The important task of comprehensively improving educational quality, promoting educational fairness, and speeding up educational modernization raises the bar for educational informatization. This study investigates and analyses the wisdom teaching design mode of college English, based on English curriculum standards, wisdom education concept, and the theoretical foundation of the wisdom classroom. The evaluation method of college graduates’ learning efficiency is established using a multifactor model, DEA (data envelopment analysis), and other statistical methods. The accuracy of model performance is improved by more than 75 percent and the recall rate is improved by more than 65 percent when the feature dimension is increased from 5 to 100. The multifactor model is widely applicable, and it can also be used to investigate the evaluation of college English teaching in the new era.

1. Introduction

College English, as the primary focus of college foreign language education, is a required public basic course for the majority of non-English undergraduate students, and it plays an essential role in personnel development. The development of wisdom education is a global challenge and opportunity for all countries. Wisdom education, on the other hand, is a tool whose primary goal is to improve citizens’ basic literacy. After the concept of basic literacy was introduced, it quickly spread throughout the world in all walks of life. In addition, in the field of higher education, the concept of basic literacy plays an indispensable role. Experts and scholars have conducted numerous beneficial investigations in recent years in order to establish a scientific and effective teaching evaluation system, with positive results. However, there are few studies on how to effectively conduct multiple evaluations in the context of college enrollment expansion and large class teaching. As a result, creating a smart classroom environment is not only the only way to achieve smart education, but it is also a critical embodiment of smart education practise.

Education’s main goal is to cultivate people, and the classroom is where this happens. Only by grasping the core of the classroom can education truly develop [1]. The limitations of traditional classrooms [2]: teaching presuppositions based on experience, according to Huang. Teachers’ teaching presuppositions are based solely on teaching experience and lack a scientific foundation because teachers cannot know students’ mastery of relevant knowledge in time before class. According to Liu Yesheng, every teacher must demonstrate a sensitive, quick, and accurate ability to judge and act in complex English teaching situations, and this ability is the teacher’s English teaching wisdom [3]. Naryatmojo believes that English teaching wisdom is a combination of practical and actionable wisdom, as evidenced by how teachers deal with the relationship between presupposition and generation in the classroom [4]. By creating a learning environment with integrated technologies, Bruya and others believe that the essence of wisdom education is to enable teachers to demonstrate efficient teaching methods and learners to obtain appropriate personalised learning services and beautiful development experiences [5]. The research described above defines the main elements of wisdom education and provides extensive theoretical support for the creation of specific wisdom classrooms, but most of them remain in a broad sense. Education informatization is an important indicator of a country’s or region’s educational development. It is critical to promote educational informatization vigorously in order to achieve educational modernization, innovate English teaching modes, and improve educational quality.

There is a lot of valuable teaching data in school education and teaching activities in the era of big data [6]. An intelligent classroom uses data to improve the wit of English teaching and promote improvement and implementation, based on dynamic learning assessment and data analysis. The shift in personalised English instruction of new ideas and directions has emerged as a result of the English teaching reform. There is currently little research in China on the relationship between intelligent teaching systems and college English listening and speaking instruction. As a result, the author hopes to discover and summarise intelligent teaching, propose corresponding implementation strategies, and evaluate its effect using a multifactor model as part of his exploration of practical application. Innovation of this research is as follows:(1)From the perspective of the overall design of a smart classroom and from the perspective of teaching and learning, this paper focuses on the whole process steps before, during, and after class and analyzes the construction process of a smart classroom. Practical experience for wisdom teaching of college English is provided.(2)This paper constructs a multifactor model for the effect evaluation of college English wisdom teaching mode, and through further practical research and investigation, determines the complex interactive relationship between each influencing factor and each material component, and synthesizes technical knowledge and development mode.

