# Optimization of English Classroom Quality Evaluation Model with AHP

Authors: Shuang Yang
Journal: Security and Communication Networks
 (2022)

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## Abstract

This work takes English classroom quality as the research object. Improving the quality of English classrooms is not only an unavoidable requirement for the current curriculum reform’s deepening and development, but also a necessary development trend of the evaluation system reform, and the call of the majority of classroom teaching position. The country invested financial, labor, and material resources, and students have also invested a lot of study time, but the English classroom effect is not satisfactory. The main issue is a lack of adequate evaluation of the teaching process as well as a scientific, fair, and practical technique for assessing classroom quality. This work combines AHP and BP networks to propose a method (AHP-IGA-BP) for evaluating the quality of English classrooms. The content of this work is as follows: (1) A hierarchical model and index system for English classroom quality evaluation based on AHP are constructed. Through the process of constructing a judgment matrix, single-level ranking, consistency check, and total-level ranking, the complete English classroom quality evaluation index weight is finally obtained, which realizes the transformation of subjective information into data with specific weight. (2) combine the original data with the aforementioned index weight data produced using AHP, as well as the neural network’s learning and training samples. Build a BP network structure to effectively evaluate the quality of English classrooms. (3) The BP network is optimized based on the improved GA algorithm.


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## Body

## 1. Introduction

With the further deepening of the country’s foreign reform and opening up and the proposal of the national Belt and Road strategy, the deepening development of exchanges and contacts between China and foreign countries requires more foreign language talents. Therefore, improving the quality of English classroom teaching and generally improving the level of students’ English communication is an urgent requirement of the current teaching reform. Curriculum reform of basic education is an important measure in the field of basic education in this century. After more than ten years of curriculum reform, the new curriculum concept based on talent training and centered on quality and innovation is rooted in people and has been recognized by most teachers, students, and parents. National, local, and school-based courses are in parallel, and a variety of parallel teaching materials have appeared in the country for students to choose and use, and students’ practical courses have been added. The dominant position of teachers and the dominant position of students have been established, the relationship between teachers and students is increasingly democratic, autonomous learning and active learning have gradually become the inner call and practice of students. New ways of teaching and learning have brought surprising changes. In the past, summative evaluation is gradually being replaced by process evaluation and multivariate evaluation. Now, the curriculum reform has advanced to a new stage, from the stage of scale development to the stage of connotation development. A substantial rise in educational standards is a major measure of societal advancement. Build a student-centered, teacher-led curriculum teaching style in the classroom rather than relying on instructors and instructional resources. Classroom management, such as how pupils spend their time in class, how much homework they have, and how much rest they get, is an important part of the teacher’s job. Give students more space for active learning, so that students’ autonomy, independence, initiative, and creativity can be developed and improved, and ultimately achieve the goal of further improving the quality of education [1–5].Although the assessment and evaluation system has played an important role in the past, it also has some severe flaws. The reform of the assessment and evaluation system has been an important aspect of education reform since the end of the twentieth century. The evaluation’s content has shifted from focusing on the examination of basic knowledge and basic abilities in the past to evaluating the whole quality and ability, including double bases. Thirdly, the evaluation method has changed from a unified paper and pencil test to a variety of forms, from standardized tests to the current qualitative and quantitative evaluations, and the addition of subjective evaluation questions. Finally, the subject of student evaluation has changed from teachers and schools to the diversity of subjects. In addition to the evaluation of schools and teachers, there are also diverse subjects such as student self-evaluation. Reform of the evaluation system has made the content more extensive, the forms more diverse, the target requirements higher, and the methods more flexible, which requires teachers to pay attention to the quality of classroom teaching [6–10].Improving the quality of English classroom education and teaching and effectively changing the problems in practical teaching are the most urgent practical needs of current English classroom teaching. Secondly, the diversification of evaluation methods needs to enhance the English teachers’ awareness of classroom quality and improve the quality of English classroom teaching. Finally, new curriculum reform requires adjusting the curriculum structure, rationally allocating educational and teaching resources, and paying more attention to improving classroom teaching. This directly requires us to study the English classroom ecology and improve the quality of classroom teaching through curriculum reform. New curriculum reform is in full swing, correspondingly, the new assessment and evaluation system is also in full swing, and the new teaching quality system is also established accordingly. In this unprecedented era of change, English classroom teaching faces many difficulties and many opportunities. In the actual education and teaching reform work, we must pay attention to the classroom, take the classroom as the main position of teaching and the main battlefield of teaching research, and study how to evaluate the quality of English classroom teaching [11–15]. According to the literature [16–18], which summarized the previous studies, teachers’ organizational skills, instructional style, and classroom management skills should be evaluated.The following is an organization of the research: Section2 discussed the related work. Section 3 discusses the method of the proposed work. Section 4 discusses the experiments and results. Finally the conclusion brings the study to a finish in Section 5.


