Teaching file optimization system

Abstract

The invention provides a teaching file optimization system, and belongs to the field of file optimization. The problem of complex file optimization is solved; the data acquisition module is used for acquiring teaching information; the data processing module is used for processing the teaching information and solving an error rate according to error conditions of the students for different knowledge points to obtain an average error rate; calculating an actual difficulty level according to the statistical difficulty level of the knowledge point and the evaluation difficulty level; the teaching optimization module is used for acquiring teaching duration and the number of knowledge points; the teaching duration of each knowledge point is optimized in combination with the average error rate and the actual difficulty level; the data storage module is used for correspondingly storing the knowledge points and the teaching duration of each optimized knowledge point; according to the method, the error rate and difficulty of the knowledge points are acquired and processed, so that the teaching duration is optimized, and the optimization difficulty is reduced.

Description

Technical Field

[0001] The present invention discloses a teaching file optimization system, which relates to the field of file optimization. Background Art

[0002] The existing teaching file optimization systems have the following deficiencies: Single optimization direction: The current teaching file optimization systems mainly focus on optimizing file storage, while ignoring other important aspects, such as user experience, file retrieval accuracy, security, or teaching data analysis; As a result, the system is difficult to meet the diverse needs of users, performs poorly in teaching support effects, and affects user satisfaction and the practical application value of the system; Limited data acquisition: The current teaching file optimization systems obtain single data and ignore other potential data sources, such as teaching content, student behavior data, external teaching platform data, etc.; It limits the comprehensive understanding of files and teaching activities, resulting in insufficient data support for optimization decisions and limited optimization effects; Simple data processing method: The system adopts a simple data processing method, making it difficult to mine valuable information and patterns from a large amount of data, limiting the accuracy and efficiency of optimization, and affecting the optimization level of the system. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a teaching file optimization system, aiming to solve the problem of complex file optimization.

[0004] To achieve the above purpose, the present invention is implemented through the following technical solutions: A teaching file optimization system, characterized in that the optimization system includes: Data acquisition module: Obtain the pitch angle of the student, the error rate of the student for different knowledge points, and the ratio of the student's listening duration, and form the class data of the student; Obtain the teaching duration, the number of knowledge points, the evaluation difficulty level of the knowledge points, and the statistical difficulty level of the knowledge points, and combine with the class data of the student to form teaching information; Data processing module: Process the teaching information, calculate the average error rate from the class data of the student by combining the error rate of the student for different knowledge points with the ratio of the student's listening duration; Combine with the pitch angle of the student during class, map the average error rate to the difficulty level to obtain the mapped difficulty level; Calculate the actual difficulty level based on the statistical difficulty level, evaluation difficulty level, and mapped difficulty level of the knowledge points; Teaching optimization module: Optimize the teaching duration of each knowledge point according to the teaching duration and the number of knowledge points; Combine with the average error rate and the actual difficulty level. Data storage module: Correspondingly save the knowledge points and the teaching duration of each optimized knowledge point, and the teacher teaches each knowledge point according to the teaching duration of each optimized knowledge point.

[0005] Furthermore, obtain teaching information as follows: Obtain the teaching duration t, the number of knowledge points m; obtain the number of teachers js in the teaching group, the teaching duration jt(a) of the teacher, and the teacher evaluates the difficulty of the knowledge points to obtain the initial evaluation level dj(a), and calculate the initial evaluation level dj(a) to obtain the evaluation difficulty level PG of the knowledge points. ; Obtain n students for a questionnaire survey, and the students choose the difficulty levels of different knowledge points. According to the number of people who choose different difficulty levels, count the number of people, obtain the number of people rs1 corresponding to the selected difficulty of 1, and thus obtain the number of people rsw corresponding to the selected difficulty of w. According to the number of people who choose different difficulty levels, calculate the average value of the difficulty levels of the knowledge points to obtain the statistical difficulty level JND of the knowledge points. i ; ; Obtain the head pitch angle of the student, the ratio of the listening duration of the student, and the error rate of the student for different knowledge points to form the class data of the student.