2.1. Wisdom Teaching Research

Anggraeni et al. analyzed the practical method of turning the classroom into a smart classroom and expounded the design ideas of smart learning space [7]. On the basis of the characteristics of teaching and learning in a smart classroom, Mardhatillah et al. analyzed the four levels of integration of a smart classroom and teaching and put forward the concrete construction method [8]. Li et al. elaborated on the intelligent learning activities of Maker Education 2.0 from the design of intelligent classroom activity space and the implementation of activities [9]. Sun et al. put forward five individualized learning and teaching modes, including the individualized learning and teaching mode based on an intelligent classroom [10]. Wang and Yang put forward a data collection model of intelligent teaching through context-aware technology [11]. Yu et al. designed the intelligent classroom teaching mode under the background of “internet plus” and applied it to specific teaching. The conclusion shows that this mode improves the learning effect and satisfaction of college students and meets the learning needs of contemporary college students [12]. Feng put forward the “student-centered” constructivist teaching model and teaching design and defined the principles and content steps of constructivist teaching design. He believed that blended learning was the symbol of the in-depth development of educational technology theory and reflected on constructivist teaching design [13].

Sun et al. think that setting situational interference or providing examples can better promote students’ learning and knowledge transfer, which is a very effective way to increase related cognitive load [14]. Chen points out that engineering design course can cultivate freshmen’s inductive and deductive abilities [15]. Zhang and Feng discussed mind mapping as the cognitive and thinking framework of design teaching and proposed that mind mapping can make learners acquire more accurate knowledge and encourage collaborative learning among learners [16].

2.2. Research on Teaching Effect Evaluation

The test scores of college students are an important indicator of their learning efficiency and state. Because the elements of the teaching system interact, it is difficult to conduct a quantitative analysis of learning efficiency. Yuan asserted that students can achieve ideal learning outcomes if they understand how to learn. On this foundation, he developed the shallow learning and deep learning theories [17]. Ming et al. evaluated students’ academic achievements and assessed the value of school teaching or education [18], providing various management decision-making services for the management department. Yang uses learning evaluation to identify students’ academic achievements, judges the value of school teaching or education, and provides various management decision-making services for management departments [19]. Ruiying divides the factors that influence college students’ learning into categories, each of which contains several single factors and then quantifies each one to obtain specific quantitative data [20]. Ls et al. use the correlation of external and internal factors in sports events to analyse and determine the key factors that influence specific college students’ sports events so that training methods can be changed [21].

3. Methodology

3.1. Construction of College English Wisdom Teaching Mode

Smart classroom is a learning mode that promotes students’ autonomous learning with the help of modern educational information equipment. The interactive and interesting nature of intelligent classroom can encourage students to respond positively. Based on the analysis of big data information, students’ learning behavior can be improved rapidly. Feedback can strengthen in time, reduce the error rate and help students learn. Cognitive theory holds that students’ learning process is a process of internalizing and reorganizing knowledge and information in students’ minds, and students’ active learning contributes to cognitive development. The colorful, interactive, and interesting learning resources in the smart classroom can stimulate students’ interest in learning, make students learn actively, and promote their cognitive development.

Teachers can introduce the new curriculum’s content in a variety of ways, including before-class feedback, assessment exercises, and the creation of English learning situations. Students present their self-study achievements in front of the class, give English speeches, and share their thoughts. Interactional space English intelligent classroom interaction can encourage the sharing of high-quality English education resources and the expertise of English teachers, as well as promote educational equity. You can watch classroom scenes from other schools on the big screen in the classroom at the same time thanks to information technology. Students’ interest in learning can also be cultivated through interaction with animation, games, or robots with the help of science and technology. The network-assisted instruction flow chart is the external manifestation of network-assisted instruction, and it more clearly reflects the connotation of network-assisted instruction (see Figure 1).

Teachers use a combination of classroom and network to deliver synchronous, real-time, asynchronous, and nonreal-time instruction based on the teaching objectives and contents. Students select learning methods based on various learning contents and then report their findings to teachers in order to update teaching materials and improve teaching objectives. Intelligent classroom teaching design is based on the release of information technology and emphasises student-centered teaching methods, allowing students to apply what they’ve learned and apply feedback information to their own behaviours to solve practical problems. The design principles of intelligent classroom teaching will also be examined based on three main factors: teaching objectives, teaching strategies, and teacher evaluation.

