A study on the relationship between digital capacity building of the teaching force and the improvement of the quality of basic education

Education informatization, as an important part of the national informatization strategy, is entering a new period of digital transformation, and the call to reshape the education power with the education digitalization strategy is rising day by day [1-2]. From the perspective of the practice of basic education reform and development around the world, there are still certain practical dilemmas in digital empowerment of basic education curriculum teaching. How to effectively promote the digital empowerment of basic education classroom is the current urgent need to solve the core issues [3-4].

Digital educational resource services are based on the content of educational resources in digital form, based on various educational platforms at all levels, and aiming to meet the teaching and learning needs of users, so that users can conveniently obtain resource content from various types of resource objects, carry out resource sharing, and apply all activities such as resource services, which are course materials stored in digital form [5-6]. The strategic action of education digitization has established the world’s first large educational teaching resource base, spawned a series of high-quality digital educational resources such as flipped classroom, high-quality courses, and generated a series of government-type and enterprise-type teaching resource platforms, which provide rich resources and platforms for classroom teaching in basic education under the technical support guarantee. The common construction and sharing of digital educational resources is the basic project and key link in the development of digital education, but there are still some problems [7-8]. In the construction of digital educational resources, there are problems such as unbalanced supply, lack of top-level design, lack of deep-level development, and difficulty in supporting deep learning in the construction of digital resources for basic education, in the supply of digital educational resources, there is a structural imbalance in supply, misalignment of supply and demand for resources, limited resource supply power, unbalanced regional configuration, too much homogeneous supply, and insufficient differentiated supply, and so on, and in the supply of digital education service In terms of digital education services, China’s basic education digital resources public services have problems such as imperfect service functions, service levels to be improved, and gaps in service value [9-10]. Research by many scholars shows that the overall construction and service situation of digital education resources is not comprehensive, digital education resources are diverse and rich in content, but the quality of resources is uneven, and the service model needs to be improved [11].

With the renewal of information technology and the continuous development of teaching reform, teachers’ digital literacy and digital teaching ability have gradually become an important factor constraining the development of high quality education. Promoting the digitalization of education is part of the “Digital China Strategy”, which is an important part of realizing a strong country in education, science and technology, and talents [12-13]. As the disseminator of knowledge and the backbone of talent cultivation, college teachers should have a deep understanding of the significance of education digitization, and have the ability to teach digitally, so as to cultivate digital citizens who can adapt to the future digital era. Promoting the digitalization of education not only opens up a broad space for the development of diversified and personalized lifelong learning, but also points out the direction for colleges and universities to promote the reform of education and teaching [14]. As an important base for talent training, colleges and universities should improve the digital literacy and digital teaching ability of teachers in the context of education digitalization, so as to provide an important guarantee for the development of education modernization and the great cause of building a strong educational country [15].

Yelubay, Y et al. analyzed the study on digital basics of teachers of the Faculty of Education of the Abai State Pedagogical University in Kazakhstan, deepened the understanding of digital knowledge and digital cognition of teachers, and made targeted recommendations for the development of digital competence of teachers [16]. Beardsley, M et al. examined the transformation of teachers’ digital teaching competencies and attitudes in the context of the epidemic through the interview channel and found a gradual increase in teachers’ digital teaching competencies, and that teachers’ confidence in teaching digitally as well as their motivation to teach digitally was enhanced to some extent [17]. Fitria, H et al. illustrated that the development of education in the digital era has placed new demands on teachers’ digital competence and adaptability, and pointed out that teachers, as agents of educational change in the digital context, significantly influence their teaching, words and actions on their students’ vision of the times, their social sensitivity, and their sense of responsibility [18]. Starkey, L systematically summarized the research literature related to teachers’ digital pedagogical readiness, found that the research in the field of education focuses on teachers’ digital pedagogical boards, and identified three areas involved in the research, including teachers’ generic digital competencies, teachers’ digital pedagogical competencies, and teachers’ emerging professional digital competencies, and lastly constructed a model of occupational digital competencies to enable the assessment of teachers’ digital pedagogical competencies to be assessed [19]. Gudmundsdottir, G. B et al. explored the status quo of teachers’ information technology practice in basic education and were informed through a national survey report that teachers demonstrated very poor results in the early stages of new technology education practice, including training in information technology teaching as well as informational digital teaching, proposing an in-depth assessment and analysis of the reasons for the lack of quality in information technology education, and the study made a positive contribution to the optimization has made a positive contribution [20]. Hatlevik, O. E scientifically explained and analyzed the phenomena of teachers’ digital teaching self-efficacy, information assessment strategies, and digital teaching competence based on structural equation modeling [21]. AlAli, R et al. assessed the digital digital transformation program to address the phenomenon of wasted resources in basic education in Irbid governorate and illuminated the barriers and key points of digital transformation in education through the collection, integration and analysis of relevant data, including the establishment and optimization of an integrated digital teaching and learning system, digital teaching and learning methodologies, and the training related to digital teaching and learning competencies and perceptions of teachers [22]. Mosehlana, M. B et al. studied in depth the effectiveness of the 7Cs protocol in practice and pointed out that the protocol acts as a guide in leading the digital informatization reforms in the educational system and that the protocol contributes to the realization of the strategy of informatization in education [23]. Zhang, X combined with literature research and analysis methods, comprehensively examined the barriers and preferred paths of education in Germany, and emphasized the need for government departments to explore the educational needs of the public, promote the scientific allocation of educational resources, and promote the digital development and construction of education, including the cultivation of students’ digital cognition and digital ability, and the training and strengthening of teachers’ digital teaching ability [24].

