessment of the quality and sustainability
potential of higher education. Therefore, based on the indicators
for measuring the quality of the higher education system, as
confirmed by historical studies, we created the QSM.
Symbol table and assumptions
We propose the following assumptions to better model the pro-
blem studied in this paper:
●The higher education system includes universities, acade-
mies and colleges. It represents levels 5–8 of the 2011
International Standard Classification of Education (ISCED)
structure. Levels 5–8 refer to tertiary education, from short-
cycle tertiary education to doctoral level, and comprise the
ISCED definition of higher education, which is the
standard used in many data sources.
●Higher education in each country functions independently
of that in other countries, meaning it is not necessary to
calculate synergistic cooperation between the higher
education systems of each country.
●There are minor variations in higher education levels
within countries or regions, and some average indicators
are therefore representative.
●The assumption is made that indicators are purely
positively or negatively correlated and that no critical
values change the logic of the indicators.
The abbreviations used in this paper and their corresponding
definitions are shown in Table 1. The meanings and definitions of
the 13 indicators can be found in Table 2.1 in Appendices B—
Tables.
The QSM of higher education
Two models and indicator selection. To assess the quality and
sustainability of the higher education system, we built the QSM. It
is a multidimensional assessment tool that assesses multiple
dimensions of a country’s higher education system, including
economic support, innovation capacity, student development,
and academic integrity, to produce an overall assessment of the
country’s higher education system. The model is divided into two
parts: quality (QL) and sustainable development (SD), which are
used to assess the higher education system’s current quality
Table 1 Symbol definition.
Symbol Meanings Definition
tTime Between 2010 and 2018
W
i
Weight The Weight of S
i
in the evaluation
system
S
i
Indicator The function ibetween 1 and 13
S
ij
Indicators for Each
Country
The function Jis the country serial
number
S
ij S
ij
Normalised Matrix Standardisation methods are Min-
max method
QL Quality Model of QSM QL
1
+QL
2
+QL
3
+QL
4
SD Sustainability Model of
QSM
SD
1
+SD
2
+SD
3
+SD
4
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vitality and future development potential, respectively. The
quality of the higher education system is concerned with the
current quality of the system, while sustainability refers to the
integration of environmental, social, and economic considera-
tions into the policies, practices, and operations of universities
and colleges. It involves a commitment to meeting the present
generation’s needs without compromising future generations’
ability to meet their own needs. The model results of each country
can be compared with the evaluation results of other countries to
discover the shortcomings of the system and the direction of
improvement and to provide objective and effective decision
support for government decision-makers and members of the
system.
As mentioned in the literature review, historical studies have
identified some factors that are inevitably taken into account
when assessing higher education, and they directly reflect the
quality of higher education and are undoubtedly elements of the
quality and sustainability of the higher education system in this
study, such as the number of papers published and cited, the
proportion of international students, and the salary level of
graduates. These indicators are also commonly used in educa-
tional assessments to assess the quality of higher education
institutions. However, considering that we are assessing the level
of higher education within a certain region, these data cannot be
compared directly but are divided by the corresponding base,
such as the total population of the region, economic base
indicators, and the total number of higher education institutions
within the region.
Innovation capacity and research capacity correspond to the
average number of patents and publications per 1,000 citizens.
Scientific research is an essential task for the sustainable
development of higher education, and the higher the research
capability, the more robust the sustainable development cap-
ability of higher education. Therefore, many past studies have
explored the academic and innovation capacities of some regions
around these two indicators. For example, Xu et al. (2013)
proposed a multi-attribute comprehensive evaluation method of
individual research output (IRO), which can assess the academic
ability of scholars by the number and quality of their publications
and overcome the one-sidedness of a single indicator to some
extent by considering more elements. Chen et al. (2009) analysed
Chinese patent filing activity in eight economic regions of China
from 1999 to 2004, exploring the relationship among gross
domestic product (GDP), research and development, and patent
filing in various regions and organisations in China. These studies
show that publications and patents can reflect technological
development and higher education in a country or region. They
embody the academic and creative capacity that is the essential
foundation of the higher education system and supports high-
quality and sustainable development.
