# Innovating Pedagogical Practices for Handmade Courses in Preschool Education Using Artificial Intelligence

Authors: Jun Zhao; Na Wang
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

Handmade is an important part of preschool education, which was aimed at improving children’s ability to work with their hands. Preschool education is the most basic and important aspect of a country’s educational system. As a result, individuals pursuing a degree in preschool education take on a lot of responsibility. The preschool education handmade course has become an important component of preschool education due to its practicality and creativity. Preschool education major offers classes in traditional crafts such as paper cutting, paper dyeing, origami, paper three-dimensional modeling, and ornamental painting. The teaching methods for custom-made preschool education courses are always evolving with the progress of society. The question of how to assess the efficacy of unique teaching methodologies for handmade courses has become crucial. This study employs artificial intelligence to create a neural network for assessing the creativity of teaching approaches for handmade courses in preschool education. The following is the specific work: Firstly, the idea, as well as the benefits and drawbacks of genetic algorithms, is investigated. To build an improved genetic algorithm (IGA), the chromosome encoding, fitness function, and three operation options are enhanced. Secondly, by improving the genetic algorithm, the selection of weights and thresholds in the BP neural network model is improved, and a combined model (IGA-BP) is created by integrating the improved genetic algorithm with the BP network. Finally, rigorous and systematic tests confirm the work’s efficacy and viability.


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## Body

## 1. Introduction

In preschool education, handmade education is very important. The primary goal of preschool education handcrafted classes is to develop pupils’ capacity to work with their hands. Today’s preschool education handmade courses mainly cultivate students’ imitation ability, hands-on operation ability, and knowledge ability. Preschool education is the first step in cultivating students, it is the enlightenment education for many students, and has a very important impact on the overall education of the nation. Handmade in preschool education is very usable and innovative, and handmade has become an important course for education professionals. The education courses for students should carry out targeted education and training according to students’ craft skills and learning ability and cultivate students’ four abilities: first is the ability to express the aesthetic sense of shape, second is the learning ability of craft skills, third is the ability to transfer skills widely, and fourth is the independent creativity. Handmade courses have inherent advantages. The main teaching content and learning content revolve around handmade ability, so the classroom atmosphere is more active and relaxed. Teachers should encourage students to cooperate and communicate with each other to solve problems. In addition, teachers should also encourage students in a timely manner. The perfection of handmade products not only gives students confidence in handcrafting but also cultivates students’ conscientiousness in doing things and perfecting the fine quality of details [1–5].When the handmade artwork is completed, it will bring the confidence of the students in handmade, improve the students’ seriousness in doing things, and improve the details of the handicrafts and other good qualities. Different handicrafts also have different meanings. Teachers should interact with students first before evaluating handicrafts and then base their judgments on the meaning and purpose of the students’ completion of handicrafts. Teachers should not use the scoring mechanism, appearance, and perfection to evaluate the handicrafts made by students and should not ignore the thoughtful works only from the appearance of defects. On the premise that the students determine the situation that needs to be improved, the teacher should improve the students’ crafts and recognize the students’ ideological understanding of the crafts. Only by fully respecting the ideas and concepts of students can students make continuous progress and improvement, and the connotation and appearance of students’ handicrafts can be perfected [1, 6–8].The multitype teaching of handmade enables students to fully understand handmade art under the guidance of teachers, deepens art modeling, enables students to generate new concepts, enhances their interest in hand-making, enhances students’ flexibility in thinking and creativity, and promotes students’ comprehensive and efficient development. In the hands-on practice of manual learning, students can exercise their hands-on ability, improve their physical flexibility and coordination, and strengthen their abilities in all aspects, comprehensive application of technologies of various departments, which enrich the content of teaching in classrooms and attract students’ attention. At this stage, students have an irresistible charm for new things and are easily attracted by new things. Therefore, handmade multitype teaching, deepening art modeling, and comprehensive application of technologies from various departments can effectively improve students’ abilities, concentrate students’ attention, and facilitate teachers’ subsequent educational work. Handmade can improve students’ hands-on ability in many ways and improve students’ creative ability in practice. Practice is the only criterion for testing, and practice is also an important criterion for testing students’ coordination abilities in education. Hands-on operation is the nature of students. In the process of hands-on practice, it can effectively improve the development of students’ creative thinking. Innovation is an important source of inspiration in creation. Handmade can help cultivate students’ innovative thinking awareness and promote the formation of students’ comprehensive quality. In the development of students, innovative consciousness can motivate students to move forward [9–14].Today’s educational concept is child-centered, the master of the classroom is the child, and the teacher should cooperate with the auxiliary master and constantly reform and innovate teaching methods. Teachers should combine previous teaching experience and teaching plans to educate children’s development needs. It should combine existing resources, upgrade and reform the teaching mode, and devote itself to research and innovative teaching. Therefore, it is very important to evaluate the innovative effect of the teaching method of handmade courses in preschool education. This paper designs a neural network based on artificial intelligence to complete this task.The following is the paper’s organization paragraph: The paper’s related work is presented in Section2. The method of the proposed study is examined in Section 3. The experiments and results are discussed in Section 4. Finally, the research job is completed in Section 5.


