# Improvement Design of Functional Modules of College English Network Teaching System Based on Decision Tree Algorithm

Authors: Hui Li
Journal: Mobile Information Systems
 (2022)

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## Abstract

English courses are a compulsory course for non-English core courses. Studying English at university is an important part of studying at a non-English university. The purpose of a university education in English is to enhance the comprehensive language skills of students in English. In this way, they can communicate effectively in English during future work, study, and social interactions, while strengthening their ability to meet the needs of social development and international exchange. The functional improvement design of the college English network teaching system will help the system to provide technical support and services for college English teaching more effectively. It can more effectively cultivate students’ language communicative ability and autonomous learning ability. This article first improves the practical units of the education system and then adds the decision tree algorithm. It uses improved decision-making algorithms to evaluate student performance with an improved learning system. The number of leaf nodes of the improved decision tree algorithm on the three datasets is 19, 14, and 15, respectively, and the number of summary points is 27, 23, and 7, respectively.


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## Body

## 1. Introduction

In recent years, China’s higher education has been developing rapidly with the increase of investment in education, the growth of the number of students, and the expansion of school-running channels. Educational reform must constantly adapt to changes in the field of talents, including reforms in educational ideas and concepts, curriculum systems and teaching materials, teaching methods, and educational management. The reform of the functional modules of the education system is crucial to the education of students.In higher education, the assessment of student achievement is one of the most important aspects of higher education. Although not as important as the college entrance examination, student evaluation also has a potential management role, especially in the introduction and promotion of credit transfer. Scientific and reasonable evaluation of students’ academic performance is directly related to the talents cultivated by a school. It is also related to the students’ own future and destiny to a certain extent. Education leaders and administrators at all levels have always taken this seriously.Regarding the decision tree algorithm, relevant scientists have done the following research. Li et al. proposed an improved Kalman filter-based gradient boosting decision tree algorithm. Because the performance degradation of GBDT is usually caused by noise overfitting in the signal, so through the work of the implementation, service providers can predict where and when network congestion will occur. Therefore, they can take action in advance [1]. Sonza et al. presented IT-based solutions. The priority system is based on gender analysis. They also designed work with evaluation systems such as decision-making algorithms. This approach is more appropriate to find appropriate solutions to improve higher education and achieve equity goals [2]. Ayinla proposed a collaborative pruning model to improve the classification efficiency of decision trees. This model consistently outperforms other adopted decision tree classification algorithms. It has the smallest tree size and almost perfect classification [3]. Segin et al. sought to accurately predict gender using the latest decision-making algorithms. It determines the coordinates of the signal and sets parameters such as angles and lengths with different combinations. A multi-parameter decision beam algorithm for pelvic scanners can improve the performance of gender prediction [4]. Mao and Zhang used the decision tree and ambiguous mathematics as the basis for modeling algorithms. They developed an algorithm based on an analysis of traditional algorithms. Based on the improved decision-making algorithms, an assessment system for innovation and entrepreneurship education was developed in secondary and university education, and various proposals were presented based on experimental results [5]. Mijwil and Abattan used genetic algorithms to reduce the effects of obesity on decision trees. This dataset analysis consists of four databases. The experimental results confirm the effectiveness of the genetic algorithm in reducing the effectiveness of obesity in the four datasets and increasing the confidence factor in the decision tree [6]. Balasubramaniam proposed that the integration of decision trees was carried out by using a regression tree algorithm. There is a correlation between the skills and some of the analytical rules [7]. Yu et al. investigated the influence and importance of case safety on the implementation of the sorting algorithm decision tree. It demonstrates the reliability of the case in the formulation and trimming of classification and regression beam algorithms. Algorithms can dramatically improve general performance and prevent obesity to some degree [8]. Kumar used decision tree algorithms and functional combinations to represent tourist destinations with a database of previously discovered visitors. It allows decisions to be made using a two-step property selection process to minimize system integration [9]. Nurkholis and Sittanggang aimed to assess the suitability of oil palm soils by using local decision tree algorithms. The algorithm is a general modification of the decision tree for the classification of spatial data and the addition of ground connections. This study led to three samples. The best sample is obtained based on the accuracy of optimization and sample time [10]. Hammed proposed decision tree algorithms that are augmented by regression analysis to create a highly efficient fraud detection system. The system covers all aspects of credit card fraud [11]. Chanmee and Kesorn presented a knowledge-based approach to improve data quality. The method uses ontology as background information to aid in the classification of decisions in data processing. Experiments show that the proposed method can actually improve the data classification process [12]. Bian and Wang explored a new system of school-business collaboration based on improved decision-making algorithms. They have improved the decision tree algorithm and made the improved algorithm more suitable for collaboration between schools and companies. Based on cloud computing and data collection, they developed a new system of collaboration between schools and companies [13]. Li et al. analyzed two packages for mobile activity data to predict future mobile traffic in the city. They proposed an improvement in the beam algorithm. They found that, by performing this work, service providers were able to predict where and when Internet congestion would occur [1]. Zhao discussed the basic criteria for personal credit assessment. Using blockchain, decision trees, and other techniques, he designed the credit assessment process and developed his own credit rating system. Experimental analysis shows that this technology can improve the transparency of personal credit information in online finance [14]. The above study carried out a detailed analysis of the decision beam algorithm. It is no wonder that these studies have made a significant contribution to the development of similar fields. We can learn a lot from methods and data analysis. However, little research has been done on decision-making algorithms in the field of intellectual production and it is necessary to fully implement these algorithms for research in this field.In this paper, we propose a new algorithm to simplify decision trees and an improved idea to solve the polynomial distortion problem by introducing weighting. In particular, the algorithm improves in computational time and complexity and solves the problem of polynomial bias by selecting test features. It does not require complicated operations, so that the classification results do not change. It uses the improved decision tree algorithm to analyze and process students.


