Research on Strategies for Enhancing Teachers’ Digital Resource Integration Capabilities with Artificial Intelligence Technology

With the development of the times, the network has become more and more a convenient and fast means of communication, which is widely penetrated into people’s daily life and learning. People can acquire knowledge through the network, forming a kind of networked teaching and learning [1-3]. And teaching resources on the network as an important part of the whole networked learning system, it should break through the multiple limitations of the traditional in-school teaching resources in terms of personnel, time and space, and provide a large number of comprehensive and open materials to serve and provide the necessary protection for networked learning [4-6].

Teaching resources have different multimedia forms, such as text, images, animation, audio and video, teaching resources have different platforms, such as cross-platform and dedicated platforms, and there are many types and contents of teaching resources, such as materials, test questions, courses, information, lesson plans, etc., and the structure of teaching resources includes unstructured original resources, semi-structured resources, and structured resources [7-10]. It is because of the complexity of teaching resources, making the current large number of network teaching resources mostly stay at a low level of autonomy sharing, numerous resources can not be large-scale, effective information sharing, is a waste of teaching resources and even human resources, it is necessary to integrate them [11-13]. At the same time, there is no unified specification of association between teaching resources and learners and subject knowledge models. For most learners, they are usually lost when utilizing the Internet containing massive teaching resources for learning [14-15]. In order to improve the utilization efficiency of quality teaching resources on the Internet, there is an urgent need to integrate the ever-increasing amount of teaching resources on the Internet and provide personalized learning navigation services for learners. The methods of teaching resources integration and learning navigation have been widely studied [16-17].

With the rapid development of science and technology, artificial intelligence technology has become an important driving force for innovation in various industries, and its application in the field of education is particularly prominent [18]. ChatGPT-like artificial intelligence technology provides a new path for the integration of digital teaching resources in colleges and universities with its powerful natural language processing capability and deep learning mechanism. The technology can automatically classify, label, retrieve and recommend teaching resources, helping teachers and students to quickly access the required content [19-21].

The application of digital teaching is on a gradual upward trend, and in the context of the epidemic, it has gained more rapid development. Literature [22] combines self-reported and questionnaire data to clarify that the integration of information and communication technology (ICT) and education can effectively respond to distance education in the context of the epidemic, in which digital technology training can be provided to improve teachers’ ability to integrate digital technology and digital resources. In the rapid development, there are also some difficulties and obstacles, the literature [23] analyzed the digital teaching environment and the obstacles faced in the process of teaching with digital teaching resources, which provides an important reference for the integration of digital teaching resources and the optimization of digital teaching mode. Among them, the problem of digital teaching resources integration has been of great concern, scholars try to use big data technology and other advanced technologies to optimize the integration of digital teaching resources, literature [24] based on the depth of understanding of digital technology and open learning resources, proposed to integrate big data technology into teaching in order to achieve personalized teaching and to strengthen the teaching effect of online teaching mode. Literature [25] designed a technical tool for digital resource integration based on the research of expert digital resource integration, improved the performance of digital storage of teaching resources and knowledge, and verified the trustworthiness through SPSS course. Literature [26] attempts to build an educational resource platform with cloud computing technology as the underlying architecture, integrates digital teaching resources, and applies the principle of minimum-maximum to realize the on-demand distribution of digital teaching resources, which effectively improves the status quo of the utilization of teaching resources, and significantly enhances the teaching efficiency. Meanwhile, some scholars also pay attention to the influence factors affecting the use and integration of teachers’ digital teaching resources, and the literature [27] reveals through empirical inquiry analysis that rural teachers’ attitudes toward digital education significantly affect teachers’ digital teaching resources integration and application, while self-efficacy, gender and school level have obvious effects. Research on digital teaching resources integration lacks thinking about intelligence and automation, so it is meaningful to explore the performance of artificial intelligence technology in digital teaching resources integration.

Using the principal component analysis algorithm, Pearson’s correlation and multiple regression analysis model as research tools, this paper analyzes the specific factors affecting the improvement of teachers’ digital resource integration ability, and explores the mechanism of its role, so as to propose a strategy for artificial intelligence to help teachers’ digital resource integration ability. First, the variable composition of teachers’ digital resource integration ability is elaborated from four dimensions: cognitive ability, design ability, evaluation ability and reflection ability. Then, the questionnaire survey method was used to obtain the data of influencing factor indicators, and based on the principal component analysis algorithm, the three principal components of social environment constraints, school environment constraints, and personal constraints were extracted. Then, Pearson correlation was used to analyze the correlation between the principal components of the influencing factors and the dimensions of each competency, and multiple regression analysis was performed to explore the influence mechanism among the variables. Finally, specific strategy designs are proposed.

2

Components of teachers’ capacity to integrate digital resources

This study combines the upper level theory and the real policy basis to inductively derive the components of teachers’ digital teaching resources integration competence, which is subdivided into four parts: cognitive ability, design ability, evaluation ability and reflection ability.

2.1

Cognitive abilities

Teacher competence is an important component of teachers’ professional development, and the enhancement of teachers’ cognitive competence is a source of motivation for teachers to continuously and consciously promote self-growth. Cognitive ability not only includes the accumulation and learning of teachers’ teaching experience and practice, but also emphasizes the information processing ability inherent in the knowledge gained, which means that teachers are not only capable of grasping the connection between themselves and the external world, but also have the ability to treat their own development as the object of their own cognition and the object of their own practice, and are able to construct their own internal world. Teachers’ cognitive abilities specifically include teachers’ knowledge, perception, thinking, and the constitution and development of teaching expertise.

Currently, the school’s focus on teachers’ teaching outcomes is more skewed toward teachers’ behaviors and ignores teachers’ cognitive processes. The enhancement of teachers’ cognitive ability needs to be supported by solid subject knowledge, and the acquisition of knowledge is a two-way constructive process: on the one hand, the acquisition of new knowledge needs to be based on prior knowledge and experience. On the other hand, the intake of new knowledge will change the original knowledge and experience to some extent, so that it can be enriched, adjusted or transformed. This requires the teacher to be familiar with the previous knowledge structure, to be able to integrate the teaching content, to construct a new knowledge system, and on this basis, to continuously seek independent development. To a certain extent, the level of teachers’ cognitive level determines the quality of teaching.

2.2

Design capacity

Teachers’ ability to design digital teaching resources is the ability of teachers to integrate and construct their own subject knowledge and digital teaching resources, which is a basic ability necessary for teachers to implement digital teaching. In the process of teaching design, the test is the teacher’s mastery of subject knowledge, understanding of digital teaching resources, and most importantly, based on the development of students, thinking about how to make students achieve the expected teaching goals through the reorganization of the constructed teaching content, which requires that the teacher needs to have a sense of creativity and innovation.

With the emergence of modern information technology such as the Internet, big data, artificial intelligence and so on, teachers also need to reflect on their traditional teaching mode, and try to create a new way of learning suitable for the age of knowledge as well as a corresponding brand-new education and teaching at both the theoretical and practical levels through the support of technology.

