# Research on Evaluation Method of Physical Education Teaching Quality in Colleges and Universities Based on Decision Tree Algorithm

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

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## Abstract

Ensuring and improving quality in higher education is a never-ending theme in university construction and development. College teaching quality includes the quality of physical education instruction. Physical education’s unique focus necessitates the use of teaching quality evaluation tools. This paper develops a decision tree algorithm-based evaluation index system for college physical education teaching quality. The evaluation index and weight of the system for evaluating the quality of physical education teaching are determined using the expert consultation method and analytic hierarchy process. To put it another way, while discussing data mining theory in general, the paper focuses on decision tree algorithm flow and structure. To develop a decision tree model for the established PE teaching quality evaluation system, the modified C4.5 algorithm was applied to classroom teaching evaluation data from 125 teachers, and the model’s correctness and applicability were verified. When it is all said and done, it highlights key decision-making attributes that have an impact on how well physical education teachers are evaluated, and it offers some sound advice based on these findings.


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## Body

## 1. Introduction

When discussing the teaching quality evaluation method, we are talking about a good way to screen and determine evaluation indicators as well as to collect, analyze, and interpret evaluation data. It is the most important factor in determining how effective a physical education program is. As part of their “Research on the Evaluation System of Physical Education Teaching Quality in Colleges and Universities,” Mao used “affinity map” technology to create initial evaluation indicators based on a theory for evaluating teaching quality in higher education. Analyzing the correlation between indicators makes use of a factor analysis method, and weighing indicators is done using an analytic hierarchy process. The analytic hierarchy process, normally called AHP, is a powerful yet simple method for making decisions. AHP elicits ranking preference for alternatives and weighting preference for attributes using a scale from 1 to 9; higher scores indicate greater relative preference. The relative rankings are organized in a series of hierarchical matrices and overall ranking derived through linear algebraic procedures. The comprehensive evaluation decision support system underwent a preliminary analysis, and a teaching quality assessment management system was developed as a result. Decision tree algorithm is to used create a model that predicts the value of a target variable, for which it uses the tree representation to solve the problem, the leaf node corresponds to a class label, and attributes are represented on the internal node of the tree. For two consecutive years (four semesters), the system was used to assess the quality of teaching at Tianjin University, and the results were generally positive [1]. Another study published in the same year, entitled “Research on the Application of Decision Tree Technology in the Evaluation of Physical Education Quality,” utilized decision tree technology in the data mining system to investigate current systems for evaluating physical education quality. You should propose an evaluation method for fitness programs using decision tree technology to ensure that fitness programs are more equitable, reasonable, and effective [2]. Students’ learning outcomes are reflected by a variety of target elements that make up the quality of physical education, according to Yu Sumei’s 2014 article, “From the Trinity Target System on Strategies to Promote the Quality of Physical Education.” Physical education’s three-in-one goal system is aimed at improving students’ physical health while also teaching them sports skills and cultivating a positive attitude. How to better achieve these objectives can be cut from how to improve the quality of teaching. Several studies have found a strong correlation between achieving physical health promotion goals and deciding on the content of a lesson, determining teaching goals, and selecting teaching methods. In addition, strengthening important and difficult aspects of sports skills, arranging a reasonable classroom, and providing appropriate evaluations are all linked to the ultimate goal of mastering sports skills [3].Due to the systematicity and complexity of college and university physical education, we must first pursue a comprehensive system of evaluation content and ensure that the evaluation content has a clear priority in the evaluation process; secondly, we must respect each album in the process of evaluation. The formation of centralized decision-making and judgment is based on opinions. As a final measure of objectivity, quantitative evaluation is frequently employed. A quantitative assessment, on the other hand, will not be able to fully capture the current state of physical education teaching. Many variables are elusive to pin down. College physical education quality evaluation will in the future be characterized by a mix of quantitative and qualitative methods of assessment.


