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Research on innovative strategies of college students’ English teaching under the blessing of big data

Publié en ligne: 21 Oct 2022
Volume & Edition: AHEAD OF PRINT
Pages: -
Reçu: 30 Apr 2022
Accepté: 15 Jun 2022
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Format
Magazine
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2444-8656
Première parution
01 Jan 2016
Périodicité
2 fois par an
Langues
Anglais
Preface

In recent years, with the rapid development of information technology, the network has played a very important role, and even produced the situation of ‘data explosion’. A large number of complex data have different values and can be applied to the development of human science and technology. The era of big data has arrived, which provides a new way for college students’ English education and learning. It can convey learning information in a simple way. In this case, college students can more easily obtain relevant information and learn. However, contemporary college students generally lack the ability to distinguish information, which will lead them to fall in the flood of big data [1, 2]. Therefore, in the era of big data, teachers should be a qualified guide to guide college students to a bright road. However, this requires English teachers to constantly improve the teaching quality in the actual teaching process and rely on big data for teaching innovation [3, 4, 5].

In the era of big data, the method of precision teaching came into being. It relies on the technology of big data to conduct relevant teaching analysis, so as to obtain more targeted College English teaching programmes [6, 7, 8]. This innovative method has many advantages. For students, this teaching method can enable them to carry out personalised learning and give full play to their learning enthusiasm and subjective initiative. For teachers, this teaching method can help them formulate corresponding teaching plans according to the personalised characteristics of students, so as to more fully understand the current learning situation of college students in any particular course, and then improve the teaching effect [9, 10]. Therefore, precision teaching in the era of big data can promote teaching in three aspects: precision, personalisation and intelligence. It is also necessary to implement certain teaching optimisation and innovation tasks to provide scientific support for innovative education and teaching decisions.

The meaning of College English teaching under the blessing of big data

The new era has new requirements for College English education and shoulders the mission of cultivating high-quality and international talents for national strategic development. As a compulsory course in Colleges and universities, College English teaching should keep pace with the times and use big data to innovate teaching methods, so as to cultivate more high-quality talents [11, 12].

However, compared with the traditional teaching mode, College English teaching based on big data is an era trend of combining information technology with education. In order to ensure the quality of English teaching and improve the effectiveness of English teaching, teachers need to adhere to the spirit of scientific, efficient, realistic and professional work, widely apply information technology means such as network and multimedia to English teaching, make it provide support for the development, application and maintenance of College English Teaching Courseware, further improve the quality of English teaching, and ensure that English teaching does not deviate from the development law of the times. Based on the fact that this courseware has been formulated in a such a manner as to have it retain the original advantages and characteristics of the same course in the Chinese language, we may assert that this information technology–incorporating coursework meets the new requirements for college students in the information age, especially by putting forward strategies for innovative teaching of College English by employing big data as the principal means. Precision teaching is the main innovative means. With the support of big data, innovative precision teaching aims mainly to give targeted teaching guidance according to students’ characteristics and learning behaviour, so as to improve teaching effect and promote teaching and learning [13, 14]. In the era of big data, accurate College English teaching analyses the basic information data of college students, student learning behaviour data and other data generated in the process of education and teaching, depicts student characteristics and student behaviour, and then carries out targeted teaching to improve the teaching effect. Therefore, in short, with the blessing of big data, the most important thing for innovative and accurate College English teaching is to provide targeted guidance to students.

Advantages of College English teaching supported by big data

If colleges and universities want to realise the optimal benefits of English teaching, they must start from the actual teaching situation and combine big data with English teaching. Only by recognising the advantages of College English teaching based on big data can colleges and universities give full play to its effectiveness. Generally speaking, colleges and universities rely on the network platform to innovate the teaching methods of College English courses, which can effectuate a variety of advantages.

Educational resource sharing

Internet technology can bring resource sharing [15,16]. There are a lot of teaching resources in the network, which is more important for College English teaching than hardware teaching facilities such as multimedia classroom and voice laboratory. The educational resources of the network platform include various databases, teaching materials, educational information and teaching software. On the one hand, teachers can make full use of the above resources to enrich teaching content, strengthen teaching effect and share teaching results with students; on the other hand, students can flexibly select suitable teaching resources according to their own ability and learning development, and actively carry out independent learning outside the classroom. The sharing of educational resources without time and space constraints is conducive to avoiding the loss and dispersion of resources, which is of great value to college English teaching.

