# Cultivation Method Analysis for Teachers’ Teaching Ability Driven by Artificial Intelligence Technology

Authors: Yanfang Chen; Shasha Xu
Journal: Advances in Multimedia
 (2022)

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## Abstract

Strengthening construction for teaching staff is an eternal theme of development and construction for colleges, and it is also the focus of personnel management in colleges. China is swiftly transitioning from the industrial age to the era of intelligence as a result of the rapid growth of information technology and artificial intelligence. Colleges and universities have reached a new stage in their evolution, one marked by intelligent use of technology, as represented by the fourth generation of information technology: cloud computing, big data, and artificial intelligence. Higher standards for college professors’ teaching abilities have been imposed by this new policy. It is therefore beneficial to evaluate teachers’ teaching abilities from an artificial intelligence perspective to improve the overall quality of college education. First, this work researches and improves the teaching ability training strategies of college teachers driven by artificial intelligence from different levels. Second, this work proposes a neural network (IPSO-BP) for evaluating the teaching ability of college teachers via artificial intelligence technology. Aiming at the issues in BP network, this work constructs IPSO by improving the weight decay strategy and learning factor of PSO algorithm. Then, it uses IPSO to optimize the BP to construct IPSO-BP. Third, the results of the experiments in this work suggest that the strategy proposed here is both feasible and preferable.


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## Body

## 1. Introduction

China is moving rapidly from the era of industrialization to the era of informationization and intelligence, and the cause of higher education has undergone profound changes. Teachers are the first identity, teaching is the first job, and class is the first responsibility. Faced with the impact of the intellectualized era, how should traditional undergraduate college teachers face it, and how should the corresponding teaching ability be further cultivated and improved? Big data, cloud computing, artificial intelligence, and other cutting-edge scientific and technological advancements have made significant strides in the real world and are now being broadly incorporated into higher education reform and development. The current era of intelligence has largely prevented most undergraduate colleges and universities from making timely and appropriate modifications to the reform of higher education, and they remain in the traditional teaching method. In the past, these traditional teaching methods were effective in undergraduate education because they were practical. Many flaws in teaching profession have been uncovered, as technology like computers and artificial intelligence continues to advance at breakneck speed [1–4].Higher education has the primary responsibility of creating individuals with a creative spirit and practical skill as a foundation for the development of highly specialized individuals. The only way to advance higher education is to raise the level of instruction at these institutions. To improve teaching quality in higher education, teachers are the driving force behind it, and their construction level directly affects the quality of their teaching. My country’s colleges and universities have been steadily growing enrolment since 1999, with an annual increase in total students of roughly 30 percent and an increase in overall enrollment. Despite this, the number of college and university professors is increasing at a slower rate, and there is a tendency of teacher shortage. The improvement of college and university teaching quality has become the emphasis of educational development. The quality of talent training has been given a lot of emphasis since the rise of higher education. In order to improve the quality of higher education instruction, college professors’ teaching abilities must be continuously improved on multiple levels. As a result, college professors are held to higher standards in terms of their capacity to impart knowledge [5–8].In the era of knowledge economy, the production, update, flow, and death rate of knowledge and the total amount of knowledge are all showing an exponential rise. From the perspective of teachers’ professional development, teachers must constantly learn and enrich themselves in the ever-changing era of knowledge economy and improve their own abilities by constantly interacting with the environment of the times. College professors, in their roles as educators and educators’ educators, should be well-versed in a wide range of disciplines, including biology, chemistry, physics, anthropology, linguistics, and other relevant fields, as well as the essential conditional knowledge. A college instructor should be able to construct lesson plans, monitor and control classrooms, and teach students in accordance with their aptitude in the ever-changing background of the times. It is possible for colleges and universities to increase college instructors’ teaching abilities and set the groundwork for the nurturing of talent in colleges and universities by developing teachers’ professional development [9–12].Education is undergoing a change thanks to artificial intelligence. Schools and educators have been reimagined in the age of AI. Teachers, too, have both opportunities and challenges as a result of the use of artificial intelligence in education. The new era has raised the bar for college professors in terms of their overall aptitude. AI has a profound impact on teachers’ understanding of educational concepts, teaching methods, and modes of delivery. Technology-savvy instructors will eventually replace those who are not as tech-savvy, but they will not be completely replaced by AI-savvy teachers just yet. As a result, the teaching profession has been impacted and challenged in a way that has never before been seen in history. Teachers are no longer the gatekeepers of knowledge, but rather the ones who plan, coordinate, and even provide services for their students’ education. A new age of human-computer coteaching will dawn in the future, and in order to prepare for this new era, educators must begin embracing human-computer coteaching now. Teachers must constantly enhance their teaching abilities if they are to keep pace with the changes in education that will occur in the new millennium [13–15].It is therefore beneficial to evaluate teachers’ teaching abilities from an artificial intelligence perspective to improve the overall quality of college education. First, this work researches and improves the teaching ability training strategies of college teachers driven by artificial intelligence from different levels. Second, this work proposes a neural network (IPSO-BP) for evaluating the teaching ability of college teachers via artificial intelligence technology. Aiming at the issues in BP network, this work constructs IPSO by improving the weight decay strategy and learning factor of PSO algorithm. Then, it uses IPSO to optimize the BP to construct IPSO-BP. Third, the results of the experiments in this work suggest that the strategy proposed here is both feasible and preferable.


