Student comprehensive quality evaluation system based on five-raising

Abstract

The invention discloses a student comprehensive quality evaluation system based on five kinds of education, and the evaluation system evaluates the cognition level of students in the aspects of subject knowledge, subject ability and thinking ability through a cognition module, evaluates the emotion aspect of the students through an emotion module, evaluates the physical quality and health conditions of the students through a physical ability module, and evaluates the comprehensive quality of the students through an evaluation module. The labor module evaluates the labor aspect of the students, the beauty module evaluates the development of the students in the aspects of aesthetic interest and artistic cultivation, and the comprehensive analysis module obtains the evaluation result of the five-time cultivation of the students, comprehensively calculates the evaluation result of the five-time cultivation of the students based on a polynomial algorithm, and generates quality assignment for each student. The set division module performs set division on all the students according to a comparison result of the quality assignment and the gradient threshold, wherein the set division comprises a first set, a second set and a third set. The evaluation system can evaluate the quality of the students after comprehensively analyzing the five-fertility conditions of the students, better inspect the personality and potential of the students, and provide support for personalized education.

Description

Technical Field

[0001] The present invention relates to the technical field of evaluation systems, and particularly to a comprehensive student quality evaluation system based on the simultaneous promotion of five educations. Background Art

[0002] For a long time, traditional education evaluations have mainly focused on exam scores, ignoring the individual differences of students and the cultivation of comprehensive qualities. With the change of educational concepts, more and more people realize that students not only need to learn knowledge, but also need to cultivate comprehensive qualities such as innovation ability, teamwork ability, and practical problem-solving ability.

[0003] The present invention proposes a comprehensive student quality evaluation system based on the simultaneous promotion of five educations, which can comprehensively evaluate students' qualities in combination with the five educations of students, better examine the individuality and potential of students, and provide support for personalized education. Summary of the Invention

[0004] The purpose of the present invention is to provide a comprehensive student quality evaluation system based on the simultaneous promotion of five educations to solve the deficiencies in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: A comprehensive student quality evaluation system based on the simultaneous promotion of five educations, including a cognitive module, an emotional module, a physical fitness module, a labor module, an aesthetic education module, a comprehensive analysis module, a set partitioning module, and a suggestion module.

[0006] Cognitive module: Evaluate the cognitive level of students in terms of subject knowledge, subject ability, and thinking ability.

[0007] Emotional module: Evaluate the emotional aspects of students.

[0008] Physical fitness module: Evaluate the physical fitness and health status of students.

[0009] Labor module: Evaluate the labor aspects of students.

[0010] Aesthetic education module: Evaluate the development of students in terms of aesthetic taste and artistic accomplishment.

[0011] Comprehensive analysis module: After obtaining the evaluation results of the five educations of students, comprehensively calculate the evaluation results of the five educations of students based on a polynomial algorithm, and generate a quality assignment for each student.

[0012] Set partitioning module: Partition all students according to the comparison results of the quality assignment and the gradient threshold, including the first set, the second set, and the third set.

[0013] Suggestion module: Generate corresponding decision suggestions for students in the first set, the second set, and the third set respectively.

[0014] Preferably, the comprehensive analysis module obtains the cognitive dimension score, emotional dimension score, physical fitness dimension score, labor dimension score, and aesthetic education dimension score of the student;

[0015] The cognitive dimension score, emotional dimension score, physical fitness dimension score, labor dimension score, and aesthetic education dimension score are weighted and calculated to obtain the quality assignment of the student. The function expression is:

[0016]

[0017] ; In the formula, Szf is the quality assignment, Q is the correction coefficient, with a value of 1.265, representing the value of the quality assignment when all parameters are absent. rzw, qhw, tnw, ldw, and myw are the cognitive dimension score, emotional dimension score, physical fitness dimension score, labor dimension score, and aesthetic education dimension score respectively, and ω 1 , ω 2 , ω 3 , ω 4 , ω 5 are the weight coefficients of the cognitive dimension score, emotional dimension score, physical fitness dimension score, labor dimension score, and aesthetic education dimension score respectively, and ω 1 + ω 2 + ω 3 + ω 4 + ω 5 = 1;

[0018] After obtaining the quality assignments of all students, establish an assignment set for all the quality assignments, and after performing standard deviation analysis on the assignment set, judge the overall student situation of the school;