Combined with the era of digitalization, this paper analyzes the digital path to improve the quality of basic education. In order to realize the urgent requirement of strengthening teachers’ capacity building in the path, the relationship between the digital capacity of the teaching force and the quality of basic education was explored through questionnaire survey, factor analysis and structural equation. The covariance of the questionnaire data was diagnosed, and the covariance problem of the independent variables in the data was reduced by principal component analysis, and four comprehensive factors were extracted to establish regression equations and solve the regression coefficients of the original independent variables. After solving the standardized coefficients and verifying the important contribution of teachers’ digital technology application ability to the quality of education, the factors affecting teachers’ digital technology application ability are further explored. Relevant hypotheses are developed and tested with structural equation modeling, and mediation effect tests are conducted for the hypotheses that do not hold, in order to find out the different paths of influence of the relevant factors on the quality of education. Finally, corresponding strategies for building the teacher workforce are proposed based on the results of the hypothesis testing.

2

Digital pathways for quality improvement in basic education

With the advent of the digital era, basic education is accelerating its digital transformation to adapt to new changes, grasp new opportunities and meet new challenges. Relying on digital technology, the digital transformation of basic education can promote the high-quality development of basic education in terms of innovation in teaching mode, optimization of educational evaluation, sharing of digital resources, and enhancement of the effectiveness of learning, so as to achieve a more equitable and quality basic education.

Under the requirement of improving the quality of basic education, it is necessary to establish an institutional system suitable for the digital transformation of education, especially the strategic planning and top-level design of education digitalization, in order to address institutional dependence. To address technological dependence, it is important to promote the integration and application of digital technology in education through innovative application scenarios, learning and training activities, and the establishment of incentive mechanisms. For cognitive dependence, it is not only necessary to change the mindset and cognition of the subjects in various ways, but also necessary to cultivate digital cultural genes by improving the digital literacy of the subjects.

2.1

Formulate strategic planning and build an institutional system

Strategic planning is an important component of the system, and it is therefore necessary to take strategic planning as a starting point for gradually building a system suitable for the digital transformation of basic education, and to “de-embed” it from the original rules and system through the “conscious deviation” of the main body of action.

In terms of overall orientation, it emphasizes the implementation of the goal of high-quality development and the cohesion of a consensus on the value of the digital transformation of basic education. It is necessary to make overall deployment of sub-targets such as digital technology-driven pedagogical innovation, regional coordinated development, green operation and maintenance and governance, openness of high-quality educational resources, and sharing of digital rights and interests.

In terms of basic content, strategic planning, standards and norms, organizational systems, operational mechanisms, management systems, guarantee systems, laws and regulations, evaluation systems, etc. should be improved, emphasizing an integrated top-level design under the concept of coordination, and a high-quality institutional mechanism should be gradually created.

In terms of hierarchical structure, it adheres to the unity of macro, meso, and micro, including the strategic planning of the country at the macro level, the systems of digital teaching, evaluation, and incentives of education administration departments and schools around the country at the meso level, and the systems related to the education model, teachers’ competence, and learning environment in the education ecology at the micro level.

In the way of construction, it insists on demand-driven. In the process of formulating relevant policies as well as constructing systems, it is necessary to gather multiple education stakeholders, deeply grasp their needs for the betterment of education, and form a consensus of interests. Specifically, a decision-making community can be formed by opening up bottom-up channels for the expression of interests and creating a mechanism for sharing the knowledge of multiple subjects.

2.2

Transform ideological cognition and realize conceptual renewal

Scientific cognition can drive the actors towards the right path and lay the ideological foundation for the effective implementation of digital transformation actions in basic education. It is necessary to both form an essential cognition of the digital transformation of basic education and strengthen the value cognition of high-quality development.

First, initial acceptance. Official propaganda and expert lectures can be used to interpret the state’s education digital transformation policy and high-quality development policy, explain the essential connotation of the digital transformation of basic education and the core requirements of high-quality development, emphasize the necessity and importance of the digital transformation of basic education oriented towards high-quality development, and allow the relevant subjects to gradually accept the transformation action.

Second, enhance recognition. Through industry exchanges to share experiences and lessons learned from the digital transformation of schools, and through case sharing to exchange the application and effectiveness of digital technology in teaching, learning, management, assessment and evaluation, teachers and education administrators can deepen their recognition of the value of the digital transformation of basic education. In addition, a specialized team can be established to help school members enhance their knowledge and understanding of digital transformation in education.