## 2. Related Work

There is evidence in the literature [16] to support the idea that good teaching performance in a range of educational situations is supported by a comprehensive personal characteristic that includes knowledge, abilities, and attitudes. An individual’s ability is a potential and long-lasting competency trait that gauges individual job performance, according to literature [17]. Literature [18] thinks that professional attitudes, knowledge, and abilities that can adapt to varied teaching conditions as well as individuals meet their own duties and successfully affect the learning process of learners constitute teaching competence. Reference [19] regards teaching ability as a psychological characteristic and considers it a special professional ability manifested in specific subject teaching activities, which is necessary to complete teaching activities and affects teaching efficiency. Literature [20] believes that teaching ability includes four parts: teaching organization and monitoring ability, ability to communicate and cooperate with students or other teachers, ability to use modern information technology, and teaching research ability. Literature [21] believes that educational ability is a kind of practical ability, which is the ability of teachers to conduct teaching research and carry out teaching practice activities on the basis of cognition, understanding, mastery, and application of teaching academics. Literature [22] believes that the structure of teaching ability should interpret the connotation of teaching ability and construct its multidimensional structure from the three disciplinary dimensions of pedagogy, psychology, and sociology. Taking teaching cognitive ability as the basis of teaching design ability, teaching organization ability is specific of teaching operation ability, and teaching research ability and teaching monitoring ability affect each other. The ability of teaching communication and teachers’ self-education ability that extends beyond the classroom provides the necessary guarantee for the smooth progress of teaching. Literature [23] believes that teaching ability is composed of various components. Teaching ability includes the ability to understand syllabus and teaching materials at the skill level, teaching design and preparation ability, knowledge transfer, language expression, and classroom organization and management ability. It also includes the teaching reflection ability and teaching monitoring ability at the teacher’s reflection level.According to the literature [24], educational artificial intelligence enables students to better participate in teachers’ instructional activities via smart devices and teaching platforms. Using intelligent technology and software in the era of artificial intelligence can help teachers adopt more personalized instruction. Internet-era multimedia equipment serves primarily to aid educators in delivering lessons in a more natural and engaging manner. Sensor gadgets are being employed increasingly frequently in the intelligent age to keep tabs on students’ learning environments and habits. Tutor assistants, intelligent assessments, learning partners, data mining, and learning analysis are just a few examples of how artificial intelligence is being used in education. There is an urgent need to train more cross-border talent in the sphere of education in light of the rise of artificial intelligence [25]. Studies in artificial intelligence technology [26] show that teachers can have a better understanding of their students’ learning outcomes by utilizing artificial intelligence analysis software. It is also capable of assessing how well students understand the material being taught and identifying any learning gaps. AI-assisted classroom management allows educators to make timely adjustments to their resource allocation based on the most basic of student data. According to literature [27], teachers can benefit from artificial intelligence by having their students’ homework and other papers intelligently marked. With the help of big data and artificial intelligence, teachers may better target their lessons based on their students’ homework and tests. Learning qualities and demands of students can be better understood by artificial intelligence, according to research on teaching design in literature [28]. It aids teachers in clarifying teaching objectives, enhancing teaching efficiency, and enhancing teaching quality by evaluating huge numbers of data samples. Literature [29] pointed out that the development of artificial intelligence education can better assist teachers in teaching, and artificial intelligence can help teachers achieve personalized teaching. Smart devices in the smart age can monitor and record students’ learning process, learning paths, and even learning choices in real time. Teachers can use the analysis engine to monitor each student’s learning progress in real time and provide personalized guidance. Literature [30] believes that although artificial intelligence education will have a significant impact or even impact on traditional teaching methods and teaching methods, the teaching profession will not disappear. The main responsibility of teachers is to design teaching courses, and artificial intelligence education applications can assist teachers to design courses faster, better, and more efficiently.


