The Role and Impact of Social Network Analysis Algorithms in Students’ Intercultural Competence Development and English Teaching and Learning
Online veröffentlicht: 21. März 2025
Eingereicht: 02. Nov. 2024
Akzeptiert: 09. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0572
Schlüsselwörter
© 2025 Yueqin Liu, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
With the continuous innovation of Internet technology, nowadays what we call social networks mostly refer to social networks based on the Internet. At present, there are many forms of social networks, such as Facebook, MySpace and LinkedIn, which simulate interactions between different actors; TikTok and Instagram, which are social platforms in the form of sharing videos; and Flickr and Youtube, which are used to share online media content. Such social networks are extremely rich, they contain a large amount of content, such as text, images, audio or video content, and can be used in all directions [1-3].
The application part of social network analysis has been at the forefront of social network research because it has been widely commercialized and productized, especially in the field of public security, where criminal network analysis is an important tool for public security authorities to analyze the intelligence of criminal gangs [4-6]. Traditional social network analysis mainly focuses on the static network structure [7]. However, in practical application scenarios, the structure of social network groups will change with time. Nowadays, most of the cross-cultural exchanges between countries are delivered through social networks, for example, in TikTok platform, students or social figures from different countries will have a live PK to simulate face-to-face cultural exchanges and learning. There are also teaching teachers who post videos on the platform to teach spoken English, word usage, grammar comprehension and analysis, or bloggers who explain the beliefs, etiquette, customs and other cultural backgrounds of a certain place for outsiders to understand and learn [8-11].
Most of the existing social network analysis research focuses on centrality degree, clustering, shortest path, etc., while its algorithms have good performance in node classification, relationship prediction, etc [12-15]. The node centrality algorithm in social network analysis algorithm is a measure of the importance of nodes, which can help us to discover the core nodes and key influencers in social networks [16-17]. And the community discovery algorithm aims to classify nodes in the network into subgroups with strong internal connections and weak connections to other community nodes [18-20]. It can reveal the structure of many communities in a social network, which helps to understand the organizational structure and information dissemination characteristics of social networks. In addition, influence propagation algorithm is one of the important phenomena in social networks, which can predict the influence of a specific node on the whole network by modeling the information propagation process between nodes [21-22]. It has important application value in the field of social network marketing, viral communication and so on.
Good social network analytics algorithms can greatly improve user satisfaction and website traffic, and provide a stable channel for cross-cultural communication.
Literature [23] measures the robustness and reliability of the interaction aspect of students with their grades with the help of social network analysis centrality algorithms, which also serves as a way to monitor the role of the interaction aspect of collaborative learning environments to understand the core nodes that influence students’ grades. Similarly, literature [24] analyzes the impact of teacher’s teaching-related keywords on student achievement through centrality algorithm. Then the algorithm can be utilized in English education to adjust teachers’ teaching phrases, such as greeting and complimenting students in common English. Literature [25] outlines the application of clustering and detection in community discovery in online learning environments to analyze user interaction patterns. Literature [26] used fuzzy clustering algorithm to optimize the clustering time, which is useful for the integration and sharing of English education resources. Teachers will not become the player of teaching videos and lose the role of teaching in the era of online teaching, and students can reasonably utilize all kinds of teaching resources to improve their learning. Similarly, the literature [27] also analyzes the SEIR model through social networks, the network environment under the optimization of the algorithm Chinese culture communication coverage increased, and the frequency of interaction also affects the speed and breadth of communication. This algorithm plays a good role in cross-cultural communication and thus improves cross-cultural competence. And literature [28] constructed an online social network link prediction, which improved the path of data sharing, solved the privacy and copyright problems in the process of cultural communication, and increased the possibility of cross-cultural communication between students and foreigners, which paved the way for the improvement of intercultural competence. In addition, the literature [29] used social network analysis to measure results showing that social behaviors such as questioning, gratitude, self-sharing, resource replacement, and appealing ability are positively correlated with scholars’ reputation in the network. Therefore, scholars can utilize their influence in social networks to positively drive cultural exchange and learning based on their own intercultural competence and learning experience, which makes more students emulate their learning process and further develop students’ intercultural competence and English proficiency.
This paper gives a description of the social network analysis method, including the connotation of social network analysis method, constituent elements, expression form and applicability of the method. Next, appropriate indicators are selected from the overall, local, and individual levels, and the concepts and calculation formulas for each indicator are provided. Then, the centrality analysis and cohesive subgroup analysis of students’ intercultural competence in a school were conducted using the social network analysis algorithm. Subsequently, a teaching model for students’ intercultural communicative competence development was explored on this basis, which was theoretically guided by the task teaching method and the output-oriented teaching method, and tried to build a classroom interaction model incorporating intercultural competence development in the English classroom. Finally, students from a school were selected as subjects for a teaching experiment, aiming to verify the effectiveness of this paper’s methodology through changes in students’ intercultural knowledge, attitudes, behaviors, and English performance.