Setting teaching goals is the premise and foundation of teaching activities, and teaching design is a series of goal-oriented activities. Through classroom wisdom teaching, the goal of wisdom teaching is to promote the generation of students’ wisdom and the cultivation of intelligent talents. The goal of intelligent teaching is to follow the development of the above-mentioned teaching objectives, combine the three-dimensional goals with the goal of cultivating people with basic literacy, and stick to the teaching goal of cultivating students’ advanced thinking ability and innovative ability, which is broad.

Smart classroom teaching emphasizes that information technology can promote the reform of teaching methods and processes, that is, make full use of the cloud-based diagnostic analysis system, feedback system and remedial system, and make full use of the accurate analysis of classroom teaching. Through information technology, provide teachers with the possibility of precise teaching. Among them, formative evaluation focuses on the evaluation of students’ learning effect in the learning process, while summative evaluation is the evaluation of the final teaching effect. The evaluation of intelligent classroom teaching is guaranteed by formative evaluation and summative evaluation, and various evaluation methods such as teacher feedback, student-student mutual evaluation, and personal evaluation are added. According to the research data, literature review, college English curriculum characteristics, and instructional design principles, the author has made an instructional design model of an intelligent classroom, as shown in Figure 2.

Before class, the learning analysis report generated by the data collected by the cloud diagnosis and analysis system, the teacher combined with the report to analyze the targeted push preview resources, and the students conducted the preview self-test and timely fed back the preview results. In this process, teachers and students interact and share the preview results, so as to prepare for the links in class. In class, teachers adopt a problem-oriented approach and introduce topics through the teaching situations designed before class to stimulate students’ interest in learning and attract students’ attention. Teachers use task-based and group cooperation to encourage students to ask questions independently, explore, and solve problems cooperatively. Use the system to automatically prepare exercises, test students’ knowledge, and evaluate teaching. This link can be combined with self-evaluation and others’ evaluation, and the reward mechanism in the system can be used to realize the diversification of evaluation methods. After class, teachers use mobile terminals to send homework and personalized learning resources to students. After students finish their homework, submit it, and the teacher will check the homework and provide feedback in real time. Through the communication and interaction with teachers after class, we can expand and improve our self-knowledge.

3.2. Implementation of College English Wisdom Teaching
3.2.1. Preparation Before Class

Students should contact and learn English as much as possible through various channels and methods, experience the language, and accumulate language experience. A large number of convenient and easy-to-use high school English learning resources is needed to fundamentally change the traditional classroom English teaching structure. First and foremost, the smart classroom is resource-rich and user-friendly, simulating the real-world classroom environment. Teachers can organise and prepare materials according to the chapters of English textbooks with the help of textbook resources. Teachers can browse resources shared by other teachers across the country and filter them based on their own requirements. You can either gather or download the necessary resources or upload and save them locally. It assists teachers with secondary processing and resource innovation by providing leading editing tools for documents, audio, and video resources.

Teachers can accurately grasp students’ first-hand learning information, preset the English teaching objectives for this course, and promote microclass or rich media preview and examination methods using the analysis of students’ homework results provided by the intelligent classroom information platform. The core of the smart classroom is big data analysis. During the preview process, students can look at teacher-created rich media content, complete and submit preview topics, participate in relevant discussions on forums or platforms, ask questions, and record questions. We can decide the teaching according to the learning situation, determine the objectives, contents, and methods, and optimise the design of the English teaching plan based on the analysis results of teachers’ learning situations and statistical feedback from students’ previews.

3.2.2. Intraclass Stage

The learning stage is mainly to enable students to master English knowledge and skills in an immersive way, integrate traditional multimedia, mobile technology, and listening and speaking courses and at the same time improve the effectiveness of foreign language classroom with the help of cognitive ability. The rich online learning resources and information technology of tools can cultivate students’ ability to solve complex problems, as shown in Figure 3.

Teachers organise students to debate and discuss related topics, promote student interaction, practise language organisation and originality, learn to analyse problems thoroughly, learn to communicate with others, and improve language application and hands-on ability. Finally, teachers should assess students’ personal performance and language ability in interactive activities, assessing whether pronunciation and intonation are correct, writing is appropriate, language expression is adequate, and opinions are correct.