[0006] Furthermore, obtain the class data of the student as follows: Record the class data of the student to obtain knowledge points of different difficulty levels, count the head pitch angle of the student when explaining knowledge points of different difficulties, and evaluate the difficulty level according to the head pitch angle of the student, and the head pitch angle interval of the student. Obtain the teaching duration jxs of the knowledge point, obtain the maximum listening angle and the minimum listening angle; obtain the head pitch angle of the student within the teaching duration jxs, and according to the head pitch angle of the student, obtain the duration Dsc when the head pitch angle is greater than the maximum listening angle and the duration Xsc when the head pitch angle is less than the minimum listening angle, and calculate the ratio of the listening duration of the student to obtain the listening duration ratio TJ. ; Statistically record the error situations of the students for different knowledge points and calculate the error rate of the knowledge points.

[0007] Furthermore, evaluate the difficulty level as follows: Obtain the head pitch angle of the student, analyze the distribution of the head pitch angle of the student, obtain the distribution curve of the head pitch angle of the student, and obtain the pitch angle JZ corresponding to the polar axis of the distribution curve; analyze the pitch angle JZ; By processing the head pitch angles of students for knowledge points of different difficulties, obtain the pitch angle JZ(S) corresponding to the corresponding polar axis, where S represents the difficulty level, and calculate according to the pitch angle corresponding to the polar axis to obtain the head pitch angle interval of the student; Obtain the angle JZY of the right adjacent polar axis of the pitch angle JZ(S), and obtain the angle JZZ of the left adjacent polar axis of the pitch angle JZ(S); obtain the minimum value zx of the head pitch angle α of the student; obtain the maximum value zd of the head pitch angle α of the student; calculate the head pitch angle interval of the student: ; ; When JZY does not exist, QJY(S) = zd; When JZZ does not exist, QJZ(S) = zx; Where: JZ(S) refers to the pitch angle corresponding to the polar axis of the knowledge point with difficulty S, QJY(S) refers to the right interval of the head pitch angle of the student for the knowledge point with difficulty S, and QJZ(S) refers to the left interval of the head pitch angle of the student for the knowledge point with difficulty S; Obtain the head pitch angle intervals of the student at different time periods of knowledge point teaching, combine the intervals of different time periods, and calculate to obtain the final interval.

[0008] Furthermore, calculate the final interval as follows: Respectively obtain the head pitch angle interval Q1(QJZ(S), QJY(S)) of the student before knowledge point teaching, the head pitch angle interval Q2(QJZ(S), QJY(S)) of the student during knowledge point teaching, and the head pitch angle interval Q3(QJZ(S), QJY(S)) of the student after knowledge point teaching; According to the head pitch angle intervals Q1(QJZ(S), QJY(S)), Q2(QJZ(S), QJY(S)), Q3(QJZ(S), QJY(S)) of the student, obtain the interval ranges QF1, QF2, QF3; the interval mid-axes QZ1, QZ2, QZ3; evaluate the change value of the head pitch angle interval of the student; obtain the interval mid-axis QZ and the interval range QF of the final interval; ; ; Calculate the mid-axis and range of the final interval to obtain the left interval LJZ(S) and right interval LJY(S) of the final interval; ; .

[0009] Furthermore, calculate the error rate of the knowledge point as follows: Obtain the total score zf of the knowledge point and the score df of the student for this knowledge point j ; Combine the number of students to obtain the average error of this knowledge point, and combine the average listening duration ratio of the students to weight the average value; obtain the average error rate cw i : ; Where: df j refers to the score of the j-th student for this knowledge point, TJ j refers to the listening duration ratio of the j-th student for this knowledge point, 1 ≤ j ≤ n; cw i refers to the average error rate of the i-th knowledge point, 1 ≤ i ≤ m, where m refers to the number of knowledge points.

[0010] Furthermore, obtain the actual difficulty level as follows: Obtain the pitch angle of the students during class, calculate the average pitch angle of the students' heads, compare the average pitch angle of the students' heads with the pitch angle interval of the students' heads to obtain the performance difficulty level; Obtain the difficulty level of the knowledge point, map the average error rate to the difficulty level, combine the performance difficulty level to obtain the mapped difficulty level, and obtain the weights β1 of the evaluation difficulty level and β2 of the average difficulty level according to the mapped difficulty level combined with the evaluation difficulty level and the average difficulty level; According to the weights β1 of the evaluation difficulty level and β2 of the average difficulty level, combine the evaluation difficulty level and the statistical difficulty level of the knowledge point for calculation to obtain the actual difficulty level SJ; .

