Teaching quality evaluation system based on big data

Abstract

The invention relates to the technical field of education quality evaluation, in particular to a teaching quality evaluation system based on big data, and the system comprises a data collection module which is used for collecting course score data, student employment rate data and attendance rates of students during each course; the score analysis module is used for evaluating the learning effect of the student and constructing a learning effect index; the attendance analysis module is used for constructing attendance indexes of students in each course; the employment analysis module is used for constructing employment indexes; the contribution degree analysis module is used for analyzing the contribution degree of each course to the employment rate; and the quality evaluation module is used for evaluating the teaching quality of each course according to the construction result of the score state index, the construction result of the attendance index of the student, the construction result of the employment index and the analysis result of the contribution degree of each course to the employment rate, and pushing the evaluation result to the user. According to the invention, the teaching quality evaluation accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of education quality assessment, and in particular to a teaching quality assessment system based on big data. Background Art

[0002] With the increasing emphasis on teaching quality in the education field, especially in higher education, modern education management systems usually adopt traditional evaluation methods, which are often based on teachers' subjective evaluation and students' direct feedback, lacking systematic quantitative data support, resulting in insufficient accuracy and effectiveness of evaluation results. Therefore, it is particularly important to develop a teaching quality evaluation system based on big data, which can provide a more objective and comprehensive teaching quality evaluation model through multi-dimensional data analysis.

[0003] Chinese patent publication number CN118365493A discloses a teaching quality evaluation method and system for an educational robot, which relates to the technical field of education quality evaluation. If the student's academic performance is not on an upward trend, the participation degree is constructed according to the interaction between the educational robot and the student when participating in the teaching, and the corresponding teaching optimization plan is matched for the educational robot according to the teaching evaluation report. After executing the teaching optimization plan, the test results are used as input, and the test results analysis model is used to analyze and obtain the student's knowledge point mastery, and thus the student's mastery of the knowledge point is constructed. If the mastery does not exceed the expectation, the corresponding teaching optimization plan is selected for the educational robot, and the feasibility is generated according to the change of the improved mastery and participation degree. If the feasibility is lower than the feasible threshold, an alarm instruction is issued to the outside. The teaching quality of the educational robot is comprehensively evaluated. If the teaching quality is poor, the educational robot is improved and adjusted; it can be seen that when evaluating the teaching quality, the scheme does not analyze the learning effect and employment status, and there is a problem of low accuracy in the teaching quality evaluation. Summary of the invention

[0004] The object of the present invention is to provide a teaching quality evaluation system based on big data to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A teaching quality evaluation system based on big data, comprising: Data collection module, used to collect course performance data, student employment rate data and student attendance rate of each course and class period; The performance analysis module is used to evaluate the students' learning effects based on the collected course grades of the current academic year and the course grades of each historical academic year, and to construct a learning effect index based on the evaluation results; The attendance analysis module is used to analyze the attendance status of students according to the collected attendance rates of students in each course and class, and to construct the attendance index of students in each course according to the analysis results; The employment analysis module is used to analyze the employment status of students based on the collected student employment rate data and to construct an employment index based on the analysis results; The contribution analysis module is used to analyze the contribution of each course to the employment rate based on the collected course opening years, course score data and student employment rate data; The quality assessment module is used to evaluate the teaching quality of each course based on the construction results of the performance status index, the construction results of the student attendance index, the construction results of the employment index and the analysis results of the contribution of each course to the employment rate, and push the evaluation results to users.

[0006] Furthermore, the grade analysis module includes a grade analysis unit, which is used to analyze the grade status of the students of the i-th course based on the collected i-th course grade di of the current academic year and the i-th course grades of each historical academic year. The grade status includes passed and unqualified. If the grade status of the students of the i-th course in the current academic year is unqualified, the i-th course grade status index is set to CZ1; if the grade status of the students of the i-th course in the current academic year is qualified, the i-th course grade status index is set to CZ2.