## 2. Related Work

One of the most important areas of database research is knowledge discovery and data mining. People all over the world are starting to realize its significance. Data warehouse was proposed by Inmon, W. H. in the early stages of data mining and solved the problem of data preparation successfully; the US government developed the sequoia2000 project as a data analysis tool in large databases [4]. Freidman successfully stimulated interest in the development, application, and research of data mining in 1997, leading to the emergence of ultralarge databases. This business opportunity was seized by a large number of commercial enterprises. Banks and retail industries, for example, use business data [5] to better understand and master their customers’ reputations, habits, and consumer psychology and then adjust their market strategies as necessary to maximize their gains. The concept learning system was the first to use decision tree technology, laying the groundwork for future decision tree learning algorithms. In the late 1970s, Quinlan came up with the infamous ID3 algorithm. ID3 stands for Iterative Dichotomiser 3 and is named such because the algorithm iteratively (repeatedly) dichotomizes (divides) features into two or more groups at each step. Attribute classification was used to determine the split attribute using the concept of entropy from information theory for the first time in this algorithm. When dealing with large-scale database issues, this algorithm has some distinct advantages. However, there are some obvious flaws in it. When selecting split nodes, attributes with more values are preferred over those with a single value.Quinlan later improved the algorithm and came up with the C4.5 algorithm, which is widely used today. C4.5 is often referred to as a classifier. The C4.5 algorithm is used in data mining as a decision tree classifier which can be employed to generate a decision, based on a certain sample of data (univariate or multivariate predictors). For selecting split attributes, the C4.5 algorithm uses an information gain ratio estimate, which compensates for the ID3 algorithm’s inability to handle continuous attributes and missing values to a large extent. In the late 1990s, Mehta et al. proposed the SLIQ classification algorithm, which was fast and scalable. SLIQ is a decision tree classifier that can handle both numeric and categorical attributes. It uses a novel presorting technique in the tree-growth phase. SLIQ also uses a new tree-pruning algorithm that is inexpensive and results in compact and accurate trees. The combination of these techniques enables SLIQ to scale for large data sets and classify data sets irrespective of the number of classes, attributes, and examples (records), thus making it an attractive tool for data mining [6].The SPRINT algorithm, proposed by Shafer et al., is a scalable and parallel inductive decision tree algorithm similar to SLIQ [7]. SPRINT algorithm is a classical algorithm for building a decision tree that is a widely used method of data classification. However, the SPRINT algorithm has high computational cost in the calculation of attribute segmentation. This algorithm is unrestricted by memory. But there are also some shortcomings. For example, finding the best segmentation point of discrete attributes needs a large amount of calculation, and the partition of continuous attributes is unreasonable. The PUBLIC algorithm was proposed by Rajeev and Kyuseok from the perspective of “integrating building and pruning” [8]. The rain forest algorithm was later proposed by Gehrke et al. to make the most of memory resources. China’s data mining research is lagging that of other countries. Research in this area was first supported by the Chinese government’s National Natural Science Foundation in 1993. Tsinghua University is currently competing with the Chinese Academy of Sciences, Fudan University, and other well-known domestic colleges and universities to carry out fundamental theories and applied data mining research [9]. As a result of these findings, our country has made rapid progress in this area. Decision trees have been studied extensively in my country, and the results can be found in the following areas: One goal is to enhance precision. The second step is to cut back on the number of options. The third option is to use other technologies in conjunction with your own. The realization of software is the fourth step. One of the research directions is to realize decision tree softwareization. In my country, the use of data mining technology in college and university teaching administration has evolved gradually [10]The rest of the article is organized according to the following pattern. Methodology is discussed in Section3. Experiments and discussion are mentioned under Section 4, and the journal is concluded in Section 5.


## 3. Method

### 3.1. Decision Tree Technology

#### 3.1.1. Introduction to Decision Trees

A decision tree is an inductive learning method that uses a set of unordered and irregular instances to infer the classification rules of the expression form of the decision tree [11, 12]. The decision tree is a tree structure, which has many similarities with the flowchart drawn by ordinary people. Each branch node corresponds to a specific value of the attribute, and each path from the root node to the leaf node is a rule of the target variable. So in fact, what the entire decision tree wants to express is a set of disjunctive expression rules.


#### 3.1.2. Establishment of Decision Tree

The generation of the decision tree is a process of gradual refinement and gradual division of the training set. The process of splitting is from top to bottom, gradually growing from the root of the tree. First, according to a certain split attribute as the evaluation criterion, the optimal attribute in the original sample data set is taken as the split attribute of the root node. At the same time, the optimal split point of the corresponding split attribute is selected as the boundary of the branch. Then, the initial sample set is divided into several disjoint subsets according to the selected column attributes and split points to form different branch nodes. Then, split each generated child node in the same way until all the leaf nodes are generated [13].


