An online education management system based on artificial intelligence

Abstract

The present invention relates to the field of educational technology, and discloses an online education management system based on artificial intelligence, comprising a data collection module, a data analysis module, a data diagnosis module, a data optimization module and a data management module, wherein the data collection module is responsible for acquiring data, the data analysis module calculates a student learning demand index Ezl, a teacher teaching efficiency index Wej and a teacher-student cooperation effect index Fvg, the data diagnosis module cooperates with a data optimization module, the diagnosis module performs result data diagnosis according to the student learning demand index Ezl, the teacher teaching efficiency index Wej and the teacher-student cooperation effect index Fvg, the data optimization module adds or deletes module functions according to the diagnosis results, and the data management module issues optimization instructions to the module according to the optimization information, and the above process can invest resources raised from various aspects into teacher methods with excellent teaching effects, thereby improving overall educational resources and achieving a state of balanced resource allocation.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and in particular to an online education management system based on artificial intelligence. Background Art

[0002] In the current education management environment, despite the introduction of a variety of convenience measures and technologies, the problem of irrational resource allocation is still significant. This problem is mainly reflected in the lack of effective monitoring and analysis of the use of educational resources, resulting in waste or uneven distribution of resources. For example, some courses or projects may receive too many resources, while other equally important areas face insufficient resources. This imbalance not only affects the quality and effectiveness of education, but also exacerbates educational inequality.

[0003] To solve these problems, online education management systems based on artificial intelligence have emerged. This system uses advanced artificial intelligence technologies, such as big data analysis and machine learning, to achieve real-time monitoring and intelligent analysis of the use of educational resources. Through these technologies, managers can more accurately understand the efficiency and demand of various resources, thereby making more scientific resource allocation and optimization. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides an online education management system based on artificial intelligence, which has the advantage of reasonable resource allocation and solves the problem of unreasonable resource allocation.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an online education management system based on artificial intelligence, comprising a data collection module, a data analysis module, a data diagnosis module, a data optimization module and a data management module;

[0008] The data collection module includes a student learning data unit, a student behavior pattern unit, a teacher's teaching method unit and a teacher-student cooperation data unit. The student learning data unit obtains student online learning data through the use records, classroom feedback and evaluation of the online learning platform. The student behavior pattern unit obtains student online behavior pattern expression data through the online learning platform and classroom interactive information. The teacher's teaching method unit obtains the teacher's teaching method data through the teaching process record and student feedback collection. The teacher-student cooperation data unit obtains the teacher-student cooperation data through the teaching achievement evaluation. The student learning data unit, the student behavior pattern unit, the teacher's teaching method unit and the teacher-student cooperation data unit classify and count the obtained data and connect with the data analysis module through the network;

[0009] The data analysis module includes a student learning behavior analysis unit, a teacher teaching method supplement unit and a teacher-student cooperation results analysis unit. The student learning behavior analysis unit calculates the student learning demand index Ezl according to the student online learning data and the student online behavior pattern expression data. The teacher teaching method supplement unit calculates the teacher teaching efficiency index Wej according to the teaching method data and the teacher-student cooperation data. The teacher-student cooperation results analysis unit calculates the teacher-student cooperation effect index Fvg according to the teacher-student cooperation data. After performing data calculation, the student learning behavior analysis unit, the teacher teaching method supplement unit and the teacher-student cooperation results analysis unit are connected to the data diagnosis module through the network;

[0010] The data diagnosis module cooperates with the data optimization module. The diagnosis module performs result data diagnosis based on the student learning demand index Ezl, the teacher teaching efficiency index Wej and the teacher-student cooperation effect index Fvg. The data optimization module adds or deletes module functions based on the diagnosis results. The data optimization module and the data management module are connected via a network.

[0011] The data management module issues optimization instructions to the module according to the optimization information.

[0012] Preferably, the student learning data unit numbers the online duration of different courses studied by the students according to the characteristics of the students' online learning data, and the online duration of different courses studied by the students is numbered as T1, T2, T3, ... T n .

[0013] Preferably, the student behavior pattern unit numbers the course completion rates of different courses studied by the students according to the data characteristics of the student online behavior pattern expression, and the course completion rates of different courses studied by the students are numbered as W1, W2, W3, ... W n .