We measure a country’s level of international exchange by the
percentage of its students that are international. The cross-border
mobility of students can profoundly impact the development of
higher education and is a reflection of its good reputation and
quality (Abdullah et al., 2017). Therefore, a higher percentage of
international students reflects a high level of internationalisation
in local higher education and a higher quality level. However,
recruiting international students presents several challenges for
the higher education system. Sherry et al. (2010) studied the
University of Toledo, which has an increasing number of
international students, and found that international students face
issues such as a new culture, language, and finances. Mature
higher education systems often have a reputation for good quality
and can defuse their challenges and those of international
students, playing an important role in international academic
exchange. Thus, the level of internationalisation is an indication
of the quality of the higher education system.
The relative position of the salary level of college graduates in
society can visualise the degree value of higher education. The pay
gap between those with and without higher education reflects the
value of higher education in the job market, which helps it be
valued and promotes sustainability. Drydakis (2016) compared
Bachelor of Science graduates in economics from several U.K.
schools in a field study and found that graduates from top-ranked
universities can receive more interview invitations and higher
entry salaries than other graduates, reflecting the impact of
university quality on graduate salaries. High-value degrees can be
the foundation of sustainable higher education by attracting
capable students and maintaining the vitality of colleges and
universities.
The complexity of the higher education system makes it more
challenging to focus on assessment activities, especially when we
need to explore their quality and sustainability. While we can
accurately judge and compare a university by its student
performance and research outcomes, when looking at the entire
higher education system, we have to focus on academic integrity,
financial commitment, and gender equity as elements of
sustainability. After compiling historical research, we selected a
number of these indicators to measure the quality of the higher
education system, including barriers to educational entry,
government attention, student development input, gender equity,
academic integrity, faculty salaries, and speculation. In addition,
because higher education systems often contain many univer-
sities, we can combine existing university rankings to determine
the number of quality universities within a system and thus
estimate the quality of the higher education system in that region.
Although applied and validated in different education system
assessment scenarios, the selected indicators were once rarely
combined with traditional indicators of higher education quality
to explore the quality and sustainability of higher education
systems within a country or region. These innovations help assess
the quality of the higher education system more comprehensively
and reveal where the potential for development lies, enhancing
the value of the model’s application.
Barriers to educational entry can be measured by the gross
enrolment rate of higher education. Reflecting how many people
have access to higher education in a country or region, high levels
of access reflect that the country’s higher education system is of
higher quality and has the potential to grow and be sustainable.
Enrolment rates visually represent how many young people of the
right age in a country can enter the higher education system.
Higher education enrolment rates are generally higher in
developed countries than in developing countries. Jiménez et al.
(2017) studied the impact of educational enrolment on national
entrepreneurship in different countries in Latin America. They
found that countries with higher enrolment in higher education
have lower entrepreneurship because higher education reduces
information asymmetry and perceptions of adverse selection, thus
increasing employer trust and promoting business development.
Enrolment rates also indicate the supply and demand for higher
education. De Campos et al. (2018) analysed data obtained from
the Brazilian census and studied indicators related to the quality
of higher education in Brazil. They examine the balance between
supply and demand in higher education in Brazil. In evaluating
the higher education system in the United States, for example,
there has been a significant amount of scholarly research on
enrolment rates (Fortin, 2006). Luo et al. (2018) and Bozick et al.
(2016) examined changes in college enrolment in the United
States and the factors that influenced them and explored the
impact of enrolment on social development. Based on historical
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research, we use higher education enrolment rates to measure the
quality and sustainability of the system.
Government attention and student development input are
measured by the ratio of financial investment in education to
GDP, total expenditure per student, and GDP per capita. They are
all financial indicators of the level of higher education. The
government’s investment in higher education is conducive to
quality and higher education development. High government
investment in students contributes to building talent within
higher education institutions, improving organisational quality,
and the sustained output of higher education talent. Tregub and
Buffet (2019) studied the impact of French investments in the
education system at all levels on French society. It developed
recommendations to optimise financial flows in education. Wu
and Liu (2009) studied the Chinese government’s investment in
higher education from 1985 to 2004, subjected the data to
correlation analysis and the Granger causality test with economic
growth, tested for series smoothness and cointegration analysis,
and found a significant positive correlation between the two.
Therefore, it is concluded that investment in higher education
improves its quality and promotes socioeconomic development.