2.3

Evaluation capacity

The purpose of teaching evaluation is to promote the improvement of teachers’ integration of digital teaching resources and the improvement of teachers’ integration ability, and the evaluation results have a feedback effect on teachers’ integration of digital teaching resources, while the evaluation can also provide a basis for teachers’ reflection on the work of thinking and improvement.

Under the guidance of constructivist learning theory, teaching evaluation is not only for hard indicators to measure teachers’ digital teaching resources integration ability, but also to support teachers to construct meaningful digital resources integration work, the development of teachers’ digital resources integration ability in the learning environment is based on dynamic and continuous developmental evaluation, which constantly presents the teachers’ construction process and learners’ progress. At the same time, teaching evaluation is carried out throughout the entire teaching process, and cannot be based only on the summative digital resource integration results as the only consideration of the evaluation content, but needs to include the preparation before teaching, the implementation during the teaching process, and the reflection after class in the evaluation system.

The school’s evaluation of teachers is the most common evaluation stereotype in current teaching evaluation, and to a certain extent, the value and significance of self-evaluation, students’ evaluation of teaching, teachers’ and students’ mutual evaluation, peer review, and parents’ evaluation are ignored by the utilitarian evaluation mechanism. Either aspect of the lack of evaluation will certainly make teachers unable to comprehensively view and correctly deal with the integration of digital teaching resources.

2.4

Reflective capacity

Teaching reflection is the process of reviewing and refining teachers’ daily work, and it is also one of the most effective ways to rapidly improve teachers’ professional ability. Teaching reflection is an important process for teachers to improve their ability to integrate digital teaching resources and accumulate teaching experience and knowledge, as well as the process of discovering problems, investigating causes, solving problems and reconstructing knowledge structure. According to the constructivist learning theory, individual experience and knowledge are in the process of interaction between the old and the new, and the process of conformity and assimilation, in which the individual forms a specific cognitive structure, and with the accumulation of experience and knowledge for a long period of time, the cognitive structure is constantly being integrated, constructed and re-constructed. The reorganization of the teacher’s knowledge structure is a reflection of the continuous internal reflection.

The form of reflection not only includes teachers’ self-reflection, but also in the process of exchanging and discussing with others, effective information conducive to reflection will be found, so that teachers can break through the original thinking in reflection. Personal reflection among teachers is the most common and frequent teaching practice, but limited by their own quality, thinking content, reflection perspective and other reasons, so that the depth and breadth of reflection can not achieve the desired results, thus narrowing the teaching reflection to a certain extent. For this reason, it is very necessary to strengthen the combination of personal reflection and collective reflection. Enhancing the integration ability of digital teaching resources is the main theme of the teachers’ reflection process, which takes teachers’ own professional ability as the medium, and spontaneously generates the resonance from cognition to reflection in the general environment of digital teaching resources in order to realize the integration of digital teaching resources and the enhancement of teachers’ reflection ability.

3

Analytical model of factors influencing teachers’ ability to integrate digital resources

This paper mainly adopts principal component analysis and multiple regression analysis to explore the main factors affecting the improvement of teachers’ digital resource integration ability, and lays the foundation for the design of strategies for the improvement of teachers’ digital resource integration ability with the help of artificial intelligence technology.

3.1

Principal Component Analysis Algorithm

3.1.1

Definition and Derivation of Algorithms

Principal component analysis refers to the fact that when the same individual is subjected to a number of observational studies, a large number of random variables with a certain degree of correlation will usually be obtained x₁, x₂, x₃, ⋯, x_p, due to the large number of variables and a certain degree of correlation makes the subsequent study more and more complex, so usually, in order to simplify the object of the study, it is hoped that one or a few composite indexes can summarize the various aspects of the information contained in these variables and it is hoped that the composite indexes represent one aspect of nature independently of each other. Independently of each represents the nature of a particular aspect of the class of statistical methods to summarize the information of many indicators into a few independent indicators [28].

The specific definition of the principal component analysis algorithm is as follows: Let x₁, x₂, x₃, ⋯, x_p be p random variables with correlation (after standardization), now they are linearly combined by coordinate transformation into one or several uncorrelated variables y_i, that is, into the following formula: (1) ${\begin{matrix} y_{1} = u_{11} x_{1} + u_{12} x_{2} + \dots + u_{1 p} x_{p} \\ y_{2} = u_{21} x_{1} + u_{22} x_{2} + \dots + u_{2 p} x_{p} \\ ⋮ \\ y_{p} = u_{p 1} x_{1} + u_{p 2} x_{2} + \dots + u_{p p} x_{p} \end{matrix}$ $$\left\{ {\begin{array}{*{20}{c}} {{y_1} = {u_{11}}{x_1} + {u_{12}}{x_2} + \cdots + {u_{1p}}{x_p}} \\ {{y_2} = {u_{21}}{x_1} + {u_{22}}{x_2} + \cdots + {u_{2p}}{x_p}} \\ \vdots \\ {{y_p} = {u_{p1}}{x_1} + {u_{p2}}{x_2} + \cdots + {u_{pp}}{x_p}} \end{array}} \right.$$

where $u_{i 1}^{2} + u_{i 2}^{2} + \dots + u_{i p}^{2} = 1$ $$u_{i1}^2 + u_{i2}^2 + \cdots + u_{ip}^2 = 1$$, $(i = 1, 2, \dots, p)$ $$\left( {i = 1,2, \cdots ,p} \right)$$, turns out to be the formula for the uncorrelated variables i.e. $\vec{Y} = U' \vec{X}$ $$\vec Y = U\prime \vec X$$, where U is the matrix formed by the eigenvectors of the correlation coefficient matrix of the p random variables, y_i is called (with respect to $\vec{X}$ $$\vec X$$) the ith principal component (i = 1, 2, ⋯, p), will be $λ_{i} / \sum_{i = 1}^{p} λ$ $$\lambda_i \bigg/\sum\limits_{i = 1}^p \lambda $$ used as the contribution of the ith principal component, will be $\sum_{i = 1}^{k} λ_{i} / \sum_{i = 1}^{p} λ$ $$\sum\limits_{i = 1}^k \lambda_i\bigg /\sum\limits_{i = 1}^p \lambda $$ used as the cumulative contribution of the first k principal components, and will be λ_i used as the eigenvalues of the correlation coefficient matrix of the p random variables.