## 2. Related Work

According to the literature [16], which summarizes numerous studies on teaching quality, teachers’ teaching quality can be evaluated in three ways: in terms of their organization, structure, or clarity of instruction, as well as in terms of their ability to communicate with students and their own teaching abilities. According to the literature [17], which summarized the previous studies, teachers’ organizational skills, instructional style, and classroom management skills should be evaluated. An evaluation of a teacher’s teaching quality has been proposed in the literature [18], and it includes 32 evaluation indicators in nine dimensions, including the ability to learn and teach with enthusiasm and organization, group interaction, and interpersonal harmony as well as the breadth of a teacher’s knowledge. According to the literature [19], the classroom evaluation index system includes teaching objectives, material, procedure, methodologies, and performance assessment. The teaching quality evaluation index system’s concepts have been written forth in the literature [20]. The five aspects of teaching design, teaching methodologies, teaching basic skills, teaching attitude, and teaching outcome are the first-level indicators. For each of the five first-level indications, the AHP is used to determine which indicators have the most weight. There are difficulties with the current classroom teaching quality evaluation index system, and a new set of classroom teaching quality evaluation indexes are proposed in the literature [21]. Among the first-level indicators of this system’s evaluation are the goals of instruction as well as the techniques and content used to convey those goals. Teachers’ teaching quality has been evaluated using the fuzzy assessment method, as proposed in the literature [22]. Examples demonstrate the effectiveness and practicality of this method, and the first-level indicators for the evaluation of the teaching quality of teachers, including teaching level, teaching method, teaching attitude, teaching effect, and teaching and educating people, are put forward. An index system including experts, leaders, and students as evaluation subjects has been established in the literature [23] to study the application of multi-level fuzzy mathematics comprehensive assessment in classroom teaching quality evaluation.A vast number of decomposition and complete training of instructors’ classroom teaching and teaching behaviour strategies was designed by literature [24]. And created a technical evaluation index system and an index analysis of instructors’ classroom teaching behaviour that can be used by teachers at all levels, from kindergarten to university. Classroom teaching audio language technology, classroom teaching tangible language technology, classroom teaching logic technology, classroom teaching management technology are the first-level indicators of teachers’ classroom teaching evaluation. Literature [25] analyzes the theoretical basis of formulation of evaluation indicators from three aspects: the formulation principle, evaluation content, and evaluation subject of classroom teaching quality evaluation indicators. It thinks that evaluation indicators should place a strong emphasis on application, science, and comprehensiveness. With a total of 35 questions in five categories, literature [26] has developed indicators to distinguish teachers’ teaching quality. The five dimensions are teachers’ teaching skills, knowledge, style, personality, and the teacher-student relationship. Literature [27] pointed out that the newly revised evaluation index system fully pays attention to the lack of evaluation items from the perspective of pedagogy. English classroom teaching is evaluated using a mathematical model based on the evaluation methods used in the literature [28]. It attempts a more scientific technique of evaluating the quality of English classroom teaching using quantitative measurements that combines qualitative and quantitative methods. American teachers’ classroom teaching is evaluated according to a new set of standards that incorporates the teacher-student interaction into the evaluation process, making teaching evaluation more rational. Literature [30] believes that the use of formative assessment in English classroom teaching can effectively make up for some deficiencies in English classroom teaching evaluation. Literature [31–33] combines the types and standards of teaching evaluation and various modern educational evaluation concepts and expounds on evaluation methods.


## 3. Method

To evaluate the quality of English classrooms, this work first uses AHP to assign weights to each indicator and then uses the formed sample data to train the BP network. At the same time, the improved GA algorithm is utilized to optimize BP and improve evaluation performance for English classroom quality.


### 3.1. English Classroom Quality Evaluation Index System

This section examines the interplay between the teaching, learning, and administration spheres and devises an index for assessing the standard of an English classroom. It is possible to break down the English classroom teaching system into three primary level subsystems and twelve secondary level subsystems. An evaluation index system for English classroom quality, as presented in Table1, is built based on this information.Table 1
The framework of system evaluation index system.
First-level indexSecond-level indexTeaching subsystemTeaching attitudeTeaching contentTeaching methodTeaching effectLearning subsystemLearning managementLearning processLearning satisfactionManagement subsystemSocial needsTraining programFacility servicesTeaching evaluationStudy and employment