Gender equity can be reflected in higher education enrolment
rates for male and female students. The more significant the
gender gap is in enrolment, the lower the quality of higher
education is in the country, as it indicates a gender gap in the
higher education system and that this is unhelpful for the
sustainability of higher education. In the last few years, David
(2011), Devos (2012), Johnson et al. (2015), and many other
scholars have discussed gender equality in the higher education
system to find a way to develop equity in higher education.
We measure the university quality of a country or region
basedonthenumberofitsuniversitiesthatappearinthetop
1000 of the QS World University Rankings (QS Rankings) as a
percentage of the total number of universities in the region.
University quality reflects the level of higher education and
international recognition and is an element of the sustainable
development of higher education. The QS rankings are credible
third-party rankings of world universities based on academic
reputation, employer reputation, faculty/student ratio, citations
per faculty, and international faculty ratio. The quality of
universities is judged based on academic reputation, employer
reputation, faculty/student ratio, citations per faculty, interna-
tional faculty ratio, and international student ratio Craig (2022).
Dowsett (2020) has examined the relationship between the
international rankings of four Australian universities and their
development strategies, showing that specificchangesin
strategic direction can improve a university’s market position
and contribute to significant improvements in its ranking. High-
quality universities are numerous and can reflect the overall high
quality of a higher education system, so this indicator can be
used to measure the sustainability potential of the higher
education system.
Academic integrity is reflected in the retraction rate of journal
articles. The higher retraction rates of high-level papers reflect
higher levels of academic misconduct and lower quality within
higher education. Nagella and Madhugiri (2020) studied the
retraction rate of journals in the medical field and found that the
number of retractions is proportional to the number of papers
published by the journal and the citation metrics of the journal.
Meanwhile, Yeo-Teh and Tang (2021) and Abritis et al. (2021)
both found an “alarming”and “exceptionally high”retraction rate
for papers on the coronavirus disease (COVID-19) in recent
years, which may be due to errors or fraud in the study.
Generally, the lower the retraction rate, the better the academic
integrity. A high-quality higher education system should main-
tain a low rate of retraction of academic output.
Faculty salaries within the higher education system can
motivate college faculty and administrators and indirectly
influence the quality of a region’s higher education system. High
faculty salaries reflect that higher education talent is respected,
contributes to higher education’s organisational quality, attracts
more talent to higher education, and promotes sustainable
development. Cao and Yu (2020), Guo and Wang (2017), and
other scholars have studied college teachers’salaries and incentive
systems to analyse how to motivate college teachers. These
findings all support the idea that high compensation for higher
education faculty can serve as an incentive to improve higher
education’s quality and sustainability potential.
The literature’s frequency of citations can reflect the Research
Value of the higher education system; accordingly, an excessive
self-citation rate is associated with speculative behaviour. The
average number of citations of papers within a country indicates
the value of higher education research output, which affects
higher education sustainability. The high self-citation rate is a
well-known phenomenon of academic speculation, illustrating
academic dishonesty as detrimental to higher education’s quality
and sustainability. Pasterkamp et al. (2007) examined 8864
articles from nine countries and found that self-citations were
more common in cardiovascular-related journals and that the
United States had the highest research output, with more citations
of papers from that country. As a commonly used measure of
academic integrity, this indicator will help us analyse the quality
and sustainability of the education system.
We have selected 13 quantifiable indicators to help us assess
the quality and sustainability of a country’s higher education:
barriers to educational entry (access to education), innovation
capacity, research capacity, international exchange, government
attention, student input, gender equity, academic quality,
academic integrity, faculty salaries, degree value, research value,
and speculation. In particular, we introduce here three indicators
of negative logic: gender injustice (S
7
), academic misconduct (S
9
),
and speculative behaviour (S
13
), which are detrimental to the
quality and sustainability of higher education when they are
higher, as shown in Table 3 in Appendices B—Tables.
We selected indicators from the United States, Australia,
Germany, the Netherlands, Japan, Norway, Canada, Sweden and
the United Kingdom as measures for the evaluation of higher-
level higher education. These countries all have well-developed
higher education systems and credible publicly available data. The
same measures can be applied across Asia, the Americas, Europe
and Australia, which balances the differences caused by
geographical location. Using the data from these countries to
validate the model will facilitate our ability to improve the
model’s credibility and open a discussion on the development of
higher education systems in these countries. The specific values of
these countries’indicators are shown in Tables 2.1 and 2.2 in
Appendices B—Tables.
