An educational and teaching effect evaluation method and system based on artificial intelligence

Abstract

The present invention discloses an education and teaching effect evaluation method and system based on artificial intelligence, and the method includes: data collection, education evaluation data analysis, construction of education and teaching evaluation model, education and teaching evaluation report display and feedback optimization. The present invention belongs to the field of educational technology, and specifically refers to an education and teaching effect evaluation method and system based on artificial intelligence. This scheme adopts preprocessing of education and teaching evaluation data, calculation of clustering matrix and fuzzy equivalent matrix, extraction of attribute features and association with other related variables, visualization display, and multi-faceted analysis of education and teaching evaluation data; and construction of education and teaching evaluation model, specifically determining the indicator system, constructing judgment matrix, calculating weight vector of each indicator, calculating compatibility index, ensuring rationality and reliability of evaluation system, and calculating final evaluation score.

Description

Technical Field

[0001] The present invention belongs to the field of educational technology, and specifically relates to an educational teaching effect evaluation method and system based on artificial intelligence. Background Art

[0002] The AI-based teaching effect evaluation method is a method that uses AI technology to achieve large-scale collection, analysis and processing of student learning data, thereby providing more diverse evaluation indicators and personalized evaluation methods, aiming to more accurately and objectively evaluate students' learning performance and effects in education, assist teachers and educational institutions to better understand students' learning situation, adjust teaching methods and course content in a timely manner, promote students' all-round development and improve teaching effects. However, the existing AI-based teaching effect evaluation methods have technical problems such as inaccurate analysis of teaching evaluation data and unclear relationships between data, which makes it difficult to improve and optimize teaching; there are many evaluation indicators, making it difficult to comprehensively and systematically determine the evaluation dimensions, and to conduct quantitative evaluation based on various indicators. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an education and teaching effect evaluation method and system based on artificial intelligence. In view of the technical problems that the analysis of education and teaching evaluation data is inaccurate and the relationship between data is unclear, which makes it difficult to improve and optimize education and teaching, the education and teaching evaluation data is pre-processed, the clustering matrix and the fuzzy equivalence matrix are calculated, the attribute features are extracted and associated with other related variables, and a visual display is performed to analyze the education and teaching evaluation data in many aspects; in view of the technical problems that there are many evaluation indicators and it is difficult to comprehensively and systematically determine the evaluation dimensions and conduct quantitative evaluation by integrating various indicators, an education and teaching evaluation model is constructed, specifically, the indicator system is determined, the judgment matrix is ​​constructed, the weight vector of each indicator is calculated, the compatibility index is calculated, the rationality and reliability of the evaluation system are ensured, and the final evaluation score is calculated.

[0004] The technical solution adopted by the present invention is as follows: The present invention provides an education and teaching effect evaluation method based on artificial intelligence, the method comprising the following steps:

[0005] Step S1: Data collection, collecting students’ learning data in education through online learning platforms;

[0006] Step S2: Education evaluation data analysis, specifically preprocessing the education and teaching evaluation data, calculating the clustering matrix and the fuzzy equivalent matrix, extracting attribute features and associating them with other related variables, performing visual display, and performing multi-faceted analysis of the education and teaching evaluation data;

[0007] Step S3: Constructing an education and teaching evaluation model, specifically determining the indicator system, constructing a judgment matrix, calculating the weight vector of each indicator, calculating the compatibility index, ensuring the rationality and reliability of the evaluation system, and calculating the final evaluation score;

[0008] Step S4: Education and teaching evaluation report presentation, showing in detail the comparative analysis of teaching objectives and achievement, the richness and applicability of teaching content, and comprehensive evaluation of teacher performance and student learning effect data and case analysis;

[0009] Step S5: Feedback optimization.

[0010] Furthermore, in step S1, the data collection includes the following steps:

[0011] Step S11: Evaluate the learning effect of students by designing a special questionnaire to ask students about their interest in the course, their understanding, and their feelings about what they have gained; observe students' participation, enthusiasm for speaking, and interactive performance in class, and record their performance as educational evaluation data;

[0012] Step S12: Evaluate the teacher, observe and evaluate the teaching process of the teacher as the education evaluation data;

[0013] Step S13: Preprocess the education assessment data, perform data cleaning, remove invalid data and duplicate data in the education assessment data, standardize data of different dimensions, and normalize the standardized data according to specific rules.

