# Performance Management of Education and Teaching Reform Based on Convolutional Neural Network

Authors: Donghua Zhou; Shan He
Journal: Mobile Information Systems
 (2022)

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## Abstract

The teaching reform of higher education has always attracted much attention, and higher education has been the key object of the national education reform. The quality of higher education, the quality of talent training, the quality of scientific research, the quality of social service, and the structure to produce characteristics must all be improved. The success of higher education teaching reform is not only related to the quality of university personnel training and important task of serving the society, but also related to connotative development of universities and sustainable development of higher education. Education and teaching reform is an all-round and systematic project, which involves reforms in different aspects, such as teaching management, teacher teaching, professional setting, and curriculum development. The outcome of the extended efforts and practical advancements in education teaching reforms is to evaluate the effects and achievements of these reforms. However, the reforms and achievements of education and teaching are much higher and effective in comparison to improvements in theoretical research and practical experience. Therefore, this work is based on the CNN (Convolutional Neural Network) and designed an intelligent model for the evaluation and management of peer-to-peer education and teaching reform achievements. This work contributes in the following three manners: (I) It includes construction of a deep one-dimensional CNN and sends relevant data of college education and teaching reform into network with the objective to realize adaptive feature extraction and high-accuracy evaluation. (II) It also realizes the management of higher education teaching quality. (III) It aims to show a greater impact of hyperparameters in the CNN performance, genetic algorithm. This proposition is to optimize hyperparameters. It proves the validity and authenticity of this work through systematization, logic, and experiment.


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## Body

## 1. Introduction

As China’s economic and social development enters a new phase and reform enters a deep-water zone of change, new conditions, concerns, and challenges await the country. Several people are concerned about the reform and development of higher education teaching, and the blueprint for the national long-term education reform plan includes explicit instructions for the improvement of higher education instruction. To produce distinctive characteristics, one of the most important goals of the national education and teaching reform is to improve the quality of higher education, as well as talent training and development, scientific research and social service capacity, and the structure of the system itself. The report of the 18th National Congress of the Communist Party of China emphasized the importance of promoting the connotative development of higher education as well as deepening the comprehensive reform in educational domain, with the overall goal of constructing a prosperous society as the overarching objective [1–5].In the provinces of Jiangsu, Hubei, and Heilongjiang, the Ministry of Education initiated into conducting pilot projects on higher education reform. For the purpose, a joint national comprehensive reform pilot zone was created. The comprehensive reform of higher education teaching in the initial five years since the establishment of the pilot program has resulted in the development of establishing comprehensive reform strategies, deepening reform promotion mechanisms, enhancing collaborative innovation capabilities, and comprehensively improving quality for higher education. Being a regional project, strong regional characteristics and development advantages have been made. It includes the optimization of the education system and structure of disciplines, reformation of the talent training model. It also included the ability to radiate high-quality resources in addition to exhibiting the advantages of the regional higher education resources. All this can play as a model and experience for comprehensive promotion of higher education reform. However, there are several deep-rooted problems that need to be addressed at once. The general difficulties faced wereImperfect management of the institutions of higher learning, insufficient education coordination power of the provincial government and lack of autonomy and independence in running universities, lack of guarantees for the quality education and enhanced teaching, and deficiency of the mechanism of industry-university-research cooperation [6–10].Achieving a successful reform of higher education teaching is tied not only to the quality of university staff training and the critical role they play in serving society, but also to the connotative growth of Chinese universities and the long-term health of the country’s higher educational institutions. The reform of higher education teaching is an all-encompassing and methodical undertaking that calls for changes to be made in a variety of areas, including administration, instruction, professional setting, and curriculum design. However, as the reform of higher education continues to deepen, obstacles in concepts, systems, and strategies that restrict the reform of education and teaching can no longer be resolved by previous fragmented individual reforms. Instead, it is necessary to gradually advance the education and teaching reforms in a comprehensive, systematic, and overall reform manner. Instead of individual fragmented efforts, some colleges across the country started to enhance comprehensive educational and teaching reforms. These efforts are constituted of further research on target thinking, practice, and strategic planning of the comprehensive teaching reform [11–15].Teaching is the soul of a university. The success or failure of the comprehensive reform of higher education teaching is not only related to quality for university personnel training and important task of serving the society. It is more related to connotative development of Chinese universities and the sustainable development of higher education. Therefore, the evaluation and management of the achievements of higher education teaching reform is a subject of practical significance. But so far, there have been few studies in this field. This work is dedicated to relying on CNN to design a strategy that can efficiently evaluate and manage the achievements of higher education teaching reform. This research will not only have practical value for improving and perfecting teaching work of higher education teaching management departments. Moreover, it provides decision-making basis and guidance for reform decision-makers and reform subjects to further change their concepts, reform talent production methods and production processes, strengthen the construction of teaching staff, improve teaching quality monitoring and evaluation systems, and optimize teaching service guarantees.


