# Evaluation of the Online Music Flipped Classroom under Artificial Intelligence and Wireless Networks

Authors: Meng Tan; Yiheng Cao
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The study aims to explore the online music flipped classrooms based on artificial intelligence (AI) and wireless networks. A backpropagation neural network (BPNN) algorithm optimized by the genetic algorithm (GA) is proposed to evaluate the teaching quality of music flipped classrooms and analyze the problems in the current teaching mode. First, an evaluation index system is established for online music flipped classrooms; second, a questionnaire is designed according to the index system. After the data are collected, the GA-BPNN evaluation model is used to evaluate the teaching quality of the music flipped classrooms. Finally, the model’s performance is evaluated based on the forecast accuracy compared with the model implemented only by the BPNN. The simulation results show that the GA-BPNN evaluation model can effectively evaluate the teaching quality of flipped classrooms, and the evaluation results are objective and accurate. The model overcomes the shortcomings of traditional evaluation methods. The study has great practical significance and provides a basis for improving the teaching quality of online flipped classrooms.


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## Body

## 1. Introduction

Since the 21st century, many new, popular, and convenient technologies have appeared, of which artificial intelligence (AI) penetrates people’s lives profoundly and rapidly. AI is added to the curriculum in colleges and universities, realizing the integration of AI and education and making intelligent programming gradually enter education [1]. AI is the main content in computer technology, and it is comprehensive and cross-cutting [2]. Research on big data and AI is inseparable from the wireless local area network (WLAN), which promotes the deep integration of megadata, AI, education, and the training industry [3]. Therefore, teachers tend to use flipped classrooms to keep up with the pace of the times. However, flipped classrooms are changing the roles between teachers and students inside and outside the classroom. The primary goal of flipped classrooms is to realize the “student-centered” classroom teaching mode [4]. Thus, students can learn knowledge autonomously rather than passively, and students’ autonomous learning ability can be strengthened, thereby improving the teaching quality of flipped classrooms [5], but there are still some problems and constraints in implementing the plan. In this case, AI appears and is applied to all aspects of human life. It has the unique characteristics of deep learning, cross-border integration, man-machine cooperation, and group intelligence. The relevant literature on AI is increasing when it is applied to education, and significant breakthroughs have been made.At present, many schools are actively taking the teaching mode of flipped classrooms, and teachers and scholars in China and foreign countries are also turning the flipped classroom into practice. Cheng et al. argued that flipped classrooms could improve teaching efficiency, promote the relationship between teachers and students, arouse students’ interest in the study, and enable them to study independently compared with traditional classrooms [6]. However, the research on teaching quality evaluation of flipped classrooms is rare, such as the construction of the Evaluation Index System (EIS) of the teaching quality of flipped classrooms [7, 8]. Goedhart et al. used a fuzzy analytic hierarchy process (AHP) to evaluate the teaching quality of flipped classrooms [9]. The current research on the evaluation of the teaching quality of flipped classrooms only stays in constructing the EIS of the teaching quality of flipped classrooms and is rarely on the weight of indexes in the system [10]. AI in neural networks is an emerging discipline in information science and technology. It is an information processing system imitating the structure and function of the human brain. Neural networks can overcome the shortcomings of the traditional methods and become an effective way to evaluate the quality of classroom teaching because they can simulate human thinking and perform nonlinear transformation and self-learning. However, they also lead to a convergence speed, fall into local minimum, and cause many prediction errors. A teaching quality evaluation method based on the AHP and neural networks is proposed in response to the above problems. A teaching quality evaluation model is established using neural networks. The simulation results show that the method simplifies the structure of neural networks’ systems and improves the evaluation accuracy of classroom teaching. Wang and Chen used neural networks and FPGA to teach students music, test what the students have learned in music courses, and evaluate the teaching quality [11]. Li developed an English learning system based on the improved hierarchical neural network system [12]. Neural networks are widely used in education. AI + educational applications include an intelligent tutor system, an educational robot, an intelligent evaluation system, and an intelligent agent. The intelligent tutor system is one of the critical applications of AI in education.The music flipped classrooms are set as the research object. After teaching quality is analyzed, the EIS of the teaching quality model of music flipped classrooms under the backpropagation neural network (BPNN) based on the improved and optimized genetic algorithm (GA) is implemented. The simulation experiment is carried out by MATLAB 2013b. This model can be used to evaluate the teaching quality of music flipped classrooms more effectively, scientifically, and objectively, laying a foundation for the reform and the improvement of the teaching quality of flipped classrooms. The technology with AI as the core has a significant influence on education. It can solve the contradiction and reconstruct the development pattern of education.


## 2. Materials and Methods

### 2.1. BPNN

The short form of the BP neural network is BPNN. It is formed by adding a BP algorithm to the structure of the feedforward network. It has input and output nodes and one or more hidden layer nodes. It is a forward network with multiple layers and unidirectional propagation [13, 14]. The basic idea of this method is to reduce the mean square error (MSE) between the actual outputs and search the output node using gradient search technology.Figure1 shows the structure of a two-layer BPNN, which has only one hidden layer.Figure 1
Structure of the BPNN.The specific expression is(1)WR×S=W0,0W0,1⋯W0,S−1W1,0W1,1⋯W1,S−1⋮⋮⋮⋮WR−1WR−1⋯WR−1,S−1,(2)WS×N=W0,0W0,1⋯W0,S−1W1,0W1,1⋯W1,S−1⋮⋮⋮⋮WR−1WR−1⋯WR−1,S−1.The matrixV and W are the weight matrix of the output and hidden layers and the weight matrix of the hidden layer and the input layer.If the thresholds of the neurons in the hidden layer and output layer areα and β, respectively, the expressions are as follows:(3)α=α0,…,αS−1,(4)β=β0,…,βS−1.The input vector is set asX, and its expression is as follows:(5)X=X0,X1,…,XR−1.The network input of the hidden layer is set asnetj, and its expression is as follows:(6)netj=∑i=0R−1XiWi,j−αj,j=0,1…,S−1.In the above equation,X is the input vector, W is the connection weight matrix between the hidden layer and the input layer, and α is the neuron threshold in the hidden layer.The output of the hidden layer iszj, and its expression is as follows:(7)zj=gnetj,j=0,1…,S−1.The output of the output layer is set asyk, and its expression is as follows:(8)yk=g∑j=0S−1ZjVj,k−βkk=0,1,…,N−1.In the above equation,V is the weight matrix of the output layer and the hidden layer, β is the neuron threshold of the output layer, and zj is the net output of the hidden layer.If the output vector isY, the expression is as follows:(9)Y=y0,y1,…,yN−1.The corresponding expected output value is set asO, and its expression is(10)O=o0,o1,…,oN−1.The working process of the BPNN is shown in Figure2.Figure 2
Flowchart of the BPNN.


