Journal of Sensors
Volume 2022, Issue 1 9979275
Research Article
Open Access

[Retracted] Evaluation and Analysis of Assisted Instruction and Ability Improvement Based on Artificial Intelligence

Zhi Li, 

Zhi Li

Academy of Tourism and Media, Xi’an Siyuan University, Xi’an, Shaanxi 710038, China xasyu.cn

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Zhao Guang, 

Zhao Guang

Academy of Business, Xi’an Siyuan University, Xi’an, Shaanxi 710038, China xasyu.cn

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Wen Sun, 

Corresponding Author

Wen Sun

Air Force Engineering University, Xi’an, Shaanxi 710043, China afeu.cn

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First published: 19 July 2022
https://doi.org/10.1155/2022/9979275
Citations: 2
Academic Editor: Yuan Li

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.

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.

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 Figure 1. Given N training samples {X ∈ ℝN×m, t ∈ ℝN}, the regression model can be expressed as
(1)
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, 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.
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Figure 1
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ELM network structure diagram.
Simplify the above formula to obtain
(2)
where , , and H are hidden layer mapping matrices of ELM.
Then, the objective function of ELM is shown in
(3)
The output weight calculation formula is shown in
(4)
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)
The conditional probability distribution of t is shown in
(6)
The probability distribution of β is shown in
(7)
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)
(9)
The parameters in the above formula need to be solved by iteration, and the specific derivation process is shown in
(10)
(11)
(12)
For a given input sample xq, the corresponding mean and variance are shown in
(13)
(14)

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. 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 in BELM model is defined as , 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 .
(15)
The overall variance change of the model is defined as
(16)

3.2.2. Sample Selection Strategy of Bayesian Extreme Learning Machine

In order to improve the efficiency of the algorithm, we propose
(17)
where h(x′) 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 .
Combined with formula (9), the posterior variance can be expressed as
(18)
(19)
where is the hidden layer mapping vector corresponding to , the Sherman-Morrison-Woodbury criterion is used to expand formula (19), and formula (20) is obtained.
(20)
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 is added is shown in
(21)
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)
The BELM strategy is shown in
(23)

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 is ns, the BELM sample selection strategy is updated to
(24)
After updating the training set, as shown in
(25)

According to the updated XL, 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 Figures 2–4, 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.

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Figure 2
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Degree of hardware facilities meeting teachers’ teaching needs.
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Figure 3
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Failure frequency of multimedia equipment in college during class.
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Figure 4
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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 Figures 5–7, we can know that most teachers are not confident in their multimedia technology and need to receive training.

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Figure 5
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Teachers’ training needs for multimedia technology.
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Figure 6
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Teachers’ demand for multimedia technology types.
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Figure 7
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Difficulties encountered in the process of making courseware.

4.1.3. Investigation on Teachers’ Sharing of Resources

Observing the results in Figures 8 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.

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Figure 8
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Teachers’ communication on multimedia courseware making.
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Figure 9
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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 Table 1.

Table 1. KMO and Bartlett tests.
Kaiser-Meyer-Olkin metric with sufficient sampling 0.854
Bartlett’s sphericity test Approximate chi-square 4222.902
Df 946
Sig. 0

From the data obtained in Table 1, 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 Table 2 are obtained:

Table 2. Reliability statistics.
Scale name Number of projects Cronbach coefficient
Teaching cognitive ability 7 0.818
Instructional design ability 14 0.806
Teaching implementation ability 9 0.783
Teaching reflection ability 7 0.72
Teaching research ability 7 0.842
Total amount table 44 0.912

Looking at Table 2, 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 Table 3.

Table 3. Sample sources of questionnaires.
School name Survey sample Questionnaire recovery number Recovery rate
Heilongjiang University 40 35 88%
Harbin Normal University 43 43 100%
Jiamusi University 50 48 96%
Harbin Engineering University 46 41 89%
Harbin University of Commerce 40 38 95%
Qiqihar University 41 41 100%
Overall 260 246 95%

In order to verify whether the presupposition theory is reasonable, we carried out factor analysis in Table 4.

Table 4. Test of total amount table and subscale.
KMO value Bartlett’s sphericity test
Subscale 1: teaching cognitive ability 0.799 0
Subscale 2: instructional design ability 0.88 0
Subscale 3: teaching implementation ability 0.821 0
Subscale 4: teaching reflection ability 0.825 0
Subscale 5: teaching research ability 0.84 0
Total amount table 0.895 0

The results in Table 3 show that the questionnaires all meet the criteria of factor analysis.

Factor analysis is performed on each data, and the results are shown in Table 5.