[0011] Furthermore, obtain the weights as follows: Obtain the pitch angle jd of the heads of n students during the explanation of the knowledge point j , and calculate the average pitch angle of the students' heads: ; Compare the average pitch angle FYJ of the students' heads with the pitch angle intervals of the knowledge points at different difficulty levels to obtain the performance difficulty level bx; If LJZ(S) ≤ FYJ ≤ LJY(S); then the performance difficulty level bx = S; Obtain the maximum value max1 and the minimum value min1 of the average error rate; obtain the maximum value max2 and the minimum value min2 of the difficulty level of the knowledge points; combine the average error rate cw of the current knowledge point i and the performance difficulty level bx for calculation to obtain the mapped difficulty level YS i ; ; Obtain the minimum value α, and according to the mapped difficulty level YS i respectively calculate the difference from the evaluated difficulty level PG of the knowledge point i and the average difficulty level JND i to obtain the weights β1 and β2 according to the proportional relationship of the differences.

[0012] Furthermore, optimize the teaching duration as follows: According to the number of knowledge points, obtain the actual difficulty level of each knowledge point, allocate the difficulty level of the knowledge points according to the proportional coefficient, and at the same time add weights to the proportional coefficient according to the average error rate of the knowledge points to obtain the allocation coefficient; reasonably allocate the total teaching duration according to the allocation coefficient to obtain the teaching duration of each knowledge point; The specific process of obtaining the allocation coefficient is as follows: According to the number of knowledge points m and the actual difficulty level SJ i , combined with the average error rate cw of the knowledge point i calculate to obtain the allocation coefficient XS i ; ; Allocate the total teaching duration t according to the allocation coefficient and the number of knowledge points m to obtain the teaching duration sc of each knowledge point i ; .

[0013] Compared with the prior art, the beneficial effects of the present invention are: Improvement and optimization direction: The present invention starts from the teaching content itself, dynamically adjusts the teaching duration according to the importance and difficulty of the knowledge points; allocates more teaching time to complex knowledge points and streamlines simple knowledge points, thereby improving teaching efficiency; Comprehensive data analysis: The present invention conducts a detailed analysis of the class data of students, classifies the performance of students by analyzing the head pitch angle of students during class, saves the in-class test situation during class, and combines the student performance and the in-class test situation to judge the difficulty of the learning content; Optimization processing steps: In the present invention, by statistically analyzing the head pitch angles of students during the explanation of different knowledge points and combining with data images, a data interval is obtained; according to the data interval, the class situation of the current student is judged, and the difficulty of the knowledge point is determined. Brief Description of the Drawings

[0014] By reading the following detailed description of the non-restrictive embodiments with reference to the accompanying drawings, other features, objects, and advantages of the present invention will become more apparent: Figure 1 It is a schematic diagram of the system of the present invention; Figure 2 It is a schematic diagram of the main data acquisition of the present invention; Figure 3 It is a schematic diagram of the main data processing of the present invention; Figure 4 It is a schematic diagram of the main data change of the present invention. Detailed Embodiments