Third, deep thinking. All education stakeholders should make it clear that technology serves education, look at the advantages and shortcomings of digital technology dialectically and rationally, and form a correct concept of technology. On this basis, learning-centeredness, human-computer collaboration, personalized wisdom generation and comprehensive development are emphasized in the teaching philosophy.

2.3

Enhancing capacity-building for a digital culture

Based on the requirements of high-quality development for talent cultivation, one of the core tasks of the digital transformation of basic education is to cultivate digital talents who can adapt to the digital era. In order to break through the cognitive dependence, it is necessary not only to change the ideology and cognition of each subject through various ways, but also to improve the digital literacy and skills of teachers and education administrators, and to cultivate the digital culture gene.

First, develop a framework for digital literacy and skills and build an evaluation system. Taking into account the conceptual definition of digital literacy and skills in China’s Program of Action for Enhancing Digital Literacy and Skills of All People, and referring to the relevant frameworks of the European Union and UNESCO, we should construct a general framework of digital literacy and skills for Chinese citizens. On this basis, a framework for digital literacy and skills of basic education students and teachers is formulated, and a digital literacy and skills evaluation index system and assessment standards for basic education students and teachers are constructed.

Second, integrate digital literacy and skills into the curriculum. For students, digital literacy and skills can be integrated with existing curricula and specialized digital literacy and skills courses can be developed. In primary and secondary schools, digital learning should be emphasized in the language and mathematics curricula, and IT and digital technology courses should be used as a starting point for teaching artificial intelligence courses and organizing intelligent robotics clubs. For teachers, relevant content can be incorporated into the mandatory courses of teacher school training programs.

Third, professional training should be carried out, focusing on improving teachers’ ability to use digital technology for teaching and improving education administrators’ ability to use digital technology for leadership and governance. Relying on the national smart education platform for primary and secondary schools, courses such as digital literacy interpretation, the application of digital technology in teaching, and digital ethics can be introduced in the “Teacher Training” section. In addition, schools can invite relevant experts to conduct offline training activities on a regular basis.

3

Mathematical modeling of the research methodology in this paper

3.1

Mathematical model for factor analysis

Factor analysis was first proposed by the British scholar Spearman in 1904, and the analysis method is widely used in various fields such as sociology, economics and meteorology. Factor analysis is a statistical method that realizes data dimensionality reduction by extracting potential common factors. Its purpose is to extract a small number of important indicators from the massive raw data, through which the original information can be represented to the greatest extent. In the process of actual data analysis, there are often many complex indicators, which can comprehensively reflect the characteristics of the research things, but in the process of actual modeling, these too many variables will lead to the problem of overlapping information, which affects the efficiency of model calculation. Factor analysis method can well solve the above shortcomings, by calculating the correlation between each original variable, dividing the variables with strong correlation into a group, with high correlation within the group and low correlation between the group, and extracting the comprehensive factors that can represent the original data to carry out data dimensionality reduction, so as to minimize the loss of the original information [25].

Factor analysis can solve the statistical problem of complex correlations between many raw indicators. The first step is to calculate the correlation matrix, correlation array eigenvalues, and eigenvectors of the original data. Next, the factor loadings of the model are solved by principal component analysis, and the coordinate system is rotated by orthogonal transformation to facilitate factor interpretation [26].

The mathematical model for factor analysis is as follows.

Assuming that there is $n$ sample of data, $p$ indicators to be observed for each sample, and that there is a correlation between the indicators, the indicators can be represented using the $m (m < p)$ common factors $F_{1}, F_{2}, \dots, F_{m}$ . Suppose the model satisfies the conditions: 1)

$X = (X_{1}, X_{2}, \dots, X_{p})'$ is an observable random variable with mean vector $E (X) = 0$ , covariance matrix $C o v (X) = \sum$ and correlation matrix $R$ equal.

2)

$F = (F_{1}, F_{2}, \dots, F_{m},)' (m < p)$ is an unobservable variable with mean vector $E (F) = 0$ and covariance matrix $C o v (F) = 1$ , i.e., the components of vector $F$ are independent of each other.

3)

The special factors $ε = (ε_{1}, ε_{2}, \dots, ε_{p})'$ and $F$ are independent of each other and the covariance matrix $Σ_{ε}$ of $E (F) = 0, ε$ is a diagonal square matrix, the mathematical formula of which is given in equation (1): (1) $C o v (ε) = \sum_{ε} = [\begin{matrix} σ_{11}^{2} & 0 \\ σ_{22}^{2} \\ ⋱ \\ 0 & σ_{p p}^{2} \end{matrix}]$

And the components of $ε$ are independent of each other. The model of equation (2) is the factor model: (2) $\{\begin{matrix} X_{1} = a_{11} F_{1} + a_{12} F_{2} + \dots + a_{1 m} F_{m} + ε_{1}, \\ X_{2} = a_{21} F_{1} + a_{22} F_{2} + \dots + a_{2 m} F_{m} + ε_{2}, \\ X_{3} = a_{31} F_{1} + a_{32} F_{2} + \dots + a_{3 m} F_{m} + ε_{3}, \\ \dots \\ X_{p} = a_{p 1} F_{1} + a_{p 2} F_{2} + \dots + a_{p m} F_{m} + ε_{m} . \end{matrix}$

