Group Cognition Assessment in the Arts Cooperative Learning

Introduction

Cooperative learning in English education requires the cooperation of teachers and students, mutual assistance, and teamwork, while integrating the competitive model into cooperative learning, to stimulate students' enthusiasm for learning and enhance the learning effect. The traditional English learning manner is mainly based on teacher'south preaching. Teachers get the main trunk of the classroom, especially for linguistic communication subjects, students with poor basic ability, it is difficult to integrate into the classroom, and students often feel boring. The game-based English teaching model has emerged and achieved proficient results. Although the quality of scientific research output is discussed in this article, it is reasonable to produce different results considering of the differences in the selection of research objects and indicators. Inspired by the research, nosotros tin can continue to explore whether the U-shaped relationship between interdisciplinary and team output quality depends on other factors, such as team member recognition or team advice.

Through literature review and empirical analysis, Eknc and others believe that unplanned direction and team member conflict are the master causes of research failure (Eknc, 2020). Siddiqui's inquiry points out that the lack of face-to-face advice will lead to members who are geographically far abroad from the cadre of the team to feel dislocated almost team goals and tasks (Siddiqui and Georgiadis, 2020). Freedberg believes that game design in the didactics process can trigger users' learning motivation, generate learning achievements, enrich learning experience, and correct learning attitudes (Freedberg et al., 2019). Liao first put frontward the give-and-take "Gamification," but the utilize of gamification design thinking to amend the feel of Internet products has gradually get popular since 2010 (Liao et al., 2018). Corell proposed that the game pattern is a "people-oriented design," which ways that in the not-game context, game thinking and interesting elements are used to stimulate user behavior and meet human psychological motivation and needs (Corell et al., 2018).

In terms of educational methods, Qinchen found that proper autonomous learning (such every bit video and game interaction) tin can effectively improve students' performance by irresolute teaching strategies and comparison students' performance changes before and afterward learning, and can change the style of autonomous learning according to different learning objectives (Cao et al., 2021). Cano plant that although at that place was no meaning departure between the game grouping and the traditional group in learning environment and curriculum attitude, the game group was better than the traditional group in grouping cohesion, score, and squad evaluation (Cano and Villón, 2018). Lobov believes that breezy institutional differences will hinder communication and exchange betwixt the 2 sides of cooperation, which will take a negative impact on transnational cooperation and innovation (Lobov et al., 2020). Li W believes that appropriate informal institutional distance volition bring differentiated complementary knowledge and provides impetus for cooperative innovation, and the relationship between informal institutional distance and Transnational Cooperative Innovation is inverted U-shaped (Li and Gu, 2020). Through empirical analysis, Zidan et al. plant that the touch on of informal institutional altitude on Transnational Cooperative Innovation is not meaning (Zidan et al., 2019). The above studies all recognize the disadvantages of the traditional not-squad learning model, but most of them evaluate the learning situation from the perspective of English learners' behavior and exercise not form a complete set of English learning models, and so it is the lack of inquiry value for the wide applicability of learners.

This article constructs a cooperative and competitive model of English learning based on multimodal information fusion. The primary manifestation is that students form groups in small groups, and there is a competitive relationship betwixt the groups, it can stimulate students' interest in learning, give play to their enthusiasm for learning, and enhance the interaction in learning. The competition among students in learning is the common interest of the entire grouping, so that the overall interests of each student volition be more competitive. This commodity refers to the main body association model in the literature to adjust English grammar, vocabulary, and language perception ability: learn together in team communication to better students' multifaceted abilities. Finally, a questionnaire was designed.

