# Learning Quality Evaluation of Course Implementation Supported by Online Teaching Platform

Authors: Yuewang Cao
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the rapid development of science and technology, the use of network information technology means more and more widely; network teaching platform has become an important measure to promote the development of education and improve the quality of teaching. The network teaching platform has been widely used and plays a vital role in the construction of teaching informationization and modernization. However, the study of student learning quality evaluation under the network teaching platform has become a huge challenge. In order to solve this problem, this paper takes the teaching of basic Python course as an example, adopts data-driven theory, and combs the teaching process of network teaching platform. Curriculum can be divided into class leading learning stage, stage of studies in class, and after class extension stage and uses the panel method, literature query method, mathematical statistics, performance appraisal, and questionnaire survey method to analyze the factors influencing the quality of learning curriculum implementation, and compared with the traditional classroom teaching, build more indicators of curriculum implementation under the network teaching platform to support students’ learning quality evaluation scheme. The experimental results show that, through the statistics of the evaluation index data, using the analysis method of the KMO coefficient and the KMO test, the three stages of the curriculum implementation of the KMO coefficient and the KMO test prove that the constructed learning quality evaluation scheme has high credibility and good structural validity. Based on class leading learning stage, stage of inquiry learning in the class, after class extension stage, and students’ learning quality correlation analysis, using Pearson correlation test, it is concluded that different index correlations with students’ academic performance prove students’ input and participation, learning ability and innovation ability, self-study ability and students’ learning achievement of great relevance. It has a direct impact on students’ learning quality. The results of this study provide powerful data and theoretical support for educators to carry out network teaching.


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## Body

## 1. Introduction

With the rapid economic and social development, information technology such as computer and artificial intelligence has been widely applied, from around the world to the intelligent, digital education under the network environment, which attaches great importance to constantly invest a lot of economic strength, through the development of network teaching platform to improve the school education, to meet the demand of all the learner’s differentiation [1–3]. Along with the rapid development of digital information technology and Internet, people access to the knowledge from the offline mode to free access to online; this brings to the traditional teaching pattern of great impact and challenges; network teaching platform of the new teaching mode has changed the traditional teaching mode and gradually attention and favor. With the rapid construction of network infrastructure and the improvement of network speed, the functions of various network teaching management platforms are constantly improved, providing good development opportunities for regional network teaching [4–6].The online teaching platform, also known as learning management system or e-learning platform, provides support and services for the teaching process through network technology and is the basis of online teaching. Network teaching platform has interactive, online, real-time, tracking, evaluation, management, and other functions, can realize real-time online communication, and can improve the teaching effect and quality [7, 8]. At present, a number of online teaching platform software have been developed at home and abroad, such as B1ackBoard, Sakai, online courses, and MOOC courses, realizing the online use of teaching content and teaching methods, as well as the online communication and interaction between teachers and students. At present, the teaching mode is mainly lecturing. Through the Internet and artificial intelligence technology such as the network teaching platform, the time and space limitation of teaching is broken, and the method is more flexible and the content is more vivid [9–13]. With the rapid development of network teaching platform, digital teaching course resources have been quite rich, further improving the quality of teaching. Some scholars have carried out a questionnaire survey on online teaching among college students and found that 80% students hold a positive attitude towards online teaching, believing that online teaching can promote learning more. It is concluded that online teaching has the advantages of realizing independence, independent learning, unlimited time, rich course resources, and convenient communication and search. At the same time, through the network teaching platform, it can realize the automatic distribution and correction of homework and make a good statistics of error-prone knowledge points, which can also effectively prevent plagiarism [14–16].With the development of online teaching platform, scholars at home and abroad have carried out studies on learning quality evaluation. Some scholars put forward that in accordance with ISO 9000 standards, the implementation of online teaching courses can be divided into the process of course introduction, knowledge point explanation, homework, interaction, and communication. Through the implementation of online courses, it is found that the interaction between students and teachers has increased significantly, and the teaching quality has been constantly improved. Meanwhile, through the feedback of homework, teachers can accurately master each student’s learning and knowledge of the degree of mastery. Some scholars have proposed to evaluate the interactive effect of online teaching by using such indicators as the social and teaching purpose of the interaction, the type of technology, the method of use, and the influence on learners’ behavior and have established a quantitative evaluation model of the interactive level of online education courses. At the same time, the indicators of teacher-student interaction design, interaction content, and interaction monitoring are also added to the learning quality evaluation index system of online course implementation. By investigating and analyzing the current situation of online course implementation in colleges and universities, some scholars have established the learning quality evaluation index system of online course implementation from the aspects of course resources, faculty allocation, teaching process, and interactive evaluation and carried out case studies [17–19].With the development of network teaching platform, teaching resources have been able to meet the needs and the functions of the platform have been gradually improved. However, there are relatively few studies on the evaluation of learning quality of curriculum implementation. With Python primer, this paper is to the teaching of the course as an example, the theory of data-driven process of network teaching platform teaching mode, curriculum can be divided into class leading learning stage, stage of studies in class, and after-class extension stage, by adopting the method of discussion method, literature query method to analyze the factors influencing the quality of learning curriculum implementation. In this way, the evaluation scheme of students’ learning quality implemented under the support of online teaching platform is constructed to provide powerful data and theoretical support for educators to carry out online teaching.


