The Construction of Training and Assessment System of Informatization Teaching Ability of Language Teachers in Colleges and Universities

The theoretical framework for the integration of information technology and teaching focuses on the integration of modern information technology into the teaching and learning process in order to create a more efficient, interactive and personalized learning environment. Based on the principles of cognitive science and educational technology, this framework emphasizes the application of information technology to optimize the presentation of teaching content, enhance teacher-student interaction, promote collaborative learning, and support students’ independent learning [1-3]. Under this framework, teachers are not only the transmitters of knowledge, but also the guides and coordinators of the learning process, while students are encouraged to actively explore and participate in deeper learning using a variety of digital tools and resources [4-6]. This integration aims to fully utilize the advantages of information technology to improve the quality of teaching and learning effectiveness, while providing students with richer and more diverse learning experiences [7-8].

At present, the information literacy of secondary school language teachers still has certain problems, and the era needs language teachers to have the awareness of the integration of information technology and language curriculum, which needs to be from the administrative point of view, the school point of view and the teachers’ own point of view, to improve the information literacy of language teachers [9-10]. In order to actively promote the development of “Internet+Education” and promote the modernization of education with education informatization, how to improve the information literacy of language teachers is the most urgent issue at present [11-12].

In language teaching in colleges and universities, the application of informatization teaching is mainly reflected in the use of various information technologies and digital tools to enrich the teaching content, enhance the interactivity of teaching and improve the learning effect [13]. This includes the use of multimedia and Internet resources to supplement traditional literature and composition teaching, the use of online platforms for course management and interactive discussions, and the use of digital resources such as e-books and online libraries to increase the accessibility and diversity of teaching [14-15]. Informatized teaching also supports personalized and differentiated learning, enabling students to choose suitable learning paths and materials according to their needs and interests, and providing teachers with more flexible and innovative teaching methods, such as flipped classroom and project-based learning, thus effectively improving the quality of language teaching and students’ interest in learning [16-17].

The composition of teachers’ informatized teaching competence includes a number of key aspects, the first of which is the proficiency in the mastery and application of information technology, including the use of various types of educational software, platforms and digital tools. The second is the ability to design and implement information technology teaching activities, effectively integrating information technology and teaching content to create an interactive and productive learning environment. Third is the ability to guide and tutor students in information technology and help them make full use of these technologies for learning. It also includes the ability to assess and provide feedback on the effectiveness of information technology teaching, as well as to continuously learn and update relevant knowledge and skills to adapt to the rapid development of educational technology [18-19]. Together, these abilities constitute the comprehensive quality and competence of teachers in the informatized teaching environment [20].

In the current higher education environment, although information technology teaching has become an important trend, language teachers in higher education face several challenges and problems in this regard [21]. Many college language teachers have significant deficiencies in the use of information technology, which is mainly reflected in their lack of proficiency in the use of emerging teaching techniques and tools. In addition there are relatively few digital teaching and learning resources available for language education in higher education compared to science or business courses [22]. This leads to teachers’ difficulties in finding suitable and high-quality digital language teaching and learning materials, as well as a lack of awareness among some tertiary language teachers of the importance of information technology in teaching and learning, especially in understanding the role of information technology in enhancing the quality of teaching and students’ learning experience. This lack of awareness leads to the fact that they tend to neglect the application of information technology in the teaching process and still follow the traditional teaching methods [23-24]. The lack of attention and in-depth understanding of information technology teaching limits their innovation and improvement in teaching design, and also affects students’ ability to adapt to new technologies and their competitiveness in the future job market [25].

Education in the information age is gradually popularized, and information technology has brought subversive changes to education, so the heat of research on information education is high. Among them, there are studies related to the study of teachers’ information literacy. Literature [26] explains the concept and meaning of teachers’ information literacy and reviews the relevant studies that study the level and training of teachers’ information literacy, dialectically discovering the inadequacies pointed out by these relevant studies, and providing a certain amount of new theories for the academic field of the improvement of educators’ information literacy. Literature [27] based on bibliometrics, keyword co-occurrence method, social network analysis and visualization knowledge theory, in-depth analysis of the relevant research and practice of teacher information literacy, enriching the arguments and results of the academic field of teacher information literacy. There are many factors influencing teachers’ information literacy, the level of regional economic development, information literacy development, informationalized teaching behaviors and personal experiences. Literature [28] conducted a large-scale online assessment of teachers’ information literacy practices and pointed out that Chinese teachers perform well in awareness, security and ethics, but are weak in information literacy, especially in western provinces and non-urban areas, and need to be balanced in the development of information literacy. Literature [29] used digital questionnaires to collect data and information related to the study of teachers’ information literacy, and used SPSS 26.0 software for testing, based on which it was found that teachers’ information literacy was related to personal experiences, such as overseas employment, relevant training, and the level of informationization of employment positions. Literature [30] proposed nine three-level indicators around richness, diversity, usefulness, and timeliness, and analyzed the performance group differences by combining propensity score matching (PSM) and difference test, and finally used supervised learning model to make predictions, pointing out that information teaching and related behaviors are the most predictive of high and low levels of teachers’ information literacy.