[0014] Furthermore, in step S2, the education assessment data analysis includes the following steps:

[0015] Step S21: data processing, pre-processing the collected education and teaching evaluation data, checking the missing values, error values ​​and abnormal values ​​in the data, correcting and deleting them, integrating the multi-channel data, and obtaining the pre-processed education and teaching evaluation data;

[0016] Step S22: Calculate the clustering degree matrix, which is used to measure the degree of clustering between different data points, and divide the data points with higher similarities and clustering tendencies in the preprocessed education and teaching evaluation data into appropriate subgroups. By using fuzzy clustering, the education and teaching evaluation data with a greater degree of clustering are placed in the same subgroup. The formula used is as follows:

[0017] ;

[0018] In the formula, K represents the dimension of the clustering matrix, and Represents different education and teaching evaluation data points, represents the clustering matrix;

[0019] Step S23: Calculate the fuzzy equivalent matrix to further clarify the relationship between data points. The formula used is as follows:

[0020] ;

[0021] In the formula, represents the fuzzy equivalent matrix, Refers to the best parameter value that can achieve the best clustering result. When the corresponding value is 1, it means that the correlation between the two data points is strong, and when the corresponding value is 0, it means that the correlation between the two data points is weak.

[0022] Step S24: extracting attribute features, analyzing the typical features of the education and teaching evaluation data in each cluster, finding representative features from the education and teaching evaluation data, exploring the correlation between the clustering results and other related variables, and conducting in-depth mining;

[0023] Step S25: Visualization display: Visualization display of the extracted attribute features is performed to intuitively observe and understand the characteristics of different clusters, discover potential problems, and provide targeted suggestions and strategies for the improvement and optimization of education and teaching.

[0024] Furthermore, in step S3, the construction of the education and teaching evaluation model includes the following steps:

[0025] Step S31: Determine an education and teaching evaluation index system, where the education and teaching evaluation index system covers teaching content, teaching methods, student participation, moral cultivation, and cultural heritage awareness;

[0026] Step S32: construct a judgment matrix, use the 1-9 scale method to compare each indicator in pairs, judge the relative importance, and fill in the factors of the judgment matrix according to the comparison results;

[0027] Step S33: Calculate the weight vector of each indicator, the formula used is as follows:

[0028] ;

[0029] In the formula, n represents the number of indicators, i and j represent the indexes of traversing each indicator, Indicates the judgment value of the relative importance between the i-th indicator and the j-th indicator, Represents the weight vector of the i-th indicator, which is used to reflect the relative importance of the indicator in the entire evaluation system and better measure the influence on the education and teaching evaluation;

[0030] Step S34: Calculate the compatibility index to ensure the rationality and reliability of the evaluation system and check whether the compatibility and correlation between the indexes meet the requirements. The formula used is as follows:

[0031] ;

[0032] ;

[0033] In the formula, represents the compatibility index, A represents the overall framework of education and teaching evaluation, represents the feature matrix, Indicates the degree of correlation between the i-th indicator and the j-th indicator, represents the weight of the i-th indicator, represents the weight of the jth indicator;

[0034] Step S35: Calculate the final evaluation score, conduct a comprehensive evaluation of education and teaching based on the calculated weight vector and consistency test results, multiply the actual performance of each indicator by the corresponding weight, sum them up, and obtain the final evaluation score.

[0035] Furthermore, in step S4, the education and teaching evaluation report is displayed, including the following steps:

[0036] Step S41: Display the comparative analysis between the teaching objectives and the actual achievement, so as to intuitively see the achievements of students in terms of knowledge acquisition and moral character formation;

[0037] Step S42: List the evaluation results of the richness and applicability of the teaching content in detail, and conduct a comprehensive evaluation of the teacher's performance, from professional quality to teaching enthusiasm.

