Research on the Innovation Path of Civic Education for College Students under the New Media Environment

At present, new media has penetrated the daily life of college students, which brings great challenges to college students’ civic education [1], and it is urgent for civic educators to seek innovative paths and methods of civic education [2]. As a powerful tool, new media can enhance the interactivity of Civic and Political education while providing targeted information and strengthen the connection between educational institutions and students [3-4]. Introducing this innovative method can make ideological and political education more attractive and more in line with students’ needs, and help to cultivate college students with all-round development and a sense of social responsibility.

The ideological and political education of college students in the new media environment is facing new challenges [5]. Traditional ideological and political education mainly relies on teachers’ classroom lectures and textbook reading, but college students in the new media era have formed a new learning style and learning habits, they pay more attention to interactive, practical and experiential learning, and they need more diversified, varied and interesting educational contents and forms [6-8].

In order to innovatively develop the ideological and political education of college students under the new media environment, it is necessary to recognize the current situation, reform on the basis of the current situation of ideological and political education of college students under the new media environment, find problems and solve them in time, and continuously optimize the system of its new media ideological and political education [9-11].

The massive amount of information on the new media platform makes the resources available to teachers no longer limited to books, and there are also information resources from all over the world on the Internet, which greatly enriches the content of ideological and political education [12-13]. The information in multiple fields such as current affairs hotspots on the new media platform can be used as resources for the ideological and political education of college students [14]. Teachers can significantly enhance the timeliness of ideological and political education by introducing these educational resources into the ideological classroom [15]. Therefore, the topics that cause network public opinion on the new media platform can be introduced into ideological and political education, which can realize ideological correction and modification while arousing the emotional resonance of college students, and help college students establish correct ideological concepts. The ideological and political education resources on the new media platform are mostly presented in the form of videos, which are more entertaining and have better sensory experience, and their introduction into the ideological and political classroom can enhance the attractiveness of ideological and political education [16-18].

By analyzing the operation mechanism of algorithmic recommendation technology, this study elaborates three characteristics of the dissemination of college students’ civic and political education in the new media environment.Based on the LDA model for new media recommendations, fuzzy decision scheduling is carried out on the topic of college students’ civic and political education, and the optimization search results of new media recommendations are obtained. Then, the information dissemination of new media is analyzed based on the hyper-network model in order to explore the influencing factors of college students’ Civic and political education in the new media environment. Finally, through the questionnaire survey method, the effectiveness of the current discourse dissemination of college students’ civic and political education is sorted out, and the problems of the discourse dissemination of civic and political education are analyzed.

2

Accurate Civic Education for College Students Based on Algorithmic Recommendation

2.1

Algorithmic Recommendation Mechanisms in New Media

The rapid development of the proposed algorithm has a great influence on people’s lives, and the proposed technique has a great influence on the mastery and ascension of the college student network.The new media application algorithm recommends using technology to create a voice, and the voice of the Internet is also having a huge impact.However, government education can also use algorithms to promote Internet thinking and education.By using the algorithm, we can fully understand the main directions of the new media and improve the students’ learning.

2.1.1

Similarity calculation for new media

The similarity calculation of new media is the foundation and key to the accurate recommendation of college students’ ideological and political education, and only by discriminating the similarity of new media can we implement personalized recommendation in a more targeted way to meet the user’s information needs and preferences.

For the implied theme M_s of a topic s, the topic S is preprocessed to obtain the keyword set L as the descriptive word set of the implied theme M_s of the topic s. The set of words is used as the probability distribution corresponding to the words in the implied topic M_s. The LDA model is a Bayesian probability model containing three layers of words, text, and topics, and it has a wide range of applications in the analysis of text topic-related tasks. The LDA model is used to analyze the user preferences and the model is shown in Fig. 1.

Combine the probability distribution P(M_hs) of topic M_hs in the user one topic distribution k_s to judge the user’s preference for topic M_hs. Using the word distribution vector r_hs corresponding to topic M_hs and the word frequency vector F_s of new media, calculate the similarity sim(γ_hs,F_s) between new media topics and topic M_hs, so as to judge the user’s preference for topic M_hs similar new media s.