## 3. Method

First, this work researches and improves the teaching ability training strategies of college teachers driven by artificial intelligence from different levels. Second, this work proposes a neural network (IPSO-BP) for evaluating the teaching ability of college teachers via artificial intelligence technology. Aiming at the issues in BP network, this work constructs IPSO by improving the weight decay strategy and learning factor of PSO algorithm. Then, it uses IPSO to optimize the BP network to construct IPSO-BP.


### 3.1. Teaching Ability Cultivation Strategies

This work puts forward the training strategies to improve teaching ability for college teachers from different levels. First, strengthen the preservice training of teachers. The growth and development of teachers are a process, and the professional development of teachers consists of three stages: preservice training, qualification appointment, and on-the-job training. Preservice training is the initial stage of teachers’ professional development and has a crucial impact on their future professional growth. Through rigorous preservice training, they can better adapt to the role transition from student to teacher. Prejob training plays an important role in helping young teachers to enter the role of teachers as soon as possible and to be better qualified for education and teaching in colleges.Second, implement teacher growth mentoring system. The so-called mentoring system is a system in which incoming teachers, through the pairing of old teachers, help and teach in an organized, planned, and purposeful way and comprehensively cultivate new teachers’ abilities in teaching and management in the early stages of professional growth. This can help new teachers grow up quickly and effectively carry out teaching ability training. The quality of tutors is an important factor that determines whether young teachers can grow and develop and also determines the effectiveness of the implementation of the tutor system. Tutors should impart teaching experience to young teachers, communicate more with new teachers through academic exchanges, symposiums, etc., to help them familiarize themselves with teaching work and master teaching skills. Mentors should guide according to the characteristics of each person, give play to strengths, and point out weaknesses and problems. Tutors should help these teachers adapt to new teaching environment from aspects of teaching design, teaching execution, and teaching research.Third, implement the mutual aid and training of teachers’ growth team. The construction of teachers’ teaching ability training system is inseparable from the mutual assistance of peers. Young teachers are in a new stage of combining independent work with knowledge and practice. Communication and cooperation with others can broaden their thinking, which is an important driving force for cultivating teaching ability. By using teaching resources of teachers, we conduct a comprehensive discussion on the problems arising in the exchange of teaching activities and display the results, so as to make up for the deficiencies, exchange experience, and gain success in the discussion. In the construction of teacher growth team mutual assistance, teacher training should be incorporated into the discipline and professional construction system according to the needs of the discipline. This enables each young teacher to find his own position in the entire subject system and professional system, forming a teacher growth team.Fourth, insist on on-the-job training and continuous training. The country has gradually increased its emphasis on on-the-job training and continuing education. This is also a significant part of teaching ability training. In foreign universities, a mature system of on-the-job training and continuous training has long been formed, and there are many experiences for reference. Teachers should cultivate the ability to solve problems in teaching, improving their original theoretical system through on-the-job training and continuous training. They should learn from the current more advanced teaching methods or teaching methods and use new media means such as TV and movie clips to enrich their teaching content. Schools should organize and encourage in-service teachers to participate in advanced training activities, instruct teachers to formulate study and research plans, and gradually complete the institutionalization of in-service teachers’ advanced education. This makes the current on-the-job training and continuous training of college teachers tend to be normalized and normalized.