The matrix form of model (2) can be summarized in equation (3): (3) $X = A F + ε$ where matrix $A$ is represented by equation (4): (4) $A = [\begin{matrix} a_{11} & a_{12} & a_{1 m} \\ a_{21} & a_{22} & a_{2 m} \\ ⋱ \\ a_{p 1} & a_{p 2} & a_{p m} \end{matrix}]$

The greater the absolute value of element $a_{i}_{i}$ in matrix $A$ , the higher the correlation between $X_{i}$ and $F_{j}$ , i.e., the greater the amount of public factor $F_{j}$ loading on variable $X_{i}$ .

3.2

Mathematical modeling of structural equations

Structural equation modeling consists of two parts, a measurement model to describe the relationship between the observed indicators and latent variables because we are indirectly measuring the latent variables through the observed indicators, and a latent variable model that describes the interactions between the latent variables, which is also called a structural model because the interactions between the latent variables reflect the structure of the model [27].

The mathematical model of the structural model is as follows: (5) $η = B η + Γ ξ + ζ$ where $η$ is the $(m \times 1)$ vector of endogenous latent variables, $ξ$ is the $(n \times 1)$ vector of exogenous latent variables, $B$ is the matrix of coefficients representing $(m \times m)$ the interactions between $η$ each other, $Γ$ represents the matrix of coefficients representing $(m \times n)$ the influence of $ξ$ on $η$ each other, and $ζ$ is the vector of residuals of the model $E (ζ) = 0$ and $ζ$ is uncorrelated with $ξ$ .

The mathematical model of the measurement model is as follows: (6) $\begin{matrix} y = Λ_{y} η + ε \\ x = Λ_{x} ξ + δ \end{matrix}$ where $y$ is the $(p \times 1)$ vector of endogenous observables, $x$ is the $(q \times 1)$ vector of exogenous observables, $Λ_{γ}$ is $y$ the factor loading matrix of $(p \times m)$ on $η$ to represent the relationship between $γ$ and $η$ , $Λ_{x}$ is $x$ the factor loading matrix of $(q \times n)$ on $ξ$ to represent the relationship between $x$ and $ξ$ , $ε$ represents the $(p \times 1)$ vector of measurement errors of observables $η$ , $δ$ is the measurement error of observables $ξ$ of the $(q \times 1)$ vector, and $ε$ is not correlated with $η, ξ, ζ$ the $E (ε) = 0$ .

4

Digital elements for quality improvement in basic education

4.1

Survey respondents and reliability tests

A total of 757 questionnaires were distributed and 726 valid questionnaires were issued, with a recovery rate of 100% and an effective rate of 95.9%. The questionnaire is divided into dimensions such as “students’ digital technology application ability”, “teachers’ digital technology application ability”, “digital infrastructure”, “digital resources”, “digital management”, and “integration of digital technology and education and teaching”. The reliability analysis results of the questionnaire showed that the total reliability coefficient of the questionnaire was greater than 0.9, and the ɑ coefficient of all dimensions was greater than 0.8. In addition, the overall KMO value of the questionnaire was 0.953, and the Bartlett spherical test P=0<0.01. Indicates that the questionnaire meets the requirements for reliability and validity.

4.2

Analysis of questionnaire data

In order to understand the elements of digital influence that promote the quality of basic education, the study builds a linear regression model with the quality of basic education as the explanatory variable, and the dimensions of teachers’ ability to apply digital technology ( $X_{1}$ ), digital infrastructure ( $X_{2}$ ), digital resources ( $X_{3}$ ), digital management ( $X_{4}$ ), the integration of digital technology with education and teaching ( $X_{5}$ ), and students’ ability to apply digital technology ( $X_{6}$ ) as the explanatory variables. The covariance diagnosis of digitalization element dimensions and education quality is shown in Table 1.

A preliminary regression analysis was conducted on the opposite data to determine whether there would be multiple covariance between the digital element dimensions, and a covariance diagnostic of the data found that there was covariance for digital infrastructure, digital resources, digital management, and integration of digital technology and education with an inflation factor (VIF) > 10.

Table 1.