Since topic s belongs to set H, the frequency set F_v={f₁,f₂,⋯,f_n} is obtained. assume that the distribution vector of keywords in set H under topic M_hs is $γ_{h s}^{*}$ $\gamma _{hs}^{*}$ for: (1) $γ_{h s}^{*} = {P^{*} (h_{1}), P^{*} (h_{2}), \dots, P^{*} (h_{n})}$ \[\gamma _{hs}^{*}=\left\{ {{P}^{*}}\left( {{h}_{1}} \right),{{P}^{*}}\left( {{h}_{2}} \right),\cdots ,{{P}^{*}}\left( {{h}_{n}} \right) \right\}\]

Where P*(h_n) is the probability distribution of keywords under topic M_hs.

If there is no keyword under topic M_hs in the set, then the probability is 0. The probability distribution in distribution vector $γ_{h s}^{*}$ $\gamma _{hs}^{*}$ is normalized to obtain distribution vector γ_hs={P(h₁),P(h₂),⋯,P(h_n)}, and the normalization formula is: (2) $P (h_{n}) = \frac{γ_{h s}^{*}}{\sum_{n = 1}^{s} P^{*} (h_{n})}$ \[P\left( {{h}_{n}} \right)=\frac{\gamma _{hs}^{*}}{\sum\limits_{n=1}^{s}{{{P}^{*}}}\left( {{h}_{n}} \right)}\]

For the similarity sim(γ_hs,F_s) between the new media topic and topic M_hs, the textual similarity JS distance in the LDA model is used to calculate the similarity coefficient, while the textual similarity JS distance in the LDA model is converted to the similarity coefficient, which is calculated by the formula: (3) $U (γ_{h s}, F) = \frac{1}{2} {\sum_{n = 1}^{s} P (h_{n}) L n \frac{2 P (h_{n})}{P (h_{n}) + f_{s}} + \sum_{n = 1}^{s} f_{s} L n \frac{2 f_{s}}{P (h_{n}) + f_{s}}},$ \[U\left( {{\gamma }_{hs}},F \right)=\frac{1}{2}\left\{ \sum\limits_{n=1}^{s}{P}\left( {{h}_{n}} \right)Ln\frac{2P\left( {{h}_{n}} \right)}{P\left( {{h}_{n}} \right)+{{f}_{s}}}+ \right.\left. \sum\limits_{n=1}^{s}{{{f}_{s}}}Ln\frac{2{{f}_{s}}}{P\left( {{h}_{n}} \right)+{{f}_{s}}} \right\},\] (4) $s i m (γ_{h s}, F) = \frac{1}{U (γ_{h s}, F)}$ \[sim\left( {{\gamma }_{hs}},F \right)=\frac{1}{U\left( {{\gamma }_{hs}},F \right)}\]

2.1.2

User behavior feature extraction

The extraction of user behavioral characteristics is key to implementing accurate recommendations for college students’ Civic and Political Education in the new media environment.Only by accurately identifying the behavioral characteristics of users can we recommend Civics content more scientifically and effectively, so that the diverse and personalized needs of users can be satisfied.The new media recommendation algorithm performs association rule scheduling by calculating the similarity degree.Finally, the behavioral characteristics of new media users are taken into account.

The aim of extracting user behavioral features based on similarity calculation method is to obtain the amount of new media recommendation behavioral features through association rules and calculate information entropy on this basis.Its label-based personalized recommendation method is used to detect the behavioral features of new media users and obtain the joint distribution probability density function of new media recommendations. Finally, based on the collaborative filtering recommendation method for the process of new media recommendation for continuous training and adaptive learning, C mean is the fusion factor, and finally get the semantic ontology function f(x) of new media personalized recommendation, that is: (5) $f (x) = \sum_{i = 1}^{n} (α_{i} - α_{i}^{*}) K (x_{i}, x_{j}) + b$ \[f(x)=\sum\limits_{i=1}^{n}{\left( {{\alpha }_{i}}-\alpha _{i}^{*} \right)}K\left( {{x}_{i}},{{x}_{j}} \right)+b\]

Where α_i and $α_{i}^{*}$ $\alpha _{i}^{*}$ are the semantic feature quantities of personalized recommendation labels, K(x_i,x_j) is the fuzzy kernel function, and b is the recommendation threshold value for the set of new media users and the set of items.