### 3.2. BP Network

An error-correction algorithm and signal monitoring are used in BP’s multilayer feedforward neural network. As a result of continuous error reverse learning, the sum of the squared errors is minimized by using the gradient descent approach to alter network weights and thresholds. The network structure is relatively simple, the algorithm is easy to understand, the error is small after adjustment, and the accuracy of the entire network is high. BP network can be divided into three-layer or multilayer neural networks according to the number of layers of the network, including input, hidden, and output layers. The hidden layer can be set to one or more layers, and its topology is demonstrated in Figure1.Figure 1
BP pipeline.There are two steps to the BP network learning algorithm, which is also known as a tutored learning algorithm. Forward propagation is the initial step. The neurons in the hidden layer receive input information from the input layer. In the hidden layer, neurons digest the information, and finally, it is sent out through the input layer. All connections between neurons remain unchanged during forward propagation, and only forward propagation is carried out. Backpropagation is the second stage. Backpropagation occurs when the error between the output of the output layer and the real value is too great to meet the criteria. This can be done by altering each layer of the neural network, which results in a better overall accuracy. The two-stage calculation process is as follows:(1)oi=f∑i,jwijxij+bij,(2)E=∑ioi−yi2,(3)w′=w+∆w,(4)b′=b+∆b,where w is weight, b is bias, and y is true label.BP network has strong adaptability when processing data. After training and learning, various mappings from input to output can be done well. Combining this mapping with nonlinear features can solve various nonlinear problems well. But the network also has some drawbacks. First, there is no theoretical basis for determining the network structure. So far, there is no unified rule and method for determining hidden layers and neuron nodes. If hidden layers are set too small, the final output error will be large and the prediction accuracy will be low. If there are too many settings, it will cause the neural network to learn slowly and inefficiently. Second, the convergence rate is slow. BP network uses the gradient descent to correct the connection weights and thresholds, but the calculation process of the gradient descent method has limitations. When training reaches certain times, only a small error can be reduced each time. The purpose of introducing learning rate is to speed up convergence speed, but its determination is a very complicated problem. In general, it is artificially set to judge its accuracy by comparing the rate of decline of the sum of squared errors. When it is too small, network takes too long to learn. However, if learning rate is large, it causes network to oscillate in the process of reducing the error, preventing the entire learning process from converging. Finally, it is prone to falling into local minima. The error backpropagation algorithm used in BP network is a nonlinear and unconstrained optimization process. In general, its error function has many local minima. When the BP network is given such a wide range of beginning values, it is bound to find different local minima or global minima. During training, the neural network’s accuracy will be drastically lowered when it reaches a local minimum point. Therefore, how to set the initial weight value of the network is an important issue.


### 3.3. PSO Algorithm

The PSO algorithm is an intelligent algorithm that simulates intelligent behavior of groups and realizes a global search through the cooperation and cooperation between individuals to find global optimal solution. In PSO algorithm, potential solutions of each optimization problem are in space, and each solution is like a bird, which is called a particle. All particles in the solution space have a value determined by objective function, which is called fitness. On the one hand, particles remember their best position based on their own experience. On the other hand, it also relies on the experience of other particles to obtain the best positions for other particles other than itself. Combining these two experiences, each particle moves continuously in the solution space and follows the particle at the current optimal position at a certain speed, which determines the distance and direction of the particle. Usually after several generations of continuous search, the optimal solution is finally found. Compared with other intelligent algorithms, the PSO algorithm requires fewer variable parameters to adjust. It is a group intelligent random search algorithm based on mutual cooperation and cooperation between groups, and it is easy to achieve ideal results when used. Due to its fast convergence and excellent global search performance. BP network uses gradient descent to update parameters, which has certain defects. Therefore, this paper chooses to find optimal parameter through PSO algorithm, which makes the prediction model more accurate. PSO pipeline is demonstrated in Figure2.Figure 2
PSO pipeline.


### 3.4. Improved PSO and IPSO-BP

Particles’ ability to optimize itself may be harmed by the addition of inertia weights. Specifically, there are advantages and disadvantages to both big and small inertia weights for the algorithm’s global and local search capabilities. Various particle swarm algorithms have been developed based on various inertia weights. Some of the most common weighting systems are adaptive weights, which can be linearly decreasing or random. Linearly decaying inertial weights (LDIWs) are employed in this study.(5)w=wmax−wmax−wminttmax,where wmax and wmin are the upper and lower bounds of the weights, t is current iteration, and tmax is max iteration.Particles converge to local extrema too early because the first learning factor is too large and the second learning factor is too small in the early search stage. This makes the particles linger in the big picture and the lack of diversity of the particles in the later stage. To solve issue effectively, a strategy is found to adjust acceleration factor, namely, the concave function. This method accelerates the change of the learning factor value in the early stage, results show that the method is feasible, but the improvement effect is not obvious. The problem occurs in the later stage, the particles still lack diversity, and the problem of the linear adjustment strategy has not been fundamentally solved.The adjustment method for the learning factor in this paper can be constructed by the method of the cosine function, which is named as the inverse cosine learning factor (ICLF). In order to enable the PSO algorithm to enter the local search as soon as possible, the strategy of the inverse cosine function is to change the acceleration factor at the beginning of the algorithm. The arc cosine function sets a more appropriate learning factor than the linear and concave function strategies in the later stage. The advantage of this method is that it can avoid the premature start of convergence and allow the particles to have a certain search speed.(6)c1=c1min+c1max−c1min1−arccos−2t/n+1π,(7)c2=c2min+c2max−c2min1−arccos−2t/n+1π,where c1max and c1min are max and min learning factor, c2max and c2min are max and min learning factor, and t is the current iteration.This study combines the advantages of both to establish a model IPSO-BP based on IPSO to optimize BP network parameters and uses this model to evaluate teachers’ teaching ability. The IPSO-BP pipeline is demonstrated in Figure3.Figure 3
IPSO-BP pipeline.The first stage is determining the BP network’s structure and setting parameters such as the inertia weight and population size. BP network and particle swarm parameters are initialized in the second stage. The fitness value of each particle is then calculated in the third phase. Individual optimal positions and global optimal positions are determined in this step. The particle velocity and position are updated in the fifth step. The next step is to determine whether the termination condition is met. If it is still not pleased, go back to the third step and try again. Otherwise, the global optimal solution is output as the best possible position based on the current fitness score, and the search is terminated. The weights and thresholds obtained at this time are the optimal weight parameters. In the seventh step, the BP network makes predictions according to the optimized weights and thresholds and obtains the prediction results.


