Digital transformation of the educational landscape raises the issue of equal opportunities in education for all. National and international authorities work together on a strategy for the education system that combines equal opportunities and digitalization. Digitalization should not be seen as an end in itself. Instead, the concrete measures must be analyzed based on their contribution to equal opportunities in education. This article aims to analyze education digitalization indicators and other indicators of accessibility of education and digital key performance indicators from the point of view of their role in determining educational equity in Ukraine. The analytical tool of the principal component method was used. The main finding of the analysis is that providing households with fixed Internet and the availability of computers connected to the Internet in schools are the most essential factors of educational equity in Ukraine. This conclusion can be used to develop state digital and education policy in Ukraine.
The integration of information technologies in education is taking place worldwide, in particular thanks
to the support of governments for the digital transformation of education at various levels. In the
conditions of digital transformation, the requirements for ensuring access to information in the learning
process are put forward. An OECD study [
National and international policies denote serious attention to the impact of digitalisation on
educational equity. The result of high-level discussions explicitly justifies the need for a strategy for
an education system that combines equal opportunities and digitalisation. It is stated that concrete
measures must always be analysed by their contribution to equal opportunities in education [
Thus, the availability of digital educational technologies, educational institutions, and teacher availability are becoming determining factors of equity in education in the modern world.
The urgency of access to education in the context of digitalisation is demonstrated by the attention
paid to them at the highest level. Thus, the European Commission published the “Digital Education
Action Plan (2021–2027)” which specifically notes that the creation of education and training systems
adapted to the development of the digital era should be carried out to achieve more efective, sustainable
and equitable development of digital education [
Ukraine implemented the Concept of Development of the Digital Economy and Society of Ukraine for
2018–2020 [
Ukrainian State Strategy for Regional Development until 2027 provides unimpeded high-speed
Internet access for all populated areas (primarily rural and small towns) and social institutions [
An essential analytical task in this regard is to incorporate education digitalisation indicators, as well as other indicators of accessibility of education and digital key performance indicators, into a single analytical model to determine their role in determining educational equity.
The main result of the education system’s functioning is the formation of human capital with a particular
set of knowledge, skills and qualifications, as well as cognitive and communication skills necessary
for the successful self-realisation of individuals in dynamic environments, such as the labour market
or in diferent social groups. In the current conditions of intensification of global shocks (natural
disasters, military conflicts, epidemics), and also taking into account the challenges of poor management
quality, gaps in resource allocation planning and reporting on their use, an uninformative system
for assessing the quality of educational services, etc., all countries face problems in the educational
sphere. Despite the significant attention governments and international organisations have paid to
education issues, the efectiveness of educational systems is often insuficient. Negative, economically
and socially undesirable phenomena arise, such as knowledge or learning gaps, skills mismatch, lack of
qualifications for the needs of the labour market, educational exclusion of specific population groups,
reduction of school enrollment rates, etc. In world practice, indicators have been developed to analyse
the state of educational systems, which are fairly universal evidence of the changes in the educational
system. For example, the “Class size and ratio of students to teaching staf” is a D2 indicator in the
OECD Educational Statistics Methodology [
Today’s realities require the inclusion of educational digitalisation indicators in the analysis of educational equity. Digitalisation in the educational sphere is considered a means of solving educational problems and deepening some of them. Thus, educational digitalisation made it possible to develop educational technologies, which allowed millions of children to continue learning during the COVID-19 pandemic.
On the other hand, millions of children in the poorest countries have not received access to knowledge remotely, which has exacerbated the knowledge losses in these countries. One way or another, educational digitalisation significantly impacts educational systems, changing their landscape, the distribution of resources in the educational sector, and the educational outcomes obtained. Much hope is pinned on the digitalisation of education as a means of accelerating the acquisition and improving the quality of knowledge obtained [? 7], and therefore, solving the issue of the eficiency of educational expenditures.