[0007] Furthermore, the performance analysis module also includes a fluctuation analysis unit, which is used to analyze the fluctuation state of the student grades of the i-th course based on the collected student grades of the i-th course in the current academic year. The fluctuation state of the student grades includes a fluctuating state and a stable state. If the fluctuation state of the student grades of the i-th course is a stable state, the fluctuation abnormality coefficient of the i-th course is set to BYi1; if the fluctuation state of the student grades of the i-th course is a fluctuating state, the fluctuation abnormality coefficient of the i-th course is set to BYi2.

[0008] Furthermore, the performance analysis module also includes a learning effect analysis unit, which is used to construct the i-th course learning effect index XGi according to the construction result of the i-th course performance status index and the construction result of the i-th course fluctuation abnormality coefficient.

[0009] Furthermore, the attendance analysis module is used to analyze the attendance status of students according to the attendance rate of each class hour of the i-th course, and the attendance status of students includes normal and abnormal, and construct the attendance index CQi of the students of the i-th course according to the analysis results of the attendance status of each class hour of the i-th course.

[0010] Furthermore, the employment analysis module includes an employment analysis unit, which is used to analyze the employment status of students based on the student employment rate M1 of the current academic year and the student employment rate M2 of the adjacent historical academic year. The employment status includes normal employment status, low employment status and high employment status. If the student's employment status is normal employment status, the student employment index is set to JZ1; if the student's employment status is low employment status, the student employment index is set to JZ2; if the student's employment status is high employment status, the student employment index is set to JZ3.

[0011] Furthermore, the employment analysis module also includes a historical analysis unit, which is used to analyze the abnormality of the student employment rate in the current academic year based on the student employment rate M1 in the current academic year and the average student employment rate M0 in historical academic years. The abnormality of the employment rate includes normal and abnormal. If the student employment rate in the current academic year is abnormal, the first preset fluctuation ratio is adjusted to η1', and the second preset fluctuation ratio is adjusted to η2'.

[0012] Furthermore, the employment analysis module also includes a student number analysis unit, which is used to analyze the student number growth status by comparing the number of students R in the current school year with the number of students n2 in the adjacent school year. The student number growth status includes normal and abnormal. If the student number growth status is abnormal, the preset adjustment ratio will be adjusted to P1.

[0013] Furthermore, the contribution analysis module is used to compare the collected number of years the i-th course is offered with the preset number of years t0 to analyze the abnormality of the number of years the i-th course is offered. The abnormality of the number of years offered includes normal and abnormal. If the number of years the i-th course is offered is normal, the contribution of the i-th course to the employment rate is set to Gi; if the number of years the i-th course is offered is abnormal, the contribution of the i-th course to the employment rate is set to G0, where G0 is the preset contribution threshold.

[0014] Furthermore, the quality assessment module includes a quality assessment unit, which is used to construct a teaching quality assessment index ZLi of the i-th course based on the construction results of the learning effect index, the construction results of the student's attendance index, the construction results of the employment index and the analysis results of the contribution of each course to the employment rate, and compare the teaching quality assessment index ZLi of the i-th course with a preset quality assessment threshold ZL0. If ZLi≤ZL0, the quality assessment unit determines that the teaching quality of the i-th course is unqualified; if ZLi>ZL0, the quality assessment unit determines that the teaching quality of the i-th course is qualified.

[0015] The beneficial effects of the present invention are as follows: by integrating data collection, grade analysis, attendance analysis, employment analysis and contribution analysis modules, the ability of colleges and universities to evaluate teaching quality is comprehensively improved. The system realizes data-driven precise analysis, quantitatively evaluates students' learning effects through detailed grade and fluctuation analysis, attendance analysis provides insights into student participation, employment analysis ensures that school education is in line with market demand, and the quality assessment module integrates various indicators to provide managers with clear and comprehensive teaching quality feedback. The synergy of these functions enables colleges and universities to formulate strategies and policies more accurately to improve teaching quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a structural diagram of the teaching quality evaluation system based on big data in this embodiment.

[0018] Figure 2 Schematic diagram of the structure of the performance analysis module of this embodiment.

[0019] Figure 3 Schematic diagram of the structure of the employment analysis module of this embodiment.