Teachers can compare and show this work on the blackboard to achieve specific English teaching goals. This can save a lot of classroom time and improve the efficiency of English teaching. Interactive classroom English teaching software provides teachers with classroom recording function. When explaining the key and difficult points in class, you can record the screen to save the explanation process. Students can watch videos after class to achieve the review effect. Teachers can record multiple videos in class according to their needs, students can review the explanations of knowledge points after class, and they won’t miss every key point in class. Teachers can also view the recorded courses to reflect on English teaching. In listening and speaking classes, teachers make full use of fascinating, culturally meaningful, and well-informed content to organize students to read aloud, understand, and discuss English listening comprehension. Students can make full use of mobile devices to listen to the recording materials repeatedly, and at the same time, they can practice imitation and counting. In a word, in the classroom teaching process, teachers should adopt a scaffolding teaching strategy, give students the subject of learning, and let students actively think and experience language in real situations, so as to carry out meaningful learning.

3.2.3. Consolidate after Class

Teachers can assign test assignments, picture assignments, audio assignments, and other types of assignments in the wisdom class. While sending homework to students, teachers can send homework notices to parents’ mobile phones. Every cycle counts each subject’s average score, compares it to the class’s average score, determines their strengths and weaknesses, and then conducts targeted intensive exercises. Students can see how they rank in their classes. Simultaneously, the system promotes supporting intensive courses based on mastery of various disciplines. Learning pressure is reduced and your academic performance is improved by being clear and fully aware of your strengths and weaknesses. Targeted classes and exercises are taken.

Simultaneously, teachers should use mobile and wireless communication technologies to encourage students to participate fully, track their entire learning process, and provide timely individual guidance via mobile devices. Teachers go over the test questions in terms of knowledge points, chapters, test paper names, and keywords, then find and group the appropriate test questions and types in real time. To correct English teaching and improve specific courses, it can be divided into different types of reports based on the different roles of users, such as student reports, parent reports, teacher reports, class teacher reports, subject leader reports, grade leader reports, and so on. This study uses a diversified evaluation mechanism and establishes a quantitative evaluation system that combines formative and summative evaluation to ensure the rationality and objectivity of the evaluation results. Teachers can use the growth record bag evaluation method to objectively and comprehensively assess students’ progress in the learning process, which includes self-made recordings, listening and speaking assignments, group cooperation results, questionnaires, and learning experiences. Teachers can use mobile terminal technology to assess students’ progress, final skills, and language development in a timely and efficient manner.

3.3. Effect Evaluation

Teachers carefully designed the selection of teaching contents, the selection and use of teaching resources, the design of teaching links, and the evaluation of teachers in the application of the new intelligent teaching mode of college English, with the goal of cultivating basic literacy. The ultimate goals are basic learning ability, thinking ability, and cultural awareness. Complete control of your learning is taken before, during, and after class. The intelligent teaching mode, which aims to cultivate basic literacy, provides a platform for cultivating students’ listening, speaking, and communication skills while also allowing them to improve their basic literacy and develop the quality of cultural thinking and awareness.

Clustering is used to categorise data by collecting similarity. Cluster analysis is theoretically based on a variety of fields. Computer science, mathematics, biology, statistics, economics, and other disciplines have all benefited from the advancement of science and technology. This section introduces the cluster analysis method for determining the set of sample units to evaluate decision-making units and conducts a comparative study that demonstrates the method’s viability.

There are decision-making units , and their corresponding output index values are written in vector form as .

Among them,

A DEA (Data Envelopment Analysis) model based on model and only output is given as follows:where represents the decision-making unit of evaluation, .

In order to overcome the relative irrationality of model evaluation, a generalized DEA model with only output based on model is given. Assuming that there are decision units to be evaluated and reference sample units, the output index value of the -th decision unit is

The output index value of the sample unit is

A generalized DEA model with only output based on model- model is given as follows:

In this paper, the support vector machine is selected as the second stage multiclassifier, which can use limited sample information to find the best compromise between the complexity of the model and the learning ability of machine learning, so as to adapt to the multiclassification scene and solve the linear inseparable problem.