Table 5. Summary of factor analysis results of measured questionnaire.
Subscale name Cumulative interpretation rate Factor name Behavior realization Factor load
Subscale 1: teaching cognitive ability 67.15% Self-cognition B1 0.832
B2 0.8
B3 0.793
B4 0.771
Student cognition B5 0.895
B6 0.732
B7 0.725
Subscale 2: instructional design ability 60.73% Teaching objectives B8 0.613
B9 0.881
B11 0.661
Teaching structure B12 0.677
B13 0.751
B14 0.753
B15 0.784
B16 0.753
B17 0.571
Teaching method B19 0.797
B20 0.733
B21 0.699
Subscale 3: teaching implementation ability 65.12% Transmit teaching information B22 0.721
B23 0.789
B24 0.587
Stimulate interest in learning B25 0.621
B26 0.785
B30 0.621
Classroom regulation B27 0.789
B28 0.799
B29 0.621
Subscale 4: teaching reflection ability 67.59% Self-reflection B31 0.543
B32 0.912
Reflection on teaching activities B33 0.558
B34 0.863
B35 0.755
Subscale 5: teaching research ability 74.59% Teaching theory research B39 0.867
B40 0.856
B41 0.799
Teaching practice research B42 0.658
B43 0.845
B44 0.809

Then, the reliability analysis of the questionnaire is carried out, and the results are shown in Table 6:

Table 6. Statistical table of reliability analysis of measured questionnaire.
Scale name Number of projects Cronbach coefficient
Subscale 1: teaching cognition 7 0.819
Self-cognition 4 0.837
Student cognition 3 0.734
Subscale 2: instructional design 12 0.875
Teaching objectives 3 0.714
Teaching structure 6 0.852
Teaching method 3 0.723
Subscale 3: teaching implementation 9 0.809
Transmit teaching information 3 0.618
Stimulate interest in learning 3 0.7
Classroom regulation 3 0.745
Subscale 4: teaching reflection 5 0.758
Self-reflection 2 0.602
Reflection on teaching activities 3 0.699
Subscale 5: teaching research 6 0.886
Teaching theory research 3 0.865
Teaching practice research 3 0.765
Total amount table 39 0.935

Looking at Table 6, 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 Tables 7 and 8, we can conclude that most teachers are stressed, and only a small part are not stressed.

Table 7. Test demographic data.
Background disguise Frequency (person) Percentage (%)
Gender Male 85 34.6
Woman 161 65.4
Graduate of Fan College Yes 101 41.1
No 145 58.9
Age Under 30 years old 74 30.1
31-35 years old 95 38.6
36-40 years old 77 31.3
Teaching experience Less than one year 50 20.3
2-3 years 69 28
4-5 years 22 8.9
Over 5 years 105 42.7
Educational background College and below 0 0
Undergraduate 14 5.7
Master graduate student 125 50.8
Doctoral students 107 43.5
Professional title Teaching assistants 31 12.6
Lecturer 143 58.1
Associate professor 44 17.9
Teachers 28 11.4
Type of institution Double first-class universities and colleges 86 34.9
Double first-class colleges and universities 83 33.7
Nondual institutions 91 31.4
Table 8. Statistical table of work stress of respondents.
Frequency Percentage Effective percentage
Effective Very large 50 20.3 20.3
Larger 99 40.2 40.2
General 85 34.6 34.6
Less 4 1.6 1.6
No 8 3.3 3.3
Total 246 100 100

From Table 9, 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.
Frequency Percentage Effective percentage
Effective Personal interest 62 25.2 25.2
The wishes of parents and others 53 21.5 21.5
Professional restriction 28 11.4 11.4
The occupation is relatively stable 103 41.9 41.9
Total 246 100 100

Looking at Figures 10 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.

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Figure 10
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Emphasis of respondents on scientific research and work.
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Figure 11
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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 Table 10.

Table 10. Descriptive statistical analysis.
Sample size Maximum value Minimum value Average Standard deviation
Subscale 1: teaching cognition ability 246 1 3 2.4123 0.36967
Self-cognition 246 1 3 2.3325 0.43911
Student cognition 246 1 3 2.5786 0.42901
Subscale 2: instructional design ability 246 1 3 2.5843 0.35199
Teaching objectives 246 1 3 2.4702 0.44033
Teaching structure 246 1 3 2.6463 0.38632
Teaching method 246 1 3 2.4255 0.42336
Subscale 3: teaching implementation ability 246 1 3 2.3785 0.38577
Transmit teaching information 246 1 3 2.5542 0.42217
Stimulate interest in learning 246 1 3 2.1667 0.52748
Classroom regulation 246 1 3 2.0146 0.49366
Subscale 4: teaching reflection ability 246 1 3 2.4236 0.42147
Self-reflection 246 1 3 2.4472 0.51035
Reflection on teaching activities 246 1 3 2.3058 0.44635
Subscale 5: teaching research ability 246 1 3 2.0935 0.55661
Teaching theory research 246 1 3 1.9702 0.66121
Teaching practice research 246 1 3 2.2168 0.5714
Total amount table 246 1 3 2.4128 0.31983

From 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.