[0020] Figure 4 Schematic diagram of the structure of the quality assessment module of this embodiment. DETAILED DESCRIPTION

[0021] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0022] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0023] See also Figure 1 As shown, it is a structural diagram of the teaching quality evaluation system based on big data in this embodiment, and the system includes: The data collection module is used to collect course grade data, student employment rate data and student attendance rate of each course and each class hour; the course grade data includes the course grades of the current academic year and the course grades of each historical academic year, and the student employment rate data includes the student employment rate of the current academic year and the student employment rate of each historical academic year.

[0024] Specifically, the data collected in the data collection module of this embodiment is data for students of a certain major in a university. In this embodiment, there is no specific limitation on the collection method of course grade data, student employment rate data and student attendance rate of each course. Technical personnel in this field can set it freely, and only need to meet the collection requirements of course grade data, student employment rate data and student attendance rate of each course. Among them, course grade data, student employment rate data and student attendance rate of each course can be obtained through the academic affairs system.

[0025] Please continue reading Figure 1 As shown, the system also includes a performance analysis module, which is used to evaluate students' learning effects based on the collected current school year course grades and each historical school year course grades, and to construct a learning effect index based on the evaluation results. The performance analysis module is connected to the data collection module.

[0026] See also Figure 2 As shown, the performance analysis module includes: The performance analysis unit is used to analyze the performance status of the students of the i-th course according to the collected i-th course performance di of the current academic year and the i-th course performance of each historical academic year, and to construct the i-th course performance status index according to the analysis results, so as to conduct a detailed analysis of the student performance status of the current academic year and quantify the student's learning effect, wherein: If di≤cip×(1-α), the performance analysis unit determines that the performance status of the student of the i-th course in the current academic year is unqualified, and sets the i-th course performance status index to CZ1, setting: CZ1=0; If di>cip×(1-α), the performance analysis unit determines that the performance status of the students in the i-th course in the current academic year is qualified, and sets the performance status index of the i-th course to CZ2, setting: CZ2=[di-cip×(1-α)] / cip, cip is the average performance of the i-th course in each historical academic year, , cij is the course grade of the i-th course in the j-th historical academic year, J is the number of years the i-th course is offered, and α is the fluctuation range threshold; the grade analysis unit conducts a detailed analysis of the student grade status in the current academic year based on the historical grade level of each course, and quantifies the student's learning effect, which provides a direct indicator for the evaluation of the overall teaching quality.

[0027] Specifically, the number of years the i-th course is offered in this embodiment can be collected through the academic affairs system, and the i-th course grade di in the current academic year is the average grade of the i-th course in the current academic year, which can be collected through the academic affairs system. In this embodiment, no specific limitation is made on the setting of the fluctuation range threshold, and technical personnel in this field can set it freely as long as the setting requirements of the fluctuation range threshold are met. Among them, the optimal value of α is 0.03.

[0028] Please continue reading Figure 2 As shown, the performance analysis module also includes: The fluctuation analysis unit is used to analyze the fluctuation state of the student scores of the i-th course according to the collected scores of each student of the i-th course in the current academic year, and to construct the fluctuation abnormality coefficient of the i-th course according to the analysis results, so as to identify and quantify the learning stability and abnormal fluctuation, wherein: The fluctuation analysis unit sets the fluctuation coefficient of the student's grade of the i-th course in the current academic year as Bi, and sets: , where cir is the score of the rth student in the i-th course in the current academic year, and R is the number of students in the current academic year; The fluctuation analysis unit compares the student grade fluctuation coefficient Bi of the i-th course in the current academic year with the preset fluctuation coefficient B0, and analyzes the fluctuation state of the student grade of the i-th course according to the comparison result, and constructs the fluctuation abnormality coefficient according to the analysis result. If Bi≤B0, the fluctuation analysis unit determines that the fluctuation state of the student grade of the i-th course is a stable state, and sets the fluctuation abnormality coefficient of the i-th course to BYi1, setting: BYi1=0; if Bi>B0, the fluctuation analysis unit determines that the student grade fluctuation state of the i-th course is a fluctuating state, and sets the fluctuation abnormality coefficient of the i-th course to BYi2, setting: BYi2=Bi-B0; by analyzing the fluctuation of student grades, learning stability and abnormal fluctuations are identified and quantified, thereby providing further support for teaching effect analysis.