## 2. Related Work

In the process of analyzing the educational reform model, the literature [16] divided the model based on the real needs of development. The first model is the diffusion development and research model. This model has a relatively wide range of influence. This model can be used to promote innovation of curriculum and education models in the process of large-scale reform of education system. The second model is social interaction model. This social interaction model takes new systems and new ideas as the core, seeking better solutions and approaches. Through effective publicity and promotion of the values and ideas of the current society, education reform mainly focuses on communication and communication between people. The third model is the participatory problem-solving model. This model takes reform as the core and foundation and actively solves the various problems that need to be faced in the reform process and continuously improves the problem-solving ability and creativity. Literature [17] proposes that higher education must focus on improving teaching quality and actively promote the innovation of internal education models. Starting from different aspects such as education as well as teaching, teacher education, school reform, and teaching conditions, strengthen attention and understanding of students’ learning conditions. Judging from the current educational model of American universities, many experts say they are not optimistic. The chairman of the National College Student Science Research Council has clearly emphasized in public that the current teaching methods in the United States are relatively backward, which makes it difficult for many students to truly win and achieve their own long-term development. Most students are less satisfied with the existing teaching methods. The study [18] about the current university teaching situation is based on in-depth research and thorough analysis, which emphasizes that the process of adopting and promoting the reform of American teaching mode may not guarantee the reduction of the number of students in class in relatively short duration of time. Best is to develop and promote a teaching model of 100-person classroom based on two key reasons. First is to take account of and focus on the real needs of students in learning process to understand their actual situation and learning capability, such as obtaining information, ensuring the rationality of teaching reforms and education. Secondly, there must be student-centric practical teaching reform to ensure that such teaching reform is advanced and contemporary to guarantee the progressive demand of students’ development.In study [19], Yang and Clarke conducted in-depth research and analysis on the substantive value of education reform. They believe that under the market reform model, many colleges have begun to actively implement educational values and continue to promote teaching activities and changes in college education activities. In the process of value orientation, the educational management system of colleges strictly follows the real needs of social development and is based on the personal development of students. In the process of analyzing the existing university teaching model, it was pointed out that many universities must take the student-centered teaching as the core in the reform process, actively guarantee the popularization of education and teaching, and promote international reform of teaching model. According to the real needs of China’s educational model reform, [20] proposed that in the socialist market, with continuous establishment for social and economic system, reform of education and teaching in my country has received a greater impact. Therefore, in the process of college reform, the leaders of colleges must combine the real needs of the development of the times to ensure that the education reform is consistent with the existing market mechanism. Literature [21] has conducted an in-depth analysis and research on the actual needs of education and teaching reform in the industrial age and believes that the improvement of modern information technology can effectively promote teaching reform. Corresponding to the actual requirements of the development of a learning society, [22] proposed that China must take effective measures in the process of university reform to ensure students improve learning ability. Literature [23] stood in the perspective of internationalization, and in response to the actual needs of globalized higher education reforms, it was proposed that in the process of education reform in Chinese universities, it is necessary to update existing education and teaching concepts and promote construction for the teaching force of the university team. In the process of analyzing the education and teaching reform model, the report [24] understands the various problems that exist in process of higher education reform, putting forward certain solutions. Reference [25] has conducted an in-depth analysis of actual situation of education and teaching model, since reform emphasized the innovation of the talent training model must be the core during the reform of the teaching model. The case study [26] reflects on various problems and deficiencies arising in international education and teaching, combines teaching methods, teaching content, teaching goals and teaching concepts, and carries out further reforms and analyses in different aspects.


## 3. Method

This project designs a one-dimensional CNN for the evaluation and management of educational and teaching reform. The network designs a multiscale block (MSB) to parallel multiscale feature extraction to the upper layer output, enhances the feature extraction ability of the convolutional layer, and uses genetic algorithms (GA) to optimize the network. This network is named GA-MS-CNN.