#### 3.1.3. Attribute Selection Metrics

Attribute selection metric is a selection split criterion, which occupies a core position in the process of decision tree construction. The decision tree ID3 algorithm uses “information gain” as the attribute selection metric [14]. The definition of information gain is as follows:LetS be a data set composed of s samples, which belong to n different categories. Suppose si is the number of samples of class Ci. Then, the information value of the sample is defined as follows:
(1)Is1,s2,⋯sn=−∑i=1nsislog2sisLet attributeA have V values. Use attribute A to divide data set S into v subsets s1,s2⋯sv. Then, the information value generated by dividing the sample set with attribute A is follows:
(2)EA=∑j=1vs1j+s2j+⋯+snjsIs1j,⋯,snjThe information gain of attributeA represents the information value reduced by dividing the data set with attribute A.
(3)GainA=Is1,s2,⋯,sn−EAThe decision tree C4.5 algorithm uses “information gain rate” as the attribute selection metric [15]. The expression of the information gain rate is as follows:
(4)GainRatioA=GainAspliInfos


### 3.2. Hierarchical Order and Consistency Check

#### 3.2.1. Hierarchical Single Ordering

Hierarchical single ordering is to calculate the maximum value of the eigenvalues of each judgment matrix and its corresponding eigenvectors to obtain the hierarchical single ordering and obtain the importance data sequence of the index layer to the target layer. To obtain the optimal decision, the specific steps are first to solve the maximum eigenvalueℷmax of the judgment matrix A, and then, use the formula Aω=ℷmaxω. Solve the feature vector ω corresponding to ℷmax. After standardization, ω is the ranking weight of the relative importance of the corresponding element in the same level to a factor in the previous level [16].


### 3.3. Consistency Test

The consistency test is designed to assess onenecessary, but not sufficient, aspect of robustness. Consistency plays a vital role in the determination of compressive strength of concrete or workability test for concrete.It is to test the consistency of the matrix. Although it is impossible to require all judgments to be completely consistent, the judgments should be made roughly consistent. Therefore, it is necessary to check the consistency of the judgment matrix. First, calculate the consistency indexCI of matrix A.
(5)CI=ℷmax−nn−1In the above formula,n is the order of the judgment matrix. When A has complete consistency, CI=0. The larger the CI, the worse the consistency of matrix A. To test whether the matrix has satisfactory consistency, it is also necessary to compare C1 with the average random consistency index RI. For the judgment matrix of order 1-9, RI is given, as shown in Table 1.Table 1
Average stochastic one-time indicator.
Order123456789RI000.580.901.121.241.321.411.45For 1 and 2 order matrices, RI is formal. Whenn is greater than 2, the consistency of the matrix is represented by CR, CR=CI/RI. When CR<0.1, it is considered that the judgment matrix A has a satisfactory consistency; otherwise, A must be readjusted until it has a satisfactory consistency.


### 3.4. Consistency Check and Hierarchical Total Ranking

#### 3.4.1. Consistency Inspection

To evaluate the consistency test of the total ranking of the hierarchy, it is necessary to calculate the index of the consistency test. CI is the consistency index. RI is the average random consistency index; CR is the random consistency ratio.(6)CI=∑i=1naiCIiIn the formula,CIi is the consistency index of layer B corresponding to ai.
(7)RI=∑i=1naiRIiIn the formula,RIi is the average random consistency test index of the B layer corresponding to ai.
(8)CR=CIRISimilarly, whenCR≤0.1, it is considered that the calculation results of the total ranking have satisfactory consistency.


### 3.5. Data Collection

#### 3.5.1. Collection Object

Randomly select 25 teachers from a certain college as the research object. Among them, the age group of the 25 physical education teachers selected by each school should involve young, middle-aged, and old; titles and levels should include assistants, lecturers, associate professors, and professors; degrees should involve doctors, masters, and bachelors. In addition, for each teacher, 35 students were randomly selected based on the teaching class. Use teacher physical education evaluation form to evaluate teachers.


#### 3.5.2. Collecting Data

The survey method in the research process of the project is mainly the questionnaire survey method, which mainly includes the issuance and recovery of the questionnaire. Questions about the questionnaire items can be answered and explained at any time to improve the accuracy and completeness of the questionnaire. The collection of questionnaires should reach a certain proportion and generally should be no less than 70% of the issued quantity; otherwise, it will affect the representativeness of the information obtained. A total of 3,824 student questionnaires were distributed in this survey. 3658 copies were recovered, of which 3371 were valid questionnaires.