## 2. Improvement Methods of the Functional Modules of the Teaching System

### 2.1. Decision Tree Algorithm

Decision tree algorithm is one of the most widely used data mining techniques. This basic algorithm builds a top-down decision tree using a recursive approach. The input for building a decision tree is a dataset of class labels. The dataset is divided into two parts, one of which is used as training data for building decision trees and the other as test data to verify the accuracy of decision trees. The constructed decision trees are divided into two categories: internal nodes, which represent attributes or groups of attributes; leaf nodes, which represent categories that can be classified. During classification, internal nodes are used to compare attribute values. It then determines the downward branch of the node, which is finally used for the classification result of the top node of the decision tree. The specific process is as follows.The first step is to build and optimize a decision tree model using the training set, describing the known dataset. It assumes that each item belongs to a predefined category. The dataset analyzed to create the model is the training dataset. The items in the training dataset are called training samples and are randomly selected from the sample population. Since a class name is assigned to each training sample, this classification method is also known as supervised learning. The construction of the decision tree is recursive until all leaf nodes contain the same class assignment. This process involves knowledge acquisition and machine learning from datasets. Some algorithms also include a pruning process to reduce the effect of noisy data on the correct classification rate. If the decision tree it builds does not represent the internal rules of the data well, adjust the decision tree by pruning and adding nodes until a “good” decision tree is built.In the second step, a decision tree model is used for classification. The accuracy of the constructed decision tree model is evaluated before classification. There are several ways to evaluate the classification performance of decision trees. One of them is the holdout method, which uses a set of class-labeled samples to generate samples at random, independent of the training samples. The training dataset can be used to estimate the test set. But in this case, the estimates are likely to be optimistic. The test set is used because the classification model in training fits the data well. For test data, it compares the trained test results with the known class labels of the sample tree. If the accuracy of the comparison is acceptable, then the decision tree is used to classify the class labels, unknown data, or elements of the samples.The classification process consists of searching the input data starting from the base node, checking the attribute value of each internal node in turn, until reaching a leaf node that provides the input classification. Choosing different field values results in different final classifications. This will affect the growth rate and performance of the decision tree and eventually lead to inconsistent information on the classification indicators produced. Therefore, the technical problem and the main problem of decision tree algorithm is how to choose suitable features for decomposition, that is, which feature metric to use for feature decomposition. The main difference between decision tree algorithms is the selection of features. The decision tree classification method flow is shown in Figure1.Figure 1
Decision tree classification method flow.A method similar to the one above is used in the study of decision trees. It compares the attribute values with the internal nodes in the decision tree and evaluates the downward branch of the node according to the different attribute values and draws inferences about the leaf nodes in the decision tree. Thus, the path from the roots to the leaf nodes complies with the compound law. The whole decision tree conforms to a set of rules of expression. The decision tree of the generation algorithm is divided into two steps. One is the tree generation, which starts with all the data from the root node and then executes the data sharing operation with each other. The second is to cut down trees to get some data that may be strong or abnormal. The condition for terminating the decision tree sharing is that the node data belongs to the same category. There are no features that can be used to share information.As a popular classification algorithm, decision trees have the following advantages: they can generate easy-to-understand classification rules. They can handle many different types of features at the same time, such as numbers, symbols, etc. Since decision trees use only a fixed or continuous set of variables, which are independent variables, they are robust to measurement error or bias in the independent variables. Some decision trees can be handled directly.(1)IoD=−∑u=1klcu,Dloglcu,D,SptD,T=−∑u=1qDuDlogDuD.D is sample collection, C is category collection, D is the potential of the set, and lcu,D is the proportion in the sample set.(2)Wv=1B∑uwuv.wuv is the score of the algorithm on the dataset, and Wv is average score of the algorithm.(3)εF2=15Bkk+1∑vWv2−kk+125.B is the number of datasets and k is the number of decision tree algorithms.(4)z=Wu−Wvkk+1/6B.z determines the probability of a normal distribution.(5)CS=wαkk+112B.CS is critical value.(6)RX=∑u=1jLu+BuL+B∗RGu.RX is information entropy of root classification.(7)CD=∑ubulog2bbu+c−12log2b2+log2πc/2ιc/2.CD is the cost of building a decision tree.(8)Pl,b=−ll+blog2ll+b+bl+blog2bl+b.Pl,b is the amount of information required for classification.(9)WX=∑u=1jlu+bul+bWGu.X is the root of the decision tree, l is the number of positive examples, and b is the number of counterexamples.(10)UT=−∑u=1blu×log2lu.T is training set and p is distribution probability.(11)Uv=−loga−1∑uazuvlogzuv.Uv is information entropy value and v is ordinal number of the indicator.(12)qv=Gv∑v=1bGv,∑v=1bqv=1.qv is relative weights of indicators.(13)UD=−∑u=1alogLu,WX=∑v=1jUDvb1v+…+bavb.WX is information entropy for dividing subsets and buv is the number in the attribution category.