Teaching evaluation system based on artificial intelligence and big data

Abstract

The invention discloses a teaching evaluation system based on artificial intelligence and big data, relates to the field of education evaluation, solves the problems that existing teaching evaluation depends on artificial questionnaire survey or examination scores, and is high in subjectivity and single in data dimensionality, and comprises a data processing module, a basic analysis module, an expansion analysis module and a comprehensive evaluation module, the data processing module is used for processing the average score of each culture course test of the class, sending the score data obtained by processing to the basic analysis module, and sending the expansion data obtained by processing to the expansion analysis module; the basic analysis module is used for analyzing the score level and stability of a class, the expansion analysis module is used for analyzing the expansion ability of students, and the result output module is used for generating comprehensive teaching evaluation for a class owner of the class, and comprehensive evaluation of the teaching condition is realized based on artificial intelligence and big data.

Description

Technical Field

[0001] The present invention belongs to the technical field of educational assessment, and specifically is a teaching evaluation system based on artificial intelligence and big data. Background Art

[0002] Teaching evaluation is an activity that makes value judgments on teaching processes and results based on teaching objectives and serves teaching decision-making. It aims to evaluate the actual or potential value of teaching activities, covering the research on the value of teachers’ teaching and students’ learning. Usually, teaching evaluation involves the consideration of multiple factors in the teaching process, including teachers, students, teaching content, teaching methods, teaching environment, and teaching management. Its core focus is on the evaluation of students’ learning effects and the evaluation of teachers’ teaching work process.

[0003] Existing teaching evaluation relies on manual questionnaires or test scores, which are highly subjective and have a single data dimension. At the same time, most existing teaching evaluations focus on the overall intellectual development of the class, and do not conduct an overall evaluation of teaching from the perspective of the overall development of the class in multiple aspects. To this end, the present invention proposes a teaching evaluation system based on artificial intelligence and big data. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a teaching evaluation system based on artificial intelligence and big data.

[0005] The technical problems to be solved by the present invention are: How to achieve comprehensive evaluation of teaching situations based on artificial intelligence and big data.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A teaching evaluation system based on artificial intelligence and big data, including a database, a test classification module, a data acquisition module, a data processing module, a basic analysis module, an extended analysis module and a comprehensive evaluation module; The database is used to store all types of historical tests conducted by the class throughout the semester, and send all types of historical tests conducted by the class throughout the semester to the test classification module; the test classification module is used to classify all types of historical tests conducted by the class throughout the semester into basic tests or extended tests; the data collection module is used to collect test parameters of extended tests, and send the test parameters of extended tests to the data processing module; The database is also used to store the average score of each cultural course test of the class, and send the average score of each cultural course test of the class to the data processing module; the data processing module is used to process the average score of each cultural course test of the class, send the processed score data to the basic analysis module, and send the processed extended data to the extended analysis module; The basic analysis module is used to analyze the performance level and stability of the class, and the analysis generates a continuous rising signal, a continuous falling signal, a continuous stable signal, a violent fluctuation signal or a slow fluctuation signal, which is sent to the result output module; the expansion analysis module is used to analyze the students' expansion ability, and the analysis generates an expansion improvement signal or an expansion decline signal, which is sent to the result generation module; The result output module is used to generate a comprehensive teaching evaluation of the class teacher.

[0007] Furthermore, the basic test is a test of cultural subjects conducted by the class throughout the semester, and the extended test is a test of reaction and memory conducted by the class throughout the semester.

[0008] Furthermore, the reaction ability test is implemented by testing the students' visual reactions, specifically: The subject's hand is placed on the test button. A display screen is installed on one side of the subject. During the test, the display screen randomly lights up different colors. When the designated color target light is on, the subject needs to press the test button of the corresponding color as quickly as possible. The memory test is implemented by testing students' short-term memory of digital positions, specifically: The display screen is divided into several cells. A group of numbers will appear randomly on the screen, and each number is located in a cell. Students need to memorize them within a fixed time, which is recorded as the memory time. When the memory time is over, the display screen will randomly display one of the numbers in the group, and students need to fill in the number in the corresponding cell within the answering time.