## 2. Related Work

Literature [15] pointed out that the nature and functions of preschool education mainly include enlightenment, unity of education and conservation, public welfare and service, compensation, and connection. It emphasizes that preschool education prepares children for school and must promote the healthy growth of children. At the same time, through the development of rich educational activities and game activities, children are guided to accumulate social experiences and master the necessary emotions, attitudes, knowledge, skills, etc. before entering school, so as to make full preparations for children to enter primary school. Literature [16] has a great agreement on the nature, function, and orientation of preschool education. It pointed out that the nature of preschool education is basic and leading, welfare and public welfare. The function and main task of preschool education are to promote the all-around development of children and lay the foundation for children to enter the nine-year compulsory education, so as to solve the worries of parents, reduce the burden of parenting, and provide education compensation for children in disadvantaged positions. The positioning of preschool education is the first link of the school system and an important part of basic education. The academic aptitude of young children is restricted, according to literature [17], and the cornerstone of early childhood education is not in any type of learning region. It is to allow children to express their natural selves, to amass their experiences, and to explore and develop their interests. Literature [18] believes that early childhood education should provide an appropriate education for the development of each child so that it can develop as a whole. Early childhood education must adhere to the concept of children first and make it the center of education. Literature [19] analyzed the differences between early childhood education and primary education. It pointed out that, in nature, early childhood education is still noncompulsory education. From the perspective of educational tasks, early childhood education pays equal attention to protection and education. From the perspective of curriculum setting, early childhood education implements integrated courses or comprehensive theme courses. From the perspective of educational methods, early childhood education implements games as a basic activity. Therefore, there is a clear difference between early childhood education and primary education in the adjacent school stage and should not be confused with primary education.The literature [20] elaborated the specific requirements of different fields according to the objectives, content requirements, and guiding points of the five fields of the kindergarten curriculum. Therefore, the content of preschool education courses must follow certain principles, not all knowledge. There is no unified national teaching book and specific curriculum format for preschool education. Therefore, the kindergarten-based curriculum is a common task and practical need of every kindergarten. Literature [21] pointed out that the three-level management system of the basic education curriculum is the diversification under the unified national requirements and standards, and the garden-based curriculum is the specification and diversification of the national curriculum. All localities and kindergartens should develop unique kindergarten-based courses under the guidance of the outline and closely integrate their actual needs. Therefore, the development of the garden-based curriculum is also carried out under the guidance of policy documents, and the selection is based on certain values. Literature [22] pointed out that in the content of the kindergarten curriculum, knowledge has the problem of how to choose and choose. The knowledge that can be recognized by young children and appropriate to their age must be carefully selected from human culture. And put forward the basis and standard of selection should be whether knowledge can improve and optimize the quality of children’s learning and life. Literature [23] pointed out that the kindergarten curriculum organizes content around themes, and different curriculum designers may have different theme selection tendencies due to differences in values. Literature [24] pointed out that the kindergarten curriculum has the function of cultural dissemination, and it adjusts or integrates the mainstream culture and subculture in the society at the same time by criticizing and choosing the cultural content of the previous generation. Literatures [25, 26] compare the kindergarten textbook market to a vegetable market. Some textbooks are contaminated and some are fake and shoddy. But it is not easy to be identified, which makes a mixed curriculum entering kindergarten, which is really worrying. Literature [27] pointed out that the textbooks used in kindergartens in various places often coexist with multiple programs and focus on their own educational conditions, characteristics, or other specific circumstances. Some of these curriculum plans or teaching materials are created by relying on the professional strength of preschool education in colleges or kindergarten teachers. Some are developed with the help of local administrative departments or teaching and research units, while others are led by publishing and distribution units to invite professionals and lead preschool teachers from the front line with rich teaching experience to jointly compile.


## 3. Method

This work will combine the improved genetic algorithm with the back-propagation (BP) network and complete the evaluation of the innovation effect of the teaching method of handmade courses in preschool education based on artificial intelligence technology.