2.1.3

LDA Model Recommendation Algorithm

Combining the probability distribution P(M_hs) of topic M_hs in the user one topic distribution k_s, the similarity sim(γ_hs,F_s) of digital media topics as well as topic M_hs, the user’s interest in digital media X_{M_hs}(a,s) is calculated using the formula: (6) $X_{M_{h s}} (a, s) = P (M_{h s}) \cdot s i m (γ_{h s}, F_{s})$ \[{{X}_{{{M}_{hs}}}}(a,s)=P\left( {{M}_{hs}} \right)\cdot sim\left( {{\gamma }_{hs}},{{F}_{s}} \right)\]

By calculating the user’s interest in digital media to obtain the user’s interest set for each topic in digital media, the interest set is sorted by the size of the interest. Eventually, different digital media content can be recommended to different users. The personalized and diversified needs of digital media users for digital media information can be effectively satisfied. In order to further improve the accuracy of digital media recommendation, the fuzzy C-mean clustering analysis method is introduced into the digital media recommendation algorithm to obtain the adaptive clustering recommendation algorithm based on LDA model. The joint information entropy of digital media user behavior is calculated, and the joint probability distribution function E^ω of digital media recommendation is obtained under the constraint of association rules, i.e.: (7) $\begin{matrix} E^{ω} (c_{1}, c_{2}) = μ \cdot l e n g t h (C) + v \cdot a r e a (i n s i d e (C)) \\ + λ_{1} \int_{i n s i d e (C)} {| I - c_{1} |}^{2} d x d y + λ_{2} \int_{o u t s i d e (C)} {| I - c_{2} |}^{2} d x d y \end{matrix}$ \[\begin{align} & {{E}^{\omega }}\left( {{c}_{1}},{{c}_{2}} \right)=\mu \cdot length(C)+v\cdot area(inside(C)) \\ & +{{\lambda }_{1}}\int\limits_{inside(C)}{{{\left| I-{{c}_{1}} \right|}^{2}}}dxdy+{{\lambda }_{2}}\int\limits_{outside(C)}{{{\left| I-{{c}_{2}} \right|}^{2}}}dxdy \end{align}\]

Where c₁,c₂ is the personalized evolutionary feature coefficient, length(C) is the length of digital media waiting to be recommended, and area(inside(C)) is the regional distribution.

According to the established LDA model for new media recommendation, fuzzy decision scheduling is carried out for various college students’ ideological and political education topics, and the LDA-based new media recommendation optimization model is finally obtained as shown in Fig. 2. In this paper, the new media recommendation algorithm program based on LDA model is established in the environment of Internet of Things, and the decision-making investigation of college students’ civic and political education is realized through the fuzzy association rule scheduling method.

2.2

Construction of Hypernetwork Model of New Media Information Dissemination

In the process of researching the dissemination of college students’ Civic and Political Education under the new media environment, it is found that factors such as the dissemination of public opinion information, the dissemination of information on emergencies, and the dissemination of virtual information of the new media all affect the socialist core values of college students. Therefore, this paper analyzes the information dissemination of new media from the perspective of hypernetwork in order to explore the influencing factors of college students’ Civic and Political Education in the new media environment.

2.2.1

Hypernetwork elements

The basic elements of a hypernetwork include nodes and edges. Node elements represent the physical elements that describe an object, and there can be multiple types of node elements. The edges connecting each node to each other are called relations, and there are many kinds of node relations, i.e., many kinds of edges, in the hypernetwork. Therefore, before constructing a hypernetwork, the first thing that should be clear is to clarify the nodes, edges, and subnetworks in the hypernetwork, which is the prerequisite for constructing a hypernetwork.

1)

Definition of node elements of hypernetwork

The set of points can be defined as X={x₁,X₂,…,X_m} or Y={y₁,y₂,…,y_n} for different types of node elements, respectively, and the letters X,Y are used to denote different types of sets of nodes in the node elements, x,y to denote different types of node elements, respectively, and m,n to denote the number of node elements in the set.

2)

Definition of Edge of Hyper Network

In defining the edges of the hypernetwork, it is categorized into two cases, one of which is the set of relationships of edges between nodes in the same set of nodes can be expressed as: (8) $Q_{x - x} = {(x_{m}, y_{n}) | x_{m}, y_{n} \in X}$ ${{Q}_{x-x}}=\left\{ \left( {{x}_{m}},{{y}_{n}} \right)|{{x}_{m}} \right.,\left. {{y}_{n}}\in X \right\}$

where edge (x_m,y_n) represents the link between nodes x_m and y_n.