Table 11 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.
F Sig. T value Degree of freedom Significance (bilateral) Mean difference
Subscale 1: teaching cognition ability Assuming methods are equal 1.067 0.303 1.162 244 0.247 0.4027
Assuming methods are not equal 1.155 168.303 0.25 0.4027
Subscale 2: instructional design ability Assuming methods are equal 0.776 0.379 -0.603 244 0.547 -0.3422
Assuming methods are not equal -0.594 163.404 0.554 -0.3422
Method design Assuming methods are equal 7.795 0.006 -1.856 244 0.065 -0.3145
Assuming methods are not equal -1.688 132.324 0.094 -0.3145
Subscale 3: teaching implementation ability Assuming methods are equal 2.37 0.125 -1.22 244 0.224 -0.56719
Assuming methods are not equal -1.172 153.196 0.243 -0.56719
Classroom regulation Assuming methods are equal 5.171 0.024 -1.979 244 0.049 -0.39065
Assuming methods are not equal -1.847 141.576 0.067 -0.39065
Subscale 4: teaching reflection ability Assuming methods are equal 8.481 0.004 -3.036 244 0.003 -0.8437
Assuming methods are not equal -2.818 139.519 0.006 -0.8437
Self-reflection Assuming methods are equal 9.944 0.002 -2.389 244 0.018 -0.32386
Assuming methods are not equal -2.231 141.83 0.027 -0.32386
Reflection on teaching activities Assuming methods are equal 7.223 0.008 -2.94 244 0.004 -0.51984
Assuming methods are not equal -2.779 146.355 0.006 -0.51984
Subscale 5: teaching research ability Assuming methods are equal 2.485 0.116 -1.072 244 0.285 -0.47965
Assuming methods are not equal -1.026 151.827 0.306 -0.47965
Theoretical research Assuming methods are equal 5.532 0.019 -0.567 244 0.571 -0.15097
Assuming methods are not equal -0.544 152.724 0.587 -0.15097
Total amount table Assuming methods are equal 2.184 0.141 -1.095 244 0.275 -1.83003
Assuming methods are not equal -1.063 157.428 0.29 -1.83003

4.3.4. Differences between Different Educational Backgrounds

Observing Table 12 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.
F Sig. T value Degree of freedom Significance (bilateral) Mean difference
Subscale 1: teaching cognition ability Assuming methods are equal 16.079 0 3.837 244 0 1.25224
Assuming methods are not equal 4.008 240.781 0 1.25224
Self-cognition Assuming methods are equal 7.43 0.007 3.939 244 0 0.87115
Assuming methods are not equal 4.028 230.799 0 0.87115
Student cognition Assuming methods are equal 13.454 0 2.305 244 0.022 0.38109
Assuming methods are not equal 2.389 237.764 0.018 0.38109
Subscale 2: instructional design ability Assuming methods are equal 10.159 0.002 4.382 244 0 2.31417
Assuming methods are not equal 4.603 242.414 0 2.31417
Goal design Assuming methods are equal 4.575 0.033 3.046 244 0.003 0.51287
Assuming methods are not equal 3.144 235.879 0.002 0.51287
Process design Assuming methods are equal 6.344 0.012 3.688 244 0 1.08037
Assuming methods are not equal 3.841 239.831 0 1.08037
Method design Assuming methods are equal 4.027 0.046 2.483 244 0.014 0.40451
Assuming methods are not equal 2.587 239.835 0.01 0.40451
Subscale 3: teaching implementation ability Assuming methods are equal 0.451 0.503 5.178 244 0 2.21632
Assuming methods are not equal 5.293 230.474 0 2.21632
Teaching information rack transmission Assuming methods are equal 15.271 0 3.791 244 0 0.60601
Assuming methods are not equal 3.945 239.387 0 0.60601
Subscale 4: teaching reflection ability Assuming methods are equal 2.834 0.094 5.484 244 0 1.41605
Assuming methods are not equal 5.678 237.322 0 1.41605
Reflection on teaching activities Assuming methods are equal 8.389 0.004 5.262 244 0 0.86719
Assuming methods are not equal 5.482 239.889 0 0.86719
Subscale 5: teaching research ability Assuming methods are equal 1.775 0.184 4.662 244 0 1.93745
Assuming methods are not equal 4.62 208.361 0 1.93745
Theoretical research Assuming methods are equal 0.034 0.853 4.626 244 0 1.14278
Assuming methods are not equal 4.636 216.78 0 1.14278
Total amount table Assuming methods are equal 1.672 0.197 6.048 244 0 9.13622
Assuming methods are not equal 6.243 235.856 0 9.13622

According 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.

Conflicts of Interest

The authors declared that they have no conflicts of interest regarding this work.

Data Availability

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

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