[0019] Analyze the standard deviation of the assignment set. The function expression is:

[0020]

[0021] In the formula, SQ is the standard deviation of the assignment set, i = {1, 2, 3,..., n}, n represents the number of students in the school, n is a positive integer, Szfi represents the quality assignment of the i-th student, represents the average quality assignment;

[0022] If the quality assignment ≥ the first division threshold and the standard deviation of the assignment set ≤ the standard deviation threshold, analyze that the overall student quality situation of the school is excellent. If the quality assignment ≥ the first division threshold and the standard deviation of the assignment set > the standard deviation threshold, analyze that the overall student quality situation of the school is good. If the quality assignment < the first division threshold, analyze that the overall student quality situation of the school is poor.

[0023] Preferably, after the set division module obtains the quality assignment, it compares the quality assignment with the gradient threshold, and the gradient threshold includes the first division threshold and the second division threshold;

[0024] If the quality assignment ≥ the second division threshold, it is analyzed that the comprehensive quality of the student is high, and the student is classified into the third set;

[0025] If the first division threshold ≤ the quality assignment < the second division threshold, it is analyzed that the comprehensive quality of the student is medium, and the student is classified into the second set;

[0026] If the quality assignment < the first division threshold, it is analyzed that the comprehensive quality of the student is poor, and the student is classified into the first set.

[0027] Preferably, the aesthetic education module collects the aesthetic education evaluation data of students from aspects of art activities, creative expression, and cultural literacy, integrates the evaluation data obtained from different sources into a unified data format, standardizes different evaluation indicators, extracts features from the standardized data, including the participation degree, creative expression, and cultural literacy of students in art activities, trains an aesthetic education analysis model using machine learning technology, the aesthetic education analysis model automatically infers the level of students in aesthetic education from the extracted features, and after training the aesthetic education analysis model, generates the evaluation results of the level of students in aesthetic education through an automated process.

[0028] Preferably, the labor module collects the labor evaluation data of students from aspects of social practice and internship experience, integrates the evaluation data obtained from different sources into a unified data format, standardizes different evaluation indicators, extracts features from the standardized data, trains a labor ability analysis model using machine learning technology, the labor ability analysis model automatically infers the level of students in labor from the extracted features, and after training the labor ability analysis model, generates the evaluation results of the level of students in labor through an automated process.

[0029] Preferably, the physical fitness module collects the physical fitness evaluation data of students from aspects of physical education curriculum records, sports competition results, and physical fitness tests, integrates the evaluation data obtained from different sources into a unified data format, standardizes different evaluation indicators, extracts features from the standardized data, including the physical fitness and sports ability of students, trains a physical fitness analysis model using machine learning technology, the physical fitness analysis model automatically infers the physical fitness level of students from the extracted features, and after training the physical fitness analysis model, generates the evaluation results of the physical fitness level of students through an automated process.

[0030] Preferably, the emotion module collects evaluation data on students' emotional attitudes, values, and interpersonal communication, including students' moral records, participation in social activities, and performance in teamwork projects. It integrates the evaluation data obtained from different sources into a unified data format, standardizes different evaluation indicators, extracts features from the standardized data, including students' social skills, sense of responsibility, and teamwork ability, and uses machine learning technology to train an emotion analysis model. The emotion analysis model automatically infers the comprehensive level of students' emotions from the extracted features. After being trained by the emotion analysis model, the comprehensive level of students' emotions generates an evaluation result through an automated process.

[0031] Preferably, the cognitive module collects evaluation data on students' subject knowledge, subject ability, thinking ability, etc., including exam scores, classroom participation records, subject competition results, etc. It integrates the collected data into a unified data format, standardizes different evaluation indicators, extracts features from the standardized data, and the features include subject knowledge level, problem-solving ability, and creative thinking. It uses machine learning technology to train a cognitive analysis model. The cognitive analysis model automatically infers the comprehensive cognitive level of students from the extracted features. After being trained by the cognitive analysis model, the comprehensive cognitive level of students generates an evaluation result through an automated process.