### 3.2. AHP

AHP refers to a simple decision-making method that decomposes the relevant factors that have an impact on decision-making into three levels: target, criterion and program level, and uses the relationship between these factors to carry out quantitative and qualitative analysis. This method is widely used in multi-level and multi-objective comprehensive decision-making and is used to select the optimal mathematical method from the candidate programs affected by multiple factors. The AHP is regarded as a system from the impact factor to the entire decision-making goal, and the goal is decomposed layer by layer to form multiple goals or criteria, which are subsequently decomposed into multiple index levels. The target elements at the same level are compared in pairs to quantify the importance of the two elements, and then the eigenvector judgment matrix is established according to the comparison results. Then, the judgment matrix of the feature vector is established, and the weight ratio of the elements of this level compared to the elements of the previous level is calculated respectively. Then, the weight ratio of each element in the final target layer is calculated by weighting. Generally speaking, the scheme with the largest final weight ratio is selected as the optimal scheme. Because AHP can quantify subjective comparisons and can be used to solve decision-making problems that are difficult to quantitatively describe, it is a feasible choice in English classroom quality assessment.Hierarchical structure enables the core elements of AHP to reflect the mutual influence between elements. In the minds of different decision-makers, each element in the criterion layer has different weights when measured relative to the target. Since each person has a different perception of the degree of importance when comparing elements pairwise, it is necessary to construct a pairwise judgment matrix to comprehensively judge the importance of elements. The importance and assignment of matrix elements are illustrated in Table2.Table 2
Importance level and scale value.
ScaleMeaning1Both are equally important3The former is slightly strong5The former is strong7The former is obviously strong9The former is absolutely strong2, 4, 6, 8The effect of the former is between above two adjacent levels1, 1/2, …, 1/9The ratio of influence is exactly the reciprocal ofaijIf there are too many elements in pairwise comparison, people’s subjective cognition will be affected and the result of judgment will be affected. It can be known from historical experience that it is more appropriate to compare the number of elements within the range of 7 ± 2, and with 9 as the upper limit, and it is suitable to use a scale of 1–9 to indicate the difference between elements. When doing pairwise comparisons, it is usually necessary to maken(n−1)/2 judgments in order to obtain more information. In addition, in order to obtain a reasonable ranking, repeated comparisons can be made through different aspects.In the judgment matrix obtained by comparing the elements pairwise, it is necessary to calculate the importance order of the elements related to the element in the previous layer. Hierarchical single ordering is the name given to this method. The pairwise judgment matrix’s eigenvalues and eigenvectors are calculated using single-level sorting. To calculate the eigenvalues and eigenvectors, you need to use the formula:(1)AW=λmaxW.The pairwise judgment matrix’s eigenvector and maximal eigenvalue can be calculated using the normalized summation approach or the root method. The normalized summation method is employed to arrive at the results in this research. Step-by-step instructions for the calculation are as follows:(1)
Ensure that the total of the entries in each column of the judgment matrix equals 1.(2)
After normalizing each column, the judgment matrix is averaged by row.(3)
The weight vector is obtained by normalizing the vector.(4)
Calculation is used to determine the maximum characteristic root of the judgment matrix.Because the important order of the judgment matrix needs to be transmitted according to a certain logic. But in fact, it may happen thatC1 is more important than C2, C2 is more important than C3 , and C3 is more important than C1 . Therefore, it is necessary to carry out a consistency check on the conclusion that violates transitivity. If the test result is within a reasonable range, the matrix has no logical problem. If it is not within a reasonable range, it means that the matrix needs to be modified. Based on the above reasons, it is necessary to perform a consistency check on the judgment matrix provided by the decision-maker to determine whether the judgment matrix meets the requirements. The specific inspection steps are as follows.UsingCI to measure the deviation of the eigenvectors of the judgment matrix from the scale, the consistency index CI is calculated as follows:(2)CI=λmax−nn−1.The mean of the random consistency metric means RI. For the RI mean, multiple replicates of the eigenvalues of the matrix are judged randomly.By calculating the ratio ofCI value of judgment matrix A to RI, it is the consistency ratio CR. When the consistency ratio CR is less than or equal to 0.1, it is considered the degree of inconsistency of the judgment matrix is acceptable, and the test is passed. When the consistency and consistency ratio, the consistency ratio CR is greater than or equal to 0.1, it is considered inconsistency of the judgment matrix has not passed the test, and the judgment matrix needs to be properly revised until the test is passed. The consistency ratio CR is calculated as follows:(3)CR=CIRI.Finally, the total sorting of the layers is carried out, which is a layer-by-layer calculation. First, the weights of each index of the criterion layer to the target layer are calculated, and then the weight of each index of the scheme layer relative to the target layer is calculated.