[0015] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] Embodiment 1 Please refer to Figure 1 , a teaching document optimization system includes: a data acquisition module, a data processing module, a teaching optimization module, and a data storage module; Data acquisition module: Obtain the pitch angle of the student and the error rate of the student for different knowledge points to form the class data of the student; obtain the teaching duration, the number of knowledge points, the evaluation difficulty level of the knowledge point, and the statistical difficulty level of the knowledge point, and combine with the class data of the student to form teaching information; According to the teaching arrangement and syllabus of the teacher, obtain the teaching duration t and the number of knowledge points m; Obtain the number of teachers js in the teaching group, the teaching duration jt(a) of the teacher, and the teacher evaluates the difficulty of the knowledge point to obtain the initial evaluation level dj(a), and calculate the initial evaluation level dj(a) to obtain the evaluation difficulty level PG of the knowledge point; ; It should be noted that: the difficulty level of the knowledge point is set to w, 1 ≤ dj(a) ≤ w, 1 ≤ PG ≤ w; where 1 is extremely easy and w is extremely difficult; Obtain the number of students n, conduct a questionnaire survey on n students, and the students select the difficulty level of different knowledge points, such as the question ("How difficult do you think 'drawing the quadratic function graph' is?"), and the options (difficulty level: 1, 2, 3,..., w), where: 1 is extremely easy and w is extremely difficult; According to the number of people who choose different difficulty levels, count the number of people, obtain the number of people rs1 corresponding to the selection difficulty of 1, obtain the number of people rs2 corresponding to the selection difficulty of 2, ……, obtain the number of people rsw corresponding to the selection difficulty of w; rs1 + rs2 + …… + rs5 = n; According to the number of people who choose different difficulty levels, calculate the average difficulty level of the knowledge points to obtain the statistical difficulty level JND of the knowledge points i ; ; For example, when w is 5, for the first knowledge point, there are 10 students participating in the questionnaire survey. The survey results show that the number of students who choose the difficulty level of 1 is 1, the number of students who choose the difficulty level of 2 is 3, the number of students who choose the difficulty level of 3 is 5, the number of students who choose the difficulty level of 4 is 1, and the number of students who choose the difficulty level of 5 is 0. Substitute the data into the formula: ; The statistical difficulty level of the first knowledge point is obtained as 2.6; Please refer to Figure 2 , through a photographic device, record the class data of students, count the head pitch angles of students when explaining knowledge points of different difficulties, and evaluate the difficulty level according to the head pitch angles of students to obtain the head pitch angle intervals of knowledge points of different difficulty levels; It should be noted that: the head pitch angle of a student refers to the angle between the head and the ground, and the angle when the student lies on the table is set to 0 degrees; Specifically as follows: Obtain the head pitch angle of a student, analyze the distribution of the head pitch angles of the student through a graphing software to obtain the distribution curve of the head pitch angles of the student, and according to the distribution curve, obtain the pitch angle JZ corresponding to the polar axis of the distribution curve; analyze the pitch angle JZ corresponding to the polar axis of the distribution curve; It should be noted that: the pitch angle corresponding to the polar axis represents the largest proportion of the number of people at this angle; Specifically as follows: It should be noted that: the graphing software refers to mathematical data processing software that can display the data distribution image by receiving data; Please refer to Figure 3 ; By processing the head pitch angles of students for knowledge points of different difficulties, obtain the corresponding pitch angle JZ(S) corresponding to the polar axis, where S represents the difficulty level (1, 2, 3, 4, 5), and calculate according to the pitch angle corresponding to the polar axis to obtain the head pitch angle interval of the student; Obtain the angle JZY of the right adjacent polar axis of the pitch angle JZ(S), and obtain the angle JZZ of the left adjacent polar axis of the pitch angle JZ(S); obtain the minimum value zx of the student's head pitch angle α; obtain the maximum value zd of the student's head pitch angle α; calculate the student's head pitch angle interval: ; ; When JZY does not exist, QJY(S) = zd; When JZZ does not exist, QJZ(S) = zx; Where: JZ(S) refers to the pitch angle corresponding to the polar axis of the knowledge point with difficulty S, QJY(S) refers to the right interval of the student's head pitch angle for the knowledge point with difficulty S, and QJZ(S) refers to the left interval of the student's head pitch angle for the knowledge point with difficulty S; It should be noted that: the student's head pitch angle interval refers to the interval value obtained by dividing the range between the polar axis and its left and right adjacent polar axes; Respectively obtain the student's head pitch angle interval Q1(QJZ(S), QJY(S)) before knowledge point teaching, the student's head pitch angle interval Q2(QJZ(S), QJY(S)) during knowledge point teaching, and the student's head pitch angle interval Q3(QJZ(S), QJY(S)) after knowledge point teaching is completed; According to the student's head pitch angle intervals Q1(QJZ(S), QJY(S)), Q2(QJZ(S), QJY(S)), Q3(QJZ(S), QJY(S)), obtain the interval ranges QF1, QF2, QF3; the interval mid-axes QZ1, QZ2, QZ3; evaluate the change value of the student's head pitch angle interval; obtain the interval mid-axis QZ and interval range QF of the final interval; ; ; It should be noted that: the interval range refers to the value range of the angle interval as QJY(S) - QJZ(S); the interval mid-axis refers to the central value of the left and right intervals, which is the sum of the left and right interval values divided by 2; It should be noted that: the larger the interval range, the more unstable the interval. When calculating, a lower weight is assigned to it. For example, the larger QF1 is, the smaller the value obtained after subtracting it from 1; Calculate the interval mid-axis and interval range of the final interval to obtain the left interval LJZ(S) and right interval LJY(S) of the final interval; ; ; Please refer toFigure 4 Obtain the teaching duration jxs of the knowledge point, obtain the maximum listening angle and the minimum listening angle; obtain the head pitch angle of the students within the teaching duration jxs, and according to the head pitch angle of the students, obtain the duration Dsc when the head pitch angle is greater than the maximum listening angle and the duration Xsc when the head pitch angle is less than the minimum listening angle, and calculate the ratio of the listening duration of the students to obtain the listening duration ratio TJ; ; After explaining the knowledge point, check the learning situation of the knowledge point and count the error situations of the students for different knowledge points; The head pitch angle of the students, the ratio of the listening duration of the students, and the error situations of the students for different knowledge points constitute the class data of the students; Data processing module: Process the teaching information, From the class data of the students, calculate the error rate of the students for different knowledge points, combine it with the ratio of the listening duration of the students to obtain the average error rate, and combine it with the pitch angle of the students during class to map the average error rate to the difficulty level; obtain the mapped difficulty level; According to the statistical difficulty level, evaluation difficulty level, and mapped difficulty level of the knowledge point, calculate the actual difficulty level; Process the error situation as follows: According to the error situations of the students for different knowledge points, obtain the total score zf of the knowledge point and the score df of the students for the knowledge point j ; Combine the number of students to calculate the error mean of the knowledge point, and combine the average listening duration ratio of the students to weight the mean; obtain the average error rate cw i : ; where: df j refers to the score of the j-th student for the