Teachers’ information literacy has a positive significance for the improvement of teaching effectiveness and teaching innovation reform based on information technology, and literature [31] based on empirical investigation and analysis learned that teachers’ information literacy is positively correlated with innovative teaching behaviors, in which the innovation atmosphere of colleges and universities positively regulates the positive correlation, which provides some effective suggestions for the construction of information technology in colleges and universities and the improvement of teachers’ information literacy. How to improve teachers’ information literacy and promote the construction of information technology education is a hot topic for future exploration. Literature [32] aims to improve the information literacy planning ability, information security awareness and cooperation spirit of international Chinese language teachers, and gives specific suggestions and strategies for international Chinese language teachers’ information literacy improvement, which makes a positive contribution to the balanced development of teachers’ information literacy.

This paper constructs an evaluation index system of informatization teaching ability of college language teachers containing 5 first-level indicators and 13 second-level indicators. Principal component analysis and Logistic regression model were applied to explore the level of informatization teaching ability of college language teachers after training and the main factors affecting teachers’ informatization teaching ability, respectively. To improve the robustness of the Logistic regression model, the weighted penalized log-likelihood function was combined with the Logistic regression model. One hundred language teachers were selected from five universities to verify the effectiveness of the application of principal component analysis and Logistic regression model in the task of establishing a scientific training and assessment system for teachers’ informatization teaching ability.

2

Construction of a training and assessment system for teachers’ informatization teaching ability

2.1

Evaluation index system of teachers’ informatization teaching ability

Traditional teaching requires basic knowledge and skills that language teachers in colleges and universities should master. Teachers’ informatization teaching ability should be built on top of the traditional mastery of basic professional knowledge, and the integration and innovation ability of teachers’ technology and curriculum should be based on the cornerstone of mastering the basic knowledge and skills of professional and information technology. The evaluation index system of informatization teaching ability of college language teachers, which was modified and determined after the consultation of experts, contains 5 first-level indexes and 13 second-level indexes. The evaluation system is specifically shown in Table 1.

Table 1.

Teacher’s information teaching ability evaluation index system

Primary indicator	Secondary indicator
Basic ability of informationization teaching X1	Information teaching consciousness and attitude Y1
Basic ability of informationization teaching X1	Basic knowledge and skills of informationization teaching Y2
Information teaching design ability X2	Ability to acquire and integrate teaching resources Y3
	Information teaching analysis ability Y4
	Information teaching design ability Y5
Information teaching implementation ability X3	Information teaching and monitoring ability Y6
	Information teaching application ability Y7
	Information teaching evaluation and reflection ability Y8
Information professional teaching skills X4	Improve professional knowledge and skills Y9
Information professional teaching skills X4	Professional skills Y10
Information innovation and development ability X5	Informationization teaching research Y11
	Professional development Y12
	Cooperation and communication Y13

2.2

Evaluation methods for teachers’ informatization lesson competence training

2.2.1

Principal Component Analysis

Principal Component Analysis [33] utilizes the idea of dimensionality reduction, the analysis process is to transform multiple variables into a smaller number of integrated variables (principal components), the transformed principal components are not related to each other, are the form of linear combinations of the original variables, so through the form of linear combinations can show a large amount of information, and this information will not be repeated between the variables used to describe the object of study assumptions are Z₁, Z₂…, Z_p a total of P variables, Z₁, Z₂…, Z_p is a P-dimensional random variable, Z is composed of the assumed P variables. Z is assumed to be a random vector, Σ is the covariance matrix of Z, and μ is the mean of Z. A linear combination of the original variables is considered and the random vector Z is treated as linearly varying: (1) ${\begin{matrix} F_{1} = μ_{11} Z_{1} + μ_{12} Z_{2} + \dots + μ_{1 p} Z_{p} \\ F_{2} = μ_{21} Z_{1} + μ_{22} Z_{2} + \dots + μ_{2 p} Z_{p} \\ \dots \dots \\ F_{m} = μ_{m 1} Z_{1} + μ_{m 2} Z_{2} + \dots + μ_{m p} Z_{p} \end{matrix}$ $$\left\{ {\begin{array}{*{20}{c}} {{F_1} = {\mu_{11}}{Z_1} + {\mu_{12}}{Z_2} + \ldots + {\mu_{1p}}{Z_p}} \\ {{F_2} = {\mu_{21}}{Z_1} + {\mu_{22}}{Z_2} + \ldots + {\mu_{2p}}{Z_p}} \\ { \cdots \cdots } \\ {{F_m} = {\mu_{m1}}{Z_1} + {\mu_{m2}}{Z_2} + \ldots + {\mu_{mp}}{Z_p}} \end{array}} \right.$$

Linear combination F₁, F₂, …, F_m(m ≤ p) is uncorrelated with the principal component, and the largest variance in linear combination Z₁, Z₂, …, Z_p is F₁, in the linear combination uncorrelated with F₁, F₂ is the largest variance, and in the linear combination uncorrelated with F₁, F₂, …, F_m; F_m is the largest variance.