### 2.2. Strengths and Weaknesses of the BPNN

#### 2.2.1. Strengths

(1) The nonlinear mapping of inputs and outputs is realized; that is, neural networks can be used to approach any nonlinear continuous functions, which are suitable for the multidimensional feature construction in data mining, and the BPNN can use the gradient descent algorithm to optimize the parameters and reduce errors [15]. (2) The BPNN has the generalization ability. (3) The BPNN can generate a network with few trained sample data, which can ensure certain accuracy in a particular range [16]. And the generalization ability of the BPNN is related to various parameters; the neural network can adjust the weights when the new data are trained. (4) It has different transfer functions. The difference between the BPNN model and other neuron models is their functions, such as pureline and logsig of sigmoid [17]. The output characteristics of the BPNN are determined by the features of the neurons in the last layer. For example, when the output of the entire network is any value, it means that the pureline function is used. However, when the output of the whole network is limited to a small range, it means that the sigmoid function is used [18].


#### 2.2.2. Weaknesses

(1) Local minimization: it is well known that gradient descent may produce a local minimum. A global minimum is needed, which makes the weights converge to the local minimum and makes the algorithm the local extremum, leading to the failure of the network training [19, 20]. (2) The convergence speed of the BPNN algorithm: because the optimized objective function is very complex, there is a flat area when the neuron output is close to 0 or 1. Because the weight error changes slightly, the training process will almost pause [21, 22]. (3) The design of the structure of networks: there is no theoretical guidance for selecting the number of hidden layers and the number of nodes in each hidden layer. (4) The contradiction between the BPNN training ability and prediction ability: usually, the predictive ability will change with the change of the training ability. The predictive ability will also be affected accordingly when the training ability becomes stronger or weaker [23]. However, the prediction ability will increase with the decline of training ability since the learning model cannot reflect the law contained in the sample. Therefore, the degree is essential in practice.


### 2.3. GA

GA is also known as an evolutionary algorithm. Its main advantage is that there is no restriction on function continuity and derivation, and it can directly operate on structural objects. Therefore, GA is different from other algorithms for finding the optimal solution [24]. Another advantage is that GA can realize probabilistic optimization without using specific rules and adjusting the search direction [25].GA comprises a coding process, a fitness function, a genetic operator, and operation parameters. The encoding process is that GA abstracts the population into a string of specific symbols according to a mechanism and compiles several individuals into a “chromosome” to select the optimal solution according to the change of genetic operators. There are many main methods for encoding, and the most common ones are binary format encoding and accurate number encoding. Fitness is used to measure the adaptability of the population to individuals. The fitness function can measure whether an individual reaches the optimal adaptation. Usually, it is necessary to select the appropriate fitness function according to the specific requirements. The genetic operator is the genetic process of distribution and combination by imitating the individual genes in biological evolution, including selection, crossover, and mutation. In operation, it is necessary to preset the initial values such as population size and iteration times and specify the occurrence probability of each genetic operator. If the control parameters cannot be preset, the optimal parameter values need to be determined through the one-by-one test, improving the accuracy of the calculation. The calculation process of GA is shown in Figure3.Figure 3
Calculation process of GA.GA is widely used in many scientific fields because it has strong robustness. It is not dependent on the issue, gradient information, or other auxiliary knowledge. It provides a general framework for solving complex system problems [26, 27] and can overcome the disadvantages of the BPNN effectively. Therefore, it can optimize and improve the BPNN and help establish the evaluation model of the teaching quality of music flipped classrooms.


### 2.4. Implementation of the Evaluation Model Based on the Optimized BPNN

#### 2.4.1. Index Setting

The implementation of the teaching quality evaluation model involves the basic elements in establishing the EIS of the teaching quality of flipped classrooms. It needs to refer to and compare the opinions of some experienced teachers and experts in flipped classrooms and the management department. Also, much relevant literature and other evaluation index systems of the teaching quality of music flipped classrooms should be considered. The established EIS of teaching quality includes four first-grade indexes: teaching resources, teaching process, teaching materials, and overall evaluation systems. They are numbered W1–W24, as shown in Table1. The teaching quality evaluation results of the music flipped classroom are divided into five grades: A (excellent) is [0.9, 1], B (good) is [0.8, 0.89], C (medium) is [0.7, 0.79], D (passing) is [0.6, 0.69], and E (failing) is [0, 0.59].Table 1
EIS of the teaching quality of music flipped classrooms.
First-grade indexesSecond-grade indexesNumberTeaching resourcesSatisfaction with existing teaching resourcesW1Satisfaction of the music flipped classroom in existing teaching placesW2Satisfaction with the information exchange platform (networks, QQ group, and WeChat group)W3Teaching processSatisfaction with teachers’ working ability and classroom teaching contentW4Satisfaction with teachers’ performance and attitudesW5Satisfaction with teaching courseware, video, and specimenW6The clarity of the teaching objectives, learning objectives, and tasksW7Satisfaction with the number and matching of team membersW8Satisfaction with the teaching progressW9Teaching effectSatisfaction with the classroom discussion atmosphereW10Satisfaction with the enthusiasm and activity of the groupW11Flipped classroom provides a better and effective teaching modeW12Satisfaction with the knowledge and skills in the flipped classroomW13Overall evaluationSatisfaction with the teaching ways of flipped classroomsW14The teaching ways of flipped classrooms are more attractive and arouse students’ interest in the studyW15The teaching ways of flipped classrooms make students learn more activelyW16Better interaction with teachers and classmatesW17Teachers are more timely in answering students’ questionsW18Flipped classrooms are based on preclass learning, and the classroom teaching environment design is more targetedW19Learning effect is better than traditional teachingW20Flipped classrooms help to improve students’ comprehensive qualityW21Teachers provide rich courseware, specimen, and videosW22Courseware, videos, and the specimen can effectively support students for autonomous learningW23Flipped classrooms promote teaching reform of music coursesW24