### 3.1. BP Network

Three or more layers of feed-forward neural networks without feedback or connections are typical of BP networks. A typical three-layer BP network is shown in Figure1. If a layer resides between the first and last layers of a stack, it is referred to as a hidden layer. Although there are no connections between neurons in the same layer in this neural network, all neurons between layers are connected.Figure 1
The structure of BP network.A guided learning strategy is used to train and learn the BP network. Since the activation values of neurons propagate across each hidden layer, each neuron in the output layer is able to get the true output response of the network when a pair of learning patterns is presented to BP networks. Each neuron in the output layer is compared to its predicted output, and the error between the two is calculated. As a result, each connection weight is rectified one layer at a time from the output to each concealed level and finally back to the input layer. BP network’s learning and training process will come to an end after the error has been decreased to an acceptable level, and the forward computation output-back propagating error loop will be repeated indefinitely. A neural network is constantly being repaired through the back-propagation of errors, which improves its ability to recognize input patterns. BP network’s learning process is known as the error reverse algorithm, which is a learning algorithm in which the error function decreases as the gradient decreases.In the context of BP neural network learning, supervised learning is the norm. There are four processes in the average 3-layer BP network. As soon as a network receives an input mode, it propagates it to the hidden layer units via mode forwarding. The output layer unit processes the hidden layers one at a time before sending them to the output layer unit, which then generates an output mode. This is a process known as forwarding propagation, which updates the state of the system layer-by-layer. An error back-propagation can be used if there is a discrepancy between an output response and an expected output pattern, layer by layer, along the course of the connection, pass the erroneous value, and correct the weight of each layer of the connection. Repetition and alternation are key components of memory training, and the connection settings between the layers must be tweaked to account for any differences between the actual and intended values. It is the goal of education and training to reduce the discrepancy between what you expect and what you get. Learning convergence is the process by which the global error of the network decreases. There will be convergence when the actual output value is nearly identical to what was predicted.Mode forward propagation and error back-propagation, memory training, and learning convergence are all part of the process. The forward- and error-back-propagation operations are continually repeated using a training pattern network for a given set of training patterns. The BP network has been learned if each training mode matches the requirements.The input is propagated forward, and the value of hidden neurons is(1)bj=σ∑i=1nWjixi+αj.The value of each neuron in the output layer is(2)yk=σ∑i=1nWkixi+αk.Use the sum of squared errors as a measure of whether to stop training:(3)E=0.5∑i=1Noi−yi2.If the incorrect sum of squares does not fulfill the requirements, the error signal must be back-propagated. The output layer gradually updates the input layer with new model parameters during this process. Calculate the gradient of the weights using the error function, and then move the weight vector from the output layer to the hidden layer to apply the changes:(4)w′=w−Δw,b′=b−Δb.The pipeline of the BP network is illustrated in Figure2.Figure 2
The pipeline of BP.A mapping is completed by the BP network. It is especially well suited to tackling nonlinear mathematical problems with complicated internal mechanisms and multiple variables influencing the final solution. With a high degree of self-learning capacity and great model generalization and promotion ability, the stable network structure established during the training phase can be directly applied to fresh data. When modeling, you only need to obtain input and output. The model integrates information storage and information processing and has a high degree of parallelism, which can quickly process complex tasks. The model has a certain fault-tolerant ability, and the trained network has strong generalizability, and can still maintain normal operation even if it is subject to local interference and damage. However, with the further development of research, BP neural network also began to expose some shortcomings and deficiencies, mainly the local search algorithm and gradient descent method used in it, which are easy to fall local minimum [28, 29].


### 3.2. Improved Genetic Algorithm

Genetic algorithms are drawn from the concept of biological evolution and are based on the principles of natural selection and genetics. It is a highly powerful global optimization search approach. Artificial evolution is used by the genetic algorithm to optimize the search for the target space in a manner similar to biological evolution. According to Darwinian criteria of survival of the fittest, a better group is constantly formed by evaluating each member against the predetermined target fitness function. A global parallel search is used to find the optimal solution for the optimization group at the same time as searching for the best individual.Selection, crossover, and mutation are the only three basic genetic operators in the basic genetic algorithm. This approach is employed in one of the most basic and extensively used genetic algorithms. The basic genetic algorithm is broken down in the following sections.


#### 3.2.1. Chromosome Encoding Method

Using a fixed-length binary string to represent individuals in the population and a binary symbol set for each allele, the basic genetic algorithm encodes chromosomes. Random integers with a uniform distribution can be used to create the gene values for each member of the starting population.


#### 3.2.2. Individual Fitness Evaluation

Using a simple genetic algorithm, the possibility of each item being inherited into the next generation is determined by the probability proportional to their individual fitness. The fitness of all individuals must be positive or zero in order to calculate this probability accurately. Individual fitness can be calculated from objective function values by following a set of conversion rules, which must be specified in advance for each type of problem. This is especially true for cases in which the objective function value is negative.