[0032] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0033] 1. The present invention evaluates the cognitive level of students in terms of subject knowledge, subject ability, and thinking ability through the cognitive module, evaluates the emotions of students through the emotion module, evaluates the physical fitness and health status of students through the physical fitness module, evaluates the labor aspects of students through the labor module, and evaluates the development of students in terms of aesthetic taste and artistic accomplishment through the aesthetic education module. After the comprehensive analysis module obtains the evaluation results of the five educations of students, it comprehensively calculates the evaluation results of the five educations of students based on the polynomial algorithm, generates a quality assignment for each student, and the set partitioning module partitions all students according to the comparison result between the quality assignment and the gradient threshold, including the first set, the second set, and the third set. This evaluation system can evaluate students' qualities after comprehensively analyzing the five educations of students, better examine students' personalities and potential, and provide support for personalized education.

[0034] 2. The present invention obtains the cognitive dimension score, emotion dimension score, physical fitness dimension score, labor dimension score, and aesthetic education dimension score of students through the comprehensive analysis module, and calculates the weighted scores of the cognitive dimension score, emotion dimension score, physical fitness dimension score, labor dimension score, and aesthetic education dimension score to obtain the quality assignment of students. The larger the quality assignment, the better the comprehensive quality of students. Through the comprehensive analysis mode, the quality analysis of students is more comprehensive. Description of the Drawings

[0035] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other accompanying drawings can also be obtained based on these drawings.

[0036] Figure 1 It is the method flow chart of the present invention. Specific embodiments

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0038] Embodiment: Please refer to Figure 1 As shown, a student comprehensive quality evaluation system based on the simultaneous promotion of five educations in this embodiment includes a cognitive module, an emotional module, a physical fitness module, a labor module, an aesthetic education module, a comprehensive analysis module, a set partitioning module, and a suggestion module;

[0039] Cognitive module: Evaluate the cognitive level of students in aspects such as subject knowledge, subject ability, and thinking ability. This may include evaluations in aspects such as subject grades, classroom performance, and subject competitions. The evaluation results are sent to the comprehensive analysis module;

[0040] Data collection: Evaluation data of students in aspects such as subject knowledge, subject ability, and thinking ability are collected. This may include exam scores, classroom participation records, and subject competition results.

[0041] Data integration: Integrate the collected data into a unified data format for convenient subsequent processing and analysis.

[0042] Data standardization: Standardize different evaluation indicators to ensure comparability between different evaluation indicators. This can be achieved through normalization or other statistical methods.

[0043] Feature extraction: Extract key features from the standardized data. These features may include subject knowledge level, problem-solving ability, creative thinking, etc.

[0044] Model training: Use machine learning or other analysis techniques to train an analysis model that can automatically infer the comprehensive cognitive level of students from the extracted features.

[0045] Evaluation result generation: After model training, the comprehensive cognitive level of students is used to generate evaluation results through an automated process, which may be presented in the form of numerical scores, grades, or other forms.

[0046] Result sending: The generated evaluation results are automatically sent to the comprehensive analysis module for subsequent overall comprehensive analysis.

[0047] Emotion module: Focus on the development of students' emotional attitudes, values, interpersonal communication, etc. This includes evaluations of students' moral character, sense of responsibility, teamwork ability, and other emotional aspects. The evaluation results are sent to the comprehensive analysis module;

[0048] Data collection: Collect evaluation data on students' emotional attitudes, values, interpersonal communication, etc. This may include students' moral character records, participation in social activities, performance in teamwork projects, etc.

[0049] Data integration: Integrate the evaluation data obtained from different sources into a unified data format for subsequent processing and analysis.

[0050] Data standardization: Standardize different evaluation indicators to ensure their comparability. Standardization may involve data normalization or other statistical methods.

[0051] Feature extraction: Extract key features from the standardized data. These features may include students' social skills, sense of responsibility, teamwork ability, etc.

[0052] Model training: Use machine learning or other analysis techniques to train an emotion analysis model that can automatically infer the comprehensive emotional level of students from the extracted features.

[0053] Evaluation result generation: After model training, the comprehensive emotional level of students is used to generate evaluation results through an automated process, which may be presented in the form of numerical scores, grades, or other forms.

[0054] Result sending: The generated evaluation results are automatically sent to the comprehensive analysis module for subsequent overall comprehensive analysis.