[0014] Preferably, the teacher's teaching method unit numbers the satisfaction scores of different students, the number of classroom interactions, and the time of homework correction according to the data characteristics of the teacher's teaching method, and the satisfaction scores of different students are numbered as R1, R2, R3, ... R n The classroom interaction times of different students are numbered as Y1, Y2, Y3, ...Y n , the homework grading time of different students is numbered as U1, U2, U3, ...U n .

[0015] Preferably, the teacher-student cooperation data unit numbers the student's score improvement rate in different subjects and the learning resource utilization efficiency according to the teacher-student cooperation data characteristics, and the student's score improvement rate in different subjects is numbered as K1, K2, K3, ... K n, the learning resource utilization efficiency is numbered as L1, L2, L3, ...L n .

[0016] Preferably, the student learning behavior analysis unit calculates the student learning demand index Ezl according to the student online learning data and the student online behavior pattern expression data, and the calculation formula is:

[0017]

[0018] In the formula, Ezl represents the student learning demand index, T1, T2, T3, ...T n represents the online time of different courses studied by students, T i represents the online duration of different courses studied by the i-th student, represents the average learning time, W1, W2, W3, …W n represents the course completion rate of different courses studied by students, W i represents the course completion rate of different courses studied by the i-th student, represents the average course completion rate, a1 and a2 represent the online duration of different courses studied by students and the weight of the course completion rate in the student learning demand index, respectively, and n represents the number of students counted.

[0019] Preferably, the teacher teaching method supplement unit calculates the teacher teaching efficiency index Wej according to the teaching method data and the teacher-student cooperation data, and the calculation formula is:

[0020]

[0021] In the formula, Wej represents the teacher teaching efficiency index, R1, R2, R3, ... R n represents the satisfaction scores of different students, R i represents the satisfaction score of the i-th student, represents the average student satisfaction score, Y1, Y2, Y3, …Y n represents the number of classroom interactions between different students, Y i represents the number of classroom interactions of the i-th student, represents the average number of interactions between students in the teacher's class, U1, U2, U3, ...U n represents the time for grading homework of different students, U i represents the time the teacher corrects the homework of the i-th student, represents the average time for correcting homework for all students, b1, b2, and b3 represent the weights of different student satisfaction scores, number of classroom interactions, and homework correction time in the teacher's teaching efficiency index, respectively, and n represents the number of students counted.

[0022] Preferably, the teacher-student cooperation achievement analysis unit calculates the teacher-student cooperation effect index Fvg according to the teacher-student cooperation data, and the calculation formula is:

[0023]

[0024] In the formula, Fvg represents the teacher-student cooperation effect index, K1, K2, K3, ...K n Indicates the improvement rate of students’ grades in different subjects, K i represents the improvement rate of the i-th student’s grades in different subjects, Indicates the average improvement rate of students' subject scores, L1, L2, L3, ...L n represents the efficiency of learning resource utilization, L i represents the utilization efficiency of the i-th learning resource, represents the average value of resource utilization efficiency, c1 and c2 represent the weights of students’ grade improvement rate in different subjects and learning resource utilization efficiency in the teacher-student cooperation effect index, and n represents the number of students counted.

[0025] Preferably, the data diagnosis module presets a standard range based on the parameters after implementation of the student learning demand index Ezl, the teacher teaching efficiency index Wej and the teacher-student cooperation effect index Fvg combined with historical data.

[0026] Preferably, the data diagnosis module sets a preset standard range based on historical parameters. When it is diagnosed that the student learning demand index Ezl is not within the preset standard range, the data optimization module adds a function of student interest or deletes a function of student low interest in the student learning data unit and the student behavior pattern unit. When the data diagnosis module diagnoses that the teacher teaching efficiency index Wej is not within the preset standard range, the data optimization module adds a function of adding a demand table for each subject of the students in the teacher's teaching method unit. When the data diagnosis module diagnoses that the teacher-student cooperation effect index Fvg is not within the preset standard range, the data optimization module adds a function of interaction between subject teachers and students in the teacher-student cooperation data unit.

