Innovation and Practice of Ideological and Political Education Communication Mode Driven by New Media Technology

In today’s rapid development of information technology, new media, as a major medium of information dissemination, has had a profound impact on people’s thinking, value orientation, and behavioral patterns. The popularization of new media has brought more space for development and more resources to the ideological and political education work in colleges and universities, as well as new challenges to the traditional education methods.

With its digital, virtual, personalized and interactive features, new media provide unprecedented opportunities for ideological and political education innovation. The digital features of new media have greatly enriched the expression of ideological and political education information, and the combination of text, pictures, audio, video, etc. not only brings rich information experience, but also accelerates the speed of dissemination of ideological and political education information [1–2]. With the help of virtual reality and augmented reality and other technologies, new media can create a “surreal” interactive environment for ideological and political education, which not only broadens the boundaries of people’s cognition, but also promotes the innovation of social communication mode [3]. New media can also make use of big data, artificial intelligence and other advanced technologies to accurately analyze the interests, needs and behavioral habits of ideological and political education targets, so as to provide them with personalized information content [4]. In addition, in the new media environment, the transmission of information is two-way interactive, and users can interact with other users, which not only enriches the transmission of information, but also contributes to the diversification of information [5].

The open and free Internet environment and fragmented communication mode in the new media era have created good conditions for the development of ideological and political education, but they have also brought serious challenges to ideological and political education in colleges and universities to a large extent. Wang, J. believes that ideological and political education in colleges and universities should give full play to the advantages of the new media environment, and improve the content of the mode of ideological and political education by changing the way of students’ information access and learning mode, in order to improve the efficiency and quality of education [6]. Hou, X. analyzed the innovative path of new media technology in ideological and political education in colleges and universities, which can be used to actively disseminate ideological and political knowledge by using new media technology and platforms, and can also be combined with interdisciplinary fusion education mode to cultivate students’ comprehensive abilities, and combined with diversified education modes to improve students’ personalized learning effects [7]. Zhang, L. elaborated the significant impact of new media technology on ideological and political education, new media technology provides convenient conditions for communication between teachers and students, which is conducive to the development of ideological and political education in colleges and universities, but the openness it possesses weakens the authority of ideological and political work in colleges and universities and affects the results of education [8]. Yixuan, L. stressed that new media technology in the development of active learning and personalized education at the same time, but also for the production and dissemination of a variety of harmful information to provide a favorable condition, in the face of the massive amount of information brought about by the new media, colleges and universities are more need to develop a reasonable ideological and political education to improve the ideological and political literacy of college students [9]. Gao, H. describes the characteristics of new media information dissemination, and explores the innovative teaching methods of ideology and politics in the new media era at the level of teaching concept, teaching mechanism, teaching resources, and teaching methods, which significantly improves the teaching effect of ideology and politics courses in colleges and universities [10]. Hu, Z. et al. investigated the problems of college students’ ideological and political education in the age of information media, and analyzed the characteristics of traditional ideological and political teaching methods, on the basis of which they put forward the relevant strategies for improving and perfecting ideological and political education in the new era, to further improve the effectiveness of college students’ ideological and political education [11]. Ran, X. pointed out that new media technology not only changed the ideological concepts, learning styles and habits of students in colleges and universities, but also greatly improved the effectiveness of ideological and political education through the establishment of a new media platform, innovation of educational channels and optimization of the educational environment of the ideological and political courses [12]. Jin, T. designed an innovative platform for online Civics teaching in the new media era based on the B/S model, which has a teaching interaction module, an evaluation and feedback module and a socialization module, and realizes the enhancement of the online teaching effect by deepening the communication between users [13]. Li, L. put forward the path to innovate the ideological and political education work in colleges and universities in the new media era, the macro aspect should improve the new media information supervision system and strengthen the construction of network culture, so that the media information can form a positive guidance to the thinking of college students, and the micro level should improve the teachers’ and students’ new media literacy, and consolidate the basic content of the values [14].

This paper objectively evaluates the level of ideological and political education communication by constructing an evaluation index system for ideological and political education communication. The comprehensive empowerment method combines the subjective empowerment of hierarchical analysis method and the objective empowerment of entropy weight method to empower indicators. The communication power of ideological and political education in media platforms of 50 colleges and universities is selected as the research object and evaluated in the dimensions of network communication subject, network communication audience, network communication content, network communication medium, network communication effect and network communication risk. Structural equation modeling is used to verify the influencing factors of the communication innovation and practice of ideological and political education, and then the path coefficients of the model are examined by structural equation modeling, and the moderating effect is examined by hierarchical regression, so as to provide references for the enhancement of the communication of ideological and political education.

2

Measuring the dissemination of ideological and political education supported by new media technologies

2.1

Innovations in the dissemination of ideological and political education

2.1.1

Traditional ideological and political education communication methods

Traditional ideological and political communication combines five elements: educator, message, medium, educated, and effect. The educator presents a single method of communication during message dissemination.