[0009] Further, the test parameters of the reaction test are the starting time when the display screen lights up the target light of the specified color, the answering time when the subject presses the test button, and the corresponding color of the target light and the test button pressed by the subject; The test parameter of the memory test is the number of correct answers given by the students within the answering time.

[0010] Furthermore, the processing process of the data processing module is as follows: Get the average score of each cultural course test of the class, and calculate the average score of the cultural course test of the class for the entire semester; Then calculate the standard deviation of the average scores of the class's cultural course tests throughout the semester; The average score of each cultural subject test of the class and the standard deviation of the average score of the cultural subject test of the class throughout the semester are recorded as the performance data.

[0011] Furthermore, the processing process of the data processing module also includes: The test parameters of the reaction test are processed, and the designated color of the target light is compared with the corresponding color of the test button pressed by the test subject; if the two colors are the same, the answer time minus the start time is obtained to obtain the answer time of the corresponding test subject; if the two colors are different, the answer time of the corresponding test subject is recorded as zero; then the average answer time of the class reaction test is calculated; Process the test parameters of the memory test and calculate the accuracy of the class memory test; The average answering time of the reaction test and the accuracy of the memory test are recorded as extended data.

[0012] Furthermore, the analysis process of the basic analysis module is as follows: Subtract the average score of the class's current cultural course test from the average score of the previous cultural course test to get the adjacent change; If all adjacent changes are greater than zero, a continuous rising signal is generated; If all adjacent changes are equal to zero, a continuous stable signal is generated; If all adjacent changes are less than zero, a continuous decline signal is generated; If adjacent changes are both greater than or equal to zero and less than zero, the standard deviation of the average scores of the class's cultural course tests for the entire semester will be compared with the standard threshold. When the standard deviation is greater than or equal to the standard threshold, a violent fluctuation signal is generated; when the standard deviation is less than the standard threshold, a slow fluctuation signal is generated.

[0013] Furthermore, the analysis process of the extended analysis module is as follows: Calculate the class's extension score; If the extension result falls within the first standard range, an extension improvement signal is generated; If the extension result falls within the second standard range, an extension decline signal is generated; wherein, the values ​​within the first standard range are all greater than the values ​​within the second standard range.

[0014] Furthermore, the working process of the result output module is as follows: When the result output module receives a continuous rising signal, X1 is added to the initial value of the basic teaching score; When the result output module receives a continuous decline signal, it subtracts X1 from the initial value of the basic teaching score; When the result output module receives a continuous and stable signal, X2 is added to the initial value of the basic teaching score; When the result output module receives a slow fluctuation signal, it subtracts the average score of each cultural course test of the class from the average score of the cultural course test of the entire semester to obtain the overall average score, and counts the number of overall average scores greater than or equal to zero and the number of overall average scores less than zero; If the number of students with an overall average score greater than or equal to zero is greater than or equal to the number of students with an overall average score less than zero, it means that the average score of the cultural course test of the class throughout the semester is generally on the rise. Then add X3 to the initial value of the basic teaching score. If the number of students with an overall average score greater than or equal to zero is less than the number of students with an overall average score less than zero, it means that the average score of the cultural course test of the class throughout the semester has generally declined. Then, subtract X3 from the initial value of the basic teaching score. Similarly, when the result output module receives a violently fluctuating signal, it subtracts the average score of each cultural course test of the class from the average score of the cultural course test of the entire semester to obtain the overall average score, and counts the number of overall average scores greater than or equal to zero and the number of overall average scores less than zero; If the number of students with an overall average score greater than or equal to zero is greater than or equal to the number of students with an overall average score less than zero, it means that the average score of the cultural course test of the class throughout the semester is generally on the rise. Then add X4 to the initial value of the basic teaching score. If the number of students with overall average scores greater than or equal to zero is less than the number of students with overall average scores less than zero, it means that the average scores of the class's cultural course tests throughout the semester have generally declined. In this case, subtract X4 from the initial value of the basic teaching score.