(14)WX=b1UD1+b2UD2b,GX>log2b−1+ΔX,D1,D2b.WX is division point information entropy and b is the size of the sample.(15)DpX=−∑u=1qdudlog2du.DpX is breadth and balance.(16)limb⟶∞lϕb−blblw≤m=12π∫−∞πe−t2/2dt,c=lf−ww1−w/B>z.ϕn is random variables, B is total number of instances, f is misclassification rate of training data, and w is probability of misclassification.Unclear decision tree algorithms are the algorithms of traditional decision tree algorithms. They include vague mathematical terms. A node of a tree can be considered as an indeterminate subset of decision size. Each pattern belongs to a specific size for each node. Each leaf node of the hairy corresponds to the bitter law. The whole vague decision tree is a collection of many vague rules. Each obscure rule has a specific membership.It creates a decision tree model from a specific exercise dataset that allows it to classify cases correctly. Decision tree analysis is a ranking database principle produced by a training database. There can be more than one decision tree that classifies exercise data correctly. When selecting a decision tree, the decision tree should be chosen that is inconsistent with the training data and has good general properties. And the opportunity model selected conditions not only fit well with the training data, but also predict the unknown data.Classification accuracy describes the classification effect of a decision tree model when applied to a test dataset or an unknown dataset. This measure provides the most direct information and is crucial for policymakers. A good decision tree model extracts the most relevant and valuable information from a large training dataset and processes it into a set of rules that clearly describe the classification problem. Finally, a decision tree with high accuracy is built. Accurate decision information and classification rules will always bring convenience to decision-makers, and also bring great benefits and value to decision-makers.According to the different attributes of the decision tree, there are the following different decision trees:(1)
The test attribute of the inner node of the decision tree may be univariate; that is, each inner node contains only one attribute. It may also be multivariate, and there are inner nodes containing multiple attributes.(2)
According to the number of different attribute values of the test attribute, each inner node may have two or more branches. If each inner node has only two branches, it is called a binary decision tree.(3)
Each attribute may be a value type or an enumeration type. Binary decision trees can be viewed as both the former and the latter.(4)
The classification result may be either two classes or multiple classes. If the result of a binary decision tree can only have two types, it is called a Boolean decision tree. Boolean decision trees can be easily represented in disjunctive normal form. And the most natural case of learning in decision trees is to learn disjunctive concepts.The complexity of the tree is mainly based on the rules generated by the decision tree. The ultimate purpose of the decision tree classification algorithm is to allow decision-makers to make correct decisions on classification problems through decision trees. Therefore, the classification rules of the final generated decision tree are simpler and clearer. The easier it is for decision makers to understand, the more popular it will be. For example, the classifier construction method expressed by semantic rules has received extensive attention because it can provide a simple and easy-to-understand description method of classification rules. In contrast, the description of the classification results by the neural network method is very unclear, which limits its further development.The purpose of pruning is to make the wood simpler. This removes the excess leaf fragment and makes it fall into the main lump or even a higher lump. It then converts the parent node or higher into a new node. Decision tree construction corresponds to local sampling and decision tree felling corresponds to international sampling. The creation of a decision tree is based solely on local preferences and cutting the decision tree to the best international standards.The hold method is a simple method for testing with a sample of class labels. When using the holdout method, the dataset is randomly divided into two separate datasets: a training set and a test set. It uses the training set to build the model and then uses the test set to evaluate the model. It usually takes two-thirds of the data for the training set and the rest for the test set. To ensure the accuracy of the evaluation, the hold method can be repeatedK times. It randomly divides the dataset into different training and testing sets each time. The total accuracy is the average of K times of accuracy, and this method is called the repetition-holding method. The biggest problem with repetition hold is that there is overlapping data between different test sets.Usually, the objects that data mining technology needs to solve are massive datasets. Therefore, the problem of computational cost has to be considered. Therefore, a good classifier can reduce the computational complexity and save the computational cost on the premise of ensuring the classification accuracy. The robustness of the model can also be understood as robustness. It is complementary to other metrics for evaluating classification ability and is the key to model survival in abnormal and endangered face situations. In classification problems, there are often situations such as data noise or missing data. Whether the model can accurately classify unknown data is the key to the research. The objects that data mining technology needs to deal with are usually massive datasets. Data incompleteness is common in such datasets. Therefore, a good decision tree model should have quite good processing ability and adaptability.Data analysis is the process of supporting decisions and in-depth methods of data analysis. It is no wonder that using computer technology to analyze student score is very effective. It can fully identify the hidden internal relationship between test results and other factors. The data processing implementation process is shown in Figure2.Figure 2
Data mining implementation process.