Enrich learning approaches

In classroom teaching, with the help of English original animation, film, music and other resources on the network platform, teachers can effectively build a dynamic and rich language environment, and guide students to deeply understand the knowledge points in class through immersive cultural experience and understanding. Real language situations can help students better internalise the content of English knowledge, expand the scope of their knowledge and empower them to realise an overall improvement in language ability and comprehensive quality [17]. The network platform can provide technical support for students to carry out group learning. Given a situation in which every student has the same interests and knowledge-level—and actualising such a situation in the classroom atmosphere is an effective way to give full play to the advantages of group learning—group learning can provide an enhanced learning opportunity for each student. In addition, through the network platform, students can share more knowledge and information; this platform also promotes learning and communication, and provides a convenient and fast oral and listening training platform for college students. Here, students can play English learning audio according to their own weaknesses and learning needs to realise self-learning. At the same time, students can also obtain listening, speaking and reading materials suitable for their English level from the database to meet their changing and developing learning needs. Thus, the flexible application of network platform in College English teaching provides students with rich and practical learning ways.

Innovative teaching mode

With the support of the network platform, some colleges and universities have enriched and innovated the English teaching mode by comprehensively considering the needs of students and the specific situation of the school. Teachers can flexibly use multimedia courseware to carry out English teaching. This mode is the most common. Teachers use electronic courseware to explain the teaching content, attract students’ attention with the help of sound and image, and enrich and vivify classroom teaching methods, the objective being to stimulate students’ subjective initiative and guide them to actively think about ways to solve problems. There is also a self-regulated learning model supported by the network platform, which is a new direction of the development of College English teaching. In addition, students can actively collect English knowledge and relevant information through network operation, and conduct independent learning and research with the help and guidance of teachers [18,19]. This model helps to improve students’ abilities in the directions of independent learning and research-oriented thinking, and cultivates in them a habit of lifelong learning.

Innovative strategies of College English teaching under the blessing of big data
Precision teaching of College English with big data

Several innovations are needed to ensure accurate College English teaching, and the core of these efforts lies in improving the classroom teaching effect of College English, which is mainly based on students’ characteristics and behaviour [20]. The accurate description of teaching effect and students’ characteristics and behaviour is the premise of formulating accurate teaching plans, and the data and big data analysis technology generated in the process of education provide strong support for this. In general, precise College English teaching in the era of big data is not a specific teaching method. It mainly pursues a decision-making function, which can take students’ characteristics and behaviour performance as the basis for innovation, so as to formulate a set of accurate College English teaching plans. It should be precise and accurate. The College English teaching plan for each student is different. The final aim of such plans is to obtain the optimal decision function of College English teaching effect. Therefore, the formulation of accurate College English teaching plan can essentially be transformed into an optimisation problem [21].

In the optimisation process of College English teaching, the problem should be transformed into an optimal decision function d: XA. The teaching effect Y is optimised through continuous optimisation. The selected indicators are shown in Table 1.

Index selection table

Indicator category Corresponding variable Decision function d

Teaching programme A d:XA
Teaching effectiveness Y
Student characteristic performance X

For multi-stage accurate College English teaching programmes, the characteristics of students will be different in different stages, and thus the application programmes formulated in the process of College English teaching will also become different [11]. The College English teaching process of the study consists of K stages, and the College English teaching scheme used in stage k is recorded as Ak(k = 1, …,K). Student characteristics are recorded as Xk(k = 1, …, K). At this time, the data corresponding to different stages are [22, 23]: X1,A1,X2,A2,,Xk,A2,,XK,AK,Y. {X_1},{A_1},{X_2},{A_2}, \cdots ,{X_k},{A_2}, \cdots ,{X_K},{A_K},Y. Xk, Ak, Hk, Y (k = 1, …, K) are random variables. The values are recorded as xk, ak, hk, y(k = 1, …, K). The relationship between them can be shown in Figure 1.

Fig. 1

Schematic diagram of multi-stage College English teaching process

It can be seen that the teaching scheme AK adopted in one stage will affect the student characteristics and behaviour performance XK in the next stage. After all K stages, the teaching effect y is obtained. In order to better construct the College English precise teaching model, the variables mentioned above need to meet the following two assumptions.

Hypothesis 1: A student's College English teaching effect Y will not be affected by other teaching schemes. In the process of College English teaching practice, this hypothesis can be satisfied by de-tagging or invisible-tagging students.