[0029] Specifically, the scores and number of students in the i-th course of the current academic year described in this embodiment can be collected through the academic affairs system. In this embodiment, no specific limitation is made on the setting of the preset fluctuation coefficient, and technical personnel in this field can set it freely as long as the setting requirements of the preset fluctuation coefficient are met. Among them, the best value of B0 is 0.15.

[0030] Please continue reading Figure 2 As shown, the performance analysis module also includes: The learning effect analysis unit is connected to the performance analysis unit and the fluctuation analysis unit. The learning effect analysis unit is used to construct the learning effect index XGi of the i-th course according to the construction results of the performance status index of the i-th course and the construction results of the fluctuation abnormality coefficient of the i-th course. The setting is: XGi=the performance status index of the i-th course-β×the fluctuation abnormality coefficient of the i-th course, β is a preset proportional coefficient; by combining the performance status and fluctuation, the learning effect index of each course is constructed to help evaluate the effectiveness of the course design and teaching methods. Provide quantitative basis for subsequent teaching adjustment and optimization.

[0031] Specifically, in this embodiment, the setting of the preset proportional coefficient is not specifically limited, and those skilled in the art can set it freely as long as the setting requirements of the preset proportional coefficient are met. Among them, the best value of β is 0.45.

[0032] Please continue reading Figure 1 As shown, the system also includes: The attendance analysis module is connected to the performance analysis module. The attendance analysis module is used to analyze the attendance status of students according to the attendance rate of students in each class of the i-th course, and to construct the attendance index of students in the i-th course according to the analysis results, wherein: If Qik≤Q0, the attendance analysis module determines that the attendance status of the student in the kth class of the i-th course is abnormal, and Q0 is the preset attendance rate; If Qik>Q0, the attendance analysis module determines that the attendance status of the student in the kth class of the i-th course is normal; The attendance analysis module constructs the attendance index CQi of the students of the i-th course according to the analysis results of the attendance status of each class hour of the i-th course, and sets: CQi=Yk / K, where Yk is the number of classes in which the attendance status of the students of the i-th course is abnormal, and K is the number of classes in the i-th course; by analyzing the students' attendance status and constructing the attendance index, it not only evaluates the students' learning habits and participation, but also reveals the relationship between the teacher's teaching strategy and the students' motivation, thereby improving the accuracy of the teaching quality evaluation.

[0033] Specifically, this embodiment does not specifically limit the setting of the preset attendance rate, and those skilled in the art can freely set it as long as the setting requirements of the preset attendance rate are met. Among them, the best value of Q0 is 0.95.

[0034] Please continue reading Figure 1 As shown, the system also includes: An employment analysis module is used to analyze the employment status of students based on the employment rate of students in the current academic year and the employment rate of students in adjacent historical academic years, and to construct an employment index based on the analysis results. The employment analysis module is connected to the attendance analysis module.

[0035] See also Figure 3 As shown, the employment analysis module includes: The employment analysis unit is used to analyze the employment status of students based on the employment rate of students in the current academic year M1 and the employment rate of students in the adjacent historical academic year M2, and to construct a student employment index based on the analysis results to quantify the employment status of students, where: When M1<M2, if (M2-M1) / M2≤η1, the employment analysis unit determines that the employment status of the student is a normal employment status, and sets the student employment index to JZ1, and sets: JZ1=L0; if (M2-M1) / M2>η1, the employment analysis unit determines that the employment status of the student is a low employment status, and sets the student employment index to JZ2, and sets: JZ2=L0-[(M2-M1) / M2-η1] / △M, η1 is the first preset fluctuation ratio, △M is the preset fluctuation interval, and L0 is the employment index threshold; When M1>M2, if (M1-M2) / M2≤η2, the employment analysis unit determines that the student's employment status is a normal employment status, and sets the student's employment index to JZ1; if (M1-M2) / M2>η2, the employment analysis unit determines that the student's employment status is a high employment status, and sets the student's employment index to JZ3, setting: JZ3=L0+[(M1-M2) / M2-η2] / △M, η2 is the second preset fluctuation ratio; by comparing the historical and current data of the student's employment rate to evaluate the employment status and construct the employment index, the accuracy of the teaching quality evaluation is improved.