[0015] Furthermore, the working process of the result output module also includes: When the result output module receives the expansion improvement signal, Y1 is added to the initial value of the expansion teaching score; When the result output module receives the extended decrease signal, Y1 is subtracted from the initial value of the extended teaching score; wherein X1, X2, X3, X4 and Y1 are all positive numbers, and the value of X1 is greater than the value of X2, the value of X2 is greater than the value of X3, and the value of X3 is greater than the value of X4; The value obtained by adding or subtracting the initial value of the basic teaching score is recorded as the basic teaching score, and the value obtained by adding or subtracting the initial value of the extended teaching score is recorded as the extended teaching score. Count the final basic teaching scores and extended teaching scores, and calculate the comprehensive teaching score; If the comprehensive teaching score belongs to the first interval, it means that the teaching ability of the teacher of the corresponding subject in the class is excellent; if the comprehensive teaching score belongs to the second interval, it means that the teaching ability of the teacher of the corresponding subject in the class is good; if the comprehensive teaching score belongs to the third interval, it means that the teaching ability of the teacher of the corresponding subject in the class is qualified; if the comprehensive teaching score belongs to the fourth interval, it means that the teaching ability of the teacher of the corresponding subject in the class is unqualified.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The present invention divides all types of history tests conducted by the class throughout the semester into basic tests or extended tests, and then collects test parameters of the extended tests and obtains the average score of each cultural course test of the class, processes the average score of each cultural course test of the class, and processes the score data and extended data obtained by processing. The grade level and stability of the class are analyzed based on the score data, and a continuous rising signal, a continuous falling signal, a continuous stable signal, a violent fluctuation signal or a slow fluctuation signal are analyzed and generated. The extension ability of the students is analyzed based on the extended data, and an extension improvement signal or an extension decline signal is analyzed and generated. Finally, a comprehensive teaching evaluation of the class teacher is performed in combination with different signals. The present invention realizes a comprehensive evaluation of the teaching situation based on artificial intelligence and big data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0018] Figure 1 is the overall system block diagram of the present invention; Figure 2 It is a schematic diagram of the scene of the reaction force test in the present invention; Figure 3 It is a schematic diagram of the scene of the memory test in the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figure 1-Figure 3 As shown, the technical solution provided by the present invention is: a teaching evaluation system based on artificial intelligence and big data, which is used to evaluate the teaching level of teachers corresponding to subjects in different classes. The system specifically includes a database, a test classification module, a data acquisition module, a data processing module, a basic analysis module, an extended analysis module and a comprehensive evaluation module; In this embodiment, the database is used to store all historical test types conducted by the class throughout the semester, and send all historical test types conducted by the class throughout the semester to the test classification module; The test classification module is used to classify all the history tests conducted in the class throughout the semester into basic tests or extended tests; basic tests are cultural tests conducted in the class throughout the semester, and extended tests are reaction tests and memory tests conducted in the class throughout the semester; In this embodiment, the reaction ability test is implemented by testing the students' visual reaction, specifically: The subject's hand is placed on the test button. A display screen is installed on one side of the subject. During the test, the display screen randomly lights up different colors. When the designated color target light is on, the subject needs to press the test button of the corresponding color as quickly as possible. Among them, the memory test is realized by testing students' short-term memory of digital positions, specifically: The display screen is divided into several cells. A group of numbers will appear randomly on the screen, and each number is located in a cell. Students need to memorize it within a fixed time, which is recorded as the memory time. When the memory time is over, the display screen will randomly display one of the numbers in the group, and students need to fill in the number in the corresponding cell within the answering time. Specifically, the extended test is only conducted at the beginning and end of a semester.