### 3.1. Basic Theory of CNN

CNN, due to its motivation by human vision and its designing being resistant to invariant transformations such as scaling, translation, and rotations, has grown to be a de facto standard for computer vision tasks during this decade. Being a Deep Learning algorithm, CNN can be used in an input image, allocate importance (learnable weights and biases) to different aspects/objects in the image, and be able to differentiate between them.The convolutional layer is the most important layer in the CNN, and it is also an important sign that distinguishes it from the traditional artificial neural network. The main work of the convolutional layer is to use a trainable convolution kernel to convolve the input data and output the result. It is a process of extracting features from input data. The convolutional layer generally contains many convolution kernels, the purpose of which is to be able to extract richer features. After the convolution operation, it is also necessary to use a nonlinear function, namely, the activation function in the artificial neural network, to process the output data so that the model can obtain the nonlinearity and compress the output within a limited range to facilitate subsequent data processing. The calculation formula of the convolutional layer is(1)xjl=f∑i∈Mjxil−1∗kijl+bjl,where x represents the feature map, k is the convolution kernel, b is the bias term, and f is the activation function.The downsampling layer is also called the pooling layer. Generally speaking, the feature data needs to be downsampled after the convolution operation. The downsampling operation is a nonlinear downsampling method, which greatly reduces the amount of feature data from the convolutional layer through this down-sampling method. The downsampling operation has the characteristics of rotation, scale, and scaling invariance, while reducing the computational complexity, improving the stability of the pattern recognition results, and reducing the phenomenon of overfitting. The calculation formula is(2)xjl=fβjldownxjl−1+bjl,where down is sampling function and β is the weight.After a CNN passes through a series of convolutional layers and pooling layers, it is generally connected to a fully connected layer before the output layer. Each node neuron point on the fully connected layer is connected to all the neuron nodes of the previous layer, and the function is to connect the features obtained from the previous convolutional layer and the pooling layer in series. Similar to the traditional neural network, the fully connected layer first calculates the dot product between the input vector and the weight, then adds a bias term, and finally outputs the result through the activation function, which requires the size of the input to be fixed. The fully connected layer is essentially a special convolutional layer, which can be replaced by a convolutional layer.The CNN first calculates the loss error value between the predicted value and the label value and the output results of each layer through the forward propagation algorithm. Then, use the optimization function to update the weight and bias of each layer according to the chain derivation rule. When the number of times to be trained reaches the set value or the cross-entropy error value meets the accuracy allowable range, the network structure is saved. The training process of the CNN is shown in Figure1.Figure 1
CNN training process.The convolutional neural network calculates the errors of each layer through the forward propagation algorithm and then updates the weight parameters of the network through the optimization function. The typical optimization algorithm principle is as follows:(3)w=w−η∂E∂w,b=b−η∂E∂b,where η is learning rate. If η is small, it will cause slow network fitting and longer training time. If the value is too large, the weight parameter update span of each layer will be too large and it will be difficult to stabilize. The stochastic gradient descent (SGD) algorithm is often used in deep neural network training. The algorithm needs to set the learning rate before the network starts training, which has good versatility.Sparse connection and weight sharing are two main characteristics of the CNN. Relative to full connection, the idea of sparse connection implies that the convolution kernel in the convolutional layer is only connected to the part of the convolution area to extract the local features of the area. If a piece of data is taken and worked on, the increase in distance causes a weak correlation between the elements. However, the use of sparse connections produces the convolution kernel to extract the local features of the elements. To obtain a more comprehensive feature of the data segment, the extracted local features through multiple types of convolution kernels are summarized. Such use of sparse connections can considerably decrease the number of parameters to be trained that help to create a deeper network structure. The multiple convolution kernels in the convolution layer perform convolution operations with the output of the previous layer to extract more comprehensive features that can characterize the characteristics of the data. When each convolution kernel performs a convolution operation on the input data, regardless of the length of the data in the convolution area, the constant weight parameters are used. Otherwise, the construction of the deep convolution network will cause backwards due to too many parameters. It is difficult to update the parameters during propagation, which causes the problem that the network is difficult to fit. In the CNN, multiple convolution kernels have multiple weight parameters, which play the function of multilevel feature extraction.


### 3.2. Multiscale CNN

The CNN uses multiple convolution kernels to summarize the extracted local features as the output of the convolutional layer of this layer and then uses multiple convolutional layers and pooling layers to extract more abstract and expressive features. Generally, a deeper CNN is easier to obtain better recognition and classification results, but there are also problems with too many parameters to be trained and overfitting of the network structure. This chapter constructs a multiscale CNN. First, a multiscale block is proposed, which uses different sizes and types of convolution kernels to extract multiscale features from the upper output of the network in parallel. This can improve the adaptive feature extraction capability of the convolutional layer and also leave room for building a deeper CNN. The multiscale block structure proposed in this paper is shown in Figure2.Figure 2
The structure of multiscale block.Use 1 × 3, 1 × 5, and 1 × 7 multisize convolution kernels to perform feature extraction on the upper-layer output in parallel. Add BN processing after the convolution operation and before the activation function. Then, the output after the activation function ReLU mapping is subjected to the maximum pooling operation. Finally, the pooled output is combined to obtain a set of feature vectors. The larger convolution kernel is easy to extract the global features of the input signal, and the smaller convolution kernel is easy to extract the detailed features of the input signal. This parallel multiscale extraction method can enhance the feature extraction ability of the convolutional layer and connect multiple MSB layers together to form a multiscale convolutional neural network (MS-CNN) to evaluate and manage education and teaching.The MS-CNN constructed in this paper consists of 1 convolutional layer, 3 MSB layers, and fully connected layers. BN processing is added to each convolutional layer and MSB layer to enhance the consistency of the distribution of the data in the training set and the test set and then use the ReLU activation function to map the output of this layer in a suitable interval. The first convolution layer uses a larger convolution kernel to perform convolution operations on the original teaching and education reform data and extract the global characteristics of the data. After the three connected MSB layers, the output of the upper layer is multiscale feature extraction. After the fully connected layer, add a dropout process with a ratio of 0.5 to improve the generalization performance of the network. Finally, use the softmax function to map and classify the extracted feature data. The structure of MS-CNN constructed in this paper is shown in Figure3.Figure 3
The structure of MS-CNN.The number of 4-layer convolution kernels in the network is set to 128, 64, 64, and 32. The number of convolution kernels is in an inverted pyramid distribution, which reduces the number of parameters to be trained and can speed up the convergence of the network structure. The MSB layer uses 64 convolution kernels with sizes of 1 × 3, 1 × 5, and 1 × 7 to extract parallel multiscale features from the output of the previous layer. The main parameters in the training process of MS-CNN are shown in Table1.Table 1
Main parameters of the MS-CNN training process.
LayerSizeActivationConv1 × 5BN + ReLUMaxpool1 × 3—MSB1 × 3, 1 × 5, 1 × 7BN + ReLUMSB1 × 3, 1 × 5, 1 × 7BN + ReLUMSB1 × 3, 1 × 5, 1 × 7BN + ReLUFC—Softmax + dropoutFC—Softmax + dropout