The second is between nodes of different sets of nodes, and the set of relationships of edges between nodes can be defined as: (9) $Q_{x - y} = {(x_{m}, y_{n}) | x_{m} \in X, y_{n} \in Y}$ ${{Q}_{x-y}}=\left\{ \left( {{x}_{m}} \right. \right.,\left. \left. {{y}_{n}} \right)|{{x}_{m}}\in X,{{y}_{n}}\in Y \right\}$

where edge (x_m,y_n) denotes the link between nodes x_m and y_n.

3)

Definition of hypernetwork subnetwork

Based on the above definitions of node elements and edges, a subnetwork consisting of the same node element can be defined as G, then: (10) ${\begin{cases} G_{x} = (X, Q_{x - x}) \\ G_{y} = (Y, Q_{y - y}) \end{cases}$ \[\left\{ \begin{align} & {{G}_{x}}=\left( X,{{Q}_{x-x}} \right) \\ & {{G}_{y}}=\left( Y,{{Q}_{y-y}} \right) \\ \end{align} \right.\]

where G_x denotes a sub-network consisting of X node set elements and G_y denotes a sub-network consisting of Y node set elements.

2.2.2

Hypernetwork construction steps

In the process of studying the construction of the hypernetwork model, this paper roughly divides the hypernetwork model into three steps, which are described as follows:

1) Elaborating the interrelationships between the elements and nodes of the nodes in the hypernetwork by certain means

Through the study, taking the description of the key entity elements in the research object and the relationship between the elements as an example, in accordance with the method of defining the elements of the supernetwork, the different entity elements and relationships in the supernetwork model are described separately, and a collection of node elements of this type is constructed.

2) Constructing and describing the relationship of each type of sub-network

For the entity elements within the same set, construct the sub-network of that type of entity elements respectively, the nodes are the entity elements in each sub-network in the set, and the edges are the relationships between the entity primitives in the sub-network within the same set. If there is no connection between two nodes, then there is no connection between two nodes in the sub-network edges.

3) Constructing and describing the mapping relationship between sub-networks

For the relationship between the entity elements of each type of subnetwork and the entity elements in other subnetworks, construct the edges between each node in that type of subnetwork and the nodes in other subnetworks, and finally derive the mapping relationship between that type of subnetwork and other subnetworks.

For example, denote the mapping relationship between subnets Q_x and Q_y by Q_x–y

Then Q_x–y=X(y_n)+Y(x_n), where: (11) $X (y_{m}) = {y_{m} | y_{m} \in Y, α (x_{n}, y_{m}) = Z, m = 1, \dots, n}$ \[X\left( {{y}_{m}} \right)=\left\{ {{y}_{m}}|{{y}_{m}}\in Y,\alpha \left( {{x}_{n}},{{y}_{m}} \right)=Z,m=1,\ldots ,n \right\}\] (12) $Y (x_{n}) = {x_{n} | x_{n} \in X, α (y_{m}, x_{n}) = Z, n = 1, \dots, n}$ \[Y\left( {{x}_{n}} \right)=\left\{ {{x}_{n}}|{{x}_{n}}\in X,\alpha \left( {{y}_{m}},{{x}_{n}} \right)=Z,n=1,\ldots ,n \right\}\]

Z is taken as follows: (13) $Z = {\begin{matrix} 1, & There is a link between x_{n} and y_{m} \\ 0 & There is no linkage between x_{n} and y_{m} \end{matrix}$ \[Z=\left\{ \begin{matrix} 1, & \text{There is a link between }{{x}_{n}}\text{ and }{{y}_{m}} \\ 0 & \text{There is no linkage between }{{x}_{n}}\text{ and }{{y}_{m}} \\ \end{matrix} \right.\]

The above formula description allows for the establishment of multiple mapping relationships between nodes of each type of subnetwork based on multiple relationships between multiple types of entity elements.A one-to-many mapping of one node in each subnetwork in a homogeneous node subnetwork and another heterogeneous node subnetwork, respectively, is constructed and described.Then, by establishing the interactions between all the sub-nodes, all the individual sub-nodes with different categories are linked together. Ultimately, a hypernetwork is formed to explain the individual nodes and their interactions in a large and complex network.