#### 2.4.2. Data Acquisition

The questionnaire is designed according to the four first-grade indexes and 24 second-grade indexes and the overall evaluation system of the teaching quality of music flipped classrooms. The questionnaires are sent to the students, and their answersg are scored. After the scores are calculated, the results are collected, and the experimental data are obtained. Since the total score is 100, the scoring data of students must be standardized by the maximum and minimum method to facilitate the experiment, and the normalized value should be between [0, 1]. The reason for choosing the method is that it can effectively save the data original meaning. The normalized value is calculated by the following equation:(11)X=I−IminImax−Imin.X is the normalized score, namely, the input value of the BPNN, I is the unprocessed score, Imin is the minimum teaching quality score, and Imax is the maximum value. 400 sets of experimental data are standardized. 360 data are used to train the model, and the remaining 40 sets of data are used for testing to detect the model’s performance better, obtaining the optimized structure of the BPNN. The students’ evaluation data are shown in Table 2. 1–24 are listed as input data, and the last data are the training output.Table 2
Standardized evaluation sample data of students.
Index numberSample number123456789101112W10.900.800.760.790.870.860.750.990.960.910.720.86W20.950.920.600.730.910.920.950.600.550.540.870.80W30.590.920.500.960.980.790.830.860.480.510.760.76W40.860.890.480.760.840.890.860.840.930.820.910.87W50.870.780.850.890.930.790.940.810.630.810.910.87W60.920.810.840.890.870.830.690.780.820.790.870.90W70.940.910.810.780.890.910.690.560.860.730.870.83W80.870.670.940.850.910.870.810.780.740.910.820.79W90.940.880.790.650.780.870.910.950.870.900.770.96W100.890.900.760.870.890.900.910.880.740.690.680.79W110.710.690.670.890.910.540.730.910.890.690.740.78W120.860.780.810.480.620.810.590.870.910.940.810.86W130.920.820.430.780.790.910.870.580.900.430.600.79W140.850.870.890.810.480.970.570.900.930.720.700.74W150.910.690.890.870.680.800.590.490.790.890.860.82W160.830.880.680.790.890.490.810.700.910.870.790.91W170.870.690.790.890.690.780.940.780.810.790.890.85W180.930.650.900.980.870.790.780.870.890.900.860.88W190.890.780.680.890.890.690.820.760.730.780.580.64W200.910.560.890.910.760.870.870.910.890.790.870.58W210.780.710.780.870.890.790.790.890.690.640.690.69W220.880.680.790.790.780.640.690.940.830.910.780.80W230.890.900.820.920.820.800.680.870.610.920.790.91W240.890.820.900.830.790.910.760.840.720.860.890.83Results0.870.770.780.820.900.860.840.820.730.690.810.82


#### 2.4.3. Establishment and Optimization of the Model

The process of GA optimizing the BPNN is shown in Figure4. The most critical step in this process is to use the optimal global solution obtained by GA to realize the optimization objective, which reduces the complexity of the training process and optimizes the BPNN timely. The fitness calculation and selection operation procedures are described in detail.Figure 4
Flowchart of the BPNN optimized by GA.(1) Fitness calculation: the fundamental reason for determining the fitness function is that the evolution direction of GA is increasing the value of the fitness function. The error in the learning process is shown in equation (12), and the fitness function is shown in equation (13).(12)E=∑k=1p∑j=1iyjk−ojk2,(13)fitness=1E.In equations (12) and (13), p is the number of samples, E is the error, l is the number of output nodes, and yjk−ojk is the error of the kth sample relative to the jth output node.(2) Genetic operation: proportional selection or replication selection is employed, and it is the so-called roulette. The first step is to calculate an individual’s fitness value and its probability of being selected. The total fitness value is the probability of the selected individual during the selection process. The selection probability is calculated by the following equation:(14)zaj=faj∑j=1dfaj.faj is the fitness value of individual aj, zaj is the probability of an individual to be selected, and aj represents the probability that individual a in the group is selected.When each individual’s probability is calculated, it is time to confirm whether the determined individual can be inherited to the next generation. The selection is based on the size of the cumulative probability. The calculation equation is shown in the following equation:(15)qak=∑j=1kzaj.In equation (15), qak is the accumulation probability of individual ak. The next generation of new populations will be obtained after the previous step. Then, the fitness value is calculated, and the above steps are repeated. When the population is stable, the calculation process stops.GA adopts real code numbers, and the length of the actual number is 101. After the experiment, parameters are obtained, and they are shown in Table3.Table 3
Parameter settings.
NameParametersNameParametersInitial population size20Code length101Cross-probability0.6Evolution termination algebra100Mutation probability0.05Structure of the BPNN18-5-1Output layer transfer functionPurelineHidden layer transfer functionLogsigPrecision0.00001Maximum iteration1000When the model is implemented, the collected data are processed. 360 groups are selected to be trained, and the remaining 40 groups are tested. The input value of the network test is the data value of 24 secondary indexes contained in the 1–24 columns of each group of samples, namely, teaching resources, teaching process, teaching effectiveness, and overall evaluation.


## 2.1. BPNN

The short form of the BP neural network is BPNN. It is formed by adding a BP algorithm to the structure of the feedforward network. It has input and output nodes and one or more hidden layer nodes. It is a forward network with multiple layers and unidirectional propagation [13, 14]. The basic idea of this method is to reduce the mean square error (MSE) between the actual outputs and search the output node using gradient search technology.Figure1 shows the structure of a two-layer BPNN, which has only one hidden layer.Figure 1
Structure of the BPNN.The specific expression is(1)WR×S=W0,0W0,1⋯W0,S−1W1,0W1,1⋯W1,S−1⋮⋮⋮⋮WR−1WR−1⋯WR−1,S−1,(2)WS×N=W0,0W0,1⋯W0,S−1W1,0W1,1⋯W1,S−1⋮⋮⋮⋮WR−1WR−1⋯WR−1,S−1.The matrixV and W are the weight matrix of the output and hidden layers and the weight matrix of the hidden layer and the input layer.If the thresholds of the neurons in the hidden layer and output layer areα and β, respectively, the expressions are as follows:(3)α=α0,…,αS−1,(4)β=β0,…,βS−1.The input vector is set asX, and its expression is as follows:(5)X=X0,X1,…,XR−1.The network input of the hidden layer is set asnetj, and its expression is as follows:(6)netj=∑i=0R−1XiWi,j−αj,j=0,1…,S−1.In the above equation,X is the input vector, W is the connection weight matrix between the hidden layer and the input layer, and α is the neuron threshold in the hidden layer.The output of the hidden layer iszj, and its expression is as follows:(7)zj=gnetj,j=0,1…,S−1.The output of the output layer is set asyk, and its expression is as follows:(8)yk=g∑j=0S−1ZjVj,k−βkk=0,1,…,N−1.In the above equation,V is the weight matrix of the output layer and the hidden layer, β is the neuron threshold of the output layer, and zj is the net output of the hidden layer.If the output vector isY, the expression is as follows:(9)Y=y0,y1,…,yN−1.The corresponding expected output value is set asO, and its expression is(10)O=o0,o1,…,oN−1.The working process of the BPNN is shown in Figure2.Figure 2
Flowchart of the BPNN.