#### 3.5.3. Data Processing

It can be concluded from the related literature and materials that the teachers’ qualities, including their teaching age, degree, professional title, and the knowledge base of the students in the class, also affect the quality of the teacher’s classroom teaching to a certain extent. To determine whether there is a relationship between them, this part of the information is also added to the input attributes of the decision tree. Therefore, data integration preprocessing is needed here. The information in the teacher’s basic information table JSJBXX and the table JSKTPJ obtained from the Office of Academic Affairs is integrated, and the result is stored in table JSPJXX (Teacher Evaluation Information).This chapter mainly describes the design process of teachers’ classroom teaching quality evaluation index, as well as the process of data collection and processing. First, the expert consultation method is adopted, using the experts’ professional experience and knowledge, and the original indicators are modified by the way of soliciting opinions, and finally, the classroom teaching quality evaluation system of colleges and universities is established.


## 3.1. Decision Tree Technology

### 3.1.1. Introduction to Decision Trees

A decision tree is an inductive learning method that uses a set of unordered and irregular instances to infer the classification rules of the expression form of the decision tree [11, 12]. The decision tree is a tree structure, which has many similarities with the flowchart drawn by ordinary people. Each branch node corresponds to a specific value of the attribute, and each path from the root node to the leaf node is a rule of the target variable. So in fact, what the entire decision tree wants to express is a set of disjunctive expression rules.


### 3.1.2. Establishment of Decision Tree

The generation of the decision tree is a process of gradual refinement and gradual division of the training set. The process of splitting is from top to bottom, gradually growing from the root of the tree. First, according to a certain split attribute as the evaluation criterion, the optimal attribute in the original sample data set is taken as the split attribute of the root node. At the same time, the optimal split point of the corresponding split attribute is selected as the boundary of the branch. Then, the initial sample set is divided into several disjoint subsets according to the selected column attributes and split points to form different branch nodes. Then, split each generated child node in the same way until all the leaf nodes are generated [13].


### 3.1.3. Attribute Selection Metrics

Attribute selection metric is a selection split criterion, which occupies a core position in the process of decision tree construction. The decision tree ID3 algorithm uses “information gain” as the attribute selection metric [14]. The definition of information gain is as follows:LetS be a data set composed of s samples, which belong to n different categories. Suppose si is the number of samples of class Ci. Then, the information value of the sample is defined as follows:
(1)Is1,s2,⋯sn=−∑i=1nsislog2sisLet attributeA have V values. Use attribute A to divide data set S into v subsets s1,s2⋯sv. Then, the information value generated by dividing the sample set with attribute A is follows:
(2)EA=∑j=1vs1j+s2j+⋯+snjsIs1j,⋯,snjThe information gain of attributeA represents the information value reduced by dividing the data set with attribute A.
(3)GainA=Is1,s2,⋯,sn−EAThe decision tree C4.5 algorithm uses “information gain rate” as the attribute selection metric [15]. The expression of the information gain rate is as follows:
(4)GainRatioA=GainAspliInfos


## 3.1.1. Introduction to Decision Trees

A decision tree is an inductive learning method that uses a set of unordered and irregular instances to infer the classification rules of the expression form of the decision tree [11, 12]. The decision tree is a tree structure, which has many similarities with the flowchart drawn by ordinary people. Each branch node corresponds to a specific value of the attribute, and each path from the root node to the leaf node is a rule of the target variable. So in fact, what the entire decision tree wants to express is a set of disjunctive expression rules.


## 3.1.2. Establishment of Decision Tree

The generation of the decision tree is a process of gradual refinement and gradual division of the training set. The process of splitting is from top to bottom, gradually growing from the root of the tree. First, according to a certain split attribute as the evaluation criterion, the optimal attribute in the original sample data set is taken as the split attribute of the root node. At the same time, the optimal split point of the corresponding split attribute is selected as the boundary of the branch. Then, the initial sample set is divided into several disjoint subsets according to the selected column attributes and split points to form different branch nodes. Then, split each generated child node in the same way until all the leaf nodes are generated [13].