### 2.2. Functional Modules of the Teaching System

A learning and teaching system is a functional unit composed of various elements. These elements are organically linked through relationships and interactions to achieve a specific goal. A learning system is a hierarchical system, and all these different layers are important in the design of the learning system. The learning system can be divided into four levels: the system at the institutional level, the system at the administrative level, the system at the educational level, and the system at the learning level. Function module means that the college English network teaching system can provide effective function settings for users at all levels. The functional characteristics of the teaching platform are directly related to the teaching quality of the online classroom. Figure3 shows the functional modules of the network teaching system.Figure 3
Functional modules of the web-based teaching system.The setting of class appointment notice allows teachers to choose whether to allow students of other similar courses to audit, which reflects the openness of online teaching to a certain extent and increases students’ right to choose. An English website is specially established for users, providing various services and resources. Teachers and students of each school can access the server of the English online teaching system after possessing the user password. The language materials in the network system and the language materials in the CD-ROM are both derived from paper textbooks. In order to obtain the credits of network English stipulated by the school, students must use network exercises in the network teaching system, thus abandoning the use of CD-ROMs. In the entire product portfolio, the system provides the data transfer function between the CD-ROM in the students’ teaching materials and the network teaching system. This not only effectively improves the utilization rate of the optical disc, but also solves the time-consuming problem of transmitting video files over the network, which is a good strategy for killing two birds with one stone. This function can record the practice records submitted by students using the audio-visual CD-ROM. The data uploaded to the network system includes the answering time and correct rate of each question type. At the same time, it also sets up functions for teachers to grade and comment for each question type. The system provides teachers with the functions of viewing and analyzing class answers and controlling the individual progress of CD-ROM learning.Design factors and evaluation factors have a certain consistency. Design research and evaluation research are like two sides of the same coin. The general evaluation is to measure the quality of the network system after it is built. But this requires a lot of resources to redesign the system, which is unrealistic for a completed platform. Therefore, the best approach is to evaluate the system before it goes into development and then proceed to design development. Figure4 shows the dimensions and premise of the learning effect.Figure 4
Dimensions and prerequisites for learning effectiveness.Students can see the assignments posted by the teacher in the assignment function and can upload their own assignments as attachments. The forum function is an independent forum system, where teachers and students can communicate in terms of study and life. In the class appointment function, students can apply for face-to-face class appointments to teachers and wait for teachers to provide time and location information for group tutoring. If the number of students is full, the application of individual students may be rejected. Students can evaluate the teacher’s teaching in the evaluation function. Students can directly select the score on the teaching evaluation index, which is conducive to improving the quality of online teaching.There is a graded test system in the college English online teaching platform, which can help students make a diagnostic evaluation at the beginning of the course study and choose the learning content that suits them. The unit test function in the network test system enables students to instantly know the test scores and correct answers. It provides timely feedback for learning and also helps teachers collect grades. The online question answering system enables teachers to answer students’ questions online and to summarize and analyze these questions. Teachers can keep abreast of individual and overall needs of students in order to adjust teaching methods and improve teaching effects in a timely manner. The learning progress management system has a detailed record of the students’ learning situation, including independent study time and learning progress, etc., which enables teachers to have a more comprehensive grasp of the students’ situation. Homework management, teaching evaluation, and other systems can also play a role in effectively monitoring and managing the learning process and enhancing teaching and learning control.Teachers have limited permissions to manage class members. The registration of each student in his own class must be approved by the administrator, which causes a lot of trouble for teachers in the management of class members. In response to this design problem, the school adopted an administrator user and password for each teacher user, thereby increasing his authority to manage class members. Teachers still had to go through a tedious set of operations to enroll class members. The teacher thinks that the monitoring function of the system is not strong, and the real situation cannot be reflected only by recording the student’s login time and the rate of answering questions. After logging in to the system in the self-learning center, many students simply browse the web on the Internet and fill in the answers on the system with reference books. The workload of teachers’ online Q&A and evaluation is very large, and this part of the workload is not measured; that is, no salary is paid. Most of the teaching and auxiliary resources in the system are listening and speaking materials, such as movie clips. But for students newly promoted to undergraduate institutions, the material is a bit difficult. Although students like to read, it does not help much in learning and is not conducive to self-directed learning and learning evaluation.Before students learn independently online, teachers store the most common difficult questions in this course in a local Q&A database in a certain way. Then the system can search for relevant content in the local Q&A database according to the student’s question and then present it to the student according to the degree of relevance. For questions that are not in the question bank or students are dissatisfied with the search results, the system will send the questions to teachers or discuss them on the teacher-student exchange platform. The solved questions can be updated to the local Q&A database, so that other students can give timely and automatic answers when they encounter similar questions.