[0036] Specifically, in this embodiment, there is no specific limitation on the setting of the employment index threshold, the preset fluctuation range and each preset fluctuation ratio. Technical personnel in this field can set them freely, and they only need to meet the setting requirements of the employment index threshold, the preset fluctuation range and each preset fluctuation ratio. Among them, the optimal value of η1 is 0.1, the optimal value of η2 is 0.06, the optimal value of L0 is 0.5, and the optimal value of △M is 0.1.

[0037] Please continue reading Figure 3 As shown, the employment analysis module also includes: A history analysis unit is connected to the employment analysis unit, and is used to analyze the abnormality of the employment rate of students in the current academic year based on the employment rate M1 of the current academic year and the average value M0 of the employment rate of students in historical academic years, and to adjust the analysis process of the employment status based on the analysis result, so as to dynamically adjust the employment status based on the employment data, wherein: When M1≤M0, the historical analysis unit determines that the student employment rate of the current academic year is abnormal, and adjusts the first preset fluctuation ratio to η1', setting η1'=η1×{1-P×ln[3×(M0-M1) / M0+1] / ln4}, and adjusts the second preset fluctuation ratio to η2', setting η2'=η2×{1+P×ln[3×(M0-M1) / M0+1] / ln4}, where P is the preset adjustment ratio; When M1>M0, the historical analysis unit determines that the student employment rate in the current academic year is normal and does not make any adjustments; by analyzing the abnormality of employment data, the employment status analysis method is adjusted to ensure the accuracy and reliability of the analysis, thereby reflecting the actual employment situation and improving the accuracy of teaching quality evaluation.

[0038] Specifically, the average value M0 of the student employment rate in the historical academic year described in this embodiment can be collected through the academic affairs system. This embodiment does not specifically limit the setting of the preset adjustment ratio, and technical personnel in this field can set it freely as long as the setting requirements of the preset adjustment ratio are met. Among them, the optimal value of P is 0.36.

[0039] Please continue reading Figure 3 As shown, the employment analysis module also includes: A student quantity analysis unit is connected to the history analysis unit, and is used to analyze the student quantity growth status by comparing the number of students R in the current school year with the number of students n2 in the adjacent school year, and to make secondary adjustments to the analysis results of the student employment status according to the analysis results, so as to ensure that the results of the employment analysis are not disturbed by sudden changes in the number of students, wherein: When R<n2, the student number analysis unit determines that the growth state of the student number is normal and does not make any adjustments; When R>n2, if (R-n2) / n2≤zz, the student number analysis unit determines that the growth status of the student number is normal and no adjustment is made; if (R-n2) / n2>zz, the student number analysis unit determines that the growth status of the student number is abnormal, and adjusts the preset adjustment ratio to P1, setting P1=P×exp{[zz-(R-n2) / n2] / zz}, where zz is the preset growth ratio; by detecting the growth status of the student number and adjusting the analysis strategy, it is ensured that the results of the employment analysis are not disturbed by sudden changes in the number of students, and support is provided for policy adjustments.

[0040] Specifically, in this embodiment, the setting of the preset growth ratio is not specifically limited, and technical personnel in this field can set it freely as long as the setting requirements of the preset growth ratio are met, where the optimal value of zz is 0.1.

[0041] Please continue reading Figure 1 As shown, the system also includes: A contribution analysis module is connected to the employment analysis module, and is used to analyze the contribution of each course to the employment rate according to the collected years of each course offering, the course grades of each historical academic year, and the employment rate of students in each historical academic year, so as to adjust the employment index, wherein: When Ti>t0, the contribution analysis module determines that the number of years the i-th course is offered is normal, and sets the contribution of the i-th course to the employment rate as Gi, setting: , where cip is the average score of the i-th course in each history academic year, jyj is the employment rate of the j-th history academic year, jyp is the average employment rate of each history academic year, J is the number of years the i-th course is offered, and t0 is the preset number of years; When Ti≤t0, the contribution analysis module determines that the number of years the i-th course has been offered is abnormal, and sets the contribution of the i-th course to the employment rate to G0, where G0 is a preset contribution threshold; by quantitatively analyzing the contribution of each course to employment, it helps to identify the courses that are most helpful to students' employment, thereby improving the accuracy of the teaching quality evaluation of each course.