## 2. Course Implementation Process and Research Method

### 2.1. Course Implementation Process

The case course is placed on the network teaching platform, which includes management module, learning module, communication module, and assessment module in Figure1, so as to achieve authority management, online teaching, interactive communication, and performance assessment. In the course teaching process, it can be divided into three parts: preclass guidance stage, in-class research stage, and after-class extension stage.Figure 1
Network teaching platform architecture.


#### 2.1.1. Prestudy Stage

The preclass guided learning stage is the initial stage of online class, in which teachers stimulate their interest in learning and lay a solid foundation for in-class learning through humorous lectures. First of all, by analyzing students’ personalized requirements and differences in learning ability and cognitive level, learning materials are distributed to each student to preview and understand new knowledge in advance. Secondly, according to the knowledge of students to master the situation, assign learning tasks, students through the completion of their own self-learning ability. Finally, through the evaluation function of the network teaching platform, the difficult problems in learning are summarized to provide a basis for later targeted teaching courses.


#### 2.1.2. Middle Study Stage

In-class research stage is also known as offline classroom stage. The specific process is as follows: teachers give personalized guidance according to students’ feedback. According to the group as a unit, the centralized exploratory research is carried out in the way of coordination and individual combination, and targeted guidance is carried out by teachers. Finally, students will display and share their personal learning achievements and answer the existing questions, so as to complete the learning tasks of the course.


#### 2.1.3. Extension after Class

The after-class extension stage is also called the stage of combining online and offline classes. The specific process is as follows: after class, students continuously improve their application ability of knowledge and skills through independent learning. Teachers can master students’ learning data and homework completion through online teaching evaluation. The network teaching platform automatically pushes systematic learning programs to students, constantly improving self-learning ability and mastering more knowledge. Finally, optimize and perfect teaching activities to improve the rationality, scientificity, and accuracy of teaching management.


### 2.2. Research Methods

This paper mainly uses discussion method, literature search method, mathematical statistics, performance assessment, questionnaire survey, and other research methods to carry out the learning quality factors affecting the implementation of the curriculum.The discussion method is mainly to carry out on-site discussion by randomly selecting students and analyze the feedback of completing the basic Python course through the network teaching platform.The literature search method is to search the relevant literatures about the application of network teaching platform to Python basic course in the databases of CNKI, Tongfang, and Wanfang.In mathematical statistics, Excel, SPSS, and other software are used for statistical investigation data, and comparison method is used for variance analysis, andP<0.05 is used to indicate significant difference.In the performance assessment method, the scores of practical operation, daily routine, and final examination are taken as the indicators for the assessment of this course, and the proportion of each part is given (Figure2) As can be seen from Figure 2, the proportion of practical operation test scores and final exam scores is the same, reaching 40%, and the proportion of behavioral performance scores such as answering questions in class is 20%. The main purpose of this course is to test students’ computer operation ability and mastery of theoretical knowledge, and teachers can make course summary reports based on course assessment scores.Figure 2
Course assessment ratio.Questionnaire survey is to carry out satisfaction survey on students’ self-learning ability, hands-on ability, literature retrieval ability, knowledge mastery, teaching effect, etc. The scoring criteria are shown in Table1. The teaching quality is evaluated based on the satisfaction obtained from the questionnaire survey and applied to the teaching effect evaluation.Table 1
Scoring standard of satisfaction survey.
Score (score)Scoring criteria1Not satisfied2Not satisfied3Satisfied4Satisfactory5Very satisfied


### 2.3. Construction of Learning Quality Evaluation System for Curriculum Implementation

The establishment of learning quality evaluation system is a complicated and long process, which needs strong rigor and systematicness. In the stage of preclass guidance, the evaluation indicators of participation mainly include students’ input and participation, guidance, and evaluation. In the research stage, the evaluation indexes mainly include contribution, collaboration ability, and real-time online evaluation. In the extension stage after class, the evaluation indexes include self-study ability, academic performance, learning effect, and teacher evaluation. In order to further improve the accuracy and authenticity of the evaluation results of learning quality in the course implementation process, teachers adhere to the combination of online and offline assessment methods and adopt qualitative or legal evaluation schemes to monitor and evaluate the whole process of students’ learning in real time.