## 3.1. Teaching Ability Cultivation Strategies

This work puts forward the training strategies to improve teaching ability for college teachers from different levels. First, strengthen the preservice training of teachers. The growth and development of teachers are a process, and the professional development of teachers consists of three stages: preservice training, qualification appointment, and on-the-job training. Preservice training is the initial stage of teachers’ professional development and has a crucial impact on their future professional growth. Through rigorous preservice training, they can better adapt to the role transition from student to teacher. Prejob training plays an important role in helping young teachers to enter the role of teachers as soon as possible and to be better qualified for education and teaching in colleges.Second, implement teacher growth mentoring system. The so-called mentoring system is a system in which incoming teachers, through the pairing of old teachers, help and teach in an organized, planned, and purposeful way and comprehensively cultivate new teachers’ abilities in teaching and management in the early stages of professional growth. This can help new teachers grow up quickly and effectively carry out teaching ability training. The quality of tutors is an important factor that determines whether young teachers can grow and develop and also determines the effectiveness of the implementation of the tutor system. Tutors should impart teaching experience to young teachers, communicate more with new teachers through academic exchanges, symposiums, etc., to help them familiarize themselves with teaching work and master teaching skills. Mentors should guide according to the characteristics of each person, give play to strengths, and point out weaknesses and problems. Tutors should help these teachers adapt to new teaching environment from aspects of teaching design, teaching execution, and teaching research.Third, implement the mutual aid and training of teachers’ growth team. The construction of teachers’ teaching ability training system is inseparable from the mutual assistance of peers. Young teachers are in a new stage of combining independent work with knowledge and practice. Communication and cooperation with others can broaden their thinking, which is an important driving force for cultivating teaching ability. By using teaching resources of teachers, we conduct a comprehensive discussion on the problems arising in the exchange of teaching activities and display the results, so as to make up for the deficiencies, exchange experience, and gain success in the discussion. In the construction of teacher growth team mutual assistance, teacher training should be incorporated into the discipline and professional construction system according to the needs of the discipline. This enables each young teacher to find his own position in the entire subject system and professional system, forming a teacher growth team.Fourth, insist on on-the-job training and continuous training. The country has gradually increased its emphasis on on-the-job training and continuing education. This is also a significant part of teaching ability training. In foreign universities, a mature system of on-the-job training and continuous training has long been formed, and there are many experiences for reference. Teachers should cultivate the ability to solve problems in teaching, improving their original theoretical system through on-the-job training and continuous training. They should learn from the current more advanced teaching methods or teaching methods and use new media means such as TV and movie clips to enrich their teaching content. Schools should organize and encourage in-service teachers to participate in advanced training activities, instruct teachers to formulate study and research plans, and gradually complete the institutionalization of in-service teachers’ advanced education. This makes the current on-the-job training and continuous training of college teachers tend to be normalized and normalized.