The impact of the use of principal component analysis is positive, the advantage of this method is that it can improve the problem of teachers’ informatization teaching training status not being able to be truly reflected due to the single evaluation index in colleges and universities, enriching the original college and university language teachers’ informatization teaching training indexes, and further organizing the enriched indexes so that they are grouped into a small number of principal components to be analyzed, which will make the teacher informatization teaching training status more precise and scientific, and also simplify the originally complex process. This way can not only make the teacher informatization teaching training situation more accurate and scientific, but also simplify the original complex process.

When using the principal component analysis method for analysis, the analysis process generally has the following six steps: 1)

pre-processing of the original variables, in the process of statistical analysis, because the original data itself in the nature of certain differences will affect the statistical analysis, the purpose of pre-processing is to eliminate the impact: standardization of the original variable data, after processing the original variable data in the number of levels and the differences in the quantitative outline of the data will be eliminated, and then get the analytical convenience of the Standardized matrix.

2)

R is the covariance matrix, established on the basis of standardized data matrix, covariance matrix R, the larger the value, it indicates that the data for principal component analysis of the necessity of the stronger, the covariance matrix can show the standardization of data after the statistics of the data correlation between the closeness of the relationship. In the covariance matrix, R_ij = (i, j = 1, 2, …, p) represents the correlation coefficient between the original variables Z_i and Z_j. Since R is a real symmetric matrix (i.e., R_ij = R_ji), only the elements of the lower or upper triangles are used in the calculation of R. The corresponding formula is shown in (2): (2) $R_{i j} = \frac{\sum_{k - 1}^{n} (Z_{k j} - Z_{t}) (Z_{k j} - Z_{j})}{\sqrt{\sum_{k - 1}^{n} {(Z_{k i} - Z_{i})}^{2} {(Z_{k i} - Z_{j})}^{2}}}$ $${R_{ij}} = \frac{{\sum\limits_{k - 1}^n {({Z_{kj}} - {Z_t})} ({Z_{kj}} - {Z_j})}}{{\sqrt {\sum\limits_{k - 1}^n {{{({Z_{ki}} - {Z_i})}^2}} {{({Z_{ki}} - {Z_j})}^2}} }}$$

3)

To carry out the determination of the number of principal components, it is necessary to calculate the covariance matrix to find its cumulative variance contribution, principal component contribution, and eigenvalues. The eigenequation |λE − R| = 0 is solved and the eigenvalues are solved as λ_i(i = 1, 2, …, P). The eigenvalues of the covariance matrix R are ranked in descending order λ₁ ≥ λ₂ ≥ … ≥ λ_i ≥ 0, and the eigenvalues solved for R are all positive because of the nature of the R matrix as a positive definite matrix. The variance contribution of each principal component is the eigenvalue of R. The magnitude of the influence of the principal components is related to the magnitude of the eigenvalues. The variance contribution of principal component Fi is $W_{t} = λ_{j} / \sum_{j = 1}^{p} λ_{j}$ $${W_t} = {\lambda_j}/\sum\limits_{j = 1}^p {{\lambda_j}}$$ and the cumulative variance contribution is $\sum_{j = 1}^{n} λ_{j} / \sum_{j = 1}^{p} λ_{j}$ $$\sum\limits_{j = 1}^n {{\lambda_j}} /\sum\limits_{j = 1}^p {{\lambda_j}}$$.

4)

And then on the selection of principal components, to follow the principle that in the eigenvalue is greater than 1 on the basis of its cumulative contribution rate to reach 80%, the eigenvalue of λ₁, λ₂, …, λ_n in the corresponding 1, 2, …, m(m ≤ p), the number of the final selection of the principal components is m (m is an integer).