### 3.3. Optimization with GA

There are many hyperparameters in CNN. Different generalization performance of the model depends on the different combinations of these hyperparameters. Due to the existence of many hyperparameters in the model, their various combination, therefore, will make the model to perform in many ways. In order to obtain the optimal hyperparameter combination, this chapter combines GA and MS-CNN. On the one hand, this method can take advantage of the feature that the CNN does not need to manually input features (input the original data directly), and on the other hand, it can take advantage of the optimization characteristics of genetic algorithm.GA is a method of simulating the process of natural evolution to search for the optimal solution. Fitness functions and probability transformation rules are used to guide the search direction. Selection, crossover, and mutation are several basic operations in genetic algorithms. The specific optimization process is as follows.Initialize the population and coding. Randomly generate a populationXm×n, each individual X1×n represents a hyperparameter distribution, n represents the number of hyperparameters, and m is the initial population size.Determine the fitness function. Taking the squared reconstruction error of the training samples of the MS-CNN model as the fitness function of GA, the calculation formula is(4)fitness=∑i=1N∑j=1Cyij−oij2,where y is the expected output and o is the actual output.Selection. Using the selection roulette method, the selection probability pi of each individual i is(5)fi=kFi,pi=fi∑j=1Nfj,where Fi represents the fitness value of the individual, N represents the number of individuals in the population, and k represents the coefficient.Crossover. Since the individual uses real number coding, the crossover operation method adopts the real number crossover method. Mutations. Based on the probability of mutation, a new individual is generated. Calculate the fitness value. Judge whether it reaches the maximum evolutionary algebra; if it reaches, end the search process; otherwise, return to the selection operation.After the genetic algorithm optimization is over, the best individual obtained is used as the hyperparameter combination of MS-CNN.The specific steps of GA-MS-CNN are as follows: (1) Collect original teaching and education reform data, and divide the training set and test set. (2) Generate an initial population, set the number of populations toN, and set the number of evolutionary generations to M. (3) Train each MS-CNN model, and use the squared reconstruction error of the training sample as the fitness function. (4) Perform selection, crossover, and mutation operations in sequence to generate a new population, and judge whether it has reached the evolutionary algebra. If the discriminant conditions are met, the optimal network hyperparameters are output. Otherwise, repeat steps 3 and 4 until the judgment conditions are met. (5) Input the test set into the optimized model, and get the final result evaluation management result.


## 3.1. Basic Theory of CNN

CNN, due to its motivation by human vision and its designing being resistant to invariant transformations such as scaling, translation, and rotations, has grown to be a de facto standard for computer vision tasks during this decade. Being a Deep Learning algorithm, CNN can be used in an input image, allocate importance (learnable weights and biases) to different aspects/objects in the image, and be able to differentiate between them.The convolutional layer is the most important layer in the CNN, and it is also an important sign that distinguishes it from the traditional artificial neural network. The main work of the convolutional layer is to use a trainable convolution kernel to convolve the input data and output the result. It is a process of extracting features from input data. The convolutional layer generally contains many convolution kernels, the purpose of which is to be able to extract richer features. After the convolution operation, it is also necessary to use a nonlinear function, namely, the activation function in the artificial neural network, to process the output data so that the model can obtain the nonlinearity and compress the output within a limited range to facilitate subsequent data processing. The calculation formula of the convolutional layer is(1)xjl=f∑i∈Mjxil−1∗kijl+bjl,where x represents the feature map, k is the convolution kernel, b is the bias term, and f is the activation function.The downsampling layer is also called the pooling layer. Generally speaking, the feature data needs to be downsampled after the convolution operation. The downsampling operation is a nonlinear downsampling method, which greatly reduces the amount of feature data from the convolutional layer through this down-sampling method. The downsampling operation has the characteristics of rotation, scale, and scaling invariance, while reducing the computational complexity, improving the stability of the pattern recognition results, and reducing the phenomenon of overfitting. The calculation formula is(2)xjl=fβjldownxjl−1+bjl,where down is sampling function and β is the weight.After a CNN passes through a series of convolutional layers and pooling layers, it is generally connected to a fully connected layer before the output layer. Each node neuron point on the fully connected layer is connected to all the neuron nodes of the previous layer, and the function is to connect the features obtained from the previous convolutional layer and the pooling layer in series. Similar to the traditional neural network, the fully connected layer first calculates the dot product between the input vector and the weight, then adds a bias term, and finally outputs the result through the activation function, which requires the size of the input to be fixed. The fully connected layer is essentially a special convolutional layer, which can be replaced by a convolutional layer.The CNN first calculates the loss error value between the predicted value and the label value and the output results of each layer through the forward propagation algorithm. Then, use the optimization function to update the weight and bias of each layer according to the chain derivation rule. When the number of times to be trained reaches the set value or the cross-entropy error value meets the accuracy allowable range, the network structure is saved. The training process of the CNN is shown in Figure1.Figure 1
CNN training process.The convolutional neural network calculates the errors of each layer through the forward propagation algorithm and then updates the weight parameters of the network through the optimization function. The typical optimization algorithm principle is as follows:(3)w=w−η∂E∂w,b=b−η∂E∂b,where η is learning rate. If η is small, it will cause slow network fitting and longer training time. If the value is too large, the weight parameter update span of each layer will be too large and it will be difficult to stabilize. The stochastic gradient descent (SGD) algorithm is often used in deep neural network training. The algorithm needs to set the learning rate before the network starts training, which has good versatility.Sparse connection and weight sharing are two main characteristics of the CNN. Relative to full connection, the idea of sparse connection implies that the convolution kernel in the convolutional layer is only connected to the part of the convolution area to extract the local features of the area. If a piece of data is taken and worked on, the increase in distance causes a weak correlation between the elements. However, the use of sparse connections produces the convolution kernel to extract the local features of the elements. To obtain a more comprehensive feature of the data segment, the extracted local features through multiple types of convolution kernels are summarized. Such use of sparse connections can considerably decrease the number of parameters to be trained that help to create a deeper network structure. The multiple convolution kernels in the convolution layer perform convolution operations with the output of the previous layer to extract more comprehensive features that can characterize the characteristics of the data. When each convolution kernel performs a convolution operation on the input data, regardless of the length of the data in the convolution area, the constant weight parameters are used. Otherwise, the construction of the deep convolution network will cause backwards due to too many parameters. It is difficult to update the parameters during propagation, which causes the problem that the network is difficult to fit. In the CNN, multiple convolution kernels have multiple weight parameters, which play the function of multilevel feature extraction.