## 3.2. BP Network

An error-correction algorithm and signal monitoring are used in BP’s multilayer feedforward neural network. As a result of continuous error reverse learning, the sum of the squared errors is minimized by using the gradient descent approach to alter network weights and thresholds. The network structure is relatively simple, the algorithm is easy to understand, the error is small after adjustment, and the accuracy of the entire network is high. BP network can be divided into three-layer or multilayer neural networks according to the number of layers of the network, including input, hidden, and output layers. The hidden layer can be set to one or more layers, and its topology is demonstrated in Figure1.Figure 1
BP pipeline.There are two steps to the BP network learning algorithm, which is also known as a tutored learning algorithm. Forward propagation is the initial step. The neurons in the hidden layer receive input information from the input layer. In the hidden layer, neurons digest the information, and finally, it is sent out through the input layer. All connections between neurons remain unchanged during forward propagation, and only forward propagation is carried out. Backpropagation is the second stage. Backpropagation occurs when the error between the output of the output layer and the real value is too great to meet the criteria. This can be done by altering each layer of the neural network, which results in a better overall accuracy. The two-stage calculation process is as follows:(1)oi=f∑i,jwijxij+bij,(2)E=∑ioi−yi2,(3)w′=w+∆w,(4)b′=b+∆b,where w is weight, b is bias, and y is true label.BP network has strong adaptability when processing data. After training and learning, various mappings from input to output can be done well. Combining this mapping with nonlinear features can solve various nonlinear problems well. But the network also has some drawbacks. First, there is no theoretical basis for determining the network structure. So far, there is no unified rule and method for determining hidden layers and neuron nodes. If hidden layers are set too small, the final output error will be large and the prediction accuracy will be low. If there are too many settings, it will cause the neural network to learn slowly and inefficiently. Second, the convergence rate is slow. BP network uses the gradient descent to correct the connection weights and thresholds, but the calculation process of the gradient descent method has limitations. When training reaches certain times, only a small error can be reduced each time. The purpose of introducing learning rate is to speed up convergence speed, but its determination is a very complicated problem. In general, it is artificially set to judge its accuracy by comparing the rate of decline of the sum of squared errors. When it is too small, network takes too long to learn. However, if learning rate is large, it causes network to oscillate in the process of reducing the error, preventing the entire learning process from converging. Finally, it is prone to falling into local minima. The error backpropagation algorithm used in BP network is a nonlinear and unconstrained optimization process. In general, its error function has many local minima. When the BP network is given such a wide range of beginning values, it is bound to find different local minima or global minima. During training, the neural network’s accuracy will be drastically lowered when it reaches a local minimum point. Therefore, how to set the initial weight value of the network is an important issue.


## 3.3. PSO Algorithm

The PSO algorithm is an intelligent algorithm that simulates intelligent behavior of groups and realizes a global search through the cooperation and cooperation between individuals to find global optimal solution. In PSO algorithm, potential solutions of each optimization problem are in space, and each solution is like a bird, which is called a particle. All particles in the solution space have a value determined by objective function, which is called fitness. On the one hand, particles remember their best position based on their own experience. On the other hand, it also relies on the experience of other particles to obtain the best positions for other particles other than itself. Combining these two experiences, each particle moves continuously in the solution space and follows the particle at the current optimal position at a certain speed, which determines the distance and direction of the particle. Usually after several generations of continuous search, the optimal solution is finally found. Compared with other intelligent algorithms, the PSO algorithm requires fewer variable parameters to adjust. It is a group intelligent random search algorithm based on mutual cooperation and cooperation between groups, and it is easy to achieve ideal results when used. Due to its fast convergence and excellent global search performance. BP network uses gradient descent to update parameters, which has certain defects. Therefore, this paper chooses to find optimal parameter through PSO algorithm, which makes the prediction model more accurate. PSO pipeline is demonstrated in Figure2.Figure 2
PSO pipeline.