3.1.2

Algorithm implementation steps

The steps for implementing the principal component analysis algorithm are shown below: 1)

Before carrying out principal component analysis, the original random variable data should first be standardized. Then the new index data obtained after standardization is analyzed based on the principal component method. In this paper, the Z-score data standardization method is used, i.e., the original p random variable with correlation x₁, x₂, x₃, ⋯, x_p data standardization, i.e., the original data: (2) $X = [\begin{matrix} x_{11}, x_{12}, \dots, x_{1 p} \\ x_{21}, x_{22}, \dots, x_{2 p} \\ ⋮ \\ x_{n 1}, x_{n 2}, \dots, x_{n p} \end{matrix}]$ $$X = \left[ {\begin{array}{*{20}{c}} {{x_{11}},{x_{12}}, \cdots ,{x_{1p}}} \\ {{x_{21}},{x_{22}}, \cdots ,{x_{2p}}} \\ \vdots \\ {{x_{n1}},{x_{n2}}, \cdots ,{x_{np}}} \end{array}} \right]$$

Standardization as: (3) $A = [\begin{matrix} a_{11}, a_{12}, \dots, a_{1 p} \\ a_{21}, a_{22}, \dots, a_{2 p} \\ ⋮ \\ a_{n 1}, a_{n 2}, \dots, a_{n p} \end{matrix}]$ $$A = \left[ {\begin{array}{*{20}{c}} {{a_{11}},{a_{12}}, \cdots ,{a_{1p}}} \\ {{a_{21}},{a_{22}}, \cdots ,{a_{2p}}} \\ \vdots \\ {{a_{n1}},{a_{n2}}, \cdots ,{a_{np}}} \end{array}} \right]$$

The standardized indicator data have a mean of 0 and a standard deviation of 1. where: (4) $a_{i j} = (x_{i j} - {\bar{x}}_{j}) / \sqrt{\frac{1}{n} {(x_{i j} - {\bar{x}}_{j})}^{2}}, i = 1, 2, \dots, n$ $${a_{ij}} = {{\left( {{x_{ij}} - {{\bar x}_j}} \right)} \bigg / {\sqrt {\frac{1}{n}{{\left( {{x_{ij}} - {{\bar x}_j}} \right)}^2}} }},i = 1,2, \cdots ,n$$ (5) ${\bar{x}}_{j} = \frac{1}{n} \sum_{i = 1}^{n} x_{i j}, j = 1, 2, \dots, p$ $${\bar x_j} = \frac{1}{n}\sum\limits_{i = 1}^n {{x_{ij}}} ,j = 1,2, \cdots ,p$$

2)

The correlation matrix of the indicator data (after normalization) is then derived as follows: (6) $R = [\begin{matrix} r_{11}, r_{12}, \dots, r_{1 p} \\ r_{21}, r_{22}, \dots, r_{2 p} \\ ⋮ \\ r_{p 1}, r_{p 2}, \dots, r_{p p} \end{matrix}]$ $$R = \left[ {\begin{array}{*{20}{c}} {{r_{11}},{r_{12}}, \cdots ,{r_{1p}}} \\ {{r_{21}},{r_{22}}, \cdots ,{r_{2p}}} \\ \vdots \\ {{r_{p1}},{r_{p2}}, \cdots ,{r_{pp}}} \end{array}} \right]$$

Among them: (7) $r_{j k} = \frac{\sum_{i = 1}^{n} (a_{i j} - {\bar{a}}_{j}) (a_{i k} - {\bar{a}}_{k})}{\sqrt{\sum_{i = 1}^{n} {(a_{i j} - {\bar{a}}_{j})}^{2} \sum_{i = 1}^{n} {(a_{i k} - {\bar{a}}_{k})}^{2}}}, i = 1, 2, \dots n, j, k = 1, 2, \dots, p$ $${r_{jk}} = \frac{{\sum\limits_{i = 1}^n {\left( {{a_{ij}} - {{\bar a}_j}} \right)} \left( {{a_{ik}} - {{\bar a}_k}} \right)}}{{\sqrt {\sum\limits_{i = 1}^n {{{\left( {{a_{ij}} - {{\bar a}_j}} \right)}^2}} \sum\limits_{i = 1}^n {{{\left( {{a_{ik}} - {{\bar a}_k}} \right)}^2}} } }},i = 1,2, \cdots n,j,k = 1,2, \cdots ,p$$ (8) ${\bar{a}}_{j} = \frac{1}{n} \sum_{i = 1}^{n} a_{i j}$ $${\bar a_j} = \frac{1}{n}\sum\limits_{i = 1}^n {{a_{ij}}}$$

3)

Next find the eigenvectors and eigenvalues of the correlation matrix. This is shown below:

By virtue of $| R - λ_{j} E | = 0$ $$\left| {R - {\lambda_j}E} \right| = 0$$, j = 1, 2, ⋯, p obtain the eigenvalues λ_j, j = 1, 2, ⋯, p, where E is the unit matrix. The eigenvectors u_k, k = 1, 2, ⋯, p are obtained by virtue of $(R - λ_{j} E) u_{k} = 0$ $$\left( {R - {\lambda_j}E} \right){u_k} = 0$$, j, k = 1, 2, ⋯, p, where λ_j, j = 1, 2, ⋯, p are the eigenvalues obtained previously.

4)

Next, find the principal components. Arrange the eigenvalues obtained above according to their size to obtain λ₁ > λ₂ > ⋯ > λ_p, determine the number of principal components k according to the principle of $\sum_{i = 1}^{k} λ_{i} / \sum_{i = 1}^{p} λ_{i} \geq 70 %$ $${{\sum\limits_{i = 1}^k {{\lambda_i}} } \bigg/ {\sum\limits_{i = 1}^p {{\lambda_i}} }} \geq 70\%$$, according to the order of the eigenvalues and the eigenvectors ${\vec{u}}_{1}, {\vec{u}}_{2}, \dots, {\vec{u}}_{k}$ $${\vec u_1},{\vec u_2}, \cdots ,{\vec u_k}$$, and then determine the selected eigenvectors according to the value of the number of principal components k previously determined, multiply the original p standardized indicator variable data and the eigenvectors can be obtained by multiplying the k composite principal component indicator data, as shown below: (9) $[\begin{matrix} a_{11}, a_{12}, \dots, a_{1 p} \\ a_{21}, a_{22}, \dots, a_{2 p} \\ ⋮ \\ a_{n 1}, a_{n 2}, \dots, a_{n p} \end{matrix}] \times [{\vec{u}}_{1}, {\vec{u}}_{2}, \dots, {\vec{u}}_{k}]$ $$\left[ {\begin{array}{*{20}{c}} {{a_{11}},{a_{12}}, \cdots ,{a_{1p}}} \\ {{a_{21}},{a_{22}}, \cdots ,{a_{2p}}} \\ \vdots \\ {{a_{n1}},{a_{n2}}, \cdots ,{a_{np}}} \end{array}} \right] \times \left[ {{{\vec u}_1},{{\vec u}_2}, \cdots ,{{\vec u}_k}} \right]$$

5)

Finally, the composite score is calculated. Multiply the data of k integrated principal component index with the corresponding principal component contribution ratio above and sum it up to be the comprehensive score.

3.2

Multivariate linear regression analysis models

Multiple regression analysis is used to study the complex relationship between several independent variables with several dependent variables, i.e., mathematical modeling by varying the number of two or more independent variables with one dependent variable [29]. In this case, the regression analysis between one dependent variable and several independent variables is called one-to-many regression analysis, and the regression analysis between several dependent variables and several independent variables is called many-to-many regression analysis. It can also be categorized as linear regression and nonlinear regression. The multiple linear regression model is an extension of the univariate linear regression model with similar principles, except that the calculation is more complex, and its general form is: (10) $y = b_{0} + b_{1} x_{1} + b_{2} x_{2} + \dots + b_{k} x_{k} + ε$ $$y = {b_0} + {b_1}{x_1} + {b_2}{x_2} + \cdots + {b_k}{x_k} + \varepsilon$$

where b₀ is a constant term that represents the estimate of y when the independent variable is zero. ε is a random disturbance term that obeys a normal distribution $N (0, σ^{2})$ $$N\left( {0,{\sigma^2}} \right)$$ and x is an influence factor. There are k influence factors.