[0021] In this embodiment, the data acquisition module is used to collect test parameters of the extended test and send the test parameters of the extended test to the data processing module; In this embodiment, the test parameters of the reaction test are the starting time when the designated color target light is lit on the display screen, the answering time when the test subject presses the test button, and the designated color of the target light and the corresponding color of the test button pressed by the test subject; the test parameters of the memory test are the number of correct answers given by the student within the answering time; specifically, the number of correct answers given by the student within the answering time can be recorded by a counter; The database is also used to store the average score of each cultural course test of the class, and send the average score of each cultural course test of the class to the data processing module.

[0022] The data processing module is used to process the average score of each cultural course test of the class, and the processing process is as follows: Get the average score of each cultural course test in the class, through the formula Calculate the average score WP of the class's cultural course test for the entire semester, using the formula Calculate the standard deviation oj of the average score of the cultural course test of the class throughout the semester; where j is the serial number of the cultural course test, j=1, 2, ..., m, m is a positive integer, and WPj represents the average score of the jth cultural course test of the class; The average score of each cultural course test of the class and the standard deviation of the average score of the cultural course test of the class throughout the semester are recorded as the score data; The test parameters of the reaction test are processed, and the designated color of the target light is compared with the corresponding color of the test button pressed by the test subject. If the two colors are the same, the answer time is subtracted from the start time to obtain the corresponding test subject's answer time. If the two colors are different, the corresponding test subject's answer time is recorded as zero; through the formula The average time taken to answer the class reaction test is calculated, where ti is the time taken to answer the i-th test subject; The test parameters of the memory test are processed by the formula The accuracy rate of the class memory test was calculated, where ZQ is the number of correct answers given by the test taker within the test time, and TS is the total number of all questions; Specifically, the average time taken to answer the reaction test conducted by the class at the beginning of the semester is recorded as TP1, the average time taken to answer the reaction test conducted by the class at the end of the semester is recorded as TP2, the accuracy rate of the memory test conducted by the class at the beginning of the semester is recorded as ZQL1, and the accuracy rate of the memory test conducted by the class at the end of the semester is recorded as ZQL1; The average answering time of the reaction test and the accuracy of the memory test are recorded as extended data.

[0023] The data processing module sends the score data to the basic analysis module and sends the extended data to the extended analysis module.

[0024] The basic analysis module is used to analyze the performance level and stability of the class. The analysis process is as follows: Subtract the average score of the class's j-th cultural course test from the average score of the class's j-1-th cultural course test to get the adjacent change; If all adjacent changes are greater than zero, it means that the average scores of each cultural course test of the class are increasing, and a continuous rising signal is generated; If all adjacent changes are equal to zero, it means that the average scores of each cultural course test of the class are stable, and a continuous stability signal is generated; If all adjacent changes are less than zero, it means that the average scores of each cultural course test of the class are on a downward trend, and a continuous decline signal is generated; If the adjacent changes are both greater than or equal to zero and less than zero, it means that the average score of each cultural course test of the class fluctuates, and then go to the next step; The standard deviation of the average score of the cultural course test of the class throughout the semester is compared with the standard threshold. If the standard deviation is greater than or equal to the standard threshold, it means that the average score of the cultural course test of the class throughout the semester fluctuates greatly, and a violent fluctuation signal is generated; if the standard deviation is less than the standard threshold, it means that the average score of the cultural course test of the class throughout the semester fluctuates slightly, and a slow fluctuation signal is generated; The basic analysis module sends a continuously rising signal, a continuously falling signal, a continuously stable signal, a violently fluctuating signal or a slowly fluctuating signal to a result output module.

[0025] The extension analysis module is used to analyze students' extension ability, and the analysis process is as follows: The class extension score is calculated by the formula TZ = (TP2-TP1) × k1 + (ZQL2-ZQL1) × k2, where k1 and k2 are weight coefficients; If the extension score falls within the first standard range, it means that the extension ability of the class has improved, and an extension improvement signal is generated; If the extension score falls within the second standard range, it means that the extension ability of the class has declined, and an extension decline signal is generated; Among them, the values ​​in the first standard interval are all greater than the values ​​in the second standard interval; The expansion analysis module sends the expansion increase signal or the expansion decrease signal to the result generation module.

