# Evaluation and Analysis of Assisted Instruction and Ability Improvement Based on Artificial Intelligence

Authors: Zhi Li; Zhao Guang; Wen Sun
Journal: Journal of Sensors
 (2022)

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## Abstract

Teachers are a very important part of university education. They have the responsibility of teaching and educating people, and it is also their unshakable responsibility to train all-round talents for the country. If we want to improve students’ quality, we must improve teachers’ teaching quality and pay attention to the research of teachers’ teaching ability. This paper analyzes the connotation of artificial intelligence-assisted instruction. Then, Bayesian active learning modeling is used. This paper mainly adopts the way of questionnaire and empirical research methods and launches a basic investigation on the teaching ability of university teachers. Through investigation, the following problems are summarized: (1) insufficient self-knowledge reserve and weak teaching theoretical foundation and (2) inaccurate orientation of teaching objectives and single teaching methods. Schools need to enrich training methods, establish multiple effective mechanisms for evaluation, meet the basic requirements of each teacher, and play the role of inspiring teachers. As for teachers, they need to have a good attitude, be full of interest in teaching and educating people, have a strong sense of responsibility, and constantly improve themselves and improve themselves.


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## Body

## 1. Introduction

This paper emphasizes the importance of task environment as the decisive factor of agent proper design. The work of artificial intelligence is explained by the definition of intelligent agent and its functions in production system, reactive agent, real-time conditional planner, neural network, and rich decision system [1]. How teachers’ ability will directly affect the cultivation of students. Therefore, in order to cultivate innovative talents, teachers should improve their teaching level and teaching ability [2]. Constrained programming is a powerful paradigm for solving combinatorial search problems, which absorbs a wide range of technologies from artificial intelligence, computer science, databases, programming languages, and operational research. Based on constrained programming, the manual provides a fairly comprehensive coverage of work in all these areas, enabling readers to have a fairly accurate concept of the whole field and its potential [3]. This paper is mainly aimed at advanced undergraduates who want to engage in Bayesian network technology and computer science. The first is what I call practitioners. Practitioners are interested in learning sufficient material on the subject to be able to assist domain experts in building Bayesian network systems [4]. Information teaching has new requirements for teachers. This paper analyzes the practical guidance, teaching reflection, and other aspects and gives which aspects to train teachers from [5]. This book shows that most of the ideas behind intelligent systems are simple and clear, and the methods used in the book have been widely tested through several courses provided by the author. The book introduces the field of computer intelligence. In university settings, this book can be used as an introductory course for computer science, information systems, or engineering departments [6]. This paper explains how matrix theory appears and effectively participates in a process and has a feasible application in game theory. Matrix technology shows itself to be essential, and their introduction can provide us with a simple and accurate method to find solutions [7]. This book emphasizes the importance of task environment as the decisive factor of agent proper design, interprets agent learning as expanding programmer’s scope in unknown environment, and shows how this role limits its design, which is beneficial to the representation of knowledge and explanatory reasoning [8]. In this paper, we review the extensive research on time representation and reasoning without focusing on any specific applications. We outline the basic problems, methods, and results in these two fields and summarize the latest developments in related fields [9]. This paper introduces the application of finite element analysis software ANSYS buckling analysis in the teaching of material mechanics. The advanced CAE method makes the column stable and makes full use of computer simulation means to make up for the lack of practice and improve students’ knowledge level [10]. This paper uses neural network technology to build a teaching quality evaluation model. On the basis of introducing the neural network model and teaching quality evaluation, the paper also verifies its effect, and the results are almost the same as expected. Finally, the information processing in teaching evaluation is discussed [11]. With the development of the times, it is more and more common to add computer technology in the teaching process. This paper presents an online intelligent diagnosis and evaluation scheme based on J2EE. As an auxiliary teaching algorithm, the system has many functions such as teaching, diagnosis, testing, and feedback through automatic modification of subjective and objective questions and personalized design of diagnosis results [12]. In the past decade, many computer-based interactive physics programs have emerged at the university level. This paper considers one such project, the Cognitive and Emotional Results Studio Physics Program, which integrates the initial implementation of the unified physics learning environment [13]. This article, through the teacher’s trial lesson for video, let all the participants to evaluate and then put forward the teaching methods need to improve the place. The results show that their teaching ability has been improved and they have learned new teaching methods [14]. The application of electronic card in student escort is to design and build a system based on Arduino Uno microcontroller and RFID module. The system is expected to facilitate lecturers to check attendance, reduce students’ habit of checking attendance, and increase the use intensity of student identification cards [15].


## 2. Connotation of Artificial Intelligence-Assisted Instruction

### 2.1. Help the Common Improvement of Machine Intelligence and Human Intelligence

In the era of artificial intelligence, machine teaching is more flexible and humanized in technology, which can effectively improve the learning ability of teachers and students. Under the future educational situation, the symbiotic evolution of man and machine will become the inevitable trend of the combination of artificial intelligence and education, and the intelligent evolution of teaching machines will certainly contribute to human understanding of nature and people. With the in-depth development of knowledge transfer, interaction, and knowledge sharing among teachers, teachers and students, and students, massive knowledge, behavioral data, teaching tasks, and cases are spreading into the infinite wisdom sharing system, becoming the original floating force for continuous intelligent learning. At the same time, the highly intelligent teaching machine is constantly updating the comprehensive accumulation of human wisdom, using comprehensive wisdom and big data to break the blind spot of human thinking, generating a large number of new information that is difficult to extract by traditional methods, and providing it to teachers and students. Learning machines also use data storage capacity to enhance the depth and breadth of memory, improve the science and technology-art interaction between teachers and students through man-machine dialogue, support decision-making, improve the efficiency and effect of teacher education management, and expand widely the wisdom and skills of teachers and students.


### 2.2. Break the Dualism of Subject and Object of Education

In the era of artificial intelligence, the traditional connotation of machines has changed. First of all, machine-assisted training supported by teaching machines has become an important part of teaching and education, which almost penetrates into all directions of the training process. Intelligent machines have more and more human abilities through experiential learning and teacher feedback. Teachers and machines become each other’s topics and objects in the process of education, and they coexist and develop harmoniously. Second, in the era of artificial intelligence, machine-assisted training retains the characteristics of resource carrier and computer-assisted, but with the improvement of intelligence level, machines have become teachers, and the characteristics of resources have weakened and the subjective components have increased. Machines do most of the work for human teachers, so it is difficult for students to feel the difference between real teachers and machine teachers. Third, technological innovation accelerates man-machine integration. The communication between people and machines has become smoother. Machines can provide people with arithmetic and memory support and give people the ability to think and act that they could not do before.


### 2.3. Promote the Cooperation and Integration of Teachers, Machines, and Students

Machine-assisted instruction in the era of artificial intelligence is dedicated to building a paradigm connecting teachers, machines, and students, emphasizing the cooperation and evolution among teachers, machines, and students, and promoting man-machine integration and teaching. In contrast, human teachers are more experienced in teaching and problem-solving. At the same time, people’s advanced characteristics such as abstract thinking, logical reasoning, and learning have strong adaptability and adaptability to educational scenes, which is conducive to teaching interaction and enhances learning effect. Massive data storage, calculation, retrieval, and other functions of intelligent machines can help teachers quickly process and analyze data and perform many complex tasks on their behalf. Students who use intelligent teaching machines can get accurate personalized services, and students’ feedback data can also support the improvement of machine-assisted functions. In the era of artificial intelligence, machine-assisted education combines the interests of teachers and machine students. Human intelligence and intelligent machines capable of dichotomy, mutual adaptation, and spiral coevolution realize social cooperation. Training methods can also undergo qualitative changes.


## 2.1. Help the Common Improvement of Machine Intelligence and Human Intelligence

In the era of artificial intelligence, machine teaching is more flexible and humanized in technology, which can effectively improve the learning ability of teachers and students. Under the future educational situation, the symbiotic evolution of man and machine will become the inevitable trend of the combination of artificial intelligence and education, and the intelligent evolution of teaching machines will certainly contribute to human understanding of nature and people. With the in-depth development of knowledge transfer, interaction, and knowledge sharing among teachers, teachers and students, and students, massive knowledge, behavioral data, teaching tasks, and cases are spreading into the infinite wisdom sharing system, becoming the original floating force for continuous intelligent learning. At the same time, the highly intelligent teaching machine is constantly updating the comprehensive accumulation of human wisdom, using comprehensive wisdom and big data to break the blind spot of human thinking, generating a large number of new information that is difficult to extract by traditional methods, and providing it to teachers and students. Learning machines also use data storage capacity to enhance the depth and breadth of memory, improve the science and technology-art interaction between teachers and students through man-machine dialogue, support decision-making, improve the efficiency and effect of teacher education management, and expand widely the wisdom and skills of teachers and students.