[0027] Compared with the prior art, the present invention provides an online education management system based on artificial intelligence, which has the following beneficial effects:

[0028] 1. The present invention calculates the student learning demand index Ezl by comprehensively calculating the online time and course completion rate of different courses studied by students. The system can accurately evaluate each student's learning needs for online courses, thereby providing a basis for students' personalized learning plans and teaching content push.

[0029] 2. The present invention calculates the teacher-student cooperation effect index Fvg. The system can analyze the performance of each student in different subjects and under the guidance of different teachers. This process provides personalized feedback and suggestions for students and teachers, thereby helping them identify their respective strengths and areas that need improvement. The system will evaluate the teacher-student cooperation effect index Fvg based on the students' learning data and the teachers' teaching methods. This not only helps schools to reasonably allocate educational resources, but also can invest resources raised from various aspects into the methods of teachers with excellent teaching effects. Teachers with excellent teaching results can help other teachers improve their teaching quality by sharing experiences and guidance, thereby improving overall educational resources and achieving a state of balanced resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0032] See also Figure 1 , an online education management system based on artificial intelligence, including a data collection module, a data analysis module, a data diagnosis module, a data optimization module and a data management module;

[0033] The data collection module includes a student learning data unit, a student behavior pattern unit, a teacher's teaching method unit and a teacher-student cooperation data unit. The student learning data unit obtains student online learning data through the use records of the online learning platform, classroom feedback and evaluation. The student behavior pattern unit obtains student online behavior pattern expression data through the online learning platform and classroom interactive information. The teacher's teaching method unit obtains the teacher's teaching method data through the teaching process record and student feedback collection. The teacher-student cooperation data unit obtains the teacher-student cooperation data through the teaching achievement evaluation. The student learning data unit, the student behavior pattern unit, the teacher's teaching method unit and the teacher-student cooperation data unit classify and count the obtained data and connect them to the data analysis module through the network;

[0034] The data analysis module includes a student learning behavior analysis unit, a teacher teaching method supplement unit and a teacher-student cooperation results analysis unit. The student learning behavior analysis unit calculates the student learning demand index Ezl based on the student online learning data and the student online behavior pattern expression data. The teacher teaching method supplement unit calculates the teacher teaching efficiency index Wej based on the teaching method data and the teacher-student cooperation data. The teacher-student cooperation results analysis unit calculates the teacher-student cooperation effect index Fvg based on the teacher-student cooperation data. After performing data calculation, the student learning behavior analysis unit, the teacher teaching method supplement unit and the teacher-student cooperation results analysis unit are connected to the data diagnosis module through the network.

[0035] The data diagnosis module cooperates with the data optimization module. The diagnosis module performs result data diagnosis based on the student learning demand index Ezl, the teacher teaching efficiency index Wej and the teacher-student cooperation effect index Fvg. The data optimization module adds or deletes module functions based on the diagnosis results. The data optimization module and the data management module are connected through the network.

[0036] The data management module issues optimization instructions to the module based on the optimization information.

[0037] The student learning data unit numbers the online duration of different courses studied by students according to the characteristics of their online learning data. The online duration of different courses studied by students is numbered as T1, T2, T3, ...T n .

[0038] The student behavior pattern unit numbers the course completion rates of different courses studied by students according to the data characteristics of the students' online behavior patterns. The course completion rates of different courses studied by students are numbered as W1, W2, W3, ...W n .

[0039] The teacher's teaching method unit numbers the satisfaction scores of different students, the number of classroom interactions, and the time of homework correction according to the data characteristics of the teacher's teaching method. The satisfaction scores of different students are numbered as R1, R2, R3, ... R n , the number of classroom interactions of different students is numbered as Y1, Y2, Y3, ...Y n , the time for marking the homework of different students is numbered as U1, U2, U3, ...U n .

[0040] The teacher-student cooperation data unit numbers the student's score improvement rate and learning resource utilization efficiency in different subjects according to the characteristics of the teacher-student cooperation data. The student's score improvement rate in different subjects is numbered as K1, K2, K3, ...K n , the learning resource utilization efficiency is numbered as L1, L2, L3, ...L n .