Social network analysis is a broad method for studying social structure [30]. Social network analysis is defined as an interdisciplinary quantitative research method developed on the basis of mathematical methods, graph theory, etc., focusing on the relationship between individuals and individuals, localities and wholes, as well as the characteristics of individual attributes, and the characteristics of the network as a whole.
A “social network” is a collection of actors (or nodes) and interconnections (or relationships) that represent the interactions between different actors. The nodes, the relationships and the organizational context constitute the most basic components of a social network.
Graph Theory Social networks are developed on the basis of graph theory, an important branch of mathematics that focuses on graphs composed of mostly point-to-point connections. Where graphs are representations used to describe nodes and relationships between nodes in a network. Define graph Matrix Matrix, is another form of mathematical representation of a network composed of nodes and relationships. Social network as a complex system, there is a diversity of its research objects, so different research objects need to be represented using different types of matrices. For example, in equation (1)
Graph theory
In recent years, social network analysis has been gradually introduced into stakeholder research, transforming the perspective from the binary level to the network level, intuitively analyzing the relationship between stakeholders from a global perspective, and putting forward targeted suggestions for the development of relationship management, which is a new attempt to study the field of organizational management by social network analysis. Stakeholders are constantly interacting with each other in terms of information resources, and they may also influence each other to achieve their own goals, which makes it difficult to identify the relationships between stakeholders. Simply identifying and categorizing stakeholders does not give us an idea of their position in the global network, but social network analysis provides a new way of thinking about the study of stakeholder relationships, as the object of study is actor relationships.
Overall network analysis The structural characterization of the relationship between stakeholders in the overall network mainly examines the cohesion and stability of the network as a whole, which usually includes information such as network size, network density, central potential, average path length, etc., to judge the characteristics of the overall relationship network of stakeholders in the construction project from a macroscopic point of view. Cohesive subgroup analysis Cohesive subgroup refers to a subset of actors with relatively close and frequent information exchanges and connections in the relationship network. This section selects faction as the indicator for cohesive subgroup analysis. In the project organization system, there are relatively closely linked stakeholder groups, which have common goals and interests, and can generate links between each other, such as sharing information resources and conducting technical exchanges, etc. The analysis of cohesive subgroups is conducive to the targeted management of small groups in the relationship network by project managers. Individual node analysis The node analysis of the relationship network concentrates on the role and position of each stakeholder in the project organization system, including the number of direct contacts between stakeholders. Actors that are in the most active position in the relational network compared to other organizations [31]. Individual network analysis in the social network analysis method is used to analyze the nodes of the relationship network in detail, using indicators such as structural holes and centrality. In this section, the quantitative measurement of the closeness of stakeholder cooperative relationship is carried out to determine the closeness of stakeholder cooperative relationship through the degree of information sharing and the intensity of mutual communication, and further analyze the meanings represented by different indicators and the research objectives, to determine the basis for the selection of indicators for the closeness of stakeholder cooperative relationship. The dimensions and indicators of the analyzed stakeholder cooperative relationship social network are shown in Figure 2.
Social network characteristics analysis dimension and index
Social network analysis method is characterized by indicators of social network quantitative research, indicator analysis can reflect the characteristics of the relationship network, this section selects the measurement indicators suitable for the social network analysis of stakeholder relationships in construction projects. From the overall level of the stakeholder relationship network density, central potential indicators to measure the analysis, mainly to examine the cohesion and stability of the network as a whole.
Network density In social networks, the ratio of the number of real relationships existing in the network to the maximum possible number of relationships is an important indicator of the closeness of relationships between nodes. In a certain size network, as the number of connecting lines in the network increases, the density of the network and the tightness of the relationship between nodes also increase. The larger the value of the overall density of the network, the higher the frequency of communication between the subjects, the more rapid information exchange and resource transfer in the network, and the higher coordination and cooperation of the overall network. The network density is represented by
Where: Degree centrality potential The degree-centered potential describes the degree of centrality of the overall network, and is used to indicate the degree to which all points in a network graph are concentrated towards one of the points. Calculation of degree-centered potential needs to find the maximum centrality of the relationship network, and then through the calculation of the maximum centrality and the centrality of other nodes of the “difference”, to get the sum of these differences, and finally divide the sum of the difference by the maximum possible sum of the difference. The formula is shown in equation (3).
Where:
The so-called cohesive subgroups are a number of subgroups formed by actors in a network of relationships through close association. The results of the analysis can reflect not only the size of the subgroups’ relationships, but also the participation of stakeholders in the organizational network in the various relationships. Many types of subgroups exist in different social organizations, and these groups not only affect the relationships between members of the organization, but also create obstacles to the achievement of organizational goals and development.
Individual network measurement focuses on obtaining the main stakeholders in the network and quantifying the extent to which different stakeholders influence each other from two perspectives. These are the ability of nodes to influence other nodes represented by “power” and the extent to which nodes are constrained by other nodes. Therefore, in this section, the centrality and structural holes of the “restriction system” are selected as the indicators of individual networks.