## 3.4. Improved PSO and IPSO-BP

Particles’ ability to optimize itself may be harmed by the addition of inertia weights. Specifically, there are advantages and disadvantages to both big and small inertia weights for the algorithm’s global and local search capabilities. Various particle swarm algorithms have been developed based on various inertia weights. Some of the most common weighting systems are adaptive weights, which can be linearly decreasing or random. Linearly decaying inertial weights (LDIWs) are employed in this study.(5)w=wmax−wmax−wminttmax,where wmax and wmin are the upper and lower bounds of the weights, t is current iteration, and tmax is max iteration.Particles converge to local extrema too early because the first learning factor is too large and the second learning factor is too small in the early search stage. This makes the particles linger in the big picture and the lack of diversity of the particles in the later stage. To solve issue effectively, a strategy is found to adjust acceleration factor, namely, the concave function. This method accelerates the change of the learning factor value in the early stage, results show that the method is feasible, but the improvement effect is not obvious. The problem occurs in the later stage, the particles still lack diversity, and the problem of the linear adjustment strategy has not been fundamentally solved.The adjustment method for the learning factor in this paper can be constructed by the method of the cosine function, which is named as the inverse cosine learning factor (ICLF). In order to enable the PSO algorithm to enter the local search as soon as possible, the strategy of the inverse cosine function is to change the acceleration factor at the beginning of the algorithm. The arc cosine function sets a more appropriate learning factor than the linear and concave function strategies in the later stage. The advantage of this method is that it can avoid the premature start of convergence and allow the particles to have a certain search speed.(6)c1=c1min+c1max−c1min1−arccos−2t/n+1π,(7)c2=c2min+c2max−c2min1−arccos−2t/n+1π,where c1max and c1min are max and min learning factor, c2max and c2min are max and min learning factor, and t is the current iteration.This study combines the advantages of both to establish a model IPSO-BP based on IPSO to optimize BP network parameters and uses this model to evaluate teachers’ teaching ability. The IPSO-BP pipeline is demonstrated in Figure3.Figure 3
IPSO-BP pipeline.The first stage is determining the BP network’s structure and setting parameters such as the inertia weight and population size. BP network and particle swarm parameters are initialized in the second stage. The fitness value of each particle is then calculated in the third phase. Individual optimal positions and global optimal positions are determined in this step. The particle velocity and position are updated in the fifth step. The next step is to determine whether the termination condition is met. If it is still not pleased, go back to the third step and try again. Otherwise, the global optimal solution is output as the best possible position based on the current fitness score, and the search is terminated. The weights and thresholds obtained at this time are the optimal weight parameters. In the seventh step, the BP network makes predictions according to the optimized weights and thresholds and obtains the prediction results.


## 4. Experiment

### 4.1. Evaluation on IPSO-BP

First, this work collects the corresponding teaching ability data of college teachers to construct a dataset. The indicators and characteristics contained in each sample are demonstrated in Table1, and the corresponding label is the teacher’s teaching ability evaluation grade. This work uses accuracy and recall as evaluation metrics for IPSO-BP.
(8)Accuracy=TP+TNTP+FP+TN+FN,(9)Rec=TPTP+FN.Table 1
Teaching ability evaluation index.
IndexMarkTeaching background analysis abilityx1Teaching goal setting abilityx2Teaching process design abilityx3Information communication abilityx4Classroom change abilityx5Multidirectional interaction abilityx6Classroom control abilityx7Learning guidance abilityx8To verify the feasibility of IPSO-BP for teaching ability evaluation, this work compares it with other methods. The comparison data is demonstrated in Figure4.Figure 4
Method comparison.IPSO-BP can achieve the highest accuracy and recall. Compared with any of the compared methods, hints on the indicators can be obtained.IPSO-BP uses a linearly decaying inertial weight (LDIW) strategy. To verify the superiority of this strategy, this work compares the performances corresponding to fixed inertial weight (FIW) and linear weight, as demonstrated in Figure5.Figure 5
Comparison of FIW and LDIW.Compared with the FIW strategy, after using the LDIW strategy, the accuracy and recall rates of IPSO-BP are improved by 2.1% and 1.5%. These improvements support the superiority of using the LDIW strategy.IPSO-BP uses an inverse cosine learning factor (ICLF) strategy. To verify the superiority of this strategy, this work compares the performances corresponding to traditional learning factor (TLF) and ISLF, as demonstrated in Figure6.Figure 6
Comparison of TLF and ICLF.Compared with the TLF strategy, after using the ICLF strategy, the accuracy and recall rates of IPSO-BP are improved by 1.3% and 1.2%. These improvements support the superiority of using the ICLF strategy.This work utilizes IPSO to optimize the BP network. To verify the feasibility of this measure, this work compares the correct and recall rates of BP, PSO-BP, and IPSO-BP, respectively, as demonstrated in Figure7.Figure 7
Comparison of BP, PSO-BP, and IPSO-BP.The accuracy and recall rate obtained by the traditional BP network are the lowest, and the performance of PSO-BP is improved. The best evaluation performance is obtained after the BP network is optimized with the improved PSO.


### 4.2. Evaluation on Teaching Ability Cultivation Strategies

This work proposes a series of teaching ability cultivation measures. To verify the feasibility for these measures, this work compares the teaching ability before and after using these measures. The analyzed indexes are the same as those in Table1, and the comparative data are demonstrated in Table 2.Table 2
Evaluation on teaching ability cultivation strategies.
Indexx1x2x3x4x5x6x7x8Before75.286.091.386.581.973.885.290.6After78.189.592.790.385.679.289.193.3After using the teaching ability cultivation measures proposed in this work, the teaching level of college teachers has been significantly improved. Each teaching ability evaluation index score can be improved to a certain extent. This proves the superiority of the teaching ability cultivation measures proposed in this work.