Hypothesis 2: For any teaching scheme āK, as Ak(Xk+1(a¯k),,XK(a¯K1),Y(a¯K))|Hkk=1,,Ko {A_k} \bot \left({{X_{k + 1}}\left({{{\bar a}_k}} \right), \cdots ,{X_K}\left({{{\bar a}_{K - 1}}} \right),Y\left({{{\bar a}_K}} \right)} \right)|{H_k}\forall k = 1, \cdots ,{K_o}

Here, āK = (a1, …,aK) represents the sequence of teaching schemes adopted in all k stages. k = (X1, …, Xk) for all student characteristics up to stage K. Āk−1 = (A1, …, Ak−1) indicates all the teaching programmes adopted before stage K. Hk = (k,Āk−1) represents the history up to stage K, that is, all student characteristics and behaviour performance up to stage K and the College English teaching scheme adopted.

If the decision functions at different stages are represented by a vector d = (d1,d2, …,dK), among dK(k = 1,2, …,K), the decision function would represent stage K. Then the problem can be transformed into an optimal decision function d = (d1,d2,…,dk), in order to make the teaching effect y optimal.

However, the solution to this optimisation problem of College English teaching is based on the accurate description of the effect of College English teaching and students’ characteristics pertaining to their performance [24]. However, the accurate description of College English teaching effect and students’ characteristics needs a lot of data as support. In addition, the description and selection methods of the model in this process also need a lot of data to run. In other words, all kinds of optimisation problems need data to process and solve. In general, the approach used is to systematically analyse the data generated in the process of College English teaching by using relevant statistical methods and big data analysis technology in combination with the basic requirements involved in the process, extract the characteristics of students and then explore the internal development occurring in the process, so as to formulate accurate College English teaching plans and improve the quality of College English teaching, and simultaneously ensure that this teaching method based on big data also proves useful towards application in the teaching of other subjects.

Creating the best teaching model of College English

In order to analyse the optimal selection and fusion clustering of College English teaching mode, combined with the statistical big data analysis method of College English teaching mode, the analysis model of the optimal fusion characteristic parameters of College English teaching mode is constructed by using big data mining technology, and the data mining model of College English teaching mode is constructed by using adaptive association rule mining method, The phase space distribution W of big data for the optimal selection of College English teaching mode is obtained. This is a control matrix for the optimal selection of College English teaching mode of n×m [25]. Under the optimal selection mode of College English teaching mode, the feature distribution vector is constructed as pq, together with constructing a probability distribution function P(ni) = {pk|prk j = 1,k = 1,2,…,m}. Through the fuzzy rule feature recognition of College English teaching model statistical big data, it is obtained that the sample set of College English teaching big data statistical feature distribution node vi is [26]: W¯i=1mq=1mW(vi,pq) {\bar W_i} = {1 \over m}\sum\limits_{q = 1}^m {W({v_i},{p_q})}

Here C is the task set of big data scheduling for College English teaching, and C(vi,vj) is the link control set selected for College English teaching mode. Under the strategy of College English teaching mode selection, the calculation between constraint characteristic quantity vi and vj is obtained. Constructing the segmented feature detection method of statistical big data of College English teaching model, the regression analysis model is obtained as the following: x(t)=i=0pa(θi)si(t)+n(t) x(t) = \sum\limits_{i = 0}^p {a({\theta _i})} {s_i}(t) + n(t)

Here p is the number of conditional probability distributions selected for College English teaching mode, n(t) is the big data scheduling interference item of College English teaching, si(t) is the statistical characteristic of big data in College English teaching and a(θi) is the big data modulation component of College English teaching. The SL set of task scheduling is constructed in the feature fusion centre of College English teaching model statistical big data, and the fuzzy membership function of College English teaching model statistical big data recognition is constructed by using fuzzy cluster analysis method: Rs(0)=n=0k(Rs(n),dm)dm+Rs(k+1) R_s^{(0)} = \sum\limits_{n = 0}^k {(R_s^{(n)},{d_m})} {d_m} + R_s^{(k + 1)}

Here Rs(0) R_s^{(0)} is the scale characteristic quantity of big data transmission node of College English teaching model statistics, dm is the dimension of data reconstruction and Rs(k+1) R_s^{(k + 1)} is the distribution frequency shift of big data of College English teaching model statistics. Then the adaptive feature decomposition process of College English teaching model statistical big data is carried out. Here the quantitative regression analysis method is adopted, and the feature decomposition formula is as follows: G(t)=min{G1(t)+G2(t)}=min{[Fμ(t)×sign(kμ(t))]+w[|ΔTm(t)|KμΘ]}, G(t) = \min \left\{{{G_1}(t) + {G_2}(t)} \right\} = \min \left\{{\left[{- \int {{F_\mu}} (t) \times {\rm{sign}}\left({{k_\mu}(t)} \right)} \right] + w\left[{\int {{{\left| {\Delta {T_m}(t)} \right|}_{{K_\mu} \in \Theta}}}} \right]} \right\}, where kμ (t) is the sampling bandwidth of College English teaching mode statistical big data at time t, ΔTm(t) is the quantitative feature set of College English teaching model statistical big data at time t, w is relative weight and Θ is the probability condition of kμ (t). Combined with the limited steady-state conditions of this part, the balanced scheduling of pattern statistical big data is the next step. It combines the linear regression analysis method to mine the association rules of data output, so as to improve the ability of information fusion.