## 2.1. Decision Tree Algorithm

Decision tree algorithm is one of the most widely used data mining techniques. This basic algorithm builds a top-down decision tree using a recursive approach. The input for building a decision tree is a dataset of class labels. The dataset is divided into two parts, one of which is used as training data for building decision trees and the other as test data to verify the accuracy of decision trees. The constructed decision trees are divided into two categories: internal nodes, which represent attributes or groups of attributes; leaf nodes, which represent categories that can be classified. During classification, internal nodes are used to compare attribute values. It then determines the downward branch of the node, which is finally used for the classification result of the top node of the decision tree. The specific process is as follows.The first step is to build and optimize a decision tree model using the training set, describing the known dataset. It assumes that each item belongs to a predefined category. The dataset analyzed to create the model is the training dataset. The items in the training dataset are called training samples and are randomly selected from the sample population. Since a class name is assigned to each training sample, this classification method is also known as supervised learning. The construction of the decision tree is recursive until all leaf nodes contain the same class assignment. This process involves knowledge acquisition and machine learning from datasets. Some algorithms also include a pruning process to reduce the effect of noisy data on the correct classification rate. If the decision tree it builds does not represent the internal rules of the data well, adjust the decision tree by pruning and adding nodes until a “good” decision tree is built.In the second step, a decision tree model is used for classification. The accuracy of the constructed decision tree model is evaluated before classification. There are several ways to evaluate the classification performance of decision trees. One of them is the holdout method, which uses a set of class-labeled samples to generate samples at random, independent of the training samples. The training dataset can be used to estimate the test set. But in this case, the estimates are likely to be optimistic. The test set is used because the classification model in training fits the data well. For test data, it compares the trained test results with the known class labels of the sample tree. If the accuracy of the comparison is acceptable, then the decision tree is used to classify the class labels, unknown data, or elements of the samples.The classification process consists of searching the input data starting from the base node, checking the attribute value of each internal node in turn, until reaching a leaf node that provides the input classification. Choosing different field values results in different final classifications. This will affect the growth rate and performance of the decision tree and eventually lead to inconsistent information on the classification indicators produced. Therefore, the technical problem and the main problem of decision tree algorithm is how to choose suitable features for decomposition, that is, which feature metric to use for feature decomposition. The main difference between decision tree algorithms is the selection of features. The decision tree classification method flow is shown in Figure1.Figure 1
Decision tree classification method flow.A method similar to the one above is used in the study of decision trees. It compares the attribute values with the internal nodes in the decision tree and evaluates the downward branch of the node according to the different attribute values and draws inferences about the leaf nodes in the decision tree. Thus, the path from the roots to the leaf nodes complies with the compound law. The whole decision tree conforms to a set of rules of expression. The decision tree of the generation algorithm is divided into two steps. One is the tree generation, which starts with all the data from the root node and then executes the data sharing operation with each other. The second is to cut down trees to get some data that may be strong or abnormal. The condition for terminating the decision tree sharing is that the node data belongs to the same category. There are no features that can be used to share information.As a popular classification algorithm, decision trees have the following advantages: they can generate easy-to-understand classification rules. They can handle many different types of features at the same time, such as numbers, symbols, etc. Since decision trees use only a fixed or continuous set of variables, which are independent variables, they are robust to measurement error or bias in the independent variables. Some decision trees can be handled directly.(1)IoD=−∑u=1klcu,Dloglcu,D,SptD,T=−∑u=1qDuDlogDuD.D is sample collection, C is category collection, D is the potential of the set, and lcu,D is the proportion in the sample set.(2)Wv=1B∑uwuv.wuv is the score of the algorithm on the dataset, and Wv is average score of the algorithm.(3)εF2=15Bkk+1∑vWv2−kk+125.B is the number of datasets and k is the number of decision tree algorithms.(4)z=Wu−Wvkk+1/6B.z determines the probability of a normal distribution.(5)CS=wαkk+112B.CS is critical value.(6)RX=∑u=1jLu+BuL+B∗RGu.RX is information entropy of root classification.(7)CD=∑ubulog2bbu+c−12log2b2+log2πc/2ιc/2.CD is the cost of building a decision tree.(8)Pl,b=−ll+blog2ll+b+bl+blog2bl+b.Pl,b is the amount of information required for classification.(9)WX=∑u=1jlu+bul+bWGu.X is the root of the decision tree, l is the number of positive examples, and b is the number of counterexamples.(10)UT=−∑u=1blu×log2lu.T is training set and p is distribution probability.(11)Uv=−loga−1∑uazuvlogzuv.Uv is information entropy value and v is ordinal number of the indicator.(12)qv=Gv∑v=1bGv,∑v=1bqv=1.qv is relative weights of indicators.(13)UD=−∑u=1alogLu,WX=∑v=1jUDvb1v+…+bavb.WX is information entropy for dividing subsets and buv is the number in the attribution category.(14)WX=b1UD1+b2UD2b,GX>log2b−1+ΔX,D1,D2b.WX is division point information entropy and b is the size of the sample.