## 3.1.3. Attribute Selection Metrics

Attribute selection metric is a selection split criterion, which occupies a core position in the process of decision tree construction. The decision tree ID3 algorithm uses “information gain” as the attribute selection metric [14]. The definition of information gain is as follows:LetS be a data set composed of s samples, which belong to n different categories. Suppose si is the number of samples of class Ci. Then, the information value of the sample is defined as follows:
(1)Is1,s2,⋯sn=−∑i=1nsislog2sisLet attributeA have V values. Use attribute A to divide data set S into v subsets s1,s2⋯sv. Then, the information value generated by dividing the sample set with attribute A is follows:
(2)EA=∑j=1vs1j+s2j+⋯+snjsIs1j,⋯,snjThe information gain of attributeA represents the information value reduced by dividing the data set with attribute A.
(3)GainA=Is1,s2,⋯,sn−EAThe decision tree C4.5 algorithm uses “information gain rate” as the attribute selection metric [15]. The expression of the information gain rate is as follows:
(4)GainRatioA=GainAspliInfos


## 3.2. Hierarchical Order and Consistency Check

### 3.2.1. Hierarchical Single Ordering

Hierarchical single ordering is to calculate the maximum value of the eigenvalues of each judgment matrix and its corresponding eigenvectors to obtain the hierarchical single ordering and obtain the importance data sequence of the index layer to the target layer. To obtain the optimal decision, the specific steps are first to solve the maximum eigenvalueℷmax of the judgment matrix A, and then, use the formula Aω=ℷmaxω. Solve the feature vector ω corresponding to ℷmax. After standardization, ω is the ranking weight of the relative importance of the corresponding element in the same level to a factor in the previous level [16].


## 3.2.1. Hierarchical Single Ordering

Hierarchical single ordering is to calculate the maximum value of the eigenvalues of each judgment matrix and its corresponding eigenvectors to obtain the hierarchical single ordering and obtain the importance data sequence of the index layer to the target layer. To obtain the optimal decision, the specific steps are first to solve the maximum eigenvalueℷmax of the judgment matrix A, and then, use the formula Aω=ℷmaxω. Solve the feature vector ω corresponding to ℷmax. After standardization, ω is the ranking weight of the relative importance of the corresponding element in the same level to a factor in the previous level [16].


## 3.3. Consistency Test

The consistency test is designed to assess onenecessary, but not sufficient, aspect of robustness. Consistency plays a vital role in the determination of compressive strength of concrete or workability test for concrete.It is to test the consistency of the matrix. Although it is impossible to require all judgments to be completely consistent, the judgments should be made roughly consistent. Therefore, it is necessary to check the consistency of the judgment matrix. First, calculate the consistency indexCI of matrix A.
(5)CI=ℷmax−nn−1In the above formula,n is the order of the judgment matrix. When A has complete consistency, CI=0. The larger the CI, the worse the consistency of matrix A. To test whether the matrix has satisfactory consistency, it is also necessary to compare C1 with the average random consistency index RI. For the judgment matrix of order 1-9, RI is given, as shown in Table 1.Table 1
Average stochastic one-time indicator.
Order123456789RI000.580.901.121.241.321.411.45For 1 and 2 order matrices, RI is formal. Whenn is greater than 2, the consistency of the matrix is represented by CR, CR=CI/RI. When CR<0.1, it is considered that the judgment matrix A has a satisfactory consistency; otherwise, A must be readjusted until it has a satisfactory consistency.


## 3.4. Consistency Check and Hierarchical Total Ranking

### 3.4.1. Consistency Inspection

To evaluate the consistency test of the total ranking of the hierarchy, it is necessary to calculate the index of the consistency test. CI is the consistency index. RI is the average random consistency index; CR is the random consistency ratio.(6)CI=∑i=1naiCIiIn the formula,CIi is the consistency index of layer B corresponding to ai.
(7)RI=∑i=1naiRIiIn the formula,RIi is the average random consistency test index of the B layer corresponding to ai.
(8)CR=CIRISimilarly, whenCR≤0.1, it is considered that the calculation results of the total ranking have satisfactory consistency.


## 3.4.1. Consistency Inspection

To evaluate the consistency test of the total ranking of the hierarchy, it is necessary to calculate the index of the consistency test. CI is the consistency index. RI is the average random consistency index; CR is the random consistency ratio.(6)CI=∑i=1naiCIiIn the formula,CIi is the consistency index of layer B corresponding to ai.
(7)RI=∑i=1naiRIiIn the formula,RIi is the average random consistency test index of the B layer corresponding to ai.
(8)CR=CIRISimilarly, whenCR≤0.1, it is considered that the calculation results of the total ranking have satisfactory consistency.