The nonlinear time series analysis method is used to analyse and predict the characteristics of College English teaching model statistical big data. Through the preliminary statistical value of College English teaching mode statistical big data, the constraint index parameter set of teaching mode optimisation is analysed in embedded system teaching, and the correlation detection method is used for big data fusion processing of College English teaching optimal mode. The output rules of College English teaching mode statistical big data are obtained by using multiple regression analysis method: pj(t+1)=a1pj(t)+a2pg(t)a1+a2, {p_j}(t + 1) = {{{a_1}{p_j}(t) + {a_2}{p_g}(t)} \over {{a_1} + {a_2}}}, mbest(t+1)=1nj=1npj(t), mbest(t + 1) = {1 \over n}\sum\limits_{j = 1}^n {{p_j}} (t), Xj(t+1)=pj(t+1)±β×|mbest(t+1)Xj(t)|×ln(1uj(t+1)), {X_j}(t + 1) = {p_j}(t + 1) \pm \beta \times |mbest(t + 1) - {X_j}(t)| \times \ln \left({{1 \over {{u_j}(t + 1)}}} \right),

Here Xj(t) is the fuzzy rule set of statistical big data of the t-generation College English teaching model, and the quantitative feature decomposition of College English teaching model statistical big data is carried out at the position of data element j. In the big data clustering, the scheduling analysis of College English teaching mode statistical big data is carried out, the iterative formula is constructed and the univariate time series of College English teaching mode statistical big data is {xn}. We use uj(t + 1) as the association rule set of statistical big data that changes within the range of [0, 1]. Combined with the attenuation vector analysis method, the similarity feature change is carried out, and mbest (t + 1) is the semantic relevance feature of big data. The optimal location of the cluster centre is obtained using pj (t + 1) as the fuzzy constraint parameter of statistical big data in the t + 1 cluster centre. The individual regression vectors of big data of College English teaching model statistics are a1 and a2. In the M-dimensional random vector, the statistical characteristic pg(t) of College English teaching model statistical big data is defined as: pg(t)=argmin{f(pj(t))|j=1,2,,n}, {p_g}(t) = {\rm{argmin}}\left\{{f\left({{p_j}(t)} \right)|j = 1,2, \cdots ,n} \right\}, where f (pj(t)) is the optimal position of College English teaching model statistical big data in cluster centre j. We carry out feature search in the optimal location to obtain the fitness value of the optimal location searched by the t-generation. The quantitative regression allocation coefficients a1 and a2 of the statistical big data of College English teaching model are determined by Eq. (11): a1 = c1r1,a2 = c2r2, \matrix{{{a_1}{\rm{}} = {\rm{}}{c_1}{r_1},} \cr {{a_2}{\rm{}} = {\rm{}}{c_2}{r_2},} \cr}

Here r1, r2 is M-dimensional random vector, c1 is the association rule vector set of statistical big data of College English teaching model and c2 is the characteristic quantity of diameter distribution. According to the above analysis, the feature extraction and fuzzy scheduling of College English teaching mode statistical big data are realised, and the ability of feature extraction and quantitative regression analysis of College English teaching mode statistical big data is improved by combining the optimal mode selection method. The next step is to carry out the statistical data fusion processing of the optimal model of College English teaching, for which we construct the English teaching model statistical big data time series {x (t0 + iΔt)}, i = 0,1, …,N − 1. The fuzzy two degree of freedom control method is adopted to fuse and adaptively update the statistical data of the optimal mode of College English teaching. The information rules of the statistical data of the optimal mode of College English teaching are as follows: λ=11+α(St)2,k˙μ(t+1)=k˙μ(t)+Q(t+1)×[Fμ/MgtStkμ(t)], \lambda = {1 \over {1 + \alpha {{\left({{{\partial S} \over {\partial t}}} \right)}^2}}},{\dot k_\mu}(t + 1) = {\dot k_\mu}(t) + Q(t + 1) \times \left[{{{\partial {F_\mu}/{M_g}} \over {\partial t}} - {{\partial S} \over {\partial t}}{k_\mu}(t)} \right],