[0038] Furthermore, in step S5, the feedback optimization is specifically to summarize the advantages and disadvantages according to the education and teaching evaluation model, and put forward accurate and highly feasible improvement suggestions, which include introducing diversified teaching methods, encouraging teachers to share high-quality courseware and cooperating with museums to provide guidance for the high-quality development of education.

[0039] The present invention provides an education and teaching effect evaluation system based on artificial intelligence, comprising a data acquisition module, a feature extraction module, a teaching evaluation model construction module, an education and teaching evaluation report display module and a feedback optimization module;

[0040] The data collection module specifically collects students’ learning data in education through an online learning platform;

[0041] The education evaluation data analysis module specifically pre-processes the education and teaching evaluation data, calculates the clustering matrix and the fuzzy equivalent matrix, extracts attribute features and associates them with other related variables, performs visual display, and performs multi-faceted analysis of the education and teaching evaluation data;

[0042] The module for constructing the education and teaching evaluation model is specifically to determine the indicator system, construct a judgment matrix, calculate the weight vector of each indicator, calculate the compatibility index, ensure the rationality and reliability of the evaluation system, and calculate the final evaluation score;

[0043] The education and teaching evaluation report display module specifically displays in detail the comparative analysis of teaching objectives and achievement, the richness and applicability of teaching content, and comprehensively evaluates the data and case analysis of teacher performance and student learning effects;

[0044] The feedback optimization module specifically summarizes the strengths and weaknesses of the system based on the evaluation model, and puts forward accurate and highly feasible improvement suggestions to provide guidance for the high-quality development of education.

[0045] The beneficial results achieved by the present invention using the above scheme are as follows:

[0046] (1) In order to solve the technical problems of inaccurate analysis of education and teaching evaluation data and unclear relationships between data, which make it difficult to improve and optimize education and teaching, the education and teaching evaluation data is pre-processed, the clustering matrix and fuzzy equivalent matrix are calculated, attribute features are extracted and associated with other related variables, and a visual display is performed to analyze the education and teaching evaluation data from multiple aspects;

[0047] (2) In order to solve the technical problem that there are too many evaluation indicators and it is difficult to comprehensively and systematically determine the evaluation dimensions and conduct quantitative evaluation based on various indicators, we adopt the method of constructing an education and teaching evaluation model. Specifically, we determine the indicator system, construct a judgment matrix, calculate the weight vector of each indicator, calculate the compatibility index, ensure the rationality and reliability of the evaluation system, and calculate the final evaluation score. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of an artificial intelligence-based education and teaching effect evaluation method provided by the present invention;

[0049] Figure 2 A schematic diagram of an education and teaching effect evaluation system based on artificial intelligence provided by the present invention;

[0050] Figure 3 is a schematic flow chart of step S2;

[0051] Figure 4 It is a schematic diagram of the process of step S3.

[0052] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0054] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0055] Example 1, see Figure 1 The present invention provides an artificial intelligence-based education and teaching effect evaluation method, which includes the following steps:

[0056] Step S1: Data collection, collecting students’ learning data in education through online learning platforms;

[0057] Step S2: Education evaluation data analysis, specifically preprocessing the education and teaching evaluation data, calculating the clustering matrix and the fuzzy equivalent matrix, extracting attribute features and associating them with other related variables, performing visual display, and performing multi-faceted analysis of the education and teaching evaluation data;

[0058] Step S3: Constructing an education and teaching evaluation model, specifically determining the indicator system, constructing a judgment matrix, calculating the weight vector of each indicator, calculating the compatibility index, ensuring the rationality and reliability of the evaluation system, and calculating the final evaluation score;

[0059] Step S4: Education and teaching evaluation report presentation, showing in detail the comparative analysis of teaching objectives and achievement, the richness and applicability of teaching content, and comprehensive evaluation of teacher performance and student learning effect data and case analysis;

[0060] Step S5: Feedback optimization.