Following a model-building process, described in Appendices
A—QSM modelling, we developed the following QSM equation
(the specific data during the calculation as shown in Appendices
C), whereby a higher QSM index represents a state of better
quality and sustainability in higher education. The function S
i
∗is
the standardised index data.
QSM ¼∑
n
i¼1
wiS*
i
Application of QSM: preliminary assessment of nine
countries
Using the model to evaluate data from nine countries in 2018.
The QSM assesses higher education systems in a country or
region. Therefore, the corresponding country or region needs a
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certain number of higher education institutions to improve the
accuracy of the model assessment. At the same time, the higher
education system of the country or region needs to be regularly
counted and evaluated to produce comparable data as the basis
for the model study.
The QSM gives us higher education quality and sustainability
indexes for nine countries in the previous constituency. This
model’s results will be analysed in detail in the third section of
this chapter. Table 2shows the specific indices and rankings of
each country’s indicators.
In the QSM results, Australia was ranked first in the QL
rankings, the UK was ranked first in the SD rankings, while US
higher education, which is traditionally perceived as large and
leading, did not perform as well as expected. Figures 1and 2show
the performance of these countries.
Evolution of the countries in 2010–2016. In addition to
applying the QSM to the data from nine countries for 2018, we
reviewed changes in higher education in the United States,
Australia, Japan and Germany between 2010 and 2016. These
four countries are in North America, Oceania, Asia and Europe,
respectively, and are considered representative of their continents.
They are among the most economically and educationally
developed countries in their regions. At the same time, due to
their educational traditions and geography, there are significant
differences between their educational systems, and it is valuable to
compare them in a comprehensive study. The results of the QSM
are shown in Figs. 3and 4.
2018 Data discussion. We found something interesting in the
results of our QSM assessment. Although the United States has a
very high reputation for higher education, its higher education
quality and sustainability indicators are not optimal for statistical
results. Many of the same factors exist in our QSM–QL and
QSM–SD, closely related to a country’s higher education quality
and sustainability indices. However, some countries, including
the United Kingdom and Japan, still show a large gap between the
indicators. The model has different weights in measuring higher
education quality and sustainable development on each indicator.
We used the model to play a fundamental role in analysing these
countries.
Let us dissect why Australia achieved the top ranking among
several countries on quality indicators. Although Australia shows
low levels of innovation capacity (S
2
) and gender equity (S
7
)in
higher education, these indicators are not highly weighted in the
QSM–QL, as shown in Fig. 1. Conversely, Australia has excellent
performance on highly weighted and critical indicators, such as
barriers to educational entry (S
1
) and academic integrity (S
9
).
Australia has made many efforts to achieve a high-quality higher
education system, first and foremost in its higher education
participation and partnerships program (HEPPP). It has been
implemented since 2009, and its primary goal is to create more
opportunities for higher education for people of low socio-
economic status, citizens of remote areas, and indigenous people,
including financial support for educational programmes and
students, increasing access to higher education in remote areas,
and promoting female access to education and employment. It
guarantees equality of opportunity for Australian citizens to
access higher education, makes the threshold for higher education
in Australia lower compared with other countries studied, and
plays an important role in the quality of Australia’s higher
education system. Government support, both policy and
financial, has provided the higher education system with a better
basis for achieving universal access, which allows Australia to be a
place where higher education can be rated highly in terms of
quality, as it is a place where many people have access to quality
higher education.
Let us dissect why the U.K. has jumped to the top on the
sustainability indicators. In the same vein as Australia, the U.K.
performs well on the highly weighted indicators, especially
student development input (S
6
), as shown in Fig. 1. Even though
the U.K. does not have a high percentage of university students
enrolled (S
1
), the government is very willing to improve the
Table 2 Quality and sustainability index for higher education
in selected countries.