knowledge point, TJ j refers to the ratio of the listening duration of the j-th student for the knowledge point, 1 ≤ j ≤ n; cw i refers to the average error rate of the i-th knowledge point, 1 ≤ i ≤ m, where m refers to the number of knowledge points; import numpy as np def calculate_weighted_error_rate(student_data, zf): """student_data: List of student data, each student is a dictionary containing 'df' and 'TJ' zf: Total score of the knowledge point """ # Extract each student's error score and lecture time ratio error_scores = [zf - student['df'] for student in student_data] TJs = [student['TJ'] for student in student_data] # Calculate the mean error for each student (error score / total score) error_means = np.array(error_scores) / zf # Weighted processing (mean error * proportion of lecture time) weighted_errors = error_means * np.array(TJs) # Calculate the weighted average error rate cwi = np.mean(weighted_errors) return cwi It should be noted that the error rate is expressed in the formula by the loss rate, that is, by the difference between the overall and the scoring rate; where 1 represents the overall; For example, for the first knowledge point, the total score is 100; there are 3 students, and their scores are 70, 75, and 80 respectively; the proportion of lecture time is 70%, 80%, and 90% respectively; substitute the formula for calculation: ; Get CW 1 =20%, then the average error rate for the first knowledge point is 20%; The difficulty level of the knowledge points is processed as follows: According to the evaluation difficulty level of the knowledge point and the statistical difficulty level of the knowledge point, combined with the average error rate of the knowledge point, two weights β1 and β2 are obtained; where: β1+β2=1; The details are as follows: According to the difficulty level of the knowledge points, the average error rate is mapped to the difficulty level. Combined with the pitch angle of the students during class, the mapped difficulty level represented by the students' class situation is used to obtain the weight β1 of the evaluation difficulty level and the weight β2 of the mean difficulty level according to the mapped difficulty level. Get the head pitch angle jd of n students when explaining the knowledge point j , calculate the mean value of the students' head pitch angle: ; Compare the average pitch angle FYJ of the student's head with the knowledge points of different difficulty levels and the head pitch angle range of the student to obtain the performance difficulty level bx; If LJZ(S) ≤ FYJ ≤ LJY(S); then the performance difficulty level bx = S; Obtain the maximum value max1 and the minimum value min1 of the average error rate; obtain the maximum value max2 and the minimum value min2 of the difficulty level of the knowledge points; calculate by combining the average error rate and the performance difficulty level of the current knowledge point to obtain the mapped difficulty level YS i ; ; Obtain the minimum value α, calculate the difference between the evaluated difficulty level and the average difficulty level of the knowledge points according to the mapped difficulty level respectively, and obtain the weights β1 and β2 according to the proportional relationship of the differences; ; ; It should be noted that: the smaller the difference between the mapped difficulty level and the evaluated difficulty level and the average difficulty level of the knowledge points, the higher the similarity between the two, and a higher weight should be assigned to it, which is achieved by taking the difference from 1; where the minimum value α is any minimum value, which has no impact on the calculation result during normal calculation. When the differences between the mapped difficulty level and the evaluated difficulty level and the average difficulty level of the knowledge points are all 0, α ensures the rationality of the formula and makes the weights β1 and β2 have the same value; According to the value range of the average error rate, the maximum value max1 of the average error rate = 100%, and the minimum value min1 = 0%; according to the value range of the difficulty level of the knowledge points, the maximum value max2 of the difficulty level of the knowledge points = 5, and the minimum value min2 = 1; obtain the average error rate of the first knowledge point as cw 1 = 20%, and the performance difficulty level bx = 2; substitute it into the calculation formula of the mapped difficulty level YS: ; Get YS = 1.9; Obtain the evaluated difficulty level PG = 2 of the first knowledge point, and obtain the average difficulty level JND of the first knowledge point i = 2.6; substitute it into the formula: ; ; Get β1 = 0.875, β2 = 0.125; Calculate the actual difficulty level SJ by combining the weight β1 of the evaluation difficulty level and the weight β2 of the average difficulty level, and integrating the evaluation difficulty level of the knowledge point and the statistical difficulty level of the knowledge point. ; Among them: PG i refers to the evaluation difficulty level of the i-th knowledge point; JND i refers to the statistical difficulty level of the i-th knowledge point; It should be noted that: the formula for obtaining the actual difficulty level combines the teacher's evaluation of the difficulty of the knowledge point, the students' choice of the difficulty of the knowledge, and at the same time weights the difficulty calculation by the error rate, and the calculation result is more in line with the actual situation and has accuracy; For example, obtain the weights β1 = 0.875 and β2 = 0.125 for the first knowledge point; the evaluation difficulty level PG = 2 of the knowledge point and the statistical difficulty level JND i = 2.6 for calculation; ; Get the actual difficulty level SJ = 2.075; Teaching optimization module: Optimize the teaching duration of each knowledge point according to the total teaching duration t and the number of knowledge points m; combine the average error rate and the actual difficulty level. According to the number of knowledge points, obtain the actual difficulty level of each knowledge point, allocate the difficulty level of the knowledge point according to the proportional coefficient, and at the same time add weights to the proportional coefficient according to the average error rate of the knowledge point to obtain the allocation coefficient; reasonably allocate the total teaching duration according to the allocation coefficient to obtain the teaching duration of each knowledge point; The specific process of obtaining the allocation coefficient is as follows: Allocate the teaching duration according to the number of knowledge points and the actual difficulty level. For knowledge points with a higher actual difficulty level, allocate a longer teaching duration, and increase the teaching duration through the average error rate of the knowledge point to obtain the allocation coefficient XS i ; ; It should be noted that: 1 + cw refers to calculating the weight of the allocation coefficient according to the average error rate of the knowledge point, so that for knowledge points with a large error rate, the allocation coefficient increases accordingly; Allocate the total teaching duration t according to the allocation coefficient and the number of knowledge points m to obtain the teaching duration sc of each knowledge point i ; ; It should be noted that: calculate the proportion according to the allocation coefficient of the knowledge point and the sum of the allocation coefficients of all knowledge points to obtain the proportion of a single knowledge point in the total teaching duration; Data storage module: Correspondingly save the knowledge points and the teaching duration of each optimized knowledge point, and the teacher conducts teaching on each knowledge point according to the teaching duration of each optimized knowledge point. The teacher conducts teaching based on the optimized data. After the teaching is completed, the teaching information is retrieved again. According to the data processing module, the average error rate and the actual difficulty level are obtained; the teaching duration of each knowledge point is continuously optimized, and the optimization results are saved. The above formulas are all calculated by removing the dimension and taking their numerical values. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. For example, if there are weight coefficients and proportionality coefficients, the sizes of their settings are to obtain a specific numerical value by quantifying each parameter, which is convenient for subsequent comparison. Regarding the sizes of the weight coefficients and proportionality coefficients, as long as they do not affect the proportional relationship between the parameters and the quantified numerical values, it is fine.

