Research on “One and Two Wings” Teaching Mode of Higher Vocational Public English under Industry-University-Research Integration Using Particle Swarm Algorithm

In recent years, with the rapid development of information technology and the integration of industry, academia and research, English education in higher vocational colleges and universities is facing unprecedented challenges and opportunities. The traditional English teaching mode can no longer meet the needs of students and social development, so there is an urgent need for English education in higher vocational colleges and universities to create a “one body, two wings” teaching mode [1-4].

The “one body, two wings” teaching mode refers to a teaching mode that combines traditional classroom teaching and network teaching. It emphasizes the use of the Internet and modern technology to expand students’ learning channels and provide more personalized English learning resources and learning modes, and it is of great practical significance to carry out research on the “one body, two wings” teaching mode using particle swarm algorithm [5-8].

In the “one body, two wings” teaching mode, traditional classroom teaching still plays an important role. Teachers impart knowledge through face-to-face teaching, explaining and interacting with each other to promote students’ understanding and learning. This traditional teaching can provide real-time feedback and guidance to help students solve problems and deepen their understanding [9-12].

Meanwhile, e-learning, as the other wing of the two-wing model, provides additional learning resources and support through online platforms and tools. Students can access online courses, teaching videos, e-textbooks, problems and exercises through online platforms [13-16]. This mode of teaching allows students to study and review independently, at their own pace and according to their own needs. The advantage of the “one and two wings” teaching model is that it combines traditional teaching and online teaching to maximize students’ individual needs and learning styles [17-20]. It can provide more English learning resources and learning styles to promote students’ active learning and independent development. At the same time, teachers can also use the network teaching platform for English course management, student assessment and feedback to improve the effectiveness of English teaching in higher vocational colleges and universities [21-24].

The combination of “one body and two wings” industry-academia-research is an important way to reform and develop education and to comprehensively improve the quality and efficiency of education. In this paper, we construct the “one body, two wings” University-Industry-Research Teaching Mode by enhancing the cooperation between schools and enterprises, cultivating public English talents in line with the market requirements, and promoting the development of regional economy. Using the particle swarm optimization algorithm computational model to analyze the sensitivity of knowledge transfer willingness and knowledge transfer threshold of colleges and universities, this paper proposes the optimization strategy of knowledge flow of higher vocational colleges and universities in the “one body, two wings” industry-academia-research teaching. The optimized “one body, two wings” University-Industry-Research Teaching Mode is taken as the research object of the students majoring in public English in X higher vocational college, and the optimized “one body, two wings” University-Industry-Research Teaching Mode is put into practice in teaching. Discuss the validity and reliability of it.

2

Design of Integrated Higher Vocational Public English Teaching Methods

2.1

Public English Teaching Driven by the “One Body, Two Wings” Industry-Academia- Research Project

The “One Body, Two Wings” industry-academia-research program is a project of the Foreign Language Department of the university, with “One Body” referring to the self-established enterprise entity, and “Two Wings” serving the regional cultural economy and improving the construction of related professions. The two wings are intended to serve the regional cultural economy and enhance the related professional construction. The project focuses on exploring and practicing the combination of the construction of higher vocational humanities and social sciences majors and the development of regional economy, and the “integration of industry and education” allows the advantages of professional resources in this field to be transformed into productivity on the spot and to serve the regional economy. Through the industry-academia-research project entity, the enterprise concept and market mechanism can be integrated with the relevant professions, and the professions can provide strong technical support for the project entity to promote each other and realize the win-win situation of professional construction and social benefits, and ultimately achieve the goal of integrating teaching resources, setting up a service platform, upgrading the service function, and boosting the development of the regional economic and cultural construction. Driven by the project, the public English teaching of the Foreign Language Department of the Vocational and Technical College combines with the development of the regional economy, cultivates technical and applied talents according to the requirements of the development of the local economy and the requirements of the relevant jobs on the public English proficiency, so that they can better serve the regional economy and at the same time play a positive role in promoting their own development. Higher vocational public English education serves the regional economy and supports the education form of regional leading industries. Higher vocational public English education is the part of higher public English education that is most closely linked to economic development, and it is of great significance to the development of regional economies.