English Learning Condition and Squad Learning Evaluation Model

Current Situation of Traditional English Classroom

The traditional determinative assessment of the classroom is facing many difficulties in the process of implementation. On the one hand, the traditional determinative assessment techniques and strategies put forrad high requirements for teachers, who need to accept evidence thinking, and discover and record students' process performance while pedagogy. This increases the difficulty of implementing formative assessment (Mohammed et al., 2018). On the other hand, traditional formative assessment techniques such as classroom observation records, action records, and portfolio use, newspaper materials, and the process of filling, receiving, and sorting is very time-consuming, and it is difficult to shop cess materials such as learning products (Wariyo, 2019). In recent years, although some classroom teaching systems or learning management systems have created weather for recording process data in electronic grade, they take non fundamentally solved the difficulties in the implementation of formative cess. Considering these evaluation techniques only focus on the data collection link of evaluation, they neglect to systematically solve the problem of teachers' implementation of formative evaluation (Lobov et al., 2020). With the evolution of information technology in the classroom, flipped classroom, problem-solving learning, project-based learning, and other new instruction modes have emerged one after another, which are student-centered and mainly aimed at cultivating students' high-level abilities (such equally collaborative research abilities, innovation abilities, etc.) (Hamzah and Nasri, 2020). The learning tasks of these classrooms are oriented to the formal modeling and intelligent calculation of classroom didactics evaluation. With the transformation of classroom teaching from solidifying unmarried teacher instruction to emphasizing group cooperation and participation, how to implement formative cess in the classroom with cooperative learning equally the basic characteristic has become an urgent problem to be solved in instruction evaluation reform (Liao et al., 2019). With the support of intelligent engineering, formal modeling can deconstruct the complex and child-bearing classroom teaching process to class a mathematical model; intelligent computing can evaluate students' learning state through algorithms and generate pedagogy auxiliary information according to education principles (Telaumbanua et al., 2020). The combination of the two can promote the effective integration of human intelligence and auto intelligence, and form a classroom evaluation mechanism of human being-figurer cooperation (Ghodbane and Achachi, 2019). The general framework of formal modeling and intelligent computing for classroom teaching evaluation consists of four parts: perception and storage of teaching and learning behavior, structure of instruction and learning behavior evaluation model, intelligent computing of teaching and learning condition, and generation of teaching auxiliary data. The first two parts focus on the representation of educational situations and bug, which is the key pace of formal modeling; the latter ii parts focus on the realization of the specific technical routes, which is the embodiment of the specific process and part of intelligent computing (Emerson et al., 2018). Based on the perception and storage of teaching and learning beliefs, the whole system determines the output of the model by constructing the evaluation model; so, it introduces an intelligent algorithm to calculate the model to reach the evaluation of teaching and learning status; finally, co-ordinate to the corresponding teaching principles, information technology automatically generates the information to help teachers in classroom educational activity evaluation (Mustafa, 2018). The further improvement of the full general architecture, man-estimator collaborative education, and evaluation mechanism requires researchers to work with teachers to "blueprint together." And activities are frequently complex and hard, and then grouping cooperation to solve issues and jointly create learning products are common ways (Vellayan et al., 2020). At the same time, the evolution of such circuitous didactics activities requires teachers to suit teaching strategies in time according to the students' learning progress. All of these put forward college requirements for the intelligence of classroom didactics evaluation. How to implement determinative assessment in the classroom characterized by collaborative learning has get an urgent problem to be solved in the reform of classroom teaching evaluation (Nievecela and Ortega, 2019). Some researchers propose to apply formal modeling and intelligent computing to aid and support teachers more systematically and to course a man-car collaborative classroom evaluation machinery (Carrillo et al., 2019). Formal modeling methods, originated from software engineering, aim at modeling representation based on combing the software development process and realizing the standardization of software blueprint, development, and verification. The formal modeling of classroom educational activity needs to collect data according to software and hardware equipment, institute a data set according to certain standards, and on this basis, make clear the input and output status of the model co-ordinate to the actual classroom needs (Nelli and Hartati, 2018). Intelligent computing is the process of introducing machine learning, deep learning, and other intelligent algorithms to calculate the input data later on modeling, get the output country represented past mathematics, and use this to analyze learners' potential learning characteristics and rules (Juliati, 2019). In classroom pedagogy evaluation, formal modeling is to make a symbolic representations of classroom teaching and learning behavior to form a mathematical model; intelligent computing is to analyze and calculate the represented teaching and learning behavior to assist teachers in classroom teaching evaluation (Ghufron and Ermawati, 2018). The linkage of the ii tin not only realize automation in the process of bear witness collection and explanation of classroom teaching evaluation, but also requite teachers support in the process of implementing teaching behavior, and then it has get an important direction of classroom teaching evaluation reform. The mode of cooperation between teachers and students is shown in Effigy 1.

Figure 1. The fashion of cooperation between teachers and students.

Typical Classroom Teaching Evaluation Organisation and Architecture

With the support of intelligent engineering science, the classroom management system develops apace. Considering that the classroom management system to provide classroom teaching evaluation must have the basic functions of real-time analysis, evaluation of learning process, and providing feedback data for teachers, this commodity summarizes the fact intelligent teaching management organization through research. The fact system takes students' operation behavior and answer records as input information and provides information and guidance for teachers in cognition, learning status, and cooperation with others through certain calculation methods. On the computing path, these systems follow the ideas of formal modeling and intelligent calculating, and not simply focus on students' private learning behaviors and group cooperative learning behavior in the classroom, but also provide support for the transformation of individual, grouping, and class activities. Input information are as follows: pupil's operation behavior, respond tape pupil's operation behavior, respond record student's real-fourth dimension lawmaking, group learning product output state, pupil's personal cerebral state and abnormal state, student's personal cognitive state, problem-solving country, and cooperation state, students' individual cerebral state, students' individual problem-solving progress and learning engagement state, group problem-solving progress, cooperation state, group cognitive land, problem-solving progress, and cooperation land. In the actual classroom, students may switch between private learning and cooperative learning, so the intelligent system for classroom teaching evaluation needs to pay attention to the two learning processes at the same time. The instructor'due south teaching framework is shown in Figure 2.