When k > 1, take the mathematical expectation for both sides of equation (10) to get: (11) $E Y = β_{0} + β_{1} X_{1} + \dots + β_{k} X_{k}$ $$EY = {\beta_0} + {\beta_1}{X_1} + \cdots + {\beta_k}{X_k}$$

Remember for: (12) $ς = E Y$ $$\varsigma = EY$$

The original equation is rewritten as: (13) $ς = β_{0} + β_{1} X_{1} + \dots + β_{k} X_{k}$ $$\varsigma = {\beta_0} + {\beta_1}{X_1} + \cdots + {\beta_k}{X_k}$$

The above equation is called the regression plane equation, where β₁, β₂, ⋯, β_k is the regression coefficient.

Parameter β_i(i = 0, 1, 2, ⋯, k) and the variance of the multiple linear regression model are generally unknown and need to be estimated from the sample observations. If n observations are made to obtain n sets of observations $(y_{j}, x_{1 j}, x_{2 j}, \dots, x_{k j})$ $$\left( {{y_j},{x_{1j}},{x_{2j}}, \cdots ,{x_{kj}}} \right)$$, j = 1, 2, ⋯, n, can be obtained: (14) $\begin{matrix} y_{1} = β_{0} + β_{1} x_{11} + β_{2} x_{21} + \dots + β_{k} x_{k 1} + ε_{1} \\ y_{2} = β_{0} + β_{1} x_{12} + β_{2} x_{22} + \dots + β_{k} x_{k 2} + ε_{2} \\ ⋮ \\ y_{n} = β_{0} + β_{1} x_{1 n} + β_{2} x_{2 n} + \dots + β_{k} x_{k n} + ε_{n} \end{matrix}}$ $$\left. {\begin{array}{*{20}{c}} {{y_1} = {\beta_0} + {\beta_1}{x_{11}} + {\beta_2}{x_{21}} + \cdots + {\beta_k}{x_{k1}} + {\varepsilon_1}} \\ {{y_2} = {\beta_0} + {\beta_1}{x_{12}} + {\beta_2}{x_{22}} + \cdots + {\beta_k}{x_{k2}} + {\varepsilon_2}} \\ \vdots \\ {{y_n} = {\beta_0} + {\beta_1}{x_{1n}} + {\beta_2}{x_{2n}} + \cdots + {\beta_k}{x_{kn}} + {\varepsilon_n}} \end{array}} \right\}$$

where ε_i obeys a normal distribution $N (0, σ^{2})$ $$N\left( {0,{\sigma^2}} \right)$$ and is assumed to be independent among ε₁, ε₂, ⋯, ε_n.

Equation (14) can be expressed in matrix form: (15) $Y = (\begin{matrix} y_{1} \\ y_{2} \\ ⋮ \\ y_{n} \end{matrix}), ε = (\begin{matrix} ε_{1} \\ ε_{2} \\ ⋮ \\ ε_{n} \end{matrix}), β = (\begin{matrix} β_{1} \\ β_{2} \\ ⋮ \\ β_{n} \end{matrix}), X = (\begin{matrix} 1 & x_{11} & x_{21} & \dots & x_{n 1} \\ 1 & x_{12} & x_{22} & \dots & x_{n 2} \\ ⋮ & ⋮ & ⋮ & ⋮ \\ 1 & x_{1 n} & x_{2 n} & \dots & x_{n n} \end{matrix})$ $$Y = \left( {\begin{array}{*{20}{c}} {{y_1}} \\ {{y_2}} \\ \vdots \\ {{y_n}} \end{array}} \right),\varepsilon = \left( {\begin{array}{*{20}{c}} {{\varepsilon_1}} \\ {{\varepsilon_2}} \\ \vdots \\ {{\varepsilon_n}} \end{array}} \right),\beta = \left( {\begin{array}{*{20}{c}} {{\beta_1}} \\ {{\beta_2}} \\ \vdots \\ {{\beta_n}} \end{array}} \right),X = \left( {\begin{array}{*{20}{c}} 1&{{x_{11}}}&{{x_{21}}}& \cdots &{{x_{n1}}} \\ 1&{{x_{12}}}&{{x_{22}}}& \cdots &{{x_{n2}}} \\ \vdots & \vdots & \vdots &{}& \vdots \\ 1&{{x_{1n}}}&{{x_{2n}}}& \cdots &{{x_{nn}}} \end{array}} \right)$$

Then equation (10) can be written as: (16) $Y = X β + ε$ $$Y = X\beta + \varepsilon$$

The corresponding error model is: (17) $V = X \hat{β} - Y$ $$V = X\hat \beta - Y$$

According to the principle of least squares V^TV = min, the equation is obtained: (18) $X^{T} X \hat{β} = X^{T} Y$ $${X^T}X\hat \beta = {X^T}Y$$

The least squares valuation of the regression parameters is: (19) $\hat{β} = {(X^{τ} X)}^{- 1} X^{T} Y$ $$\hat \beta = {\left( {{X^\tau }X} \right)^{ - 1}}{X^T}Y$$

Among them: (20) $X^{T} X = [\begin{matrix} n & \sum x_{1 i} & \dots & \sum x_{k i} \\ \sum x_{1 i} & \sum x_{1 i}^{2} & \dots & \sum x_{1 i} x_{k j} \\ ⋮ & ⋮ & ⋮ \\ \sum x_{k i} & \sum x_{1 j} x_{k i} & \dots & \sum x_{k i}^{2} \end{matrix}]; X^{T} Y = [\begin{matrix} \sum y_{i} \\ \sum x_{1 i} y_{i} \\ ⋮ \\ \sum x_{k j} y_{i} \end{matrix}]$ $${X^T}X = \left[ {\begin{array}{*{20}{c}} n&{\sum {{x_{1i}}} }& \cdots &{\sum {{x_{ki}}} } \\ {\sum {{x_{1i}}} }&{\sum {x_{1i}^2} }& \cdots &{\sum {{x_{1i}}} {x_{kj}}} \\ \vdots & \vdots &{}& \vdots \\ {\sum {{x_{ki}}} }&{\sum {{x_{1j}}} {x_{ki}}}& \cdots &{\sum {x_{ki}^2} } \end{array}} \right];{X^T}Y = \left[ {\begin{array}{*{20}{c}} {\sum {{y_i}} } \\ {\sum {{x_{1i}}} {y_i}} \\ \vdots \\ {\sum {{x_{kj}}} {y_i}} \end{array}} \right]$$

The least squares valuation of the regression parameters is obtained by bringing the regression parameters into equation (10): (21) $\hat{y} = {\hat{β}}_{0} + {\hat{β}}_{1} x_{1} + {\hat{β}}_{2} x_{2} + \dots + {\hat{β}}_{k} x_{k}$ $$\hat y = {\hat \beta_0} + {\hat \beta_1}{x_1} + {\hat \beta_2}{x_2} + \cdots + {\hat \beta_k}{x_k}$$

The above equation is called the empirical regression plane equation, where ${\hat{β}}_{1}, {\hat{β}}_{2}, {\hat{β}}_{3}, \dots, {\hat{β}}_{k}$ $${\hat \beta_1},{\hat \beta_2},{\hat \beta_3}, \cdots ,{\hat \beta_k}$$ is the empirical regression coefficient.