Degree of centrality
Degree of centrality Different actors in the social network have different degrees of closeness and occupy different positions, so the degree of closeness can be measured by the difference in the degree of nodes. Degree centrality is the sum of direct connections between a node and other nodes. The calculation formula is shown in equation (4).
Intermediate centrality The intermediate centrality can be expressed as the ratio of the sum of the number of shortest paths passing through node Proximity centrality Proximity centrality can be measured by the closeness or distance between nodes and is used to measure the proximity between nodes. Usually some nodes with high proximity centrality tend to be more likely to establish cooperative alliances with other organizations, and the relationship between them causes more impact on the network. The relative proximity centrality expression is shown in equation (6).
Structural hole metrics Structural holes are used to measure non-redundant relationships between nodes. When a structural hole appears between two nodes in the network, it indicates that the relationship between them is non-redundant. Structural holes in a relational network are shown in Fig. 3. In Fig. 3(a), the relationship between
Network hole
In this experiment, a total of 60 students in a school were selected as the research subjects, followed by the teacher’s social intercultural competence score for each student, which was scored in five levels, from 1-5 indicating strong intercultural competence, strong intercultural competence, average intercultural competence, weak intercultural competence and weak intercultural competence, respectively. That is, the best performing student scored 1 point, the next scored 2 points, and so on, with the lowest scoring 5 points. SPSS was used to analyze the data statistically and perform network analysis. The data was first entered to demonstrate the cooperative intercultural competence of the students in the class by analyzing parameters related to centrality and cohesive subgroups.
The incidence of individual students is shown in Figure 4, with student #8 having the greatest incidence, with an incidence value of 12. Students #7 and #15 have an incidence of 9. Student #13 has an incidence of 7. The rest of the students with lower entry were 3, 2, and 1 respectively. There are also some students who have an entry level of 0, such as students #11 and #12. These data were imported into SPSS to do a Spearman rank correlation with teachers’ subjective ratings of students’ personality, and a correlation coefficient of r=-0.432 was obtained, p<0.05 (the correlation coefficient is negative because the teachers’ subjective ratings are scored in the opposite direction to the students’ entry degree). The results indicate that the higher the entry level, the lower the subjective rating, i.e., the higher the intercultural competence of the students, and the results also indirectly indicate the reliability of the online data.
The entry degree of the individual
The frequency histogram of intercultural competence entry degree is shown in Figure 5. From the figure, it can be seen that the students’ intakes do not follow a normal distribution, and the intake values are all concentrated between 0 and 4, with the largest number of students having an intake value of 1.
Cooperative interaction frequency histogram
The student’s mediated centrality is shown in Figure 6. Student #15 had the greatest mediational centrality with a value of 0.25. This is followed by student #14 with a value of 0.16. Students #2, #3, and #10 had the same mediator neutrality of 0.13. Students #8, #13, and #29 also had a relatively high mediator neutrality of 0.12. The rest of the students had lower intermediary centrality. These data were imported into SPSS to do a Spearman rank correlation with the teachers’ subjective ratings of personality, and a correlation coefficient of r=-0.404 was obtained, p<0.05. The results indicate that the higher the mediational centrality, the lower the subjective rating, i.e., the higher the intercultural competence of the students. The proximity centrality of the students is shown in Figure 7. Students #7 and #8 had the greatest proximity centrality with a value of 0.52. Student #15 has a value of 0.42. Student #13 has a value of 0.35. Students #38 and #20 have a value of 0.33. The rest of the students had relatively low values, with some even having a near centrality of 0.00. These data were imported into SPSS to do a Spearman rank correlation with the teachers’ subjective ratings of personality, and a correlation coefficient of r=-0.587 was obtained, p<0.05. The results indicate that the higher the proximity centrality, the lower the subjective rating, i.e., the higher the intercultural competence of the students.
Student mediation center
Proximity center
The centrality of representative individuals of students is shown in Table 1. From the three centrality indicators, it can be seen that although all of them are significantly negatively correlated with subjective intercultural competence, the same nodes are different in different individual centrality indicators, the point centrality and mediator centrality of student No. 3 and student No. 10 are consistent, but their proximity centrality is different, which is 0.25 and 0.2, respectively. Again, students #32 and #34, whose pointwise centrality and proximity centrality agree, have mediator centrality of 0.07 and 0.02, respectively.