However, digitalisation creates new challenges in the educational sphere – for example, to form a new
skill of orientation in a post-truth society. Despite all the controversial views on education digitalisation,
it has already become an integral part of the indicators of educational equity. In particular, we are
talking about the D5 indicator – “Access to and use of information and telecommunication technologies”
– according to the OECD Educational Statistics Methodology [
In addition to factors derived from state policy in the field of education (provision of students with
teaching staf, material, technical, information and telecommunication resources), attention should also
be paid to the participation of households in providing additional classes for students – tutoring or
so-called “shadow education”. The ability of families to finance additional educational services afects
the equity of education and the educational achievements of students, that is, education outcomes [
Thus, for analysis, we outline three groups of educational equity factors: physical accessibility of education, digital accessibility of education, and accessibility of additional “shadow” education (figure 1).
The study measures the physical accessibility of education using the indicators of the number of students per teacher, which indicates the provision of students with teaching staf; and the number of students per general secondary education institution, which indicates the size of the school and, as a rule, indicates the material and technical support of the learning process.
The accessibility of educational digital technologies includes, in addition to the number of computers in the school connected to the Internet and the number of institutions with classrooms with interactive surfaces, the provision of households with fixed access to the Internet.
The intensity of tutoring, which indicates the accessibility of the “shadow education”, is measured
using the specialised Google Trends service. The measurement of “shadow” education in Ukraine using
the Google Trends analytical environment is explained in the works of Sarioglo V. [
The study considers all the above-mentioned indicators for 2019-2022 and by region. Thus, a set of initial data for analysis consists of many variables with diferent units of measurement linked to each other.
The evaluation of significance and analysis of the linkages between the indicators mentioned above of education accessibility are carried out using statistical analysis methods, one of the most promising of which is the principal component method. The principal component method is a way to reduce a set of directly observed features to a smaller number of implicit but objectively existing factors. Finding the principal components is reduced to identifying linear combinations of random variables with the maximum possible variance. In other words, selecting several variables from many of them and explaining the total variance of the entire set of variables at a level of at least 90% is a practical result of using the principal component method.
The principal component method applies a mathematical procedure that transforms a set of correlated variables into a smaller number of uncorrelated variables—the principal components. Principal component number one accounts for as much of the variability in the data as possible, and each further principal component accounts for as much of the remaining variability as possible.
The principal component method is based on a linear model of the type (1) [11]:
′ = ∑︁ , =1 where ′ – is the normalized value of the ℎ indicator; – is the weight of the ℎ component in the ℎ indicator; – is the ℎ principal component; n is the number of indicators; r, j =1, 2,. . . ,n.
Principal component one accounts for the maximum total variance in the observed variables. This means that the first principal component will be correlated with at least some of the observed variables.
Principal component number two will account for the maximum variance in the data set not accounted for by principal component number one. This means that the second principal component will be correlated with some of the observed variables that were not strongly correlated with principal component number one.
The second principal component does not correlate with the first principal component; that is, the correlation between components one and two is zero.
Due to the relationship between the principal components and the correlation coeficients, model (1) can be rewritten in the following form (2): ′ = 11 + 22 + 33 + 44, (2) where ′ – is the normalized value of the ℎ indicator; 1 – is the weight of the first principal component in the ℎ indicator; 1 – is the value of the first principal component in the total variance of the set of observations; 2 – is the weight of the second principal component in the ℎ indicator; 2 – is the value of the second principal component in the total variance of the set of observations; 3 – is the weight of the third principal component in the ℎ indicator; 3 – is the value of the third principal component in the total variance of the set of observations; 4 -is the weight of the fourth principal component in the ℎ indicator; 4 – is the value of the fourth principal component in the total variance of the set of observations.
A comprehensive substantiation of the crucial role of digital technologies in Ukrainian schools during the ongoing war is given in Vorotnykova et al. [12]. As stated in the study, one of the critical advantages of digital technology usage in secondary education is that it ensures educational accessibility. Agreeing intuitively with this statement, our research proves it through statistical analysis.