[0055] Physical fitness module: Evaluate students' physical fitness and health status. This may include evaluations of sports performance, physical fitness tests, health status, etc. The evaluation results are sent to the comprehensive analysis module;

[0056] Data collection: Collect students' physical fitness evaluation data from aspects such as physical education course records, sports competition results, and physical fitness tests.

[0057] Data integration: Integrate the evaluation data obtained from different sources into a unified data format for subsequent processing and analysis.

[0058] Data standardization: Standardize different evaluation indicators to ensure their comparability. Standardization may involve data normalization or other statistical methods.

[0059] Feature extraction: Extract key features from the standardized data, which may include students' physical fitness, motor ability, etc.

[0060] Model training: Use machine learning or other analysis techniques to train a physical fitness analysis model that can automatically infer students' physical fitness levels from the extracted features.

[0061] Evaluation result generation: After model training, generate evaluation results for students' physical fitness levels through an automated process, which may be presented in the form of numerical scores, grades, or other forms.

[0062] Result sending: Automatically send the generated evaluation results to the comprehensive analysis module for subsequent overall comprehensive analysis.

[0063] Labor module: Focus on students' practical ability and hands-on ability, which includes students' performance in actual work and practical activities, such as social practice, internship experiences, etc. The evaluation results are sent to the comprehensive analysis module;

[0064] Data collection: Collect evaluation data for students' labor module from aspects such as social practice and internship experiences.

[0065] Data integration: Integrate evaluation data obtained from different sources into a unified data format for subsequent processing and analysis.

[0066] Data standardization: Standardize different evaluation indicators to ensure their comparability. Standardization may involve data normalization or other statistical methods.

[0067] Feature extraction: Extract key features from the standardized data, which may include students' performance in actual work and practical activities, problem-solving ability, etc.

[0068] Model training: Use machine learning or other analysis techniques to train a labor ability analysis model that can automatically infer students' labor levels from the extracted features.

[0069] Evaluation result generation: After model training, generate evaluation results for students' labor levels through an automated process, which may be presented in the form of numerical scores, grades, or other forms.

[0070] Result sending: Automatically send the generated evaluation results to the comprehensive analysis module for subsequent overall comprehensive analysis.

[0071] Aesthetic Education Module: Evaluate the development of students in aspects such as aesthetic taste and artistic accomplishment. This may include evaluations of participation in art activities, creative expression, cultural literacy, etc. The evaluation results are sent to the Comprehensive Analysis Module;

[0072] Data Collection: Collect evaluation data of students' aesthetic education module from aspects such as art activities, creative expression, cultural literacy, etc.

[0073] Data Integration: Integrate the evaluation data obtained from different sources into a unified data format for subsequent processing and analysis.

[0074] Data Standardization: Standardize different evaluation indicators to ensure their comparability. Standardization may involve normalizing the data or other statistical methods.

[0075] Feature Extraction: Extract key features from the standardized data. These features may include the degree of students' participation in art activities, creative expression, cultural literacy, etc.

[0076] Model Training: Use machine learning or other analysis techniques to train an aesthetic education analysis model that can automatically infer the level of students' aesthetic education from the extracted features.

[0077] Evaluation Result Generation: After model training, generate evaluation results of students' aesthetic education through an automated process, which may be presented in the form of numerical scores, grades, or other forms.

[0078] Result Sending: Automatically send the generated evaluation results to the Comprehensive Analysis Module for subsequent overall comprehensive analysis.

[0079] Comprehensive Analysis Module: After obtaining the evaluation results of students' five educations, comprehensively calculate the evaluation results of students' five educations based on the polynomial algorithm, generate a quality assignment for each student, and send the quality assignment to the Set Partitioning Module;

[0080] Obtain the cognitive dimension score, emotional dimension score, physical fitness dimension score, labor dimension score, and aesthetic education dimension score of students;

[0081] Calculate the weighted sum of the cognitive dimension score, emotional dimension score, physical fitness dimension score, labor dimension score, and aesthetic education dimension score to obtain the quality assignment of students. The function expression is:

[0082]