[0026] The result output module is used to generate a comprehensive teaching evaluation of the class teacher, and the generation process is as follows: When the result output module receives a continuous rising signal, X1 is added to the initial value of the basic teaching score; When the result output module receives a continuous decline signal, it subtracts X1 from the initial value of the basic teaching score; When the result output module receives a continuous and stable signal, X2 is added to the initial value of the basic teaching score; When the result output module receives a slow fluctuation signal, it subtracts the average score of each cultural course test of the class from the average score of the cultural course test of the entire semester to obtain the overall average score, and counts the number of overall average scores greater than or equal to zero and the number of overall average scores less than zero; If the number of students with an overall average score greater than or equal to zero is greater than or equal to the number of students with an overall average score less than zero, it means that the average score of the cultural course test of the class throughout the semester is generally on the rise. Then add X3 to the initial value of the basic teaching score. If the number of students with an overall average score greater than or equal to zero is less than the number of students with an overall average score less than zero, it means that the average score of the cultural course test of the class throughout the semester has generally declined. Then, subtract X3 from the initial value of the basic teaching score. Similarly, when the result output module receives a violently fluctuating signal, it subtracts the average score of each cultural course test of the class from the average score of the cultural course test of the entire semester to obtain the overall average score, and counts the number of overall average scores greater than or equal to zero and the number of overall average scores less than zero; If the number of students with an overall average score greater than or equal to zero is greater than or equal to the number of students with an overall average score less than zero, it means that the average score of the cultural course test of the class throughout the semester is generally on the rise. Then add X4 to the initial value of the basic teaching score. If the number of students with an overall average score greater than or equal to zero is less than the number of students with an overall average score less than zero, it means that the average score of the cultural course test of the class throughout the semester has generally declined. Then, subtract X4 from the initial value of the basic teaching score. When the result output module receives the expansion improvement signal, Y1 is added to the initial value of the expansion teaching score; When the result output module receives the extended decline signal, Y1 is subtracted from the initial value of the extended teaching score; Among them, X1, X2, X3, X4 and Y1 are all positive numbers, and the value of X1 is greater than the value of X2, the value of X2 is greater than the value of X3, and the value of X3 is greater than the value of X4; The value obtained by adding or subtracting the initial value of the basic teaching score is recorded as the basic teaching score JCX, and the value obtained by adding or subtracting the initial value of the extended teaching score is recorded as the extended teaching score TZX. The final basic teaching score and extended teaching score are calculated, and the comprehensive teaching score is calculated by the formula ZH=JCX×a1+TZX×a2, where a1 and a2 are weight coefficients; If the comprehensive teaching score falls within the first range, it means that the teaching ability of the teacher of the corresponding subject in the class is excellent; If the comprehensive teaching score falls within the second range, it means that the teaching ability of the teacher of the corresponding subject in the class is good; If the comprehensive teaching score falls within the third range, it means that the teaching ability of the teacher of the corresponding subject in the class is qualified; If the comprehensive teaching score falls within the fourth range, it means that the teaching ability of the teacher of the corresponding subject in the class is unqualified; Exemplarily, the value of the first interval is [90, 100], the value of the second interval is [75, 90), the value of the third interval is [60, 75), and the value of the fourth interval is [0, 60).

[0027] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0028] Embodiment 2: Based on another concept of the same invention, a teaching evaluation method based on artificial intelligence and big data is proposed, comprising the following steps: Step S101, classifying all history tests conducted by the class throughout the semester into basic tests or extended tests; Step S102, collecting test parameters of the extended test and obtaining the average score of each cultural course test of the class; Step S103, processing the average scores of each cultural course test of the class, and processing the obtained score data and extended data; Step S104, analyzing the performance level and stability of the class according to the performance data, and generating a continuous rising signal, a continuous falling signal, a continuous stable signal, a violent fluctuation signal or a slow fluctuation signal; Step S105, analyzing the students' extension ability according to the extension data, and generating an extension improvement signal or an extension decline signal; Step S106, combining different signals to make a comprehensive teaching evaluation of the class teacher.