Early literature on the impact of information and telecommunication on the learning process and educational outcomes suggested several opposite opinions: from the possibility that computers would replace teachers in key instructional roles to realising its’ impossibility and undesirability. A comprehensive review of early research as well as results of quantitative and qualitative analysis of the impact of digitalisation on learning during 1960-2000, presented in T.S.Eng [13], resulted in several conclusions that are still relevant and that we use in our research. It is stated that higher usage of information and telecommunication positively afected school achievement both at the individual pupil level and at the school level.
More recent studies have focused on substantiating and improving information and telecommunication usage in education. For instance, Brown et. al [14] explore the factors that influence the transformative use of digital technology in schools, focusing on innovations that contribute to enhanced educational outcomes. The complexity of modern learning ecology substantiated in the study makes teachers’ professional training in the digital sphere one of the central tasks ensuring “creative teaching” for “creative learning”. One way to ensure a teacher’s preferred style of whole-class interactive teaching is using an interactive whiteboard (IWB). As stated in R. Wood and J.Ashfield [ 15], IWB had enhanced whole-class teaching and learning, increasing educational accessibility. Considering the wide use of IWB in Ukraine, this tool is analysed in our research as one of the educational accessibility factors.
Physical accessibility of education, expressed via such indicators as the ratio of students per teacher or educational institution, that identify educational equity, are widely discussed in studies by E.Hanushek [16, 17], A.Krueger [18], L.Wang [19]. There is an ongoing debate on the role of teacher-pupil ratio on educational outcomes. While A. Krueger states that the diference in the ACT test scores between students from smaller and from larger classes is statistically insignificant (19.3 to 19.2, respectively), E.Hanushek substantiates the necessity of taking into account contextual factors to make substantial conclusions, namely the teacher’s quality, and L.Wang concludes, that large classes are associated with challenges in delivering high-quality and equitable learning opportunities. The study by R. Rodriguez et al. [20] underlines the impact of the class and school size on parent’s engagement in the educational process. The authors state that the student-teacher ratio has the strongest impact on parents’ involvement in education. This conclusion is highly consistent with other studies that reveal the role of parents’ involvement in tutoring for the educational outcomes of their children (Gupta, A. [21], Ma, Y. et al. [22], Ansong, D. et al. [23]).
To take into consideration the results of the studies above and to shed some light on the role of information and telecommunication, class size, school size, and family on educational equity in Ukraine, we take into consideration such indicators: number of households provided with fixed access to the Internet, number of computers in the school connected to the Internet, number of schools with classrooms with interactive surfaces (or IWBs), number of students per teacher in school, number of students per general secondary education institution, interest in tutoring that represents parents’ involvement in educational process.
This article aims to reveal the role of education digitalisation indicators in a set of education inputs identifying educational equity in Ukraine. The hypothesis is that in a speedily digitalising world, education digitalisation indicators will appear to be dominant.
The analysis of educational equity indicators in Ukraine is carried out based on a set of relevant education accessibility indicators. We consider six indicators that correspond to the grouping of indicators of access into the educational equity factors outlined in figure 1: x1 – number of students per teacher, persons; x2 – number of students per one general secondary education institution, persons; x3 – provision of households with fixed access to the Internet, % of households in a region; x4 – number of computers in a general secondary education institution connected to the Internet, units; x5 – number of general secondary education institutions that have classrooms with interactive surface units; x6 – distribution of regions by the number of Google queries for the keyword “tutor”, where 100 points are assigned to the region with the most significant number of query points.
Specific values of the indicators are obtained from oficial publications of the State Statistics Service of Ukraine [24, 25], Ministry of Education and Science and the Institute of Educational Analytics[26, 27] National Commission for the State Regulation of Electronic Communications, Radio Frequency Spectrum and the Provision of Postal Services[28] and Google Trends analytical tool [29].
A list of observations is given in table 1.
Note: Data for Kyiv city in 2022 are not available. The number of observations is 91 (4 years * 23 regions – 1 missing observation).