[0083] ; where Szf is the quality assignment, Q is the correction coefficient, with a value of 1.265, representing the value of the quality assignment when all parameters are absent, rzw, qhw, tnw, ldw, and myw are the cognitive dimension score, emotional dimension score, physical fitness dimension score, labor dimension score, and aesthetic education dimension score respectively, and ω1 、 ω 2 、 ω 3 、 ω 4 、 ω 5 are the weight coefficients of the cognitive dimension score, the emotional dimension score, the physical fitness dimension score, the labor dimension score, and the aesthetic education dimension score respectively, and ω 1 + ω 2 + ω 3 + ω 4 + ω 5 = 1;

[0084] After obtaining the quality assignments of all students, establish an assignment set for all quality assignments, and after performing standard deviation analysis on the assignment set, judge the overall student situation of the school;

[0085] Analyze the standard deviation of the assignment set, and the function expression is:

[0086]

[0087] In the formula, SQ is the standard deviation of the assignment set, i = {1, 2, 3,..., n}, n represents the number of students in the school, n is a positive integer, and Szfi represents the quality assignment of the i-th student, represents the average quality assignment;

[0088] If the quality assignment ≥ the first division threshold and the standard deviation of the assignment set ≤ the standard deviation threshold, analyze that the overall student quality situation of the school is excellent. If the quality assignment ≥ the first division threshold and the standard deviation of the assignment set > the standard deviation threshold, analyze that the overall student quality situation of the school is good. If the quality assignment < the first division threshold, analyze that the overall student quality situation of the school is poor.

[0089] This application obtains the cognitive dimension score, the emotional dimension score, the physical fitness dimension score, the labor dimension score, and the aesthetic education dimension score of students through the comprehensive analysis module, and obtains the quality assignment of students by weighted calculation of the cognitive dimension score, the emotional dimension score, the physical fitness dimension score, the labor dimension score, and the aesthetic education dimension score. The larger the quality assignment, the better the comprehensive quality of the student. Through the comprehensive analysis mode, the quality analysis of students is made more comprehensive.

[0090] Cognitive dimension score:

[0091] Calculation formula: It can be measured by subject scores, test scores, etc., or weighted average or other indicators can be used, specifically depending on the evaluation system of the school or educational institution.

[0092] Emotional dimension score:

[0093] Calculation formula: Consider the scores in aspects such as morality, sense of responsibility, and teamwork. Weighted average can be adopted, and the weights of different aspects can be adjusted according to the school's educational philosophy and requirements.

[0094] Physical fitness dimension scoring:

[0095] Calculation formula: Use physical education scores, physical fitness test scores, etc. to measure the physical fitness level of students. Weighted average can be adopted, and the weights of different test items can be adjusted according to the school's requirements.

[0096] Labor dimension scoring:

[0097] Calculation formula: Evaluate the labor dimension of students through scores in aspects such as social practice and internship performance. Similarly, weighted average can be adopted, considering the weights of different labor items.

[0098] Aesthetic education dimension scoring:

[0099] Calculation formula: Use scores in aspects such as art activities, creative performance, and cultural literacy to evaluate the aesthetic education level of students. Similarly, weighted average can be adopted, considering the weights of different aesthetic education items.

[0100] Set partitioning module: Partition all students according to the comparison results of quality assignment and gradient thresholds, including the first set, the second set, and the third set, and send the set partitioning information to the recommendation module;

[0101] The larger the quality assignment, the higher the comprehensive quality of the student. After obtaining the quality assignment, compare the quality assignment with the gradient thresholds, and the gradient thresholds include the first partitioning threshold and the second partitioning threshold;

[0102] If the quality assignment ≥ the second partitioning threshold, analyze that the comprehensive quality of the student is high and assign the student to the third set;

[0103] If the first partitioning threshold ≤ the quality assignment < the second partitioning threshold, analyze that the comprehensive quality of the student is medium and assign the student to the second set;

[0104] If the quality assignment < the first partitioning threshold, analyze that the comprehensive quality of the student is poor and assign the student to the first set.

[0105] Recommendation module: Generate corresponding decision-making recommendations for the students in the first set, the second set, and the third set respectively;

[0106] The decision-making recommendation generated for the students in the first set is:

[0107] Specific weakness identification: Through analysis, determine the quality weaknesses of the students in the first set, that is, in which aspects there is a large room for improvement.

[0108] Formulate personalized suggestions: Based on specific weaknesses, formulate personalized suggestions for each student to promote their improvement in specific aspects.