## 2.2. Strengths and Weaknesses of the BPNN

### 2.2.1. Strengths

(1) The nonlinear mapping of inputs and outputs is realized; that is, neural networks can be used to approach any nonlinear continuous functions, which are suitable for the multidimensional feature construction in data mining, and the BPNN can use the gradient descent algorithm to optimize the parameters and reduce errors [15]. (2) The BPNN has the generalization ability. (3) The BPNN can generate a network with few trained sample data, which can ensure certain accuracy in a particular range [16]. And the generalization ability of the BPNN is related to various parameters; the neural network can adjust the weights when the new data are trained. (4) It has different transfer functions. The difference between the BPNN model and other neuron models is their functions, such as pureline and logsig of sigmoid [17]. The output characteristics of the BPNN are determined by the features of the neurons in the last layer. For example, when the output of the entire network is any value, it means that the pureline function is used. However, when the output of the whole network is limited to a small range, it means that the sigmoid function is used [18].


### 2.2.2. Weaknesses

(1) Local minimization: it is well known that gradient descent may produce a local minimum. A global minimum is needed, which makes the weights converge to the local minimum and makes the algorithm the local extremum, leading to the failure of the network training [19, 20]. (2) The convergence speed of the BPNN algorithm: because the optimized objective function is very complex, there is a flat area when the neuron output is close to 0 or 1. Because the weight error changes slightly, the training process will almost pause [21, 22]. (3) The design of the structure of networks: there is no theoretical guidance for selecting the number of hidden layers and the number of nodes in each hidden layer. (4) The contradiction between the BPNN training ability and prediction ability: usually, the predictive ability will change with the change of the training ability. The predictive ability will also be affected accordingly when the training ability becomes stronger or weaker [23]. However, the prediction ability will increase with the decline of training ability since the learning model cannot reflect the law contained in the sample. Therefore, the degree is essential in practice.


## 2.2.1. Strengths

(1) The nonlinear mapping of inputs and outputs is realized; that is, neural networks can be used to approach any nonlinear continuous functions, which are suitable for the multidimensional feature construction in data mining, and the BPNN can use the gradient descent algorithm to optimize the parameters and reduce errors [15]. (2) The BPNN has the generalization ability. (3) The BPNN can generate a network with few trained sample data, which can ensure certain accuracy in a particular range [16]. And the generalization ability of the BPNN is related to various parameters; the neural network can adjust the weights when the new data are trained. (4) It has different transfer functions. The difference between the BPNN model and other neuron models is their functions, such as pureline and logsig of sigmoid [17]. The output characteristics of the BPNN are determined by the features of the neurons in the last layer. For example, when the output of the entire network is any value, it means that the pureline function is used. However, when the output of the whole network is limited to a small range, it means that the sigmoid function is used [18].


## 2.2.2. Weaknesses

(1) Local minimization: it is well known that gradient descent may produce a local minimum. A global minimum is needed, which makes the weights converge to the local minimum and makes the algorithm the local extremum, leading to the failure of the network training [19, 20]. (2) The convergence speed of the BPNN algorithm: because the optimized objective function is very complex, there is a flat area when the neuron output is close to 0 or 1. Because the weight error changes slightly, the training process will almost pause [21, 22]. (3) The design of the structure of networks: there is no theoretical guidance for selecting the number of hidden layers and the number of nodes in each hidden layer. (4) The contradiction between the BPNN training ability and prediction ability: usually, the predictive ability will change with the change of the training ability. The predictive ability will also be affected accordingly when the training ability becomes stronger or weaker [23]. However, the prediction ability will increase with the decline of training ability since the learning model cannot reflect the law contained in the sample. Therefore, the degree is essential in practice.


## 2.3. GA

GA is also known as an evolutionary algorithm. Its main advantage is that there is no restriction on function continuity and derivation, and it can directly operate on structural objects. Therefore, GA is different from other algorithms for finding the optimal solution [24]. Another advantage is that GA can realize probabilistic optimization without using specific rules and adjusting the search direction [25].GA comprises a coding process, a fitness function, a genetic operator, and operation parameters. The encoding process is that GA abstracts the population into a string of specific symbols according to a mechanism and compiles several individuals into a “chromosome” to select the optimal solution according to the change of genetic operators. There are many main methods for encoding, and the most common ones are binary format encoding and accurate number encoding. Fitness is used to measure the adaptability of the population to individuals. The fitness function can measure whether an individual reaches the optimal adaptation. Usually, it is necessary to select the appropriate fitness function according to the specific requirements. The genetic operator is the genetic process of distribution and combination by imitating the individual genes in biological evolution, including selection, crossover, and mutation. In operation, it is necessary to preset the initial values such as population size and iteration times and specify the occurrence probability of each genetic operator. If the control parameters cannot be preset, the optimal parameter values need to be determined through the one-by-one test, improving the accuracy of the calculation. The calculation process of GA is shown in Figure3.Figure 3
Calculation process of GA.GA is widely used in many scientific fields because it has strong robustness. It is not dependent on the issue, gradient information, or other auxiliary knowledge. It provides a general framework for solving complex system problems [26, 27] and can overcome the disadvantages of the BPNN effectively. Therefore, it can optimize and improve the BPNN and help establish the evaluation model of the teaching quality of music flipped classrooms.