### 3.3. BP Network

BP network can simulate human brain thinking, self-organized learning, and automatically discover and adapt to environmental laws. It trains the model according to the reverse transfer of the error, which is also called the error reverse learning algorithm.Input, output, and a hidden layer are all part of the BP neural network’s three-layer structure. One or more layers can be used to create the concealed layer. A three-layer BP neural network structural diagram is shown in Figure1. If there are N variables, then there are also N inputs in the model’s layer of assumptions. The neurons in a hidden layer are sometimes referred to as nodes, and a hidden layer may comprise several nodes. There can be one or more output variables depending on the output layer. The neurons in the upper and lower layers are interconnected, and information travels from the input layer to the output layer by layer. To lessen the difference between the expected output and actual output, each neuron in each output layer receives its input response, reverses feedback, and corrects the connections’ weights and limits layer by layer. Errors are constantly flagged and connected weights and thresholds are constantly adjusted to increase the response rate.Figure 1
The structure of BP network.


### 3.4. Improved BP Network with Improved GA

BP neural network still has its shortcomings, such as slow convergence speed, poor global optimization ability, and easily falling into local optimum. As a result, researching how to strengthen the BP network is critical. One of the most often used BP network optimization approaches is the classic GA-BP algorithm, which involves utilizing a genetic algorithm to optimize the BP network weight threshold and so improve the performance of the BP algorithm. The principle is to reduce the prediction error of the BP network as a guide. First, using the global optimization ability of the genetic algorithm, a better set of weights and thresholds are found in the entire solution space, and the local fitting space of the BP network is determined. Then the better weight threshold is assigned to the BP network as the initial weight threshold for the BP network to search for the optimal solution. This enables the BP network to find the set of optimal weight thresholds corresponding to the minimum prediction error around the optimal initial weight thresholds as the operation result of the BP network.The genetic algorithm is used to optimize the weights of the BP network, combining the former’s global optimization ability with the latter’s local optimization ability. This can speed up the convergence speed of the BP network and reduce the probability of the premature phenomenon of the BP algorithm. However, the traditional GA-BP algorithm does not fundamentally prevent the BP algorithm from falling into local extreme values. The reason is that the traditional genetic algorithm will appear the phenomenon of genetic degradation and the deterioration of population diversity, which weakens the overall optimization ability of the algorithm.Because the traditional GA-BP algorithm is prone to genetic degradation and population diversity degradation, this work presents two measures of genetic evolution monitoring mechanism and the alternating of life and death individuals to improve the traditional GA-BP algorithm’s performance.Genetic process monitoring and genetic result monitoring are two types of evolution monitoring. The goal of both is to determine whether genetic degeneration has occurred and to ensure that the genetic result is the best individual created during the entire genetic process. The genetic process monitoring uses the maximum individual fitnessBestFitness in each generation of the population as the standard to judge whether the genetic degradation occurs. Its judgment condition is(4)BestFitnessn+1<BestFitnessn.If the conditions are met, it means that the inheritance has not produced better individuals, and the inheritance is proceeding in the direction of degeneration. At this time, it is necessary to enable the elimination mechanism of dead individuals to increase the diversity of individuals in the population, reduce the probability of genetic degradation, and ensure that the inheritance proceeds in a favorable direction.The realization method of genetic result monitoring is to record the optimal individuals and their fitness in the whole genetic process asMinBestChrom and MinBestFitness, respectively, and record the genetic result and its fitness as EndChrom and EndFitness. Its judgment condition is(5)EndFitness<MinBestfFitness.If the conditions are met, it means that the genetic result is not the optimal individual produced in the whole genetic process. Therefore, it is necessary to replaceEndchrom with Minbestchrom as the final genetic result, and use it as the initial weight threshold of the BP neural network after decoding.The role of the interaction mechanism of life and death individuals is to enhance the global optimization ability of the algorithm by increasing the diversity of the population of individuals. Among them, dead individuals refer to individuals whose fitness is lower than the average fitness of the population in the descendant population with genetic degradation.N deceased individuals in the present offspring population are eliminated when genetic deterioration occurs. Simultaneously, N new individuals are formed at random and enriched in the population. Such a cycle of life and death individuals not only preserves the best members of the current population, but also increases the diversity of the population by introducing new members. It expands the search space of the algorithm and improves the global optimization ability of the algorithm.


### 3.5. Combination of AHP and IGA-BP

In this work, AHP is used for sample optimization of the IGA-BP network. First, the weights of each English classroom quality evaluation index are obtained based on the AHP strategy, and then the weights are combined with the original samples to generate new samples. The new samples are then used for training and testing of the BP network, and the IGA algorithm is used to optimize the BP network. This network is dubbed AHP-IGA-BP in this study, and its process is depicted in Figure2.Figure 2
The pipeline of AHP-IGA-BP.