## 2.1. Course Implementation Process

The case course is placed on the network teaching platform, which includes management module, learning module, communication module, and assessment module in Figure1, so as to achieve authority management, online teaching, interactive communication, and performance assessment. In the course teaching process, it can be divided into three parts: preclass guidance stage, in-class research stage, and after-class extension stage.Figure 1
Network teaching platform architecture.


### 2.1.1. Prestudy Stage

The preclass guided learning stage is the initial stage of online class, in which teachers stimulate their interest in learning and lay a solid foundation for in-class learning through humorous lectures. First of all, by analyzing students’ personalized requirements and differences in learning ability and cognitive level, learning materials are distributed to each student to preview and understand new knowledge in advance. Secondly, according to the knowledge of students to master the situation, assign learning tasks, students through the completion of their own self-learning ability. Finally, through the evaluation function of the network teaching platform, the difficult problems in learning are summarized to provide a basis for later targeted teaching courses.


### 2.1.2. Middle Study Stage

In-class research stage is also known as offline classroom stage. The specific process is as follows: teachers give personalized guidance according to students’ feedback. According to the group as a unit, the centralized exploratory research is carried out in the way of coordination and individual combination, and targeted guidance is carried out by teachers. Finally, students will display and share their personal learning achievements and answer the existing questions, so as to complete the learning tasks of the course.


### 2.1.3. Extension after Class

The after-class extension stage is also called the stage of combining online and offline classes. The specific process is as follows: after class, students continuously improve their application ability of knowledge and skills through independent learning. Teachers can master students’ learning data and homework completion through online teaching evaluation. The network teaching platform automatically pushes systematic learning programs to students, constantly improving self-learning ability and mastering more knowledge. Finally, optimize and perfect teaching activities to improve the rationality, scientificity, and accuracy of teaching management.


## 2.1.1. Prestudy Stage

The preclass guided learning stage is the initial stage of online class, in which teachers stimulate their interest in learning and lay a solid foundation for in-class learning through humorous lectures. First of all, by analyzing students’ personalized requirements and differences in learning ability and cognitive level, learning materials are distributed to each student to preview and understand new knowledge in advance. Secondly, according to the knowledge of students to master the situation, assign learning tasks, students through the completion of their own self-learning ability. Finally, through the evaluation function of the network teaching platform, the difficult problems in learning are summarized to provide a basis for later targeted teaching courses.


## 2.1.2. Middle Study Stage

In-class research stage is also known as offline classroom stage. The specific process is as follows: teachers give personalized guidance according to students’ feedback. According to the group as a unit, the centralized exploratory research is carried out in the way of coordination and individual combination, and targeted guidance is carried out by teachers. Finally, students will display and share their personal learning achievements and answer the existing questions, so as to complete the learning tasks of the course.


## 2.1.3. Extension after Class

The after-class extension stage is also called the stage of combining online and offline classes. The specific process is as follows: after class, students continuously improve their application ability of knowledge and skills through independent learning. Teachers can master students’ learning data and homework completion through online teaching evaluation. The network teaching platform automatically pushes systematic learning programs to students, constantly improving self-learning ability and mastering more knowledge. Finally, optimize and perfect teaching activities to improve the rationality, scientificity, and accuracy of teaching management.