Among, Q(t+1)=P(t+1)St,P(t+1)=1λ[P(t)P2(t)(St)2λ+P(t)(St)2], Q(t + 1) = P(t + 1){{\partial S} \over {\partial t}},P(t + 1) = {1 \over \lambda}\left[{P(t) - {{{P^2}(t){{\left({{{\partial S} \over {\partial t}}} \right)}^2}} \over {\lambda + P(t){{\left({{{\partial S} \over {\partial t}}} \right)}^2}}}} \right], St=rvgωwt, {{\partial S} \over {\partial t}} = {r \over {{v_g}}}{{\partial {\omega _w}} \over {\partial t}}, where λ College English teaching scheduling model is the optimal process of big data, μ is the statistical characteristic component of the optimal mode scheduling of College English teaching, ωw is the adaptive weighting coefficient and μ(t) is the estimated value of quantitative regression analysis of the optimal model of College English teaching extracted at time t. Further, P(t) is the correlation inverse matrix and α is the residual characteristic quantity. Cor3=(xnx¯)(xndx¯)(xnDx¯)(xnx¯)3, {C_{or3}} = {{\left\langle {\left({{x_n} - \bar x} \right)\left({{x_{n - d}} - \bar x} \right)\left({{x_{n - D}} - \bar x} \right)} \right\rangle} \over {\left\langle {{{\left({{x_n} - \bar x} \right)}^3}} \right\rangle}},

Here xn is the discrete characteristic sequence of statistical big data of College English teaching model. The mean value of the positive correlation characteristic component of the statistical big data of College English teaching mode is: x¯=1Ni=1N|xi| \bar x = {1 \over N}\sum\limits_{i = 1}^N {\left| {{x_i}} \right|}

The matching detection of cantaloupe is carried out in the decentralised subspace to improve the ability of optimal pattern selection. Finally, the cluster selection of the optimal mode of College English teaching should be carried out. The fuzzy convergence control function of statistical big data of College English teaching mode is given as follows: Mv=w1i=1m×n(HiSi)+Mhw2i=1m×n(SiVi)+w3i=1m×n(ViHi) {M_v} = {w_1}\sum\limits_{i = 1}^{m \times n} {\left({{H_i} - {S_i}} \right)} + {M_h}{w_2}\sum\limits_{i = 1}^{m \times n} {\left({{S_i} - {V_i}} \right)} + {w_3}\sum\limits_{i = 1}^{m \times n} {\left({{V_i} - {H_i}} \right)}

In Eq. (17), the load predicted by big data of College English teaching model statistics is Mh. After generating a set of clustering attribute features V, the College English teaching model statistical big data attribute set scheduling is carried out. The criterion of statistical big data of College English teaching model based on Sigma test is the following: if S ≥ 2.0, the 95% probability of identifying big data of College English teaching model statistics is not tenable, and the original data and classified data have the same characteristics; if S < 2.00, the identification of statistical big data of College English teaching model is established, the original data do not have the same characteristics [27].

According to the above discriminant conditions, a support vector machine model is constructed, and Ru,v is used to represent the fuzzy set quality of College English teaching model statistical big data attribute set. For College English teaching mode statistics, the association rule attribute of big data scheduling is v0Ru,v, representing the cross-correlation function. The quantitative set of optimisation indicators for statistical big data identification of College English teaching model is (RT1,RT2). Based on the clustering results, the quantitative distribution set of the optimal model of College English teaching is constructed [28]. Combined with the big data fusion method, the optimal model of College English teaching is constructed, and the limited data set of statistical big data sampling of College English teaching model is obtained: X={x1,x2,,xn}Rs, X = \left\{{{x_1},{x_2}, \cdots ,{x_n}} \right\} \subset {R^s},

There are n samples in the big data set of College English teaching model statistics. As a result of combining sample xi,i = 1,2, …,n with SVM learning method, the quantitative characteristic relationship is obtained as follows: h(t)=iai(t)ejθi(t)δ(tiTS) h(t) = \sum\limits_i {{a_i}} (t){e^{j{\theta _i}(t)}}\delta \left({t - i{T_S}} \right)

The modified support vector machine learning model is used to design the classifier of College English teaching mode, and the characteristic distribution matrix of the classification of College English teaching mode statistical big data is obtained, which meets the following requirements: [R1EETR2]>0,Ψ1=[XN*Z1]0,Ψ2=[YM*Z2]0. \left[{\matrix{{{R_1}} & E \cr {{E^T}} & {{R_2}} \cr}} \right] > 0,{\Psi _1} = \left[{\matrix{X & N \cr * & {{Z_1}} \cr}} \right] \ge 0,{\Psi _2} = \left[{\matrix{Y & M \cr * & {{Z_2}} \cr}} \right] \ge 0.