Country QSM–QL QL ranking QSM–SD SD ranking
The United States 0.3093 8 0.3186 9
Australia 0.7085 1 0.5138 3
Germany 0.4508 4 0.3870 6
The Netherlands 0.4331 5 0.3851 7
Japan 0.3390 7 0.4281 4
Norway 0.5557 3 0.3988 5
Canada 0.5730 2 0.5895 2
Sweden 0.2782 9 0.3821 8
The United Kingdom 0.3774 6 0.6601 1
Fig. 1 QSM–QL of Australia and QSM–SD of the United Kingdom. This figure shows the QL and SD ranking results of the Australia and UK with the 13
indicators.
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quality of learning for every university student, resulting from
their elite higher education that produces high-level higher
education talent.
Among the study’sfindings, indications that run counter to the
general intuition that higher education in the United States is the
most advanced are of concern. As shown in Fig. 2, the U.S. loses
to “statistics”as they are ranked at the bottom of the nine
countries on several highly weighted indicators. The U.S.’s high
population averages high results and lowers its research capacity
(S
3
) and international exchange (S
4
). Although the U.S. has many
good universities, it also has many community colleges and
specialised graduate schools, which reduces its overall quality (S
8
).
The data show more academic speculation (S
13
) in the U.S., and
although they have a high citation rate for papers, they have a
higher self-citation rate. Interestingly, although the U.S. has the
highest degree value (S
11
) among the countries, their male-to-
female enrolment ratios vary greatly. This phenomenon is
difficult to determine and is a complex pedagogical and
sociological issue that we will not discuss further here. What is
clear is that the model results show that neither the quality nor
the sustainability level of U.S. higher education is as good as
expected.
Dynamic data discussion. First, the most notable of our higher
education quality indicators is the precipitous drop in the U.S.
quality index after 2014, as shown in Fig. 3. Since our indicator
compares to the highest level in that year, the worldwide
regression around 2010 may not be readily noticeable. Combined
with the 2018 data, the U.S. declined to 0.3093, likely due to the
country not recovering from the effects of the 2008 financial
crisis. The smooth political situation in Germany and Australia
also kept their higher education quality at a high and stable level.
Therefore, we discuss changes in the level of quality and sus-
tainability of higher education in Japan and the United States.
Japan’s Higher Education Quality Index declined sharply
between 2011 and 2013 and quickly returned to higher levels.
This unusual period of movement is unparalleled in any other
Fig. 2 QSM–QL and QSM–SD of the U.S. This figure shows the QL and SD ranking results of the U.S. with the 13 indicators.
Fig. 3 Data in QSM–QL for selected countries for previous years. This figure shows the QSM–QL results of the U.S., Australia, Germany and Japan from
2010 to 2016.
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country. However, we quickly discovered the reason for this by
examining a series of efforts by Japanese society and the Ministry
of Education, Culture, Sports, Science and Technology (MEXT) at
the time. In 2011, Japan experienced the Great East Japan
Earthquake and the Fukushima nuclear power plant crisis, which
put Japanese society under tremendous pressure (Okuyama and
Inaba, 2017). As a result, Japan’s economic development,
financial situation, and educational stability have been signifi-
cantly affected during this period, putting greater pressure on the
Japanese higher education system, as reflected in the sharp
decline in the Higher Education Quality Index for two
consecutive years in our model. At the same time, the Japanese
higher education system, faced with the challenges of earthquake
damage, ageing, and globalisation, is committed to reforming its
universities in line with national and social expectations and
helping to promote a dynamic and sustainable social structure.
Japan, driven by MEXT, enacted the National University Reform
Plan in November 2013, which details a blueprint for reforming
Japan’s higher education system. Its most significant feature is
that the plan encourages each university to fully justify its
strengths and characteristics and encourages autonomous
improvement and development, thereby enhancing competitive-
ness and generating new added value. It also planned and
financially supported ways to enhance the innovative capacity of
Japanese universities and promote the improvement of human
resources and international development. Over the next many
years, MEXT effectively implemented and tracked this plan,
achieving a series of efforts, such as promoting the diversification
of higher education and tuition remission, and continued to track
and repair the damage caused by the Great East Japan Earthquake
for many years, issuing new specific plans every two to three
years. The efforts of the Japanese higher education system have
led to a rapid recovery in the quality of their higher education,
which grew at a high rate in the two years after 2013, returning to
pre-earthquake levels.