## 2.4. Implementation of the Evaluation Model Based on the Optimized BPNN

### 2.4.1. Index Setting

The implementation of the teaching quality evaluation model involves the basic elements in establishing the EIS of the teaching quality of flipped classrooms. It needs to refer to and compare the opinions of some experienced teachers and experts in flipped classrooms and the management department. Also, much relevant literature and other evaluation index systems of the teaching quality of music flipped classrooms should be considered. The established EIS of teaching quality includes four first-grade indexes: teaching resources, teaching process, teaching materials, and overall evaluation systems. They are numbered W1–W24, as shown in Table1. The teaching quality evaluation results of the music flipped classroom are divided into five grades: A (excellent) is [0.9, 1], B (good) is [0.8, 0.89], C (medium) is [0.7, 0.79], D (passing) is [0.6, 0.69], and E (failing) is [0, 0.59].Table 1
EIS of the teaching quality of music flipped classrooms.
First-grade indexesSecond-grade indexesNumberTeaching resourcesSatisfaction with existing teaching resourcesW1Satisfaction of the music flipped classroom in existing teaching placesW2Satisfaction with the information exchange platform (networks, QQ group, and WeChat group)W3Teaching processSatisfaction with teachers’ working ability and classroom teaching contentW4Satisfaction with teachers’ performance and attitudesW5Satisfaction with teaching courseware, video, and specimenW6The clarity of the teaching objectives, learning objectives, and tasksW7Satisfaction with the number and matching of team membersW8Satisfaction with the teaching progressW9Teaching effectSatisfaction with the classroom discussion atmosphereW10Satisfaction with the enthusiasm and activity of the groupW11Flipped classroom provides a better and effective teaching modeW12Satisfaction with the knowledge and skills in the flipped classroomW13Overall evaluationSatisfaction with the teaching ways of flipped classroomsW14The teaching ways of flipped classrooms are more attractive and arouse students’ interest in the studyW15The teaching ways of flipped classrooms make students learn more activelyW16Better interaction with teachers and classmatesW17Teachers are more timely in answering students’ questionsW18Flipped classrooms are based on preclass learning, and the classroom teaching environment design is more targetedW19Learning effect is better than traditional teachingW20Flipped classrooms help to improve students’ comprehensive qualityW21Teachers provide rich courseware, specimen, and videosW22Courseware, videos, and the specimen can effectively support students for autonomous learningW23Flipped classrooms promote teaching reform of music coursesW24


### 2.4.2. Data Acquisition

The questionnaire is designed according to the four first-grade indexes and 24 second-grade indexes and the overall evaluation system of the teaching quality of music flipped classrooms. The questionnaires are sent to the students, and their answersg are scored. After the scores are calculated, the results are collected, and the experimental data are obtained. Since the total score is 100, the scoring data of students must be standardized by the maximum and minimum method to facilitate the experiment, and the normalized value should be between [0, 1]. The reason for choosing the method is that it can effectively save the data original meaning. The normalized value is calculated by the following equation:(11)X=I−IminImax−Imin.X is the normalized score, namely, the input value of the BPNN, I is the unprocessed score, Imin is the minimum teaching quality score, and Imax is the maximum value. 400 sets of experimental data are standardized. 360 data are used to train the model, and the remaining 40 sets of data are used for testing to detect the model’s performance better, obtaining the optimized structure of the BPNN. The students’ evaluation data are shown in Table 2. 1–24 are listed as input data, and the last data are the training output.Table 2
Standardized evaluation sample data of students.
Index numberSample number123456789101112W10.900.800.760.790.870.860.750.990.960.910.720.86W20.950.920.600.730.910.920.950.600.550.540.870.80W30.590.920.500.960.980.790.830.860.480.510.760.76W40.860.890.480.760.840.890.860.840.930.820.910.87W50.870.780.850.890.930.790.940.810.630.810.910.87W60.920.810.840.890.870.830.690.780.820.790.870.90W70.940.910.810.780.890.910.690.560.860.730.870.83W80.870.670.940.850.910.870.810.780.740.910.820.79W90.940.880.790.650.780.870.910.950.870.900.770.96W100.890.900.760.870.890.900.910.880.740.690.680.79W110.710.690.670.890.910.540.730.910.890.690.740.78W120.860.780.810.480.620.810.590.870.910.940.810.86W130.920.820.430.780.790.910.870.580.900.430.600.79W140.850.870.890.810.480.970.570.900.930.720.700.74W150.910.690.890.870.680.800.590.490.790.890.860.82W160.830.880.680.790.890.490.810.700.910.870.790.91W170.870.690.790.890.690.780.940.780.810.790.890.85W180.930.650.900.980.870.790.780.870.890.900.860.88W190.890.780.680.890.890.690.820.760.730.780.580.64W200.910.560.890.910.760.870.870.910.890.790.870.58W210.780.710.780.870.890.790.790.890.690.640.690.69W220.880.680.790.790.780.640.690.940.830.910.780.80W230.890.900.820.920.820.800.680.870.610.920.790.91W240.890.820.900.830.790.910.760.840.720.860.890.83Results0.870.770.780.820.900.860.840.820.730.690.810.82