## 3.1. English Classroom Quality Evaluation Index System

This section examines the interplay between the teaching, learning, and administration spheres and devises an index for assessing the standard of an English classroom. It is possible to break down the English classroom teaching system into three primary level subsystems and twelve secondary level subsystems. An evaluation index system for English classroom quality, as presented in Table1, is built based on this information.Table 1
The framework of system evaluation index system.
First-level indexSecond-level indexTeaching subsystemTeaching attitudeTeaching contentTeaching methodTeaching effectLearning subsystemLearning managementLearning processLearning satisfactionManagement subsystemSocial needsTraining programFacility servicesTeaching evaluationStudy and employment


## 3.2. AHP

AHP refers to a simple decision-making method that decomposes the relevant factors that have an impact on decision-making into three levels: target, criterion and program level, and uses the relationship between these factors to carry out quantitative and qualitative analysis. This method is widely used in multi-level and multi-objective comprehensive decision-making and is used to select the optimal mathematical method from the candidate programs affected by multiple factors. The AHP is regarded as a system from the impact factor to the entire decision-making goal, and the goal is decomposed layer by layer to form multiple goals or criteria, which are subsequently decomposed into multiple index levels. The target elements at the same level are compared in pairs to quantify the importance of the two elements, and then the eigenvector judgment matrix is established according to the comparison results. Then, the judgment matrix of the feature vector is established, and the weight ratio of the elements of this level compared to the elements of the previous level is calculated respectively. Then, the weight ratio of each element in the final target layer is calculated by weighting. Generally speaking, the scheme with the largest final weight ratio is selected as the optimal scheme. Because AHP can quantify subjective comparisons and can be used to solve decision-making problems that are difficult to quantitatively describe, it is a feasible choice in English classroom quality assessment.Hierarchical structure enables the core elements of AHP to reflect the mutual influence between elements. In the minds of different decision-makers, each element in the criterion layer has different weights when measured relative to the target. Since each person has a different perception of the degree of importance when comparing elements pairwise, it is necessary to construct a pairwise judgment matrix to comprehensively judge the importance of elements. The importance and assignment of matrix elements are illustrated in Table2.Table 2
Importance level and scale value.
ScaleMeaning1Both are equally important3The former is slightly strong5The former is strong7The former is obviously strong9The former is absolutely strong2, 4, 6, 8The effect of the former is between above two adjacent levels1, 1/2, …, 1/9The ratio of influence is exactly the reciprocal ofaijIf there are too many elements in pairwise comparison, people’s subjective cognition will be affected and the result of judgment will be affected. It can be known from historical experience that it is more appropriate to compare the number of elements within the range of 7 ± 2, and with 9 as the upper limit, and it is suitable to use a scale of 1–9 to indicate the difference between elements. When doing pairwise comparisons, it is usually necessary to maken(n−1)/2 judgments in order to obtain more information. In addition, in order to obtain a reasonable ranking, repeated comparisons can be made through different aspects.In the judgment matrix obtained by comparing the elements pairwise, it is necessary to calculate the importance order of the elements related to the element in the previous layer. Hierarchical single ordering is the name given to this method. The pairwise judgment matrix’s eigenvalues and eigenvectors are calculated using single-level sorting. To calculate the eigenvalues and eigenvectors, you need to use the formula:(1)AW=λmaxW.The pairwise judgment matrix’s eigenvector and maximal eigenvalue can be calculated using the normalized summation approach or the root method. The normalized summation method is employed to arrive at the results in this research. Step-by-step instructions for the calculation are as follows:(1)
Ensure that the total of the entries in each column of the judgment matrix equals 1.(2)
After normalizing each column, the judgment matrix is averaged by row.(3)
The weight vector is obtained by normalizing the vector.(4)
Calculation is used to determine the maximum characteristic root of the judgment matrix.Because the important order of the judgment matrix needs to be transmitted according to a certain logic. But in fact, it may happen thatC1 is more important than C2, C2 is more important than C3 , and C3 is more important than C1 . Therefore, it is necessary to carry out a consistency check on the conclusion that violates transitivity. If the test result is within a reasonable range, the matrix has no logical problem. If it is not within a reasonable range, it means that the matrix needs to be modified. Based on the above reasons, it is necessary to perform a consistency check on the judgment matrix provided by the decision-maker to determine whether the judgment matrix meets the requirements. The specific inspection steps are as follows.UsingCI to measure the deviation of the eigenvectors of the judgment matrix from the scale, the consistency index CI is calculated as follows:(2)CI=λmax−nn−1.The mean of the random consistency metric means RI. For the RI mean, multiple replicates of the eigenvalues of the matrix are judged randomly.By calculating the ratio ofCI value of judgment matrix A to RI, it is the consistency ratio CR. When the consistency ratio CR is less than or equal to 0.1, it is considered the degree of inconsistency of the judgment matrix is acceptable, and the test is passed. When the consistency and consistency ratio, the consistency ratio CR is greater than or equal to 0.1, it is considered inconsistency of the judgment matrix has not passed the test, and the judgment matrix needs to be properly revised until the test is passed. The consistency ratio CR is calculated as follows:(3)CR=CIRI.Finally, the total sorting of the layers is carried out, which is a layer-by-layer calculation. First, the weights of each index of the criterion layer to the target layer are calculated, and then the weight of each index of the scheme layer relative to the target layer is calculated.