[0109] Generate decision-making suggestions: Combine specific weaknesses and overall evaluations to generate decision-making suggestions regarding subject supplementary training, social skills training, physical exercise, or other aspects.

[0110] Generate decision-making suggestions for students in the second set as follows:

[0111] Discover potential development space: Through analysis, discover the potential development space of students in the second set, that is, in which aspects they can be further improved.

[0112] Formulate suggestions: Formulate suggestions for the all-round improvement of each student, emphasizing the balanced development in different fields.

[0113] Generate decision-making suggestions: Combine the development space and overall evaluation to generate decision-making suggestions regarding continuing to cultivate existing advantages, improving weaknesses, expanding hobbies, etc.

[0114] Generate decision-making suggestions for students in the third set as follows:

[0115] Strengthen strengths: Confirm the quality strengths of students in the third set and put forward suggestions to consolidate these strengths to ensure the continued maintenance of advantages.

[0116] Enhance potential: Identify potential improvement space, that is, in which aspects there is still room for further development to enhance the all-round quality of students.

[0117] Generate decision-making suggestions: Combine strengths and improvement potential to generate decision-making suggestions regarding deeper development, expanding field interests, participating in higher-level subject competitions, etc.

[0118] This application evaluates the cognitive levels of students in terms of subject knowledge, subject ability, and thinking ability through a cognitive module, evaluates students' emotions through an emotion module, evaluates students' physical fitness and health status through a physical fitness module, evaluates students' labor aspects through a labor module, evaluates the development of students' aesthetic taste and artistic accomplishment through an aesthetic education module. After the comprehensive analysis module obtains the evaluation results of students' five educations, it comprehensively calculates the evaluation results of students' five educations based on a polynomial algorithm to generate a quality assignment for each student. The set partitioning module partitions all students according to the comparison results between the quality assignment and the gradient threshold, including the first set, the second set, and the third set. This evaluation system can evaluate students' qualities after comprehensively analyzing the five-education status of students, better examine students' personalities and potential, and provide support for personalized education.

[0119] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0120] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0121] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A comprehensive quality evaluation system for students based on the five-pronged education, characterized by: It includes cognitive module, emotional module, physical module, labor module, aesthetic education module, comprehensive analysis module, set division module and suggestion module; Cognitive module: evaluates students’ cognitive level in terms of subject knowledge, subject ability, and thinking ability; Emotional module: evaluate students’ emotional aspects; Physical fitness module: evaluate students' physical fitness and health status; Labor module: evaluate students’ labor aspects; Aesthetic Education Module: evaluates students’ development in aesthetic taste and artistic accomplishment; Comprehensive analysis module: After obtaining the evaluation results of the five aspects of students' education, the evaluation results of the five aspects of students' education are comprehensively calculated based on the polynomial algorithm to generate quality values ​​for each student; Set division module: all students are divided into sets according to the comparison results of quality assignment and gradient threshold, including the first set, the second set and the third set; Suggestion module: Generate corresponding decision suggestions for students in the first set, the second set and the third set respectively.

2. According to claim 1, a comprehensive quality evaluation system for students based on the five-pronged education is characterized by: The comprehensive analysis module obtains the student's cognitive dimension score, emotional dimension score, physical dimension score, labor dimension score, and aesthetic education dimension score; The cognitive dimension score, emotional dimension score, physical dimension score, labor dimension score, and aesthetic dimension score are weighted to obtain the student's quality assignment. The function expression is: Szf=e(Q+ω1rzw+ω2qhw+ω3tnw+ω4ldw+ω5myw); In the formula, Szf is the quality assignment, Q is the correction coefficient, which takes a value of 1.265, indicating the value of the quality assignment when all parameters do not exist, rzw, qhw, tnw, ldw, myw are the cognitive dimension score, emotional dimension score, physical dimension score, labor dimension score, and aesthetic education dimension score, respectively, ω1, ω2, ω3, ω4, ω5 are the weight coefficients of the cognitive dimension score, emotional dimension score, physical dimension score, labor dimension score, and aesthetic education dimension score, respectively, and ω1+ω2+ω3+ω4+ω5=1; After obtaining the quality assignments of all students, establish an assignment set for all quality assignments, and after performing standard deviation analysis on the assignment set, determine the overall student status of the school; Analyze the standard deviation of the value set, the function expression is: In the formula, SQ is the standard deviation of the assignment set, i = {1, 2, 3, ..., n}, n represents the number of students in the school, n is a positive integer, Szfi represents the quality assignment of the i-th student, Indicates the average quality assignment; If the quality assignment is ≥ the first division threshold and the standard deviation of the assignment set is ≤ the standard deviation threshold, the overall student quality of the school is excellent. If the quality assignment is ≥ the first division threshold and the standard deviation of the assignment set is greater than the standard deviation threshold, the overall student quality of the school is good. If the quality assignment is less than the first division threshold, the overall student quality of the school is poor.