The center of the representative individual
| Node number | Degree centrality(indegreek) | Degree centrality(indegreek) | Closeness centrality |
|---|---|---|---|
| Number 3 | 5 | 0.18 | 0.25 |
| Number 10 | 5 | 0.18 | 0.2 |
| Number 32 | 3 | 0.07 | 0.16 |
| Number 34 | 3 | 0.02 | 0.16 |
Firstly, the average pointwise degree of all vertices in the network was calculated to be 5 and the density was 0.072. Indicating that the network cohesion was good. The
Student individual’s
College English classroom teaching that incorporates intercultural teaching needs to incorporate the two teaching components of task-driven and student task execution, which have a positive effect on the cultivation of students’ intercultural awareness. Instead of emphasizing a student-centered approach, the output-oriented approach focuses on “learning” and advocates the concept of “learning to use”. Based on the “output-driven-input-enabled hypothesis”, the output-oriented approach is a localized pedagogical theory, which has three core components: driving, enabling and evaluating. In the enabling session, teachers use the teaching objectives and output tasks as teaching drivers. The facilitating session requires teachers to be able to provide input materials that lead to the output, help students select important parts for processing and practicing according to the output needs, and ensure the quality of students’ output. The evaluation session involves teachers making immediate assessments and providing remedial teaching for students’ tasks. It can be seen that the teacher acts as an intermediary in these three stages, playing the roles of leading, designing, and scaffolding, highlighting the role of the teacher in teaching. Task output is both the starting point and the goal of teaching [32-33].
In order to achieve the teaching goal of cultivating students’ language application ability and intercultural competence, this teaching practice is based on the teaching theories of task teaching method and output-oriented method, integrates intercultural teaching into traditional language teaching, and optimizes the design of the task-driven-output-oriented intercultural competence cultivation teaching mode in college English classrooms. Teachers play the roles of facilitator, leader and evaluator, and take students’ “learning” and “communication” as the center of teaching.
A one-year academic study of intercultural teaching was conducted in an English classroom of one English major class at a university. First, a teaching needs assessment was conducted on the teaching of the subject, based on the assessment results, the teaching objectives were determined, the teaching contents were clarified, the teaching activities and processes were selected and arranged, and finally the teaching was evaluated and reflected upon. The design of teaching tasks is driven by few and precise output-type tasks, which prompts students to actively learn the required intercultural knowledge and skills in the process of experiencing interculturality, and enhances their language application ability. The steps of the task-driven and output-oriented intercultural integration classroom teaching are “task-driven and setting - task implementation - task assessment and reflection”. The idea of classroom teaching is shown in Figure 9.
Classroom teaching thought
Taking one classroom teaching of a university English course as an example, we introduce and analyze the ways and effects of teaching university English in a classroom that incorporates intercultural learning.
Stage 1: Task-driven and Setting Stage (Input of Intercultural Knowledge). The goal of the task setting is to let students independently acquire as much cross-cultural knowledge and information as possible and to stimulate students’ cross-cultural awareness. Task 1: Students are assigned to use various online platforms to collect and tell in English well-known friendship stories in the past, present and future. Students look for relevant information and work in groups to tell stories of friendship between China and other countries in English, and create a video for submission. Teachers will give tips on the task. Task 2: Students find and record 10 Chinese and 10 English friendship proverbs to share in class. The teacher will give the Chinese and English proverbs and the students will translate them. Pre-lesson task 3: Write down the reasons for the suspension of friendship in the author’s writing against the content of the text and ask students to tell their own friendship stories in English. At this stage, students will acquire knowledge about the culture of friendship between Chinese and Western cultures through the preparation and completion of the pre-course tasks, and feel the differences in the expressions of their own culture and that of other countries. Teachers provide relevant input materials and guidance, and take students’ output as a task-driven process. The first phase of the task is focused on improving students’ English language skills and cultivating cultural awareness.
Stage 2: Task implementation stage (intercultural scenario implantation). In this stage, the teacher completes the tasks one by one with the students in the classroom, so that the students can produce more cross-cultural knowledge and experience cross-cultural communication. First, complete Task 1: The teacher shows the video work of the best students and points out the language problems to help them tell the story of Chinese friendship accurately in English. Student groups role-played the story of friendship between China and the West, while other groups watched and guessed the name of the story. This activity introduces cross-cultural scenarios into the classroom, allowing students to fully immerse themselves in storytelling and comprehension. Completion of Task 2: Students share proverbs about friendship, and the teacher guides students to match Chinese and foreign friendship proverbs with different friendship stories from China and the West. Students share their friendship stories to complete Task Three. Then, the teacher plays the video “Chinese friendship in the eyes of westerners”, using examples to let students feel the essential differences between Chinese and English friendship and the cultural reasons. Through this series of tasks, students gain an understanding of language and cultural knowledge and try to communicate effectively, cultivating cross-cultural sensitivity. The content of the textbook is an important material for students’ language input, which teachers should not neglect in the process of teaching.