## 3.5. Data Collection

### 3.5.1. Collection Object

Randomly select 25 teachers from a certain college as the research object. Among them, the age group of the 25 physical education teachers selected by each school should involve young, middle-aged, and old; titles and levels should include assistants, lecturers, associate professors, and professors; degrees should involve doctors, masters, and bachelors. In addition, for each teacher, 35 students were randomly selected based on the teaching class. Use teacher physical education evaluation form to evaluate teachers.


### 3.5.2. Collecting Data

The survey method in the research process of the project is mainly the questionnaire survey method, which mainly includes the issuance and recovery of the questionnaire. Questions about the questionnaire items can be answered and explained at any time to improve the accuracy and completeness of the questionnaire. The collection of questionnaires should reach a certain proportion and generally should be no less than 70% of the issued quantity; otherwise, it will affect the representativeness of the information obtained. A total of 3,824 student questionnaires were distributed in this survey. 3658 copies were recovered, of which 3371 were valid questionnaires.


### 3.5.3. Data Processing

It can be concluded from the related literature and materials that the teachers’ qualities, including their teaching age, degree, professional title, and the knowledge base of the students in the class, also affect the quality of the teacher’s classroom teaching to a certain extent. To determine whether there is a relationship between them, this part of the information is also added to the input attributes of the decision tree. Therefore, data integration preprocessing is needed here. The information in the teacher’s basic information table JSJBXX and the table JSKTPJ obtained from the Office of Academic Affairs is integrated, and the result is stored in table JSPJXX (Teacher Evaluation Information).This chapter mainly describes the design process of teachers’ classroom teaching quality evaluation index, as well as the process of data collection and processing. First, the expert consultation method is adopted, using the experts’ professional experience and knowledge, and the original indicators are modified by the way of soliciting opinions, and finally, the classroom teaching quality evaluation system of colleges and universities is established.


## 3.5.1. Collection Object

Randomly select 25 teachers from a certain college as the research object. Among them, the age group of the 25 physical education teachers selected by each school should involve young, middle-aged, and old; titles and levels should include assistants, lecturers, associate professors, and professors; degrees should involve doctors, masters, and bachelors. In addition, for each teacher, 35 students were randomly selected based on the teaching class. Use teacher physical education evaluation form to evaluate teachers.


## 3.5.2. Collecting Data

The survey method in the research process of the project is mainly the questionnaire survey method, which mainly includes the issuance and recovery of the questionnaire. Questions about the questionnaire items can be answered and explained at any time to improve the accuracy and completeness of the questionnaire. The collection of questionnaires should reach a certain proportion and generally should be no less than 70% of the issued quantity; otherwise, it will affect the representativeness of the information obtained. A total of 3,824 student questionnaires were distributed in this survey. 3658 copies were recovered, of which 3371 were valid questionnaires.


## 3.5.3. Data Processing

It can be concluded from the related literature and materials that the teachers’ qualities, including their teaching age, degree, professional title, and the knowledge base of the students in the class, also affect the quality of the teacher’s classroom teaching to a certain extent. To determine whether there is a relationship between them, this part of the information is also added to the input attributes of the decision tree. Therefore, data integration preprocessing is needed here. The information in the teacher’s basic information table JSJBXX and the table JSKTPJ obtained from the Office of Academic Affairs is integrated, and the result is stored in table JSPJXX (Teacher Evaluation Information).This chapter mainly describes the design process of teachers’ classroom teaching quality evaluation index, as well as the process of data collection and processing. First, the expert consultation method is adopted, using the experts’ professional experience and knowledge, and the original indicators are modified by the way of soliciting opinions, and finally, the classroom teaching quality evaluation system of colleges and universities is established.