If the communicator wants to achieve this purpose, then they need to understand the basic conditions of the recipients’ ideological tendencies, interests, character traits, and environment. The communicator also needs to take into account the reaction of the recipients to the information when the information is disseminated. Therefore, it is necessary to deal with the following relationships: 1)

The relationship between the educator (the disseminator of information) and the recipient (the recipient of information). The “indoctrination” mode of communication is easy to make the educated person resentful, the educator only sees himself as the main body, and sees the educated person as the passive container object, and turns the information dissemination into the “duck-filling, injecting” mode of communication, and this kind of unidirectional dissemination of the main body of communication to the object will cause the educated person to react to the information in a unilateral way. Unidirectional dissemination will cause the exclusion of the educated person’s reaction. To clarify the subject-object relationship in the dissemination process of ideological and political education is of guiding significance for the correct use of the method of “indoctrination”, which should have a certain degree of coercion, and does not exclude the object. The “indoctrination” method of communication should be somewhat mandatory, and does not exclude the questions posed by the object, the conclusions of the object’s thinking and positive feedback when receiving information from the subject;

2)

The educator should grasp the relationship between indoctrination and diversion. The “diversion method” is one of the most important methods of ideological and political education. Both “diversion” and “guidance” are indispensable. The unilateral “dredge” allows the educated to express their own views and opinions, and the unilateral “guide” allows the educated to accept the educator’s information, “dredge” and “guide” both. The dialectical unity of the two “guide” can achieve the effectiveness of information dissemination;

3)

Entertainment is also one of the methods of dissemination of ideological and political education. Such a way of information dissemination is the use of lively expression through a variety of media carriers, with fun, permeability, and infectiousness, to achieve the purpose of education.

2.1.2

Changes in the dissemination of ideological and political education in colleges and universities

The communication mode of ideological and political education in colleges and universities in the era of media integration has been transformed from the traditional one-way indoctrination mode to a new mode of ideological and political education communication that focuses on the feedback of the communicated person (the educated person), is clear about the complex and changeable environment, analyzes systematically the relationship between the elements, and makes it clear that the message receives the interference of the external noise during the process of dissemination, forming a twoway interactive communication process.

The two-way interactive communication process of ideological and political education consists of eight elements: educator, message, media, educated, effect, environment, feedback, and noise. The educator, message, medium, educated, and effect are influenced by the environment, noise, and feedback, making these 8Ws dynamic variables. This is a better fit for the current media integration environment than the traditional one-item indoctrination model.

2.1.3

Innovative Practices of Ideological and Political Education Communication Methods

1)

Deepening the development of multifaceted cooperation in the dissemination of ideological and political education under social media

To deepen the development of multifaceted cooperation in the dissemination of ideological education under social media is to provide multifaceted support for the dissemination of ideological education with the help of educational resources such as enterprises, museums, libraries, and educational research institutes, and to actively carry out the practical activities of ideological education by utilizing social media, enriching the mode of dissemination of ideological education and its content, and enhancing the relevance of the content of the dissemination of ideological education. This will be an effective strategy to solve the problem of lack of resources for Civic and Political education in social media of educational institutions. For this reason, educational institutions should integrate resources based on social media platforms, expand the cooperative development path of civic and political education, and make social media platforms an important carrier for the dissemination of civic and political education.

2)

Build a social media-based network interactive education community

Building an interactive education community based on social media means that by improving the interaction and communication between teachers and students, deepening teachers’ understanding of the mainstream cultural content of online media and students’ online discourse, helping teachers to design the content of Civic and Political education according to the logical thinking characteristics of the students, enhancing teachers’ educational influence and affinity on social media, and fully solving teachers’ problem of insufficient ability to design the content of Civic and Political education on social media. The real problem of teachers’ insufficient ability to design Civic and Political Education content has been fully resolved. For example, we set up interactive news communities on social media platforms such as Pai Pai, Sina Weibo, Baidu Post Bar and Reddit, expanding the content of Civic and Political Education according to the current news, and applying students’ familiar expressions to Civic and Political Education, so as to enhance students’ ability to learn and receive the elements of Civic and Political Education in social media.

2.2

Construction of Ideological and Political Education Communication Power Indicator System

2.2.1

Principles for the construction of evaluation indicators

Principle of significance: The comprehensive evaluation indicator system needs to describe and cover all the characteristics of the object as much as possible and make the indicators independent; only by choosing indicators that are closely related to and highly compatible with the evaluation objectives can the actual situation of the evaluation object be truly reflected. Therefore, when creating the evaluation indicator system, it is important to retain the most important and significant indicators with a high degree of fit as much as possible.