Phase 3: Task Assessment Phase (Intercultural Knowledge Task Assessment Phase (Intercultural Knowledge Reflection). The goal of this stage is to develop students’ intercultural critical awareness and critical thinking skills through in-depth intercultural comparison and reflection. The teacher will make immediate comments on the students’ video and role-playing work, suggest modifications, and invite other groups of students to comment on the work, highlighting strengths and weaknesses. Discuss the differences between Chinese and Western friendships in terms of their manifestations, and consider whether the underlying reasons for the differences are related to the values of collectivism and individualism between the East and the West. Have students write a cultural journal in English, asking them to reflect deeply on the differences between Chinese and Western friendship cultures from the perspective of the characteristics of Chinese and Western cultures and their formation, and to reflect on the differences between the values of collectivism and individualism embodied in Chinese and Western friendship cultures. Teachers transform students’ oral classroom outputs into written outputs through written assignments, which on the one hand strengthens students’ language drills and standardizes their written language application skills, and on the other hand allows students to reflect, think and empathize with Chinese and Western cultures. Finally, the teacher conducts a delayed assessment of the students’ journals.
This section of the experiment continues to take students of a school as the research object, set up experimental class (60 students) and control class (60 students), the experiment time is one semester, respectively, from the changes of students’ cross-cultural knowledge, attitudes, behaviors, and English learning achievement, to explore the role of this paper’s method in students’ cross-cultural competence and English achievement in.
In order to analyze the effect of this paper’s method on the three dimensions of intercultural communicative competence: knowledge, attitude and behavior, the author firstly statistically organizes the pre-test scores of intercultural knowledge, attitude and behavior of the experimental class and the control class. The pre-test data of intercultural communication knowledge, attitude and behavior of the experimental and control classes are shown in Table 2. The results of the independent sample t-test for the three dimensions of intercultural communicative competence of the experimental and control classes are shown in Table 3.
Pre-behavior data of the experiment class and the cross section
| Class | Case Number | Mean Value | Standard Deviation | Standard Error Mean | |
|---|---|---|---|---|---|
| Knowledge | Laboratory Class | 60 | 33.84 | 4.439 | 0.64 |
| Cross-Reference Class | 60 | 32.67 | 5.775 | 0.84 | |
| Attitude | Laboratory Class | 60 | 31 | 4.514 | 0.572 |
| Cross-Reference Class | 60 | 31.67 | 4.358 | 0.598 | |
| Behavior | Laboratory Class | 60 | 32.81 | 7.464 | 1.037 |
| Cross-Reference Class | 60 | 30.99 | 6.044 | 0.838 |
Independent sample T test results
| Levin variance equivalence test | Average equivalent t test | |||||||
|---|---|---|---|---|---|---|---|---|
| F | significance | t | freedom | Sig(Double tail) | Mean difference | Standard error | ||
| Knowledge | Assumed equal variance | 3.926 | 0.055 | 1.291 | 95 | 0.216 | 1.326 | 1.032 |
| Unassuming equal variance | 1.291 | 91.745 | 0.216 | 1.326 | 1.032 | |||
| Attitude | Assumed equal variance | 0.022 | 0.892 | -1.266 | 95 | 0.198 | -1.11 | 0.891 |
| Unassuming equal variance | -1.266 | 97.352 | 0.198 | -1.11 | 0.891 | |||
| Behavior | Assumed equal variance | 1.193 | 0.281 | 0.916 | 95 | 0.362 | 1.246 | 1.345 |
| Unassuming equal variance | 0.916 | 93.152 | 0.362 | 1.246 | 1.345 | |||
As can be seen from the table, in the dimension of intercultural knowledge, the mean values of the experimental and control classes were 33.84 and 32.67, respectively, and the standard deviations were 4.439 and 5.775, respectively, which shows that the overall gap is very small. The results of Levine’s variance equivalence test show that F corresponds to a p-value of 0.055 (>0.05), which is greater than the level of significance, so the test is assumed to be valid when the variances are equal. At this point the mean equivalence t-test results show that T=1.291 and P=0.216, both greater than 0.05, so there is no significant difference between these two sets of data, which shows that there is no significant difference between the two classes in the dimension of intercultural knowledge and it is almost at the same level. In the dimension of intercultural attitudes, the means of the experimental and control classes were 31 and 31.67 with standard deviations of 4.514 and 4.358, respectively. The result of Levine’s test for equality of variances showed that the F-value was 0.022 and the P-value was 0.892, which is greater than 0.05, so the data from the t-test assuming equality of variances was used. The line of data showed that P=0.198 which is greater than 0.05. It can be concluded that the experimental and control classes have the same level of pre-test of intercultural attitudes. In the intercultural behavior dimension, the mean values of the experimental and control classes were 32.81 and 30.99 with standard deviations of 7.464 and 6.044. The results of Levine’s variance equivalence test showed that F corresponded to a p-value of 0.281 (p>0.05), which is greater than the level of significance, assuming that the test is valid when the variances are equal. In the t-test for equivalence of means, T=0.916, P=0.362, with a p-value greater than 0.05. This proves that there is no significant difference in the intercultural behavior dimension between the two classes before the experiment. To sum up, there is no significant difference in the overall level of intercultural communicative competence and the levels of the three dimensions of knowledge, attitude and behavior between the control class and the experimental class before the experiment, which are at the same level.