5)

The score of the principal components is calculated, and the initial factor loading matrix is constructed to interpret the principal components. The correlation coefficient R_ij = (F_i, Z_j) between the principal component F_i and the original indicator Z_j is the factor loading, and the use of factor loading can better explain the economic significance contained in the principal component, indicating the degree of correlation between each variable and the principal component. Assuming that U_ij is the eigenvector corresponding to each eigenvalue, there are: (3) $U_{i j} = R_{i j} / \sqrt{λ_{i}}$ $${U_{ij}} = {R_{ij}}/\sqrt {{\lambda_i}}$$

An expression for each principal component F_i score can then be obtained: (4) ${\begin{matrix} \begin{matrix} F_{1} = U_{11} Z_{1} + U_{12} Z_{2} + \dots + U_{1 p} Z_{p} \\ F_{2} = U_{21} Z_{1} + U_{22} Z_{2} + \dots + U_{2 p} Z_{p} \\ \dots \dots \\ F_{m} = U_{m 1} Z_{1} + U_{m 2} Z_{2} + \dots + U_{m p} Z_{p} \end{matrix} & (i = 1, 2, ..., m; j = 1, 2, ..., p) \end{matrix}$ $$\left\{ {\begin{array}{*{20}{c}} {\begin{array}{*{20}{c}} {{F_1} = {U_{11}}{Z_1} + {U_{12}}{Z_2} + \ldots + {U_{1p}}{Z_p}} \\ {{F_2} = {U_{21}}{Z_1} + {U_{22}}{Z_2} + \ldots + {U_{2p}}{Z_p}} \\ { \cdots \cdots } \\ {{F_m} = {U_{m1}}{Z_1} + {U_{m2}}{Z_2} + \ldots + {U_{mp}}{Z_p}} \end{array}}&{ (i = 1,2,...,m;{\text{ }}j = 1,2,...,p)} \end{array}} \right.$$

6)

ZScore_n is a corporate composite scoring function, which is applied to calculate the composite values of listed companies, and the resulting results are ranked in descending order. When calculating the principal component composite model, the proportion of the eigenvalues of each principal component in the sum of eigenvalues of the extracted principal components is taken as the weights. The resulting model is shown in equation (5): (5) $Z S c o r e_{n} = M_{1} F_{1} + M_{2} F_{2} + ... + M_{1} F_{1}$ $$ZScor{e_n} = {M_1}{F_1} + {M_2}{F_2} + ... + {M_1}{F_1}$$

(i = 1, 2, …, m; n is the number of samples, M is the principal component, and F is the proportion of the corresponding eigenvalue to the sum of the total eigenvalues of the principal component).

2.2.2

Logistic regression model

Logistic regression [34] classifier is a classical classification method in machine learning, Logistic regression model belongs to log-linear model. The most common Logistic regression model is the binomial Logistic regression classification model, the conditional probability distribution of classification is shown below: (6) $P (Y = 1 | X) = \frac{\exp (X^{T} β)}{1 + \exp (X^{T} β)}$ $$P(Y = 1|X) = \frac{{\exp ({X^T}\beta )}}{{1 + \exp ({X^T}\beta )}}$$ (7) $P (Y = 0 | X) = 1 - P (Y = 1 | X) = \frac{1}{1 + \exp (X^{T} β)}$ $$P(Y = 0|X) = 1 - P(Y = 1|X) = \frac{1}{{1 + \exp ({X^T}\beta )}}$$

Where, $X \in ℝ^{n}$ $$X \in {\mathbb{R}^n}$$ is the input variable, Y ∈ {0, 1} is the response variable i.e. dichotomous outcome and $β \in ℝ^{p}$ $$\beta \in {\mathbb{R}^p}$$ is the model parameters. Given the input sample X, the values of P(Y = 1 ∣ X) and P(Y = 0 ∣ X) can be calculated. By comparing the magnitude of the two conditional probability values, the binomial logistic regression classification model classifies the input observation X into the category with the larger probability value and gives the corresponding response variable labeled “0” or “1”.

Logistic regression classification models have another feature. Let’s start with the definition: the ratio of the probability of an event occurring to the probability of it not occurring is called the odds of that event. If the probability of an event occurring is pro, then the odds of that event is $\frac{p r o}{1 - p r o}$ $$\frac{{pro}}{{1 - pro}}$$. The log odds, or logit function, is notated as: (8) $l o g i t (p r o) = \log \frac{p r o}{1 - p r o}$ $$logit(pro) = \log \frac{{pro}}{{1 - pro}}$$

For the Logistic regression model, the logit function for positively categorized events is calculated as: (9) $\log i t (P (Y = 1 | X)) = \log \frac{P (Y = 1 | X)}{1 - P (Y = 1 | X)} = X^{T} β$ $$\log it(P(Y = 1|X)) = \log \frac{{P(Y = 1|X)}}{{1 - P(Y = 1|X)}} = {X^T}\beta$$

It can be seen that in the Logistic regression model, the log odds of response variable Y = 1 is a model represented by a linear function of input variable X, so the Logistic regression model is also one of the generalized linear models. And it is easy to know that the larger the value of linear function X^Tβ, the closer the probability value of classification Y = 1 is to 1; conversely, the smaller the value of linear function X^Tβ, the closer the probability value is to 0.