Figure two. The instructor's pedagogy framework.

Deduction and Evaluation Model of English Competitive Learning

This article proposes an overall prediction model for cooperative prediction in English learning. First, through the cooperative network of English learning participants, the embedded vector representation of English learning participants is obtained by using the network representation learning method. The English competitive learning model is shown in Figure 3.

$\begin{array}{ll}{A}_{lim}=f(x)={\displaystyle \sum _{j\subset Q}}{c}_j\frac{x_j}{\sigma ({X^{\prime }}_j)}-p& (1)\end{array}$

$\begin{array}{l}N\left(d_i,{\mathrm{west}}_j\right)=P\left(d_i\right)P\left({\mathrm{due\; west}}_j|d_i\right);P\left({\mathrm{west}}_j|d_i\right)=\\ {\displaystyle \sum _{\mathrm{one\; thousand}=1}^K}P\left({\mathrm{westward}}_j|z_{\mathrm{thou}}\right)P\left(z_k|d_i\right)& (2)\end{array}$

The structure similarity and content similarity of English learning participants are linearly fused to obtain the node pair similarity discriminant matrix, and the threshold partitioning method is used for cooperative prediction. Side by side, this article introduces the core module of the cooperative prediction model of English learning participants based on the fusion of representation learning and content features

$\begin{array}{ll}\mathrm{Due\; south}{d}_{gai\mathrm{due\; north}}(Y)=\frac{\sigma (Y)-avg(\sigma (Y^{\prime }),\sigma (Y^{\prime }))}{\sigma (Y)}& (3)\end{array}$

$\begin{array}{ll}K\left(Y_{\mathrm{one}},Y_2,\dots ,Y_k\right)=-{\displaystyle \sum _{j=1}^m}M\left(K_j\right){log}_2\left(P\left({\mathrm{Yard}}_j\right)\right)& (4)\end{array}$

$\begin{array}{ll}F(X_f)={\displaystyle \sum _{x=\mathrm{one}}^{\mathrm{Northward}}}\frac{N_{1\mathrm{10}}+{\mathrm{Due\; north}}_{2\mathrm{10}}+\dots +N_{mx}}{T}& (\mathrm{five})\end{array}$

In add-on:

$\begin{array}{ll}I\left(M_{1x},\dots ,M_{gx}\right)=-\phi {\displaystyle \sum _{j=1}^M}{K}_{jx}{log}_2\left({\mathrm{Yard}}_{jx}\right)& (6)\end{array}$

The topic vector distribution of English learning participants is obtained through the topic model of English learning participants

$\begin{array}{ll}Q=\left\{\left(a_1,b_{\mathrm{one}}\right),\left(a_{\mathrm{two}},b_2\right),...,\left(a_n,b_{\mathrm{north}}\right)\right\}& (7)\end{array}$

$\begin{array}{ll}b=argmax{\displaystyle \sum _{a_{\mathrm{northward}}\in W_{\mathrm{Chiliad}}(a)}}|\left(b_n=c_n\right)& (8)\end{array}$

Spider web-based English learning participants' similarity calculation, model fusion calculation and parameter selection method calculation are shown in the post-obit formula:

$\begin{array}{ll}\text{Chiliad}(\text{5},\text{e})=[{\zeta }_1{\text{c}}_{\mathrm{ane}}(\text{t})+{\zeta }_2{\text{c}}_{\mathrm{two}}(\text{k})+{\zeta }_{\mathrm{three}}{\text{c}}_3(\text{one thousand})+{\zeta }_4{\text{c}}_4(\text{k})+{\zeta }_5{\text{c}}_5(\text{k})+{\zeta }_6{\text{due west}}_{\text{ik}}]& (\mathrm{ix})\end{array}$

$\begin{array}{ll}{c}_{\mathrm{one}}(t)\ge 0,c_2(\mathrm{grand})\ge 0,c_3(k)\ge 0,c_{\mathrm{iv}}(k)\ge 0,c_5(\mathrm{one\; thousand})\ge 0& (\mathrm{x})\end{array}$

Given a network G (V, e), V is the vertex set, eastward is the edge set, | 5 | = north, | e | = grand, permit the weight value of Min exist:

$\begin{array}{ll}min{w}_k(t)=[{\omega }_{\mathrm{one}}(\frac{d_g}{V})+{\omega }_{\mathrm{ii}}(\frac{d_k}{5})+{\omega }_3(\frac{T_{\mathrm{yard}}}{N{D}_{\mathrm{Yard}}})+{\omega }_1(P_K{T}_{\mathrm{Thou}})]& (11)\end{array}$

$\begin{array}{ll}\text{W}={\vartheta }^{*}=\frac{\mathrm{ii}k}{\mathrm{thou}+1}+\frac{\mathrm{two}{c}_1+c_{\mathrm{ii}}+3et-2\mathrm{east}t\zeta }{3}& (12)\end{array}$

The adjacency matrix of a graph is west. The degree of node I is Di, and the caste matrix of the graph is d. The classical network representation learning method is selected equally the baseline method for embedded representation of English learning participants.