In order to investigate how the method of this paper affects students’ intercultural knowledge, attitudes and behaviors after one semester’s teaching experiment, the author conducted an independent samples t-test on the posttest scores of the three dimensions in the two classes, and the statistics of the posttest scores of the three dimensions of intercultural communicative competence in the experimental class and the control class are shown in Table 4. The post-test scores of the experimental and control classes in the three dimensions of intercultural communicative competence were subjected to independent sample t-tests as shown in Table 5.
Statistical results of the experimental class and the comparison class
| Class | Case Number | Mean Value | Standard Deviation | Standard Error Mean | |
|---|---|---|---|---|---|
| Knowledge | Laboratory Class | 60 | 42.62 | 4.896 | 0.692 |
| Cross-Reference Class | 60 | 33.61 | 4.263 | 0.598 | |
| Attitude | Laboratory Class | 60 | 47.62 | 2.985 | 0.431 |
| Cross-Reference Class | 60 | 34.65 | 4.736 | 0.684 | |
| Behavior | Laboratory Class | 60 | 41.62 | 8.241 | 1.155 |
| Cross-Reference Class | 60 | 35.84 | 5.263 | 0.751 |
Test of independent sample t test
| Levin variance equivalence test | Average equivalent t test | |||||||
|---|---|---|---|---|---|---|---|---|
| F | significance | t | freedom | Sig(Double tail) | Mean difference | Standard error | ||
| Knowledge | Assumed equal variance | 0.645 | 0.433 | 10.005 | 97 | 0.000 | 9.144 | 0.916 |
| Unassuming equal variance | 10.005 | 97.652 | 0.000 | 9.144 | 0.916 | |||
| Attitude | Assumed equal variance | 9.638 | 0.005 | 17.165 | 97 | 0.000 | 13.549 | 0.814 |
| Unassuming equal variance | 17.165 | 82.411 | 0.000 | 13.549 | 0.814 | |||
| Behavior | Assumed equal variance | 10.322 | 0.001 | 4.958 | 97 | 0.000 | 6.875 | 1.421 |
| Unassuming equal variance | 4.958 | 83.654 | 0.000 | 6.875 | 1.421 | |||
From the two tables, it can be seen that in the dimension of intercultural knowledge, the mean values of the experimental and control classes after the experiment were 42.62 and 33.61, respectively.According to the results of the independent samples t-test in the table, the Levine’s test of equivalence of variances shows that the F-value corresponds to a P-value of 0.433, which is less than 0.05, so the test is valid when it is assumed to be unequal in terms of variances. The t-test for equivalence of means in this row showed a P-value of 0.000 (P<0.05). It indicates that there is a significant difference between the data of the two groups, i.e. there is a significant difference in the level of intercultural knowledge dimensions between the two classes after the experiment.
In the cross-cultural attitude dimension, the mean values of the experimental and control classes after the experiment were 47.62 and 34.65, respectively.In the Levine’s test of equivalence of variances, the p-value corresponding to F was 0.005, which is less than 0.05, so the test was assumed to be valid when the variances were not equal. The t-test for equivalence of means in this row showed a p-value of 0.000 (p<0.05). It indicates that the difference between these two sets of data is significant, i.e., there is a significant difference in the level of the intercultural attitude dimension between the two classes after the experiment. In the intercultural behavior dimension, the mean of the posttest scores of the experimental and control classes were 41.62 and 35.84, respectively, and the Levine’s test of equivalence of variances showed that F corresponded to a p-value of 0.001, which was less than 0.05, so the assumption of inequality of variances was established. The t-test for equivalence of means in this line showed a p-value of 0.000 (p<0.05). It indicates that there is a significant difference in the level of intercultural behavior dimensions between the two classes after the experiment. In conclusion, there is a significant difference in the level of intercultural knowledge, attitude, and behavior dimensions between the two classes following the experiment. Before the experiment the three dimensions of knowledge, attitude and behavior of the two classes were at the same level, while after the experiment the three dimensions of knowledge, attitude and behavior of the experimental class were significantly higher than those of the control class. In order to understand how this paper’s method affects the knowledge, attitude and behavior of intercultural communication competence respectively, the author conducted a paired-sample t-test on the scores of the three dimensions in the pre- and post-test of the experimental class and judged that this paper’s method has the greatest effect on that dimension of intercultural communication competence according to the magnitude of the significance.