## 3.2. Multiscale CNN

The CNN uses multiple convolution kernels to summarize the extracted local features as the output of the convolutional layer of this layer and then uses multiple convolutional layers and pooling layers to extract more abstract and expressive features. Generally, a deeper CNN is easier to obtain better recognition and classification results, but there are also problems with too many parameters to be trained and overfitting of the network structure. This chapter constructs a multiscale CNN. First, a multiscale block is proposed, which uses different sizes and types of convolution kernels to extract multiscale features from the upper output of the network in parallel. This can improve the adaptive feature extraction capability of the convolutional layer and also leave room for building a deeper CNN. The multiscale block structure proposed in this paper is shown in Figure2.Figure 2
The structure of multiscale block.Use 1 × 3, 1 × 5, and 1 × 7 multisize convolution kernels to perform feature extraction on the upper-layer output in parallel. Add BN processing after the convolution operation and before the activation function. Then, the output after the activation function ReLU mapping is subjected to the maximum pooling operation. Finally, the pooled output is combined to obtain a set of feature vectors. The larger convolution kernel is easy to extract the global features of the input signal, and the smaller convolution kernel is easy to extract the detailed features of the input signal. This parallel multiscale extraction method can enhance the feature extraction ability of the convolutional layer and connect multiple MSB layers together to form a multiscale convolutional neural network (MS-CNN) to evaluate and manage education and teaching.The MS-CNN constructed in this paper consists of 1 convolutional layer, 3 MSB layers, and fully connected layers. BN processing is added to each convolutional layer and MSB layer to enhance the consistency of the distribution of the data in the training set and the test set and then use the ReLU activation function to map the output of this layer in a suitable interval. The first convolution layer uses a larger convolution kernel to perform convolution operations on the original teaching and education reform data and extract the global characteristics of the data. After the three connected MSB layers, the output of the upper layer is multiscale feature extraction. After the fully connected layer, add a dropout process with a ratio of 0.5 to improve the generalization performance of the network. Finally, use the softmax function to map and classify the extracted feature data. The structure of MS-CNN constructed in this paper is shown in Figure3.Figure 3
The structure of MS-CNN.The number of 4-layer convolution kernels in the network is set to 128, 64, 64, and 32. The number of convolution kernels is in an inverted pyramid distribution, which reduces the number of parameters to be trained and can speed up the convergence of the network structure. The MSB layer uses 64 convolution kernels with sizes of 1 × 3, 1 × 5, and 1 × 7 to extract parallel multiscale features from the output of the previous layer. The main parameters in the training process of MS-CNN are shown in Table1.Table 1
Main parameters of the MS-CNN training process.
LayerSizeActivationConv1 × 5BN + ReLUMaxpool1 × 3—MSB1 × 3, 1 × 5, 1 × 7BN + ReLUMSB1 × 3, 1 × 5, 1 × 7BN + ReLUMSB1 × 3, 1 × 5, 1 × 7BN + ReLUFC—Softmax + dropoutFC—Softmax + dropout