Principle of operability: Operability refers to the measurability and observation cost of the evaluation indicators, which means that both qualitative and quantitative indicators in the evaluation indicator system can be collected or quantified. In addition, the observation cost of the indicators should not be too large, and the data of the evaluation indicators can be easily collected and obtained as openly and objectively as possible, and when it is difficult to obtain, the indicator can be directly discarded or other ways can be taken to obtain the indicator data in an approximate manner.

Principle of dynamism: Although the evaluation indicator system must have a certain degree of stability within a certain period of time, due to the diversity of evaluation objects and the complexity of the objectives, the indicator system must be adjusted dynamically according to the actual situation. On the one hand, adjustments can be made positively, that is, the evaluation indicators should be redesigned and optimized in accordance with the new evaluation objectives and targets; on the other hand, negative adjustments can be made, that is, some indicators can be deleted or added to the indicator system on the basis of the effects reflected in the evaluation results.

2.2.2

Establishment of evaluation indicators

This study uses the Delphi method and expert meetings to formulate evaluation indexes for the communication power of ideological and political education in colleges and universities. The results of the first round of expert consultation mainly focus on adjusting and amending the index structure, dimensions and specific expressions, eliminating some of the indexes that do not fit well with the evaluation object, and supplementing incomplete dimensions, and the results of the expert survey of the second round gradually converge, with adjustments made to the descriptions and the order of the first-level indicators belonging to some indexes only. The second round of expert surveys resulted in a gradual convergence, and only the descriptions of some indicators and the order of first-level indicators were modified. Finally, the evaluation indicators were further evaluated and optimized using the expert meeting method to improve the accuracy and scientificity of the evaluation indicator system. In summary, the evaluation index system of communication power of ideological and political education in colleges and universities is finally constructed, which contains 6 first-level indicators, 17 second-level indicators and 40 third-level indicators, as shown in Table 1.

Table 1.

Evaluation index system

Primary indicator	Secondary indicator	Tertiary index
Network communication body A1	Ideological and political educator B1	Iinfluence C1
	Ideological and political educator B1	Total release C2
	Platform manager B2	Typography C3
	Platform manager B2	Operational ability C4
Network communication audience A2	College students’ network information literacy B3	Network security awareness C5
		Network information security knowledge C6
		Network information application ability C7
		Moral cultivation of network information C8
	Student information acceptance B4	Personality demand satisfaction C9
	Student information acceptance B4	Teaching recognition C10
Network communication content A3	Content timeliness B5	Current politics update frequency C11
	Content timeliness B5	Social hotspot update frequency C12
	Content fun B6	Verbal affinity C13
	Content fun B6	Title attraction C14
	Content feature B7	Original content C15
	Content feature B7	Subject richness C16
Network media A4	The new media platform is authoritative B8	Official account C17
	The new media platform is authoritative B8	Information completeness C18
	New media platform functionality B9	Plate C19
		Message feedback C20
		Expanded cognition C21
	New media platform promotion B10	Push time C22
	New media platform promotion B10	Construction duration C23
Network propagation effect A5	Propagation span B11	Concern quantity C24
	Propagation span B11	Reading quantity/playback quantity C25
	Propagation depth B12	Collection quantity C26
	Propagation depth B12	Thumb up C27
	Spread participation B13	Sharing quantity C28
	Spread participation B13	Comment quantity C29
Network communication risk A6	Spread the risk source B14	Privacy risk C30
		Dissemination of content risksC31
		The spread of the process is not risky C32
		Privacy security risk identification C33
	Spread risk identification B15	Dissemination of content risk identification C34
	Spread risk identification B15	Information technology identification C35
	Dissemination risk assessment B16	Assessment of risk itself C36
	Dissemination risk assessment B16	Risk negative impact assessment C37
	Spread risk control B17	Spread content to control C38
		Information technology optimization C39
		Risk monitoring and warning system C40

2.2.3

Methodology for the assignment of indicators

1)

Calculation method for determining indicator weights based on hierarchical analysis method

Establishment of hierarchical structure: according to the attributes and relationships of the factors in the system, it is divided into a number of levels, the general situation is divided into the highest level, the middle level and the lowest level, and finally constructed into a ladderlike model [15].

Construct two-by-two comparison judgment matrix: after constructing the ladder model, each level is compared according to the same rules to form a judgment matrix, the establishment of the matrix is a key step in the determination of the weights, the judgment matrix is mainly formed by two-by-two comparisons of each level of each indicator corresponding to the importance of the previous level, and the final value of the matrix is mainly to reflect the importance of each element.

(1)

Hierarchical single sorting. Through the hierarchical analysis method MATABLE programming on the judgment matrix calculation, follow the value of the elements of each level of the upper level of the index layer of the weight of the operation one by one, and so on, to find the weight of each evaluation index, noted as e_i (i = 1, 2,…, m), m indicates the number of indicators.