## 2.2. Research Methods

This paper mainly uses discussion method, literature search method, mathematical statistics, performance assessment, questionnaire survey, and other research methods to carry out the learning quality factors affecting the implementation of the curriculum.The discussion method is mainly to carry out on-site discussion by randomly selecting students and analyze the feedback of completing the basic Python course through the network teaching platform.The literature search method is to search the relevant literatures about the application of network teaching platform to Python basic course in the databases of CNKI, Tongfang, and Wanfang.In mathematical statistics, Excel, SPSS, and other software are used for statistical investigation data, and comparison method is used for variance analysis, andP<0.05 is used to indicate significant difference.In the performance assessment method, the scores of practical operation, daily routine, and final examination are taken as the indicators for the assessment of this course, and the proportion of each part is given (Figure2) As can be seen from Figure 2, the proportion of practical operation test scores and final exam scores is the same, reaching 40%, and the proportion of behavioral performance scores such as answering questions in class is 20%. The main purpose of this course is to test students’ computer operation ability and mastery of theoretical knowledge, and teachers can make course summary reports based on course assessment scores.Figure 2
Course assessment ratio.Questionnaire survey is to carry out satisfaction survey on students’ self-learning ability, hands-on ability, literature retrieval ability, knowledge mastery, teaching effect, etc. The scoring criteria are shown in Table1. The teaching quality is evaluated based on the satisfaction obtained from the questionnaire survey and applied to the teaching effect evaluation.Table 1
Scoring standard of satisfaction survey.
Score (score)Scoring criteria1Not satisfied2Not satisfied3Satisfied4Satisfactory5Very satisfied


## 2.3. Construction of Learning Quality Evaluation System for Curriculum Implementation

The establishment of learning quality evaluation system is a complicated and long process, which needs strong rigor and systematicness. In the stage of preclass guidance, the evaluation indicators of participation mainly include students’ input and participation, guidance, and evaluation. In the research stage, the evaluation indexes mainly include contribution, collaboration ability, and real-time online evaluation. In the extension stage after class, the evaluation indexes include self-study ability, academic performance, learning effect, and teacher evaluation. In order to further improve the accuracy and authenticity of the evaluation results of learning quality in the course implementation process, teachers adhere to the combination of online and offline assessment methods and adopt qualitative or legal evaluation schemes to monitor and evaluate the whole process of students’ learning in real time.


## 3. Results and Analysis

### 3.1. Study Sample Selection

Based on the basic course of Python, 245 students majoring in artificial intelligence and software engineering were selected from sophomores to carry out course teaching with the help of multimedia classroom and MOOC online teaching platform. Before the course teaching, teachers upload the course materials to the MOOC platform in advance so that students can preview in advance online and master relevant basic theories. As a basic professional course, this course has 72 class hours, and the final examination period is generally in the 19th to 20th week of each semester.


### 3.2. Curriculum Implementation under the Network Teaching Platform

In order to enable undergraduate students to prepare for online courses in advance, the teacher will upload the after-class exercises of the last class and the main knowledge points and learning contents of the next class one or two days after the end of the last class before each class and import them through the online teaching platform MOOC. Through the online teaching platform, students can complete the exercises after class, and at the same time, they can preview the course content in advance and have a good idea. This paper takes the online editing program in the basic Python tutorial as an example. The teacher uploads the online editing program case in advance and constructs a learning team to learn the content in the course. According to the preview effect and the experimental teaching objective of Python language, the teacher issues 20 multiple choice questions and uploads them to the online teaching platform to get the students’ scores. Meanwhile, the teacher issues another paper to get the students’ scores in the form of group discussion, laying a solid foundation for the next learning method.In online classes, students discuss in groups and teachers work together in complex ways. Finally, all students share their achievements, especially the documents they have consulted, so that teachers can grasp the confounding difficulties of the course. After the course, each group will score each other. Teachers will explain questions online on the online teaching platform according to the test results of students’ classroom teaching. Meanwhile, students can also submit better learning suggestions for the online teaching platform.


### 3.3. Reliability and Validity Analysis

Based on the evaluation indexes of students’ learning quality implemented in the course under the network teaching platform established in Table2, this course is divided into three parts: guidance, research, and extension. The corresponding indexes are scored, respectively, and the reliability and validity indexes in mathematical statistics are used to evaluate students’ learning quality. The specific implementation is as follows: score the 9 evaluation indicators of 245 students and import the data into the network teaching platform system, which is mainly divided into the learning process (investment degree, etc.) and learning results (performance, etc.) data. The main factors affecting students’ learning quality are obtained through statistics and analysis of the data by using the analysis method of Cronbach’s coefficient. The analysis results of Cronbach’s coefficient are shown in Table 3. Result distribution maps of different learning stages are drawn in Table 3 (Figure 3) It can be seen from Figure 3 that the Cronbach coefficient reaches 0.882, 0.873, and 0.861, respectively, which fully indicates that the constructed evaluation index has high credibility. KMO test is used to calculate the structural validity of the three stages of learning, and the calculated result is 0.658, much higher than 0.5. The evaluation index proposed in this paper has good structural validity.Table 2
Curriculum learning evaluation index system.
Score the contentThe evaluation indexWeight (%)Courses leading to learnEngagement and engagement20Learning evaluation10Studies in the classContribution15Cooperation ability15Real-time online measurement20Extension after classSelf-study ability5Study result5Learning effect5The teacher evaluation5Table 3
Analysis results of Cronbach coefficients.
Total gradeLearning achievementStudies resultsExtension performanceCronbach coefficienta0.8890.8820.8730.861Figure 3
Distribution of Cronbach coefficients at different learning stages.