## 3.3. Optimization with GA

There are many hyperparameters in CNN. Different generalization performance of the model depends on the different combinations of these hyperparameters. Due to the existence of many hyperparameters in the model, their various combination, therefore, will make the model to perform in many ways. In order to obtain the optimal hyperparameter combination, this chapter combines GA and MS-CNN. On the one hand, this method can take advantage of the feature that the CNN does not need to manually input features (input the original data directly), and on the other hand, it can take advantage of the optimization characteristics of genetic algorithm.GA is a method of simulating the process of natural evolution to search for the optimal solution. Fitness functions and probability transformation rules are used to guide the search direction. Selection, crossover, and mutation are several basic operations in genetic algorithms. The specific optimization process is as follows.Initialize the population and coding. Randomly generate a populationXm×n, each individual X1×n represents a hyperparameter distribution, n represents the number of hyperparameters, and m is the initial population size.Determine the fitness function. Taking the squared reconstruction error of the training samples of the MS-CNN model as the fitness function of GA, the calculation formula is(4)fitness=∑i=1N∑j=1Cyij−oij2,where y is the expected output and o is the actual output.Selection. Using the selection roulette method, the selection probability pi of each individual i is(5)fi=kFi,pi=fi∑j=1Nfj,where Fi represents the fitness value of the individual, N represents the number of individuals in the population, and k represents the coefficient.Crossover. Since the individual uses real number coding, the crossover operation method adopts the real number crossover method. Mutations. Based on the probability of mutation, a new individual is generated. Calculate the fitness value. Judge whether it reaches the maximum evolutionary algebra; if it reaches, end the search process; otherwise, return to the selection operation.After the genetic algorithm optimization is over, the best individual obtained is used as the hyperparameter combination of MS-CNN.The specific steps of GA-MS-CNN are as follows: (1) Collect original teaching and education reform data, and divide the training set and test set. (2) Generate an initial population, set the number of populations toN, and set the number of evolutionary generations to M. (3) Train each MS-CNN model, and use the squared reconstruction error of the training sample as the fitness function. (4) Perform selection, crossover, and mutation operations in sequence to generate a new population, and judge whether it has reached the evolutionary algebra. If the discriminant conditions are met, the optimal network hyperparameters are output. Otherwise, repeat steps 3 and 4 until the judgment conditions are met. (5) Input the test set into the optimized model, and get the final result evaluation management result.


## 4. Experiment and Discussion

### 4.1. Dataset

This work uses a self-made dataset for teaching and education reform performance and evaluation management. The data set contains a total of 5982 samples, of which 3817 samples are training set, and the remaining 2165 samples are test set. The input feature of each sample are 10 educational and teaching reform performance indicators, as shown in Table2. Each indicator adopts a scoring system of 1–10 points. The final label is also a reform score of 1–10 points. The evaluation metrics are precision and recall.Table 2
The indicators of education and teaching reform.
IndexItemx1Reforms bring improvements in school disciplinex2Reform brings improvement in learning attitudex3Reform brings improvement in teaching attitudex4Reform brings improvement in innovation abilityx5Reforms bring improvements in knowledgex6Reforms bring improvements in decision-making capabilitiesx7Reforms bring improvements in executionx8Reforms bring improvements in informatization capabilitiesx9Reforms bring improvements in academic abilityx10Reform brings improvement in technical reserve capacity


### 4.2. Evaluation on the Number of MSB Layers

It is very helpful to alter convolutional layer and pooling layer for hierarchical extraction as it extracts more representative features. To improve the adaptive extraction capability of the network, it is required to construct a deeper network structure. However, it causes increase in the number of parameters to be trained, which will raise the difficulty of network training. In this work, in addition to the ordinary convolutional layer used in the first layer of the network structure, the other convolutional and pooling layers are replaced by the MSB layer, and a multiscale convolutional neural network is established to evaluate and manage the education and teaching reform achievements. The number of convolution kernels in the previous three convolutional layers is set to 128-64-64, and the number of convolution kernels in each convolutional layer is set to 32. The batch size is set to 32 as an example, and many experiments are performed for statistics. Figure4 shows the influence of the number of MSB layers on the management of reform performance evaluation.Figure 4
Evaluation on the number of MSB layers.It can be seen from the figure that when the number of MSB layers increases, the performance shows an increasing trend. This shows that a deeper network structure can enhance the ability of network adaptive feature extraction and achieve better reform results to evaluate management performance. When the number of MSB layers is set to 3, the best performance is achieved. But as the network continues to deepen, the performance increase is small. Because as the network depth reaches a certain value, the parameters to be trained in the network structure have reached the optimum, and the feature extraction capability tends to be saturated. Therefore, the number of MSB layers in GA-MS-CNN can be set to 3. Under the condition of ensuring performance, the relatively shallow network structure has low complexity, which can speed up network fitting and reduce network training and testing time.


### 4.3. Evaluation on Batch Size

The CNN is based on the backward propagation algorithm to update the parameters of the network structure. The batch size determines the number of samples of the training set selected for each backward propagation. The larger the batch size, it indicates that more training set data is used for weight update in this iteration. This article uses small sample data, so this chapter discusses the impact of batch size from 16 to 64 on network performance. In the experiment, the number of MSB layers is 3; other parameters remain unchanged. The result is shown in Figure5.Figure 5
Evaluation on batch size.From the above figure, as the batch size increases, the performance shows an increasing trend. Because this article is based on a small sample size to train GA-MS-CNN, when a smaller batch size is set, the model weight is updated with a smaller training set sample each time, which is easy to cause the model to fall into shock. Therefore, a larger batch size is used, and more samples are used to represent the data distribution of the overall sample of the training set so that the network structure is better developed in the direction of reducing the loss function. When the batch size is set to 32, its performance is optimal. When the batch size continues to increase, the performance of the model does not increase much, indicating that the network structure has reached the optimum, and the number of samples limits the further optimization of the network structure parameters.