2.2

Innovative Teaching Models of Public English Education

In terms of teaching mode, the construction of an integrated teaching program for higher vocational public English education is shown in Figure 1. Under the premise of focusing on equipping students with solid basic knowledge of public English, more emphasis is placed on extracurricular practical training and the application of public English knowledge, which is conducive to better adapting students to the needs of the workplace, and also makes greater contributions to the development of the local economy.

In order to make the teaching of higher vocational public English education better serve the local economic development, it is necessary to further deepen the school-enterprise cooperation, build a stable cooperative relationship, create conditions for students’ practical training, and promote the enhancement of students’ public English knowledge application ability. At the same time, it is also necessary to establish a mechanism for combining industry, academia and research to promote the effective integration of the three, with “industry” as the direction, “academia” as the basis and “research” as the link. It is also necessary to enhance the cooperation between schools and enterprises and adopt the “order-based” talent training model, so as to import more public English professionals for the development of the local economy. On the one hand, it is possible to set up a “professional steering committee”, hire technical personnel from enterprises to participate in the development of public English teaching programs and curriculum construction, and formulate practical training plans in accordance with the requirements of local economic development and jobs, and organize internships for students in enterprises, so as to improve the application level of students’ knowledge of public English. On the other hand, enhance cooperation with industries and associations to create a new platform for students’ public English learning. Vocational universities can cooperate with translation associations and foreign trade associations to organize students to participate in professional public English oral and translation training. It can also organize students to participate in translation and interpretation work. This can not only enhance students’ understanding of the workplace, but also exercise their oral communication and public English translation skills, as well as create conditions for teachers to participate in teaching practice and scientific research. In turn, it promotes the mechanism of industry-university-research to a deeper level, and has a positive effect on improving students’ public English knowledge and application skills, and letting higher vocational public English teaching better serve the development of local economy. The curriculum system of higher vocational public English should be constructed in accordance with the requirements of vocational positions and the work process, and should not be based on the logic of knowledge itself and the content of book subjects to organize the curriculum model.

The curriculum system should focus on vocational public English and industrial public English, highlighting the characteristics of “industry+vocational+skill” of the higher vocational public English program. Different majors have different requirements for students’ vocational quality and professional skills, and the public English course system should be reformed according to the requirements of different abilities.

3

Optimization of particle clusters of knowledge processes in the “one and two wings” industry-academia teaching and research

It is a necessary process of University-Industry-Research Cooperation to generate the flow to each subject in the University-Industry-Research Cooperation through the higher vocational colleges and universities as knowledge factories. The process of knowledge flow and transfer is a process of innovation and appreciation of value. Higher vocational colleges and universities as a knowledge-intensive organization, the flow of knowledge throughout the higher vocational colleges and universities in talent training, scientific research and social services in the three major business processes. Knowledge flow in higher vocational colleges and universities is a type of flow, similar to traditional logistics, capital flow, and information flow.

3.1

Particle Swarm Optimization Algorithm

The PSO algorithm first initializes a group of random particles, and then finds the optimal value through iterative computation. In each iteration, the particles update themselves by tracking two “extremes”, and each particle knows the current individual optimum, the optimum in the group and its corresponding position, and can track the current optimum particle. The basic particle swarm model consists of one particle, its position and velocity with respect to the number of evolutionary generations in a one-dimensional space, denoted as: (1) $P_{t}^{j} = (p_{1, t}^{i}, p_{2, t}^{i}, \dots, p_{i, t}^{j}, \dots, p_{n, t}^{j})$ $$P_t^j = (\>p_{1,t}^i,p_{2,t}^i, \cdots ,p_{i,t}^j, \cdots ,p_{n,t}^j)$$ (2) $V_{t}^{j} = (v_{1, t}^{j}, v_{2, t}^{j}, | \dots, v_{i, t}^{j}, \dots, v_{n, t}^{j})$

where J = 1, 2, ..., n represents the number of the particle; i = 1, 2, ..., n is the number of the particle position element; and t is the number of evolutionary generations. At generation t⁺1, the velocity update expression for particle j is: (3) $V (t^{+} 1) = ω V {[t]}^{+} C_{1} R a n d () {[P B e s t - V [t]]}^{+} C_{2} R a n d ([G B e s t - V [t]]$