2.3

New Media Communication Mechanisms for Civic and Political Education of University Students

No communication behavior can be separated from the communication medium or media, and media integration is based on the premise of media integration. If we view the communication of college students’ ideological education as a system, then it has an objective operating mechanism. In other words, the phenomenon of media fusion and the mechanism of media fusion must exist in this educational communication system, and the operation mode of the mechanism is shown in Figure 3. Essentially, the communication of college students’ ideological and political education is a specific form of communication. Therefore, through the hyper-network model of new media information dissemination, the new media communication mechanism of college students’ Civic and Political Education can be understood as the interconnection and constraints between the constituent elements of Civic and Political Education. And in the educational communication environment, the principle and process of playing a specific function and role.

3

Empirical analysis of civic and political innovation education in the new media environment

3.1

Analysis of Civic Education in the View of Algorithmic Recommendation

3.1.1

Basic information on the questionnaire

This survey began on February 27, 2024 and ended on March 28th. The target of the survey is college students, whose basic information is shown in Table 1. The proportion of men in this survey is 0.4881, while the proportion of women is 0.5119, which is identical for both genders. Among the school types, general undergraduate colleges and universities accounted for the highest proportion of 0.7419. The educational levels are mostly undergraduate, graduate, and specialized, accounting for 0.6247, 0.2148, and 0.1605, respectively. Specializing in this survey covers a wide range of areas, and the data are real and reliable, which can be used to study the discourse dissemination of university college students’ Civic and Political Education in the new media environment.

Table 1.

The basic information table for the survey

Categories	Variable	Frequency	Specific gravity
Gender	Man	225	0.4881
Gender	Female	236	0.5119
School type	Higher vocational colleges	61	0.1323
	General undergraduate	342	0.7419
	“Double class” universities	58	0.1258
Educational background	Specialty	74	0.1605
	Undergraduate	288	0.6247
	Graduate student	99	0.2148
Majors	Science and engineering	184	0.3991
	Social science	105	0.2277
	Humanism	133	0.2885
	Art	39	0.0847
Political appearance	Party member	156	0.3384
	Democrats	7	0.0152
	League member	231	0.5011
	Masses	64	0.1388
	Other	3	0.0065

This questionnaire has 26 questions, including 10 single-choice questions (A1~A10), 4 multiple-choice questions (B1~B4) and 1 question and answer question (C1~C2). It can basically reflect the achievements and shortcomings of the discourse communication of college students’ Civic and political education under the influence of algorithmic recommendation technology, and provide data basis for the following optimization of the benign thinking of the discourse communication of college students’ Civic and political education.

3.1.2

Data testing

The questionnaire of this paper was mainly distributed online, 500 questionnaires were distributed through Questionnaire Star on WeChat, QQ and other new media platforms, 461 questionnaires were recovered, 461 valid questionnaires, and the effective recovery rate of the questionnaire was 100%. Later, SPSS.21 was used to statistically analyze the recovered valid data in order to understand the current situation of the dissemination of ideological and political education discourse in colleges and universities.

1)

Reliability analysis

The overall reliability of the questionnaire is shown in Table 2, and it can be seen that the total reliability of the questionnaire is 0.908, and the reliability of the variables are 0.884, 0.915, 0.873, which are all above 0.7, and the reliability of each variable is good, and it has already met the requirements of the study.

Table 2.

Questionnaire reliability analysis

Name	The correction term is associated at (CITC)	The α coefficient has been removed	Cronbach’ α	Total reliability
A1	0.581	0.888	0.884	0.908
A2	0.673	0.906
A3	0.820	0.873
A4	0.759	0.866
A5	0.753	0.895
A6	0.722	0.917
A7	0.664	0.893
A8	0.824	0.915
A9	0.836	0.875
A10	0.803	0.912
B1	0.756	0.873	0.915
B2	0.751	0.856
B3	0.523	0.902
B4	0.749	0.888
C1	0.712	0.906	0.873
C2	0.741	0.873

2)

Validity and exploratory factor analysis

This study uses KMO value and Bartlett’s spherical test to analyze the validity of the discourse communication scale of college students’ civic education as shown in Table 3, which shows that the KMO value is 0.923, the Bartlett’s approximate chi-square value is 6233.491, and the degree of freedom is 163, and it passes the test at the 0.05 significance level, which indicates that this study can be done as a factor analysis. Also, this study has strong validity and the variable’s structural validity has achieved the desired effect.