The estimation of parameter β in a logistic regression model is examined. For a given sample set of observations D = {(X₁, Y₁), (X₂, Y₂), …, (X_n, Y_n)}, where $X_{i} \in ℝ^{n}, Y_{i} \in {0, 1}, i = 1, 2, 3, \dots, n$ $${X_i} \in {\mathbb{R}^n},{Y_i} \in \{ 0,1\} ,i = 1,2,3, \ldots ,n$$. Is set: (10) $π (X^{T} β) = P (Y = 1 | X)$ $$\pi ({X^T}\beta ) = P(Y = 1|X)$$

Then: (11) $1 - π (X^{T} β) = 1 - P (Y = 1 | X) = P (Y = 0 | X)$ $$1 - \pi ({X^T}\beta ) = 1 - P(Y = 1|X) = P(Y = 0|X)$$

From the principle of great likelihood estimation, the likelihood function is given as: (12) $\sum_{i = 1}^{n} {[π (X_{i}^{T} β) r]}^{Y_{i}} {[1 - π (X_{i}^{T} β) r]}^{1 - Y_{i}}$ $$\sum\limits_{i = 1}^n {{{[\pi (X_i^T\beta )r]}^{{Y_i}}}{{[1 - \pi (X_i^T\beta )r]}^{1 - {Y_i}}}}$$

The log-likelihood function [35] is: (13) $\begin{array}{l} L (β) = \sum_{i = 1}^{n} [Y_{i} \log π (X_{i}^{T} β) + (1 - Y_{i}) \log (1 - π (X_{i}^{T} β))] \\ = \sum_{i = 1}^{n} [Y_{i} \log \frac{π (X_{i}^{T} β)}{1 - π (X_{i}^{T} β)} + \log (1 - π (X_{i}^{T} β))] \end{array}$ $$\begin{array}{l} L(\beta ) = \sum\limits_{i = 1}^n {[{Y_i}\log \pi (X_i^T\beta ) + (1 - {Y_i})\log (1 - \pi (X_i^T\beta ))]} \\ = \sum\limits_{i = 1}^n {[{Y_i}\log \frac{{\pi (X_i^T\beta )}}{{1 - \pi (X_i^T\beta )}} + \log (1 - \pi (X_i^T\beta ))]} \\ \end{array}$$

To wit: (14) $L (\begin{matrix} β \end{matrix}) = \sum_{i = 1}^{n} [Y_{i} (X_{i}^{T} β) - \log (1 + \exp (X_{i}^{T} β))]$ $$L(\begin{array}{*{20}{c}} \beta \end{array}) = \sum\limits_{i = 1}^n {[{Y_i}(X_i^T\beta ) - \log (1 + \exp (X_i^T\beta ))]}$$

At this point, the maximum value point of L(β), you can get the great likelihood estimate of the regression parameter β.

It can be seen that the problem of solving the parameters of the Logistic regression model becomes an optimization problem with the log-likelihood function as the objective function. The method of solving the problem generally uses the proposed Newton method or gradient descent method. If the great likelihood estimate of β is remembered to be solved as $\hat{β}$ $$\hat \beta$$, the estimation results of the Logistic regression model are as follows: (15) $P (Y = 1 | X) = \frac{\exp (X^{T} \hat{β})}{1 + \exp (X^{T} \hat{β})}$ $$P(Y = 1|X) = \frac{{\exp ({X^T}\hat \beta )}}{{1 + \exp ({X^T}\hat \beta )}}$$ (16) $P (Y = 0 | X) = \frac{1}{1 + \exp (X^{T} \hat{β})}$ $$P(Y = 0|X) = \frac{1}{{1 + \exp ({X^T}\hat \beta )}}$$

For the Logistic regression model, variable selection is achieved while estimating the regression parameters by adding a penalty term to its log-likelihood function. The solution of this penalized regression model is based on the great likelihood estimation method, which is usually very sensitive to outliers and weakly robust in the solution process. In order to improve the robust variable selection of the Logistic penalized regression model, a weighted penalized logarithmic [36] likelihood function is therefore proposed.

3

Results of the evaluation of teachers’ training in information technology teaching skills

Since all the respondents of this survey were language teachers from five randomly selected colleges and universities in a certain region that had conducted training on teachers’ IT teaching ability, the test questions were designed in such a way that the questions were in line with the cognitive level of the language teachers in the colleges and universities. The indicator evaluation matrix was obtained by testing 100 language teachers from the five schools. By applying IBM SOSS software, the teachers’ indicators were analyzed and the total correlation coefficient matrix was obtained as in Table 2. The highest correlation coefficient value of 0.785 was found for X1 and X2, which indicated the highest correlation between teachers’ basic competence in informatized teaching and their instructional design ability.