[0061] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, the data collection includes the following steps:

[0062] Step S11: Evaluate the learning effect of students by designing a special questionnaire to ask students about their interest in the course, their understanding, and their feelings about what they have gained; observe students' participation, enthusiasm for speaking, and interactive performance in class, and record their performance as educational evaluation data;

[0063] Step S12: Evaluate the teacher, observe and evaluate the teaching process of the teacher as the education evaluation data;

[0064] Step S13: Preprocess the education assessment data, perform data cleaning, remove invalid data and duplicate data in the education assessment data, standardize data of different dimensions, and normalize the standardized data according to specific rules.

[0065] Example 3, see Figure 1 and Figure 3 This embodiment is based on the above embodiment. In step S2, the education assessment data analysis includes the following steps:

[0066] Step S21: data processing, pre-processing the collected education and teaching evaluation data, checking the missing values, error values ​​and abnormal values ​​in the data, correcting and deleting them, integrating the multi-channel data, and obtaining the pre-processed education and teaching evaluation data;

[0067] Step S22: Calculate the clustering degree matrix, which is used to measure the degree of clustering between different data points, and divide the data points with higher similarities and clustering tendencies in the preprocessed education and teaching evaluation data into appropriate subgroups. By using fuzzy clustering, the education and teaching evaluation data with a greater degree of clustering are placed in the same subgroup. The formula used is as follows:

[0068] ;

[0069] In the formula, K represents the dimension of the clustering matrix, and Represents different education and teaching evaluation data points, represents the clustering matrix;

[0070] Step S23: Calculate the fuzzy equivalent matrix to further clarify the relationship between data points. The formula used is as follows:

[0071] ;

[0072] In the formula, represents the fuzzy equivalent matrix, Refers to the best parameter value that can achieve the best clustering result. When the corresponding value is 1, it means that the correlation between the two data points is strong, and when the corresponding value is 0, it means that the correlation between the two data points is weak.

[0073] Step S24: extracting attribute features, analyzing the typical features of the education and teaching evaluation data in each cluster, finding representative features from the education and teaching evaluation data, exploring the correlation between the clustering results and other related variables, and conducting in-depth mining;

[0074] Step S25: Visualization display: Visualization display of the extracted attribute features is performed to intuitively observe and understand the characteristics of different clusters, discover potential problems, and provide targeted suggestions and strategies for the improvement and optimization of education and teaching.

[0075] By performing the above operations, the education and teaching evaluation data are preprocessed, the clustering matrix and the fuzzy equivalence matrix are calculated, the attribute features are extracted and associated with other related variables, and a visual display is performed. The education and teaching evaluation data are analyzed in many aspects, which solves the technical problems of inaccurate analysis of education and teaching evaluation data and unclear relationship between data, which make it difficult to improve and optimize education and teaching.

[0076] Example 4, see Figure 1 and Figure 4 This embodiment is based on the above embodiment. In step S3, the construction of the education and teaching evaluation model includes the following steps:

[0077] Step S31: Determine an education and teaching evaluation index system, where the education and teaching evaluation index system covers teaching content, teaching methods, student participation, moral cultivation, and cultural heritage awareness;

[0078] Step S32: construct a judgment matrix, use the 1-9 scale method to compare each indicator in pairs, judge the relative importance, and fill in the factors of the judgment matrix according to the comparison results;

[0079] Step S33: Calculate the weight vector of each indicator, the formula used is as follows:

[0080] ;

[0081] In the formula, n represents the number of indicators, i and j represent the indexes of traversing each indicator, Indicates the judgment value of the relative importance between the i-th indicator and the j-th indicator, Represents the weight vector of the i-th indicator, which is used to reflect the relative importance of the indicator in the entire evaluation system and better measure the influence on the education and teaching evaluation;

[0082] Step S34: Calculate the compatibility index to ensure the rationality and reliability of the evaluation system and check whether the compatibility and correlation between the indexes meet the requirements. The formula used is as follows:

[0083] ;

[0084] ;

[0085] In the formula, represents the compatibility index, A represents the overall framework of education and teaching evaluation, represents the feature matrix, Indicates the degree of correlation between the i-th indicator and the j-th indicator, represents the weight of the i-th indicator, represents the weight of the jth indicator;

[0086] Step S35: Calculate the final evaluation score, conduct a comprehensive evaluation of education and teaching based on the calculated weight vector and consistency test results, multiply the actual performance of each indicator by the corresponding weight, sum them up, and obtain the final evaluation score.