(15)DpX=−∑u=1qdudlog2du.DpX is breadth and balance.(16)limb⟶∞lϕb−blblw≤m=12π∫−∞πe−t2/2dt,c=lf−ww1−w/B>z.ϕn is random variables, B is total number of instances, f is misclassification rate of training data, and w is probability of misclassification.Unclear decision tree algorithms are the algorithms of traditional decision tree algorithms. They include vague mathematical terms. A node of a tree can be considered as an indeterminate subset of decision size. Each pattern belongs to a specific size for each node. Each leaf node of the hairy corresponds to the bitter law. The whole vague decision tree is a collection of many vague rules. Each obscure rule has a specific membership.It creates a decision tree model from a specific exercise dataset that allows it to classify cases correctly. Decision tree analysis is a ranking database principle produced by a training database. There can be more than one decision tree that classifies exercise data correctly. When selecting a decision tree, the decision tree should be chosen that is inconsistent with the training data and has good general properties. And the opportunity model selected conditions not only fit well with the training data, but also predict the unknown data.Classification accuracy describes the classification effect of a decision tree model when applied to a test dataset or an unknown dataset. This measure provides the most direct information and is crucial for policymakers. A good decision tree model extracts the most relevant and valuable information from a large training dataset and processes it into a set of rules that clearly describe the classification problem. Finally, a decision tree with high accuracy is built. Accurate decision information and classification rules will always bring convenience to decision-makers, and also bring great benefits and value to decision-makers.According to the different attributes of the decision tree, there are the following different decision trees:(1)
The test attribute of the inner node of the decision tree may be univariate; that is, each inner node contains only one attribute. It may also be multivariate, and there are inner nodes containing multiple attributes.(2)
According to the number of different attribute values of the test attribute, each inner node may have two or more branches. If each inner node has only two branches, it is called a binary decision tree.(3)
Each attribute may be a value type or an enumeration type. Binary decision trees can be viewed as both the former and the latter.(4)
The classification result may be either two classes or multiple classes. If the result of a binary decision tree can only have two types, it is called a Boolean decision tree. Boolean decision trees can be easily represented in disjunctive normal form. And the most natural case of learning in decision trees is to learn disjunctive concepts.The complexity of the tree is mainly based on the rules generated by the decision tree. The ultimate purpose of the decision tree classification algorithm is to allow decision-makers to make correct decisions on classification problems through decision trees. Therefore, the classification rules of the final generated decision tree are simpler and clearer. The easier it is for decision makers to understand, the more popular it will be. For example, the classifier construction method expressed by semantic rules has received extensive attention because it can provide a simple and easy-to-understand description method of classification rules. In contrast, the description of the classification results by the neural network method is very unclear, which limits its further development.The purpose of pruning is to make the wood simpler. This removes the excess leaf fragment and makes it fall into the main lump or even a higher lump. It then converts the parent node or higher into a new node. Decision tree construction corresponds to local sampling and decision tree felling corresponds to international sampling. The creation of a decision tree is based solely on local preferences and cutting the decision tree to the best international standards.The hold method is a simple method for testing with a sample of class labels. When using the holdout method, the dataset is randomly divided into two separate datasets: a training set and a test set. It uses the training set to build the model and then uses the test set to evaluate the model. It usually takes two-thirds of the data for the training set and the rest for the test set. To ensure the accuracy of the evaluation, the hold method can be repeatedK times. It randomly divides the dataset into different training and testing sets each time. The total accuracy is the average of K times of accuracy, and this method is called the repetition-holding method. The biggest problem with repetition hold is that there is overlapping data between different test sets.Usually, the objects that data mining technology needs to solve are massive datasets. Therefore, the problem of computational cost has to be considered. Therefore, a good classifier can reduce the computational complexity and save the computational cost on the premise of ensuring the classification accuracy. The robustness of the model can also be understood as robustness. It is complementary to other metrics for evaluating classification ability and is the key to model survival in abnormal and endangered face situations. In classification problems, there are often situations such as data noise or missing data. Whether the model can accurately classify unknown data is the key to the research. The objects that data mining technology needs to deal with are usually massive datasets. Data incompleteness is common in such datasets. Therefore, a good decision tree model should have quite good processing ability and adaptability.Data analysis is the process of supporting decisions and in-depth methods of data analysis. It is no wonder that using computer technology to analyze student score is very effective. It can fully identify the hidden internal relationship between test results and other factors. The data processing implementation process is shown in Figure2.Figure 2
Data mining implementation process.