[0029] 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 invention to only specific implementation methods. Obviously, many modifications and changes 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 understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A teaching evaluation system based on artificial intelligence and big data, characterized in that: It includes a database, a test classification module, a data acquisition module, a data processing module, a basic analysis module, an extended analysis module and a comprehensive evaluation module. The database is used to store all types of historical tests conducted by the class throughout the semester, and send all types of historical tests conducted by the class throughout the semester to the test classification module; the test classification module is used to classify all types of historical tests conducted by the class throughout the semester into basic tests or extended tests; the data acquisition module is used to collect test parameters of extended tests, and send the test parameters of extended tests to the data processing module; The database is also used to store the average score of each cultural course test of the class, and send the average score of each cultural course test of the class to the data processing module; the data processing module is used to process the average score of each cultural course test of the class, send the processed score data to the basic analysis module, and send the processed extended data to the extended analysis module; The basic analysis module is used to analyze the class's academic performance and stability, and the analysis generates a continuous rising signal, a continuous falling signal, a continuous stable signal, a violent fluctuation signal or a slowly fluctuating signal, which is sent to the result output module; the extended analysis module is used to analyze the students' extended ability, and the analysis generates an extension improvement signal or an extension decline signal, which is sent to the result generation module; the result output module is used to generate a comprehensive teaching evaluation of the class teacher.

2. According to claim 1, a teaching evaluation system based on artificial intelligence and big data is characterized in that: The basic test is a test of cultural subjects conducted by the class throughout the semester, and the extended test is a test of reaction and memory conducted by the class throughout the semester.

3. The teaching evaluation system based on artificial intelligence and big data according to claim 2 is characterized in that: The reaction test is achieved by testing the students' visual reactions, specifically: The subject's hand is placed on the test button. A display screen is installed on one side of the subject. During the test, the display screen randomly lights up different colors. When the designated color target light is on, the subject needs to press the test button of the corresponding color as quickly as possible. The memory test is implemented by testing students' short-term memory of digital positions, specifically: The display screen is divided into several cells. A group of numbers will appear randomly on the screen, and each number is located in a cell. Students need to memorize them within a fixed time, which is recorded as the memory time. When the memory time is over, the display screen will randomly display one of the numbers in the group, and students need to fill in the number in the corresponding cell within the answering time.

4. The teaching evaluation system based on artificial intelligence and big data according to claim 1 is characterized in that: The test parameters of the reaction test are the starting time when the designated color target light is lit on the display screen, the answer time when the subject presses the test button, and the designated color of the target light and the corresponding color when the subject presses the test button; The test parameter of the memory test is the number of correct answers given by the students within the answering time.

5. The teaching evaluation system based on artificial intelligence and big data according to claim 4 is characterized in that: The processing process of the data processing module is as follows: Get the average score of each cultural course test of the class, and calculate the average score of the cultural course test of the class for the entire semester; Then calculate the standard deviation of the average scores of the class's cultural course tests throughout the semester; The average score of each cultural subject test of the class and the standard deviation of the average score of the cultural subject test of the class throughout the semester are recorded as the performance data.

6. The teaching evaluation system based on artificial intelligence and big data according to claim 5 is characterized in that: The processing process of the data processing module also includes: The test parameters of the reaction test are processed, and the designated color of the target light is compared with the corresponding color of the test button pressed by the test subject; if the two colors are the same, the answer time minus the start time is obtained to obtain the answer time of the corresponding test subject; if the two colors are different, the answer time of the corresponding test subject is recorded as zero; then the average answer time of the class reaction test is calculated; Process the test parameters of the memory test and calculate the accuracy of the class memory test; The average answering time of the reaction test and the accuracy of the memory test are recorded as extended data.