### 3.4. Correlation Analysis between Prelesson Learning and Students’ Learning Quality

The Pearson correlation test is used to analyze the correlation between students’ investment and participation in learning and their total academic performance. According to the calculated result: students’ learning commitment and participation of Pearson correlation coefficient is 0.812 and Pearson correlation coefficient of 0.808 of the student’s total grade; there was a positive correlation relationship between them at the same time, which shows the higher input and participation, students’ learning achievement will continue to improve, and learning quality is becoming more and more ideal.


### 3.5. Correlation Analysis between Middle School Research and Students’ Learning Quality

Pearson’s correlation test was used to analyze the correlation between students’ contribution to research, collaborative innovation, real-time online evaluation, and their total score. Pearson’s correlation coefficients of students’ learning ability and their total score were 0.853, indicating that there was an obvious correlation between them. The Pearson correlation coefficients of the collaborative innovation ability and the total score are 0.683, and there is a significant correlation around 0.01. It shows that the stronger the students’ learning ability and innovation ability, the more ideal their academic performance and the better the quality of their learning.


### 3.6. Correlation Analysis between After-Class Extension and Students’ Learning Quality

Pearson’s correlation test is used to analyze the correlation between students’ self-study ability, final score, learning effect, teacher evaluation, and total score. According to the calculation results, the Pearson correlation coefficient of students’ self-learning ability is 0.833, which is basically consistent with the Pearson correlation coefficient of students’ total score of 0.821, and the two are positively correlated. Pearson correlation coefficient of students’ final academic performance is basically the same as that of the total score, which is also positive correlation. Therefore, the stronger the students’ self-study ability, the better their academic performance, and the higher the quality of their students.


## 3.1. Study Sample Selection

Based on the basic course of Python, 245 students majoring in artificial intelligence and software engineering were selected from sophomores to carry out course teaching with the help of multimedia classroom and MOOC online teaching platform. Before the course teaching, teachers upload the course materials to the MOOC platform in advance so that students can preview in advance online and master relevant basic theories. As a basic professional course, this course has 72 class hours, and the final examination period is generally in the 19th to 20th week of each semester.


## 3.2. Curriculum Implementation under the Network Teaching Platform

In order to enable undergraduate students to prepare for online courses in advance, the teacher will upload the after-class exercises of the last class and the main knowledge points and learning contents of the next class one or two days after the end of the last class before each class and import them through the online teaching platform MOOC. Through the online teaching platform, students can complete the exercises after class, and at the same time, they can preview the course content in advance and have a good idea. This paper takes the online editing program in the basic Python tutorial as an example. The teacher uploads the online editing program case in advance and constructs a learning team to learn the content in the course. According to the preview effect and the experimental teaching objective of Python language, the teacher issues 20 multiple choice questions and uploads them to the online teaching platform to get the students’ scores. Meanwhile, the teacher issues another paper to get the students’ scores in the form of group discussion, laying a solid foundation for the next learning method.In online classes, students discuss in groups and teachers work together in complex ways. Finally, all students share their achievements, especially the documents they have consulted, so that teachers can grasp the confounding difficulties of the course. After the course, each group will score each other. Teachers will explain questions online on the online teaching platform according to the test results of students’ classroom teaching. Meanwhile, students can also submit better learning suggestions for the online teaching platform.