## 2.2. Break the Dualism of Subject and Object of Education

In the era of artificial intelligence, the traditional connotation of machines has changed. First of all, machine-assisted training supported by teaching machines has become an important part of teaching and education, which almost penetrates into all directions of the training process. Intelligent machines have more and more human abilities through experiential learning and teacher feedback. Teachers and machines become each other’s topics and objects in the process of education, and they coexist and develop harmoniously. Second, in the era of artificial intelligence, machine-assisted training retains the characteristics of resource carrier and computer-assisted, but with the improvement of intelligence level, machines have become teachers, and the characteristics of resources have weakened and the subjective components have increased. Machines do most of the work for human teachers, so it is difficult for students to feel the difference between real teachers and machine teachers. Third, technological innovation accelerates man-machine integration. The communication between people and machines has become smoother. Machines can provide people with arithmetic and memory support and give people the ability to think and act that they could not do before.


## 2.3. Promote the Cooperation and Integration of Teachers, Machines, and Students

Machine-assisted instruction in the era of artificial intelligence is dedicated to building a paradigm connecting teachers, machines, and students, emphasizing the cooperation and evolution among teachers, machines, and students, and promoting man-machine integration and teaching. In contrast, human teachers are more experienced in teaching and problem-solving. At the same time, people’s advanced characteristics such as abstract thinking, logical reasoning, and learning have strong adaptability and adaptability to educational scenes, which is conducive to teaching interaction and enhances learning effect. Massive data storage, calculation, retrieval, and other functions of intelligent machines can help teachers quickly process and analyze data and perform many complex tasks on their behalf. Students who use intelligent teaching machines can get accurate personalized services, and students’ feedback data can also support the improvement of machine-assisted functions. In the era of artificial intelligence, machine-assisted education combines the interests of teachers and machine students. Human intelligence and intelligent machines capable of dichotomy, mutual adaptation, and spiral coevolution realize social cooperation. Training methods can also undergo qualitative changes.


## 3. Active Learning Modeling of Bayesian Extreme Learning Machine

### 3.1. Bayesian Extreme Learning Machine Modeling Method

#### 3.1.1. Extreme Learning Machine

ELM is a neural network model. Its network structure is shown in Figure1. Given N training samples X∈ℝN×m,t∈ℝN, the regression model can be expressed as
(1)t^i=∑k=1Mhai,bi,xiβk,where βk is the output weight from the k-th hidden layer node to the output layer, ai and bi are the weight and offset of the i-th hidden layer node, respectively, t^i is the predicted output of xi, h⋅ is the activation function, and the activation function in this paper is sigmoid function, which makes ELM have nonlinear fitting ability.Figure 1
ELM network structure diagram.Simplify the above formula to obtain(2)t^=Hβ,where β=β1,β2,⋯,βMT, t^=t∧1,t∧2,⋯,t∧MT, and H are hidden layer mapping matrices of ELM.Then, the objective function of ELM is shown in(3)mint∧−t2=minHβ−t2.The output weight calculation formula is shown in(4)β=H+t,where H+ is the generalized inverse of H.


#### 3.1.2. Bayesian Extreme Learning Machine

BELM is an ELM algorithm based on Bayesian framework. Similar to ELM, the regression model of BELM can be expressed as(5)t=hβ+ε.The conditional probability distribution oft is shown in
(6)pth,β,σ2=Nh⋅β,σ2.The probability distribution ofβ is shown in
(7)pβα=N0,α−1⋅I,where I is the identity matrix and α is the hyperparameter. Assuming that the prior function and likelihood function of β obey Gaussian distribution, the maximum likelihood estimation is shown in
(8)β^=σ−2SHTt,(9)S=αI+σ−2HTH−1.The parameters in the above formula need to be solved by iteration, and the specific derivation process is shown in(10)γ=M−α⋅trS,(11)α=γβ∧Tβ^,(12)σ2=∑i=1Nti−hiβ∧2N−γ.For a given input samplexq, the corresponding mean and variance are shown in
(13)yq=hqβ^,(14)σq2=σ2+hq⋅S⋅hqT.


### 3.2. Active Learning Methods

#### 3.2.1. Definition of Global Variance Change

Sample sets are divided into labeled and unlabeled.nl and nu are the number of labeled samples and unlabeled samples, respectively, and m is the number of auxiliary variables. xiu is an unlabeled sample in XU, x′ is a sample to be tested, and the prediction variance change of sample to be tested x′ after adding sample xiu in BELM model is defined as Δσ2x′,xiu, as shown in Formula (15). Because the change of super parameter is not considered, this index does not depend on the actual label value of xiu.
(15)Δσ2x′,xiu=σ2x′xL−σ2x′XLxiu.The overall variance change of the model is defined as(16)η=∑x′∈XΔσ2x′,xiu.


#### 3.2.2. Sample Selection Strategy of Bayesian Extreme Learning Machine

In order to improve the efficiency of the algorithm, we propose(17)Δσ2x′,xiu=hx′Sn−Sn+1hx′T,where hx′ is the hidden layer mapping vector corresponding to x′, Sn is the posterior variance of β without adding unlabeled samples, and Sn+1 is the posterior variance of β after adding xiu.Combined with formula (9), the posterior variance can be expressed as
(18)Sn=aI+σ−2HLTHL−1,(19)Sn+1=aI+σ−2HLhiuTHLhiu−1=aI+σ−2HLTHL+σ−2hiuThiu−1,where hiu is the hidden layer mapping vector corresponding to xiu, the Sherman-Morrison-Woodbury criterion is used to expand formula (19), and formula (20) is obtained.
(20)Sn+1=aI+σ−2HLTHL−1−Snσ−2hiuThiuSn1+hiuSnσ−2hiuT=Sn−Snσ−2hiuThiuSn1+hiuSnσ−2hiuT.Formula (20) is substituted into formula (17) for simplification, and the predicted variance change amount of the sample x′ to be measured after the sample xiu is added is shown in
(21)Δσ2x′,xiu=hx′Snσ−2hiuThiuSn1+hiuSnσ−2hiuThx′T.By substituting formula (21) into formula (16) to further simplify η, the overall variance change η of the BELM model can be expressed as
(22)η=∑x′∈XΔσ2x′,xiu=trhXSnσ−2hiuThiuSn1+hiuSnσ−2hiuThXT.The BELM strategy is shown in(23)x∗=argmaxhiu∈XUη.The generalization performance of the model is maximized by using formula (23).


#### 3.2.3. Modeling Process

In order to avoid the increase of operation cost, this chapter designs a batch sample selection and labeling method without considering the change of BELM model parameters. Assuming that the number of batch labeled samples in the iterative process isns, the BELM sample selection strategy is updated to
(24)XL≔XLxiu,XU≔xiui=1,⋯,i−1,i+1,⋯nu.After updating the training set, as shown in(25)Sn≔Sn−Snσ−2hiuThiuSn1+hiuSnσ−2hiuT.According to the updatedXL, XU, and Sn after iteration, sample evaluation is carried out again by using formula (23), and new unlabeled samples are selected. When the number of selected unlabeled samples reaches a preset ns, ns unlabeled samples selected in the iteration process are labeled in batches, and BELM model parameters are reoptimized, and a new soft sensing model is established at the same time.


## 3.1. Bayesian Extreme Learning Machine Modeling Method

### 3.1.1. Extreme Learning Machine

ELM is a neural network model. Its network structure is shown in Figure1. Given N training samples X∈ℝN×m,t∈ℝN, the regression model can be expressed as
(1)t^i=∑k=1Mhai,bi,xiβk,where βk is the output weight from the k-th hidden layer node to the output layer, ai and bi are the weight and offset of the i-th hidden layer node, respectively, t^i is the predicted output of xi, h⋅ is the activation function, and the activation function in this paper is sigmoid function, which makes ELM have nonlinear fitting ability.Figure 1
ELM network structure diagram.Simplify the above formula to obtain(2)t^=Hβ,where β=β1,β2,⋯,βMT, t^=t∧1,t∧2,⋯,t∧MT, and H are hidden layer mapping matrices of ELM.Then, the objective function of ELM is shown in(3)mint∧−t2=minHβ−t2.The output weight calculation formula is shown in(4)β=H+t,where H+ is the generalized inverse of H.