4

Empirical research on factors influencing teachers’ ability to integrate digital resources

4.1

Study design

4.1.1

Objects of study

In this study, the secondary teachers in Province H were selected as the research object, and 20 secondary schools were surveyed by stratified random sampling method. A total of 960 questionnaires were distributed to each secondary school teacher, and 784 valid questionnaires were recovered, with a recovery rate of 81.7%.

4.1.2

Research tools

Designing the questionnaire of “Secondary Teachers’ Digital Resource Integration Competence” and combing it with the previous literature to classify the digital resource integration competence into four dimensions: cognitive competence, design competence, evaluation competence, and reflective competence. The cognitive ability dimension includes four sub-dimensions: knowledge level, perception ability, thinking ability, and teaching expertise. The design ability dimension contains 3 sub-dimensions: mastery of subject knowledge, understanding of digital teaching resources, and creative awareness and innovativeness. The evaluation competence dimension contains 3 sub-dimensions: self-evaluation, evaluation of students, and peer review. The reflective ability dimension contains 2 sub-dimensions of self-reflection and collective reflection.

The whole questionnaire was designed in three parts with 29 questions. It includes 5 questions on teachers’ basic information, which are demographic variables such as gender, age, teaching age, and title. Teaching ability status 12 questions, this part of the questions using the Likert 5-point scale to quantify the teacher’s digital resource integration ability, each dimension from strong to weak were assigned with a score of 5 to 1, respectively. Teachers’ digital resource integration ability influencing factors design 12 questions, this part of the questionnaire design refers to the existing research literature, and adapted with the actual secondary school. The questionnaire was statistically analyzed using SPSS28.0.

4.1.3

Reliability and validity tests of the questionnaire

The results of the test of reliability and validity of the questionnaire showed that the reliability coefficient of the whole questionnaire is 0.928, the data of the questionnaire has high reliability and the results have a referable value. The result of validity test showed that the overall validity of the questionnaire reached 0.919 and the result of significance test showed a p-value of 0.000, which indicates that the questionnaire has good structural validity and can be analyzed in terms of factor and principal components.

4.2

Analysis of the current status of digital resource integration capacity and factors affecting it

4.2.1

Descriptive statistics on the ability to integrate digital resources

Of the 784 teachers surveyed, 362 were male and 422 were female, accounting for 46.17% and 53.83% of the total, respectively.56, 98, 163, 334 and 133 teachers were 25 years old and below, 26-30 years old, 31-40 years old, 41-50 years old and 51-60 years old, accounting for 7.14%, respectively, 12.50%, 20.79%, 42.60% and 16.96% respectively. There were 4 persons with doctoral degree (0.51%). 38 persons with master’s degree (4.85%). There are 696 teachers with bachelor’s degree, accounting for 88.77%. Another 46 are college degree holders, accounting for 5.87%. There were 32 full senior lecturers, and the numbers of associate senior lecturers, lecturers, assistant lecturers and unclassified lecturers were 193, 389, 152 and 18 respectively, accounting for 4.08%, 24.62%, 49.62%, 19.39% and 2.29% of the total number. There were 42 teachers with less than 1 year of teaching experience (excluding 1 year), 69 with 1~3 years, 120 with 4~10 years, 356 with 11~20 years and 161 with more than 20 years (excluding 20 years). There were 236 dual-teacher teachers, accounting for 30.10% of the total number of teachers surveyed. There are 548 non-dual teachers, accounting for 69.90% of the total number of surveyed teachers. The status of digital resource integration ability was measured by means of teachers’ self-assessment, and the results of the survey on the status of secondary school teachers’ digital resource integration ability are shown in Table 1.

Table 1.

Present situation of digital resource integration ability survey

Dimensions	Categories	Number of respondents	Mean value	Expectation level
Cognitive ability	Knowledge level	784	4.02	4.14
	Perceptive ability	784	3.89	4.14
	Thinking ability	784	4.02	4.18
	Teaching expertise	784	3.90	4.09
Design capability	Mastery of subject knowledge	784	3.58	4.09
	Understanding of digital teaching resources	784	3.57	4.18
	Innovation consciousness and innovation power	784	3.94	4.12
Evaluation ability	Self-evaluation	784	3.98	4.10
	Evaluation of students	784	3.95	4.02
	Peer-reviewed	784	3.91	4.01
Reflective ability	Self-reflection	784	3.64	4.04
Reflective ability	Collective reflection	784	3.55	4.12

As can be seen from Table 1, the overall level scores of secondary teachers’ digital resource integration competence ranged from 3.5 to 4.1, with a mean value of 3.80. In the dimension of cognitive ability, teachers’ knowledge level and thinking ability performed the best, with a mean value of 4.02. This was followed by pedagogical expertise and perceived competence, with mean values of 3.90 and 3.89, respectively. Comparing the dimensions of perceived competence, teachers generally perceived themselves as having a high level of knowledge but a slightly lower level of perceived competence in digital teaching and learning resources, and a lack of personal characteristics in digital teaching and learning. On the design competency dimension, teachers considered the strongest to be creativity and innovativeness, with a mean value of 3.94, while lacking in both subject matter knowledge acquisition and knowledge of digital teaching resources. In the dimension of thinking ability, teachers think that their ability in self-evaluation, evaluation of students, and mutual evaluation of teachers are all high, but there is still a slight gap from the ideal value. In the dimension of reflective ability, the mean values of self-reflection and collective reflection were 3.64 and 3.55 respectively, which scored low relative to the other dimensions, indicating that reflective ability needs to be strengthened. On the whole, the current status of teachers’ competence in each dimension is generally lower than the level they expect to achieve, indicating that the teachers’ ability to integrate digital resources at this stage is still lower than expected, and still needs a substantial improvement.

4.2.2

Analysis of factors affecting the ability to integrate digital resources

In order to avoid the existence of correlation between the constraints so that the information between the variables overlap, this paper firstly factor analyzed the 12 impact category questions in the questionnaire, and the analyzed factor loadings and the results of the cumulative variance are interpreted as shown in Table 2.

Table 2.

Total variance interpretation

	Initial eigenvalue			Extract the sum of squared loads			Rotating load sum of squares
Module	Total	Percentage of variance	Cumulative /%	Total	Percentage of variance /%	Cumulative /%	Total	Percentage of variance /%	Cumulative /%
1	4.278	38.085	38.085	4.278	38.085	38.085	3.724	32.458	32.458
2	2.230	19.711	57.796	2.230	19.711	57.796	2.708	23.865	56.323
3	1.690	16.876	74.672	1.690	16.876	74.672	1.986	18.349	74.672
4	0.616	4.436	79.108
5	0.529	4.162	83.27
6	0.464	3.685	86.955
7	0.442	3.529	90.484
8	0.421	2.416	92.9
9	0.386	2.121	95.021
10	0.368	2.038	97.059
11	0.343	1.709	98.768
12	0.267	1.232	100

The results in Table 2 show that the initial eigenvalues of the three factors extracted from the questionnaire are 4.278, 2.230 and 1.690, respectively, and the cumulative explainable variances are 38.085%, 57.796% and 74.672%, respectively, indicating that the three extracted factors can better reflect the main information in the original 12 variables.