## 3.3. BP Network

BP network can simulate human brain thinking, self-organized learning, and automatically discover and adapt to environmental laws. It trains the model according to the reverse transfer of the error, which is also called the error reverse learning algorithm.Input, output, and a hidden layer are all part of the BP neural network’s three-layer structure. One or more layers can be used to create the concealed layer. A three-layer BP neural network structural diagram is shown in Figure1. If there are N variables, then there are also N inputs in the model’s layer of assumptions. The neurons in a hidden layer are sometimes referred to as nodes, and a hidden layer may comprise several nodes. There can be one or more output variables depending on the output layer. The neurons in the upper and lower layers are interconnected, and information travels from the input layer to the output layer by layer. To lessen the difference between the expected output and actual output, each neuron in each output layer receives its input response, reverses feedback, and corrects the connections’ weights and limits layer by layer. Errors are constantly flagged and connected weights and thresholds are constantly adjusted to increase the response rate.Figure 1
The structure of BP network.


## 3.4. Improved BP Network with Improved GA

BP neural network still has its shortcomings, such as slow convergence speed, poor global optimization ability, and easily falling into local optimum. As a result, researching how to strengthen the BP network is critical. One of the most often used BP network optimization approaches is the classic GA-BP algorithm, which involves utilizing a genetic algorithm to optimize the BP network weight threshold and so improve the performance of the BP algorithm. The principle is to reduce the prediction error of the BP network as a guide. First, using the global optimization ability of the genetic algorithm, a better set of weights and thresholds are found in the entire solution space, and the local fitting space of the BP network is determined. Then the better weight threshold is assigned to the BP network as the initial weight threshold for the BP network to search for the optimal solution. This enables the BP network to find the set of optimal weight thresholds corresponding to the minimum prediction error around the optimal initial weight thresholds as the operation result of the BP network.The genetic algorithm is used to optimize the weights of the BP network, combining the former’s global optimization ability with the latter’s local optimization ability. This can speed up the convergence speed of the BP network and reduce the probability of the premature phenomenon of the BP algorithm. However, the traditional GA-BP algorithm does not fundamentally prevent the BP algorithm from falling into local extreme values. The reason is that the traditional genetic algorithm will appear the phenomenon of genetic degradation and the deterioration of population diversity, which weakens the overall optimization ability of the algorithm.Because the traditional GA-BP algorithm is prone to genetic degradation and population diversity degradation, this work presents two measures of genetic evolution monitoring mechanism and the alternating of life and death individuals to improve the traditional GA-BP algorithm’s performance.Genetic process monitoring and genetic result monitoring are two types of evolution monitoring. The goal of both is to determine whether genetic degeneration has occurred and to ensure that the genetic result is the best individual created during the entire genetic process. The genetic process monitoring uses the maximum individual fitnessBestFitness in each generation of the population as the standard to judge whether the genetic degradation occurs. Its judgment condition is(4)BestFitnessn+1<BestFitnessn.If the conditions are met, it means that the inheritance has not produced better individuals, and the inheritance is proceeding in the direction of degeneration. At this time, it is necessary to enable the elimination mechanism of dead individuals to increase the diversity of individuals in the population, reduce the probability of genetic degradation, and ensure that the inheritance proceeds in a favorable direction.The realization method of genetic result monitoring is to record the optimal individuals and their fitness in the whole genetic process asMinBestChrom and MinBestFitness, respectively, and record the genetic result and its fitness as EndChrom and EndFitness. Its judgment condition is(5)EndFitness<MinBestfFitness.If the conditions are met, it means that the genetic result is not the optimal individual produced in the whole genetic process. Therefore, it is necessary to replaceEndchrom with Minbestchrom as the final genetic result, and use it as the initial weight threshold of the BP neural network after decoding.The role of the interaction mechanism of life and death individuals is to enhance the global optimization ability of the algorithm by increasing the diversity of the population of individuals. Among them, dead individuals refer to individuals whose fitness is lower than the average fitness of the population in the descendant population with genetic degradation.N deceased individuals in the present offspring population are eliminated when genetic deterioration occurs. Simultaneously, N new individuals are formed at random and enriched in the population. Such a cycle of life and death individuals not only preserves the best members of the current population, but also increases the diversity of the population by introducing new members. It expands the search space of the algorithm and improves the global optimization ability of the algorithm.