(2)

Consistency test. Construct the judgment matrix to follow the principle of consistency, the same is also picked up layer by layer. When the matrix is the same, the matrix is consistent with λ_max = n; not the same as λ_max > n, then use λ_max – n operation difference, so as to test the consistency of the size, recorded as CI: 1 $C I = \frac{λ_{\max} - n}{n - 1}$

Where λ_max represents the maximum value of the matrix, n is the matrix order; if CI the smaller the value, it indicates a high degree of consistency of the indicators. At the same time, there must be a satisfactory consistency indicator RI comparison, resulting in a test value CR: 2 $C R = \frac{C I}{R I}$

The test for each judgment matrix is required to be CR < 0.1, because when CR < 0.1, the matrix has satisfactory consistency. RI is denoted as the average consistency indicator, whose value is taken according to the different orders of the matrix

2)

Calculation method of determining indicator weights based on entropy weight method

The entropy weight method fully considers the amount of change of each indicator, uses the information entropy to calculate the entropy weight of each indicator, and then uses the entropy weight to correct the weight of each indicator to ensure that the value of the weight of the indicator can truly respond to the situation in line with reality [16]. In general, if the information entropy of an indicator is smaller, the greater the change in the value of the indicator, the more information is provided, the greater the role of the indicator in the comprehensive evaluation, and the greater the weight; and vice versa, which indicates that the information gun is inversely proportional to the weight.

The calculation process is as follows:

Taking the research of this paper as an example, assuming that there are n ways of evaluating ideological and political communication, and there are m evaluation indexes of their ways, each index value is expressed by q_ij (i = 1……m, j = 1……n), and a multi-pair judgment matrix can be obtained, and the matrix is noted as: 3 $Q' = [\begin{matrix} {q^{'}}_{11} & \dots & {q^{'}}_{1 n} \\ \dots & \dots & \dots \\ {q^{'}}_{m 1} & \dots & {q^{'}}_{m n} \end{matrix}]$

Where q_ij is the rating value of the j nd evaluation object on the i rd evaluation level. In order to facilitate the calculation of uniformity, Q is standardized to form the standardized matrix of each indicator. Since the data of q_ij, min|q_ij| and max |q_ij| have an impact on the final numerical result of q_j, the extreme value method is applied to standardize the statistical data. The standardized matrix is denoted as Q = (q_ij)_m×n (i = 1,…,mj = 1,…,n), and the formula for calculating q_ij is as follows: 4 $q_{i j} = \frac{q_{i j} - \min_{j} | q_{i j} |}{\max_{j} | q_{i j} | - \min_{j} | q_{i j} |}$

Normalization of statistical data allows the calculation of the information yes for each indicator. The i st indicator of is H_i can be defined as: 5 $H_{i} = - k \sum_{i = 1}^{m} f_{i j} \ln f_{i j}$

Where $f_{i j} = \frac{q_{i j}}{\sum_{j = 1}^{m} q_{i j}}$ , $k = \frac{1}{\ln n}$ (assumption: when f_ij = o, f_ij ln f_ij = o)

After the value of the indicator is determined it is possible to determine the yes weight w_i of the i th indicator according to the following formula: 6 $w_{i} = \frac{1 - H_{i}}{m - \sum_{i = 1}^{m} H_{i}}$

3)

Calculation method of comprehensive weight

Comprehensive weights are a combination of the weights calculated by the subjective assignment method and the weights calculated by the objective assignment method of the entropy weight method, so that the importance of the opinions of experts and other information on the indicators is combined with the dynamics of the data, which is the basis for the final calculation of the assessment object.

That is, the determination of the combined weight W₁(W_i, W₂, …, W_n), that is, the subjective weight value e₁ and the objective weight entropy weight $w_{i}^{H}$ using formula (7) formula (8) for the combined calculation: 7 $W_{t} = \frac{e_{i} w_{i}^{H}}{\sum_{i = 1}^{n} e_{i} w_{i}^{H}}$ 8 $\begin{matrix} \bar{W} = {\frac{w_{1} e_{1}}{\sum_{j = 1}^{n} w_{j} e_{j}}, \frac{w_{2} e_{2}}{\sum_{j = 1}^{n} w_{j} e_{j}}, \dots, \frac{w_{n} e_{n}}{\sum_{j = 1}^{n} w_{j} e_{j}}} \\ = (W_{1}, W_{2}, \dots, W_{n}) \\ s . t . \sum_{j = 1}^{n} W_{j} = 1, W_{j} > 0 \end{matrix}$

Eq. i = 1,2,…, m.