Then, we construct the number chain of statistical big data distribution of College English teaching model in continuous finite state space S = {1,2,…,N}, and generate metadata ϒ = (rij)N*N. Combined with the fusion method of big data, the construction of the optimal model of College English teaching is realised, and the statistical characteristic quantity is as follows: P(r(t+Δ))={rijΔ+oΔ1+rijΔ+oΔ P(r(t + \Delta)) = \left\{{\matrix{{{r_{ij}}\Delta + o\Delta} \hfill \cr {1 + {r_{ij}}\Delta + o\Delta} \hfill \cr}} \right.

If Δ > 0 and rij > 0, this represents the time lag item of College English teaching model statistical big data: rij=jirij {r_{ij}} = - \sum\limits_{j \ne i} {{r_{ij}}}

Combined with the optimal mode selection method, this paper realises the empirical test and analysis of the optimal mode of College English teaching.

Innovate and establish the teaching consciousness of network informatisation

College English teachers should recognise the importance of network informatisation, make good use of the network platform in the era of big data and information, and promote the innovative development of English teaching. Educators need to give full play to their subjective initiative and complete various teaching tasks on the premise of improving teaching quality. Group teaching is based on the principle of flexible setting of students’ teaching situation, and group teaching must follow the basic direction of students’ initiative. Therefore, it is necessary for College English teachers to design teaching methods that can arouse students’ interest and improve students’ creativity from the perspective of students’ development and combined with students’ actual needs [29,30]. It should be noted that teachers must maintain an objective understanding of network information-based teaching, establish systematic and scientific curriculum standards, and implement teaching tasks according to the established objectives and plans, so as to avoid a situation wherein English teaching that relies on the network platform cannot give full play to its advantages due to the deviation of understanding. In addition, College English teachers should combine the advantages of network information teaching with the objectives of English teaching courses, build an efficient teaching model on this basis and introduce this new teaching model into classroom teaching practice.

In conclusion

The rapid global development of social science and technology, as well as the fact that students need to have an ability to grasp—especially scientific and technological—research findings generated around the world to keep their knowledge abreast of the developments in various fields, indicates that the ability to understand and express oneself in English is a major concern, and therefore, College English occupies a very important position. The era of big data has thus imposed higher requirements for the teaching of English. In order to improve the teaching quality of College English, innovative strategies are needed. This paper improves the mathematical model of accurate teaching strategy and establishes the empirical model of the optimal model of College English teaching based on big data. It also puts forward relevant innovative teaching plans, improves the content of College English classroom teaching knowledge through the rational use of big data technology and updates the form of College English classroom teaching, so as to achieve improvements in the efficiency of English classroom learning, the comprehensive learning effect of college students’ English and the learning quality with which college students imbibe a knowledge of English that would help them in enhancing their professional skills and careers in the future.

Fig. 1

Schematic diagram of multi-stage College English teaching process
Schematic diagram of multi-stage College English teaching process

Index selection table

Indicator category Corresponding variable Decision function d

Teaching programme A d:XA
Teaching effectiveness Y
Student characteristic performance X

Huang Lei Innovation of College English education and teaching from the perspective of multiculturalism [J] Scientific consultation (education and scientific research), 2021 (11): 43–45. LeiHuang Innovation of College English education and teaching from the perspective of multiculturalism [J] Scientific consultation (education and scientific research) 2021 11 43 45 Search in Google Scholar

Li Feng Discussion on College English teaching mode under the background of big data technology – Comment on English writing teaching and research in the era of big data [J] Forest products industry, 2021, 58 (09): 144. FengLi Discussion on College English teaching mode under the background of big data technology – Comment on English writing teaching and research in the era of big data [J] Forest products industry 2021 58 09 144 Search in Google Scholar

Xiang kunmao Construction path of College English ecological classroom teaching model under big data [J] Overseas English, 2021 (24): 177–178. kunmaoXiang Construction path of College English ecological classroom teaching model under big data [J] Overseas English 2021 24 177 178 Search in Google Scholar