The common linear diagnosis of information elements and education quality

Model	Unnormalized coefficient		Normalized coefficient	t	Sig.	Common linear statistics
Model	β	SE	Beta	t	Sig.	Tolerance	VIF
Constants	88.661	45.515		1.994	0.043
	0.909	0.050	0.743	28.310	0.024	0.955	1.056
	-4.413	2.484	-0.787	-1.786	0.057	0.001	302.953
	5.197	3.259	0.657	1.615	0.125	0.007	236.756
	-2.539	1.833	-0.506	-1.447	0.212	0.011	214.488
	5.378	3.031	1.181	1.703	0.118	0.002	736.914
	0.015	0.032	0.015	0.153	0.836	0.985	1.017

In the process of analyzing the promotion of basic education quality improvement, each digital element has a different degree of influence on the improvement of basic education quality, and there is also an intricate relationship of influence between each digital element, with a variety of co-linear phenomena. In order to accurately quantify the improvement of the quality of basic education, it is necessary to reduce the dimensionality of the digital elements to several unrelated composite digital elements before conducting research and analysis. Principal component analysis is an effective method of dimensionality reduction in multivariate statistical analysis, which enriches the information and simplifies the analysis problem to a large extent. Table 2 shows the extraction of principal component factors.

Table 2 shows that the eigenvalues of the first 4 components are close to the range of 1 and have a cumulative contribution of 98.402%, which can be used to explain 98.402% of all variables. Therefore, these 4 variables meet the requirements of the analysis. The eigenvalues of the first 4 principal components are $λ_{1}$ =2.664, $λ_{2}$ =1.338, $λ_{3}$ =1.011, and $λ_{4}$ =0.906, respectively.The linear composite coefficients are obtained through the quotient of the eigenvalues and the square root of the loadings, and the linear composite coefficients of the columns are multiplied by the corresponding variables and cumulatively added up to obtain the linear composite expressions of the components.After linear transformation, the standardized variables can be converted into linear composites, which can be used to extract from variable 6 the 4 composite factors were extracted. This analysis method is mainly to reduce the dimensionality as well as to simplify the analyzed factors, and since the four composite factors are orthogonal matrices, it means that there is no correlation between them, thus solving the problem of multicollinearity.

Table 2.

Main component factor extraction

Component	Summary	Initial feature variance percentage	Accumulation (%)	Summary	Percentage of variance explained by extracted loadings	Accumulation (%)
1	2.664	44.588	44.588	2.664	44.588	44.588
2	1.338	22.514	67.102	1.338	22.514	67.102
3	1.011	16.666	83.768	1.011	16.666	83.768
4	0.906	14.634	98.402	0.906	14.634	98.402
5	0.108	1.584	99.986
6	0.001	0.014	100

Table 3 shows the table of regression coefficients for the dimensions of digitalization elements and quality of education. It can be seen that the tolerance of $Z_{1}$ , $Z_{2}$ , $Z_{3}$ and $Z_{4}$ is 1.000 and the expansion factor (VIF) is also 1.000, so it can be seen that the values of the tolerance and expansion factor (VIF) of each variable are within the required range and there is no multicollinearity between the independent variables. T-tests showed no statistical significance for the constants, and p-values <0.05 for $Z_{1}$ , $Z_{2}$ , $Z_{3}$ , and $Z_{4}$ were statistically significant.

Table 3.

The regression coefficient of information elements and education quality

Model	Unnormalized coefficient		Normalized coefficient	t	Sig.	Common linear statistics
Model	β	SE	Beta	t	Sig.	Tolerance	VIF
Constants	-1.237E-14	0.024		0	1.000
	0.084	0.008	0.118	4.722	0.002	1.000	1.000
	0.415	0.022	0.472	19.262	0.004	1.000	1.000
	-0.242	0.033	-0.264	-10.154	0.001	1.000	1.000
	0.521	0.021	0.486	19.945	0.000	1.000	1.000

Therefore, the regression equation is modeled as: (7) $Z_{Y} = 0.084 Z_{1} + 0.415 Z_{2} - 0.242 Z_{3} + 0.521 Z_{4}$

The covariance diagnosis of the data found that the theoretical values of the inflation factor (VIF) of students’ digital awareness and attitude, students’ digital learning skills, and students’ digital learning practices are close to 1, the multiple covariance between the explanatory variables is weak, and the model is reasonably designed.

Substituting the linear expressions of $Z_{1}$ , $Z_{2}$ , $Z_{3}$ , and $Z_{4}$ into (7), the linear relationship between the quality of education and the original digital factor dimensions can be obtained: (8) $\begin{matrix} y & = \sum_{i = 1}^{4} β_{j} Z_{i} + β_{0} = \sum_{i = 1}^{4} β_{i} \sum_{j = 1}^{6} α_{j i} Z X_{j} + β_{0} \\ = \sum_{i = 1}^{4} β_{i} \sum_{j = 1}^{6} α_{j j} \frac{X_{j} - \bar{X_{j}}}{σ_{j}} + β_{0} \\ = \sum_{j = 1}^{6} \sum_{i = 1}^{4} α_{j i} \frac{β_{i} X_{j}}{σ_{j}} - \sum_{j = 1}^{6} \sum_{i = 1}^{4} α_{j i} \frac{β_{i} \bar{X_{j}}}{σ_{j}} + β_{0} \end{matrix}$

Reducing to the original variables according to (8), the regression coefficients of each original variable are obtained, and the regression coefficients of the original independent variables are shown in Table 4.

Table 4.