[0041] The student learning behavior analysis unit calculates the student learning demand index Ezl based on the student online learning data and the student online behavior pattern expression data. The calculation formula is:

[0042]

[0043] In the formula, Ezl represents the student learning demand index, T1, T2, T3, ...T n represents the online time of different courses studied by students, T i represents the online duration of different courses studied by the i-th student, represents the average learning time, W1, W2, W3, …W n represents the course completion rate of different courses studied by students, W i represents the course completion rate of different courses studied by the i-th student, represents the average course completion rate, a1 and a2 represent the online duration of different courses studied by students and the weight of the course completion rate in the student learning demand index, respectively, and n represents the number of students counted.

[0044] The advantages are: by calculating the student learning needs index Ezl and comprehensively calculating the online time and course completion rate of different courses studied by students, the system can accurately evaluate each student's learning needs for online courses, thereby providing a basis for students' personalized learning plans and teaching content push.

[0045] The teacher teaching method supplement unit calculates the teacher teaching efficiency index Wej based on the teaching method data and teacher-student cooperation data. The calculation formula is:

[0046]

[0047]

[0048] In the formula, Wej represents the teacher teaching efficiency index, R1, R2, R3, ... R n represents the satisfaction scores of different students, R i represents the satisfaction score of the i-th student, represents the average student satisfaction score, Y1, Y2, Y3, …Y n represents the number of classroom interactions between different students, Y i represents the number of classroom interactions of the i-th student, represents the average number of interactions between students in the teacher's class, U1, U2, U3, ...U n represents the time for grading homework of different students, U i represents the time the teacher corrects the homework of the i-th student, represents the average time for correcting homework for all students, b1, b2, and b3 represent the weights of different student satisfaction scores, number of classroom interactions, and homework correction time in the teacher's teaching efficiency index, respectively, and n represents the number of students counted.

[0049] The advantages are: by calculating the teacher's teaching efficiency index Wej, and by comprehensively calculating different student satisfaction scores, classroom interaction times, and homework grading time, the system can accurately evaluate the teaching efficiency of each teacher, thereby providing teachers with personalized teaching suggestions and support to help them improve their teaching quality.

[0050] The teacher-student cooperation results analysis unit calculates the teacher-student cooperation effect index Fvg based on the teacher-student cooperation data. The calculation formula is:

[0051]

[0052] In the formula, Fvg represents the teacher-student cooperation effect index, K1, K2, K3, ...K n Indicates the improvement rate of students’ grades in different subjects, K i represents the improvement rate of the i-th student’s grades in different subjects, Indicates the average improvement rate of students' subject scores, L1, L2, L3, ...L n represents the efficiency of learning resource utilization, L i represents the utilization efficiency of the i-th learning resource, represents the average value of resource utilization efficiency, c1 and c2 represent the weights of students’ grade improvement rate in different subjects and learning resource utilization efficiency in the teacher-student cooperation effect index, and n represents the number of students counted.

[0053] Learning resources include: textbooks and supplementary teaching materials, multimedia teaching tools, online education resources, experimental equipment and practice sites;

[0054] Learning resource utilization efficiency: Evaluate the utilization efficiency of each learning resource by recording the ratio of the number of times each learning resource is used in the teaching process to the total number of times it is used.

[0055] The advantages are: by calculating the teacher-student cooperation effect index Fvg, the system can analyze the performance of each student in different subjects and under the guidance of different teachers. This process provides personalized feedback and suggestions for students and teachers, thereby helping them identify their respective strengths and areas for improvement. The system will evaluate the teacher-student cooperation effect index Fvg based on students' learning data and teachers' teaching methods. This will not only help schools to reasonably allocate educational resources, but also invest resources raised from various aspects into teachers' methods with excellent teaching results. Teachers with excellent teaching results can help other teachers improve their teaching quality by sharing experiences and guidance, thereby improving overall educational resources. This reasonable allocation of resources and collaboration between teachers will help to improve teaching effectiveness as a whole, promote students' learning outcomes, and thus achieve the optimal allocation of educational resources.

[0056] The data diagnosis module presets the standard range based on the parameters after implementation of the student learning demand index Ezl, the teacher teaching efficiency index Wej and the teacher-student cooperation effect index Fvg combined with historical data.