$\begin{array}{ll}(In-\alpha W)y=(In-\alpha W)X\beta +\epsilon & (\mathrm{thirteen})\end{array}$

$\begin{array}{ll}{\text{D}}_{\kappa }=\frac{\mathrm{two}\mathrm{thou}}{g+1}+[\frac{1}{2}+\frac{1}{2\mathrm{thou}}]{[\frac{c_2-c_1}{\mathrm{three}}]}^2+\frac{2(c_{\mathrm{two}}-c_1)}{3}& (\mathrm{fourteen})\end{array}$

$\begin{array}{ll}\psi ={\displaystyle \sum _{\mathrm{ten}=\mathrm{one}}^{\theta }}5x={\displaystyle \sum _{x=\mathrm{i}}^{\vartheta }}(\frac{\mathrm{Due\; west}x}{{\displaystyle \sum _{\mathrm{one}}^n}{\mathrm{West}}_{\Im }}\mathrm{Due\; south}\mathrm{10})& (15)\end{array}$

The Stochastic Slope descent (SGD) method is used to update the numerical value convergence

$\begin{array}{ll}{w}_{i\mathrm{chiliad}}={\displaystyle \sum _a^n}{\tau }_{\mathrm{i}}{X}_{im}+{\displaystyle \sum _b^n}{\tau }_{2}U(Y_{ik})+B_{ik}& (17)\end{array}$

$\begin{array}{ll}{{\text{w}}_{\text{G}}}^{\text{SGD}}=max\left\{0,W_G·\epsilon ({f_G}^{A_i},{f_{\mathrm{1000}}}^{A_j})\right\}& (18)\end{array}$

In deep walk model, a random walk is used to generate a vertex sequence. Each node sequence is similar to a judgement in the language, and each node pair is similar to a give-and-take. Skip gram method is used for learning and training, and the vector representation of the node is obtained.

The representation vectors of nodes with strong proximity are closer, and the objective of offset-order optimization is

$\begin{array}{l}P\left(d_i,w_j\right)=P\left(d_i\right)P\left(w_j|d_i\right);P\left(w_j|d_i\right)=\\ {\displaystyle \sum _{k=1}^{\mathrm{Chiliad}}}P\left({\mathrm{due\; west}}_j|z_k\right)P\left(z_k|d_i\right)& (22)\end{array}$

$\begin{array}{l}\lambda (Wi,Wj)=\\ \left[log(\frac{|x_{A_i}-a_{A_j}|}{{\mathrm{due\; west}}_{A_j}}),log(\frac{|y_{A_i}-y_{A_j}|}{h_{A_j}}),log(\frac{w_{A_i}}{w_{A_j}}),log(\frac{h_{A_i}}{h_{A_j}})\right]& (23)\end{array}$

The objective of second-order optimization is as follows

$\begin{array}{ll}\mathrm{Grand}\mathrm{Eastward}\mathrm{South}(y,y^{\prime })=\frac{{\sum }_{i=\mathrm{i}}^{\mathrm{grand}}(y_i-y_i^{\prime })}{m}& (24)\end{array}$

Because the algorithm is uncomplicated, the result of line node prediction is not very stable and the dependence on the initial value is also serious.

Figure 3. The English language competitive learning model.

Structure of the Way of Cooperation and Competition in English learning

Content

This article constructs a cooperative and competitive model of English learning based on multimodal information fusion. The primary performance is that students form teams in groups, then there is a competitive relationship between groups. The competition of students in learning is the mutual interest of the whole grouping, so that each educatee will be more competitive for the overall interest. This commodity refers to the bailiwick association model in the literature and makes the following adjustments: English language grammar, vocabulary, and language sense abilities acquire together in team communication and improve students' abilities in many aspects. Finally, a questionnaire was designed to investigate the participants.

Methods and Steps

The students are divided into groups. The groups usually take four to six people. Go on the group level close. Each grouping member maintains a logical divergence in many aspects, including personality, language, foundation, and hobbies. In particular, avoid the situation where the group members are all cross-graded and introverted students, so that the students in the group take their own characteristics, learn from each other's strengths, and help each other. At the same time, it solves the diversity and fairness of collaborative learning. This work can be done by the monitor and teacher together to promote mutual cooperation and interdependence between the team members. Then the instructor can choose the advisable group leader for each group. Leaders demand to have advantages in academic performance, interpersonal relationships, learning attitudes, organizational skills, etc. Team leaders need to have certain skills to organize English learning activities under the guidance of teachers. For instance: Motivate squad members to actively participate, supervise everyone to express in English, discuss topics, sympathise the progress of activities, etc. Create a expert learning environment, stimulate students' learning motivation, and work hard in the process of collaborative learning. Team members work together for the same learning goal, communicate with each other, supervise each other, encourage each other, strengthen mutual emotions between members, and cultivate students' autonomy and learning abilities.