Obviously the combined weights W and $w_{i}^{H}$ and e, are as similar as possible, according to the principle of minimum information entropy there: 9 $\min F = \sum_{j = 1}^{n} W_{j} (\ln W_{j} - \ln w_{j}) + \sum_{j = 1}^{n} W_{j} (\ln W_{j} - \ln e_{j})$

Solving the upper optimization problem using the Lagrange multiplier method yields: 10 $W_{j} = \frac{{(w_{j} e_{j})}^{1 / 2}}{\sum_{j = 1}^{n} {(w_{j} e_{j})}^{1 / 2}}$

Eq. j = 1,2,…,m.

2.3

TOPSIS method of analyzing weights for comprehensive evaluation

2.3.1

Principle of the TOPSIS two-point basic law

The two-point basic law is mainly used for multi-objective decision-making comprehensive scoring method, based on the normalization of the original data, the calculation of the positive ideal solution and the negative ideal solution, to determine the optimal solution by the comprehensive score number of the scheme and the distance between the positive ideal solution and the negative ideal solution of the ranking criterion, if the comprehensive score number of a particular scheme is ranked close to the positive ideal solution and far away from the negative ideal solution, then the scheme is the optimal solution.

2.3.2

Steps for combining evaluation weights

Step 1:

Construct the original matrix: 11 $C = [\begin{matrix} C_{11} & \dots & C_{1 n} \\ ⋮ & ⋱ & ⋮ \\ C_{m 1} & \dots & C_{m n} \end{matrix}]$

Where C is the original evaluation indicator data matrix, C_ij represents the evaluation indicator data for year j for indicator i, i represents year (i = 1,2,3,…, n), and j represents indicator (j = 1,2,3,…m).

Step 2:

Normalize matrix C to obtain the normalized matrix: 12 $M = [\begin{matrix} \frac{C_{11}}{\sum_{i = 1}^{m} C_{1 j}^{2}} & \dots & \frac{C_{1 n}}{\sum_{i = 1}^{m} C_{1 j}^{2}} \\ ⋮ & ⋱ & ⋮ \\ \frac{C_{m 1}}{\sum_{i = 1}^{m} C_{m j}^{2}} & \dots & \frac{C_{m n}}{\sum_{i = 1}^{m} C_{m j}^{2}} \end{matrix}]$

Step 3:

Use the above AHP-entropy weighting method to obtain the combination weights ${W^{'}}_{i} = [{W^{'}}_{1}, {W^{'}}_{2}, {W^{'}}_{3}, \dots {W^{'}}_{n}]$ .

Step 4:

Calculate the indicator weighted evaluation matrix: 13 $V = [\begin{matrix} \frac{C_{11} * {w^{'}}_{1}}{\sum_{i = 1}^{m} C_{1 j}^{2}} & \dots & \frac{c_{1 n} * {w^{'}}_{n}}{\sum_{i = 1}^{m} C_{1 j}^{2}} \\ ⋮ & ⋱ & ⋮ \\ \frac{c_{m 1} * {w^{'}}_{1}}{\sum_{i = 1}^{m} c_{m j}^{2}} & \dots & \frac{c_{m n} * {w^{'}}_{n}}{\sum_{i = 1}^{m} c_{m j}^{2}} \end{matrix}]$ where v_ij = m_ij * w′_i

Step 5:

Determine the positive ideal solution and the negative ideal solution, the larger the value of element Z_ij in the decision matrix Z, the better the solution.

Positive Ideal Solution: 14 $Z^{+} = (z_{1}^{+}, z_{2}^{+}, z_{3}^{+}, \dots z_{n}^{+}) = {\max Z_{i j} | j = 1, 2, 3, \dots m}$

Negative Ideal Solution: 15 $Z^{-} = (z_{1}^{+}, z_{2}^{+}, z_{3}^{+}, \dots z_{n}^{+}) = {\min Z_{i j} | j = 1, 2, 3, \dots m}$

Step 6:

Calculate the distance to the positive ideal and the distance to the negative ideal for each indicator: 16 $D_{j}^{+} = \sqrt{\sum_{i = 1}^{m} {(Z_{j}^{+} - Z_{i j})}^{2}}$ 17 $D_{j}^{-} = \sqrt{\sum_{i = 1}^{m} {(Z_{j}^{-} - Z_{i j})}^{2}}$

Step 7:

Calculate the relative closeness of the evaluation values for each year to arrive at a composite evaluation score for each year, with higher values indicating a higher level of energy development and vice versa. 18 $S_{j} = \frac{D_{j}^{-}}{(D_{j}^{+} + D_{j}^{-})}$

3

Communication analysis of ideological and political education supported by new media technologies

3.1

Hierarchical analysis-entropy weighting method to determine the comprehensive weights