The original independent variable regression coefficient

Variable	Normalized coefficient	Original coefficient
X₁	0.530	15.691
X₂	0.102	0.032
X₃	0.232	0.076
X₄	0.130	0.077
X₅	0.166	0.053
X₆	0.179	11.381

From Table 4, the regression equation of the dimensions of the original digital elements and the quality of education can be obtained: (9) $Y = 15.691 X_{1} + 0.032 X_{2} + 0.076 X_{3} + 0.077 X_{4} + 0.053 X_{5} + 11.381 X_{6}$

In Table 4, expressing the standardized coefficient as a percentage, for every 1% improvement in teachers’ digital technology application ability, the quality of education improves by 53% on average, indicating that teachers’ digital application ability is the main channel to promote the improvement of education quality. The improvement of teachers’ digital technology application ability is considered an important soft power to crack the bottleneck of education digital development, promote the quality of basic education and facilitate teachers’ professional development. New teaching tools and technologies are developing rapidly. Teachers should not only go to adapt to the technologies, but also combine the developmental rules of students and their own professional needs to promote the in-depth integration of digital technology and education teaching, and improve the quality of education teaching.

For every 1% increase in students’ ability to use digital technology, the quality of education improves by 17.9% on average. Through the enhancement of their own digital awareness and attitude, students are able to independently choose the knowledge and methods suitable for their own learning, and can make use of the online learning space to communicate and discuss with their teachers and classmates across geographical areas, and complete the conversion of multiple roles in the discussion process. In the process of constantly using digital technology, students turn to active access to resources for learning, constructive learning, independent and collaborative learning, cross-school learning, and in digital practice activities, complete the cultivation and enhancement of students’ sense of innovation, research learning ability, and communicative ability to communicate with others, so as to improve students’ interest in learning and the efficiency of classroom teaching.

In addition, for every 1% increase in digital infrastructure investment, the quality of education improves by 10.2% on average. For every 1% increase in digital resources, the quality of education improves by 23.2% on average. For every 1% increase in digital management and planning, the quality of education increases by 13% on average. A 1% increase in the integration of digital technology into education and teaching increases the quality of education by 16.6% on average. This indicates that all of the above variables affect the quality of basic education to varying degrees.

5

Factors affecting the digital competence of the teaching force and strategies to enhance it

5.1

Research hypotheses

From the analysis in the previous section, it can be seen that the digital competence of the teacher team is an important factor affecting the improvement of the quality of basic education, so it is necessary to further analyze the influencing factors of the digital competence of the teacher team, so as to put forward the corresponding enhancement strategies in a targeted manner to achieve the improvement of the quality of education.

According to related research, this paper makes several assumptions about teachers’ digital technology application ability, and Table 5 shows the model relationship assumptions of the factors influencing teachers’ digital technology application ability.

Table 5.

The hypothesis of factors that affect teachers’ ability to apply their digital skills

Number	Hypothesis
H1	Sense of self-efficacy can significantly affect teachers’ ability of digital technology using.
H2	Skills base will significantly affect teachers’ ability of digital technology using.
H3	Awareness attitudes will significantly affect teachers’ ability of digital technology using.
H4	Perceptual usefulness will significantly affect teachers’ ability of digital technology using.
H5	Perceptual ease of use will significantly affect teachers’ ability of digital technology using.
H6	Perceptual ease of use will significantly affect perceptual usefulness.
H7	Application atmosphere will significantly affect teachers’ ability of digital technology using.
H8	Application atmosphere will significantly affect teachers’ sense of self-efficacy.
H9	Application atmosphere will significantly affect teachers’ awareness attitudes towards digital technology using.
H10	School management will significantly affect teachers’ ability of digital base using.
H11	School management will significantly affect teachers’ awareness attitudes towards digital technology using.
H12	Training support will significantly affect teachers’ ability of digital technology using.
H13	Training support will significantly affect teachers’ sense of self-efficacy.
H14	Training support will significantly affect the skills base of teachers.
H15	Training support will significantly affect teachers’ awareness attitude towards digital technology using.

5.2

Structural equation modeling

In this study, structural equation modeling was conducted using AMOS24.0 software with perceived ease of use, perceived usefulness, application climate, school management system, and training support as independent variables, self-efficacy, awareness attitude, and perceived usefulness as mediator variables, and ability to apply digital technology as dependent variable.

All the hypotheses presented above were tested and analyzed using AMOS 24.0 software, and the structural model parameter test values and the specific results of the research hypotheses validation are shown in Table 6. The test results of the structural equation modeling showed that the p-value of hypothesis H10 (p=0.3053 > 0.05) and hypothesis H12 (p=0.4978 > 0.05) did not reach the level of statistical significance and therefore these two hypotheses were not supported. This indicates that the positive effect of school management system and training support on digital technology application skills is not significant. Whereas, the p-value of the other 13 hypotheses were below 0.05 and met the criterion of significance, therefore these hypotheses were confirmed indicating that they have a significant effect on teachers’ competence in the use of digital technology.

Table 6.