7. The teaching evaluation system based on artificial intelligence and big data according to claim 1 is characterized in that: The analysis process of the basic analysis module is as follows: Subtract the average score of the class's current cultural course test from the average score of the previous cultural course test to get the adjacent change; If all adjacent changes are greater than zero, a continuous rising signal is generated; If all adjacent changes are equal to zero, a continuous stable signal is generated; If all adjacent changes are less than zero, a continuous decline signal is generated; If adjacent changes are both greater than or equal to zero and less than zero, the standard deviation of the average scores of the class's cultural course tests for the entire semester will be compared with the standard threshold. When the standard deviation is greater than or equal to the standard threshold, a violent fluctuation signal is generated; when the standard deviation is less than the standard threshold, a slow fluctuation signal is generated.

8. The teaching evaluation system based on artificial intelligence and big data according to claim 7 is characterized in that: The analysis process of the extended analysis module is as follows: Calculate the class's extension score; If the extension result falls within the first standard range, an extension improvement signal is generated; If the extension result falls within the second standard range, an extension decline signal is generated; wherein, the values ​​within the first standard range are all greater than the values ​​within the second standard range.

9. The teaching evaluation system based on artificial intelligence and big data according to claim 8 is characterized in that: The working process of the result output module is as follows: When the result output module receives a continuous rising signal, X1 is added to the initial value of the basic teaching score; When the result output module receives a continuous decline signal, it subtracts X1 from the initial value of the basic teaching score; When the result output module receives a continuous and stable signal, X2 is added to the initial value of the basic teaching score; When the result output module receives a slow fluctuation signal, it subtracts the average score of each cultural course test of the class from the average score of the cultural course test of the entire semester to obtain the overall average score, and counts the number of overall average scores greater than or equal to zero and the number of overall average scores less than zero; If the number of students with an overall average score greater than or equal to zero is greater than or equal to the number of students with an overall average score less than zero, it means that the average score of the cultural course test of the class throughout the semester is generally on the rise. Then add X3 to the initial value of the basic teaching score. If the number of students with an overall average score greater than or equal to zero is less than the number of students with an overall average score less than zero, it means that the average score of the cultural course test of the class throughout the semester has generally declined. Then, subtract X3 from the initial value of the basic teaching score. Similarly, when the result output module receives a violently fluctuating signal, it subtracts the average score of each cultural course test of the class from the average score of the cultural course test of the entire semester to obtain the overall average score, and counts the number of overall average scores greater than or equal to zero and the number of overall average scores less than zero; If the number of students with an overall average score greater than or equal to zero is greater than or equal to the number of students with an overall average score less than zero, it means that the average score of the cultural course test of the class throughout the semester is generally on the rise. Then add X4 to the initial value of the basic teaching score. If the number of students with overall average scores greater than or equal to zero is less than the number of students with overall average scores less than zero, it means that the average scores of the class's cultural course tests throughout the semester have generally declined. In this case, subtract X4 from the initial value of the basic teaching score.

10. The teaching evaluation system based on artificial intelligence and big data according to claim 9 is characterized in that: The working process of the result output module also includes: When the result output module receives the expansion improvement signal, Y1 is added to the initial value of the expansion teaching score; When the result output module receives the extended decrease signal, Y1 is subtracted from the initial value of the extended teaching score; wherein X1, X2, X3, X4 and Y1 are all positive numbers, and the value of X1 is greater than the value of X2, the value of X2 is greater than the value of X3, and the value of X3 is greater than the value of X4; The value obtained by adding or subtracting the initial value of the basic teaching score is recorded as the basic teaching score, and the value obtained by adding or subtracting the initial value of the extended teaching score is recorded as the extended teaching score. Count the final basic teaching scores and extended teaching scores, and calculate the comprehensive teaching score; If the comprehensive teaching score belongs to the first interval, it means that the teaching ability of the teacher of the corresponding subject in the class is excellent; if the comprehensive teaching score belongs to the second interval, it means that the teaching ability of the teacher of the corresponding subject in the class is good; if the comprehensive teaching score belongs to the third interval, it means that the teaching ability of the teacher of the corresponding subject in the class is qualified; if the comprehensive teaching score belongs to the fourth interval, it means that the teaching ability of the teacher of the corresponding subject in the class is unqualified.

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