The maximum variance method was used to rotate the data so that the extracted factors could be interpreted more rationally. The rotated factor component matrix is shown in Table 3.

Table 3.

The component matrix after rotation

	Module
	1	2	3
Insufficient financial support from the government and educational institutions.	0.078	0.753	0.109
The content of teachers’ digital resource integration ability training is not in line with reality.	0.095	0.812	0.124
The ways and means of teacher training lack diversification.	0.123	0.819	0.057
School-enterprise cooperation, the integration of industry, university and research is not deep, and teaching lacks pertinence.	0.135	0.806	0.053
The school is not supported enough in terms of funds, salaries and benefits.	0.829	0.134	0.034
The construction of teaching infrastructure and digital equipment are backward.	0.861	0.113	0.063
Divorced from front-line positions, lack of practice in using digital resources.	0.867	0.082	0.081
It is difficult to juggle work and study.	0.849	0.055	0.066
Lack of time and space to communicate with other teachers.	0.826	0.126	0.098
Study and use of digital teaching resources cannot be met.	0.067	0.111	0.782
Lack of initiative and enthusiasm for their own development.	0.108	0.085	0.827
Their own development energy is insufficient, limited ability.	0.047	0.052	0.824

The results shown in Table 3 loading values show that the rotated factors have a good degree of differentiation. The first factor has a larger loading value in the insufficient support of school funding, salary and treatment, backward teaching infrastructure construction and digital equipment, lack of practice in the use of digital resources due to detachment from the frontline teaching position, difficulty in balancing work and study, lack of time and space for common communication with other teachers, which basically reflects the problems related to school management, and is considered to be named as constraints in the school environment . The second factor has a larger loading value in the problems of insufficient financial support from the government, educational institutions, etc., teachers’ digital teaching resources integration and training content not matching the actual situation, lack of diversification of teachers’ training methods, in-depth cooperation between schools and enterprises, integration of industry, academia and research, and lack of relevance of teaching, etc. Considering that it is related to the behavior of the government and other functional departments in the society, it will be named as a constraint in the social environment. The third factor with larger loading values includes issues such as the digital teaching resources used for learning cannot be satisfied, the lack of initiative and motivation for their own development, and the lack of energy and limited capacity for their own development, all of which are closely related to the teachers’ own learning and development, and will be named as personal constraints.

4.3

Effect of influencing factors on the ability to integrate digital resources

On the basis of exploring the influencing factors of digital resource integration capability through principal component analysis, this study uses SPSS software to perform multiple linear regression analysis in order to analyze the influencing mechanism of digital resource integration capability more comprehensively, with a view to providing an important basis for the strategy of improving digital resource integration capability.

4.3.1

Correlation analysis between the influencing factors and the dimensions of competencies

Pearson bivariate correlation analysis was performed with “school environmental constraints”, “social environmental constraints”, and “personal constraints” as dependent variables, and four predictors of cognitive ability, design ability, evaluation ability, and reflective ability [30], and the correlation analysis results are shown in Table 4.

Table 4.

Correlation analysis between the influencing factors and the ability dimensions

Correlation	Cognitive ability	Design capability	Evaluation ability	Reflective ability
School environment constraints	0.552**	0.583**	0.728**	0.722**
Social and environmental constraints	0.638**	0.647**	0.567**	0.514**
Personal constraint	0.678**	0.724**	0.669**	0.764**
p<0.05, *p<0.01

The analysis of the data showed that the four dimensions of the ability to integrate digital resources showed positive correlations with the three influencing factor variables. The strongest correlations were with the personal constraints and reflective competence dimensions, with a correlation coefficient of 0.764, followed by the school environment constraints and evaluation competence dimensions, which indicated that the school environment and system had a more significant impact on teachers’ evaluation competence. Once again, it is the design ability dimension and personal constraints, where teachers’ design ability is closely related to their own level of subject knowledge, knowledge of digital resources, and innovative thinking. As for the dimension of cognitive ability, personal constraints and social environment constraints have a higher degree of correlation with teachers’ knowledge level, perception ability, thinking ability, and teaching expertise, while school environment constraints have a weaker effect on teachers’ cognitive ability. The correlation analysis reveals that the individual teacher and the environmental climate of the school in which he or she works are the key variables affecting multiple dimensions of teachers’ ability to integrate digital resources.

4.3.2

Regression Analysis of Influencing Factors and Dimensions of Capabilities

1)

Regression Analysis of Influential Factors and Cognitive Ability

In this study, the cognitive ability dimension of teachers’ digital resource integration ability is first taken as the dependent variable, and school environment constraints, social environment constraints and personal constraints are taken as the independent variables, and linear regression methods are adopted to analyze the influence mechanism among the variables. The results of the regression analysis of the influencing factors and cognitive ability are shown in Table 5.

Through the comprehensive judgment of the model, it is found that the combination of independent variables has a strong explanatory power (R² = 0.632) for technological literacy. The model also passed the ANOVA test and the multiple covariance test without autocorrelation problems, and the results showed that the model was highly robust. Analyzing the independent effects of the independent variables, it was found that both social environment constraints (β=0.125) and personal constraints (β=0.148) showed a positive and significant effect on cognitive ability. Among them, personal constraints had the greatest effect, which indicates that teachers’ subjective initiative is the key to enhance cognitive ability. While school environment constraints had no significant effect on cognitive ability.

2)

Regression Analysis of Influential Factors and Design Ability

This section takes the design ability dimension of teachers’ digital resource integration ability as the dependent variable and examines the influence mechanism between the variables. The results of the regression analysis of influencing factors and design ability are shown in Table 6.

The model validity test shows that the combination of independent variables can effectively explain the variation of planning and preparation with an explanatory power of 65.2%. The model also passed the ANOVA test without multicollinearity and autocorrelation problems, and the results were robust. The effect analysis of the independent variables revealed that both social environmental constraints (β=0.191) and personal constraints (β=0.128) had a positive effect on cognitive ability and passed the test of significance. This indicates that these variables are key influences in improving teachers’ planning and preparation skills. Whereas, school environment constraints did not significantly affect design competencies (p=0.416>0.05).

3)

Regression Analysis of Influential Factors and Evaluation Ability

Next, this study takes the evaluation ability dimension of teachers’ digital resource integration ability as the dependent variable and examines the influence mechanism between the variables. The specific results of the regression analysis of influencing factors and evaluation ability are shown in Table 7.

The model validity test showed that the combination of independent variables could effectively explain the changes in evaluation competence with an explanatory power of 58.6%. Meanwhile the model passed the ANOVA test without multicollinearity and autocorrelation problems, and the results were robust. Examination of the effects of the independent variables revealed that personal constraints (β = 0.226) and school environment constraints (β = 0.086) generated a significant positive effect on evaluative ability. This suggests that improving these two factors is the key to improving teachers’ evaluation competence. The effect of school environment constraints (β=0.091), on the other hand, was not significant.