The traditional prediction methods based on network representation learning only consider the structural characteristics of the cooperative network of English learning participants, and do not consider the affect of the mutual research content betwixt two English language learning participants on scientific research cooperation. How to mensurate the similarity of the content between two English learning participants is too worth considering. The traditional content similarity calculation methods utilise ane hot model, Term Frequency-Changed Document Frequency (TF-IDF) representation, etc. When the number of words is large, it volition bring the trouble of dimension disaster. Based on the deductive and evaluative model of English competitive learning described in this article, this article proposes that we should pay attention to the influence of five factors on classroom communication: professional cognition, language knowledge, strategic ability, language environment, and personal characteristics. Based on the above model, this article needs to start from these five aspects to redesign the strategies and methods used in instruction. This article besides gives some specific suggestions. In recent years, the curriculum practise shows that this new educational activity mode has achieved skillful results in activating the classroom atmosphere and promoting active learning, and has significantly improved the teaching quality and curriculum satisfaction. The cooperative learning model introduces a competitive mechanism. This type of grouping collaborative learning method can generate cohesion and centrifugal force between groups. Working together for the honor of the team has strengthened the spirit of unity and cooperation, and the resulting group consciousness can promote the development of usual collaborative learning activities. At the aforementioned time, this article believes that this concept and model can also be used for reference by other non-English majors. At this stage, the teaching modes adopted in this article are: students preview textbooks, fill in the experimental written report, sentry the teaching video in class, teachers explain the experimental steps subsequently class experimental data processing, and consummate the experimental report. The teacher scored according to the experimental operation, preview, experimental study, and other aspects. At the same time, some experimental projects were also opened. Students learned by watching Massive Open Online Courses (MOOC) video and completed the experimental report. Some students similar to written report independently and think that collaborative learning takes a lot of time, while others feel that they have to have courses with weak foundations. All these require authors and colleagues to continuously explore the educational procedure to help students to better their ability to larn English. However, information technology should also be noted that the shortcomings of the learning cooperative competition model are that it may cause some unnecessary conflicts betwixt students. The form of cooperation may cause negative emotions in individual students and affect the efficiency of English language learning.

Questionnaire Pattern

The sampling survey method adopts the method of random learning, and the questionnaire is distributed randomly to the survey subjects to ensure the randomness of the results. Two different scales were used in this examination, one for pretest and one for the posttest, and one for the eye test. Utilize unlike perspectives to design the questionnaires for teachers and students, and consider the differences betwixt teachers and students to ensure the wide applicability of the questionnaire. Co-ordinate to the content of the experiment, 15 single choice questions were designed for the pretest and posttest questions, with 1 bespeak for each question, a total of 15 points. The midterm exam is composed of 15 single option questions and 12 subjective questions designed by the experimental content of the required stage. Considering our students only need to complete iv experiments at this phase, they but demand to choose iv of them to answer 1 point for each question, and the full score is four points. In this fashion, the measured results of students are more than accurate to prevent students from being unfamiliar with other experiments, resulting in the concluding test data are not accurate enough. At the same time, this commodity also adds some subjective surveys in the second half of the questionnaire combined with cooperative learning and autonomous learning, and attaches the analytic hierarchy process scale. In particular, the main purpose of setting the midterm test questions in this project is to utilise the subjective test questions to screen out the students who have independently joined the advisable cooperative learning or autonomous learning fashion in the experimental process, to provide a certain range in the later information processing. The purposes of this test are: first, the type of the test is to provide a new idea for the future college English experimental test; second, to investigate the cognitive level of unlike levels of students in cooperative learning way or autonomous learning mode nether the framework of the new test.

Structure of the Mode of Cooperation and Competition in English Learning

Students' learning motivation is shown in Table ane. According to Table ane, both external motivation and internal motivation are below 3 points, and the average value of internal motivation is lower than the boilerplate value of external motivation 0.12. This shows that amongst the respondents, internal reading motivation withal dominates. The average score for unmotivated is to a higher place three and is college than the average score of i.1 for internal motivation, indicating that students still have a certain learning motivation when learning English language, and they know why they want to learn English.

Table i. Students' learning motivation.