Table 3.

KMO and bartlett test

KMO sampling availability number	0.923
Bartlett sphericity test	Calorie value	Freedom	Significance
Bartlett sphericity test	6233.491	163	0.05

3.2

Analysis of the Communication Effectiveness of Civic and Political Education under New Media

3.2.1

Content Analysis of Discourse Communication in Civic Education

With the arrival of the new media era, the discourse dissemination of college students’ ideological education is characterized by openness and diversification, and the discourse content is richer, which is mainly reflected in the following two aspects:

1) Discourse communication resources are constantly enriched to meet the personalized needs of the educated.

2) The current discourse communication categories of college students’ ideological education are constantly enriched, which greatly satisfy the needs of college students’ thoughts.

The results of the survey on “college students’ favorite types of information on new media platforms in colleges and universities” are shown in Figure 4. Among them, A~G refer to Party History and Party Building (A), Chinese Excellent Traditional Culture (B), Volunteer Service (C), Current Affairs (D), Study Courses (E), Life and Leisure (F), Science and Technology (G), and Others (H) respectively. It can be seen that the top three categories are Chinese excellent traditional culture (B), current political news (D) and party history and party building (A), accounting for 0.6723, 0.5846 and 0.5496, respectively. The number of college students favoring the study course category (E) and life and leisure information (F) is as much as the number of college students, accounting for about 0.54. It shows that the discourse communication categories of college students’ ideological education are no longer limited to traditional contents such as political discourse, document discourse and policy discourse, but are developing towards diversified forms.

3.2.2

Problem Analysis of Discourse Communication in Civic and Political Education

When investigating the “problems encountered by college students in the process of using new media platforms to disseminate ideological education”, the results of the survey are shown in Figure 5. Among them, A~J, respectively, refer to “the platform has a single function and lacks interaction and feedback mechanism (A)”, “the platform is stuck and the effect of using it is not smooth (B)”, “the platform fails to convey the mainstream counseling in a timely manner (C)”, “Information is a mixture of good and bad, and it is difficult to distinguish between truth and falsehood (D)”, “Remarks of polarized groups affect the mood (E)”, “A single form of information expression (F)”, “Information content is the same (G)”, “Information has a strong sense of romance and is detached from people’s lives (H)”, “Fragmented information is not enough to express the whole picture of things (I)”, “Others (J)”. It can be intuitively found that college students encounter more problems when using social media platforms. Among them, the ratios of the two problems, “the platform has a single function and lacks interaction and feedback mechanism (0.5645)” and “the information is mixed, and it is difficult to distinguish the truth from the falsehood (0.5349)”, are above 0.5, which have a greater impact.

We summarize the problems of disseminating the discourse of civic and political education through new media as follows:

1) The decentralized recommendation method has weakened the guiding power of the discourse dissemination of college students’ Civic and political education.

2) Fragmentation of the dissemination method weakens the radiation power of the discourse dissemination of college students’ Civic and Political Education.

3) The information narrowing recommendation method dissolves the discourse dissemination control power of college students’ ideological and political education.

3.3

Analysis of college students’ quality dimensions in the new media environment

Under the new media environment, college students also need to have certain qualities of learning and innovation. Learning and innovation are especially important in this environment. College students need to be both virtuous and talented, not only need to have good professional business quality, knowledge structure quality, scientific quality and learning and innovation quality. At the same time, they must also have certain moral and political qualities. Under the new media environment, the ideological and political education of college students is for the service of socialist construction and the dissemination of socialist scientific and cultural knowledge. Therefore, moral and political qualities are indispensable.

The results of the statistical analysis of college students’ quality dimensions in the new media environment are shown in Table 4. Among the 461 people in the survey, 403 people (0.8742) think that the quality of knowledge structure is the most important quality that college students should have in the new media environment. It refers to the knowledge reserves that scientific and technological communication talents should possess, such as sociological knowledge, communication knowledge, mathematical and physical knowledge, and computer knowledge.

Table 4.