4)

Regression Analysis of Influential Factors and Reflective Ability

Table 5.

Regression analysis results of influencing factors and cognitive ability

Model	Nonnormalized coefficient		Standardization coefficient	t	p	Collinearity diagnosis
Model	B	Standard error	β	t	p	VIF	Toqlerance
Constant	0.492	0.138	-	3.842	0.000**	-	-
School environment constraints	-0.048	0.046	-0.042	-1.128	0.324	2.317	0.459
Social and environmental constraints	0.126	0.056	0.125	2.673	0.011*	2.819	0.375
Personal constraint	0.197	0.049	0.148	4.075	0.000**	2.687	0.396
R²	0.632
Adjusted R²	0.626
F	F=112.368p=0.000
D-W value	2.114
Note: p<0.05, *p<0.01

Table 6.

Regression analysis results of influencing factors and design capability

Model	Nonnormalized coefficient		Standardization coefficient	t	p	Collinearity diagnosis
Model	B	Standard error	β	t	p	VIF	Tolerance
Constant	0.224	0.121	-	1.812	0.078	-	-
School environment constraints	0.036	0.042	0.035	0.841	0.416	2.327	0.458
Social and environmental constraints	0.179	0.043	0.191	4.423	0.000**	2.836	0.375
Personal constraint	0.129	0.046	0.128	3.105	0.002**	2.605	0.393
R²	0.652
Adjusted R²	0.644
F	F=121.458p=0.000
D-W value	1.929
Note: p<0.05, *p<0.01

Table 7.

Regression analysis results of influencing factors and evaluation ability

Model	Nonnormalized coefficient		Standardization coefficient	t	p	Collinearity diagnosis
Model	B	Standard error	β	t	p	VIF	Tolerance
Constant	0.136	0.135	-	0.997	0.334	-	-
School environment constraints	0.089	0.045	0.086	2.107	0.042*	2.203	0.482
Social and environmental constraints	0.095	0.048	0.091	1.885	0.059	2.839	0.375
Personal constraint	0.232	0.049	0.226	4.853	0.000**	2.684	0.396
R²	0.586
Adjusted R²	0.581
F	F=89.617p=0.000
D-W value	1.985
Note: p<0.05, *p<0.01

Finally, this study takes the reflective ability dimension of teachers’ digital resource integration ability as the dependent variable and examines the influence mechanism between the variables. The specific results of the regression analysis of influencing factors and reflective ability are shown in Table 8.

Table 8.

Regression analysis results of influencing factors and reflective ability

Model	Nonnormalized coefficient		Standardization coefficient	t	p	Collinearity diagnosis
Model	B	Standard error	β	t	p	VIF	Tolerance
Constant	-0.049	0.142	-	-0.364	0.735	-	-
School environment constraints	0.112	0.047	0.098	2.417	0.019*	2.316	0.458
Social and environmental constraints	0.092	0.049	0.093	2.127	0.042*	2.838	0.379
Personal constraint	0.276	0.051	0.258	5.722	0.000**	2.687	0.398
R²	0.605
Adjusted R²	0.597
F	F=99.564p=0.000
D-W value	1.949
Note: p<0.05, *p<0.01

The model validity test showed that the combination of independent variables could effectively explain the variation of planning and preparation with an explanatory power of 60.5%. The model also passed the ANOVA test without multicollinearity and autocorrelation problems, and the results were robust. Examination of the effects of the independent variables revealed that school environment constraints (β=0.098), social environment constraints (β=0.093), and personal constraints (β=0.258) all exerted a significant and positive relationship on reflective ability. Among them, personal factors of teachers have the most significant effect on reflective ability (p=0.000).

In conclusion, the strength of influence of the influencing factor variables on the four sub-dimensions of teachers’ ability to integrate digital resources is determined by the magnitude of their explanatory variance. 1)

Cognitive ability is affected by influencing factors in the following order: personal constraints>social environment constraints, with personal constraints having the greatest intensity of influence. School environment constraints do not play a significant role in influencing cognitive ability.

2)

Design ability is affected by the influencing factors in the following order: social environment constraints > personal constraints. The most influential factor is social environment constraints. School environment constraints do not play a significant role in influencing design ability.

3)

Evaluation competence is influenced by the factors affecting it in the following order: personal constraints > school environment constraints. The most influential factor is personal constraints. Social environment constraints do not play a significant role in influencing evaluation competence.

4)

Reflective ability is influenced by the factors affecting it in the following order: personal constraints > school environment constraints > social environment constraints. The most influential factor is personal constraints.

5

AI-based strategies for improving teachers’ ability to integrate digital resources

On the basis of the analysis of the influencing factors of teachers’ digital resource integration ability in the previous paper, this paper proposes the strategy design of artificial intelligence technology to help teachers’ digital resource integration ability from the three levels of social environment constraints, school environment constraints and personal constraints. It mainly starts from the following aspects. 1)

Building a solid guarantee mechanism for teachers’ digital resource integration ability

The improvement of teachers’ digital resource integration ability has entered a new stage of development based on the new generation of information technology revolution. The improvement of teachers’ digital resource integration ability in the new stage needs a more comprehensive guarantee mechanism from the external point of view.

First, strengthen the institutional guarantee. In order to adapt to the rapid development of digital technology, the state should formulate plans and implementation programs at the macro level, refine the laws and regulations related to in-service teacher education, strengthen the guarantee for the development of teachers’ digital resource integration capacity, and ensure that the policies are implemented and put into practice. Schools around the world should take into account the actual situation of their localities and expeditiously introduce implementation measures or rules that are compatible with teachers’ digital resource integration capacity, so that teachers have rules to follow when problems arise in specific operations.

Second, construct a perfect standard system for teachers’ digital teaching. Relevant departments should aim at adapting to the needs of the new stage of AI development, take into full consideration the development trend of AI platforms and products, put forward new requirements for teachers’ digital resource integration ability, and formulate relevant standards and norms to lead teachers to make efficient use of digital teaching resources in education teaching and professional development.

Third, implement financial security. Schools everywhere should strengthen top-level design, provide good organizational guidance, strengthen financial security, increase investment, and create good conditions for teachers to learn and apply digital technology.

2)

Build teachers’ digital network platform resources

In the era of Artificial Intelligence, a huge amount of knowledge and information comes into the view of teachers and students, how to extract the cocoon in the complicated data, classify them into different categories, and analyze the knowledge and information that is useful for teaching is particularly important. The construction of teachers’ digital network platforms and resources can not only help teachers quickly search and locate the knowledge they need, but also share these platforms and resources nationwide, effectively alleviating the dilemma of the lack of educational resources for teachers.

First, dig deeper into digital teaching resources and use artificial intelligence and other information technology means to create a teaching environment. It is necessary to continue to carry out the construction of digital teaching resource libraries for teachers, high-quality resource sharing courses and other projects, and to increase the relevant hardware investment, so as to ensure that teachers are provided with teaching resources that are complete in terms of subject matter. In addition, schools and local enterprises can work together to develop more specific and targeted digital network resources to effectively respond to the development status of the region, the school and the discipline, and the focus of the school-enterprise development of digital network resources is not only for group teachers, but also on the accuracy, characteristics and professionalism of the construction resource library, so as to adapt to the industrial development of the school and the region, and provide a replicable learning experience for the improvement of the integration ability of local teachers’ digital resources.