#### 3.2.3. Genetic Operators

There are three fundamental types of genetic operators in the genetic algorithm. Selective selection, crossover, and mutation operations all use the same fundamental bit mutation operator or uniform mutation operator for their respective operations.Regardless of the problem domain or kind, genetic algorithms provide a general framework for solving complex system optimization challenges. The following stages can be used to build a genetic algorithm to address a real-world application problem that calls for optimization calculations: identify the decision factors and their restrictions, establish an optimization model, which means deciding on the type of objective function and the mathematical description or quantification approach for that objective function, identify the chromosomal encoding method that can be implemented, figure out how to decode the data, which means figuring out the relationship between each unique genotype and its matching phenotype. Individual fitness can be assessed quantitatively. It is also necessary to consider genetic operators in terms of their operating procedures, such as how crossovers and mutations are handled. The key working parameters of the genetic algorithm should be specified. As can be observed from the preceding construction processes, a genetic algorithm’s coding method and genetic operator design are two important subjects to consider while building a genetic algorithm. They are also two vital steps in creating a genetic algorithm. For various optimization problems, multiple coding approaches and genetic operators with various operations are necessary. Figure3 shows the fundamental genetic algorithm’s operation flow.Figure 3
The pipeline of GA.The specific optimization of IGA mainly includes five parts. The chromosomes are encoded differently, the function is selected, and one that meets the fitness function is found. The third to fifth steps are three genetic operations, which can be divided into selection operations, crossover operations, and mutation operations.The coding of chromosomes is an important part of the genetic algorithm, and it is also an important part of optimizing the BP neural network. This paper decides to use real numbers for encoding. The initial weights and initial thresholds determine the length of chromosome encoding, and the BP neural network determines which thresholds and weights.The fitness level can basically determine the quality of the network prediction model, and fitness level is determined by absolute error. Only when the gap of absolute error is small can it prove prediction accuracy is better, and the value of fitness is equal to the gap between test value and actual value. Assuming thatNth individual fitness is F, and its corresponding absolute error is Ex, then the formula for the function value of the fitness value is(5)Fi=EXi.From this formula, fitness can express the direct performance of the individual to varying degrees. The genetic algorithm will reduce the fitness value of the individual to be optimized as much as possible until the fitness value is 0.The three genetic manipulations are crossover, selection, and mutation. Next, three different manipulations are analyzed in detail. There are many methods for selecting operations, such as the well-known roulette method. The roulette method is mostly used in the traditional genetic algorithm BP network prediction model. However, the fitness used in this paper is the absolute error, whichever is closer to 0 and smaller, the better the prediction effect. However, the traditional selection operation method is not suitable for the experimental method. In this paper,i represents the individual, F represents the corresponding fitness, l represents the adjustment coefficient, and the probability is P. The formula for finding the probability is as follows:(6)Pi=l/Fi∑i=1Nl/Fi.Because the real numbers are used for chromosome encoding, the crossover operation is the crossover between real numbers:(7)axj=1−μaxj+∂ayj,ayj=1−μayj+∂axj.The mutation operation is(8)fg=d1−ggmax2,cmn=cmn+cmn−cmaxfg,r>0.5,cmn=cmn+cmin−cmnfg,r<0.5.


### 3.3. IGA-BP Network

Because the BP neural network must be given appropriate weights and thresholds when building the model, however, due to the characteristics of the BP network, the BP network is very sensitive to the selection of weights as well as thresholds. Once the weights or thresholds are improperly selected, the accuracy of the BP network will be greatly reduced. If it is heavy, it causes the network to fail for training. If it is light, it will make the whole prediction result show a large difference. The result may be that the training results of the BP network are not as good as those of some traditional linear prediction models.On the premise of discarding the traditional roulette method in genetic algorithms, this paper adopts an operation method that improves fitness. An optimization model with the improvement of GA is proposed to optimize the content structure for the BP network, which is referred to as IGA-BP. The model is divided into three parts:(1)
Determine various structures of the BP neural network, mainly to determine the number of layers in the BP network and required nodes(2)
Optimizing the network with IGA, the main content is to adjust initial weights as well as thresholds in the network according to the selection in the genetic algorithm(3)
Apply the optimized model to practice, and test whether the optimized model is better than the traditional network