Table 2.

Correlation matrix

		X1	X2	X3	X4	X5
Correlation	X1	1.000	0.785	0.665	0.521	0.423
	X2	0.785	1.000	0.655	0.562	0.474
	X3	0.665	0.655	1.000	0.613	0.478
	X4	00521	0.562	0.613	1.000	0.587
	X5	0.423	0.474	0.478	0.587	1.000
Sig.	X1		0.000	0.000	0.000	0.000
	X2	0.000		0.000	0.000	0.000
	X3	0.000	0.000		0.000	0.000
	X4	0.000	0.000	0.000		0.000
	X5	0.000	0.000	0.000	0.000

The total variance explained is shown in Table 3.

Table 3.

Principal component 1 has the highest cumulative contribution of 56.458%.

	Initial eigenvalue			Extract the sum of squares and load			Rotate the squares and load
	Tot	VAR/%	CUM/%	Tot	VAR/%	CUM/%	Tot	VAR/%	CUM/%
1	3.589	56.458	56.458	3.589	56.458	56.458	2.089	30.411	30.411
2	0.859	14.568	71.026	0.859	14.568	71.026	1.359	21.598	52.099
3	0.785	12.569	83.595	0.785	12.569	83.595	1.082	19.457	71.466
4	0.523	9.584	93.179	0.523	9.584	93.179	1.021	15.541	87.007
5	0.389	6.821	100.000	0.389	6.821	100.000	1.019	12.993	100.000

The loadings of each of the five principal components are shown in Table 4.

Table 4.

Various load component matrix

	Component
	1	2	3	4	5
X1	0.825	-0.235	-0.356	-0.095	0.189
X2	0.851	-0.175	-0.275	-0.205	0.075
X3	0.809	-0.295	-0.095	0.159	-0.178
X4	0.775	-0.228	0.339	0.251	-0.288
X5	0.701	-0.002	0.609	-0.156	0.355

The contribution rate of each principal component is taken as the weight, that is, there is K=(0.56458,0.14568,0.12569,). Combining the values of each principal component of the teacher, the comprehensive evaluation score of each teacher can be obtained In this paper, the normal distribution test is performed, and the histogram of the comprehensive evaluation of teachers as well as the P-P chart are obtained. The evaluation histograms and P-P plots are shown in Figures 1 and 2, respectively.

Among these teachers to be examined, all of them have a certain degree of informatization teaching ability, and for basic informatization teaching, they can have basic teaching ideas and frameworks, and can use some informatization language to describe some problems in teaching. However, the proficiency in complex informatization teaching tools is not high enough, which may be mainly due to the fact that the teachers have not made full use of the training courses on information technology, and their understanding of informatization teaching is at a basic stage. It is not good enough for teaching innovation. For teachers, there is no systematic mastery of informatization teaching methods, so that they have not performed well enough in terms of innovation. This may be related to the fact that teachers often use traditional teaching methods, or it may be related to the environment in which they live.

It can also be seen that the difference of each teacher’s informatization teaching ability is not very obvious either. Teachers are still relatively rigorous in their thinking about informationalized teaching. In summary, it can be seen that the 100 teachers surveyed are basically the same in terms of cognitive ability and informatization teaching ability. The innovation ability still needs to be further strengthened. At the same time, special attention should be paid to improving the comprehensive ability of teachers’ informatization teaching.

4

Analysis of the factors influencing the dimensions of teachers’ informatization teaching ability

Through principal component analysis, the principal component factors of each dimension of informatization teaching ability of college teachers can be extracted. Given that the informatization teaching ability of college teachers is measured by a five-level scale, and the data results are ordered categorical variables, even after principal component analysis, the data still satisfy the state of discrete distribution, with a progressive relationship from low to high scores, so this paper adopts ordered logistics regression to conduct measurement analysis of the dimensions of the Influence factors for econometric analysis, the results are shown in Table 5. The results are shown in Table 5. *,**,*** denote 10%, 5% and 1% significance, respectively.

Table 5.