The types of learning motivation and academic performance are shown in Table 2. The student's academic performance is closely related to the internal and external learning motivation. The internal motivation (2.52) of the students with good academic performance is significantly higher than the external motivation (3.02). There is no obvious difference betwixt the internal motivation (2.78) and the external motivation (2.81) of the students of eye learning. Students with poor bookish performance have significantly lower internal motivation (three.06) and external motivation (two.82). In full general, the college the students' internal motivation, the better their performance in learning, and vice versa. Internal motivation tin can provide students with long-term enthusiasm and motivation for learning. The English class itself is a kind of enjoyment for them, and English learning makes them feel happy.

Table ii. The types of learning motivation and academic performance.

As shown in Figure 4, for students with unlike learning abilities and grades, the same teaching method may have unlike effects, which is due to the differences between different individuals. With the evolution of science and engineering science, research in various fields has shown a tendency of rapid differentiation and integration, and enquiry on individual differences has likewise been minimized and integrated. Individuals are afflicted past the interaction of genetics and environment during their evolution, which leads to individual manifestations of various physical and psychological characteristics. Therefore, the influence of cooperative learning fashion and democratic learning mode on students' operation in different grades may be different. To explore the difference of this influence, this survey further refines the scale on the footing of the original scale of students' experimental ability, so that students only demand to answer the relevant questions of their experiments at the end of the course, to improve the effectiveness of the questionnaire. At the same fourth dimension, the students who have chosen the cooperative learning mode or autonomous learning way in the experiment are selected, and they are divided into different levels according to the exam scores, then, whether cooperative learning or autonomous learning improves their scores significantly is studied.

Figure 4. Results of differences between individuals.

As shown in Table 3, pocket-sized-scale team is the best style to acquire tacit knowledge and the most creative organizational unit. English team learning is easier to improve the overall team functioning by stimulating the individual creativity of members. Moreover, in large-scale teams, the mediating path of squad interaction and team cohesion has the to the lowest degree effect. The reason is that when students are in the English learning squad, they have the to the lowest degree upshot. When the number of members is large, the difficulty of interaction and cooperation among members increases, and the demand for team internal personnel direction increases. Therefore, the squad interaction process is piece of cake to be blocked, and information technology is difficult to be transformed into team performance. In terms of the heterogeneity of team members' schoolhouse limerick, English squad learning has a greater touch on on team innovation performance.

Table iii. Small-scale teams are tacit noesis learning.

As shown in Effigy five, whether the squad members come up from the aforementioned type of school or not, the direct effect of English language squad learning on team innovation operation is greater than the indirect outcome. Comparing the contribution of unlike paths, it is found that the contribution rate of direct path and the mediating path through team interaction and cohesion in the heterogeneous squad formed by schools is slightly less than that in the homogeneous team formed by schools; the contribution rate of mediating path through member inventiveness in the heterogeneous team formed by schools is greater than that in the homogeneous squad formed by schools.

Figure 5. English language team learning to team innovation functioning.

The results of the questionnaire are shown in Table four. First, when the members come from different levels of colleges and universities, the interaction process and the formation of cohesion among team members are affected past background differences and lack of tacit understanding. Therefore, English language team learning is more than probable to impact team performance by stimulating the private creativity of the members. Second, when the members come up from the same level of colleges and universities, due to the lack of academy training, it is easier to translate English squad learning platform support into team performance or to influence team performance by improving team members' interaction and cohesion.

Tabular array 4. Touch on the interaction process and cohesion between team members.

As shown in Figure 6, in terms of the heterogeneity of team members' subject field composition, when the number of subjects in the squad is large, the overall impact of English team learning on team innovation functioning is greater, and multidisciplinary teams can promote the functioning transformation of English language learning space. By comparison the team path coefficients of different disciplines, information technology is found that the contribution rate of promoting performance transformation by improving member inventiveness is greater in teams with fewer disciplines than in teams with more disciplines; in teams with more disciplines, information technology is easier to amend team cohesion by promoting team interaction process and to promote team innovation functioning transformation. The average value of the didactics method survey is shown in Table 5.

Figure 6. Heterogeneity of team members.

Table 5. The average value of the education method survey.

Every bit shown in Effigy 7, when the number of subjects in the English language learning team is pocket-sized, it is easy to produce agin furnishings due to the lack of vocalism in different professional fields; notwithstanding, due to the relatively full-bodied disciplinary groundwork of the members and the team members' in-depth discussion of professional knowledge, the team mainly relies on the private inventiveness of the members to promote the team'due south innovation functioning; second, the English learning team is built-in the greater the differences in the professional fields of the members, the more they tin can examine and solve issues from a diversified perspective, the richer the content of ideological commutation and collision among the members, in the cease, a benign interaction will be formed in the artistic process, and then that the procedure of transforming the support of the English squad learning platform into team innovation performance is smoother. As shown in Table 6, English language team learning, equally the principal carrier of innovation and entrepreneurship activities, has an of import impact on the innovation operation of students' English learning teams.