## 3.1. BP Network

Three or more layers of feed-forward neural networks without feedback or connections are typical of BP networks. A typical three-layer BP network is shown in Figure1. If a layer resides between the first and last layers of a stack, it is referred to as a hidden layer. Although there are no connections between neurons in the same layer in this neural network, all neurons between layers are connected.Figure 1
The structure of BP network.A guided learning strategy is used to train and learn the BP network. Since the activation values of neurons propagate across each hidden layer, each neuron in the output layer is able to get the true output response of the network when a pair of learning patterns is presented to BP networks. Each neuron in the output layer is compared to its predicted output, and the error between the two is calculated. As a result, each connection weight is rectified one layer at a time from the output to each concealed level and finally back to the input layer. BP network’s learning and training process will come to an end after the error has been decreased to an acceptable level, and the forward computation output-back propagating error loop will be repeated indefinitely. A neural network is constantly being repaired through the back-propagation of errors, which improves its ability to recognize input patterns. BP network’s learning process is known as the error reverse algorithm, which is a learning algorithm in which the error function decreases as the gradient decreases.In the context of BP neural network learning, supervised learning is the norm. There are four processes in the average 3-layer BP network. As soon as a network receives an input mode, it propagates it to the hidden layer units via mode forwarding. The output layer unit processes the hidden layers one at a time before sending them to the output layer unit, which then generates an output mode. This is a process known as forwarding propagation, which updates the state of the system layer-by-layer. An error back-propagation can be used if there is a discrepancy between an output response and an expected output pattern, layer by layer, along the course of the connection, pass the erroneous value, and correct the weight of each layer of the connection. Repetition and alternation are key components of memory training, and the connection settings between the layers must be tweaked to account for any differences between the actual and intended values. It is the goal of education and training to reduce the discrepancy between what you expect and what you get. Learning convergence is the process by which the global error of the network decreases. There will be convergence when the actual output value is nearly identical to what was predicted.Mode forward propagation and error back-propagation, memory training, and learning convergence are all part of the process. The forward- and error-back-propagation operations are continually repeated using a training pattern network for a given set of training patterns. The BP network has been learned if each training mode matches the requirements.The input is propagated forward, and the value of hidden neurons is(1)bj=σ∑i=1nWjixi+αj.The value of each neuron in the output layer is(2)yk=σ∑i=1nWkixi+αk.Use the sum of squared errors as a measure of whether to stop training:(3)E=0.5∑i=1Noi−yi2.If the incorrect sum of squares does not fulfill the requirements, the error signal must be back-propagated. The output layer gradually updates the input layer with new model parameters during this process. Calculate the gradient of the weights using the error function, and then move the weight vector from the output layer to the hidden layer to apply the changes:(4)w′=w−Δw,b′=b−Δb.The pipeline of the BP network is illustrated in Figure2.Figure 2
The pipeline of BP.A mapping is completed by the BP network. It is especially well suited to tackling nonlinear mathematical problems with complicated internal mechanisms and multiple variables influencing the final solution. With a high degree of self-learning capacity and great model generalization and promotion ability, the stable network structure established during the training phase can be directly applied to fresh data. When modeling, you only need to obtain input and output. The model integrates information storage and information processing and has a high degree of parallelism, which can quickly process complex tasks. The model has a certain fault-tolerant ability, and the trained network has strong generalizability, and can still maintain normal operation even if it is subject to local interference and damage. However, with the further development of research, BP neural network also began to expose some shortcomings and deficiencies, mainly the local search algorithm and gradient descent method used in it, which are easy to fall local minimum [28, 29].


## 3.2. Improved Genetic Algorithm

Genetic algorithms are drawn from the concept of biological evolution and are based on the principles of natural selection and genetics. It is a highly powerful global optimization search approach. Artificial evolution is used by the genetic algorithm to optimize the search for the target space in a manner similar to biological evolution. According to Darwinian criteria of survival of the fittest, a better group is constantly formed by evaluating each member against the predetermined target fitness function. A global parallel search is used to find the optimal solution for the optimization group at the same time as searching for the best individual.Selection, crossover, and mutation are the only three basic genetic operators in the basic genetic algorithm. This approach is employed in one of the most basic and extensively used genetic algorithms. The basic genetic algorithm is broken down in the following sections.


### 3.2.1. Chromosome Encoding Method

Using a fixed-length binary string to represent individuals in the population and a binary symbol set for each allele, the basic genetic algorithm encodes chromosomes. Random integers with a uniform distribution can be used to create the gene values for each member of the starting population.


### 3.2.2. Individual Fitness Evaluation

Using a simple genetic algorithm, the possibility of each item being inherited into the next generation is determined by the probability proportional to their individual fitness. The fitness of all individuals must be positive or zero in order to calculate this probability accurately. Individual fitness can be calculated from objective function values by following a set of conversion rules, which must be specified in advance for each type of problem. This is especially true for cases in which the objective function value is negative.