### 4.4. Evaluation on the Number of Convolution Kernels

In a deep CNN, multiple convolution kernels in the convolution layer can extract more comprehensive features. Then, multiple convolutional layers will extract the higher-dimensional and more abstract features as the input of the classifier to realize the evaluation and management of the educational reform achievements. The number of multiconvolutional-layer convolution kernels usually has two distributions. One is a decreasing type. With this decreasing distribution, a larger number of convolution kernels in the first layer can extract more global features, and the training parameters are relatively small, which is beneficial to network fitting. The other is an incremental distribution. The more backward the convolutional layer, the more the number of convolution kernels. This method helps to extract more detailed features in the higher convolutional layer. In order to explore the influence of the number distribution of convolution kernels in the convolution layer on the network performance, in the experiment, the number of MSB layers is 3 and the batch size is set to 32. The experimental results are shown in Figure6.Figure 6
Evaluation on the number of convolution kernels.It can be seen from the figure that the recognition accuracy of the decreasing distribution of the number of multiconvolutional-layer convolution kernels is higher than that of the increasing distribution. Mainly because of the former distribution method, more global features can be extracted in the lower convolutional layer. Then, the alternate convolutional layer and pooling layer adaptively select the extracted features, and there are relatively few training parameters before the fully connected layer, which is conducive to better network fitting and better performance. Experiments show that the decreasing distribution of the number of multiconvolutional layer convolution kernels can effectively evaluate and manage the educational and teaching reform achievements.


### 4.5. Evaluation on MSB

In the experiment, the method used in this article has a good effect on the performance evaluation management of education and teaching reform. In order to further explore the role of multiscale blocks in the established model, the MSB layer in GA-MS-CNN is replaced with a common convolutional layer and a pooling layer. The convolutional layers are also processed by BN to build a CNN network with 4 convolutional layers. The results are shown in Figure7.Figure 7
Evaluation on MSB.From Figure7, GA-MS-CNN has achieved 97.7% precision and 94.8% recall, which is higher than the CNN network structure with 4 convolutional layers. This shows that the same depth of CNN, GA-MS-CNN, a parallel multi-scale feature extraction method, can enhance the feature extraction ability of the network structure and has better modeling performance for the education and teaching reform performance evaluation management.


### 4.6. Evaluation on GA

John Holland and his collaborators developed the GA in 1960s and 1970s. This model or abstraction of biological evolution is founded on the theory of natural selection by Charles Darwin. It reveals the process of natural selection where the fittest individuals are selected for reproduction. The aim of this selection is the reproduction of offspring of the next generation. The current studies apply the GA algorithm to optimize the one-dimensional CNN. In order to verify the effectiveness of this strategy, this paper conducts a comparative experiment. This paper compares the network performance when using GA and when not using GA, and the results are shown in Table3.Table 3
Evaluation on GA.
MethodPrecision (%)Recall (%)GA-MS-CNN97.794.8MS-CNN95.993.7Obviously, when using GA optimization, the network can obtain the best performance. You can get an 1.8% increase in precision, and you can get a 1.1% increase in recall. This proves the effectiveness and correctness of this work using GA to optimize the CNN.


## 4.1. Dataset

This work uses a self-made dataset for teaching and education reform performance and evaluation management. The data set contains a total of 5982 samples, of which 3817 samples are training set, and the remaining 2165 samples are test set. The input feature of each sample are 10 educational and teaching reform performance indicators, as shown in Table2. Each indicator adopts a scoring system of 1–10 points. The final label is also a reform score of 1–10 points. The evaluation metrics are precision and recall.Table 2
The indicators of education and teaching reform.
IndexItemx1Reforms bring improvements in school disciplinex2Reform brings improvement in learning attitudex3Reform brings improvement in teaching attitudex4Reform brings improvement in innovation abilityx5Reforms bring improvements in knowledgex6Reforms bring improvements in decision-making capabilitiesx7Reforms bring improvements in executionx8Reforms bring improvements in informatization capabilitiesx9Reforms bring improvements in academic abilityx10Reform brings improvement in technical reserve capacity


## 4.2. Evaluation on the Number of MSB Layers

It is very helpful to alter convolutional layer and pooling layer for hierarchical extraction as it extracts more representative features. To improve the adaptive extraction capability of the network, it is required to construct a deeper network structure. However, it causes increase in the number of parameters to be trained, which will raise the difficulty of network training. In this work, in addition to the ordinary convolutional layer used in the first layer of the network structure, the other convolutional and pooling layers are replaced by the MSB layer, and a multiscale convolutional neural network is established to evaluate and manage the education and teaching reform achievements. The number of convolution kernels in the previous three convolutional layers is set to 128-64-64, and the number of convolution kernels in each convolutional layer is set to 32. The batch size is set to 32 as an example, and many experiments are performed for statistics. Figure4 shows the influence of the number of MSB layers on the management of reform performance evaluation.Figure 4
Evaluation on the number of MSB layers.It can be seen from the figure that when the number of MSB layers increases, the performance shows an increasing trend. This shows that a deeper network structure can enhance the ability of network adaptive feature extraction and achieve better reform results to evaluate management performance. When the number of MSB layers is set to 3, the best performance is achieved. But as the network continues to deepen, the performance increase is small. Because as the network depth reaches a certain value, the parameters to be trained in the network structure have reached the optimum, and the feature extraction capability tends to be saturated. Therefore, the number of MSB layers in GA-MS-CNN can be set to 3. Under the condition of ensuring performance, the relatively shallow network structure has low complexity, which can speed up network fitting and reduce network training and testing time.