### 3.1.2. Bayesian Extreme Learning Machine

BELM is an ELM algorithm based on Bayesian framework. Similar to ELM, the regression model of BELM can be expressed as(5)t=hβ+ε.The conditional probability distribution oft is shown in
(6)pth,β,σ2=Nh⋅β,σ2.The probability distribution ofβ is shown in
(7)pβα=N0,α−1⋅I,where I is the identity matrix and α is the hyperparameter. Assuming that the prior function and likelihood function of β obey Gaussian distribution, the maximum likelihood estimation is shown in
(8)β^=σ−2SHTt,(9)S=αI+σ−2HTH−1.The parameters in the above formula need to be solved by iteration, and the specific derivation process is shown in(10)γ=M−α⋅trS,(11)α=γβ∧Tβ^,(12)σ2=∑i=1Nti−hiβ∧2N−γ.For a given input samplexq, the corresponding mean and variance are shown in
(13)yq=hqβ^,(14)σq2=σ2+hq⋅S⋅hqT.


## 3.1.1. Extreme Learning Machine

ELM is a neural network model. Its network structure is shown in Figure1. Given N training samples X∈ℝN×m,t∈ℝN, the regression model can be expressed as
(1)t^i=∑k=1Mhai,bi,xiβk,where βk is the output weight from the k-th hidden layer node to the output layer, ai and bi are the weight and offset of the i-th hidden layer node, respectively, t^i is the predicted output of xi, h⋅ is the activation function, and the activation function in this paper is sigmoid function, which makes ELM have nonlinear fitting ability.Figure 1
ELM network structure diagram.Simplify the above formula to obtain(2)t^=Hβ,where β=β1,β2,⋯,βMT, t^=t∧1,t∧2,⋯,t∧MT, and H are hidden layer mapping matrices of ELM.Then, the objective function of ELM is shown in(3)mint∧−t2=minHβ−t2.The output weight calculation formula is shown in(4)β=H+t,where H+ is the generalized inverse of H.


## 3.1.2. Bayesian Extreme Learning Machine

BELM is an ELM algorithm based on Bayesian framework. Similar to ELM, the regression model of BELM can be expressed as(5)t=hβ+ε.The conditional probability distribution oft is shown in
(6)pth,β,σ2=Nh⋅β,σ2.The probability distribution ofβ is shown in
(7)pβα=N0,α−1⋅I,where I is the identity matrix and α is the hyperparameter. Assuming that the prior function and likelihood function of β obey Gaussian distribution, the maximum likelihood estimation is shown in
(8)β^=σ−2SHTt,(9)S=αI+σ−2HTH−1.The parameters in the above formula need to be solved by iteration, and the specific derivation process is shown in(10)γ=M−α⋅trS,(11)α=γβ∧Tβ^,(12)σ2=∑i=1Nti−hiβ∧2N−γ.For a given input samplexq, the corresponding mean and variance are shown in
(13)yq=hqβ^,(14)σq2=σ2+hq⋅S⋅hqT.


## 3.2. Active Learning Methods

### 3.2.1. Definition of Global Variance Change

Sample sets are divided into labeled and unlabeled.nl and nu are the number of labeled samples and unlabeled samples, respectively, and m is the number of auxiliary variables. xiu is an unlabeled sample in XU, x′ is a sample to be tested, and the prediction variance change of sample to be tested x′ after adding sample xiu in BELM model is defined as Δσ2x′,xiu, as shown in Formula (15). Because the change of super parameter is not considered, this index does not depend on the actual label value of xiu.
(15)Δσ2x′,xiu=σ2x′xL−σ2x′XLxiu.The overall variance change of the model is defined as(16)η=∑x′∈XΔσ2x′,xiu.


### 3.2.2. Sample Selection Strategy of Bayesian Extreme Learning Machine

In order to improve the efficiency of the algorithm, we propose(17)Δσ2x′,xiu=hx′Sn−Sn+1hx′T,where hx′ is the hidden layer mapping vector corresponding to x′, Sn is the posterior variance of β without adding unlabeled samples, and Sn+1 is the posterior variance of β after adding xiu.Combined with formula (9), the posterior variance can be expressed as
(18)Sn=aI+σ−2HLTHL−1,(19)Sn+1=aI+σ−2HLhiuTHLhiu−1=aI+σ−2HLTHL+σ−2hiuThiu−1,where hiu is the hidden layer mapping vector corresponding to xiu, the Sherman-Morrison-Woodbury criterion is used to expand formula (19), and formula (20) is obtained.
(20)Sn+1=aI+σ−2HLTHL−1−Snσ−2hiuThiuSn1+hiuSnσ−2hiuT=Sn−Snσ−2hiuThiuSn1+hiuSnσ−2hiuT.Formula (20) is substituted into formula (17) for simplification, and the predicted variance change amount of the sample x′ to be measured after the sample xiu is added is shown in
(21)Δσ2x′,xiu=hx′Snσ−2hiuThiuSn1+hiuSnσ−2hiuThx′T.By substituting formula (21) into formula (16) to further simplify η, the overall variance change η of the BELM model can be expressed as
(22)η=∑x′∈XΔσ2x′,xiu=trhXSnσ−2hiuThiuSn1+hiuSnσ−2hiuThXT.The BELM strategy is shown in(23)x∗=argmaxhiu∈XUη.The generalization performance of the model is maximized by using formula (23).


### 3.2.3. Modeling Process

In order to avoid the increase of operation cost, this chapter designs a batch sample selection and labeling method without considering the change of BELM model parameters. Assuming that the number of batch labeled samples in the iterative process isns, the BELM sample selection strategy is updated to
(24)XL≔XLxiu,XU≔xiui=1,⋯,i−1,i+1,⋯nu.After updating the training set, as shown in(25)Sn≔Sn−Snσ−2hiuThiuSn1+hiuSnσ−2hiuT.According to the updatedXL, XU, and Sn after iteration, sample evaluation is carried out again by using formula (23), and new unlabeled samples are selected. When the number of selected unlabeled samples reaches a preset ns, ns unlabeled samples selected in the iteration process are labeled in batches, and BELM model parameters are reoptimized, and a new soft sensing model is established at the same time.


## 3.2.1. Definition of Global Variance Change

Sample sets are divided into labeled and unlabeled.nl and nu are the number of labeled samples and unlabeled samples, respectively, and m is the number of auxiliary variables. xiu is an unlabeled sample in XU, x′ is a sample to be tested, and the prediction variance change of sample to be tested x′ after adding sample xiu in BELM model is defined as Δσ2x′,xiu, as shown in Formula (15). Because the change of super parameter is not considered, this index does not depend on the actual label value of xiu.
(15)Δσ2x′,xiu=σ2x′xL−σ2x′XLxiu.The overall variance change of the model is defined as(16)η=∑x′∈XΔσ2x′,xiu.


## 3.2.2. Sample Selection Strategy of Bayesian Extreme Learning Machine

In order to improve the efficiency of the algorithm, we propose(17)Δσ2x′,xiu=hx′Sn−Sn+1hx′T,where hx′ is the hidden layer mapping vector corresponding to x′, Sn is the posterior variance of β without adding unlabeled samples, and Sn+1 is the posterior variance of β after adding xiu.Combined with formula (9), the posterior variance can be expressed as
(18)Sn=aI+σ−2HLTHL−1,(19)Sn+1=aI+σ−2HLhiuTHLhiu−1=aI+σ−2HLTHL+σ−2hiuThiu−1,where hiu is the hidden layer mapping vector corresponding to xiu, the Sherman-Morrison-Woodbury criterion is used to expand formula (19), and formula (20) is obtained.
(20)Sn+1=aI+σ−2HLTHL−1−Snσ−2hiuThiuSn1+hiuSnσ−2hiuT=Sn−Snσ−2hiuThiuSn1+hiuSnσ−2hiuT.Formula (20) is substituted into formula (17) for simplification, and the predicted variance change amount of the sample x′ to be measured after the sample xiu is added is shown in
(21)Δσ2x′,xiu=hx′Snσ−2hiuThiuSn1+hiuSnσ−2hiuThx′T.By substituting formula (21) into formula (16) to further simplify η, the overall variance change η of the BELM model can be expressed as
(22)η=∑x′∈XΔσ2x′,xiu=trhXSnσ−2hiuThiuSn1+hiuSnσ−2hiuThXT.The BELM strategy is shown in(23)x∗=argmaxhiu∈XUη.The generalization performance of the model is maximized by using formula (23).