3. According to claim 2, a comprehensive quality evaluation system for students based on the five-pronged education is characterized by: After the set partitioning module obtains the quality assignment, the quality assignment is compared with the gradient threshold, where the gradient threshold includes a first partitioning threshold and a second partitioning threshold; If the quality assignment value is ≥ the second division threshold, the student’s comprehensive quality is analyzed to be high, and the student is classified into the third set; If the first division threshold ≤ quality assignment < second division threshold, the student's comprehensive quality is analyzed to be medium, and the student is classified into the second set; If the quality assignment is less than the first division threshold, the student's overall quality is analyzed and the student is classified into the first set.

4. According to claim 3, a comprehensive quality evaluation system for students based on the five-pronged education is characterized by: The aesthetic education module collects students' aesthetic education evaluation data from the aspects of artistic activities, creative expression, and cultural literacy, integrates the evaluation data obtained from different sources into a unified data format, standardizes different evaluation indicators, and extracts features from the standardized data, including students' participation in artistic activities, creative expression, and cultural literacy. The aesthetic education analysis model is trained using machine learning technology. The aesthetic education analysis model automatically infers students' level of aesthetic education from the extracted features. After the aesthetic education analysis model is trained, the students' level of aesthetic education is evaluated through an automated process.

5. According to claim 4, a comprehensive quality evaluation system for students based on the five-pronged education is characterized by: The labor module collects students' labor evaluation data from social practice and internship experience, integrates the evaluation data obtained from different sources into a unified data format, standardizes different evaluation indicators, extracts features from the standardized data, and uses machine learning technology to train a labor capacity analysis model. The labor capacity analysis model automatically infers the students' labor level from the extracted features. After the labor capacity analysis model is trained, the students' labor level is evaluated through an automated process.

6. A comprehensive quality evaluation system for students based on the five-pronged education according to claim 5, characterized in that: The physical fitness module collects students' physical fitness evaluation data from physical education course records, sports competition results, and physical fitness tests, integrates the evaluation data obtained from different sources into a unified data format, standardizes different evaluation indicators, extracts features from the standardized data, including students' physical fitness and athletic ability, and uses machine learning technology to train a physical fitness analysis model. The physical fitness analysis model automatically infers the students' physical fitness level from the extracted features. After the physical fitness analysis model is trained, the students' physical fitness level is evaluated through an automated process.

7. A student comprehensive quality evaluation system based on the five-pronged education according to claim 6, characterized in that: The emotion module collects evaluation data on students' emotional attitudes, values, and interpersonal relationships, including students' moral records, participation in social activities, and performance in team collaboration projects. It integrates evaluation data obtained from different sources into a unified data format, standardizes different evaluation indicators, and extracts features from the standardized data, including students' social skills, sense of responsibility, and team collaboration capabilities. It uses machine learning technology to train a sentiment analysis model, which automatically infers the comprehensive level of students' emotions from the extracted features. After the sentiment analysis model is trained, the comprehensive level of students' emotions is evaluated through an automated process.

8. The comprehensive quality evaluation system for students based on the five-pronged education of claim 7 is characterized by: The cognitive module collects evaluation data on students' subject knowledge, subject ability, thinking ability, etc., including test scores, class participation records, subject competition results, etc., integrates the collected data into a unified data format, standardizes different evaluation indicators, and extracts features from the standardized data. The features include subject knowledge level, problem-solving ability, and creative thinking. The cognitive analysis model is trained using machine learning technology. The cognitive analysis model automatically infers the student's comprehensive cognitive level from the extracted features. After the cognitive analysis model is trained, the student's comprehensive cognitive level is evaluated through an automated process.

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