Effigy 7. Fellow member's academic groundwork concentration.

Table 6. English team learning as an innovation and entrepreneurship activity.

The measurement and heterogeneity test results of English language squad learning and team innovation performance are shown in Figure 8. At nowadays, the students' English learning teams feel that the software and hardware support provided past English team learning is insufficient. In the small-scale squad and the team with loftier homogeneity of members' school sources, the common goal and vision is to create a positive creative atmosphere; while in the large-scale team and the squad with high heterogeneity of members' schoolhouse sources, a reasonable division of tasks can make up for the lack of common vision and create a good English squad learning atmosphere. In the squad with larger calibration and higher homogeneity of fellow member schools, the contribution of squad innovation accomplishment is lower; in the squad with smaller calibration and higher heterogeneity of member schools, the contribution of member innovation intention is lower. The squad members' discipline heterogeneity has no pregnant effect on English team learning platform and team innovation operation.

Figure eight. Comparing of English squad learning furnishings.

Based on the heterogeneity test results of students' English learning team characteristics, as shown in Figure 9, large-scale teams are more likely to directly transform the support provided by the English team learning platform into team performance; in minor-scale teams, English squad learning is more likely to improve the team's overall performance past stimulating individual creativity; while in large-calibration teams, the squad interaction procedure is difficult to transform into squad functioning. When the source of member universities is homogeneous, it is easier to promote squad operation through the positive interaction amongst team members, while when the source of member universities is heterogeneous, information technology is mainly through individual creativity to achieve team performance. When the members come from the same discipline, they mainly rely on the individual creativity of the members to promote the team innovation performance; when the squad members have different professional person fields, it is easy to form the positive interaction in the process of creation, then turn into the squad innovation functioning.

Figure ix. The main carrier of innovation and entrepreneurship.

As shown in Figure x, afterwards irresolute the English language team learning mode and optimizing the support organization of English language team learning for students' English language learning team, the combination mode of English language learning cooperation and competition proposed in this commodity can better the learning effect by 55–60%. With the vigorous promotion of national policies, English team learning, as an important carrier of innovation and entrepreneurship, has entered a stage of rapid development, but the speed of optimizing the internal quality of English team learning cannot grab up with the growth rate of the total scale. In add-on to providing hardware infrastructure to ensure the normal development of daily activities of English learning teams, team learning also needs to explore the establishment of a systematic internal direction systems.

Effigy 10. Improve the construction of English team learning platform.

As shown in Table 7, we should strengthen the influence of innovation atmosphere and English learning culture, and create an innovation atmosphere of "free innovation" by carrying out a serial of activities such as lectures, entrepreneurship guidance, and substitution meetings in English team learning. At the same time, English squad learning should cooperate closely with colleges and universities and improve team innovation functioning through scientific selection and scientific team formation.

Table seven. Innovative temper and the influence of English learning culture.

As shown in Table 8, English squad learning should be combined with the specific talent needs of team innovation and entrepreneurship projects, mining and gathering students with dissimilar advantages and talents, scientifically forming an English learning squad, and scientifically determining the team size and member structure characteristics. For example, for high-quality pocket-sized innovation and entrepreneurship projects, nosotros should fully explore the unique advantages of individual creativity of members, and at the same time, we should ensure the diversified member construction of the team to prevent agin effects. Afterwards the team has entered a catamenia of stable development, we should take advantage of the resource advantages of English learning space platform to employ well-known innovative and entrepreneurial talents, venture capitalists, and entrepreneurs to form an English language team learning tutor library, to improve the team performance by improving the individual creativity of members.

Table 8. English language team learning, team innovation, and entrepreneurship projects.

As shown in Figure 11, we should give full play to the "aggregation" role of English squad learning and enhance the contribution of the creative process to team innovation performance. Outset of all, English language team learning, equally a public space for communication and resources sharing in students' English learning, should create opportunities and build a platform for positive interaction and communication and cooperation amongst team members by ways of various theme activities such equally substitution salon and entrepreneurship contest, to raise team cohesion and help the output of innovation and entrepreneurship achievements.

Figure 11. The contribution of the creative process to the team's innovation performance.

Equally shown in Table 9, with the rapid increase in the number of English learning teams and members, information technology is increasingly difficult to manage and coordinate within the team. Based on the characteristics of the team, it is necessary to further explore the scientific manner of information and resource sharing among members, the institutional mechanism of team cooperation and win-win results, and the effective path of innovation and entrepreneurship achievement transformation. Finally, co-ordinate to the needs of English learning teams in different stages of innovation and entrepreneurship, combined with the field of entrepreneurial tutors, through scientific guidance and planning, we tin can improve the ability of students' English learning teams to reasonably allocate the tasks and the enthusiasm of team members, so equally to enhance the team's "joint force."