## 3.5. Combination of AHP and IGA-BP

In this work, AHP is used for sample optimization of the IGA-BP network. First, the weights of each English classroom quality evaluation index are obtained based on the AHP strategy, and then the weights are combined with the original samples to generate new samples. The new samples are then used for training and testing of the BP network, and the IGA algorithm is used to optimize the BP network. This network is dubbed AHP-IGA-BP in this study, and its process is depicted in Figure2.Figure 2
The pipeline of AHP-IGA-BP.


## 4. Experiment

In the experimental section, we explained the determination for index weight with AHP, and evaluation on AHP-IGA-BP in detail.


### 4.1. Determination for Index Weight with AHP

Figure3 depicts the hierarchical structure model for evaluating English classroom quality that was developed in this study.Figure 3
AHP model of English classroom quality.The model is divided into three layers. The top layer is the target layer A. The middle layer corresponds to the first-level index layer B, namely teacher teaching B1, student learning B2, and teaching management B3. The bottom layer is the measure layer corresponding to the second-level indicator layer C. Teacher teaching B1 includes teaching attitude C1, teaching content C2, teaching method C3, and teaching effect C4. Student learning B2 includes learning management C5, learning process C6, and learning satisfaction C7. Teaching management B3 includes social needs C8, training programs C9, facility services C10, teaching evaluation C11, and the employment rate for further studies C12.After that, the judgment matrix is constructed, and then the hierarchical single sorting is performed and the consistency of the matrix is judged. Then perform hierarchical total sorting and consistency check. Finally, the weights of the first-level indicators and the weights of the second-level indicators are obtained. The experimental results are shown in Table3 and Table 4.Table 3
The weights of the first-level indicators.
LevelB1B2B3Weight0.540.160.30Table 4
The weights of the second-level indicators.
LevelC1C2C3C4C5C6C7C8C9C10C11C12Weight0.040.260.170.080.090.050.020.040.150.020.060.02Combine these weights with the original data to construct new indicator data. Then it is used for the training of the AHP-IGA-BP network to complete the efficient evaluation of English classroom quality.


### 4.2. Evaluation on AHP-IGA-BP

First, the training process of the network is evaluated to determine whether it can achieve convergence performance. Figure4 shows the training loss analysis of this network.Figure 4
The training loss.Obviously, the network loss lowers as the number of iterations grows at the start of training. The loss is not minimized once the number of training times exceeds a particular threshold. This shows that the network has reached a state of convergence, which can also verify the feasibility of the AHP-IGA-BP network design in this work.Second, this work applies the AHP strategy to the optimization of English classroom quality assessment. To verify the effectiveness of this strategy, this work conducts comparative experiments to compare the evaluation performance without and with AHP, respectively. The experimental results are illustrated in Figure5.Figure 5
Evaluation of AHP.It is able to observe that the AHP technique provides the best results. Compared with not using AHP, 1.9% precision improvement and 1.3% recall improvement can be obtained after using this strategy. This checks the accuracy of this work, which was optimized with AHP.Third, this work applies the IGA strategy to the optimization of English classroom quality assessment. To verify the effectiveness of this strategy, this work conducts comparative experiments to compare the evaluation performance with GA and with IGA, respectively. The experimental results are illustrated in Figure6.Figure 6
Evaluation of IGA.It is easy to observe that the IGA technique provides the greatest results. Compared with using GA, 1.3% precision improvement and 1.0% recall improvement can be obtained after using this strategy. This checks the accuracy of this work by optimising it using enhanced GA.Finally, the impact of several hidden layers on the assessment of English classroom quality is examined in this study. In the BP network, the hidden layer is not static. To mine the optimal hidden layer nodes, this work conducts a comparative experiment on the number of different hidden layer nodes. The results are shown in Figure7.Figure 7
Evaluation of hidden layer.When the number of nodes in the hidden layer is set to 12, it is evident that the highest precision and recall may be reached. In the beginning, as the number of nodes increases, the evaluation performance gradually increases. However, as the number of nodes grows, the performance will steadily decline after reaching a high.