The U.S. appears to perform flat in higher education’s
sustainability indicator. However, in 2018 it dropped to 0.3186,
with student development input (S
6
) falling to almost the nine
countries’lowest level, as shown in Fig. 4. During this time,
student loan debt has been increasing within the U.S. higher
education system, and more and more graduates face difficulties
repaying their student loans. At the same time, due to the
recession and tight job market, many graduates find it difficult to
find adequate jobs to pay off their debt. This has led to increased
student loan defaults and general concerns about overburdening
higher education (Barr et al., 2019). These issues show up in the
QSM as declining values starting in 2015, given that both the U.S.
and its education system’s quality and sustainability were facing
challenges at that time.
A discussion of the static and dynamic data raises questions
regarding the design of the model: as the QSM takes a holistic
view of the many aspects that affect the quality and sustainability
of higher education, these factors may influence or even constrain
each other. This becomes increasingly evident when the QSM is
applied to higher education on a larger scale. For example, the
U.S. has many top institutions of higher education, with the result
that many people think the region has the highest quality higher
education. However, within a large higher education system,
many mediocre schools and students can go unnoticed, resulting
in a weaker performance when the QSM is used to assess the
whole region, as compared to smaller regions with consistently
higher quality schools, such as Australia and the UK.
At the same time, from the policy perspective, it is difficult to
take into account every aspect of a country’s higher education
development promotion, and a focus on increasing the QSM
indicators needs to be balanced by taking into account the impact
on other indicators and other aspects of society. For example,
there is a danger that increasing the value of a degree may result
in lower salaries for the uneducated (S
11
). Therefore, using QSM
to assess and explore the development of educational systems
contains some interesting dynamics. We will continue to explore
this issue in the following discussion.
Multidisciplinary analysis
Policy influences in countries. When we revisit the above poli-
cies, many contradictions become apparent. Immediate success is
not necessarily desirable in the development of higher education.
We cannot offer arbitrary targets that pose significant risks.
Economics tells us that if we ask the market to pay higher-
education graduates above the market’s equilibrium position,
Fig. 4 Data in QSM–SD for selected countries for previous years. This figure shows the QSM–SD results of the U.S., Australia, Germany and Japan from
2010 to 2016.
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there will be a severe market excess (Heyne et al., 2009), as shown
in Fig. 5. Allowing the QSM’s indicators to grow could be det-
rimental to the quality of society and would potentially create a
more significant economic and social crisis.
There is far more to management practice in higher education
than simply using models for assessment. As our research has
shown, the quality and sustainability of a higher education system
involve many dimensions, and the indicators interact with each
other and are even linked to other social issues in the region. For
example, increasing the average number of self-citations (S
13
) can
increase the total number of citations (S
12
). However, the two
logics are reversed in the QSM, and increasing the average of self-
citations (S
13
) decreases the country’s QSM evaluation index.
Such academic speculation does not reflect well in the QSM.
Another example occurs when, due to market excess, educators
must reduce the number of higher education graduates to solve
the job market problem. Should this happen, financial investment
in higher education and the number of international students will
decline across the board, and the recognition of higher education
in society will also decline rapidly, which is not a desirable
outcome.
However, although its practice is full of complex variables, the
QSM can be a reliable partner in helping policymakers to
understand the current situation, test the current state of
development of the education system and suggest areas where
improvements can be made. Through the collation of statistics
and the monitoring of changes, it can help managers identify
problems and generate ideas for development. For example, the
QSM does not directly tell us whether a country is over-resourced
in higher education, but when we analyse the results for that
country, we find that the Higher Degree Value is declining and
the QSM–SD is falling. Changes in these indicators can be used
alongside other statistics, such as unemployment rates, to help
managers understand the current situation and to support future
decisions.
Accordingly, the QSM can also be used as a tool for monitoring
the effectiveness of policies. Once a national government has
enacted a particular higher education-related policy initiative, the
QSM can be used to track the impact of the policy continuously
throughout the implementation process. Such monitoring and
evaluation can take place over several years, as changes in higher
education are often slow. Not only does it take time for policies to
be implemented, but ultimately, their manifestation in changes in
the quality and sustainability of higher education also takes time
to appear, especially as the impact between the aspects and
indicators of educational management also takes time to manifest.
The changes in the QSM indicators in Japan following the
enactment of the National University Reform Plan are a good
example.