## 4.1. Evaluation on IPSO-BP

First, this work collects the corresponding teaching ability data of college teachers to construct a dataset. The indicators and characteristics contained in each sample are demonstrated in Table1, and the corresponding label is the teacher’s teaching ability evaluation grade. This work uses accuracy and recall as evaluation metrics for IPSO-BP.
(8)Accuracy=TP+TNTP+FP+TN+FN,(9)Rec=TPTP+FN.Table 1
Teaching ability evaluation index.
IndexMarkTeaching background analysis abilityx1Teaching goal setting abilityx2Teaching process design abilityx3Information communication abilityx4Classroom change abilityx5Multidirectional interaction abilityx6Classroom control abilityx7Learning guidance abilityx8To verify the feasibility of IPSO-BP for teaching ability evaluation, this work compares it with other methods. The comparison data is demonstrated in Figure4.Figure 4
Method comparison.IPSO-BP can achieve the highest accuracy and recall. Compared with any of the compared methods, hints on the indicators can be obtained.IPSO-BP uses a linearly decaying inertial weight (LDIW) strategy. To verify the superiority of this strategy, this work compares the performances corresponding to fixed inertial weight (FIW) and linear weight, as demonstrated in Figure5.Figure 5
Comparison of FIW and LDIW.Compared with the FIW strategy, after using the LDIW strategy, the accuracy and recall rates of IPSO-BP are improved by 2.1% and 1.5%. These improvements support the superiority of using the LDIW strategy.IPSO-BP uses an inverse cosine learning factor (ICLF) strategy. To verify the superiority of this strategy, this work compares the performances corresponding to traditional learning factor (TLF) and ISLF, as demonstrated in Figure6.Figure 6
Comparison of TLF and ICLF.Compared with the TLF strategy, after using the ICLF strategy, the accuracy and recall rates of IPSO-BP are improved by 1.3% and 1.2%. These improvements support the superiority of using the ICLF strategy.This work utilizes IPSO to optimize the BP network. To verify the feasibility of this measure, this work compares the correct and recall rates of BP, PSO-BP, and IPSO-BP, respectively, as demonstrated in Figure7.Figure 7
Comparison of BP, PSO-BP, and IPSO-BP.The accuracy and recall rate obtained by the traditional BP network are the lowest, and the performance of PSO-BP is improved. The best evaluation performance is obtained after the BP network is optimized with the improved PSO.


## 4.2. Evaluation on Teaching Ability Cultivation Strategies

This work proposes a series of teaching ability cultivation measures. To verify the feasibility for these measures, this work compares the teaching ability before and after using these measures. The analyzed indexes are the same as those in Table1, and the comparative data are demonstrated in Table 2.Table 2
Evaluation on teaching ability cultivation strategies.
Indexx1x2x3x4x5x6x7x8Before75.286.091.386.581.973.885.290.6After78.189.592.790.385.679.289.193.3After using the teaching ability cultivation measures proposed in this work, the teaching level of college teachers has been significantly improved. Each teaching ability evaluation index score can be improved to a certain extent. This proves the superiority of the teaching ability cultivation measures proposed in this work.


## 5. Conclusion

It is imperative that colleges and universities break new ground in cultivating high-quality talents, and strengthening college professors’ capacity to teach is a certain direction in this effort. One of the most important areas of modern talent development is the development of teacher training programs. Big data, the Internet of Things, the Internet, intelligent identification, and knowledge management are all examples of artificial intelligence technologies that are influencing education. Teachers’ professional growth and development have been profoundly altered by artificial intelligence, which has brought technological innovation to higher education instruction. It is therefore beneficial to evaluate teachers’ teaching abilities from an artificial intelligence perspective to improve the overall quality of college education. First, this work researches and improves the teaching ability training strategies of college teachers driven by artificial intelligence from different levels. Second, this work proposes a neural network (IPSO-BP) for evaluating the teaching ability of college teachers via artificial intelligence technology. Aiming at the issues in BP network, this work constructs IPSO by improving the weight decay strategy and learning factor of PSO algorithm. Then, it uses IPSO to optimize the BP network to construct IPSO-BP. Third, the results of the experiments in this work suggest that the strategy proposed here is both feasible and preferable.



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