## 3.2.3. Modeling Process

In order to avoid the increase of operation cost, this chapter designs a batch sample selection and labeling method without considering the change of BELM model parameters. Assuming that the number of batch labeled samples in the iterative process isns, the BELM sample selection strategy is updated to
(24)XL≔XLxiu,XU≔xiui=1,⋯,i−1,i+1,⋯nu.After updating the training set, as shown in(25)Sn≔Sn−Snσ−2hiuThiuSn1+hiuSnσ−2hiuT.According to the updatedXL, XU, and Sn after iteration, sample evaluation is carried out again by using formula (23), and new unlabeled samples are selected. When the number of selected unlabeled samples reaches a preset ns, ns unlabeled samples selected in the iteration process are labeled in batches, and BELM model parameters are reoptimized, and a new soft sensing model is established at the same time.


## 4. Experimental Analysis

### 4.1. Teachers’ Needs in the Application Environment of Multimedia Technology

#### 4.1.1. Survey of Demand for Multimedia Facilities

Looking at Figures2–4, we can draw that 94% of teachers said that they would encounter multimedia equipment failure in the teaching process. In the investigation of whether the equipment can meet the teaching needs, almost all teachers think that it can meet their teaching needs. Multimedia equipment is the basic material of artificial intelligence-assisted teaching. Schools should provide teachers with a good teaching environment to ensure the normal teaching.Figure 2
Degree of hardware facilities meeting teachers’ teaching needs.Figure 3
Failure frequency of multimedia equipment in college during class.Figure 4
Hardware equipment that teachers think needs to be improved.In the investigation of what aspects of multimedia equipment need to be improved, teachers put forward that the network environment, computer configuration, projector, microphone, and audio need to be optimized.


#### 4.1.2. Investigation on Teachers’ Demand for Multimedia Technology

Through the data in Figures5–7, we can know that most teachers are not confident in their multimedia technology and need to receive training.Figure 5
Teachers’ training needs for multimedia technology.Figure 6
Teachers’ demand for multimedia technology types.Figure 7
Difficulties encountered in the process of making courseware.


#### 4.1.3. Investigation on Teachers’ Sharing of Resources

Observing the results in Figures8 and 9, we can know that most teachers have little communication on courseware making, which is very unfavorable to the sharing of excellent teaching resources, and will lead to the reduction of teachers’ teaching efficiency. In the same courseware making process, if teachers communicate more, they will save a lot and effectively improve courseware making.Figure 8
Teachers’ communication on multimedia courseware making.Figure 9
Teachers’ attitude towards sharing multimedia teaching resources.In the investigation of resource sharing, most teachers still hope to share resources, because sharing resources is related to the protection of teachers’ labor achievements, which can be solved by establishing courseware material resource library. Teachers can voluntarily upload the courseware they are willing to share to the resource library, which not only improves the efficiency of making courseware but also protects the success of teachers.


### 4.2. Subjects and Contents of the Survey

#### 4.2.1. Validity Analysis of Pretest Questionnaire

Usually, we judge whether a data is suitable for factor analysis according to KMO value and Bartlett spherical test.Using this analysis method, the results are shown in Table1.Table 1
KMO and Bartlett tests.
Kaiser-Meyer-Olkin metric with sufficient sampling0.854Bartlett’s sphericity testApproximate chi-square4222.902Df946Sig.0From the data obtained in Table1, it can be found that the prequestionnaire structure is very good, which is especially suitable for factor analysis.


#### 4.2.2. Reliability Analysis of Pretest Questionnaire

Whether the research results are stable or not is usually tested by reliability. According to this principle, the results in Table2 are obtained:Table 2
Reliability statistics.
Scale nameNumber of projectsCronbach coefficientTeaching cognitive ability70.818Instructional design ability140.806Teaching implementation ability90.783Teaching reflection ability70.72Teaching research ability70.842Total amount table440.912Looking at Table2, we can conclude that the questionnaire is very trustworthy and has very high internal consistency, which can be carried out as a formal questionnaire.


#### 4.2.3. Investigation and Analysis of Measured Questionnaires

After we preinvestigated the questionnaire in the previous section, we can start the formal questionnaire survey. This time, the method of sampling survey was selected, as shown in Table3.Table 3
Sample sources of questionnaires.
School nameSurvey sampleQuestionnaire recovery numberRecovery rateHeilongjiang University403588%Harbin Normal University4343100%Jiamusi University504896%Harbin Engineering University464189%Harbin University of Commerce403895%Qiqihar University4141100%Overall26024695%In order to verify whether the presupposition theory is reasonable, we carried out factor analysis in Table4.Table 4
Test of total amount table and subscale.
KMO valueBartlett’s sphericity testSubscale 1: teaching cognitive ability0.7990Subscale 2: instructional design ability0.880Subscale 3: teaching implementation ability0.8210Subscale 4: teaching reflection ability0.8250Subscale 5: teaching research ability0.840Total amount table0.8950The results in Table3 show that the questionnaires all meet the criteria of factor analysis.Factor analysis is performed on each data, and the results are shown in Table5.Table 5
Summary of factor analysis results of measured questionnaire.
Subscale nameCumulative interpretation rateFactor nameBehavior realizationFactor loadSubscale 1: teaching cognitive ability67.15%Self-cognitionB10.832B20.8B30.793B40.771Student cognitionB50.895B60.732B70.725Subscale 2: instructional design ability60.73%Teaching objectivesB80.613B90.881B110.661Teaching structureB120.677B130.751B140.753B150.784B160.753B170.571Teaching methodB190.797B200.733B210.699Subscale 3: teaching implementation ability65.12%Transmit teaching informationB220.721B230.789B240.587Stimulate interest in learningB250.621B260.785B300.621Classroom regulationB270.789B280.799B290.621Subscale 4: teaching reflection ability67.59%Self-reflectionB310.543B320.912Reflection on teaching activitiesB330.558B340.863B350.755Subscale 5: teaching research ability74.59%Teaching theory researchB390.867B400.856B410.799Teaching practice researchB420.658B430.845B440.809Then, the reliability analysis of the questionnaire is carried out, and the results are shown in Table6:Table 6
Statistical table of reliability analysis of measured questionnaire.
Scale nameNumber of projectsCronbach coefficientSubscale 1: teaching cognition70.819Self-cognition40.837Student cognition30.734Subscale 2: instructional design120.875Teaching objectives30.714Teaching structure60.852Teaching method30.723Subscale 3: teaching implementation90.809Transmit teaching information30.618Stimulate interest in learning30.7Classroom regulation30.745Subscale 4: teaching reflection50.758Self-reflection20.602Reflection on teaching activities30.699Subscale 5: teaching research60.886Teaching theory research30.865Teaching practice research30.765Total amount table390.935Looking at Table6, we can conclude that the reliability of the data is very good, and we can continue the next research.


### 4.3. Analysis of Survey Results

#### 4.3.1. Analysis of Demographic Variables

Looking at Tables7 and 8, we can conclude that most teachers are stressed, and only a small part are not stressed.Table 7
Test demographic data.
Background disguiseFrequency (person)Percentage (%)GenderMale8534.6Woman16165.4Graduate of Fan CollegeYes10141.1No14558.9AgeUnder 30 years old7430.131-35 years old9538.636-40 years old7731.3Teaching experienceLess than one year5020.32-3 years69284-5 years228.9Over 5 years10542.7Educational backgroundCollege and below00Undergraduate145.7Master graduate student12550.8Doctoral students10743.5Professional titleTeaching assistants3112.6Lecturer14358.1Associate professor4417.9Teachers2811.4Type of institutionDouble first-class universities and colleges8634.9Double first-class colleges and universities8333.7Nondual institutions9131.4Table 8
Statistical table of work stress of respondents.
FrequencyPercentageEffective percentageEffectiveVery large5020.320.3Larger9940.240.2General8534.634.6Less41.61.6No83.33.3Total246100100From Table9, we can conclude that most people choose the profession of teachers because it is more stable than other industries, and only a few people are forced to choose the profession of teachers because of professional problems.Table 9
Statistical table of respondents’ reasons for choosing jobs.
FrequencyPercentageEffective percentageEffectivePersonal interest6225.225.2The wishes of parents and others5321.521.5Professional restriction2811.411.4The occupation is relatively stable10341.941.9Total246100100Looking at Figures10 and 11, we can find that most teachers still choose both scientific research and teaching, so we can know that weighing the direct weight of the two is a big problem for teachers.Figure 10
Emphasis of respondents on scientific research and work.Figure 11
Distribution of teaching fields of respondents.