## 4.3. Evaluation on Batch Size

The CNN is based on the backward propagation algorithm to update the parameters of the network structure. The batch size determines the number of samples of the training set selected for each backward propagation. The larger the batch size, it indicates that more training set data is used for weight update in this iteration. This article uses small sample data, so this chapter discusses the impact of batch size from 16 to 64 on network performance. In the experiment, the number of MSB layers is 3; other parameters remain unchanged. The result is shown in Figure5.Figure 5
Evaluation on batch size.From the above figure, as the batch size increases, the performance shows an increasing trend. Because this article is based on a small sample size to train GA-MS-CNN, when a smaller batch size is set, the model weight is updated with a smaller training set sample each time, which is easy to cause the model to fall into shock. Therefore, a larger batch size is used, and more samples are used to represent the data distribution of the overall sample of the training set so that the network structure is better developed in the direction of reducing the loss function. When the batch size is set to 32, its performance is optimal. When the batch size continues to increase, the performance of the model does not increase much, indicating that the network structure has reached the optimum, and the number of samples limits the further optimization of the network structure parameters.


## 4.4. Evaluation on the Number of Convolution Kernels

In a deep CNN, multiple convolution kernels in the convolution layer can extract more comprehensive features. Then, multiple convolutional layers will extract the higher-dimensional and more abstract features as the input of the classifier to realize the evaluation and management of the educational reform achievements. The number of multiconvolutional-layer convolution kernels usually has two distributions. One is a decreasing type. With this decreasing distribution, a larger number of convolution kernels in the first layer can extract more global features, and the training parameters are relatively small, which is beneficial to network fitting. The other is an incremental distribution. The more backward the convolutional layer, the more the number of convolution kernels. This method helps to extract more detailed features in the higher convolutional layer. In order to explore the influence of the number distribution of convolution kernels in the convolution layer on the network performance, in the experiment, the number of MSB layers is 3 and the batch size is set to 32. The experimental results are shown in Figure6.Figure 6
Evaluation on the number of convolution kernels.It can be seen from the figure that the recognition accuracy of the decreasing distribution of the number of multiconvolutional-layer convolution kernels is higher than that of the increasing distribution. Mainly because of the former distribution method, more global features can be extracted in the lower convolutional layer. Then, the alternate convolutional layer and pooling layer adaptively select the extracted features, and there are relatively few training parameters before the fully connected layer, which is conducive to better network fitting and better performance. Experiments show that the decreasing distribution of the number of multiconvolutional layer convolution kernels can effectively evaluate and manage the educational and teaching reform achievements.


## 4.5. Evaluation on MSB

In the experiment, the method used in this article has a good effect on the performance evaluation management of education and teaching reform. In order to further explore the role of multiscale blocks in the established model, the MSB layer in GA-MS-CNN is replaced with a common convolutional layer and a pooling layer. The convolutional layers are also processed by BN to build a CNN network with 4 convolutional layers. The results are shown in Figure7.Figure 7
Evaluation on MSB.From Figure7, GA-MS-CNN has achieved 97.7% precision and 94.8% recall, which is higher than the CNN network structure with 4 convolutional layers. This shows that the same depth of CNN, GA-MS-CNN, a parallel multi-scale feature extraction method, can enhance the feature extraction ability of the network structure and has better modeling performance for the education and teaching reform performance evaluation management.


## 4.6. Evaluation on GA

John Holland and his collaborators developed the GA in 1960s and 1970s. This model or abstraction of biological evolution is founded on the theory of natural selection by Charles Darwin. It reveals the process of natural selection where the fittest individuals are selected for reproduction. The aim of this selection is the reproduction of offspring of the next generation. The current studies apply the GA algorithm to optimize the one-dimensional CNN. In order to verify the effectiveness of this strategy, this paper conducts a comparative experiment. This paper compares the network performance when using GA and when not using GA, and the results are shown in Table3.Table 3
Evaluation on GA.
MethodPrecision (%)Recall (%)GA-MS-CNN97.794.8MS-CNN95.993.7Obviously, when using GA optimization, the network can obtain the best performance. You can get an 1.8% increase in precision, and you can get a 1.1% increase in recall. This proves the effectiveness and correctness of this work using GA to optimize the CNN.


## 5. Conclusion

The teaching reform of higher education is an important part and key target of the national education reform. The goal of higher education reform is to comprehensively improve the quality of higher education and improve the quality of talent training, thereby further enhancing social productivity. The effectiveness of higher education teaching reform is not only directly related to the quality of university personnel training and the important task of serving the society, but also related to the connotative development of Chinese universities and the sustainable development of higher education. As an important subject, the reform of higher education teaching is a comprehensive system engineering. With the continuous deepening of higher education teaching reform, the evaluation and management of the results of higher education teaching reform has become a hot issue. However, there is still a lack of theoretical research and practical experience in this area. Therefore, based on the CNN, this work has designed an intelligent model for the evaluation and management of peer-to-peer education and teaching reform results.The content of this work is based on the following three points:(I)
Construction of a deep one-dimensional CNN connecting the relevant data of college education and teaching reform to the neural network recognizing the self-adaptive feature extraction and precise evaluation and management of the quality of higher education teaching.(II)
Targeting the problem that the use of many hyperparameters in the CNN has a greater impact on the network performance, a genetic algorithm is suggested to optimize the hyperparameters.(III)
Proof of the validity and reliability of the work of this paper through systematic and comprehensive experiment.



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*Source: 5321629-2022-05-28.xml*