The descriptive statistics of the pre-test data and post-test data of intercultural communicative competence are shown in Table 6, and it can be found through the analysis that the post-test scores of the three dimensions are higher than the pre-test scores, so the students’ knowledge, attitudes and behaviors have been improved. From this, it can be preliminarily judged that after one semester of teaching with the method of this paper, students’ intercultural knowledge, intercultural attitude and intercultural behavior have been improved. The author conducted paired-sample T for the pre-test and post-test data of the three dimensions of intercultural knowledge, attitude and behavior in the experimental class, and the paired-sample T test for the pre-test and post-test data of the three dimensions of intercultural in the experimental class is shown in Table 7. First of all, for the dimension of intercultural knowledge, it can be seen from the data that the average score of students’ intercultural knowledge before the experiment is about 32.84 points, and after the experiment, the average score of students’ cultural knowledge is about 42.61 points, and the difference between the average values before and after the experiment is 9.77 points. The paired-sample t-test of the pre- and post-test data showed a p-value of 0.000 (<0.05), indicating that before and after the experiment, the students in the experimental class experienced significant changes in the dimension of intercultural knowledge, which suggests that the post-test scores in the dimension of intercultural knowledge in the experimental class were significantly higher than the pre-test scores. Therefore, the following conclusion is drawn: the method of this paper can improve students’ intercultural knowledge. This shows that using this paper’s method in English teaching effectively enhances learners’ understanding of Chinese and Western language knowledge, as well as intercultural knowledge. The difference between the mean values of intercultural attitudes before and after the experiment is 16 points. The results of the paired samples t-test showed a p-value of 0.000, indicating that the students in the experimental class experienced significant changes in their level of intercultural attitudes before and after the experiment. This paper’s methodology appears to have a positive effect on students’ intercultural attitudes. In terms of the dimension of cross-cultural behavior, it can be concluded from the data that before the experiment, the average score of students’ cross-cultural behavior was about 33.65 points, and the average score of students’ cross-cultural behavior after the experiment was about 40.98 points, and the average difference before and after the experiment was 7.33 points. It is therefore concluded that the methodology of this paper has a facilitating effect on the intercultural behavioral aspects of students.
Descriptive statistics of pre-measured data and post-data data
| Mean Value | Case Number | Standard Deviation | Standard Error Mean | |
|---|---|---|---|---|
| Pre-test of Knowledge | 32.84 | 60 | 4.162 | 0.622 |
| Post-test of Knowledge | 42.61 | 60 | 4.635 | 0.695 |
| Pre-test of Attitude | 31.65 | 60 | 4.415 | 0.631 |
| Post-test of Attitude | 47.65 | 60 | 2.958 | 0.415 |
| Pre-test of Behavior | 33.65 | 60 | 7.512 | 1.026 |
| Post-test of Behavior | 40.98 | 60 | 8.244 | 1.165 |
Test of data matching sample t before and after
| Mean Value | Standard Deviation | Standard Error Mean | The difference is 95% confidence interval | t | freedom | Sig(Double tail) | ||
|---|---|---|---|---|---|---|---|---|
| Lower limit | Upper limit | |||||||
| Knowledge | -8.215 | 5.877 | 0.842 | -9.755 | -6.425 | -9.875 | 46 | 0.000 |
| Attitude | -17.622 | 5.748 | 0.811 | -18.655 | -14.362 | -19.842 | 46 | 0.000 |
| Behavior | -6.788 | 12.652 | 1.625 | -10.155 | -3.641 | -4.261 | 46 | 0.000 |
The author then further analyzed the results in conjunction with the results of the paired samples t-test, and the pre and post-test paired t-values for the three dimensions of interculturalism in the experimental class are shown in Table 8. The absolute values of the t-values for intercultural knowledge, attitude and skills are -9.482, -19.653 and -4.215 respectively. The greater the absolute value of t, the greater the difference between before and after the experiment. Therefore, the dimension of students’ intercultural attitudes changed most significantly before and after the experiment. It is finally concluded that the method used in this paper has the most significant effect on the dimensions of students’ intercultural attitudes.
Before and after the matching t
| Dimension | T value |
|---|---|
| Cross-cultural knowledge | -9.482 |
| Cross-cultural attitude | -19.653 |
| Cross-cultural behavior | -4.215 |
In summary, after one semester of teaching experiment, the overall level of intercultural competence and its three dimensions of the experimental class students have been significantly improved, while the intercultural competence and the three dimensions of the control class students before and after the experiment did not change significantly, so it can be concluded that this paper’s methodology has a positive effect on the cultivation of students’ intercultural competence, and has a positive effect on the students’ intercultural knowledge, attitudes and behaviors, especially the most obvious promotion effect on the dimension of students’ intercultural attitudes. It can be concluded that the method of this paper has a positive effect on the cultivation of students’ intercultural competence, and it can promote students’ intercultural knowledge, attitudes and behaviors in three dimensions, especially in the dimension of intercultural attitudes.
In this paper, the listening, vocabulary, reading, and composite scores of the pre-test and post-test of the experimental class were subjected to independent samples t-tests using SPSS respectively, and the data of the pre and post-test t-tests of the experimental class are shown in Table 9.N refers to the sample capacity of the subjects in the experimental study, and the sample capacity of the experimental class is 60 students, so N = 60. Firstly, we analyze the results of the listening variance chi-square test: the significance level p = Sig. = 0.691 > 0.05, which did not reach the significance level, indicating that the variance chi-square condition is valid, and the sample variance amount t = 1.098, p = 0.691, t > p, which can be regarded as a significant difference in the performance of the listening pre-test and the post-test reached. As for vocabulary, reading and composite scores, t = 0.809, 1.663 and 1.053, p = 0.586, 0.043 and 0.931, t > p. Thus, it can be concluded that there is a significant difference in the scores of the pre-tests and the post-tests for vocabulary, reading, and composite scores.