### 3.2.3. Genetic Operators

There are three fundamental types of genetic operators in the genetic algorithm. Selective selection, crossover, and mutation operations all use the same fundamental bit mutation operator or uniform mutation operator for their respective operations.Regardless of the problem domain or kind, genetic algorithms provide a general framework for solving complex system optimization challenges. The following stages can be used to build a genetic algorithm to address a real-world application problem that calls for optimization calculations: identify the decision factors and their restrictions, establish an optimization model, which means deciding on the type of objective function and the mathematical description or quantification approach for that objective function, identify the chromosomal encoding method that can be implemented, figure out how to decode the data, which means figuring out the relationship between each unique genotype and its matching phenotype. Individual fitness can be assessed quantitatively. It is also necessary to consider genetic operators in terms of their operating procedures, such as how crossovers and mutations are handled. The key working parameters of the genetic algorithm should be specified. As can be observed from the preceding construction processes, a genetic algorithm’s coding method and genetic operator design are two important subjects to consider while building a genetic algorithm. They are also two vital steps in creating a genetic algorithm. For various optimization problems, multiple coding approaches and genetic operators with various operations are necessary. Figure3 shows the fundamental genetic algorithm’s operation flow.Figure 3
The pipeline of GA.The specific optimization of IGA mainly includes five parts. The chromosomes are encoded differently, the function is selected, and one that meets the fitness function is found. The third to fifth steps are three genetic operations, which can be divided into selection operations, crossover operations, and mutation operations.The coding of chromosomes is an important part of the genetic algorithm, and it is also an important part of optimizing the BP neural network. This paper decides to use real numbers for encoding. The initial weights and initial thresholds determine the length of chromosome encoding, and the BP neural network determines which thresholds and weights.The fitness level can basically determine the quality of the network prediction model, and fitness level is determined by absolute error. Only when the gap of absolute error is small can it prove prediction accuracy is better, and the value of fitness is equal to the gap between test value and actual value. Assuming thatNth individual fitness is F, and its corresponding absolute error is Ex, then the formula for the function value of the fitness value is(5)Fi=EXi.From this formula, fitness can express the direct performance of the individual to varying degrees. The genetic algorithm will reduce the fitness value of the individual to be optimized as much as possible until the fitness value is 0.The three genetic manipulations are crossover, selection, and mutation. Next, three different manipulations are analyzed in detail. There are many methods for selecting operations, such as the well-known roulette method. The roulette method is mostly used in the traditional genetic algorithm BP network prediction model. However, the fitness used in this paper is the absolute error, whichever is closer to 0 and smaller, the better the prediction effect. However, the traditional selection operation method is not suitable for the experimental method. In this paper,i represents the individual, F represents the corresponding fitness, l represents the adjustment coefficient, and the probability is P. The formula for finding the probability is as follows:(6)Pi=l/Fi∑i=1Nl/Fi.Because the real numbers are used for chromosome encoding, the crossover operation is the crossover between real numbers:(7)axj=1−μaxj+∂ayj,ayj=1−μayj+∂axj.The mutation operation is(8)fg=d1−ggmax2,cmn=cmn+cmn−cmaxfg,r>0.5,cmn=cmn+cmin−cmnfg,r<0.5.


## 3.2.1. Chromosome Encoding Method

Using a fixed-length binary string to represent individuals in the population and a binary symbol set for each allele, the basic genetic algorithm encodes chromosomes. Random integers with a uniform distribution can be used to create the gene values for each member of the starting population.


## 3.2.2. Individual Fitness Evaluation

Using a simple genetic algorithm, the possibility of each item being inherited into the next generation is determined by the probability proportional to their individual fitness. The fitness of all individuals must be positive or zero in order to calculate this probability accurately. Individual fitness can be calculated from objective function values by following a set of conversion rules, which must be specified in advance for each type of problem. This is especially true for cases in which the objective function value is negative.


## 3.2.3. Genetic Operators

There are three fundamental types of genetic operators in the genetic algorithm. Selective selection, crossover, and mutation operations all use the same fundamental bit mutation operator or uniform mutation operator for their respective operations.Regardless of the problem domain or kind, genetic algorithms provide a general framework for solving complex system optimization challenges. The following stages can be used to build a genetic algorithm to address a real-world application problem that calls for optimization calculations: identify the decision factors and their restrictions, establish an optimization model, which means deciding on the type of objective function and the mathematical description or quantification approach for that objective function, identify the chromosomal encoding method that can be implemented, figure out how to decode the data, which means figuring out the relationship between each unique genotype and its matching phenotype. Individual fitness can be assessed quantitatively. It is also necessary to consider genetic operators in terms of their operating procedures, such as how crossovers and mutations are handled. The key working parameters of the genetic algorithm should be specified. As can be observed from the preceding construction processes, a genetic algorithm’s coding method and genetic operator design are two important subjects to consider while building a genetic algorithm. They are also two vital steps in creating a genetic algorithm. For various optimization problems, multiple coding approaches and genetic operators with various operations are necessary. Figure3 shows the fundamental genetic algorithm’s operation flow.Figure 3
The pipeline of GA.The specific optimization of IGA mainly includes five parts. The chromosomes are encoded differently, the function is selected, and one that meets the fitness function is found. The third to fifth steps are three genetic operations, which can be divided into selection operations, crossover operations, and mutation operations.The coding of chromosomes is an important part of the genetic algorithm, and it is also an important part of optimizing the BP neural network. This paper decides to use real numbers for encoding. The initial weights and initial thresholds determine the length of chromosome encoding, and the BP neural network determines which thresholds and weights.The fitness level can basically determine the quality of the network prediction model, and fitness level is determined by absolute error. Only when the gap of absolute error is small can it prove prediction accuracy is better, and the value of fitness is equal to the gap between test value and actual value. Assuming thatNth individual fitness is F, and its corresponding absolute error is Ex, then the formula for the function value of the fitness value is(5)Fi=EXi.From this formula, fitness can express the direct performance of the individual to varying degrees. The genetic algorithm will reduce the fitness value of the individual to be optimized as much as possible until the fitness value is 0.The three genetic manipulations are crossover, selection, and mutation. Next, three different manipulations are analyzed in detail. There are many methods for selecting operations, such as the well-known roulette method. The roulette method is mostly used in the traditional genetic algorithm BP network prediction model. However, the fitness used in this paper is the absolute error, whichever is closer to 0 and smaller, the better the prediction effect. However, the traditional selection operation method is not suitable for the experimental method. In this paper,i represents the individual, F represents the corresponding fitness, l represents the adjustment coefficient, and the probability is P. The formula for finding the probability is as follows:(6)Pi=l/Fi∑i=1Nl/Fi.Because the real numbers are used for chromosome encoding, the crossover operation is the crossover between real numbers:(7)axj=1−μaxj+∂ayj,ayj=1−μayj+∂axj.The mutation operation is(8)fg=d1−ggmax2,cmn=cmn+cmn−cmaxfg,r>0.5,cmn=cmn+cmin−cmnfg,r<0.5.