Secondly, through the construction of digital network platform resources, we can promote the sharing of educational resources and provide teachers in less economically developed areas with equivalent educational resources. The state can build a number of typical pilots of AI-assisted school education at the macro level, carry out an action plan to improve the ability of teachers to integrate digital resources, share the development experience of advanced schools, and then focus on breakthroughs and comprehensively promote them, leading to the improvement of the ability of teachers to integrate digital resources across the country. It can also establish a mechanism of twinning and co-construction between schools in educationally developed areas and schools in impoverished areas to create remote synchronized smart classrooms, making use of the advantages of information technology that are convenient, fast and efficient, and realizing the synchronized sharing of high-quality educational resources.

3)

Comprehensively implementing the training project for teachers’ digital resource integration capacity

Teacher training, as an important way to enhance the digital resource integration capacity of in-service teachers, has been given high priority by the state. The focus of the comprehensive implementation of the teacher digital training project is to provide all-round, whole-process training for all teachers based on national, provincial, municipal and school-level training, combined with online and offline training in a variety of ways.

First, digital training for teachers should cover all teachers, making full use of national training, provincial training and school-based training, so that teachers in all regions, at all levels and of all teaching ages can enjoy the opportunity to contact artificial intelligence and train in digital technology. The training should especially focus on schools in less economically developed regions and teachers of higher teaching ages, helping them to form the awareness and habit of using digital resources, and to realize that digital teaching is the inevitable trend of teaching in the future, and that digital training is the key to the future. To help them form the awareness and habit of using digital resources, and to realize that digital teaching is an inevitable trend in the future of teaching and learning, and that making full use of digital technology can bring about a whole new change in teaching and teachers’ work.

Secondly, teachers’ digital training involves a full range of training in basic knowledge, practical operation and application innovation of digital technology. Different levels of training courses can be set up according to the existing level of teachers’ ability to integrate digital resources, and training content with different focuses can be arranged, so that teachers with different bases of digital technology can enjoy the most appropriate and effective training, and teachers’ digital proficiency can be enhanced in a more targeted way by means of clear-cut and focused training. Through clear and focused training, teachers’ digitalization level will be improved, and teachers will be motivated to actively participate in the exploration of digital teaching.

Thirdly, teachers’ digital training should run through the whole process of teachers’ teaching and teachers’ growth. From the perspective of the teaching process, thematic digital technology training should be designed for teachers’ pre-course preparation, implementation during the course of the lesson, and post-course evaluation, so that digital technology can be fully integrated into the whole process of teaching practice; from the perspective of teachers’ growth, integrated systematic training should be formed throughout the professional growth of teachers, targeting at different groups of teachers, such as new teachers, teachers who are skilled in teaching and veteran teachers who have high levels of teaching experience. In terms of teacher growth, an integrated system of training should be formed for different groups of teachers, such as new teachers, skilled teachers and older teachers, throughout their professional growth, so as to ensure that the different needs of teachers of all ages for training in the ability to integrate digital resources are met.

4)

Motivating Teachers to Practice Digital Resource Integration

Under the guarantee of sufficient external environment, teachers’ own knowledge, understanding and practice of digital teaching is the internal motivation for teachers to improve their digital resource integration ability. On the one hand, teachers should change their previous understanding of digital technology, fully recognize the changes that may occur in digital teaching in the era of artificial intelligence, set up the awareness of continuous learning and updating concepts, make good use of artificial intelligence platforms and products in teaching, and make intelligent products play a positive role in teaching. On the other hand, in the face of the changes of learning mode, learning scene, learning ecology and learning evaluation in the age of artificial intelligence, we should be diligent in thinking and rich in innovation, fully integrate intelligent products and digital technology into the process of teaching design, teaching implementation, teaching resource integration and teaching reflection, adopt digital teaching means, constantly innovate teaching mode and boldly try various forms of online teaching strategies, and promote the comprehensive enhancement of the ability to integrate digital resources.

In the era of artificial intelligence, every participant in the field of education plays an important role, and teachers, as the leading force in the implementation of the fundamental task of establishing moral character, should always keep learning, scientifically recognize the role of “technology” as a weapon in education and teaching, and understand that improving the ability to integrate digital resources is to make technology better serve the teaching of education. Teaching and learning. Teachers should constantly try to reform and innovate, comprehensively improve their teaching design ability, teaching implementation ability, teaching resource integration ability and teaching reflection ability, and with the background of relevant national policies and strong support from schools and enterprises, they should welcome the newer artificial intelligence era with positive digital awareness and solid digital resource integration ability.

6

Conclusion

This paper explores the influencing factors of teachers’ digital resource integration ability and their functioning mechanisms by comprehensively applying principal component analysis, Pearson correlation and multiple regression analysis model, so as to put forward the strategy of artificial intelligence to help teachers’ digital resource integration ability to improve.

Based on the principal component analysis for the analysis of influencing factors of teachers’ digital resource integration ability, three principal component factors were extracted, and the cumulative explainable variance reached 74.672%, indicating that the extracted three factors could better reflect the main information of the influencing factor variables, which were named: social environment constraints, school environment constraints, and personal constraints, respectively.

Pearson’s correlation analysis showed that the four dimensions of digital resource integration ability showed positive correlation (p<0.01) with the three influencing factor variables. Among them, the variables with stronger correlations were the personal constraints and reflective competence dimensions, with a correlation coefficient of 0.764. Next, the following conclusions were drawn from the multiple regression analysis of the variables: 1)

Teachers’ cognitive ability and design ability are both significantly influenced by personal constraints and social environment constraints, while their school environment constraints do not play a significant role.

2)

Evaluation competence is significantly influenced by personal constraints and school environment constraints, while the role of social environment constraints in influencing evaluation competence is not significant.

3)

The three factors of personal constraints, school environment constraints, and social environment constraints significantly affect teachers’ reflective ability in the order of their influence from the largest to the smallest.

On this basis, this paper realizes the strategy design of artificial intelligence to help teachers’ digital resource integration ability at the same time from the aspects of building a firm guarantee mechanism for teachers’ digital resource integration ability, constructing teachers’ digital network platform resources, comprehensively implementing teachers’ digital technology training project, and stimulating teachers’ motivation for digital resource integration practice.

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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Research on Strategies for Enhancing Teachers’ Digital Resource Integration Capabilities with Artificial Intelligence Technology

Yanping Zhao

Published Online: Sep 26, 2025

Received: Dec 26, 2024

Accepted: Apr 17, 2025

DOI: https://doi.org/10.2478/amns-2025-1054

KeywordsPrincipal component analysis algorithm, Multiple regression analysis model, Digital resource integration competence, Artificial intelligence

© 2025 Yanping Zhao, published by Sciendo

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

Keywords
Principal component analysis algorithm, Multiple regression analysis model, Digital resource integration competence, Artificial intelligence