Test data of test t before and after the experimental class
| Empirical Class | N | Standard Deviation | Sig. | T | |
|---|---|---|---|---|---|
| Auditory Power | Pre-Test | 60 | 5.244 | 0.691 | 1.098 |
| Post-Test | 60 | 5.487 | |||
| Word Sink | Pre-Test | 60 | 7.727 | 0.586 | 0.809 |
| Post-Test | 60 | 7.212 | |||
| Read | Pre-Test | 60 | 4.422 | 0.043 | 1.663 |
| Post-Test | 60 | 5.409 | |||
| Heald | Pre-Test | 60 | 5.942 | 0.931 | 1.053 |
| Post-Test | 60 | 5.873 |
In this paper, the listening, vocabulary, reading, and composite scores of the pre-test and post-test of the control class were subjected to independent samples t-tests using SPSS respectively, and the data of the pre-test and post-test t-test of the control class are shown in Table 10. The sample size of the control class is 60, so N = 60. As can be seen from the table, the results of the listening test are: the significance level p = Sig. = 0.473 < 0.05, which has reached the level of significance, indicating that the condition of chi-square does not hold, and the amount of sample variance t = -0.054, p = 0.473, t < p. It can be assumed that the scores of the listening pre-test and the post-test have not reached a significant difference. As for vocabulary, reading and composite scores, t = 0.000, 0.536 and 0.214, p = 0.741, 0.526 and 0.821, t < p. It can be assumed that the difference in scores between pre-test and post-test for vocabulary, reading, and composite scores is not significant.
Test data of t-test before and after the cross-section
| Cross-Reference Class | N | Standard Deviation | Sig. | T | |
|---|---|---|---|---|---|
| Auditory Power | Pre-Test | 60 | 5.191 | 0.473 | -0.054 |
| Post-Test | 60 | 5.578 | |||
| Word Sink | Pre-Test | 60 | 6.648 | 0.741 | 0.000 |
| Post-Test | 60 | 6.475 | |||
| Read | Pre-Test | 60 | 4.663 | 0.526 | 0.536 |
| Post-Test | 60 | 4.763 | |||
| Heald | Pre-Test | 60 | 5.284 | 0.821 | 0.214 |
| Post-Test | 60 | 5.343 |
For the posttest of the experimental class and the posttest scores of the control class, independent samples t-tests for listening, vocabulary, reading, and synthesis were conducted using SPSS, and the t-test data of the posttest of the experimental class and the control class are shown in Table 11. In listening, the results of the test of variance alignment between the control class and the experimental class in listening: the significance level p = Sig. = 0.727 > 0.05, which does not reach the significance level, indicating that the condition of variance alignment is established, and the amount of sample difference t = 0.412, p = 0.727, t < p, which can be regarded as a significant difference between the performance of pre-test and post-test of listening has not been reached. However, in vocabulary, reading, and synthesis, t > p, the scores showed a significant difference.
Test data for test and cross section
| class | N | Standard deviation | Sig. | t | |
|---|---|---|---|---|---|
| Auditory power | Pre-test | 60 | 5.484 | 0.727 | 0.412 |
| Post-test | 60 | 5.583 | |||
| Word sink | Pre-test | 60 | 7.189 | 0.465 | 0.82 |
| Post-test | 60 | 6.452 | |||
| read | Pre-test | 60 | 5.418 | 0.462 | 1.271 |
| Post-test | 60 | 4.774 | |||
| 4heald | Pre-test | 60 | 5.882 | 0.175 | 0.874 |
| Post-test | 60 | 5.35 |
This paper utilizes social network analysis to analyze the indicators of students’ intercultural competence cultivation network, and proposes a college English classroom teaching model that incorporates intercultural competence cultivation.
In the three centrality indicators of social network analysis, although all of them are significantly negatively correlated with subjective intercultural competence, the same nodes are different in different individual centrality indicators. For example, Student #32 and Student #34 had the same point centrality and proximity centrality, but mediated centrality of 0.07 and 0.02, respectively.
In the results of the posttest t-test between the experimental and control classes, it was concluded that in vocabulary, reading, and synthesis, t > p. For example, in reading methods, t = 0.462 > p = 1.271, and the students’ performance reached a significant difference.
This paper verifies the effectiveness of the social network analysis algorithm on the cultivation of intercultural communicative competence on the basis of obtaining a large amount of data using the teaching experiment as a benchmark, enriches the research results in the field of intercultural communicative competence, and contributes to solving the realistic problems of intercultural communicative competence cultivation in English teaching.