[0057] The student learning demand index Ezl can timely find out whether the student's learning needs are met. The data diagnosis module sets the preset standard range according to the historical parameters. When it is diagnosed that the student learning demand index Ezl is not within the preset standard range, it means that the student's needs are not met. The data optimization module needs to add the function of student interest or delete the function of student low interest in the student learning data unit and the student behavior pattern unit. By paying attention to the students' interests, the students' learning motivation and participation can be improved, so that the learning process can increase the fun while reducing the learning of low-interest content, thereby avoiding wasting time and energy, and helping to timely adjust the teaching strategy and resource allocation to ensure that every student can obtain educational resources suitable for them;

[0058] The teacher teaching efficiency index Wej can evaluate the teacher's teaching effect and help teachers understand their teaching strengths and weaknesses, so as to improve themselves and develop professionally. When the data diagnosis module diagnoses that the teacher's teaching efficiency index Wej is not within the preset standard range, it means that the teaching effect is not good. The data optimization module needs to add a new demand form function for each subject in the teacher's teaching method unit. This model encourages active interaction between teachers and students, thereby improving teaching quality and learning effects. By collecting students' needs for each subject, it helps teachers understand students' personalized needs and adjust teaching content and methods to meet the learning needs of different students.

[0059] The teacher-student cooperation effect index Fvg can evaluate the cooperation effect between teachers and students, promote communication and collaboration between teachers and students, and improve teaching quality and learning effects. When the data diagnosis module diagnoses that the teacher-student cooperation effect index Fvg is not within the preset standard range, it means that the teacher-student cooperation effect is not good, and the data optimization module is required to add a function of interaction between subject teachers and students in the teacher-student cooperation data unit. By increasing the interaction between teachers and students, the teacher-student relationship can be enhanced, and students' learning enthusiasm and participation can be improved. At the same time, it also helps teachers to better understand students' learning situation and needs.

[0060] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An online education management system based on artificial intelligence, characterized in that: It includes data collection module, data analysis module, data diagnosis module, data optimization module and data management module; The data collection module includes a student learning data unit, a student behavior pattern unit, a teacher's teaching method unit and a teacher-student cooperation data unit. The student learning data unit obtains student online learning data through the use records, classroom feedback and evaluation of the online learning platform. The student behavior pattern unit obtains student online behavior pattern expression data through the online learning platform and classroom interactive information. The teacher's teaching method unit obtains the teacher's teaching method data through the teaching process record and student feedback collection. The teacher-student cooperation data unit obtains the teacher-student cooperation data through the teaching achievement evaluation. The student learning data unit, the student behavior pattern unit, the teacher's teaching method unit and the teacher-student cooperation data unit classify and count the obtained data and connect with the data analysis module through the network; The data analysis module includes a student learning behavior analysis unit, a teacher teaching method supplement unit and a teacher-student cooperation results analysis unit. The student learning behavior analysis unit calculates the student learning demand index Ezl according to the student online learning data and the student online behavior pattern expression data. The teacher teaching method supplement unit calculates the teacher teaching efficiency index Wej according to the teaching method data and the teacher-student cooperation data. The teacher-student cooperation results analysis unit calculates the teacher-student cooperation effect index Fvg according to the teacher-student cooperation data. After performing data calculation, the student learning behavior analysis unit, the teacher teaching method supplement unit and the teacher-student cooperation results analysis unit are connected to the data diagnosis module through the network; The data diagnosis module cooperates with the data optimization module. The diagnosis module performs result data diagnosis based on the student learning demand index Ezl, the teacher teaching efficiency index Wej and the teacher-student cooperation effect index Fvg. The data optimization module adds or deletes module functions based on the diagnosis results. The data optimization module and the data management module are connected via a network. The data management module issues optimization instructions to the module according to the optimization information.

2. The online education management system based on artificial intelligence according to claim 1, characterized in that: The student learning data unit numbers the online duration of different courses studied by the students according to the characteristics of the students' online learning data. The online duration of different courses studied by the students is numbered as T1, T2, T3, ...T n .

3. The online education management system based on artificial intelligence according to claim 1, characterized in that: The student behavior pattern unit performs data numbering on the course completion rates of different courses studied by the students according to the data features of the student online behavior pattern expression, and the course completion rates of different courses studied by the students are numbered as W1, W2, W3, ...W n .