### 2.4.3. Establishment and Optimization of the Model

The process of GA optimizing the BPNN is shown in Figure4. The most critical step in this process is to use the optimal global solution obtained by GA to realize the optimization objective, which reduces the complexity of the training process and optimizes the BPNN timely. The fitness calculation and selection operation procedures are described in detail.Figure 4
Flowchart of the BPNN optimized by GA.(1) Fitness calculation: the fundamental reason for determining the fitness function is that the evolution direction of GA is increasing the value of the fitness function. The error in the learning process is shown in equation (12), and the fitness function is shown in equation (13).(12)E=∑k=1p∑j=1iyjk−ojk2,(13)fitness=1E.In equations (12) and (13), p is the number of samples, E is the error, l is the number of output nodes, and yjk−ojk is the error of the kth sample relative to the jth output node.(2) Genetic operation: proportional selection or replication selection is employed, and it is the so-called roulette. The first step is to calculate an individual’s fitness value and its probability of being selected. The total fitness value is the probability of the selected individual during the selection process. The selection probability is calculated by the following equation:(14)zaj=faj∑j=1dfaj.faj is the fitness value of individual aj, zaj is the probability of an individual to be selected, and aj represents the probability that individual a in the group is selected.When each individual’s probability is calculated, it is time to confirm whether the determined individual can be inherited to the next generation. The selection is based on the size of the cumulative probability. The calculation equation is shown in the following equation:(15)qak=∑j=1kzaj.In equation (15), qak is the accumulation probability of individual ak. The next generation of new populations will be obtained after the previous step. Then, the fitness value is calculated, and the above steps are repeated. When the population is stable, the calculation process stops.GA adopts real code numbers, and the length of the actual number is 101. After the experiment, parameters are obtained, and they are shown in Table3.Table 3
Parameter settings.
NameParametersNameParametersInitial population size20Code length101Cross-probability0.6Evolution termination algebra100Mutation probability0.05Structure of the BPNN18-5-1Output layer transfer functionPurelineHidden layer transfer functionLogsigPrecision0.00001Maximum iteration1000When the model is implemented, the collected data are processed. 360 groups are selected to be trained, and the remaining 40 groups are tested. The input value of the network test is the data value of 24 secondary indexes contained in the 1–24 columns of each group of samples, namely, teaching resources, teaching process, teaching effectiveness, and overall evaluation.


## 2.4.1. Index Setting

The implementation of the teaching quality evaluation model involves the basic elements in establishing the EIS of the teaching quality of flipped classrooms. It needs to refer to and compare the opinions of some experienced teachers and experts in flipped classrooms and the management department. Also, much relevant literature and other evaluation index systems of the teaching quality of music flipped classrooms should be considered. The established EIS of teaching quality includes four first-grade indexes: teaching resources, teaching process, teaching materials, and overall evaluation systems. They are numbered W1–W24, as shown in Table1. The teaching quality evaluation results of the music flipped classroom are divided into five grades: A (excellent) is [0.9, 1], B (good) is [0.8, 0.89], C (medium) is [0.7, 0.79], D (passing) is [0.6, 0.69], and E (failing) is [0, 0.59].Table 1
EIS of the teaching quality of music flipped classrooms.
First-grade indexesSecond-grade indexesNumberTeaching resourcesSatisfaction with existing teaching resourcesW1Satisfaction of the music flipped classroom in existing teaching placesW2Satisfaction with the information exchange platform (networks, QQ group, and WeChat group)W3Teaching processSatisfaction with teachers’ working ability and classroom teaching contentW4Satisfaction with teachers’ performance and attitudesW5Satisfaction with teaching courseware, video, and specimenW6The clarity of the teaching objectives, learning objectives, and tasksW7Satisfaction with the number and matching of team membersW8Satisfaction with the teaching progressW9Teaching effectSatisfaction with the classroom discussion atmosphereW10Satisfaction with the enthusiasm and activity of the groupW11Flipped classroom provides a better and effective teaching modeW12Satisfaction with the knowledge and skills in the flipped classroomW13Overall evaluationSatisfaction with the teaching ways of flipped classroomsW14The teaching ways of flipped classrooms are more attractive and arouse students’ interest in the studyW15The teaching ways of flipped classrooms make students learn more activelyW16Better interaction with teachers and classmatesW17Teachers are more timely in answering students’ questionsW18Flipped classrooms are based on preclass learning, and the classroom teaching environment design is more targetedW19Learning effect is better than traditional teachingW20Flipped classrooms help to improve students’ comprehensive qualityW21Teachers provide rich courseware, specimen, and videosW22Courseware, videos, and the specimen can effectively support students for autonomous learningW23Flipped classrooms promote teaching reform of music coursesW24


## 2.4.2. Data Acquisition

The questionnaire is designed according to the four first-grade indexes and 24 second-grade indexes and the overall evaluation system of the teaching quality of music flipped classrooms. The questionnaires are sent to the students, and their answersg are scored. After the scores are calculated, the results are collected, and the experimental data are obtained. Since the total score is 100, the scoring data of students must be standardized by the maximum and minimum method to facilitate the experiment, and the normalized value should be between [0, 1]. The reason for choosing the method is that it can effectively save the data original meaning. The normalized value is calculated by the following equation:(11)X=I−IminImax−Imin.X is the normalized score, namely, the input value of the BPNN, I is the unprocessed score, Imin is the minimum teaching quality score, and Imax is the maximum value. 400 sets of experimental data are standardized. 360 data are used to train the model, and the remaining 40 sets of data are used for testing to detect the model’s performance better, obtaining the optimized structure of the BPNN. The students’ evaluation data are shown in Table 2. 1–24 are listed as input data, and the last data are the training output.Table 2
Standardized evaluation sample data of students.
Index numberSample number123456789101112W10.900.800.760.790.870.860.750.990.960.910.720.86W20.950.920.600.730.910.920.950.600.550.540.870.80W30.590.920.500.960.980.790.830.860.480.510.760.76W40.860.890.480.760.840.890.860.840.930.820.910.87W50.870.780.850.890.930.790.940.810.630.810.910.87W60.920.810.840.890.870.830.690.780.820.790.870.90W70.940.910.810.780.890.910.690.560.860.730.870.83W80.870.670.940.850.910.870.810.780.740.910.820.79W90.940.880.790.650.780.870.910.950.870.900.770.96W100.890.900.760.870.890.900.910.880.740.690.680.79W110.710.690.670.890.910.540.730.910.890.690.740.78W120.860.780.810.480.620.810.590.870.910.940.810.86W130.920.820.430.780.790.910.870.580.900.430.600.79W140.850.870.890.810.480.970.570.900.930.720.700.74W150.910.690.890.870.680.800.590.490.790.890.860.82W160.830.880.680.790.890.490.810.700.910.870.790.91W170.870.690.790.890.690.780.940.780.810.790.890.85W180.930.650.900.980.870.790.780.870.890.900.860.88W190.890.780.680.890.890.690.820.760.730.780.580.64W200.910.560.890.910.760.870.870.910.890.790.870.58W210.780.710.780.870.890.790.790.890.690.640.690.69W220.880.680.790.790.780.640.690.940.830.910.780.80W230.890.900.820.920.820.800.680.870.610.920.790.91W240.890.820.900.830.790.910.760.840.720.860.890.83Results0.870.770.780.820.900.860.840.820.730.690.810.82