This paper adopts the hierarchical analysis-entropy weighting method, which is a combination of weighting method, to make the overall results not only conform to the knowledge, experience system and opinions of experts, but also ensure that the effective information carried by the original data can be fully explored, and ensure that the weights reach the maximum degree of subjective and objective unity, and the paper uses the additive combination of the assignment of weights to composite the subjective weights of the subjective weights of the 1 and the objective weights of the 2. This paper uses the additive combination of weights to calculate the subjective weight W_i and objective weight U_i to obtain the composite weight λ_i of the bottom three-level indexes relative to the top-level target. 19 $λ_{i} = a W_{i} + (1 - a) U_{i}$

Where the value of a ranges from 0 to 1. When a = 0, this weight coefficient is the objective entropy weight method weight, and when a = 1, the result is the weight determined by the subjective hierarchical analysis method. Based on the calculated composite weights λ_i, the composite weight values of the criterion layer relative to the objective layer and the indicator layer relative to the criterion layer are then calculated. The final composite weights of the hierarchical analysis-entropy weight method are organized as shown in Table 2.

Table 2.

Evaluation index system objective weight

Primary indicator	Secondary indicator
Network communication body A1(0.1457)	Ideological and political educator B1 (0.0756)
Network communication body A1(0.1457)	Platform manager B2 (0.0701)
Network communication audience A2(0.1588)	College students’ network information literacy B3 (0.0864)
Network communication audience A2(0.1588)	Student information acceptance B4 (0.0724)
Network communication content A3(0.1675)	Content timeliness B5 (0.0524)
	Content fun B6 (0.0536)
	Content feature B7 (0.0615)
Network media A4(0.1558)	The new media platform is authoritative B8 (0.0583)
	New media platform functionality B9 (0.0647)
	New media platform promotion B10 (0.0328)
Network propagation effect A5(0.2179)	Propagation span B11 (0.0576)
	Propagation depth B12 (0.0647)
	Spread participation B13 (0.0956)
Network communication risk A6(0.1543)	Spread the risk source B14 (0.0454)
	Spread risk identification B15 (0.0316)
	Dissemination risk assessment B16 (0.0466)
	Spread risk control B17 (0.0307)

3.2

The Communication Power of Civic Education Supported by New Media Technologies

3.2.1

Overall dimensional analysis

The communication of 50 colleges and universities on social media platforms was selected for empirical analysis. The scores of each dimension of ideological and political education network communication power are shown in Figure 1. From the average score of each dimension, network communication subject A1 (3.277) > network communication content A2 (3.215) > network communication effect A3 (3.225) > network communication audience A4 (3.197) > network communication media A5 (3.223) > network communication risk A6 (3.14), which can be seen that the ideological and political education of the colleges and universities with the new media platform to establish a better ideological and political education communication form, telling good stories and spreading good voices of ideology and politics, but not enough attention has been paid to the negative impact of network communication risk.

3.2.2

Analysis of differences in the dimensions of network communication subjects

The scores of the indicators of the dimension of the main body of online communication are shown in Figure 2. From the figure, it is analyzed that influence C1 scores (3.365) performs well in this category of indicators, followed closely by the total amount of published content C2 (3.382), layout and editing ability C3 (3.269) and operation and promotion ability C4 (3.129) all of which show slightly weaker ratings. Samples with a poor rating of network communication power lag significantly behind samples with a very good, good and average rating in terms of influence, and samples with a very good, good, average and poor rating all show a stepwise decrease in their scores in this indicator.

3.2.3

Analysis of Differences in Audience Dimensions of Web Communications

The network communication audience dimension contains two second-level indicators of college students’ network information literacy and college students’ information acceptance, and the scores of the six indicators under it are shown in Figure 3. Network security awareness C5 (3.382) performs the best among the indicators of the same level, network information security knowledge C6 (3.171) and network information utilization ability C7 (3.192) have similar scores, network information ethics C8 (3.099) is the lowest, and college students’ personality needs satisfaction C9 scores higher than teaching acceptance C10. The samples with evaluation grades of very good and good have significantly higher scores than the other grades in the indicators of personality Needs Satisfaction and Teaching Approval are significantly higher than the other grades.

3.2.4

Analysis of differences in content dimensions of web communication

The scores of the network communication content dimension indicators are shown in Figure 4. Among them, the performance of the two indicators of current affairs and politics update frequency C11 and social hotspot update frequency C12 is particularly outstanding, reaching the evaluation of 3.396 and 3.333, with similar scores of language affinity C13 and theme richness C16, while content originality C14 performs the worst under the content dimension. Samples with a very good rating are close to perfect scores in the 2 indicators of social hotspot update frequency, and current affairs and politics update frequency.

3.2.5

Analysis of differences in the dimensions of online communication media

The scores of the network communication media dimension indicators are shown in Figure 5. Among them, information completeness C18 (5.484) has the best performance, message feedback C20 (3.879) and push time C22 (3.753) have not much difference in performance, and the university’s ideological education in the construction of the length of time C23 (3.199) has average performance. The samples with very good and good communication power have significantly better data results on the function board than the samples with average and poor communication power.