#### 4.3.2. Analysis of the Overall Situation of Teachers’ Teaching Ability

Through descriptive analysis of teachers’ teaching ability, we get Table10.Table 10
Descriptive statistical analysis.
Sample sizeMaximum valueMinimum valueAverageStandard deviationSubscale 1: teaching cognition ability246132.41230.36967Self-cognition246132.33250.43911Student cognition246132.57860.42901Subscale 2: instructional design ability246132.58430.35199Teaching objectives246132.47020.44033Teaching structure246132.64630.38632Teaching method246132.42550.42336Subscale 3: teaching implementation ability246132.37850.38577Transmit teaching information246132.55420.42217Stimulate interest in learning246132.16670.52748Classroom regulation246132.01460.49366Subscale 4: teaching reflection ability246132.42360.42147Self-reflection246132.44720.51035Reflection on teaching activities246132.30580.44635Subscale 5: teaching research ability246132.09350.55661Teaching theory research246131.97020.66121Teaching practice research246132.21680.5714Total amount table246132.41280.31983From the overall situation, it shows that teachers’ teaching ability is still very high. Only the low score of teaching theory shows that teachers’ cognition in this aspect is not enough and needs to be improved.


#### 4.3.3. Difference Analysis under Different Variables

Differences between genders are as follows.Table11 shows that the gender differences make teachers have obvious scores in teaching ability. Among them, the differences in instructional design are mainly reflected in the design and selection of teaching methods. The differences in teaching implementation are mainly reflected in the adjustment and control of classroom, the differences in teaching reflection are mainly reflected in teachers’ self-reflection and reflection on teaching activities, and the differences in teaching research are mainly reflected in teachers’ theoretical research.Table 11
Summary of teaching ability results of teachers of different genders.
FSig.T valueDegree of freedomSignificance (bilateral)Mean differenceSubscale 1: teaching cognition abilityAssuming methods are equal1.0670.3031.1622440.2470.4027Assuming methods are not equal1.155168.3030.250.4027Subscale 2: instructional design abilityAssuming methods are equal0.7760.379-0.6032440.547-0.3422Assuming methods are not equal-0.594163.4040.554-0.3422Method designAssuming methods are equal7.7950.006-1.8562440.065-0.3145Assuming methods are not equal-1.688132.3240.094-0.3145Subscale 3: teaching implementation abilityAssuming methods are equal2.370.125-1.222440.224-0.56719Assuming methods are not equal-1.172153.1960.243-0.56719Classroom regulationAssuming methods are equal5.1710.024-1.9792440.049-0.39065Assuming methods are not equal-1.847141.5760.067-0.39065Subscale 4: teaching reflection abilityAssuming methods are equal8.4810.004-3.0362440.003-0.8437Assuming methods are not equal-2.818139.5190.006-0.8437Self-reflectionAssuming methods are equal9.9440.002-2.3892440.018-0.32386Assuming methods are not equal-2.231141.830.027-0.32386Reflection on teaching activitiesAssuming methods are equal7.2230.008-2.942440.004-0.51984Assuming methods are not equal-2.779146.3550.006-0.51984Subscale 5: teaching research abilityAssuming methods are equal2.4850.116-1.0722440.285-0.47965Assuming methods are not equal-1.026151.8270.306-0.47965Theoretical researchAssuming methods are equal5.5320.019-0.5672440.571-0.15097Assuming methods are not equal-0.544152.7240.587-0.15097Total amount tableAssuming methods are equal2.1840.141-1.0952440.275-1.83003Assuming methods are not equal-1.063157.4280.29-1.83003


#### 4.3.4. Differences between Different Educational Backgrounds

Observing Table12 shows that teachers with different educational backgrounds are different in all aspects, and the main difference is reflected in cognition.Table 12
Summary of teaching ability results under different educational backgrounds.
FSig.T valueDegree of freedomSignificance (bilateral)Mean differenceSubscale 1: teaching cognition abilityAssuming methods are equal16.07903.83724401.25224Assuming methods are not equal4.008240.78101.25224Self-cognitionAssuming methods are equal7.430.0073.93924400.87115Assuming methods are not equal4.028230.79900.87115Student cognitionAssuming methods are equal13.45402.3052440.0220.38109Assuming methods are not equal2.389237.7640.0180.38109Subscale 2: instructional design abilityAssuming methods are equal10.1590.0024.38224402.31417Assuming methods are not equal4.603242.41402.31417Goal designAssuming methods are equal4.5750.0333.0462440.0030.51287Assuming methods are not equal3.144235.8790.0020.51287Process designAssuming methods are equal6.3440.0123.68824401.08037Assuming methods are not equal3.841239.83101.08037Method designAssuming methods are equal4.0270.0462.4832440.0140.40451Assuming methods are not equal2.587239.8350.010.40451Subscale 3: teaching implementation abilityAssuming methods are equal0.4510.5035.17824402.21632Assuming methods are not equal5.293230.47402.21632Teaching information rack transmissionAssuming methods are equal15.27103.79124400.60601Assuming methods are not equal3.945239.38700.60601Subscale 4: teaching reflection abilityAssuming methods are equal2.8340.0945.48424401.41605Assuming methods are not equal5.678237.32201.41605Reflection on teaching activitiesAssuming methods are equal8.3890.0045.26224400.86719Assuming methods are not equal5.482239.88900.86719Subscale 5: teaching research abilityAssuming methods are equal1.7750.1844.66224401.93745Assuming methods are not equal4.62208.36101.93745Theoretical researchAssuming methods are equal0.0340.8534.62624401.14278Assuming methods are not equal4.636216.7801.14278Total amount tableAssuming methods are equal1.6720.1976.04824409.13622Assuming methods are not equal6.243235.85609.13622According to the above research, we can conclude that there are significant differences in gender, graduation from normal colleges, professional titles, teaching years, and work pressure, which shows that these factors have certain influence on the improvement of teachers’ teaching ability. Therefore, schools should consider the above factors in the selection and training of teachers, including the cultivation and promotion of teaching ability.


## 4.1. Teachers’ Needs in the Application Environment of Multimedia Technology

### 4.1.1. Survey of Demand for Multimedia Facilities

Looking at Figures2–4, we can draw that 94% of teachers said that they would encounter multimedia equipment failure in the teaching process. In the investigation of whether the equipment can meet the teaching needs, almost all teachers think that it can meet their teaching needs. Multimedia equipment is the basic material of artificial intelligence-assisted teaching. Schools should provide teachers with a good teaching environment to ensure the normal teaching.Figure 2
Degree of hardware facilities meeting teachers’ teaching needs.Figure 3
Failure frequency of multimedia equipment in college during class.Figure 4
Hardware equipment that teachers think needs to be improved.In the investigation of what aspects of multimedia equipment need to be improved, teachers put forward that the network environment, computer configuration, projector, microphone, and audio need to be optimized.


### 4.1.2. Investigation on Teachers’ Demand for Multimedia Technology

Through the data in Figures5–7, we can know that most teachers are not confident in their multimedia technology and need to receive training.Figure 5
Teachers’ training needs for multimedia technology.Figure 6
Teachers’ demand for multimedia technology types.Figure 7
Difficulties encountered in the process of making courseware.


### 4.1.3. Investigation on Teachers’ Sharing of Resources

Observing the results in Figures8 and 9, we can know that most teachers have little communication on courseware making, which is very unfavorable to the sharing of excellent teaching resources, and will lead to the reduction of teachers’ teaching efficiency. In the same courseware making process, if teachers communicate more, they will save a lot and effectively improve courseware making.Figure 8
Teachers’ communication on multimedia courseware making.Figure 9
Teachers’ attitude towards sharing multimedia teaching resources.In the investigation of resource sharing, most teachers still hope to share resources, because sharing resources is related to the protection of teachers’ labor achievements, which can be solved by establishing courseware material resource library. Teachers can voluntarily upload the courseware they are willing to share to the resource library, which not only improves the efficiency of making courseware but also protects the success of teachers.


## 4.1.1. Survey of Demand for Multimedia Facilities

Looking at Figures2–4, we can draw that 94% of teachers said that they would encounter multimedia equipment failure in the teaching process. In the investigation of whether the equipment can meet the teaching needs, almost all teachers think that it can meet their teaching needs. Multimedia equipment is the basic material of artificial intelligence-assisted teaching. Schools should provide teachers with a good teaching environment to ensure the normal teaching.Figure 2
Degree of hardware facilities meeting teachers’ teaching needs.Figure 3
Failure frequency of multimedia equipment in college during class.Figure 4
Hardware equipment that teachers think needs to be improved.In the investigation of what aspects of multimedia equipment need to be improved, teachers put forward that the network environment, computer configuration, projector, microphone, and audio need to be optimized.