## 2.4.3. Establishment and Optimization of the Model

The process of GA optimizing the BPNN is shown in Figure4. The most critical step in this process is to use the optimal global solution obtained by GA to realize the optimization objective, which reduces the complexity of the training process and optimizes the BPNN timely. The fitness calculation and selection operation procedures are described in detail.Figure 4
Flowchart of the BPNN optimized by GA.(1) Fitness calculation: the fundamental reason for determining the fitness function is that the evolution direction of GA is increasing the value of the fitness function. The error in the learning process is shown in equation (12), and the fitness function is shown in equation (13).(12)E=∑k=1p∑j=1iyjk−ojk2,(13)fitness=1E.In equations (12) and (13), p is the number of samples, E is the error, l is the number of output nodes, and yjk−ojk is the error of the kth sample relative to the jth output node.(2) Genetic operation: proportional selection or replication selection is employed, and it is the so-called roulette. The first step is to calculate an individual’s fitness value and its probability of being selected. The total fitness value is the probability of the selected individual during the selection process. The selection probability is calculated by the following equation:(14)zaj=faj∑j=1dfaj.faj is the fitness value of individual aj, zaj is the probability of an individual to be selected, and aj represents the probability that individual a in the group is selected.When each individual’s probability is calculated, it is time to confirm whether the determined individual can be inherited to the next generation. The selection is based on the size of the cumulative probability. The calculation equation is shown in the following equation:(15)qak=∑j=1kzaj.In equation (15), qak is the accumulation probability of individual ak. The next generation of new populations will be obtained after the previous step. Then, the fitness value is calculated, and the above steps are repeated. When the population is stable, the calculation process stops.GA adopts real code numbers, and the length of the actual number is 101. After the experiment, parameters are obtained, and they are shown in Table3.Table 3
Parameter settings.
NameParametersNameParametersInitial population size20Code length101Cross-probability0.6Evolution termination algebra100Mutation probability0.05Structure of the BPNN18-5-1Output layer transfer functionPurelineHidden layer transfer functionLogsigPrecision0.00001Maximum iteration1000When the model is implemented, the collected data are processed. 360 groups are selected to be trained, and the remaining 40 groups are tested. The input value of the network test is the data value of 24 secondary indexes contained in the 1–24 columns of each group of samples, namely, teaching resources, teaching process, teaching effectiveness, and overall evaluation.


## 3. Results and Discussion

### 3.1. Reliability and Validity Analyses of the Questionnaire

The reliability of the questionnaire is analyzed by SPSS 25.0, and Cronbach’s alpha is 0.806, which is greater than 0.7. This shows that the questionnaire has good reliability. The statistical results of the reliability are shown in Table4.Table 4
Statistical results of the reliability.
Cronbach’s alphaNumber of items0.80624SPSS 25.0 is used for Kaiser–Meyer–Olkin (KMO) and Bartlett spherical tests, and factor analysis is used to obtain the validity of the questionnaire. KMO is 0.692, more significant than 0.6, so each question in the questionnaire is suitable for factor analysis and has good validity. The test results of KMO and Bartlett are shown in Table5.Table 5
KMO and Bartlett tests.
KMO0.692Bartlett’s spherical testsApproximate chi-square158.459df53sig0.000


### 3.2. Model Verification and Test

The prediction results of the GA-BPNN are shown in Figure5.Figure 5
Prediction results of the GA-BPNN.Figure5 shows that the prediction results of the GA-BPNN are very close to the actual evaluation values. Their difference gets smaller after the BPNN evaluation model is improved by GA. Therefore, the improved BPNN evaluation model can evaluate the teaching quality timely and scientifically.


### 3.3. Results of the Optimized BPNN

The evaluation simulation experiment on teaching quality is conducted by MATLAB 2013b programming. The prediction accuracy of the GA-BPNN is shown in Figure6, its MSE is shown in Figure 7, and its fitness function curve is shown in Figure 8.Figure 6
Prediction accuracy of the GA-BPNN.Figure 7
Mean squared errors of the GA-BPNN.Figure 8
Fitness function curve of the GA-BPNN.Figure6 shows that the prediction accuracy of 40 groups of test samples is more than 95%, of which the prediction accuracy of 39 groups is more than 96%. The prediction accuracy values of most samples are more than 98%, and the prediction accuracy of 19 groups is more than 99%. This shows that the approximation effect of the GA-BPNN is good. Figure 7 shows that the convergence speed of MSE of the GA-BPNN is fast before the fifth generation, stable between the fifth generation and the twentieth generation, and slow between the twentieth generation and the thirtieth generation. After 35 iterations, the MSE of the GA-BPNN is stable, indicating that the GA-BPNN can achieve global optimization. Figure 8 shows that the convergence speed of the fitness function of the GA-BPNN is relatively fast before the 10th generation, and it reaches a stable state between the 10th and 20th generations. After 40 iterations, it can keep a stable state. Therefore, the adaptability of the GA-BPNN is relatively high. In summary, the prediction performance of the GA-BPNN is determined by its internal mechanism, and its prediction is effective.


### 3.4. Model Comparison

The BPNN method is used to verify the performance of this model, and the data simulation and comparative tests are carried out on the samples. The absolute error comparison is shown in Figure9.Figure 9
Comparison of the absolute error of forecast results.Figure9 shows that the absolute prediction error of the teaching quality evaluation model based on the BPNN is 0–1.48, and the fundamental error of 10% samples is greater. The absolute prediction error of the evaluation model under the GA-BPNN is 0–0.05, and the fundamental error of 90% samples is within 0.1. The prediction results of individual samples of the BPNN model deviate considerably. In contrast, the prediction accuracy of the GA-BPNN is relatively stable. Compared with the BPNN teaching quality evaluation model, GA-BPNN has better performance.The performance comparison shows that the average evaluation accuracy of 40 groups based on the BPNN is 84.13%, while that of the GA-BPNN is 99.17%, increasing by 15.04%. This proves that the evaluation results of the latter are better than the BPNN algorithm. Besides, GA-BPNN is feasible for evaluating the teaching quality of music flipped classrooms.The integration of AI into education has given education a new meaning. The impact of AI on education is enormous. It solves the contradiction and reconstructs the development pattern of education. It also brings many challenges to education, especially to the teacher-student relationship. The teaching, moral, psychological, and personal relationships between teachers and students have undergone corresponding changes under AI. These changes include reducing students’ dependence on teachers and promoting their autonomous learning. AI breaks the space-time limit of teaching and learning, and the relationship between teachers and students is cooperative and equal. However, there are also negative aspects. For example, man-machine cooperation makes teachers and students lose their subjectivity and weakens the tradition of respecting teachers and loving students; the contradiction between teachers and students gets sharp. Because of the opportunities and challenges, the traditional teacher-student relationship needs to be adjusted. Therefore, the use of AI in education should be rational. An intersubjectivity relationship between teachers and students should be built, and emotional interaction between teachers and students should be strengthened.