## 2.2. Functional Modules of the Teaching System

A learning and teaching system is a functional unit composed of various elements. These elements are organically linked through relationships and interactions to achieve a specific goal. A learning system is a hierarchical system, and all these different layers are important in the design of the learning system. The learning system can be divided into four levels: the system at the institutional level, the system at the administrative level, the system at the educational level, and the system at the learning level. Function module means that the college English network teaching system can provide effective function settings for users at all levels. The functional characteristics of the teaching platform are directly related to the teaching quality of the online classroom. Figure3 shows the functional modules of the network teaching system.Figure 3
Functional modules of the web-based teaching system.The setting of class appointment notice allows teachers to choose whether to allow students of other similar courses to audit, which reflects the openness of online teaching to a certain extent and increases students’ right to choose. An English website is specially established for users, providing various services and resources. Teachers and students of each school can access the server of the English online teaching system after possessing the user password. The language materials in the network system and the language materials in the CD-ROM are both derived from paper textbooks. In order to obtain the credits of network English stipulated by the school, students must use network exercises in the network teaching system, thus abandoning the use of CD-ROMs. In the entire product portfolio, the system provides the data transfer function between the CD-ROM in the students’ teaching materials and the network teaching system. This not only effectively improves the utilization rate of the optical disc, but also solves the time-consuming problem of transmitting video files over the network, which is a good strategy for killing two birds with one stone. This function can record the practice records submitted by students using the audio-visual CD-ROM. The data uploaded to the network system includes the answering time and correct rate of each question type. At the same time, it also sets up functions for teachers to grade and comment for each question type. The system provides teachers with the functions of viewing and analyzing class answers and controlling the individual progress of CD-ROM learning.Design factors and evaluation factors have a certain consistency. Design research and evaluation research are like two sides of the same coin. The general evaluation is to measure the quality of the network system after it is built. But this requires a lot of resources to redesign the system, which is unrealistic for a completed platform. Therefore, the best approach is to evaluate the system before it goes into development and then proceed to design development. Figure4 shows the dimensions and premise of the learning effect.Figure 4
Dimensions and prerequisites for learning effectiveness.Students can see the assignments posted by the teacher in the assignment function and can upload their own assignments as attachments. The forum function is an independent forum system, where teachers and students can communicate in terms of study and life. In the class appointment function, students can apply for face-to-face class appointments to teachers and wait for teachers to provide time and location information for group tutoring. If the number of students is full, the application of individual students may be rejected. Students can evaluate the teacher’s teaching in the evaluation function. Students can directly select the score on the teaching evaluation index, which is conducive to improving the quality of online teaching.There is a graded test system in the college English online teaching platform, which can help students make a diagnostic evaluation at the beginning of the course study and choose the learning content that suits them. The unit test function in the network test system enables students to instantly know the test scores and correct answers. It provides timely feedback for learning and also helps teachers collect grades. The online question answering system enables teachers to answer students’ questions online and to summarize and analyze these questions. Teachers can keep abreast of individual and overall needs of students in order to adjust teaching methods and improve teaching effects in a timely manner. The learning progress management system has a detailed record of the students’ learning situation, including independent study time and learning progress, etc., which enables teachers to have a more comprehensive grasp of the students’ situation. Homework management, teaching evaluation, and other systems can also play a role in effectively monitoring and managing the learning process and enhancing teaching and learning control.Teachers have limited permissions to manage class members. The registration of each student in his own class must be approved by the administrator, which causes a lot of trouble for teachers in the management of class members. In response to this design problem, the school adopted an administrator user and password for each teacher user, thereby increasing his authority to manage class members. Teachers still had to go through a tedious set of operations to enroll class members. The teacher thinks that the monitoring function of the system is not strong, and the real situation cannot be reflected only by recording the student’s login time and the rate of answering questions. After logging in to the system in the self-learning center, many students simply browse the web on the Internet and fill in the answers on the system with reference books. The workload of teachers’ online Q&A and evaluation is very large, and this part of the workload is not measured; that is, no salary is paid. Most of the teaching and auxiliary resources in the system are listening and speaking materials, such as movie clips. But for students newly promoted to undergraduate institutions, the material is a bit difficult. Although students like to read, it does not help much in learning and is not conducive to self-directed learning and learning evaluation.Before students learn independently online, teachers store the most common difficult questions in this course in a local Q&A database in a certain way. Then the system can search for relevant content in the local Q&A database according to the student’s question and then present it to the student according to the degree of relevance. For questions that are not in the question bank or students are dissatisfied with the search results, the system will send the questions to teachers or discuss them on the teacher-student exchange platform. The solved questions can be updated to the local Q&A database, so that other students can give timely and automatic answers when they encounter similar questions.