Firstly, in the case of linear separability, support vector classifier tries to find an optimal classification hyperplane to maximize the separation interval. In order to find this hyperplane, the following quadratic programming problem needs to be solved:where is the normal vector, is the offset term, and is the characteristic. The solution of the above quadratic programming problem is solved by Lagrange duality.

This formula is the original problem, and its dual problem is obtained by differential formula and simplification. The relation of is

The mapping of the original training sample to the high-dimensional feature space is realized by nonlinear mapping . Therefore, the nonlinear classification problem in the input space becomes the linear classification problem in the feature space.

Based on the structural risk minimization theory, SVM constructs the optimal segmentation hyperplane in the feature space, which can make the learners get the global optimization, and the expected risk of the whole sample space meets a certain upper bound with a certain probability. Model correction is indispensable in the process of model construction. According to the hypothetical model put forward in theoretical literature, when it is found that the fitting degree with sample data is not good, it means that the hypothetical model must be corrected. The model structure diagram of the hypothetical model after appropriate correction is shown in Figure 4.

After reviewing the model, in addition to retaining the original hypothesis model, the school factor, teacher training factor, motivation factor, policy system factor, career development factor, school factor, policy system factor, teacher training factor, and motivation factor are added to the self-efficacy factor.

4. Experiment and Results

When studying educational issues, researchers should not only pay attention to the quality of the model but also have a deeper discussion. When students only perform a few actions, it is difficult for the experiment to explore the order of their daily activities and grasp their main purpose. This experiment studies whether the model is good at modeling the behavior of long sequences by comparing the behaviors of different length sequences, as shown in Figure 5.

It has been discovered that as the length of the behaviour sequence increases from 1 to 10, the model’s performance improves and the accuracy rate exceeds 75%. This demonstrates that the model can achieve better prediction if it captures more target features based on the behaviour features of existing sequences. Short-term continuous behaviour modelling can monitor students’ daily activities more sensitively and accurately reflect students’ living and learning conditions than standard academic evaluation or personal static information. In the accuracy and recall method evaluation, the experiment shows whether the feature dimensions of various training quantities will affect students’ behaviour feature selection, as shown in Figure 6.

It can be found that with the feature dimension increasing from 5 to 100, the accuracy of the model performance is improved by more than 75% and the recall rate is improved by more than 65%. It can also be found that with the increase of the number of hidden cells, the curves of the two indexes show a flat trend. However, the rendering ability of models is limited, and the accuracy of models with features greater than 50 dimensions tends to be stable. At this time, some repetitive features may appear in the process of algorithm training. Therefore, the establishment of appropriate feature dimensions is helpful to model the students’ behaviors, and it is also helpful for educators to understand the students’ behaviors and explore the information contained in the data.

Teachers’ and students’ cognition of teaching skill structure is basically consistent with that of university professors. Four common factors can be extracted, and the evaluation indexes (or skill components) contained in each common factor are similar. However, the contribution rate of each common factor is quite different; that is, teachers and students have different interpretations of the role of the components that constitute teaching ability. In this study, the components of each common factor are included in the formula, and teachers and students can get the evaluation of the importance of each component, as shown in Figure 7.

Courses and teaching skills are the most important components of college teachers’ teaching ability, as shown in Figure 7, followed by professional attitude, professional construction ability, personal characteristics, and basic knowledge. The multifactor model is then used to compare the traditional T-test method to the measured data of university teaching evaluation. The information comes from students’ evaluations of teachers at the end of their university careers. The school created the evaluation questionnaire, which includes 17 indicators divided into five categories: teaching routine, teaching content, teaching method, teaching attitude, and teaching effect. “Excellent, moderate, and poor” are represented on a five-point scale from 5 to 1. For a comparison of test results (see Table 1).

We selected the evaluation data of two teachers (T1 and T2) for comparison. According to the results of t-test, there is a significant difference between the two samples in teaching content and teaching effect (). For the comparison of the mean value of latent variables, it is necessary for the questionnaire to have structural consistency between the two groups of samples. The fitting indexes of several models of teaching content factors are shown in Table 2.