The orderly regression of each dimension

	Variable	Model 1	Model 2	Model 3	Model 4	Model 5
Personal characteristics	Gender	-0.185	-0.125**	-0.043*	-1.685**	-0.599*
	Gender	0.383	0.514	0.218	0.094	0.185
	Age	-0.156**	-2.37***	-0.092*	-1.105**	-0.945**
	Age	0.058	0.125	0.165	0.225	0.334
	Educational background	0.045	0.998	0.187	0.013	0.016*
	Educational background	0.168	0.248	0.105	0.025	0.335
	Educational title	0.075	0.064	0.145	0.108	0.278
	Educational title	0.356	0.475	0.215	0.224	0.065
	Tenure	-0.225***	-1.82***	-0.008*	-0.203	-0.047**
	Tenure	0.326	0.175	0.195	0.074	0.277
	Teaching discipline	0.123	0.008	0.638	0.598	0.021
	Teaching discipline	0.298	0.158	0.274	0.111	0.495
	Business experience	0.065	0.854	0.598	0.052*	0.078
	Business experience	0.337	0.095	0.115	0.274	0.415
	Teaching competition experience	0.098*	1.063	0.984**	0.734	0.071***
	Teaching competition experience	0.035	0.058	0.074	0.015	0.135
	Whether or not a teacher	0.194*	0.105	0.085	0.413**	0.674*
	Whether or not a teacher	0.115	0.927	0.074	0.077	0.164
Training experience	Training	0.028*	0.001	0.235	0.134	0.095
	Training	0.077	0.098	0.134	0.115	0.224
	On-the-job training	2.335**	0.065*	1.529**	0.923*	1.016*
	On-the-job training	0.012	0.034	0.023	0.017	0.131
Institutional characteristics	Hardware and hardware facilities	0.154**	0.075	0.182	0.092	0.077
	Hardware and hardware facilities	0.097	0.143	0.075	0.062	0.148
	Policy system	0.515*	0.005	0.575**	0.854*	1.725**
	Policy system	0.133	0.078	0.064	0.086	0.145
	Information teaching atmosphere	0.228***	0.042	0.954**	0.775*	0.028*
	Information teaching atmosphere	0.041	0.153	0.018	0.035	0.075
Psychological tendency	Personal interest	0.995***	2.835***	0.526*	0.184*	0.056*
	Personal interest	0.054	0.175	0.027	0.053	0.099
	Self-efficacy	0.754**	1.856*	1.185**	0.814**	0.075**
	Self-efficacy	0.016	0.125	0.045	0.038	0.105
	Self-need	0.234**	1.012	1.056*	0.998**	0.118*
	Self-need	0.156	0.095	0.072	0.036	0.0114
	Achievement motive	0.024*	0.998	0.516	0.845	0.037
	Achievement motive	0.106	0.026	0.038	0.059	0.157
	Compatibility	-0.335	-0.103	-0.054	-0.195	-0.043
	Compatibility	0.084	0.065	0.043	0.024	0.091
Constant term	—	0.599**	1.825*	0.685*	0.634*	0.114
Constant term	—	1.053	0.902	0.061	0.035	1.023
Pseudo R²	—	0.625	0.445	0.385	0.354	0.346
Logarithmic likelihood	—	-553.681	-585.365	-345.51	-393.215	-418.215

4.1

Major Influences on Teachers’ Awareness of IT Application

The explanatory variable of model (1) is the awareness of information technology application among college teachers. According to the results of logistics regression, it can be seen that ∶

First, personal background level. Age, years of service and other variables are negatively correlated, information technology competition experience, “dual-teacher” teachers are positively correlated, three types of college teachers have a higher awareness of information technology application: young teachers, teachers with experience in information technology teaching competition, “dual-teacher” teachers.

Second, the external environment. The three main factors of the institutional environment (hardware and software facilities, policies and systems, and information technology teaching atmosphere) have a significant impact on college teachers’ awareness of information technology application, and in addition, induction and on-the-job information technology teaching training also have a significant positive impact.

Third, internal psychological level. In addition to the compatibility psychology, other psychological tendencies related to information technology teaching such as, personal interest, self-efficacy, self-needs, and achievement motivation will have a significant positive effect on college teachers’ awareness of information technology application. The highest regression coefficient (β=0.995) was found for personal interest, followed by self-efficacy (β=0.754), and these two psychological dimensions dominated college teachers’ awareness of IT application.

4.2

Major Influencing Factors on Teachers’ Competence in IT Application

The explanatory variable of model (2) is the information technology application ability of college teachers. According to the results of logistics regression, it can be seen that:

First, personal background level. Gender, age, years of service and other variables are negatively correlated, which can be obtained that two types of college teachers have a higher level of IT application ability: male teachers and young teachers.

Second, external environment level. Institutional environmental factors are not statistically analyzed to have a significant effect on individual college teachers’ information technology application ability, but in-service information technology teaching training will have a significant positive effect.

Third, internal psychological level. Higher education teachers’ personal interest and self-efficacy in information technology teaching are significantly positively correlated with their information technology application ability. Among them, personal interest has the highest regression coefficient (β=2.835) and plays a major role in psychological factors.

4.3

Main Influencing Factors of Teachers’ Informatization Education and Teaching Ability

The explanatory variable of model (3) is the informatization education teaching ability of college teachers. According to the results of logistics regression, it can be seen that:

First, personal background level. Gender, age, years of service and other variables are negatively correlated, and the experience of informatization competition is positively correlated, which can be obtained that three types of college teachers have higher informatization education and teaching ability: male teachers, young teachers, and teachers with experience of informatization teaching competition.