[0087] By executing the above operations, an education and teaching evaluation model is constructed, specifically to determine the indicator system, build a judgment matrix, calculate the weight vector of each indicator, calculate the compatibility index, ensure the rationality and reliability of the evaluation system, and calculate the final evaluation score. This solves the technical problem of having too many evaluation indicators and it is difficult to comprehensively and systematically determine the evaluation dimensions and conduct quantitative evaluation based on various indicators.

[0088] Example 5, see Figure 1 This embodiment is based on the above embodiment. In step S4, the education and teaching evaluation report is displayed, including the following steps:

[0089] Step S41: Display the comparative analysis between the teaching objectives and the actual achievement, so as to intuitively see the achievements of students in terms of knowledge acquisition and moral character formation;

[0090] Step S42: List the evaluation results of the richness and applicability of the teaching content in detail, and conduct a comprehensive evaluation of the teacher's performance, from professional quality to teaching enthusiasm.

[0091] Example 6, see Figure 1 This embodiment is based on the above embodiment. In step S5, the feedback optimization is specifically to summarize the advantages and disadvantages according to the education and teaching evaluation model, and put forward accurate and highly feasible improvement suggestions. The improvement suggestions include introducing diversified teaching methods, encouraging teachers to share high-quality courseware and cooperating with museums to provide guidance for the high-quality development of education.

[0092] Embodiment 7, see Figure 2 , this embodiment is based on the above embodiment, and the present invention provides an education and teaching effect evaluation system based on artificial intelligence, including a data acquisition module, a feature extraction module, a teaching evaluation model construction module, an education and teaching evaluation report display module and a feedback optimization module;

[0093] The data collection module specifically collects students’ learning data in education through the online learning platform, including learning behavior, answer records and learning time;

[0094] The data analysis module specifically pre-processes the education and teaching evaluation data, calculates the clustering matrix and the fuzzy equivalent matrix, extracts attribute features and associates them with other related variables, performs visual display, and performs multi-faceted analysis of the education and teaching evaluation data;

[0095] The module for constructing the education and teaching evaluation model is specifically to determine the indicator system, construct a judgment matrix, calculate the weight vector of each indicator, calculate the compatibility index, ensure the rationality and reliability of the evaluation system, and calculate the final evaluation score;

[0096] The education and teaching evaluation report display module specifically displays in detail the comparative analysis of teaching objectives and achievement, the richness and applicability of teaching content, and comprehensively evaluates the data and case analysis of teacher performance and student learning effects;

[0097] The feedback optimization module specifically summarizes the strengths and weaknesses based on the evaluation model, and proposes suggestions for improvement to provide guidance for the high-quality development of education.

[0098] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0099] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0100] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An educational and teaching effect evaluation method based on artificial intelligence, characterized by: The method comprises the following steps: Step S1: Data collection, collecting students’ learning data in education through online learning platforms; Step S2: Education evaluation data analysis, specifically preprocessing the education and teaching evaluation data, calculating the clustering matrix and the fuzzy equivalent matrix, extracting attribute features for association operations, and performing visual display; Step S3: construct an education and teaching evaluation model, specifically determining the indicator system, constructing a judgment matrix, calculating the weight vector of each indicator, calculating the compatibility index, and calculating the final evaluation score; Step S4: display of education and teaching evaluation report; Step S5: Feedback optimization.

2. According to the artificial intelligence-based education and teaching effect evaluation method of claim 1, it is characterized by: In step S2, the educational assessment data analysis includes the following steps: Step S21: data processing, pre-processing the collected education and teaching evaluation data; Step S22: Calculate the clustering degree matrix, which is used to measure the degree of clustering between different data points. The formula used is as follows: ； In the formula, K represents the dimension of the clustering matrix, and Represents different education and teaching evaluation data points, represents the clustering matrix; Step S23: Calculate the fuzzy equivalent matrix to further clarify the relationship between data points. The formula used is as follows: ； In the formula, represents the fuzzy equivalent matrix, Refers to the best parameter value that can achieve the best clustering result. When the corresponding value is 1, it means that the correlation between the two data points is strong, and when the corresponding value is 0, it means that the correlation between the two data points is weak. Step S24: extracting attribute features, analyzing typical features of the education and teaching evaluation data in each cluster, and conducting in-depth mining; Step S25: Visualization display: Visualize the extracted attribute features to intuitively observe and understand the characteristics of different clusters, discover potential problems, and provide targeted suggestions and strategies for the improvement and optimization of education and teaching.