For the other four factors and the total score, use the same procedure to obtain the estimated value of the average of potential variables. It is convenient to compare with the results of T-test. It can be seen that both the T-test of observed variables and the multiple groups of confirmatory factor analysis of latent variables show that there are significant differences between the two teachers in teaching content and influencing factors, as well as the influence on students, and the evaluation of teacher T2 is better than that of teacher T1. There is no significant difference between T1 teacher and T2 teacher in teaching methods and attitudes.

The scores of the two groups of samples in two indicators (indicators 1 and 4) are very different and inconsistent, indicating that the two groups of students have very different starting points in these two indicators, according to the parameters of teaching routine factors. The T-test method finds no significant difference in teaching routine and total score between the two groups of samples due to this difference in measurement starting point. Wisdom classroom teaching has achieved phased results after a year of pilot work. This study examines the teaching effect of intelligent classrooms from the perspective of students’ English performance by examining the evaluation performance of the pilot class, the English performance of the pilot class, and the control class. For more information (see Figure 8).

Wisdom is the beginning and end of students’ wisdom development, and wisdom classroom is one of the key strategies to realize students’ wisdom development under the background of wisdom learning. Figure 9 shows the comparison of students’ initiative in the early and late stages of intelligent classroom practice.

It can be seen that in the early and late stages of the smart classroom pilot, students’ enthusiasm for submitting homework has been continuously improved, and students’ enthusiasm for actively using the tablet to view resources has declined compared with the previous stage. Students’ learning initiative has also become the main factor affecting smart classroom teaching.

Teachers and students support and agree with intelligent classroom teaching mode, according to teacher and student evaluation data on the application results of intelligent classroom teaching design mode, and 75 percent of students prefer teachers to use flat-panel teaching. Teachers believe that intelligent thematic teaching design fosters students’ communication and cooperation skills while also increasing their enthusiasm for learning. The reason for this is that the intelligent classroom teaching design mode adheres to the “student-centered” teaching philosophy and emphasises the development of students’ cooperative learning abilities and learning initiative. The model emphasises making full use of the deep integration of information technology and the classroom and encourages teachers to use the real-time feedback function of big data information to obtain the most accurate learning situation.

5. Conclusions

College English wisdom teaching extends learning time and space, effectively connects classroom and extracurricular activities, provides students with more intuitive and rich learning content and methods, and emphasises students’ dominant position as well as the role of guides and teachers’ supervision. Smart classrooms balance educational resources, increase teacher and student enthusiasm, increase classroom efficiency, reduce teacher and student workload, and improve English teaching quality. To achieve our goal of examining students’ learning efficiency, we use the generalised DEA method to distinguish the vast majority of students, and it can be sorted completely. The accuracy of the model performance is improved by more than 75 percent and the recall rate is improved by more than 65 percent when the feature dimension is increased from 5 to 100. When clustering analysis and data mining are combined, academic achievements can be classified more effectively and accurately.

Data Availability

The data used to support the findings of this study are available from the author upon request.

Conflicts of Interest

The author declares that there are no conflicts of interest.


This study was supported by the Special Projects on Smart Instruction in Universities in Henan Province: Research on the Ways of Smart Instruction for Improving Students’ Learning Performance in Common Core Course Based on Incremental Machine Learning Algorithms (Grant No. (2021)268), Projects for the Cultivation of Young Leading Teachers in Henan Province: Research on Practice and Improvement of Deeply Integrated Blended Teaching Mode of College English Based on CoI Theory (Grant No. 2019GGJS284), The Ninth Batch of the New Century Education and Teaching Reform and Talent Training Project of Zhengzhou Local Universities: An Innovative Research on Deep Integrated Blended First-class College English Course Based on SPOC (Grant No. ZZJG-A9008), Teaching Reform Project in Henan Province: Research on Practice and Promotion of Blended Teaching Mode of First-class College English Based on “SPOC + MOOC” Multimode Operation (Grant No. 2019SJGLX520), and Zhengzhou Shengda University Blended Course Project: Integrated English Course IV (Grant No. SDHHSKC-2019-A01).