The updated representation of the position of particle J is given by: (4) $P [t + 1] = P [t] + v (t)$

C₁, C₂ is a constant and Rand() is a random number uniformly distributed in the interval [0, 1]. ω is an important parameter that affects the convergence of the PSO algorithm and is used to control how much the historical velocity of the particle affects the current velocity. When ω is large, the PSO algorithm has a strong global search capability. When ω is small, the PSO algorithm favors local search. Therefore, selecting an appropriate ω can balance the global and local search ability of the PSO algorithm, thus obtaining a better solution. The general ω expression is: (5) $ω = ω_{\max} - \frac{(ω_{\max} - ω_{\min})}{I t e r_{\max}} \times I t e r$

Iter represents the current number of iterations and Iter_max represents the maximum number of iterations. In this paper, ω = 0.8 is selected based on shi and Eberhart (1998) pointed out that the adaptation degree is selected: (6) $ω_{\max} = 0.9, ω_{\min} = 0.4$

Functions for: (7) $S = E {λ_{i} v (a_{i}, b_{i}) - [A_{i} v_{i} (a_{i}, b_{i}) + 0.5 B_{i} v_{i} {(a_{i}, b_{i})}^{2}]} - R_{i}$

3.2

Modeling and its solution

Particle swarm optimization of knowledge process in “one body, two wings” industry-academia research teaching is to generate the adjacency matrix through the generated knowledge process matrix, and then to carry out the description of the knowledge process through the transformation of the adjacency matrix.

It should be especially noted that in real life, it is difficult to obtain the probability of articulation between knowledge activities, so the probability is only an estimated value, and this estimated value has no effect on the analysis effect. Since the probability of articulation between knowledge activities is an estimate, the corresponding weight matrix is also an estimate in matrix analysis. The adjacency matrix, reachability matrix and strong connectivity matrix can be generated as follows: 1)

Neighborhood matrix: (8) $A = [a_{i j} | k_{i} ℜ a_{i j}]_{a \times a}]$ $$A = \left[ {\>{a_{ij}}\>|\>{k_i}\>\Re \>{a_{ij}}\>{]_{a \times a}}} \right]$$ (9) $\begin{array}{rcl} a_{i j} & = & {\begin{array}{l} 1, Knowledge activity j is the knowledge activity after knowledge activity i \\ 0, Or else \end{array} \\ i & = & 1, 2, L, n \\ j & = & 1, 2, L, n \end{array}$

Where matrix A represents the adjacency matrix and n represents the number of nodes, i.e., the number of knowledge activities. Columns with all zeros in matrix A denote the starting knowledge activities and rows with all zeros denote the ending knowledge activities. The adjacency matrix A is represented as follows: (10) $A = [\begin{matrix} 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \end{matrix}]$ 2)

Reachability matrix R and strong connectivity matrix Q based on adjacency matrix A

Reachable matrix R = (I + A)ⁿ⁻¹, where I denotes the unit matrix, r_ij = 1 denotes the node i can reach the node j after a number of nodes, and the reachable set is denoted as $R_{j} = {j | r_{i j} = 1}$ . Matrix Q denotes the strong connectivity matrix, Q = R ∩ R^T. If there is a maximum of nodes D, i.e., contains more than one node, such that all of the q_ij = 1 (i ∈ D and j ∉ D), all nodes within the set of nodes form a knowledge activity ring. Assuming that the set of nodes of a knowledge activity ring is D, a node is a ring branching node if there are a_ij = 1 (i ∈ D and j ∉ D), and the strong connectivity matrix Q and the unit matrix I have the same representation. The reachable matrix R and the strong connectivity matrix Q, which can be obtained, are represented as follows: (11) $R = [\begin{matrix} 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 & 1 \\ 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 \\ 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \end{matrix}]$ (12) $Q = [\begin{matrix} 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \end{matrix}]$

Using the above method, it is possible to decompose and obtain a description of the knowledge process system for organizing the complete cooperation between enterprises and research academia, and the results of decomposition and description are unique. In this paper, we propose to use formula (12) to comprehensively reflect the advantages and disadvantages of the knowledge process, and introduce the particle swarm optimization algorithm to optimize the matrix formed by this knowledge process: (13) $f = \sum_{i = 1}^{n} f_{i} ω_{i}$

Where f_i represents the absolute value of the error between the i nd indicator and the ideal indicator, and ω_i represents the weights. The smaller f_i is, the better the result of optimization, so using f_i as the objective function is to minimize its value. The objective function for matrix optimization is: (14) $\min f_{\max}$

This is a nonlinear optimization problem where the objective function is not easy to compute directly, and this paper proposes to use particle swarm optimization algorithm to solve this problem.