Model parameter test value and the results of the research hypothesis

	Path	S.E.	C.R.	P	Standardized path coefficient	Hypothesis
H1	Self-efficacy→Ability of digital technology using	0.015	9.066	0.0036	0.317	True
H2	Skills base→Ability of digital technology using	0.015	11.484	0.0278	0.342	True
H3	Awareness attitude→Ability of digital technology using	0.039	3.587	0.0099	0.126	True
H4	Perceptual usefulness→Ability of digital technology using	0.018	2.975	0.0331	0.098	True
H5	Perceptual ease of use→Ability of digital technology using	0.033	3.677	0.0012	0.158	True
H6	Perceptual ease of use→Perceptual usefulness	0.038	22.852	0.0015	0.763	True
H7	Application atmosphere→Ability of digital technology using	0.041	3.831	0.0045	0.169	True
H8	Application atmosphere→Self-efficacy	0.044	7.469	0.0116	0.309	True
H9	Application atmosphere→Awareness attitude	0.032	7.822	0.0124	0.430	True
H10	School management→Ability of digital technology using	0.048	0.816	0.3053	0.031	Untrue
H11	School management→Awareness attitude	0.043	2.379	0.0291	0.111	True
H12	Training support→Ability of digital technology using	0.019	0.624	0.4978	0.032	Untrue
H13	Training support→Self-efficacy	0.037	11.111	0.0118	0.493	True
H14	Training support→Skills base	0.034	13.212	0.0091	0.545	True
H15	Training support→Awareness attitude	0.044	5.276	0.0131	0.265	True

5.3

Tests and Analysis of Intermediation Effects

The mediation effect test and analysis aims to investigate the effect of the independent variable on the dependent variable through the mediating variable. This study uses maximum likelihood estimation method for parameter estimation in structural equation modeling and 95% confidence intervals to test the mediating effect in the model through bias-corrected percentile Bootstrap method. The sample size of random sampling is set to 4000, if the confidence interval contains 0, it means that the path of the study is not significant, if the confidence interval excludes the value of 0 it means that the path of the study is significant. The results of the mediation effect test of the overall structural model are shown in Table 7.

From the table below, it can be seen that the effect of application climate on the ability to apply digital technology is partially realized through the mediating variable of self-efficacy. The mediating effect size is 0.060 with 95% confidence interval not containing 0. This indicates that a good application atmosphere can enhance teachers’ self-efficacy and thus improve the use of digital technology in teaching. The effect of application climate on the ability to use digital technology is also partially realized through the mediating variable of awareness attitude. The mediator effect size was 0.049 with a 95% confidence interval not including 0. This suggests that the influence of environment also penetrates into the level of teachers’ consciousness and attitude, enhancing the importance they attach to the application of technology, and thus promoting the enhancement of competence.

The effect of school management system on digital technology application competence can be realized through the mediating variable of awareness attitude. The mediator effect size is 0.199 and the 95% confidence interval does not contain zero.

There is a dual mediating mechanism of training support on digital technology adoption competence. One is through increasing self-efficacy with a mediation effect size of 0.158, and the other is through the mediating variable of awareness attitude with a mediation effect size of 0.023.Finally, perceived usefulness has a 95% confidence interval that does not contain 0 in the effect of perceived ease of use on digital technology adoption competence, indicating that the indirect effect is significant with an effect value of 0.133.

Table 7.

The mediation effect test result of the structural model

Path	Mediation effect value	Bootstrap 95% confidence interval
Path	Mediation effect value	Lower limit	Upper limit
Application atmosphere→Self-efficacy→Ability of digital technology using	0.060	0.051	0.156
Application atmosphere→Awareness attitude→Ability of digital technology using	0.049	0.001	0.109
School management→Awareness attitude→Ability of digital technology using	0.199	0.136	0.249
Training support→Self-efficacy→Ability of digital technology using	0.158	0.100	0.226
Training support→Skill base→Ability of digital technology using	0.013	-0.007	0.043
Training support→Awareness attitude→Ability of digital technology using	0.023	0.005	0.071
Perceptual ease of use→Perceptual usefulness→Ability of digital technology using	0.133	0.046	0.124

In summary, school management system cannot have a direct effect on digital technology application competence, but can influence digital technology application competence through the fully mediated role of conscious attitude. Training support cannot have a direct effect on digital technology application ability, but can influence digital technology application ability through the fully mediated role of self-efficacy and conscious attitude. Meanwhile, perceived ease of use directly enhances digital technology application competence and has an indirect effect through its influence on perceived usefulness.

It can be seen that the factors affecting teachers’ ability to apply digital technology cover three levels: self-efficacy, skill base, and conscious attitude at the individual level. Perceived ease of use and perceived usefulness at the technology level. As well as the application climate, school management system and training support at the environmental level. Together, these eight interacting influences shape the whole picture of teachers’ digital technology adoption competence.