Higher education and society. In addition to developing higher
education based on a multi-subject collaborative framework, it is
important to be guided by the right mindset. Educators who focus
on optimising individual indicators do not necessarily improve
the overall quality and development potential of higher education
but may instead cause a decline in quality, according to our QSM.
Including higher education within the total national capacity,
the development system is a fundamental step in development.
The state needs to improve the internal governance capacity of
higher education and external recognition holistically and
effectively use higher education as a source of human develop-
ment progress. These initiatives can promote the endogenous
growth of higher education’s quality and sustainable development
indicators rather than a single policy doing more harm than good.
Different countries have parts of their higher education
systems in which they excel, and there is an opportunity for
international exchange in education to play a more significant
role in the current development of higher education. Educators
from different countries can understand and learn from each
other through the exchange and use the development experience
of others to support the areas where they are at a disadvantage. In
modern society, higher education contributes significantly to the
development of the economy and society, undertakes a large part
of the world’s research, and is an essential pillar of scientific
knowledge (Ojeda-Romano et al., 2021). International exchange
in higher education improves the quality of education and
indirectly promotes social progress.
Research innovation and future development
The QSM provides higher education managers with a tool by
which to understand and monitor the quality and sustainability of
education. It can provide a quantitative assessment of the current
state of the higher education system in a country or region,
identifying potential areas for development, supporting ideas for
higher education development and helping practitioners monitor
policy effectiveness. The QSM inherits the advantages of both
PCA and the entropy weighting method (EWM). When weighing
Fig. 5 The impact of graduates’salaries on the market equilibrium position. This figure shows the market to pay higher-education graduates above the
market’s equilibrium position, there will be a severe market excess. P price, Q quantity, S supply, D demand.
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the indicators, we utilised the average weight method by com-
bining PCA with EWM. To some extent, this method supple-
ments the indicator’s horizontal comparison with EWM, but it
also simplifies the evaluation dimensions, allowing us to point to
the shortcomings within national or regional higher education
within a manageable range and then progress towards world-class
levels. We fully integrated our model with academic misconduct
indicators, thereby introducing a new perspective to explaining
higher education’s quality and sustainability, as guided by
the title.
The collection of certain statistics is difficult, so there is room
for improvement in the selection of indicators. Significantly
underdeveloped countries were not chosen as examples of the
model. In addition to the difficulty of obtaining data, the higher
education base in those countries is inferior. The model would
have these countries scoring high on the negative logic questions,
most likely outperforming some of the developed countries in
total ratings. Therefore, using our model to analyse higher edu-
cation in underdeveloped countries or regions is challenging. We
selected only nine countries when constructing our QSM, which
may not adequately cover the extreme and minimal values of
higher education quality and sustainability indicators. We also
selected our data for training the model from 2018, which may
have introduced some modelling errors, even though the cumu-
lative variance rate for PCA was reasonable.
Further development. The current research model has innova-
tively incorporated academic misconduct and speculative beha-
viour into assessing the quality of a country or region’s higher
education system and has validated the model’s applicability in
nine developed countries. However, the QSM has not been
applied to developing countries or countries or regions with poor
statistics on relevant indicators. Therefore, future development of
the study could focus on the following areas.
Future research could optimise the model to be better applied
to the assessment activities of developing higher education
systems. Many countries around the world, such as China, India,
and other Asian countries, are currently in a state of rapid growth
or constant change. Higher education data in these countries
change yearly, making their QSM results not always accurate and
difficult to analyse. Enhancing the generalisability of the model
worldwide would allow for a better study of the differences and
gaps between different education systems and enable the
assessment of the current state of development through a
quantitative approach. Such a study will facilitate the realisation
of comparisons between more countries, discover differences in
higher education systems at different development levels, and find
breakthroughs in development.
Research can also explore how to make better use of existing
indicators. We used 13 indicators to measure the quality and
sustainability of higher education, and realistically, administrators
will also count more indicators that are necessary for analysis and
are readily available. In particular, the research team’s initiative to
count and calculate new indicators will make it possible to assess
higher education with higher quality. Further adjustments to
existing indicators could achieve similar small states. As a result,
the model’s reliability will be improved, and we can obtain more
accurate assessment results.