## 4. Experiment and Discussion

### 4.1. Establish a Decision Tree Model for Classroom Teaching Quality Evaluation

Using the decision tree C4.5 algorithm, the preprocessed table JSPJXX (Teacher Evaluation Information) is used as the training sample data set to establish a decision tree model for the evaluation of college teachers’ classroom teaching quality and extract its classification rules. Specific steps are as follows:(1)
The Information Entropy of the Classification Attributes in the Training Set. First, use the formula to calculate the classification information entropy of the training set, and then, use the information entropy to calculate the information gain rate of each attribute. In the training data set, the total number of samples is 80. Among them, the number of samples with excellent classification attributes is 23, the number of samples with good classification attributes is 25, the number of samples with qualified classification attributes is 23, and the classification attributes are poor, and the number of samples is 9. According to formula (1), we can get the following:(9)IS1,S2,S3,S4==−2380log22380−2580log22580−2380log22380−980log980=1.913(2)
Calculate the information entropy of the subset divided by each attribute value. First, take the calculation of the subset information entropy of the “teaching age” attribute as an example. There are three values for the teaching age attribute, and here it is necessary to calculate the information entropy for the subset of each attribute value. For “teaching age” is “high,” there are 7 data samples for excellent classroom teaching performance, 6 data samples for good classroom teaching performance, 3 data samples for qualified classroom teaching performance, and 0 for poor classroom teaching performance. Calculate with formula (2):(10)IS11,S12,S13,S14==−716log2716−616log2616−316log2316=1.524Establish the root node of the decision tree. According to the above calculation, the order of the information gain rate of each attribute is as follows: student basics-teaching effect-teaching attitude-teaching method-degree-professional title-teaching age-teaching content-basic skills-emotional attitude and values. According to the C4.5 algorithm, taking the “student base” with the largest test attribute information gain rate as the root node of the decision tree in the calculation results is the best classification. Therefore, the “student foundation” is used as the test attribute to build a decision tree, and the root node of the decision tree is marked as “student foundation.” For the two values of “student foundation”: excellent and general, the corresponding subsets are established, respectively, and the corresponding branches are derived, and the above methods are also used when dividing each subset into the branches, as shown in Figure1.Figure 1
Schematic diagram of the root node of the decision tree for teacher classroom teaching quality evaluation.


### 4.2. Pruning the Decision Tree

It can be seen that the final decision tree scale is too large, which shows that the training data of the decision tree is too low, the time is relatively long, and there are long and unbalanced branches. In actual use, it is not clear which are the main results, which makes it difficult to understand and causes the processing process to become complicated. Therefore, we need to properly prun the decision tree. Here we use the postpruning method to prune the decision tree [17–20], to overcome the problem of low comprehensibility and applicability of the decision tree. The essence of decision tree pruning is to replace a subtree with a leaf node. If a decision rule detects that the misclassification ratio of its subtree is greater than the misclassification ratio of a single leaf, it needs to be replaced.


### 4.3. Classification Rule Test

This paper uses 50 test data for evaluation and comparison. The results show that the reasonable number of evaluations of the physical education quality research method set by the decision tree algorithm is 46, and the unreasonable number is 4. The comparison is shown in Figure2, and the pass rate is 92%, as shown in Figure 3.Figure 2
Comparison of evaluation results.Figure 3
Evaluation qualification rate chart.It can be known by comparison test, and it is found that the prediction accuracy of the decision tree model constructed above is high, indicating that the construction of the decision tree model studied in this paper is very reasonable. At the same time, if the collected data sample size is large enough, the accuracy rate will be further improved, and there will be higher use value in practical applications.The article adopts the decision tree C4.5 algorithm, uses SQL Server to establish a database, connects in MatLab, establishes a decision tree model for evaluating the quality of physical education in colleges and universities, and uses postpruning method to prun the decision tree. To better dig its potential laws, the decision tree has also been revised, mainly by adding sentences to the algorithm and adding records of eligible statistical data to the leaf nodes. Then, extract the corresponding classification rules. After testing, the classification rule has a high accuracy rate and a certain degree of applicability. Finally, some constructive comments and suggestions are put forward based on the conclusions of the experiment. Promote the improvement of the quality of college teachers’ physical education, and assist relevant departments to make relevant decision-making work for the quality assurance of physical education teaching in colleges and universities. It has a certain practical value for the research of college physical education.