5.4

Strategies for Building Teachers’ Capacity to Apply Digital Technology

5.4.1

Conceptual leadership

Against the backdrop of the accelerating process of digital transformation in education, teachers need to keep abreast of the times and develop a view of technology application that is in harmony with technological development and in line with educational practice. Schools should formulate relevant incentive policies to encourage teachers to expand their concepts of digital technology application, abandon old perceptions and change their attitudes towards technology. Through relevant training and incentive mechanisms, teachers’ ability to independently use technology to solve problems is enhanced, innovative design thinking is developed, teachers’ concepts of technology application and value perceptions are transformed, and the formation and development of an appropriate technological outlook is promoted to build a new ecology of teaching guided by technological concepts. This will help mobilize the subjective initiative of teachers, stimulate their endogenous motivation to use digital technology, and establish their sense of ownership in promoting digital transformation, thus impacting the solidified thinking inertia of teachers at the conceptual level.

5.4.2

Technical safeguards

In order to further enhance teachers’ digital technology application capabilities, it is necessary to increase the inclination of relevant policies towards teachers. Education departments at all levels should do a good job of coordinating the planning of digital education, develop standards for teachers’ digital teaching competence that are in line with the actual situation of schools, and provide guidelines for the development of teachers’ digital technology application capabilities. In addition, policies and norms can effectively reflect the strategic positioning and value pursuit of schools in the process of digital transformation, and clearly define the duties and responsibilities of teachers, students, administrators and technical support staff in the process of digital transformation.

High-quality infrastructure is the foundation and guarantee for promoting the development of teachers’ digital technology application ability. The Ministry of Education (MOE) and the relevant departments of the Ministry of Industry and Information Technology (MIIT) need to take measures to ensure the timely replacement and maintenance of digital equipment in schools, and to provide teachers with timely and sustainable technical support services. They should also gain an in-depth understanding of the current state of education and the teaching situation of front-line teachers, and establish a lifelong learning platform for the cultivation of teachers’ digital technology application skills, so as to meet teachers’ diversified and personalized needs for the cultivation of technological literacy.

5.4.3

Environmental optimization

School climate is an important protective factor for the improvement of teachers’ digital technology use, and it is an indispensable external condition for teachers’ professional development and growth. The positive atmosphere of surrounding colleagues’ support and peer assistance is a very important factor in enhancing teachers’ identity in technology use. Teachers are encouraged to apply newly learned technology in their classroom teaching and to conduct regular reflection and assessment. Through case studies, instructional journals, or peer reviews, teachers can gain a deeper understanding of the effectiveness of technology use and adjust instructional strategies based on student feedback and learning outcomes. Leader support and vision are critical to creating an educational environment that encourages experimentation and innovation. Leaders should demonstrate a strong commitment to technology-integrated teaching and learning and support teachers’ use of technology by developing clear policies and strategies.

Digitally intelligent technology has become a key driver of teacher professional growth. To this end, integrated digital resource platforms need to be developed and made available to provide teachers with rich teaching and learning resources. There is also a need to promote smart teaching tools, using artificial intelligence, big data and other technologies to develop smart teaching tools, such as smart homework correction systems, learning progress tracking tools and personalized learning recommendation systems, to encourage teachers to use technology for pedagogical innovations and to motivate them to explore and apply new technologies in their teaching.

6

Conclusion

This paper explores the relationship between the digital competence of the teaching force and the improvement of the quality of basic education using questionnaire survey, factor analysis and structural equation. The results show that for every 1% increase in teachers’ digital technology application ability, the quality of education increases by 53% on average, which verifies the important impact of teachers’ digital ability on the quality improvement of basic education. In addition, for every 1% unit increase in students’ digital technology application capacity, digital infrastructure, digital resources, degree of digital management and planning, and integration of digital technology with education and teaching, the quality of education increased by an average of 17.9%, 10.2%, 23.2%, 13%, and 16.6%, respectively.

In order to further explore the influencing factors of the digital competence of the teaching force, this paper establishes relevant hypotheses and builds structural equation modeling for hypothesis testing. The results show that all factors except H10 (school management → teachers’ digital technology application competence) and H12 (training support → teachers’ digital technology application competence) have a direct positive effect on teachers’ digital technology application competence. The mediating effect test of the above two hypotheses found that training support on teachers’ digital technology application ability arises through the two mediating variables of awareness attitude and self-efficacy, and the effect of school management system on digital technology application ability is realized through the mediating variable of awareness attitude.

Based on the above conclusions, this paper argues that the digital competence of the teaching force should be built from the aspects of conceptual leadership, technical support and environmental optimization.

Lingua:: Inglese

Frequenza di pubblicazione:: 1 volte all'anno
Argomenti della rivista:: Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro

Feed RSS della rivista

A study on the relationship between digital capacity building of the teaching force and the improvement of the quality of basic education

Xianggui Jing

Pubblicato online: 24 set 2025

Ricevuto: 26 gen 2025

Accettato: 30 apr 2025

DOI: https://doi.org/10.2478/amns-2025-0999

Parole chiaveDigital competence, Basic education, Structural equation, Factor analysis

© 2025 Xianggui Jing, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Digital competence, Basic education, Structural equation, Factor analysis