## 3.3. IGA-BP Network

Because the BP neural network must be given appropriate weights and thresholds when building the model, however, due to the characteristics of the BP network, the BP network is very sensitive to the selection of weights as well as thresholds. Once the weights or thresholds are improperly selected, the accuracy of the BP network will be greatly reduced. If it is heavy, it causes the network to fail for training. If it is light, it will make the whole prediction result show a large difference. The result may be that the training results of the BP network are not as good as those of some traditional linear prediction models.On the premise of discarding the traditional roulette method in genetic algorithms, this paper adopts an operation method that improves fitness. An optimization model with the improvement of GA is proposed to optimize the content structure for the BP network, which is referred to as IGA-BP. The model is divided into three parts:(1)
Determine various structures of the BP neural network, mainly to determine the number of layers in the BP network and required nodes(2)
Optimizing the network with IGA, the main content is to adjust initial weights as well as thresholds in the network according to the selection in the genetic algorithm(3)
Apply the optimized model to practice, and test whether the optimized model is better than the traditional network


## 4. Experiment and Discussion

### 4.1. Dataset and Metric

This work uses a self-made dataset to evaluate the innovativeness of teaching methods for handcrafted curricula in preschool education. The dataset contains a total of 25,937 samples, of which 18,038 samples are training samples and the remaining 7,899 samples are test samples. The features of each sample are the corresponding evaluation indicators, as illustrated in Table1, and the labels are the corresponding innovation levels. This work uses precision and recall to evaluate actual network performance.Table 1
The detailed feature indexes.
IndexItemX1Innovativeness of campaign goalsX2Innovativeness of educational contentX3Reasonableness of age characteristicsX4Rationality of the observational evaluationX5Reflections on the effect of educationX6Course safetyX7Innovation in event designX8Innovation to ease convergenceX9Innovativeness of educational strategies


### 4.2. Training Loss

In a neural network, the training loss is an important statistic for determining if the network can converge. This work undertakes tests to evaluate the loss at various phases of network training in order to verify the network’s convergence. Figure4 depicts the outcomes of the experiment.Figure 4
Training loss of IGA-BP.At the beginning of training, as the training epoch increases, the loss of the network decreases significantly. However, when the epoch reaches a certain value, the loss decreases very little, which means that the network has converged at this time. This proves the preliminary feasibility of the IGA-BP method designed in this paper.


### 4.3. Method Comparison

To verify the effectiveness of the IGA-BP method, this work compares it with other methods. The methods compared include LG, radial basis function kernel (RBF), and support vector machine (SVM). The experimental results are illustrated in Table2.Table 2
Result of method comparison.
MethodPrecisionRecallLG86.3083.60RBF90.2087.70SVM92.8091.20IGA-BP95.6093.90It is obvious that the method proposed in this work can achieve the highest performance: 95.6% precision and 93.9% recall, compared with the best-listed method SVM, which can obtain 2.8% precision improvement and 2.7% recall improvement. This can verify the effectiveness of our proposed method.


### 4.4. Effectiveness of IGA

In this work, in order to alleviate the local optimal problem of BP, the IGA algorithm is introduced. To verify the effectiveness of this strategy, first, compare the network performance without using the IGA algorithm and using the IGA algorithm. The experimental results are shown in Figure5.Figure 5
Comparison of BP and IGA-BP.Compared to not using IGA strategy, IGA-BP can obtain 2.3% precision improvement and 1.7% recall improvement. This proves the feasibility and correctness of this work using the IGA strategy to initialize the weights and thresholds of the neural network. To further verify the feasibility of improving the traditional GA algorithm, another comparative experiment was conducted in this work to compare the network performance when using GA and using IGA for optimization. The experimental results are shown in Figure6.Figure 6
Comparison of GA-BP and IGA-BP.Obviously, after improving the traditional GA algorithm, the IGA-BP algorithm can obtain better precision and recall. This proves the effectiveness of the improvement strategy proposed in this work.