The basic particle swarm model is in a n-dimensional space, consisting of m particles, and particle positions $P_{i}^{j}$ and particle velocities $V_{i}^{j}$ associated with the evolutionary algebra t, denoted as: (15) $P_{t}^{j} = (P_{1}^{j}, t^{'}, P_{2}^{j}, t^{'}, L, P_{j}^{j}, t^{'}, L, P_{n}^{j}, t)$ (16) $V_{t}^{j} = (v_{1}^{j}, t^{'}, v_{2}^{j}, t^{'}, L, v_{j}^{j}, t^{'}, L, v_{n}^{j}, t)$

where J = 1, 2, L, m denotes the number of the particle; i = 1, 2, L, n denotes the number of the particle position element; and t is the number of evolutionary generations. At generation t + 1, the velocity update expression for particle J is: (17) $V [t + 1] = ω V [t] + C_{1} R a n d () [P B e s t - V [t]] + C_{2} r R a n d () [G B e s t - V [t]]$

The position update representation of the particle is given by: (18) $P [t + 1] = P [t] + V (t)$

Where C₁, C₂ is a constant, Rand() is a random number uniformly distributed in an interval of [0, 1], and ω is an important parameter that affects the convergence of the PSO algorithm.

The particle swarm algorithm can also maximize the benefit of each member in the “one body, two wings” industry-university-research project. The core idea of cooperation and innovation between industry, academia and research is to realize the goal of “win-win” or “multi-win” through cooperation and integration. There are n industry-university-research units, each of which has only one scientific and technological achievement. That is, the objective function: (19) $S_{i} (q_{i}) = a_{i} + b_{i} v_{i} (i = 1, 2... n)$

$C_{i} (v_{i}) = A_{i} v_{i} + 0.5 B_{i} v_{i}^{2}$ A_i, B_i is a constant, and the profit function of an industry-academia-research unit is expressed as: (20) $π_{i} (x_{i}, x -_{i}) = λ_{i} (x) v_{i} (x) - [A_{i} v_{i} (x) + 0. 5 B_{i} v_{i} {(x)}^{2}]$ $${\pi _i}(\>{x_i}\>,\>x{ - _i}) = {\lambda _i}(\>x){v_i}(\>x) - [\>{{\rm{A}}_i}{v_i}(\>x) + 0.\>5{B_i}{v_i}{(\>x)^2}\>]$$

When the industry-university-research unit bids at a certain x_i ∈ (a_i, b_i), the maximum profit of the industry-university-research unit is calculated based on the expected value of the profit function, and the model can be expressed as follows: (21) $M a x E [π_{i} (x_{i}, x_{- i})]$ (22) $s \cdot t \cdot φ (x_{i}) \leq R_{i}$

4

Optimization and empirical research on “one body, two wings” industry-academia-research teaching and learning

4.1

Particle Swarm Optimization Strategy of Knowledge Process in Higher Vocational Colleges and Universities in “One Body, Two Wings” University-Industry-Research Teaching

4.1.1

Increasing the willingness of higher education institutions to transfer knowledge

As factories of knowledge and treasures of talents, higher vocational colleges and universities are sources of national economic growth and bases for the dissemination of culture and knowledge. Knowledge material is the basis of the work of higher vocational colleges and universities. As knowledge-intensive organizations, the knowledge management activities of higher vocational colleges and universities are carried out throughout the business processes of talent cultivation, scientific research and social service, and each business process is accompanied by a large number of regular as well as irregular knowledge flows.