## 4.1. Establish a Decision Tree Model for Classroom Teaching Quality Evaluation

Using the decision tree C4.5 algorithm, the preprocessed table JSPJXX (Teacher Evaluation Information) is used as the training sample data set to establish a decision tree model for the evaluation of college teachers’ classroom teaching quality and extract its classification rules. Specific steps are as follows:(1)
The Information Entropy of the Classification Attributes in the Training Set. First, use the formula to calculate the classification information entropy of the training set, and then, use the information entropy to calculate the information gain rate of each attribute. In the training data set, the total number of samples is 80. Among them, the number of samples with excellent classification attributes is 23, the number of samples with good classification attributes is 25, the number of samples with qualified classification attributes is 23, and the classification attributes are poor, and the number of samples is 9. According to formula (1), we can get the following:(9)IS1,S2,S3,S4==−2380log22380−2580log22580−2380log22380−980log980=1.913(2)
Calculate the information entropy of the subset divided by each attribute value. First, take the calculation of the subset information entropy of the “teaching age” attribute as an example. There are three values for the teaching age attribute, and here it is necessary to calculate the information entropy for the subset of each attribute value. For “teaching age” is “high,” there are 7 data samples for excellent classroom teaching performance, 6 data samples for good classroom teaching performance, 3 data samples for qualified classroom teaching performance, and 0 for poor classroom teaching performance. Calculate with formula (2):(10)IS11,S12,S13,S14==−716log2716−616log2616−316log2316=1.524Establish the root node of the decision tree. According to the above calculation, the order of the information gain rate of each attribute is as follows: student basics-teaching effect-teaching attitude-teaching method-degree-professional title-teaching age-teaching content-basic skills-emotional attitude and values. According to the C4.5 algorithm, taking the “student base” with the largest test attribute information gain rate as the root node of the decision tree in the calculation results is the best classification. Therefore, the “student foundation” is used as the test attribute to build a decision tree, and the root node of the decision tree is marked as “student foundation.” For the two values of “student foundation”: excellent and general, the corresponding subsets are established, respectively, and the corresponding branches are derived, and the above methods are also used when dividing each subset into the branches, as shown in Figure1.Figure 1
Schematic diagram of the root node of the decision tree for teacher classroom teaching quality evaluation.


## 4.2. Pruning the Decision Tree

It can be seen that the final decision tree scale is too large, which shows that the training data of the decision tree is too low, the time is relatively long, and there are long and unbalanced branches. In actual use, it is not clear which are the main results, which makes it difficult to understand and causes the processing process to become complicated. Therefore, we need to properly prun the decision tree. Here we use the postpruning method to prune the decision tree [17–20], to overcome the problem of low comprehensibility and applicability of the decision tree. The essence of decision tree pruning is to replace a subtree with a leaf node. If a decision rule detects that the misclassification ratio of its subtree is greater than the misclassification ratio of a single leaf, it needs to be replaced.


## 4.3. Classification Rule Test

This paper uses 50 test data for evaluation and comparison. The results show that the reasonable number of evaluations of the physical education quality research method set by the decision tree algorithm is 46, and the unreasonable number is 4. The comparison is shown in Figure2, and the pass rate is 92%, as shown in Figure 3.Figure 2
Comparison of evaluation results.Figure 3
Evaluation qualification rate chart.It can be known by comparison test, and it is found that the prediction accuracy of the decision tree model constructed above is high, indicating that the construction of the decision tree model studied in this paper is very reasonable. At the same time, if the collected data sample size is large enough, the accuracy rate will be further improved, and there will be higher use value in practical applications.The article adopts the decision tree C4.5 algorithm, uses SQL Server to establish a database, connects in MatLab, establishes a decision tree model for evaluating the quality of physical education in colleges and universities, and uses postpruning method to prun the decision tree. To better dig its potential laws, the decision tree has also been revised, mainly by adding sentences to the algorithm and adding records of eligible statistical data to the leaf nodes. Then, extract the corresponding classification rules. After testing, the classification rule has a high accuracy rate and a certain degree of applicability. Finally, some constructive comments and suggestions are put forward based on the conclusions of the experiment. Promote the improvement of the quality of college teachers’ physical education, and assist relevant departments to make relevant decision-making work for the quality assurance of physical education teaching in colleges and universities. It has a certain practical value for the research of college physical education.


## 5. Conclusions

In recent years, decision tree algorithms have been applied in various fields of society. For example, it was initially used for military scientific research and later gradually developed into various fields such as medical services, commercial finance, and retail. The application of the decision tree algorithms in college physical education is gradually expanding in our country. According to the current trend, we can realize that the application of the decision tree algorithms in education has begun, but it has not been further promoted. What we have to do is to continue in-depth research based on previous work. Based on the quality of college teachers’ physical education, this paper uses the C4.5 decision tree algorithm in data mining technology to build a decision tree model and researches the evaluation methods of college teachers’ physical education quality. The results show that it is feasible. In the next work, it is necessary to increase the promotion and provide feasible suggestions for college physical education through this method.



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*Source: 9994983-2022-03-22.xml*