## 4.1. Dataset and Metric

This work uses a self-made dataset to evaluate the innovativeness of teaching methods for handcrafted curricula in preschool education. The dataset contains a total of 25,937 samples, of which 18,038 samples are training samples and the remaining 7,899 samples are test samples. The features of each sample are the corresponding evaluation indicators, as illustrated in Table1, and the labels are the corresponding innovation levels. This work uses precision and recall to evaluate actual network performance.Table 1
The detailed feature indexes.
IndexItemX1Innovativeness of campaign goalsX2Innovativeness of educational contentX3Reasonableness of age characteristicsX4Rationality of the observational evaluationX5Reflections on the effect of educationX6Course safetyX7Innovation in event designX8Innovation to ease convergenceX9Innovativeness of educational strategies


## 4.2. Training Loss

In a neural network, the training loss is an important statistic for determining if the network can converge. This work undertakes tests to evaluate the loss at various phases of network training in order to verify the network’s convergence. Figure4 depicts the outcomes of the experiment.Figure 4
Training loss of IGA-BP.At the beginning of training, as the training epoch increases, the loss of the network decreases significantly. However, when the epoch reaches a certain value, the loss decreases very little, which means that the network has converged at this time. This proves the preliminary feasibility of the IGA-BP method designed in this paper.


## 4.3. Method Comparison

To verify the effectiveness of the IGA-BP method, this work compares it with other methods. The methods compared include LG, radial basis function kernel (RBF), and support vector machine (SVM). The experimental results are illustrated in Table2.Table 2
Result of method comparison.
MethodPrecisionRecallLG86.3083.60RBF90.2087.70SVM92.8091.20IGA-BP95.6093.90It is obvious that the method proposed in this work can achieve the highest performance: 95.6% precision and 93.9% recall, compared with the best-listed method SVM, which can obtain 2.8% precision improvement and 2.7% recall improvement. This can verify the effectiveness of our proposed method.


## 4.4. Effectiveness of IGA

In this work, in order to alleviate the local optimal problem of BP, the IGA algorithm is introduced. To verify the effectiveness of this strategy, first, compare the network performance without using the IGA algorithm and using the IGA algorithm. The experimental results are shown in Figure5.Figure 5
Comparison of BP and IGA-BP.Compared to not using IGA strategy, IGA-BP can obtain 2.3% precision improvement and 1.7% recall improvement. This proves the feasibility and correctness of this work using the IGA strategy to initialize the weights and thresholds of the neural network. To further verify the feasibility of improving the traditional GA algorithm, another comparative experiment was conducted in this work to compare the network performance when using GA and using IGA for optimization. The experimental results are shown in Figure6.Figure 6
Comparison of GA-BP and IGA-BP.Obviously, after improving the traditional GA algorithm, the IGA-BP algorithm can obtain better precision and recall. This proves the effectiveness of the improvement strategy proposed in this work.


## 5. Conclusion

With the emergence of the information society, there is a pressing need for individuals with a broad knowledge base who are adaptable to complex societal changes and capable of long-term development. Early childhood should be used to create the groundwork for this type of talent development. Preschool education must adapt to the needs of the times in terms of talent development and the implementation of specific knowledge education for young children. The study of preschool education knowledge supply is not only a reflection on preschool education knowledge but also an analysis of the complex relationship network of preschool education knowledge supply and the problems of interest competition and power distribution in the process of knowledge supply. Handmade education plays a very important role in preschool education. The main purpose of preschool education handmade courses is to cultivate students’ good hands-on ability. Today’s preschool education handmade courses mainly cultivate students’ imitation ability, hands-on operation ability, and knowledge-ability. With the change of society, the teaching methods of handmade courses in preschool education are constantly innovating. How to evaluate the effectiveness of the innovation of handmade course teaching methods has become a very important topic. This work builds a neural network to evaluate the innovative effectiveness of preschool handcrafted curriculum teaching methods. The specific content is that the genetic algorithm has been enhanced, a mode of the classic genetic algorithm roulette technique has been improved, and the moderation function has been adopted, allowing the BP neural network’s starting weight and threshold to reach a higher standard. The revised genetic algorithm is substituted into the BP neural network model to optimize it, resulting in a new BP neural network. The data test and experiment were carried out, and it was found that in terms of training speed and prediction accuracy, the BP neural network optimized by the improved genetic algorithm has better accuracy and can better complete the prediction.



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