4. The online education management system based on artificial intelligence according to claim 1, characterized in that: The teacher's teaching method unit numbers the satisfaction scores of different students, the number of classroom interactions, and the time of homework correction according to the data characteristics of the teacher's teaching method. The satisfaction scores of different students are numbered as R1, R2, R3, ... R n The classroom interaction times of different students are numbered as Y1, Y2, Y3, ...Y n , the homework grading time of different students is numbered as U1, U2, U3, ...U n .

5. The online education management system based on artificial intelligence according to claim 1, characterized in that: The teacher-student cooperation data unit numbers the student's score improvement rate in different subjects and the learning resource utilization efficiency according to the teacher-student cooperation data characteristics. The student's score improvement rate in different subjects is numbered as K1, K2, K3, ...K n , the learning resource utilization efficiency is numbered as L1, L2, L3, ...L n .

6. The online education management system based on artificial intelligence according to claim 3 is characterized by: The student learning behavior analysis unit calculates the student learning demand index Ezl according to the student online learning data and the student online behavior pattern expression data, and the calculation formula is: In the formula, Ezl represents the student learning demand index, T1, T2, T3, ...T n represents the online time of different courses studied by students, T i represents the online duration of different courses studied by the i-th student, represents the average learning time, W1, W2, W3, …W n represents the course completion rate of different courses studied by students, W i represents the course completion rate of different courses studied by the i-th student, represents the average course completion rate, a1 and a2 represent the online duration of different courses studied by students and the weight of the course completion rate in the student learning demand index, respectively, and n represents the number of students counted.

7. The online education management system based on artificial intelligence according to claim 4 is characterized by: The teacher teaching method supplement unit calculates the teacher teaching efficiency index Wej according to the teaching method data and the teacher-student cooperation data, and the calculation formula is: In the formula, Wej represents the teacher teaching efficiency index, R1, R2, R3, ... R n represents the satisfaction scores of different students, R i represents the satisfaction score of the i-th student, represents the average student satisfaction score, Y1, Y2, Y3, …Y n represents the number of classroom interactions between different students, Y i represents the number of classroom interactions of the i-th student, represents the average number of interactions between students in the teacher's class, U1, U2, U3, ...U n represents the time for grading homework of different students, U i represents the time the teacher corrects the homework of the i-th student, represents the average time for correcting homework for all students, b1, b2, and b3 represent the weights of different student satisfaction scores, number of classroom interactions, and homework correction time in the teacher's teaching efficiency index, respectively, and n represents the number of students counted.

8. The online education management system based on artificial intelligence according to claim 5, characterized in that: The teacher-student cooperation achievement analysis unit calculates the teacher-student cooperation effect index Fvg according to the teacher-student cooperation data, and the calculation formula is: In the formula, Fvg represents the teacher-student cooperation effect index, K1, K2, K3, ...K n Indicates the improvement rate of students’ grades in different subjects, K i represents the improvement rate of the i-th student’s grades in different subjects, Indicates the average improvement rate of students' subject scores, L1, L2, L3, ...L n represents the efficiency of learning resource utilization, L i represents the utilization efficiency of the i-th learning resource, represents the average value of resource utilization efficiency, c1 and c2 represent the weights of students’ grade improvement rate in different subjects and learning resource utilization efficiency in the teacher-student cooperation effect index, and n represents the number of students counted.

9. The online education management system based on artificial intelligence according to claim 8, characterized in that: The data diagnosis module presets a standard range based on the parameters after implementation of the student learning demand index Ezl, the teacher teaching efficiency index Wej and the teacher-student cooperation effect index Fvg combined with historical data.

10. The online education management system based on artificial intelligence according to claim 9, characterized in that: The data diagnosis module sets a preset standard range based on historical parameters. When it is diagnosed that the student learning demand index Ezl is not within the preset standard range, the data optimization module adds a function of student interest or deletes a function of student low interest in the student learning data unit and the student behavior pattern unit. When the data diagnosis module diagnoses that the teacher teaching efficiency index Wej is not within the preset standard range, the data optimization module adds a function of adding a new demand form for each subject of the students in the teacher's teaching method unit. When the data diagnosis module diagnoses that the teacher-student cooperation effect index Fvg is not within the preset standard range, the data optimization module adds a function of interaction between subject teachers and students in the teacher-student cooperation data unit.

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