## 3.3. Reliability and Validity Analysis

Based on the evaluation indexes of students’ learning quality implemented in the course under the network teaching platform established in Table2, this course is divided into three parts: guidance, research, and extension. The corresponding indexes are scored, respectively, and the reliability and validity indexes in mathematical statistics are used to evaluate students’ learning quality. The specific implementation is as follows: score the 9 evaluation indicators of 245 students and import the data into the network teaching platform system, which is mainly divided into the learning process (investment degree, etc.) and learning results (performance, etc.) data. The main factors affecting students’ learning quality are obtained through statistics and analysis of the data by using the analysis method of Cronbach’s coefficient. The analysis results of Cronbach’s coefficient are shown in Table 3. Result distribution maps of different learning stages are drawn in Table 3 (Figure 3) It can be seen from Figure 3 that the Cronbach coefficient reaches 0.882, 0.873, and 0.861, respectively, which fully indicates that the constructed evaluation index has high credibility. KMO test is used to calculate the structural validity of the three stages of learning, and the calculated result is 0.658, much higher than 0.5. The evaluation index proposed in this paper has good structural validity.Table 2
Curriculum learning evaluation index system.
Score the contentThe evaluation indexWeight (%)Courses leading to learnEngagement and engagement20Learning evaluation10Studies in the classContribution15Cooperation ability15Real-time online measurement20Extension after classSelf-study ability5Study result5Learning effect5The teacher evaluation5Table 3
Analysis results of Cronbach coefficients.
Total gradeLearning achievementStudies resultsExtension performanceCronbach coefficienta0.8890.8820.8730.861Figure 3
Distribution of Cronbach coefficients at different learning stages.


## 3.4. Correlation Analysis between Prelesson Learning and Students’ Learning Quality

The Pearson correlation test is used to analyze the correlation between students’ investment and participation in learning and their total academic performance. According to the calculated result: students’ learning commitment and participation of Pearson correlation coefficient is 0.812 and Pearson correlation coefficient of 0.808 of the student’s total grade; there was a positive correlation relationship between them at the same time, which shows the higher input and participation, students’ learning achievement will continue to improve, and learning quality is becoming more and more ideal.


## 3.5. Correlation Analysis between Middle School Research and Students’ Learning Quality

Pearson’s correlation test was used to analyze the correlation between students’ contribution to research, collaborative innovation, real-time online evaluation, and their total score. Pearson’s correlation coefficients of students’ learning ability and their total score were 0.853, indicating that there was an obvious correlation between them. The Pearson correlation coefficients of the collaborative innovation ability and the total score are 0.683, and there is a significant correlation around 0.01. It shows that the stronger the students’ learning ability and innovation ability, the more ideal their academic performance and the better the quality of their learning.


## 3.6. Correlation Analysis between After-Class Extension and Students’ Learning Quality

Pearson’s correlation test is used to analyze the correlation between students’ self-study ability, final score, learning effect, teacher evaluation, and total score. According to the calculation results, the Pearson correlation coefficient of students’ self-learning ability is 0.833, which is basically consistent with the Pearson correlation coefficient of students’ total score of 0.821, and the two are positively correlated. Pearson correlation coefficient of students’ final academic performance is basically the same as that of the total score, which is also positive correlation. Therefore, the stronger the students’ self-study ability, the better their academic performance, and the higher the quality of their students.


## 4. Conclusion

The online teaching platform has become the most important course teaching method at present. Taking the online teaching of basic Python course as an example, this paper, through the data-driven theory, summarizes the process of the online teaching mode in the stage of preclass guidance, in-class research, and after-class extension and constructs the learning quality evaluation indicators that affect the course implementation. The correlation between each index and the total score of learning is established, so as to construct an evaluation scheme of students’ learning quality supported by the network teaching platform. Main research achievements of this paper are as follows:(1)
The basic Python course is placed on the network teaching platform, and the teaching process of this course is divided into preclass guidance stage, in-class research stage, and after-class extension stage This paper analyzes the factors affecting the learning quality of curriculum implementation by means of discussion method, literature inquiry method, mathematical statistics, achievement examination, and questionnaire survey, so as to establish the evaluation system of learning quality of curriculum implementation(2)
For the teaching process of basic Python course, the course is placed on the MOOC network teaching platform for teaching, and the learning quality of the students in class is analyzed. Teachers upload course to platform in advance before class, students can prepare ahead of course content, course for middle school students through the form of group discussion learning and collaborative guidance from teachers, students share the achievements of their own learning, and teachers grasp the difficulty of mixing the team after class to review with each other and by teachers in the network teaching platform evaluation and arrange the homework(3)
By analyzing the data of 9 evaluation indexes of students and using the analysis method of KMO’s coefficient and KMO’s test, Cronbach’s coefficient and structural validity of the three stages of curriculum implementation are obtained, and it is proved that the constructed evaluation indexes have high credibility and good structural validity. Through the class leading learning stage, stage of inquiry learning in the class, after class extension stage, and students’ learning quality correlation analysis, using Pearson correlation test, it is concluded that different index correlation with students’ academic performance, students’ input and participation, learning ability, and innovation ability, self-study ability is directly related to the students’ academic performance. It has a direct impact on the learning quality of students



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*Source: 4703760-2022-07-19.xml*