[0017] Finally, it should be noted that: The above embodiments are only specific implementation manners of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: Any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A teaching file optimization system, characterized in that: The optimization system comprises: Data acquisition module: obtains the students' pitch angle, the students' error rate for different knowledge points, and the proportion of the students' lecture time to form the students' class data; The teaching time, number of knowledge points, evaluation difficulty level of knowledge points, and statistical difficulty level of knowledge points are obtained, and combined with the students' class data to form teaching information; Data processing module: Processing teaching information, using students’ class data, calculating the students’ error rates for different knowledge points and the percentage of their lecture time to obtain the average error rate; Combined with the pitch angle of students during class, the average error rate is mapped to the difficulty level to obtain the mapped difficulty level; The actual difficulty level is calculated based on the statistical difficulty level, evaluation difficulty level, and mapping difficulty level of the knowledge point; Teaching optimization module: optimize the teaching time of each knowledge point based on the teaching time, number of knowledge points, average error rate, and actual difficulty level; Data storage module: The knowledge points and the optimized teaching time of each knowledge point are saved in correspondence, and the teacher teaches each knowledge point according to the optimized teaching time of each knowledge point.

2. A teaching file optimization system according to claim 1, characterized in that: To obtain teaching information, please refer to the following: Get the teaching time t, the number of knowledge points m; get the number of teachers in the teaching group js, the teaching time jt(a) of the teachers, and the difficulty of the knowledge points evaluated by the teachers to get the initial evaluation level dj(a); calculate the initial evaluation level dj(a) to get the evaluation difficulty level PG of the knowledge points; ； Obtain n students to conduct a questionnaire survey, and the students select the difficulty level of different knowledge points; According to the number of people who choose different difficulty levels, the number of people is counted to obtain the number of people rs1 corresponding to the selection difficulty of 1, thereby obtaining the number of people rsw corresponding to the selection difficulty of w; According to the number of people who choose different difficulty levels, the mean difficulty level of the knowledge points is calculated to obtain the statistical difficulty level JND of the knowledge points. i ; ； The student's head pitch angle, the student's listening time ratio, and the student's error rate for different knowledge points are obtained to form the student's class data.