## 4.1. Determination for Index Weight with AHP

Figure3 depicts the hierarchical structure model for evaluating English classroom quality that was developed in this study.Figure 3
AHP model of English classroom quality.The model is divided into three layers. The top layer is the target layer A. The middle layer corresponds to the first-level index layer B, namely teacher teaching B1, student learning B2, and teaching management B3. The bottom layer is the measure layer corresponding to the second-level indicator layer C. Teacher teaching B1 includes teaching attitude C1, teaching content C2, teaching method C3, and teaching effect C4. Student learning B2 includes learning management C5, learning process C6, and learning satisfaction C7. Teaching management B3 includes social needs C8, training programs C9, facility services C10, teaching evaluation C11, and the employment rate for further studies C12.After that, the judgment matrix is constructed, and then the hierarchical single sorting is performed and the consistency of the matrix is judged. Then perform hierarchical total sorting and consistency check. Finally, the weights of the first-level indicators and the weights of the second-level indicators are obtained. The experimental results are shown in Table3 and Table 4.Table 3
The weights of the first-level indicators.
LevelB1B2B3Weight0.540.160.30Table 4
The weights of the second-level indicators.
LevelC1C2C3C4C5C6C7C8C9C10C11C12Weight0.040.260.170.080.090.050.020.040.150.020.060.02Combine these weights with the original data to construct new indicator data. Then it is used for the training of the AHP-IGA-BP network to complete the efficient evaluation of English classroom quality.


## 4.2. Evaluation on AHP-IGA-BP

First, the training process of the network is evaluated to determine whether it can achieve convergence performance. Figure4 shows the training loss analysis of this network.Figure 4
The training loss.Obviously, the network loss lowers as the number of iterations grows at the start of training. The loss is not minimized once the number of training times exceeds a particular threshold. This shows that the network has reached a state of convergence, which can also verify the feasibility of the AHP-IGA-BP network design in this work.Second, this work applies the AHP strategy to the optimization of English classroom quality assessment. To verify the effectiveness of this strategy, this work conducts comparative experiments to compare the evaluation performance without and with AHP, respectively. The experimental results are illustrated in Figure5.Figure 5
Evaluation of AHP.It is able to observe that the AHP technique provides the best results. Compared with not using AHP, 1.9% precision improvement and 1.3% recall improvement can be obtained after using this strategy. This checks the accuracy of this work, which was optimized with AHP.Third, this work applies the IGA strategy to the optimization of English classroom quality assessment. To verify the effectiveness of this strategy, this work conducts comparative experiments to compare the evaluation performance with GA and with IGA, respectively. The experimental results are illustrated in Figure6.Figure 6
Evaluation of IGA.It is easy to observe that the IGA technique provides the greatest results. Compared with using GA, 1.3% precision improvement and 1.0% recall improvement can be obtained after using this strategy. This checks the accuracy of this work by optimising it using enhanced GA.Finally, the impact of several hidden layers on the assessment of English classroom quality is examined in this study. In the BP network, the hidden layer is not static. To mine the optimal hidden layer nodes, this work conducts a comparative experiment on the number of different hidden layer nodes. The results are shown in Figure7.Figure 7
Evaluation of hidden layer.When the number of nodes in the hidden layer is set to 12, it is evident that the highest precision and recall may be reached. In the beginning, as the number of nodes increases, the evaluation performance gradually increases. However, as the number of nodes grows, the performance will steadily decline after reaching a high.


## 5. Conclusion

English has become an important tool and efficient method to enhance international competitiveness, and the society’s demand for compound talents with high English proficiency and international communication skills will also increase substantially. English education has run through all levels of education and has become an important part of civic education around the world. English, as a mandatory course in school, serves a critical role in improving pupils’ educational quality, broadening their knowledge, and nurturing compound skills. As a result, determining how to accurately assess the classroom quality of English instruction has become a hot topic. This study provides an AHP-based English classroom quality optimization model that is combined with a neural network in this study. The specific work is as follows. (1) Construct an AHP-based English classroom quality level model and index system. Using AHP to quantitatively analyze the English classroom quality evaluation indicators, obtain complete English classroom quality index weights, and convert subjective information into objective quality index weight data. (2) The original quality evaluation index data and the quality weight data obtained through AHP are used as the training samples of the neural network. Construct the corresponding BP neural network model to effectively evaluate the English classroom quality. (3) To improve the model’s performance, the IGA-BP network is proposed, and the network’s weights and thresholds are effectively initialized using the improved GA algorithm [29].



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*Source: 2502377-2022-05-10.xml*