## 4.1.2. Investigation on Teachers’ Demand for Multimedia Technology

Through the data in Figures5–7, we can know that most teachers are not confident in their multimedia technology and need to receive training.Figure 5
Teachers’ training needs for multimedia technology.Figure 6
Teachers’ demand for multimedia technology types.Figure 7
Difficulties encountered in the process of making courseware.


## 4.1.3. Investigation on Teachers’ Sharing of Resources

Observing the results in Figures8 and 9, we can know that most teachers have little communication on courseware making, which is very unfavorable to the sharing of excellent teaching resources, and will lead to the reduction of teachers’ teaching efficiency. In the same courseware making process, if teachers communicate more, they will save a lot and effectively improve courseware making.Figure 8
Teachers’ communication on multimedia courseware making.Figure 9
Teachers’ attitude towards sharing multimedia teaching resources.In the investigation of resource sharing, most teachers still hope to share resources, because sharing resources is related to the protection of teachers’ labor achievements, which can be solved by establishing courseware material resource library. Teachers can voluntarily upload the courseware they are willing to share to the resource library, which not only improves the efficiency of making courseware but also protects the success of teachers.


## 4.2. Subjects and Contents of the Survey

### 4.2.1. Validity Analysis of Pretest Questionnaire

Usually, we judge whether a data is suitable for factor analysis according to KMO value and Bartlett spherical test.Using this analysis method, the results are shown in Table1.Table 1
KMO and Bartlett tests.
Kaiser-Meyer-Olkin metric with sufficient sampling0.854Bartlett’s sphericity testApproximate chi-square4222.902Df946Sig.0From the data obtained in Table1, it can be found that the prequestionnaire structure is very good, which is especially suitable for factor analysis.


### 4.2.2. Reliability Analysis of Pretest Questionnaire

Whether the research results are stable or not is usually tested by reliability. According to this principle, the results in Table2 are obtained:Table 2
Reliability statistics.
Scale nameNumber of projectsCronbach coefficientTeaching cognitive ability70.818Instructional design ability140.806Teaching implementation ability90.783Teaching reflection ability70.72Teaching research ability70.842Total amount table440.912Looking at Table2, we can conclude that the questionnaire is very trustworthy and has very high internal consistency, which can be carried out as a formal questionnaire.


### 4.2.3. Investigation and Analysis of Measured Questionnaires

After we preinvestigated the questionnaire in the previous section, we can start the formal questionnaire survey. This time, the method of sampling survey was selected, as shown in Table3.Table 3
Sample sources of questionnaires.
School nameSurvey sampleQuestionnaire recovery numberRecovery rateHeilongjiang University403588%Harbin Normal University4343100%Jiamusi University504896%Harbin Engineering University464189%Harbin University of Commerce403895%Qiqihar University4141100%Overall26024695%In order to verify whether the presupposition theory is reasonable, we carried out factor analysis in Table4.Table 4
Test of total amount table and subscale.
KMO valueBartlett’s sphericity testSubscale 1: teaching cognitive ability0.7990Subscale 2: instructional design ability0.880Subscale 3: teaching implementation ability0.8210Subscale 4: teaching reflection ability0.8250Subscale 5: teaching research ability0.840Total amount table0.8950The results in Table3 show that the questionnaires all meet the criteria of factor analysis.Factor analysis is performed on each data, and the results are shown in Table5.Table 5
Summary of factor analysis results of measured questionnaire.
Subscale nameCumulative interpretation rateFactor nameBehavior realizationFactor loadSubscale 1: teaching cognitive ability67.15%Self-cognitionB10.832B20.8B30.793B40.771Student cognitionB50.895B60.732B70.725Subscale 2: instructional design ability60.73%Teaching objectivesB80.613B90.881B110.661Teaching structureB120.677B130.751B140.753B150.784B160.753B170.571Teaching methodB190.797B200.733B210.699Subscale 3: teaching implementation ability65.12%Transmit teaching informationB220.721B230.789B240.587Stimulate interest in learningB250.621B260.785B300.621Classroom regulationB270.789B280.799B290.621Subscale 4: teaching reflection ability67.59%Self-reflectionB310.543B320.912Reflection on teaching activitiesB330.558B340.863B350.755Subscale 5: teaching research ability74.59%Teaching theory researchB390.867B400.856B410.799Teaching practice researchB420.658B430.845B440.809Then, the reliability analysis of the questionnaire is carried out, and the results are shown in Table6:Table 6
Statistical table of reliability analysis of measured questionnaire.
Scale nameNumber of projectsCronbach coefficientSubscale 1: teaching cognition70.819Self-cognition40.837Student cognition30.734Subscale 2: instructional design120.875Teaching objectives30.714Teaching structure60.852Teaching method30.723Subscale 3: teaching implementation90.809Transmit teaching information30.618Stimulate interest in learning30.7Classroom regulation30.745Subscale 4: teaching reflection50.758Self-reflection20.602Reflection on teaching activities30.699Subscale 5: teaching research60.886Teaching theory research30.865Teaching practice research30.765Total amount table390.935Looking at Table6, we can conclude that the reliability of the data is very good, and we can continue the next research.


## 4.2.1. Validity Analysis of Pretest Questionnaire

Usually, we judge whether a data is suitable for factor analysis according to KMO value and Bartlett spherical test.Using this analysis method, the results are shown in Table1.Table 1
KMO and Bartlett tests.
Kaiser-Meyer-Olkin metric with sufficient sampling0.854Bartlett’s sphericity testApproximate chi-square4222.902Df946Sig.0From the data obtained in Table1, it can be found that the prequestionnaire structure is very good, which is especially suitable for factor analysis.


## 4.2.2. Reliability Analysis of Pretest Questionnaire

Whether the research results are stable or not is usually tested by reliability. According to this principle, the results in Table2 are obtained:Table 2
Reliability statistics.
Scale nameNumber of projectsCronbach coefficientTeaching cognitive ability70.818Instructional design ability140.806Teaching implementation ability90.783Teaching reflection ability70.72Teaching research ability70.842Total amount table440.912Looking at Table2, we can conclude that the questionnaire is very trustworthy and has very high internal consistency, which can be carried out as a formal questionnaire.


## 4.2.3. Investigation and Analysis of Measured Questionnaires

After we preinvestigated the questionnaire in the previous section, we can start the formal questionnaire survey. This time, the method of sampling survey was selected, as shown in Table3.Table 3
Sample sources of questionnaires.
School nameSurvey sampleQuestionnaire recovery numberRecovery rateHeilongjiang University403588%Harbin Normal University4343100%Jiamusi University504896%Harbin Engineering University464189%Harbin University of Commerce403895%Qiqihar University4141100%Overall26024695%In order to verify whether the presupposition theory is reasonable, we carried out factor analysis in Table4.Table 4
Test of total amount table and subscale.
KMO valueBartlett’s sphericity testSubscale 1: teaching cognitive ability0.7990Subscale 2: instructional design ability0.880Subscale 3: teaching implementation ability0.8210Subscale 4: teaching reflection ability0.8250Subscale 5: teaching research ability0.840Total amount table0.8950The results in Table3 show that the questionnaires all meet the criteria of factor analysis.Factor analysis is performed on each data, and the results are shown in Table5.Table 5
Summary of factor analysis results of measured questionnaire.
Subscale nameCumulative interpretation rateFactor nameBehavior realizationFactor loadSubscale 1: teaching cognitive ability67.15%Self-cognitionB10.832B20.8B30.793B40.771Student cognitionB50.895B60.732B70.725Subscale 2: instructional design ability60.73%Teaching objectivesB80.613B90.881B110.661Teaching structureB120.677B130.751B140.753B150.784B160.753B170.571Teaching methodB190.797B200.733B210.699Subscale 3: teaching implementation ability65.12%Transmit teaching informationB220.721B230.789B240.587Stimulate interest in learningB250.621B260.785B300.621Classroom regulationB270.789B280.799B290.621Subscale 4: teaching reflection ability67.59%Self-reflectionB310.543B320.912Reflection on teaching activitiesB330.558B340.863B350.755Subscale 5: teaching research ability74.59%Teaching theory researchB390.867B400.856B410.799Teaching practice researchB420.658B430.845B440.809Then, the reliability analysis of the questionnaire is carried out, and the results are shown in Table6:Table 6
Statistical table of reliability analysis of measured questionnaire.
Scale nameNumber of projectsCronbach coefficientSubscale 1: teaching cognition70.819Self-cognition40.837Student cognition30.734Subscale 2: instructional design120.875Teaching objectives30.714Teaching structure60.852Teaching method30.723Subscale 3: teaching implementation90.809Transmit teaching information30.618Stimulate interest in learning30.7Classroom regulation30.745Subscale 4: teaching reflection50.758Self-reflection20.602Reflection on teaching activities30.699Subscale 5: teaching research60.886Teaching theory research30.865Teaching practice research30.765Total amount table390.935Looking at Table6, we can conclude that the reliability of the data is very good, and we can continue the next research.