We also need to consider how the QSM can be applied to
highly underdeveloped countries to guide the building of their
education systems. These countries or regions may not have a
structured higher education system, so statistics are missing in
large numbers, and it is not easy to clarify how to make them
develop more efficiently and healthily.
Conclusion
We analysed higher education data from the World Bank, OECD,
and other authorities in nine developed countries. First, we used
principal component analysis for dimensionality reduction. We
obtained two principal model components of our QSM: [cost of
higher education, innovation, reputation, access to higher edu-
cation, and government guidance] and [sustainability of teaching
and research, sustainability of schooling, reputation, and sus-
tainability of policy]. We then assigned weights to the 13 sub-
indicators using the entropy value method to build a credible
model of higher education sustainability.
Data from nine countries were selected for cross-sectional
comparisons, and four were selected for longitudinal comparisons
from 2010 to 2016. The analysis found that Australian higher
education is highly advantageous in quality: it has a very high
tertiary education penetration rate and very few academic dis-
honesty incidents. The U.K. higher education is advantageous in
sustainability: its elite education is distinctive.
To improve the quality and sustainability of higher education,
each member of the higher education system can start from the
perspective of what it can do. It is a complex system, and many
indicators can be used to measure its quality. Thus, all 13 indi-
cators in the QSM can significantly impact the overall quality and
sustainability of the higher education system. Strengths in indi-
vidual elements of the dimensions that make up higher education
do not lead to an increase in overall levels. For example, the
United States produces more patents per capita than the other
countries in this case, but higher education’s overall quality and
sustainability are assessed at a more mediocre level.
Similarly, weaknesses in individual indicators do not necessa-
rily indicate poor quality in the education system. Using the
number of patents as an example, Australia produces the lowest
number of patents per capita of the nine countries, but its higher
education system is high quality. In reality, higher education in
different countries has its strengths, and comparing them with
each other and promoting exchange can facilitate the develop-
ment of systems across the board.
The construction and development of higher education is a
continuous and lengthy process, and balanced and stable devel-
opment can form a high-quality higher education system and
further promote social development. The QSM in this study can
help decision-makers in each country or region quantitatively
analyse the higher education system’s current situation, find
progress breakthroughs, and guide actions.
Data availability
The datasets generated and analysed during this study are available in
the supplementary files. They are also available in the World Bank
Open Data (https://data.worldbank.org/; Project Atlas Infographics:
https://www.iie.org/Research-and-Insights/Project-Atlas/Explore-
Data/Infographics/2019-Project-Atlas-Infographics); National Center
for Education Statistics (https://nces.ed.gov/programs/digest/d20/
tables/dt20_605.20.asp; OECD Data: https://data.oecd.org/); QS
World University Rankings (https://www.topuniversities.com/qs-
world-university-rankings/methodology); World Higher Education
Database (https://www.whed.net/home.php); Retraction Watch
Database (http://retractiondatabase.org/RetractionSearch.aspx?
&AspxAutoDetectCookieSupport=1); Salary Explorer (http://www.
salaryexplorer.com/); and Scimago Journal & Country Rank (https://
www.scimagojr.com/).
Received: 12 November 2022; Accepted: 12 July 2023;
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Acknowledgements
This research was supported by the National Social Science Foundation of China, grant
number 22BGJ037; the Zhejiang Provincial Federation of Social Sciences, grant number
2023N009; the Humanities and Social Sciences Research Project of Zhejiang Provincial
Department of Education, grant number Y202248811; the Zhejiang University of Technology
Humanities and Social Sciences Pre-Research Fund Project, grant number SKY-ZX-20210175;
and Teaching Reform Project of Zhejiang University of Technology, grant number JG2022045.
Author contributions
Conceptualisation: CC, TW and FS; Methodology: FS and HF; Software: SX; Validation:
CC and TW; Formal analysis: FS; Investigation: TW and SX; Resources: CC; Data
curation: SX; Writing—original draft preparation: TW; Writing—review & editing: CC
and TW; Visualisation: SX; Supervision: CC; Funding acquisition: CC; all authors
approved the final manuscript to be submitted.
Competing interests
The authors declare no competing interests.
Ethical approval
This article does not contain any studies with human participants performed by any of
the authors.
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