## 3. Improvement Experiments of the Functional Modules of the Teaching System

Because students already have certain knowledge and analytical ability, their positive logic and expository thinking are basically guaranteed. Therefore, there should be functions in the system to support collaborative topic learning. Students have a strong dependence on teachers because they are accustomed to the teacher-centered teaching method in primary education. Their intrinsic motivation for learning is weak and they are not used to independent learning. Therefore, the system must have a strong monitoring and comprehensive evaluation system. The key to online teaching lies in the implementation of strict management, tracking, and supervision of online teaching activities for students. As shown in Figure5, it is the functional improvement design framework of the student platform.Figure 5
Design framework for functional improvements to the student platform.It interviewed some student and teacher users to seek their opinions on the design of functional improvements in this study. It divides the user’s liking for each functional improvement design into different levels of evaluation. Part of the evaluation information is shown in Figure6.Figure 6
Selected evaluation information.It can be seen from the figures that both students and teachers gave full marks to the establishment of a learning resource website, indicating that teachers and students have strong functional needs in this regard. The two functions of collaborative communication system and speech recognition technology have relatively high student evaluation scores and teacher evaluation scores, indicating that these two improvements are still very popular among teachers and students.Figure7 shows the details of the dataset used in the experiments. It can be seen from the figures that the number of non-category attributes of the dataset is greater than the number of values of category attributes.Figure 7
Details of the dataset used.The classification training sample datasets are tested separately for both methods. 15 experiments were carried out for each set of data, and then the average was calculated, which made the experimental data more general. Figure8 shows the time and accuracy of the algorithm to build the decision tree. As can be seen from the figure, when there are more data in the dataset, the accuracy of classification is improved. This is because the algorithm uses the estimated misclassification rate for pruning, and when the amount of data is larger, it conforms to a normal distribution.Figure 8
Time and accuracy of algorithms to construct decision trees.A decision tree classifier system is used to further compare the performance of the algorithm before and after the improvement. Tables1–3 show the comparison of dataset data, algorithm performance, and algorithm modeling speed.Table 1
Experimental datasets.
Training setTest setNumber of attributesNumber of categoriesMk12543582Breac600100102Balas50012554Table 2
Algorithm performance comparison.
Number of nodesNumber of rulesClassification accuracyBefore improvementAfter improvementBefore improvementAfter improvementBefore improvementAfter improvementMk856350400.750.92Breac125100105930.910.95Balas4054003143000.260.4Table 3
Modeling speed of algorithms.
Sample set nameBefore improvement (ms)After improvement (ms)COD algorithm (ms)Mk502060Breac402550Balas452030Figure9 shows the number of leaf nodes and summary points of the decision tree generated by the decision tree algorithm. It can be seen from the figure that the number of leaf nodes and summary points of the improved decision tree algorithm is significantly reduced, and multi-value bias is avoided when constructing the decision tree.Figure 9
Number of leaf nodes and summary points of a decision tree.The improved decision tree algorithm is used to classify and analyze the students' grades using the improved teaching system. Figure10 shows a decision tree for good or bad grades.Figure 10
Decision tree for good grades.Data collection is a key part of data mining and the first part of data mining. The data collection of the entire data mining system should include structured and semi-structured data. It evaluates the basic situation of students' learning and summarizes the results. Table4 shows the basic dataset for student achievement analysis.Table 4
Basic dataset for student performance analysis.
Student numberWeekly on-camera timeLevel of pre-course knowledgeClassroom learningUsual gradesGeneral commentsA01≤2Basic understandingPoorPoorlyPoorA022.4Basic understandingBasic masteryBetterGoodA032.4Basic understandingBasic masteryMediumPassingA04≥4Learn someBasic masteryMediumPassing


## 4. Discussion

The university’s English language learning system has evolved from reading comprehension to listening and speaking skills. Likewise, the functioning of the higher education system must change. Students who use online tutoring for independent learning have different English backgrounds. The flexibility of the online teaching system provides different teaching methods for students with different backgrounds. If students study the e-learning system on their own, the e-learning system should present teaching materials that match the student’s current learning skills and presentations. Smart technology can enable e-learning systems to understand this type of personalized student service. Student templates can be created for online learning systems of adaptive learning systems. This student model can represent information such as students’ level of knowledge, thinking skills, motivation to study, study style, and study history. Therefore, it reflects the character and attitude of the students and forms the basis for achieving the goals of teaching, content, and teaching methods of intelligent education. Student models are based on student interactions and online teaching systems. It can vary greatly depending on the student’s study conditions. Online tutoring systems can provide students with purposeful instruction and adaptation through student models.In theory, the collaborative communication function in the network system can increase the students’ sense of belonging and identity in the self-learning network. Students have a need to communicate and interact more with their classmates and even work together to complete specific language learning tasks. Teachers also have the need to implement collaborative thematic learning. The collaborative communication function realizes the interaction between students and sets up a network platform for teachers to arrange collaborative theme learning. Collaboration features allow students to work together on an assignment or project. Coupled with the aid of an audio-conferencing system, it can improve student motivation. It also provides opportunities for students to exercise collaboration skills in an online environment. In the process of collaborative communication, topics can be proposed by teachers, and students can be motivated to use English through collaborative communication of topic-related questions. The function setting of the system should also support the teacher’s guidance in the students’ discussion process, so as to guide the discussion topics to proceed in an orderly manner.Network test is an important detection and evaluation method for network teaching. The network test system organizes the test question resources of the course unit according to a certain educational measurement theory and provides test question elements for the generation of test exercises and the arrangement of homework. When students actually use the exercises at the back of each unit of the system network courseware, they find that their order is fixed, which is basically the same as the content of the paper teaching materials. This is not only a lack of changes, but also not conducive to a true reflection of students’ learning situation. The test system gives teachers a lot of freedom, and teachers can choose the question type, test questions, and test time. The test management system mainly manages the online testing process. It manages exam time and automatically resubmits exams after deadlines. Test score analysis tools are often used to collect statistics and analyze specific learning outcomes based on information from individual questions and student responses. It adapts learning practices and activities to the specific implications of these outcomes and diagnoses and develops follow-up learning activities for individual students. It will also utilize test statistics-based learning assessment theory to analyze the quality of questions, such as discrimination and difficulty.


## 5. Conclusion

Users of decision trees do not need to have a high level of technical knowledge because the rules produced by decision trees are very easy to understand. Compared to other classification techniques, decision tree rules are slightly more precise. So it can help people make better decisions. For this reason, decision tree algorithms are one of the most active areas of research in knowledge discovery. This study presents the design reform of the implementation unit of the University’s online English learning system in relation to the theory and techniques of the development. The decision tree classification algorithm is not perfect, and it needs to rely on other database tools to manually complete the data preprocessing.



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*Source: 5110709-2022-06-22.xml*