3.2.6

Analysis of differences in the dimensions of network communication effects

The scores of the network communication effect dimension indicators are shown in Figure 6. Among them, the number of likes C27 (3.279) and the number of readings/playbacks C25 (3.267) performed the best, the number of concerns C24 (3.258) and the number of comments C29 (3.234) scored the second best, and the number of favorites C26 (3.171) and the number of shares C28 (3.14) were the weakest, and the colleges and universities with a better evaluation of the overall dissemination power scored more strongly than those with a better evaluation of overall evaluation in terms of the number of concerns and likes than the overall The colleges and universities with average scores are all strong.

3.2.7

Analysis of differences in web communication risk dimensions

In the dimension of network communication risk, there are four secondary indicators of communication risk source, communication risk identification, communication risk assessment and communication risk prevention and control, with 11 observation points, and the specific scores are shown in Figure 7. Civic education network communication in colleges and universities has the highest score in the indicator of communication content risk C31 (2.975), and the lowest score in the communication process of words and deeds risk C32 (2.828), which indicates that the privacy and security risk has not been sufficiently emphasized in Civic education in colleges and universities. The performance in the risk assessment and risk prevention and control indicators is better, and the data performance of colleges and universities of all evaluation levels in this dimension is lower than the average of the other dimensions.

3.3

Influencing Factors of Civic Education Communication Innovation and Practice

This study uses structural equation modeling to validate the influencing factors of Civic and Political Education Communication Innovation and Practice, which is a method of correlation statistics for multiple variables and is widely used in sociology, psychology, management and other disciplines. This study utilizes AMOS software to construct an equation model so as to validate the relationship between information source, information content, information medium, communication motivation, and willingness to communicate innovation and practice in Civic and Political Education, and to evaluate the theoretical model using the model structure and data relationships.

3.3.1

Effectiveness of the overall model fit

The fitting results of the model are shown in Figure 8. By examining the fit of the model, we find that the model has good fit indicators for each fit indicator, and the fit indicator is usually applied to the chi-square degrees of freedom ratio (NC), the closer the value is to 0, the better the fit of the model to the data, and less than 3, the fit is considered to be good. Benchmarking Fit Indicator (NFI), in general, the closer the value is to 1, the better the fit, and greater than 0.9 indicates a better fit. Goodness of Fit Indicator (GFI), the closer the value is to 1, the better the fit of the model, and greater than 0.9 indicates a better fit. Tucker-Lewis Indicator (TLI), the closer the value is to 1, the better the fit of the model, and greater than 0.9 indicates a better fit. Tucker-Lewis Indicator (TLI), the closer the value is to 1, the better the fit of the model, and greater than 0.9 indicates a better fit. Comparative Fit Indicator (CFI), the closer the value taken to 1 indicates a better model fit, and greater than 0.9 indicates a better model fit, Root Mean Squared Error Squared (RMSEA), usually takes a value less than 0.08 indicating a better model fit. Adjusted Goodness of Fit Index (AGFI) Normally, this takes a value greater than 0.8, indicating a good model fit. Through empirical testing, the value of the chisquare degrees of freedom ratio (NC) in this study is 2.864, which is less than 3; the value of the benchmarking fitness index (NFI) is 0.966, which is greater than 0.9; the value of the goodness of fit index (GFI) is 0.927, which is greater than 0.9; the value of the Tucker-Lewis index (TLI) is 0.909, which is greater than 0.9; and the comparative goodness-of-fit index (CFI) value is 0.912, which is greater than 0.9; the root mean square error squared (RMSEA) value is 0.077, which is less than 0.08; and the adjusted goodness-of-fit index (AGFI) value is 0.951, which is greater than 0.9. Taken together, all of the fit indices are within acceptable limits, which demonstrates that the research model in this paper has good fit.

3.3.2

Path analysis of the overall model

Through the analysis above, the relationship between the five latent variables of this paper: information source, information content, information medium, communication motivation, and communication willingness is next verified, and when the C.R value is greater than 1.96, then it indicates that the standardized path coefficient reaches significance at the level of p<0.05, and the following path coefficients can be sorted out from the overall structural equation model Fig. 8, which is shown in Table 3.

Table 3.

Model path test results

Path	C.R	Standard path coefficient	P
Spread will←Information source	2.532	0.24	**
Spread will←Information content	4.032	0.35	***
Spread will←Information medium	12.133	0.72	***
Spread will←Propagation motive	8.231	0.65	***
Propagation motive←Information source	4.131	0.37	***
Propagation motive←Information content	3.983	0.19	**
Propagation motive←Information medium	9.131	0.56	***

Note: *p<0.05;

**

p<0.01;

***

p<0.001.