Analysis of the quality dimension of college students

Serial number	Quality dimension	Frequency	Sample size	Specific gravity
1	Intellectual structure quality	403	461	0.8742
2	Business competency	386	461	0.8373
3	Innovative consciousness	365	461	0.7918
4	Innovative personality quality	339	461	0.7354
5	Communication competency	321	461	0.6963
6	Occupational ethics	303	461	0.6573
7	Managerial ability	256	461	0.5553
8	Political quality	244	461	0.5293

Under the new media environment, the quality dimensions of college students’ ideological education are broader, including the quality of knowledge structure, business quality, political quality, moral quality, innovation quality, and so on. In order to scientifically and reasonably summarize the quality dimensions that college students have in the new media environment, this paper synthesizes the research conclusions of the text analysis method and the expert interview method, and refines the six modules of political quality, moral quality, scientific quality, knowledge structure quality, professional and business quality, and learning and innovation quality.

In the new media environment, the influence of college students’ civic education is mainly composed of eight quality dimensions, namely, knowledge quality, business skills quality, management ability quality, decision-making ability quality, communication and collaboration quality, learning and innovation quality, moral quality, and political quality. According to the content of the education law on the content of the Civic and Political Education, namely, the education of patriotism, collectivism and socialism, as well as the education of ideals, morality, discipline, legal system, national defense and national unity. From an objective point of view, the contents of Civic and Political Education are very basic, but they encompass many levels of knowledge and deviate from the actual needs of college students to a certain extent. When asked about the interest in the following contents of Civic and Political Education, the results of the survey are shown in Table 5. It can be seen that the highest score (5) is 0.3087 in the content closely related to professional learning. In terms of solving practical problems such as life planning and interpersonal communication, the highest score was 0.2900, while only 0.2204 students gave a score of 5 out of 5 when asked about “the content of Marxist theory and current affairs and politics”. These data show that in the era of new media, the content of ideological education still needs to be constantly innovated, and new content outputs that match the physical and mental development and characteristics of college students under the premise of using new media need to be explored.

Table 5.

Interest in the content of ideological and political education

Investigation problem	Fractional value
Investigation problem	1	2	3	4	5
To cultivate the ideal belief, moral cultivation, scientific analysis, etc.	0.0483	0.0817	0.3015	0.3814	0.1871
Solve the practical problems of life planning and interpersonal communication	0.0405	0.0672	0.2611	0.3412	0.2900
The Marxist theory and the content of current affairs	0.0418	0.1697	0.2715	0.2966	0.2204
Closely related to professional learning	0.0503	0.0715	0.2734	0.2961	0.3087
The content of the study or the knowledge point	0.0538	0.0825	0.2611	0.3315	0.2711

4

Conclusion

New media, as a kind of technical means, are the subjects of civic and political education in the process of using new media technology for equal communication. Each subject is subconsciously enjoying the media culture brought by the new media, and at the same time, this media culture will have an impact on the cultivation and development of the ideological quality of the subjects of civic and political education. The study found that 0.5349 students think that the information in new media is mixed, and it is difficult to distinguish between good and bad, which is one of the influencing factors affecting the effectiveness of college students’ civic education. Therefore, the new media will bring positive impacts on the university’s civic and political education, but it is also difficult to avoid its negative impacts.

This essay makes the case that college students’ civic education in the age of new media should be all-around, multi-level, and broad, combining different types of education to create educational synergy. Based on this, the paper explores innovations in the concepts, subjects, contents, modes, and carriers of ideological and political education for college students and comprehensively improves the effectiveness of education. Under the condition of a new media environment, only the main body, content, method, carrier, and environment of ideological and political education are compatible with the real development needs of ideological and political education, advancing with the times, in order to effectively achieve the goal of ideological and political education in colleges and universities and to promote the comprehensive and healthy development of college students.

Further research can be conducted in future studies.First, enhance the strictness of model evaluation and introduce more performance indicators.Second, through field testing and long-term tracking research, verify the long-term stability and reliability of the proposed algorithm.Third, we will explore the moral and ethical issues of the new media environment to ensure the legitimacy and efficiency of education content.

Lingua:: Inglese

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Research on the Innovation Path of Civic Education for College Students under the New Media Environment

Xue Gong

Pubblicato online: 17 mar 2025

Ricevuto: 26 ott 2024

Accettato: 20 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0252

Parole chiaveLDA model, Personalized recommendation, Similarity calculation, Feature extraction, New media

© 2025 Xue Gong, published by Sciendo

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

Parole chiave
LDA model, Personalized recommendation, Similarity calculation, Feature extraction, New media