3. A teaching file optimization system according to claim 2, characterized in that: The following are the details of obtaining students' class data: Record the students' class data, obtain knowledge points of different difficulty levels, count the students' head pitch angles when explaining knowledge points of different difficulty levels, evaluate the difficulty level based on the students' head pitch angles, and the students' head pitch angle ranges; Obtain the teaching time jxs of the knowledge point, obtain the maximum listening angle and the minimum listening angle; obtain the head pitch angle of the student within the teaching time jxs, and according to the head pitch angle of the student, obtain the time Dsc when the head pitch angle is greater than the maximum listening angle and the time Xsc when the head pitch angle is less than the minimum listening angle, and obtain the listening time proportion of the student to obtain the listening time proportion TJ; ； Statistics were collected on students’ errors in different knowledge points, and the error rates of the knowledge points were calculated.

4. A teaching file optimization system according to claim 3, characterized in that: The difficulty level is assessed as follows: Obtain the pitch angle of the student's head, analyze the distribution of the pitch angle of the student's head, obtain the distribution curve of the pitch angle of the student's head, obtain the pitch angle JZ corresponding to the polar axis of the distribution curve; analyze the pitch angle JZ; By processing the head pitch angles of students with different difficulty levels, the pitch angle JZ(S) corresponding to the corresponding polar axis is obtained, where S represents the difficulty level. The pitch angle range of the student's head is obtained by calculation based on the pitch angle corresponding to the polar axis; Get the right adjacent polar axis angle JZY of the pitch angle JZ(S), get the left adjacent polar axis angle JZZ of the pitch angle JZ(S); get the minimum value zx of the student's head pitch angle α; get the maximum value zd of the student's head pitch angle α; calculate the student's head pitch angle interval: ； ； When JZY does not exist, QJY(S) = zd; When JZZ does not exist, QJZ(S) = zx; Among them: JZ(S) refers to the pitch angle corresponding to the polar axis of the knowledge point with difficulty S, QJY(S) refers to the right interval of the student's head pitch angle for the knowledge point with difficulty S, and QJZ(S) refers to the left interval of the student's head pitch angle for the knowledge point with difficulty S; For different time periods of knowledge point teaching, the pitch angle range of the students' head is obtained, and the ranges of different time periods are combined to calculate the final range.