X Wang. Exploration of the Reform and Innovation of College English Teaching under Humanistic Literacy Education[J]. Journal of Language Teaching and Research, 2020, 11(6):1017. WangX Exploration of the Reform and Innovation of College English Teaching under Humanistic Literacy Education[J] Journal of Language Teaching and Research 2020 11 6 1017 10.17507/jltr.1106.21 Search in Google Scholar

Dong H T, Department FL. The Connection with College English Teaching and Bilingual Education[J]. Journal of Baicheng Normal University, 2018. DongH T DepartmentFL The Connection with College English Teaching and Bilingual Education[J] Journal of Baicheng Normal University 2018 Search in Google Scholar

Zhang M. Research on the Relationship between “Internet + Education” and College English Teaching[J]. Journal of Heihe University, 2018. ZhangM. Research on the Relationship between “Internet + Education” and College English Teaching[J] Journal of Heihe University 2018 Search in Google Scholar

Xin D. Research on the Innovation of College English Teaching Model from the Perspective of Big Data[C]//2019. XinD. Research on the Innovation of College English Teaching Model from the Perspective of Big Data[C] 2019 Search in Google Scholar

Li W B. Application of Big Data Technology in College Students’ Mental Health Education Innovation[J]. Journal of Physics Conference Series, 2020, 1648:042069. LiW B Application of Big Data Technology in College Students’ Mental Health Education Innovation[J] Journal of Physics Conference Series 2020 1648:042069. 10.1088/1742-6596/1648/4/042069 Search in Google Scholar

Wei H. An Exploration on College English Writing Teaching Innovation in the Age of Big Data[M]. 2020. WeiH An Exploration on College English Writing Teaching Innovation in the Age of Big Data[M] 2020 10.1007/978-981-15-5959-4_79 Search in Google Scholar

Zhang Y. The Researches on College English Class Audio-visual Oral Teaching Theory and Practice[C]//International Symposium-reform & Innovation of Higher Engineering Education. 2014. ZhangY The Researches on College English Class Audio-visual Oral Teaching Theory and Practice[C] International Symposium-reform & Innovation of Higher Engineering Education 2014 Search in Google Scholar

Jiang X. Analysis of ESP Instructing and Innovation of College English Teaching[J]. 2015. JiangX Analysis of ESP Instructing and Innovation of College English Teaching[J] 2015 Search in Google Scholar

Ru W. Reflections on the innovation of College English teaching methods in the new media environment[C]//International Conference on Education. 2017. RuW Reflections on the innovation of College English teaching methods in the new media environment[C] International Conference on Education 2017 Search in Google Scholar

Guo J, Sun F. Vocabulary-Teaching in College English[C]//Proceedings of 2015 3rd International Conference on Education Reform and Management Innovation (ERMI 2015 V78). 2015. GuoJ SunF Vocabulary-Teaching in College English[C] Proceedings of 2015 3rd International Conference on Education Reform and Management Innovation (ERMI 2015 V78) 2015 Search in Google Scholar

Li A. Research on the Innovation of College English Teaching Method in the New Media Era[C]//8th International Conference on Education, Management, Information and Management Society (EMIM 2018). 2018. LiA Research on the Innovation of College English Teaching Method in the New Media Era[C] 8th International Conference on Education, Management, Information and Management Society (EMIM 2018) 2018 10.2991/emim-18.2018.21 Search in Google Scholar

Ma Y. A Study of College English Teaching from the Perspective of “Entrepreneurship and Innovation”[C]//2019. MaY A Study of College English Teaching from the Perspective of “Entrepreneurship and Innovation”[C] 2019 10.2991/iccese-19.2019.286 Search in Google Scholar

Martin. English as a lingua franca: an empirical study of innovation in lexis and grammar[J]. Kings College London, 2007. Martin English as a lingua franca: an empirical study of innovation in lexis and grammar[J] Kings College London 2007 Search in Google Scholar

Shiozawa T, Simmons T. Social and Administrative Parameters in Methodological Innovation and Implementation in Post-Secondary Language Schools in Japan[J]. journal of college of international studies, 1993. ShiozawaT SimmonsT Social and Administrative Parameters in Methodological Innovation and Implementation in Post-Secondary Language Schools in Japan[J] journal of college of international studies 1993 Search in Google Scholar

Hong L. Study and Practice on English Teaching Innovation of Doctors of Non-English Majors[J]. Cross-Cultural Communication, 2007, 3(2). HongL Study and Practice on English Teaching Innovation of Doctors of Non-English Majors[J] Cross-Cultural Communication 2007 3 2 Search in Google Scholar