[0042] Specifically, the average number of years for each course to be offered and the employment rate of each historical academic year described in this embodiment can be collected through the academic affairs system or through interactive collection. In this embodiment, no specific limitation is made on the setting of the preset number of years for offering courses and the preset contribution threshold. Technical personnel in this field can set them freely as long as the setting requirements of the preset number of years for offering courses and the preset contribution threshold are met. Among them, the best value of G0 is 0, and the best value of t0 is 5 years.

[0043] Please continue reading Figure 1 As shown, the system also includes: A quality assessment module is connected to the contribution analysis module. The quality assessment module is used to evaluate the teaching quality of each course based on the construction results of the performance status index, the construction results of the student's attendance index, the construction results of the employment index and the analysis results of the contribution of each course to the employment rate, and push the evaluation results to the user.

[0044] See also Figure 4 As shown, the quality assessment module includes: The quality assessment unit is used to evaluate the teaching quality of each course based on the construction results of the learning effect index, the construction results of the student attendance index, the construction results of the employment index and the analysis results of the contribution of each course to the employment rate, including: The quality assessment unit sets the teaching quality assessment index of the i-th course as ZLi, and sets: ZLi=u1×i-th ​​course learning effect index+u2×i-th ​​course student attendance index+u3×(employment index+i-th course contribution to employment rate), where u1 is the grade weight, u2 is the attendance index, u3 is the employment weight, and u1+u2+u3=1; The quality assessment unit compares the teaching quality assessment index ZLi of the i-th course with the preset quality assessment threshold ZL0, and evaluates the teaching quality of the i-th course based on the comparison result. If ZLi≤ZL0, the quality assessment unit determines that the teaching quality of the i-th course is unqualified; if ZLi>ZL0, the quality assessment unit determines that the teaching quality of the i-th course is qualified; by integrating multiple indicators (learning effect index, attendance index, employment index and course contribution), a comprehensive assessment of teaching quality is achieved, which helps schools quickly identify weak links and advantageous courses in teaching, provides information support for teachers and managers, and promotes continuous improvement of education quality.

[0045] Specifically, in this embodiment, there is no specific limitation on the setting of each weight and the preset quality assessment threshold. Technical personnel in this field can set it freely as long as the setting requirements of each weight and the preset quality assessment threshold are met. Among them, the best value of u1 is 0.5, the best value of u2 is 0.2, the best value of u3 is 0.3, and the best value of ZL0 is 0.56.

[0046] Please continue reading Figure 4 As shown, the quality assessment module also includes: A push unit is connected to the quality assessment unit and is used to push the assessment result of the teaching quality of the i-th course to the user.

[0047] Specifically, the system described in this embodiment is applied to the evaluation of the teaching quality of professional courses in colleges and universities. It conducts a comprehensive analysis of students' academic performance, attendance and employment status to accurately evaluate the teaching quality, and dynamically adjusts the analysis process through factors such as course contribution, thereby improving the accuracy of teaching quality evaluation and promoting continuous improvement of education quality.

[0048] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A teaching quality evaluation system based on big data, characterized in that: include: Data collection module, used to collect course performance data, student employment rate data and student attendance rate of each course and class period; The performance analysis module is used to evaluate the students' learning effects based on the collected course grades of the current academic year and the course grades of each historical academic year, and to construct a learning effect index based on the evaluation results; The attendance analysis module is used to analyze the attendance status of students according to the collected attendance rates of students in each course and class, and to construct the attendance index of students in each course according to the analysis results; The employment analysis module is used to analyze the employment status of students based on the collected student employment rate data and construct an employment index based on the analysis results; The contribution analysis module is used to analyze the contribution of each course to the employment rate based on the collected course opening years, course score data and student employment rate data; The quality assessment module is used to evaluate the teaching quality of each course based on the construction results of the performance status index, the construction results of the student attendance index, the construction results of the employment index and the analysis results of the contribution of each course to the employment rate, and push the evaluation results to users.