The selected indicators have diferent units and measurement scales. Therefore, it is necessary to standardise them. Standardisation of values of selected indicators [x1, x2, x3, x4, x5, x6] is carried out using formulas (3-5) [30]: ′ = ( − )/ =
1 ∑︁
=1 ⎯⎸ ⎸⎸ ∑︁( − ) = ⎷⎸ =1
− 1 (5) where – is standardized values of random variables at ℎ measurement; – is the values of random variables at ℎ measurement; – is the mean value of random variable summarized at all i measurements; – is the standard deviation ; N=91 (4 years *23 regions - 1 missing observation), j=1,2, . . . 6, i=1,2,. . . ,91.
Standardised values of selected indicators are presented in table 2.
Correlation analysis of a set of standardised values of 6 indicators (table 3) shows that the Pearson correlation coeficient exceeds 0.8, which indicates the presence of collinearity [ 31], only between such indicators: the number of students per teacher (y1) and the number of students per one general secondary education institution (y2).
Rlab software allows us to obtain new synthetic variables – principal components – from a set of standardised values (table 4) and a matrix of weight coeficients of each principal component in the j-th indicator (Table 5).
The contribution of the first four principal components to the total variance of the selected set of indicators of accessibility of general secondary education in Ukraine captures 96% of the total variance
Therefore, to establish the relationship between the principal components and the weight coeficients based on model (1), we will use the first four values of the contribution of the principal components to the total variance of the set of observations (vj) and the corresponding first four rows of table 5. The results of calculating the normalised values of indicators of general secondary education accessibility in Ukraine in 2019-2022 are as follows: y’1 – number of students per teacher is 0.23; y’2 – number of students per one general secondary education institution is 0.25; y’3 – the provision of households with fixed access to the Internet is 0.37; y’4 – number of computers in a general secondary education institution connected to the Internet is 0.29;
y’5 – number of general secondary education institutions that have classrooms with interactive surfaces is 0.1; y’6 – interest to the “shadow” education (tutoring) is (- 0.04).
It should be noted that the sum of the weights should not be equal to one but should be close to it [11]. In our case the sum of the weights is 1.2.
The normalised value of the sixth indicator – provision of households with fixed access to the Internet or Digital Transformation Index – has the most significant weight. Indicators of interest in the “shadow” education and equipping classes with interactive surfaces have the lowest values. It is possible to assess the change in some indicators of education accessibility compared to others using the geometric method by plotting the obtained results in the coordinates of the first two principal components. To do this, we constructed a graph in which all 91 observations regarding the accessibility of general secondary education in Ukraine in 2019-2022 are indicated by dots but not in the initial coordinates, namely in the coordinates of the first (plotted along the x-axis) and second (plotted along the y-axis) principal components (figure 2).
Within the coordinates of the first two principal components, the vectors depict the direction of six indicators selected to analyse the equity of general secondary education. An important conclusion is that students’ (or their families’) interest in tutoring is almost the opposite of providing students with teachers and places in educational institutions.
The results of the conducted analysis show that the most critical factors of educational equity in Ukraine refer to digital accessibility – the provision of households with fixed Internet and the availability of computers connected to the Internet in schools.
This result is essential for developing state policy to improve equity in general secondary education in Ukraine. In particular, the following are essential practical conclusions: • of the instruments of state policy to make general secondary education more equitable, the most important is providing households with fixed access to the Internet, as well as connecting school computers to the Internet; • providing a suficient number of school teachers reduces the burden on families with children since a decrease in the number of students per teacher will reduce interest in tutoring.
This justifies the attention paid to digitalisation in the State Strategy for Regional Development until 2027. The analysis results are consistent with the key performance indicators of the State Strategy implementation – to fully provide schools with fixed broadband Internet access and to cover the population on all the territories with 4G mobile networks. At same time, it should be noted that one of the most challenging tasks in this field is to raise teachers’ digital skills. As empirical studies state, “despite the significant developments in the implementation of information and communication technologies in the educational process. . . the issue of the ability and maintenance of teachers’ readiness to use still remains insuficiently resolved” [ 32]. This leaves space for further research considering teachers’ digital competencies as a factor of educational equity in Ukraine.
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