Second, the external environment level. Institutional environmental factors have a significant impact on individual college teachers’ informatization education and teaching from the aspects of policy system and informatization teaching atmosphere. In addition in-service informatization teaching training will have a significant positive effect.

Third, internal psychological level. The personal interest, self-efficacy, and self-needs of college teachers for informatization teaching are significantly positively correlated with their informatization education and teaching ability. Among them, self-efficacy has the highest regression coefficient (β=1.185) and plays a major role in psychological factors.

4.4

Main Influencing Factors of Teachers’ Informational Vocational Practical Training Ability

The explanatory variable of model (4) is the information-based vocational training ability of college teachers. According to the logistics regression results, it can be seen:

First, personal background level. The variables such as gender, age and years of service are negatively correlated, while the enterprise work experience and “double-qualified” teachers are positively correlated. It can be found that four types of college teachers have higher information-based vocational practical training ability: male teachers, young teachers, teachers with enterprise work experience, and “double-qualified” teachers.

Second, the external environment level. Institutional environmental factors have a significant impact on the individual informatization vocational training ability of college teachers in terms of policy system and informatization teaching atmosphere, in addition to on-the-job informatization teaching training will have a positive impact.

Third, internal psychological level. College teachers’ personal interest in informatization teaching, self-efficacy, and self-needs are significantly positively correlated with their informatization education and teaching ability. Among them, the regression coefficient of self-need (β=0.998) is the highest, playing a major role in psychological factors.

4.5

Main Influencing Factors of Teachers’ Informational Research and Innovation Ability

The explanatory variable of model (5) is the research and innovation ability of informationization of university teachers. According to the results of logistics regression can be seen:

First, personal background. Gender, age, years of service and other variables are negatively correlated, while academic background, experience in information-based competition and “double-qualified” teachers are positively correlated. Five types of college teachers can be found to have higher information-based research and innovation ability: male teachers, teachers with advanced education, young teachers, teachers with experience in information-based teaching competition, and “double-qualified teachers”.

Second, the external environment level. Institutional environmental factors have a significant impact on the individual informatization research and innovation ability of college teachers in terms of policy system and informatization teaching atmosphere, in addition to in-service informatization teaching training will have a positive impact.

Third, internal psychological level. College teachers’ personal interest in informatization teaching, self-efficacy, and self-needs are significantly positively correlated with their informatization research and innovation ability. Among them, the regression coefficient of self-need (β-0.118) is the highest, playing a major role in psychological factors.

5

Conclusion

This paper constructs and analyzes the training and assessment system of informatization lesson ability of language teachers in colleges and universities through principal component analysis and Logistic model.

The language teachers of five colleges and universities in a district were taken as the research subjects. The results of the study show that the principal component analysis method can effectively assess the informatization teaching ability of language teachers in colleges and universities after their informatization teaching training. After the training, the language teachers’ informatization teaching ability is generally at a comparable level, and the teachers are basically able to master the basic informatization teaching means, but they are not able to comprehend the application of the informatization teaching means at a deeper level, and they are not able to carry out the teaching innovation well, therefore, the teachers’ comprehensive ability of informatization teaching still needs to be further improved.

Logistic model can effectively extract the key dimensions affecting the information technology teaching ability of college language teachers, which provides a scientific basis for the optimization of the training system. The method identifies the core influencing factors of teachers’ information technology application awareness, teachers’ information technology application ability, teachers’ information technology education and teaching ability, teachers’ information technology vocational training ability and teachers’ information technology research and innovation ability. Among them, the regression coefficients of personal interest and self-efficacy are high, 0.995 and 0.754, which significantly affect the awareness of information technology application of college language teachers.

The above results provide directions for designing targeted training contents for information technology teaching, which can help to improve the efficiency and results of training.

Langue:: Anglais

Périodicité:: 1 fois par an
Sujets de la revue:: Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres

RSS Feed de la revue

The Construction of Training and Assessment System of Informatization Teaching Ability of Language Teachers in Colleges and Universities

Liping Lei

Xinyao Li

Wen Yan

Rui Zhang

Junyi Xu

Publié en ligne: 29 sept. 2025

Reçu: 07 janv. 2025

Accepté: 07 mai 2025

DOI: https://doi.org/10.2478/amns-2025-1105

Mots clésPrincipal Component Analysis, Assessment System, Logistic Model, Informatization Teaching Ability

© 2025 Liping Lei et al., published by Sciendo

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

Mots clés
Principal Component Analysis, Assessment System, Logistic Model, Informatization Teaching Ability