2. The teaching quality evaluation system based on big data according to claim 1 is characterized in that: The performance analysis module includes a performance analysis unit, which is used to analyze the performance status of the students of the i-th course based on the collected i-th course performance di of the current academic year and the i-th course performance of each historical academic year. The performance status includes qualified and unqualified. If the performance status of the students of the i-th course in the current academic year is unqualified, the i-th course performance status index is set to CZ1; if the performance status of the students of the i-th course in the current academic year is qualified, the i-th course performance status index is set to CZ2.

3. The teaching quality evaluation system based on big data according to claim 2 is characterized in that: The performance analysis module also includes a fluctuation analysis unit, which is used to analyze the fluctuation state of the student performance of the i-th course based on the collected student performance of the i-th course in the current academic year. The fluctuation state of the student performance includes a fluctuating state and a stable state. If the fluctuation state of the student performance of the i-th course is a stable state, the fluctuation abnormality coefficient of the i-th course is set to BYi1; if the fluctuation state of the student performance of the i-th course is a fluctuating state, the fluctuation abnormality coefficient of the i-th course is set to BYi2.

4. The teaching quality evaluation system based on big data according to claim 3 is characterized in that: The performance analysis module also includes a learning effect analysis unit, which is used to construct the i-th course learning effect index XGi according to the construction result of the i-th course performance status index and the construction result of the i-th course fluctuation abnormality coefficient.

5. The teaching quality evaluation system based on big data according to claim 4 is characterized in that: The attendance analysis module is used to analyze the attendance status of students according to the attendance rate of each class hour of the i-th course. The attendance status of students includes normal and abnormal, and constructs the attendance index CQi of the i-th course students according to the analysis results of the attendance status of each class hour of the i-th course.

6. The teaching quality evaluation system based on big data according to claim 5 is characterized in that: The employment analysis module includes an employment analysis unit, which is used to analyze the employment status of students based on the student employment rate M1 of the current academic year and the student employment rate M2 of the adjacent historical academic year. The employment status includes normal employment status, low employment status and high employment status. If the student's employment status is normal employment status, the student's employment index is set to JZ1; if the student's employment status is low employment status, the student's employment index is set to JZ2; if the student's employment status is high employment status, the student's employment index is set to JZ3.

7. The teaching quality evaluation system based on big data according to claim 6 is characterized in that: The employment analysis module also includes a historical analysis unit, which is used to analyze the abnormality of the student employment rate in the current academic year based on the student employment rate M1 in the current academic year and the average student employment rate M0 in historical academic years. The abnormality of the employment rate includes normal and abnormal. If the student employment rate in the current academic year is abnormal, the first preset fluctuation ratio is adjusted to η1', and the second preset fluctuation ratio is adjusted to η2'.

8. The teaching quality evaluation system based on big data according to claim 7 is characterized in that: The employment analysis module also includes a student number analysis unit, which is used to analyze the student number growth status based on the number of students R in the current school year and the number of students n2 in the adjacent school year. The student number growth status includes normal and abnormal. If the student number growth status is abnormal, the preset adjustment ratio will be adjusted to P1.

9. The teaching quality evaluation system based on big data according to claim 8 is characterized in that: The contribution analysis module is used to compare the collected number of years the i-th course is offered with the preset number of years t0 to analyze the abnormality of the number of years the i-th course is offered. The abnormality of the number of years offered includes normal and abnormal. If the number of years the i-th course is offered is normal, the contribution of the i-th course to the employment rate is set to Gi; if the number of years the i-th course is offered is abnormal, the contribution of the i-th course to the employment rate is set to G0, where G0 is the preset contribution threshold.

10. The teaching quality evaluation system based on big data according to claim 9 is characterized in that: The quality assessment module includes a quality assessment unit, which is used to construct a teaching quality assessment index ZLi of the i-th course according to the construction results of the learning effect index, the construction results of the student's attendance index, the construction results of the employment index and the analysis results of the contribution of each course to the employment rate, and compare the teaching quality assessment index ZLi of the i-th course with a preset quality assessment threshold ZL0. If ZLi≤ZL0, the quality assessment unit determines that the teaching quality of the i-th course is unqualified; if ZLi>ZL0, the quality assessment unit determines that the teaching quality of the i-th course is qualified.

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