## 4.3. Analysis of Survey Results

### 4.3.1. Analysis of Demographic Variables

Looking at Tables7 and 8, we can conclude that most teachers are stressed, and only a small part are not stressed.Table 7
Test demographic data.
Background disguiseFrequency (person)Percentage (%)GenderMale8534.6Woman16165.4Graduate of Fan CollegeYes10141.1No14558.9AgeUnder 30 years old7430.131-35 years old9538.636-40 years old7731.3Teaching experienceLess than one year5020.32-3 years69284-5 years228.9Over 5 years10542.7Educational backgroundCollege and below00Undergraduate145.7Master graduate student12550.8Doctoral students10743.5Professional titleTeaching assistants3112.6Lecturer14358.1Associate professor4417.9Teachers2811.4Type of institutionDouble first-class universities and colleges8634.9Double first-class colleges and universities8333.7Nondual institutions9131.4Table 8
Statistical table of work stress of respondents.
FrequencyPercentageEffective percentageEffectiveVery large5020.320.3Larger9940.240.2General8534.634.6Less41.61.6No83.33.3Total246100100From Table9, we can conclude that most people choose the profession of teachers because it is more stable than other industries, and only a few people are forced to choose the profession of teachers because of professional problems.Table 9
Statistical table of respondents’ reasons for choosing jobs.
FrequencyPercentageEffective percentageEffectivePersonal interest6225.225.2The wishes of parents and others5321.521.5Professional restriction2811.411.4The occupation is relatively stable10341.941.9Total246100100Looking at Figures10 and 11, we can find that most teachers still choose both scientific research and teaching, so we can know that weighing the direct weight of the two is a big problem for teachers.Figure 10
Emphasis of respondents on scientific research and work.Figure 11
Distribution of teaching fields of respondents.


### 4.3.2. Analysis of the Overall Situation of Teachers’ Teaching Ability

Through descriptive analysis of teachers’ teaching ability, we get Table10.Table 10
Descriptive statistical analysis.
Sample sizeMaximum valueMinimum valueAverageStandard deviationSubscale 1: teaching cognition ability246132.41230.36967Self-cognition246132.33250.43911Student cognition246132.57860.42901Subscale 2: instructional design ability246132.58430.35199Teaching objectives246132.47020.44033Teaching structure246132.64630.38632Teaching method246132.42550.42336Subscale 3: teaching implementation ability246132.37850.38577Transmit teaching information246132.55420.42217Stimulate interest in learning246132.16670.52748Classroom regulation246132.01460.49366Subscale 4: teaching reflection ability246132.42360.42147Self-reflection246132.44720.51035Reflection on teaching activities246132.30580.44635Subscale 5: teaching research ability246132.09350.55661Teaching theory research246131.97020.66121Teaching practice research246132.21680.5714Total amount table246132.41280.31983From the overall situation, it shows that teachers’ teaching ability is still very high. Only the low score of teaching theory shows that teachers’ cognition in this aspect is not enough and needs to be improved.


### 4.3.3. Difference Analysis under Different Variables

Differences between genders are as follows.Table11 shows that the gender differences make teachers have obvious scores in teaching ability. Among them, the differences in instructional design are mainly reflected in the design and selection of teaching methods. The differences in teaching implementation are mainly reflected in the adjustment and control of classroom, the differences in teaching reflection are mainly reflected in teachers’ self-reflection and reflection on teaching activities, and the differences in teaching research are mainly reflected in teachers’ theoretical research.Table 11
Summary of teaching ability results of teachers of different genders.
FSig.T valueDegree of freedomSignificance (bilateral)Mean differenceSubscale 1: teaching cognition abilityAssuming methods are equal1.0670.3031.1622440.2470.4027Assuming methods are not equal1.155168.3030.250.4027Subscale 2: instructional design abilityAssuming methods are equal0.7760.379-0.6032440.547-0.3422Assuming methods are not equal-0.594163.4040.554-0.3422Method designAssuming methods are equal7.7950.006-1.8562440.065-0.3145Assuming methods are not equal-1.688132.3240.094-0.3145Subscale 3: teaching implementation abilityAssuming methods are equal2.370.125-1.222440.224-0.56719Assuming methods are not equal-1.172153.1960.243-0.56719Classroom regulationAssuming methods are equal5.1710.024-1.9792440.049-0.39065Assuming methods are not equal-1.847141.5760.067-0.39065Subscale 4: teaching reflection abilityAssuming methods are equal8.4810.004-3.0362440.003-0.8437Assuming methods are not equal-2.818139.5190.006-0.8437Self-reflectionAssuming methods are equal9.9440.002-2.3892440.018-0.32386Assuming methods are not equal-2.231141.830.027-0.32386Reflection on teaching activitiesAssuming methods are equal7.2230.008-2.942440.004-0.51984Assuming methods are not equal-2.779146.3550.006-0.51984Subscale 5: teaching research abilityAssuming methods are equal2.4850.116-1.0722440.285-0.47965Assuming methods are not equal-1.026151.8270.306-0.47965Theoretical researchAssuming methods are equal5.5320.019-0.5672440.571-0.15097Assuming methods are not equal-0.544152.7240.587-0.15097Total amount tableAssuming methods are equal2.1840.141-1.0952440.275-1.83003Assuming methods are not equal-1.063157.4280.29-1.83003


### 4.3.4. Differences between Different Educational Backgrounds

Observing Table12 shows that teachers with different educational backgrounds are different in all aspects, and the main difference is reflected in cognition.Table 12
Summary of teaching ability results under different educational backgrounds.
FSig.T valueDegree of freedomSignificance (bilateral)Mean differenceSubscale 1: teaching cognition abilityAssuming methods are equal16.07903.83724401.25224Assuming methods are not equal4.008240.78101.25224Self-cognitionAssuming methods are equal7.430.0073.93924400.87115Assuming methods are not equal4.028230.79900.87115Student cognitionAssuming methods are equal13.45402.3052440.0220.38109Assuming methods are not equal2.389237.7640.0180.38109Subscale 2: instructional design abilityAssuming methods are equal10.1590.0024.38224402.31417Assuming methods are not equal4.603242.41402.31417Goal designAssuming methods are equal4.5750.0333.0462440.0030.51287Assuming methods are not equal3.144235.8790.0020.51287Process designAssuming methods are equal6.3440.0123.68824401.08037Assuming methods are not equal3.841239.83101.08037Method designAssuming methods are equal4.0270.0462.4832440.0140.40451Assuming methods are not equal2.587239.8350.010.40451Subscale 3: teaching implementation abilityAssuming methods are equal0.4510.5035.17824402.21632Assuming methods are not equal5.293230.47402.21632Teaching information rack transmissionAssuming methods are equal15.27103.79124400.60601Assuming methods are not equal3.945239.38700.60601Subscale 4: teaching reflection abilityAssuming methods are equal2.8340.0945.48424401.41605Assuming methods are not equal5.678237.32201.41605Reflection on teaching activitiesAssuming methods are equal8.3890.0045.26224400.86719Assuming methods are not equal5.482239.88900.86719Subscale 5: teaching research abilityAssuming methods are equal1.7750.1844.66224401.93745Assuming methods are not equal4.62208.36101.93745Theoretical researchAssuming methods are equal0.0340.8534.62624401.14278Assuming methods are not equal4.636216.7801.14278Total amount tableAssuming methods are equal1.6720.1976.04824409.13622Assuming methods are not equal6.243235.85609.13622According to the above research, we can conclude that there are significant differences in gender, graduation from normal colleges, professional titles, teaching years, and work pressure, which shows that these factors have certain influence on the improvement of teachers’ teaching ability. Therefore, schools should consider the above factors in the selection and training of teachers, including the cultivation and promotion of teaching ability.