5. A teaching file optimization system according to claim 3, characterized in that: The final interval is calculated as follows: Obtain the head pitch angle interval Q1 (QJZ (S), QJY (S)) of the students before the knowledge point teaching, the head pitch angle interval Q2 (QJZ (S), QJY (S)) of the students during the knowledge point teaching, and the head pitch angle interval Q3 (QJZ (S), QJY (S)) of the students after the knowledge point teaching is completed; According to the head pitch angle intervals Q1 (QJZ (S), QJY (S)), Q2 (QJZ (S), QJY (S)), Q3 (QJZ (S), QJY (S)), obtain the interval ranges QF1, QF2, QF3; the interval center axes QZ1, QZ2, QZ3; evaluate the change values ​​of the head pitch angle intervals of the students; obtain the interval center axis QZ and the interval range QF of the final interval; ； ； The interval center axis and the interval range of the final interval are calculated to obtain the left interval LJZ (S) and the right interval LJY (S) of the final interval; ； 。 6. A teaching file optimization system according to claim 3, characterized in that: The error rate of the knowledge points is calculated as follows: Get the total score zf of the knowledge point and the student's score df for the knowledge point j ; Combined with the number of students, the mean error value of this knowledge point is calculated, and combined with the average listening time of students, the mean value is weighted; the average error rate cw is obtained i : ； Where: df j Refers to the score of the jth student on this knowledge point, TJ j refers to the proportion of the jth student's listening time on this knowledge point, 1≤j≤n; cw i Refers to the average error rate of the i-th knowledge point, 1≤i≤m, m refers to the number of knowledge points.

7. A teaching file optimization system according to claim 5, characterized in that: The actual difficulty level is obtained as follows: Obtain the pitch angle of the students during class, calculate the average of the pitch angles of the students' heads, compare the average of the pitch angles of the students' heads and the pitch angle ranges of the students' heads to obtain the performance difficulty level; Obtain the difficulty level of the knowledge point, map the average error rate to the difficulty level, combine it with the performance difficulty level to obtain the mapping difficulty level, and calculate the weight according to the mapping difficulty level combined with the assessment difficulty level and the mean difficulty level to obtain the weight β1 of the assessment difficulty level and the weight β2 of the mean difficulty level; According to the weight β1 of the evaluation difficulty level and the weight β2 of the mean difficulty level, the actual difficulty level SJ is calculated by combining the evaluation difficulty level of the knowledge point with the statistical difficulty level of the knowledge point; 。 8. A teaching file optimization system according to claim 7, characterized in that: The weights are obtained as follows: Get the head pitch angle jd of n students when explaining the knowledge point j , calculate the mean value of the students' head pitch angle: ； Compare the mean value of the students' head pitch angle FYJ with the knowledge points of different difficulty levels and the students' head pitch angle ranges to obtain the performance difficulty level bx; If LJZ(S)≤FYJ≤LJY(S), then the performance difficulty level bx=S; Get the maximum value max1 and minimum value min1 of the average error rate; get the maximum value max2 and minimum value min2 of the difficulty level of the knowledge point; combine the average error rate cw of the current knowledge point i , calculate the performance difficulty level bx and obtain the mapping difficulty level YS i ; Get the minimum value α, according to the mapping difficulty level YS i The difficulty level of each knowledge point is PG i , Difficulty Level Average JND i The difference is calculated, and the weights β1 and β2 are obtained according to the proportional relationship of the difference.

9. A teaching file optimization system according to claim 1, characterized in that: The teaching time is optimized as follows: According to the number of knowledge points, the actual difficulty level of each knowledge point is obtained, and the difficulty level of the knowledge point is distributed according to the proportional coefficient. At the same time, the proportional coefficient is weighted according to the average error rate of the knowledge point to obtain the distribution coefficient; the total teaching time is reasonably distributed according to the distribution coefficient to obtain the teaching time of each knowledge point; The specific process of obtaining the distribution coefficient is as follows: According to the number of knowledge points m and the actual difficulty level SJ i , combined with the average error rate of knowledge points cw i Calculate and obtain the distribution coefficient XS i ; ； According to the allocation coefficient and the number of knowledge points m, the total teaching time t is allocated to obtain the teaching time sc for each knowledge point i ; 。

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