From the results of the path test, it can be seen that the standardized path coefficient of information source on communication willingness is 0.24, and the standardized path coefficient of information content on communication motivation is 0.19, both of which reach significance at the level of p<0.01. All other paths reached significance at the p<0.001 level.

3.3.3

Moderating effects of perceived risk

Through the establishment and analysis of structural equation modeling, it is found that information source, information content, information media, and communication motivation will have different influences on communication willingness, at the same time, the authors believe that the influence of communication motivation on communication willingness is also affected by perceived risk, and that the communication motivation of college students as the main body of the new media users is different under different perceived risks, and the next major analyze whether the perceived risk plays a moderating role in the process of the influence of communication motivation on communication willingness.

In order to verify the moderating role of college students’ perceived risk in the relationship between communication motivation and communication willingness, this paper utilizes hierarchical regression method to verify, firstly, the first step needs to regress the independent variable with the dependent variable to verify the main effect of the independent variable on the dependent variable; secondly, the independent variable and the moderating variable are added into the equation at the same time to verify the main effect of the moderating variable; lastly, the product term is added into the original regression based on the independent variable and the moderating variable, and we observe the product term and observe the moderating variable and the moderating variable. Finally, add the product term of the independent variable and the moderator variable to the original regression, observe the result of the product term, if the product term is significant, then it indicates that the moderator variable plays a moderating role in the effect of the independent variable on the dependent variable. In the stage of testing the moderating effect of perceived risk in this paper, the communication motives, i.e., emotional resonance, self-identification, and altruistic motives, are taken as independent variables, communication willingness is the dependent variable, and perceived risk is the moderating variable. First, the main effect of emotional empathy, self-identity and altruistic motivation on communication intention is verified, and the results show that the three independent variables have a significant effect on the dependent variable; second, perceived risk is introduced into the equation to verify the main effect of the moderating variables, and the verification result is significant; finally, the product term of emotional empathy, self-identity, altruistic motivation and perceived risk is introduced into the equation, and the results of observing the interaction term are shown in Table 4.

Table 4.

The regulation effect of perceived risk is tested

Variable	Spread will
Independent variable	Model 1	Model 2	Model 3
Emotional resonance	0.64**	0.60**	0.48**
self-identification	0.38**	0.33**	0.31**
Altruistic motivation	0.44**	0.51**	0.36**
Regulating variable
Perceived risk		-0.146*	0.275*
Interaction effect
Emotional resonance * Perceived risk			-0.181**
Self-identification * Perceived risk			-0.117**
Altruistic motivation * Perceived risk			-0.246**
F	58.12**	49.54**	55.28**
R²	0.592	0.522	0.539
Adjust R²	0.577	0.53	0.527

As can be seen from the table, the regression coefficients of the interaction terms emotional empathy and perceived risk, self-identity and perceived risk, and altruistic motivation and perceived risk in the model are significant and negative, and the interaction effects are β=-0.181, P<0.01, β=-0.117, P<0.01, and β=-0.246, P<0.01, respectively. The above results show that perceived risk has a moderating effect on the relationship between motivation to communicate and communication intention has a moderating effect.

4

Conclusion

The study takes the ideological and political education in colleges and universities as the research object, analyzes the ideological and political jacket and communication mode in the context of the new media era, and studies the size of the network communication power of ideological and political education from the dimensions of network communication subject, network communication audience, network communication content, network communication medium, network communication effect and network communication risk, and constructs the evaluation index system on this basis. After empirical analysis, this paper draws the following important conclusions: 1)

From the average score of each dimension, network communication body (3.277) > network communication content (3.215) > network communication effect (3.225) > network communication audience (3.197) > network communication media (3.223) > network communication risk (3.14), which can be seen that the ideological and political education of colleges and universities set up a better ideological and political education dissemination form by media platform. It can be seen that college students can feedback the degree of acceptance of ideological and political education information with positive mental display and behavior, but they have not yet paid attention to the negative risks brought by network communication.

2)

Perceived risk has a moderating effect on the relationship between college students ’ ideological and political communication motivation and willingness to communicate, and in the process of the influence of altruistic motivation on college students ’ willingness to communicate, the moderating significance of risk perception is higher than that of the influence of emotional resonance and self-identification on college students’ willingness to communicate.

Idioma:: Inglés

Calendario de la edición:: 1 veces al año
Temas de la revista:: Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros

RSS Feed de revista

Innovation and Practice of Ideological and Political Education Communication Mode Driven by New Media Technology

Yunbin Ma

Publicado en línea: 19 mar 2025

Recibido: 20 oct 2024

Aceptado: 13 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0392

Palabras claveIdeology and politics, Hierarchical analysis, Educational communication innovation, Entropy power method

© 2025 Yunbin Ma, published by Sciendo

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

Palabras clave
Ideology and politics, Hierarchical analysis, Educational communication innovation, Entropy power method