## 3.1. Reliability and Validity Analyses of the Questionnaire

The reliability of the questionnaire is analyzed by SPSS 25.0, and Cronbach’s alpha is 0.806, which is greater than 0.7. This shows that the questionnaire has good reliability. The statistical results of the reliability are shown in Table4.Table 4
Statistical results of the reliability.
Cronbach’s alphaNumber of items0.80624SPSS 25.0 is used for Kaiser–Meyer–Olkin (KMO) and Bartlett spherical tests, and factor analysis is used to obtain the validity of the questionnaire. KMO is 0.692, more significant than 0.6, so each question in the questionnaire is suitable for factor analysis and has good validity. The test results of KMO and Bartlett are shown in Table5.Table 5
KMO and Bartlett tests.
KMO0.692Bartlett’s spherical testsApproximate chi-square158.459df53sig0.000


## 3.2. Model Verification and Test

The prediction results of the GA-BPNN are shown in Figure5.Figure 5
Prediction results of the GA-BPNN.Figure5 shows that the prediction results of the GA-BPNN are very close to the actual evaluation values. Their difference gets smaller after the BPNN evaluation model is improved by GA. Therefore, the improved BPNN evaluation model can evaluate the teaching quality timely and scientifically.


## 3.3. Results of the Optimized BPNN

The evaluation simulation experiment on teaching quality is conducted by MATLAB 2013b programming. The prediction accuracy of the GA-BPNN is shown in Figure6, its MSE is shown in Figure 7, and its fitness function curve is shown in Figure 8.Figure 6
Prediction accuracy of the GA-BPNN.Figure 7
Mean squared errors of the GA-BPNN.Figure 8
Fitness function curve of the GA-BPNN.Figure6 shows that the prediction accuracy of 40 groups of test samples is more than 95%, of which the prediction accuracy of 39 groups is more than 96%. The prediction accuracy values of most samples are more than 98%, and the prediction accuracy of 19 groups is more than 99%. This shows that the approximation effect of the GA-BPNN is good. Figure 7 shows that the convergence speed of MSE of the GA-BPNN is fast before the fifth generation, stable between the fifth generation and the twentieth generation, and slow between the twentieth generation and the thirtieth generation. After 35 iterations, the MSE of the GA-BPNN is stable, indicating that the GA-BPNN can achieve global optimization. Figure 8 shows that the convergence speed of the fitness function of the GA-BPNN is relatively fast before the 10th generation, and it reaches a stable state between the 10th and 20th generations. After 40 iterations, it can keep a stable state. Therefore, the adaptability of the GA-BPNN is relatively high. In summary, the prediction performance of the GA-BPNN is determined by its internal mechanism, and its prediction is effective.


## 3.4. Model Comparison

The BPNN method is used to verify the performance of this model, and the data simulation and comparative tests are carried out on the samples. The absolute error comparison is shown in Figure9.Figure 9
Comparison of the absolute error of forecast results.Figure9 shows that the absolute prediction error of the teaching quality evaluation model based on the BPNN is 0–1.48, and the fundamental error of 10% samples is greater. The absolute prediction error of the evaluation model under the GA-BPNN is 0–0.05, and the fundamental error of 90% samples is within 0.1. The prediction results of individual samples of the BPNN model deviate considerably. In contrast, the prediction accuracy of the GA-BPNN is relatively stable. Compared with the BPNN teaching quality evaluation model, GA-BPNN has better performance.The performance comparison shows that the average evaluation accuracy of 40 groups based on the BPNN is 84.13%, while that of the GA-BPNN is 99.17%, increasing by 15.04%. This proves that the evaluation results of the latter are better than the BPNN algorithm. Besides, GA-BPNN is feasible for evaluating the teaching quality of music flipped classrooms.The integration of AI into education has given education a new meaning. The impact of AI on education is enormous. It solves the contradiction and reconstructs the development pattern of education. It also brings many challenges to education, especially to the teacher-student relationship. The teaching, moral, psychological, and personal relationships between teachers and students have undergone corresponding changes under AI. These changes include reducing students’ dependence on teachers and promoting their autonomous learning. AI breaks the space-time limit of teaching and learning, and the relationship between teachers and students is cooperative and equal. However, there are also negative aspects. For example, man-machine cooperation makes teachers and students lose their subjectivity and weakens the tradition of respecting teachers and loving students; the contradiction between teachers and students gets sharp. Because of the opportunities and challenges, the traditional teacher-student relationship needs to be adjusted. Therefore, the use of AI in education should be rational. An intersubjectivity relationship between teachers and students should be built, and emotional interaction between teachers and students should be strengthened.


## 4. Conclusions

Compared with previous technologies, AI has the characteristics of deep learning, cross-border integration, human-computer interaction (HCI), and group intelligence. Based on AI and wireless local area network (WLAN), online music flipped classrooms are discussed, and GA is used to optimize the BPNN model and evaluate the teaching quality of music flipped classrooms. The simulation and comparison results show that the GA-BPNN is suitable for evaluating the teaching quality of music flipped classrooms. It overcomes the shortcomings of the subjectivity and linear evaluation of traditional evaluation methods. The study has significant practice for evaluating the teaching quality of flipped classrooms and provides a basis for improving the teaching quality of flipped classrooms in the future. AI, as the latest information technology, is widely used in education. Its application to the teaching process is reflected in the technology and the influencing factors in the teaching process. Moreover, it is bound to impact education as the materialization of human intelligence significantly. However, this proposed evaluation model does not apply to the evaluation system with many samples. Therefore, an evaluation system for more samples is the focus of the follow-up research. The integration of wireless networks and AI will be realized in the future. Simply speaking, the integration of machine learning and wireless networks will appear soon. As the latest information technology, AI will be booming in education.



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*Source: 9524185-2022-03-09.xml*