Wang Z. Research on the Reform and Innovation of College English Teaching in the Context of “Internet Plus”[C]//Proceedings of the 2018 6th International Education, Economics, Social Science, Arts, Sports and Management Engineering Conference (IEESASM 2018). 2019. WangZ Research on the Reform and Innovation of College English Teaching in the Context of “Internet Plus”[C] Proceedings of the 2018 6th International Education, Economics, Social Science, Arts, Sports and Management Engineering Conference (IEESASM 2018) 2019 10.2991/ieesasm-18.2019.26 Search in Google Scholar

Chen M Q, University T. On the Innovation of College English Teaching Reform under the Background of New Media[J]. Education Teaching Forum, 2019. ChenM Q University T. On the Innovation of College English Teaching Reform under the Background of New Media[J] Education Teaching Forum 2019 Search in Google Scholar

Zhang C. Research on Blending Teaching Model Innovation of College English Based on Mobile Learning[C]//Proceedings of the 2018 6th International Education, Economics, Social Science, Arts, Sports and Management Engineering Conference (IEESASM 2018). 2019. ZhangC Research on Blending Teaching Model Innovation of College English Based on Mobile Learning[C] Proceedings of the 2018 6th International Education, Economics, Social Science, Arts, Sports and Management Engineering Conference (IEESASM 2018) 2019 10.2991/ieesasm-18.2019.97 Search in Google Scholar

Wang Lei Mathematical model of precision teaching in the era of big data [D] Central China Normal University, 2018. LeiWang Mathematical model of precision teaching in the era of big data [D] Central China Normal University 2018 Search in Google Scholar

Wang Y. On the Feedback -Interactive Teaching Mode in College English Teaching of Physical Education[C]//International symposium on sports innovation and development of universities and colleges. 0. WangY On the Feedback -Interactive Teaching Mode in College English Teaching of Physical Education[C] International symposium on sports innovation and development of universities and colleges. 0. Search in Google Scholar

Guo Y, Wu Z. Reform and Innovation Research on College English Teaching in Ethnic Regions[C]//2013. GuoY WuZ Reform and Innovation Research on College English Teaching in Ethnic Regions[C] 2013 Search in Google Scholar

Xiao G, Wu S, Song Y. Analysis on New Practical Approach to College English Innovation — A Case Study of HUEB[J]. Journal of Hebei University of Economics and Business (Comprehensive Edition), 2016. XiaoG WuS SongY Analysis on New Practical Approach to College English Innovation — A Case Study of HUEB[J] Journal of Hebei University of Economics and Business (Comprehensive Edition) 2016 Search in Google Scholar

Wu Junjie Empirical research on the optimal mode of Internet of things teaching based on big data[J] Journal of Changchun Institute of Engineering (NATURAL SCIENCE EDITION), 2019, 20 (01): 78–81 JunjieWu Empirical research on the optimal mode of Internet of things teaching based on big data[J] Journal of Changchun Institute of Engineering (NATURAL SCIENCE EDITION) 2019 20 01 78 81 Search in Google Scholar

Dai Baolin, Gong Jun. research on forgetting factor iterative learning control based on initial state learning[J]. Information and control, 2018, 47 (5): 547–552. BaolinDai JunGong research on forgetting factor iterative learning control based on initial state learning[J] Information and control 2018 47 5 547 552 Search in Google Scholar

J Zhou. Inhibitors and Innovation in College Oral English Teaching[J]. The Science Education Article Collects, 2014. ZhouJ Inhibitors and Innovation in College Oral English Teaching[J] The Science Education Article Collects 2014 Search in Google Scholar

Xian-Zhu S I. A Constructivist Perspective of College English Teaching Model Innovation: With Reference to The Interactive English Teaching Model by Beijing Jiaotong University[J]. Foreign Language and Literature, 2010. Xian-ZhuS I A Constructivist Perspective of College English Teaching Model Innovation: With Reference to The Interactive English Teaching Model by Beijing Jiaotong University[J] Foreign Language and Literature 2010 Search in Google Scholar

Chen X. Enlightenments and Challenges: English Translation of Chinese Classics in College English Teaching and Learning for Non-English Major Students[C]//2016 International Seminar on Education Innovation and Economic Management (SEIEM 2016). 2016. ChenX Enlightenments and Challenges: English Translation of Chinese Classics in College English Teaching and Learning for Non-English Major Students[C] 2016 International Seminar on Education Innovation and Economic Management (SEIEM 2016) 2016 10.2991/seiem-16.2016.131 Search in Google Scholar

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