Tabular array 9. The number of teams and members in English team learning.

Every bit shown in Figure 12, in view of some mutual problems in the current English teaching of English subjects, combined with the practical experience in the English teaching of relevant English language courses in contempo years and some reflections on applied linguistics, this article expounds some new opinions on the classroom communication and feedback link in the English teaching. In all English teaching, the ii dimensions of professional knowledge and English power grooming are non orthogonal and mutually exclusive, simply should be mutually supportive and dependent. To course an effective pedagogy mode of "educatee-centered and instructor-led," active and rich communication and feedback in the classroom are the keys, which besides help to form a gradual bicycle of pedagogy and learning.

Figure 12. New trapezoidal pattern.

At the kickoff of the semester, iv monitors of the experimental course and the comparison class were called to distribute the questionnaire forms and explain related matters to obtain relevant information. The survey results are shown in Tabular array x. It can be seen from the table that the boilerplate score of each class varies greatly, and the average scores of student motivation and teacher self-evaluation are relatively low.

Table 10. Survey results.

Figure 13 shows the pretest results comparison betwixt the experimental class and the command class. The average score of the experimental course is 0.xvi lower than that of the control class (the 2 classes are eight.59 and viii.75, respectively), and the T value is −0.164, which is for below 5% of the theoretical critical value of i.69. Therefore, we can conclude that at that place is no meaning difference between the ii classes in the pretest results.

Figure 13. Pretest results comparison betwixt the experimental class and the command form.

The test results of the two classes, afterward the experiment, are shown in Figure 14. The average number of the experimental class is one.88 higher than that of the control class (the two classes are xiv.25 and 12.37, respectively), and the T value is 2.1, which is significantly higher than v% of the theoretical disquisitional value of 1.69. From this, we can run across that the cooperative learning mode in the experimental grade played an verbal role and accomplished remarkable results, thus verifying that the cooperative learning model is significantly better than the traditional teaching method.

Figure 14. The test results of the ii classes after the experiment.

Conclusions

At present, students' English learning teams feel that the software and hardware back up provided by English team learning is insufficient. In the pocket-sized-calibration team and the squad with high homogeneity of members' schoolhouse sources, the common goal and vision create a positive creative atmosphere; while in the large-scale team and the team with high heterogeneity of members' school sources, a reasonable division of tasks tin can brand up for the lack of common vision and create a expert English team learning atmosphere. In the team with larger scale and higher homogeneity of member schools, the contribution of squad innovation accomplishment is lower; in the team with smaller calibration and higher heterogeneity of fellow member schools, the contribution of member innovation intention is lower. Team members' field of study heterogeneity has no pregnant effect on English team learning platform and team innovation performance.

By changing the English team learning model and optimizing the English team learning support system of the students' English language learning team, the English learning model based on multimode information fusion combining cooperation and contest proposed in this commodity can meliorate the learning upshot by 55–60%. With the vigorous advocacy of national policies, English language team learning has entered a stage of rapid development as an important carrier of innovation and entrepreneurship, but the optimization speed of the internal quality of English language squad learning has not kept up with the growth charge per unit of the overall calibration. In addition to providing hardware infrastructure to ensure the normal development of the daily activities of the English learning squad, team learning also needs to explore the establishment of a systematic internal management organisation.

In the process of learning English, the training process needs to spend more time and money, which profoundly increases the difficulty and toll of coordination and management, thus, further reducing the willingness and efficiency of cooperative innovation. In the aspect of trust building, people will react more negatively to opinions and behaviors that do not accommodate to the existing norms. When the altitude between the informal institutions is large, the innovative behaviors and opinions of the two sides often deviate from each other'due south informal institutional norms. Therefore, it is hard to plant a high sense of trust and identity in learning English language, which seriously hinders mutual knowledge sharing and learning, and is not conducive to the development of cooperative innovation. This article tests that squad achievement goal orientation can effectively predict team learning inventiveness, which not only promotes the evolution of team achievement goal theory in the field of learning creativity, only too expands the research scope of antecedents of team learning creativity. The next enquiry will focus on the important role of squad state of affairs factors in the germination of team learning creativity, and reveal the dynamic mechanism of team learning creativity from the perspective of positive psychology.

Information Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can exist directed to the corresponding author/s.

Ethics Argument

Ethical review and approval was not required for the study on man participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this report.

Author Contributions

HT is responsible for conceptualization, methodology, software data curation, writing, contributed to manuscript revision, read, and canonical the submitted version.

Conflict of Interest

The author declares that the inquiry was conducted in the absence of any commercial or financial relationships that could exist construed as a potential conflict of involvement.

Publisher's Note

All claims expressed in this commodity are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Whatsoever product that may exist evaluated in this commodity, or claim that may exist made past its manufacturer, is non guaranteed or endorsed by the publisher.

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Source: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.767844/full

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