Knowledge transfer willingness of higher vocational colleges and universities is a comprehensive measure of higher vocational colleges and universities’ knowledge transfer motivation, which is affected by a number of factors such as organizational distance, organizational culture differences, trust between industry, academia, and research, higher vocational colleges and universities’ benefit distribution rate, and inputs to industry-academia-research collaborative innovation platforms. The original program is based on the initial value selection and parameter settings mentioned above. Keeping the organizational distance and organizational culture differences unchanged, increasing the trust between industry, academia and research, the benefit distribution rate of higher vocational colleges and universities, and the input of the collaborative innovation platform of industry, academia and research, we get Scheme 1. Option 2 is then obtained by reducing organizational distance and organizational culture differences based on Option 1. The results are shown in Figure 2.

It can be seen that the knowledge gap between higher vocational colleges and enterprises is significantly narrowed in Scenario 2 compared with Scenario 1, and in Scenario 1 compared with the original scenario, but the trends remain consistent. With the improvement of the scheme, increasing the trust between industry, academia and research, the benefit distribution rate of higher vocational colleges and universities, the input of industry-academia-research collaborative innovation platform, and reducing the organizational distance and organizational culture differences can make the knowledge stock of the enterprise gradually increase. 1)

Give full play to the advantages of small organizational distance and fewer organizational culture differences, encourage cooperation between higher vocational colleges and enterprises in the same region, and build a university-enterprise cooperation network under regional leadership.

2)

Establish a reasonable benefit distribution mechanism. Economic interests are always the important driving force for the combination between the subjects of industry-university-research cooperation, and in the process of industry-university-research cooperation, it should be based on the actual mode of school-enterprise cooperation, such as technology transfer, technology shareholding, setting up physical enterprises, and establishing strategic alliances of industry-university-research, and so on.

3)

Give full play to the government’s role as a bridge, strengthen the construction of the industry-university-research collaborative innovation platform guided by the government, implemented by higher vocational colleges and universities, and participated by enterprises, and actively promote the development of the three major innovative activities of talent cultivation, scientific and technological innovation, and fruit transformation in the industry-university-research collaborative innovation platform, strengthen the functional role of higher vocational colleges and universities in the industry-university-research collaborative innovation platform of scientific research and talent cultivation, and give full play to the natural multidisciplinary advantages, rich human resources and multifunctionality of higher vocational colleges and universities. Strengthen the function of scientific research and talent cultivation in the platform of industry-university-research collaborative innovation, and give full play to the natural multidisciplinary advantages, rich talent resources and multifunctional characteristics of higher vocational colleges.

4.1.2

Increasing the knowledge transfer threshold for higher education institutions

The initial value selection and parameter settings above are taken as the original program; the boundary of the university knowledge transfer threshold in the equation of the amount of knowledge transferred from universities is adjusted downward to 0.7 to get Program 1. Continue to adjust the boundary of the university knowledge transfer threshold downward to 0.5 to get Program 2. The results are shown in Figure 3. With the increase of enterprise knowledge, the university’s knowledge transfer threshold reaches a critical value, and the university stops transferring knowledge to the enterprise in order to maintain its own knowledge advantage. In the next stage, as universities are stronger than enterprises in terms of knowledge innovation ability, the knowledge gap between universities and enterprises begins to expand, the university knowledge transfer threshold will be lower than the critical value, universities continue to carry out knowledge flow to enterprises, and the knowledge stock of enterprises improves until it reaches the critical value next time. 1)

Actively establish a long-term mechanism and management system for University-Industry-Research Cooperation, establish a flexible personnel system and distribution system, promote the close integration of University-Industry-Research, and improve the management ability and synergy within University-Industry-Research Cooperation. Analyze the difficulties and bottlenecks faced by industry-university-research cooperation, and explore how to solve the difficulties and bottlenecks faced through synergistic innovation and mechanism and system reform. Effectively integrate and share the rich human resources and academic resources of higher vocational colleges and universities with the social resources of enterprises, focus on the major needs of the country and society, fully release the vitality of industry-university-research cooperation, and produce high-quality and high-level innovation results.

2)

The government effectively implements the policies and measures formulated on University-Industry-Research Cooperation as well as the risk-sharing and benefit-sharing mechanisms, improves the sense of trust in the cooperation between higher vocational colleges and enterprises, promotes wider knowledge exchange, knowledge sharing and knowledge integration between higher vocational colleges and enterprises in different cultural contexts, and makes the flow of knowledge in the subject of University-Industry-Research Cooperation barrier-free to enhance the overall level of knowledge.