3. The method for evaluating the educational and teaching effects based on artificial intelligence according to claim 1, characterized in that: In step S3, the construction of the education and teaching evaluation model includes the following steps: Step S31: Determine an education and teaching evaluation index system, which covers teaching content, teaching methods, student participation, moral cultivation, and cultural heritage awareness; Step S32: construct a judgment matrix, use the 1-9 scale method to compare each indicator in pairs, judge the relative importance, and fill in the factors of the judgment matrix according to the comparison results; Step S33: Calculate the weight vector of each indicator, the formula used is as follows: ； In the formula, n represents the number of indicators, i and j represent the indexes of traversing each indicator, Indicates the judgment value of the relative importance between the i-th indicator and the j-th indicator, Represents the weight vector of the i-th indicator; Step S34: Calculate the compatibility index using the following formula: ； ； In the formula, represents the compatibility index, A represents the overall framework of education and teaching evaluation, represents the feature matrix, Indicates the degree of correlation between the i-th indicator and the j-th indicator, represents the weight of the i-th indicator, represents the weight of the jth indicator; Step S35: Calculate the final evaluation score, conduct a comprehensive evaluation of education and teaching based on the calculated weight vector and consistency test results, multiply the actual performance of each indicator by the corresponding weight, sum them up, and obtain the final evaluation score.

4. The method for evaluating the educational and teaching effects based on artificial intelligence according to claim 1, characterized in that: In step S1, the data collection includes the following steps: Step S11: Evaluate the learning effect of students, design a special questionnaire, and record the performance of students as educational evaluation data; Step S12: Evaluate the teacher, observe and evaluate the teaching process of the teacher as the education evaluation data; Step S13: Preprocess the education assessment data, perform data cleaning, remove invalid data and duplicate data in the education assessment data, standardize data of different dimensions, and normalize the standardized data according to specific rules.

5. The method for evaluating the educational and teaching effects based on artificial intelligence according to claim 1, characterized in that: In step S4, the education and teaching evaluation report is displayed, including the following steps: Step S41: displaying comparative analysis of teaching objectives and actual achievement; Step S42: List the evaluation results of the richness and applicability of the teaching content in detail, and conduct a comprehensive evaluation of the teacher's performance, from professional quality to teaching enthusiasm.

6. The method for evaluating the educational and teaching effects based on artificial intelligence according to claim 1, characterized in that: In step S5, the feedback optimization is specifically to summarize the advantages and disadvantages according to the education and teaching evaluation model, and put forward improvement suggestions.

7. An education and teaching effect evaluation system based on artificial intelligence, used to implement an education and teaching effect evaluation method based on artificial intelligence as described in any one of claims 1 to 6, characterized in that: It includes data collection module, education evaluation data analysis module, education and teaching evaluation model building module, education and teaching evaluation report display module and feedback optimization module.

8. The artificial intelligence-based education and teaching effect evaluation system according to claim 7, characterized in that: The data collection module specifically collects students’ learning data in education through an online learning platform; The education evaluation data analysis module specifically pre-processes the education and teaching evaluation data, calculates the clustering matrix and the fuzzy equivalent matrix, extracts attribute features and associates them with other related variables, performs visual display, and performs multi-faceted analysis of the education and teaching evaluation data; The module for constructing the education and teaching evaluation model is specifically to determine the indicator system, construct the judgment matrix, calculate the weight vector of each indicator, calculate the compatibility index, and calculate the final evaluation score; The education and teaching evaluation report display module specifically displays the teaching objectives and the comparative analysis of the achievement in detail; The feedback optimization module specifically summarizes the advantages and disadvantages based on the evaluation model and puts forward suggestions for improvement.

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