## 4.3.1. Analysis of Demographic Variables

Looking at Tables7 and 8, we can conclude that most teachers are stressed, and only a small part are not stressed.Table 7
Test demographic data.
Background disguiseFrequency (person)Percentage (%)GenderMale8534.6Woman16165.4Graduate of Fan CollegeYes10141.1No14558.9AgeUnder 30 years old7430.131-35 years old9538.636-40 years old7731.3Teaching experienceLess than one year5020.32-3 years69284-5 years228.9Over 5 years10542.7Educational backgroundCollege and below00Undergraduate145.7Master graduate student12550.8Doctoral students10743.5Professional titleTeaching assistants3112.6Lecturer14358.1Associate professor4417.9Teachers2811.4Type of institutionDouble first-class universities and colleges8634.9Double first-class colleges and universities8333.7Nondual institutions9131.4Table 8
Statistical table of work stress of respondents.
FrequencyPercentageEffective percentageEffectiveVery large5020.320.3Larger9940.240.2General8534.634.6Less41.61.6No83.33.3Total246100100From Table9, we can conclude that most people choose the profession of teachers because it is more stable than other industries, and only a few people are forced to choose the profession of teachers because of professional problems.Table 9
Statistical table of respondents’ reasons for choosing jobs.
FrequencyPercentageEffective percentageEffectivePersonal interest6225.225.2The wishes of parents and others5321.521.5Professional restriction2811.411.4The occupation is relatively stable10341.941.9Total246100100Looking at Figures10 and 11, we can find that most teachers still choose both scientific research and teaching, so we can know that weighing the direct weight of the two is a big problem for teachers.Figure 10
Emphasis of respondents on scientific research and work.Figure 11
Distribution of teaching fields of respondents.


## 4.3.2. Analysis of the Overall Situation of Teachers’ Teaching Ability

Through descriptive analysis of teachers’ teaching ability, we get Table10.Table 10
Descriptive statistical analysis.
Sample sizeMaximum valueMinimum valueAverageStandard deviationSubscale 1: teaching cognition ability246132.41230.36967Self-cognition246132.33250.43911Student cognition246132.57860.42901Subscale 2: instructional design ability246132.58430.35199Teaching objectives246132.47020.44033Teaching structure246132.64630.38632Teaching method246132.42550.42336Subscale 3: teaching implementation ability246132.37850.38577Transmit teaching information246132.55420.42217Stimulate interest in learning246132.16670.52748Classroom regulation246132.01460.49366Subscale 4: teaching reflection ability246132.42360.42147Self-reflection246132.44720.51035Reflection on teaching activities246132.30580.44635Subscale 5: teaching research ability246132.09350.55661Teaching theory research246131.97020.66121Teaching practice research246132.21680.5714Total amount table246132.41280.31983From the overall situation, it shows that teachers’ teaching ability is still very high. Only the low score of teaching theory shows that teachers’ cognition in this aspect is not enough and needs to be improved.


## 4.3.3. Difference Analysis under Different Variables

Differences between genders are as follows.Table11 shows that the gender differences make teachers have obvious scores in teaching ability. Among them, the differences in instructional design are mainly reflected in the design and selection of teaching methods. The differences in teaching implementation are mainly reflected in the adjustment and control of classroom, the differences in teaching reflection are mainly reflected in teachers’ self-reflection and reflection on teaching activities, and the differences in teaching research are mainly reflected in teachers’ theoretical research.Table 11
Summary of teaching ability results of teachers of different genders.
FSig.T valueDegree of freedomSignificance (bilateral)Mean differenceSubscale 1: teaching cognition abilityAssuming methods are equal1.0670.3031.1622440.2470.4027Assuming methods are not equal1.155168.3030.250.4027Subscale 2: instructional design abilityAssuming methods are equal0.7760.379-0.6032440.547-0.3422Assuming methods are not equal-0.594163.4040.554-0.3422Method designAssuming methods are equal7.7950.006-1.8562440.065-0.3145Assuming methods are not equal-1.688132.3240.094-0.3145Subscale 3: teaching implementation abilityAssuming methods are equal2.370.125-1.222440.224-0.56719Assuming methods are not equal-1.172153.1960.243-0.56719Classroom regulationAssuming methods are equal5.1710.024-1.9792440.049-0.39065Assuming methods are not equal-1.847141.5760.067-0.39065Subscale 4: teaching reflection abilityAssuming methods are equal8.4810.004-3.0362440.003-0.8437Assuming methods are not equal-2.818139.5190.006-0.8437Self-reflectionAssuming methods are equal9.9440.002-2.3892440.018-0.32386Assuming methods are not equal-2.231141.830.027-0.32386Reflection on teaching activitiesAssuming methods are equal7.2230.008-2.942440.004-0.51984Assuming methods are not equal-2.779146.3550.006-0.51984Subscale 5: teaching research abilityAssuming methods are equal2.4850.116-1.0722440.285-0.47965Assuming methods are not equal-1.026151.8270.306-0.47965Theoretical researchAssuming methods are equal5.5320.019-0.5672440.571-0.15097Assuming methods are not equal-0.544152.7240.587-0.15097Total amount tableAssuming methods are equal2.1840.141-1.0952440.275-1.83003Assuming methods are not equal-1.063157.4280.29-1.83003


## 4.3.4. Differences between Different Educational Backgrounds

Observing Table12 shows that teachers with different educational backgrounds are different in all aspects, and the main difference is reflected in cognition.Table 12
Summary of teaching ability results under different educational backgrounds.
FSig.T valueDegree of freedomSignificance (bilateral)Mean differenceSubscale 1: teaching cognition abilityAssuming methods are equal16.07903.83724401.25224Assuming methods are not equal4.008240.78101.25224Self-cognitionAssuming methods are equal7.430.0073.93924400.87115Assuming methods are not equal4.028230.79900.87115Student cognitionAssuming methods are equal13.45402.3052440.0220.38109Assuming methods are not equal2.389237.7640.0180.38109Subscale 2: instructional design abilityAssuming methods are equal10.1590.0024.38224402.31417Assuming methods are not equal4.603242.41402.31417Goal designAssuming methods are equal4.5750.0333.0462440.0030.51287Assuming methods are not equal3.144235.8790.0020.51287Process designAssuming methods are equal6.3440.0123.68824401.08037Assuming methods are not equal3.841239.83101.08037Method designAssuming methods are equal4.0270.0462.4832440.0140.40451Assuming methods are not equal2.587239.8350.010.40451Subscale 3: teaching implementation abilityAssuming methods are equal0.4510.5035.17824402.21632Assuming methods are not equal5.293230.47402.21632Teaching information rack transmissionAssuming methods are equal15.27103.79124400.60601Assuming methods are not equal3.945239.38700.60601Subscale 4: teaching reflection abilityAssuming methods are equal2.8340.0945.48424401.41605Assuming methods are not equal5.678237.32201.41605Reflection on teaching activitiesAssuming methods are equal8.3890.0045.26224400.86719Assuming methods are not equal5.482239.88900.86719Subscale 5: teaching research abilityAssuming methods are equal1.7750.1844.66224401.93745Assuming methods are not equal4.62208.36101.93745Theoretical researchAssuming methods are equal0.0340.8534.62624401.14278Assuming methods are not equal4.636216.7801.14278Total amount tableAssuming methods are equal1.6720.1976.04824409.13622Assuming methods are not equal6.243235.85609.13622According to the above research, we can conclude that there are significant differences in gender, graduation from normal colleges, professional titles, teaching years, and work pressure, which shows that these factors have certain influence on the improvement of teachers’ teaching ability. Therefore, schools should consider the above factors in the selection and training of teachers, including the cultivation and promotion of teaching ability.


## 5. Concluding Remarks

The educational level of school teachers will directly affect the educational quality of school staff and will also affect the employment and future development of students. It is a very important subject to improve the teaching ability of college teachers at present. This study mainly focuses on improving teachers’ teaching ability. Based on the actual situation of teachers in colleges and universities, through questionnaire survey, personal interview, and data collection, we can understand the first-hand information of teachers’ training and summarize the present situation. Based on the relevant literature, this paper analyzes the factors affecting the improvement of young college teachers’ teaching ability from the aspects of education administration, schools, and teachers and puts forward corresponding development strategies. The purpose of this paper is to find an effective way to improve the teaching ability of college teachers and then improve the quality of personnel training and help the progress of education.



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