4.2

Effectiveness of the “One and Two Wings” University-Industry Teaching and Research Program

The students of public English major in X higher vocational college were taken as the research object. A total of 110 students in class 2311 (experimental class) and class 2312 (control class) of grade 2023 were selected for the comparison experiment. The students in the two classes are basically the same in terms of age, learning foundation, learning environment and learning performance, with the same teaching content and experimental conditions. The difference is that class 2311 is taught by the optimized “integrated” teaching mode, and class 2312 is taught by the general teaching mode. The questionnaire design is shown in Table 1.

Table 1.

Questionnaire design

Question/Options	A	B	C	D
Q1 Will you review after class	Always	Often	Occasionally	Never
Q2 Summarize the knowledge content	Always	Often	Occasionally	Never
Q3 Find other topics to practice	Always	Often	Occasionally	Never
Q4 The end of the exam will make most of the wrong questions	Always	Often	Occasionally	Never
Q5 Find the weak link of your learning through the course of the enterprise	Always	Often	Occasionally	Never
Q6 You want to study in English class	Student self-study	Teacher explanation	The teacher explained the time distribution of self-study time with the students	It doesn’t
Q7 Your state in class	Concentration	Lecture with questions	Take notes	Wander away
Q8 There’s a lot of time to talk and think	Always	Often	Occasionally	Never
Q9 Whether it is a positive question	Always	Often	Occasionally	Never
Q10 Write tips to memorize knowledge points	Always	Often	Occasionally	Never
Q11 Take part in extracurricular training and enterprise training	Always	Often	Occasionally	Never
Q12 The time of the teacher in the English class is reasonable	Very reasonable	Reasonableness	Inadequacy	Very unreasonable
Q14 Ask your teachers and classmates for help	Always	Often	Occasionally	Never

The same questionnaire was administered to students in both classes before and after the implementation of the “one-unit, two-wing” industry-academia-research teaching model. The questionnaire content focused on the dimensions of learning in class and reviewing after class.

The results of the public English after-class revision survey are shown in Figure 4, with no obvious changes in the control class, and students in the experimental class, while maintaining their previous good revision habits, have thought about their own learning status at a deeper level. According to the public English teaching program development and curriculum developed with the participation of enterprise technicians, more than 70% of the students were able to conduct objective analysis and find their weak points in public English learning, and improve the application level of students’ public English knowledge. Conduct exercises to consolidate and summarize. In the process, most of the students have improved their self-knowledge and evaluation abilities.

The results of the questionnaire are shown in Figure 5. In terms of learning in the public English class, the experimental class, due to the change of the classroom format, gave students more time for independent thinking and communication, students became the main body of the classroom, and the teacher became a real guide, no longer a knowledge instiller from the beginning of the class to the end of the class. Through students’ communication and mutual help, the number of students who can find their own problems has increased, and more students have begun to think positively about the issues raised by the teacher and express their own views. Public English is a language tool used for communication and is a necessary skill for many industries. Teachers should focus on the vocational and practical aspects of higher vocational public English. About 80% of the students will participate in activities such as extracurricular practical training and enterprise training. This shows that most of the students have changed their concept of passive learning. However, a small number of students do not adapt to the new teaching mode.

At the end of the six-month experimental teaching, students uniformly took the final exam. The opening examination before the teaching experiment and the final examination after the experiment were analyzed specifically, and the results were as follows: the comparison of the two examination scores is shown in Table 2, the post-test scores of both the experimental class and the control class were relatively higher than the pre-test scores, but the post-test scores of the control class were slightly higher than the pre-test scores, and the post-test scores of the experimental class were relatively obvious in terms of their improvement.

Table 2.

Achievement statistics

			N	Min	Max	Median	M	SD
Group	Cross-reference class	Pretest	60	49	85	68	65.47	8.82
	Cross-reference class	Posttest	60	44	92	67	67.54	10.42
	Laboratory class	Pretest	60	40	91	65	65.86	11.12
	Laboratory class	Posttest	60	58	75	70	70.93	7.23

Separately, the data of pre-test and post-test were compared between the two groups and independent samples t-test was conducted, and the results are shown in Table 3, from the results, the pre-test data test results of the control class and the experimental class are Sig. greater than 0.05, indicating that there is no significant difference in the academic performance of the two classes before the teaching experiment. The result of the post-test data test is Sig. equal to 0.022, which is less than 0.05, indicating that through the teaching experiment, there is a significant difference in the academic performance of the two classes, and the experimental class’ performance is significantly higher than that of the control class.

Table 3.

Achievement difference analysis

	Group	N	M	SD	SDE	T	Sig
Pretest	Cross-reference class	60	65.34	8.852	1.194	-0.294	0.788
Pretest	Laboratory class	60	65.78	11.124	1.522	-0.294	0.788
Posttest	Cross-reference class	60	67.62	10.417	1.426	-2.468	0.022
Posttest	Laboratory class	60	70.95	7.213	0.975	-2.468	0.022

A paired sample t-test was done on the means of the two classes and the results of the control class are shown in Table 4. From the results, the mean of the pretest scores minus the posttest scores was -1.375 points and the t-test results were significant Sig. greater than 0.05, further indicating that there was no significant difference in the scores of the control class before and after the experiment.

Table 4.

The experimental class and the cross-section experiment were different

	M	Pair difference standard deviation	Standard error of mean	T	df	Sig.
Cross-reference class
Pretest-posttest	-1.375	6.632	0.884	-1.647	54.210	0.132
Laboratory class
Pretest-posttest	-5.122	7.845	1.125	-4.715	51.210	0.001

From the results, the mean value of the pre-test scores minus the post-test scores is -5.122 points, and the t-test Sig. is less than 0.05, which further indicates that there is a significant difference in the performance of the experimental class before and after the experiment and the post-test scores are higher than the pre-test scores, and the performance scores are significantly higher.

From the pre and post-test scores of the two groups of samples, there is no significant difference between the scores of the two groups before the experiment, which indicates that the samples are selected in such a way that there is no difference between the two groups before the experiment, eliminating the influence of the original scores on the experiment. After further experiments, it is found that the scores of the experimental group after the experiment are higher than those of the control group, and the scores of the experimental group are significantly higher than those before the experiment, while the scores of the control group before and after the experiment have no difference, which indicates that the experimental group adopts the “one body, two wings” industry-academia-research teaching mode, which effectively improves the students’ scores, and it has a certain degree of feasibility.

5

Conclusion

This paper constructs the “one and two wings” teaching mode of public English in higher vocational colleges and universities from the perspectives of integration of industry and education, regional economy, etc., optimizes the knowledge flow process of higher vocational colleges and universities under the “one and two wings” teaching mode by using particle swarm algorithm, and proposes the corresponding improvement measures. Measures. On the basis of teaching practice, we explore its effectiveness. The conclusions are as follows: 1)

The optimization strategy can be briefly summarized as building a university-enterprise cooperation network and encouraging cooperation between higher vocational colleges and enterprises. Establish a reasonable benefit distribution mechanism, a long-term mechanism for industry-university-research cooperation, and a management system. The government should act as a bridge between institutions and enterprises. Actively implement the policies formulated.

2)

After the experimental group adopts the “one body, two wings” University-Industry-Research Teaching Mode, the students’ performance can be significantly improved (Sig<0.05). It can be shown that this teaching method can well play the role of higher vocational public English in improving vocational abilities and achieving long-term economic development in the region.

Langue:: Anglais

Périodicité:: 1 fois par an
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Research on “One and Two Wings” Teaching Mode of Higher Vocational Public English under Industry-University-Research Integration Using Particle Swarm Algorithm

Daimin Wang

Publié en ligne: 24 mars 2025

Reçu: 21 oct. 2024

Accepté: 17 févr. 2025

DOI: https://doi.org/10.2478/amns-2025-0762

Mots clésParticle swarm algorithm, Two wings, Industry-academia-research, Neighbor matrix

© 2025 Daimin Wang, published by Sciendo

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

Mots clés
Particle swarm algorithm, Two wings, Industry-academia-research, Neighbor matrix