Online education student information management system based on artificial intelligence

Abstract

The present invention discloses an online education student information management system based on artificial intelligence, relates to the technical field of student information management, solves the technical problem in the prior art that students in the same channel of online teaching cannot be planned based on information analysis, and specifically comprises a progress information division unit, which is used to divide student progress information, collect information within the progress and information outside the progress, and reasonably plan the progress setting of participating online students based on information analysis; a regional information analysis unit, which is used to conduct regional impact analysis based on participating online students in different regions, collect knowledge type data and knowledge progress data, and infer whether participating online students are divided into the same channel based on data analysis; a preference ability assessment unit, which is used to conduct learning preference ability assessment on online participating students, collect preference assessment information, and obtain preference ability assessment coefficients, and further perform channel division based on coefficient threshold comparison.

Description

Technical Field

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

[0002] In today's digital education environment, effective student information management is crucial to the successful implementation of online education. It not only helps educational institutions and teachers to fully understand students' backgrounds, learning progress and needs in order to provide personalized teaching support, but also ensures the smooth organization and implementation of teaching activities, such as course scheduling, homework assignment and correction, examination and evaluation. At the same time, good student information management is also conducive to educational institutions to conduct data analysis, evaluate teaching effectiveness, and provide a basis for the optimization of teaching strategies and the rational allocation of educational resources, thereby improving the overall education quality and operational efficiency.

[0003] However, in the prior art, online education is unable to process and divide the information input by students, that is, it is impossible to plan students in the same channel of online teaching based on information analysis, and it is impossible to carry out targeted channel areas based on student preference ability assessment.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose an online education student information management system based on artificial intelligence.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The online education student information management system based on artificial intelligence includes an information management server, and the communication connections of the information management server are: An information entry unit, used to enter information about students participating in online education; The progress information division unit is used to divide the student progress information, collect the progress information and the out-of-progress information, and reasonably plan the progress setting of the online students based on the information analysis; A regional information analysis unit is used to conduct regional impact analysis based on online students participating in different regions, collect knowledge type data and knowledge progress data, and infer whether online students participating in the same channel based on data analysis; The preference ability assessment unit is used to conduct learning preference ability assessment on online participating students, collect preference assessment information, and obtain preference ability assessment coefficients, and further divide channels according to coefficient threshold comparison.

[0007] As a preferred implementation of the present invention, the on-progress information and the off-progress information are respectively the ratio of the continuous decline time of the repeated error rate of knowledge point questions within the corresponding explicit progress of the online students to the total learning time, and the increase span of the frequency of use of solutions to knowledge point questions outside the corresponding explicit progress of the online students.

[0008] As a preferred implementation of the present invention, if the information within the progress exceeds the duration ratio threshold, and the information outside the progress exceeds the frequency span threshold, a progress extension signal is generated; If the information within the progress exceeds the duration ratio threshold, and the information outside the progress does not exceed the frequency span threshold, a progress unchanged signal is generated; If the information within the schedule does not exceed the duration ratio threshold, and the information outside the schedule does not exceed the frequency span threshold, a schedule shortening signal is generated.

[0009] As a preferred implementation manner of the present invention, the knowledge type data and the knowledge progress data are respectively the cumulative number of non-overlapping knowledge point types covered by the teaching tasks of the same grade for online students participating in different regions, and the progress deviation values ​​corresponding to the teaching task nodes where the knowledge points are covered in the teaching tasks of the same grade for online students participating in different regions.

[0010] As a preferred embodiment of the present invention, if the knowledge type data exceeds the type cumulative quantity threshold, or the knowledge progress data exceeds the progress deviation threshold, a regional differentiation signal is generated; If the knowledge type data does not exceed the type cumulative quantity threshold, and the knowledge progress data does not exceed the progress deviation threshold, a regional no-impact signal is generated.

[0011] As a preferred embodiment of the present invention, the preference evaluation information includes the deviation duration of the error rate control cycle of the knowledge points of different subjects for the pre-planned same-channel students, the peak value of the continuous rising span of the error frequency of any subject knowledge points for the pre-planned same-channel students, and the cumulative time deviation of the preparation time for different subject knowledge points for the pre-planned same-channel students.

[0012] As a preferred implementation of the present invention, if the preference ability evaluation coefficient of the preset same-channel students exceeds the preference ability evaluation coefficient threshold, a channel differentiation signal is generated and sent to the information management server. After receiving the channel differentiation signal, the information management server differentiates the current preset same-channel students and divides them according to the subject of the preferred knowledge point as the differentiation standard. If the preference ability evaluation coefficient of the preset same-channel students does not exceed the preference ability evaluation coefficient threshold, a channel fitting signal is generated and sent to the information management server; after receiving the channel fitting signal, the information management server will use the current preset same-channel students as the frequency class division standard.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, by determining the learning progress of each student through data analysis and accurately locating the progress information of each student, it is possible to divide the current online education into targeted courses, improve the efficiency of online education, and ensure that each student can improve the speed and quality of learning progress through online education; Based on the regional impact analysis of the participating online students in different regions, it is inferred whether the different educational methods and strategies in different regions have an impact on the current learning progress, so as to avoid the situation where students from multiple regions participating in online teaching on the same channel are unable to adapt to the same educational model, resulting in gradual differences in the progress of online students participating in the current channel, thereby reducing the effectiveness of online education.

[0014] 2. In the present invention, the learning preference ability of online participating students is evaluated. After the teaching channels are classified by analyzing the progress information and regional information, the channels are further differentiated according to the learning preference ability evaluation of the students participating in the online teaching, so that the online teaching can more accurately divide the classroom channels and promote the efficiency of online teaching; Infer whether the current online teaching can promote students' learning efficiency and avoid obstacles to students' learning progress. Through real-time quality analysis, the quality of online teaching can be reasonably controlled and rectified and controlled in time to ensure the feasibility of online teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0016] Figure 1 This is a principle block diagram of Embodiment 1 of the present invention; Figure 2 This is a principle block diagram of the second embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, 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.

[0018] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0019] Embodiment 1 See also Figure 1 As shown, an online education student information management system based on artificial intelligence includes an information management server, which is communicatively connected to an information input unit, a progress information division unit, a regional information analysis unit, and a preference ability assessment unit; The information management server generates an information entry signal and sends the information entry signal to the information entry unit; After receiving the information input signal, the information input unit inputs information of the students participating in the online education and stores the input information. The student input information includes: Basic personal information Name, gender, age, date of birth, identity card number and other identification information, home address, contact information (mobile phone number, email address, etc.), so as to communicate with students or parents when necessary, and photos can be used for identity recognition and display on online platforms.

[0020] Student Information Academic data such as the school, grade, class, student number, etc., to clarify the student’s position in the education system; academic history information such as admission time, school system, graduation time (estimated), etc.

[0021] Learning progress information Records of completed courses, including course name, course code, lecturer, study time, grades, etc., reflect students' knowledge accumulation; currently studying courses and learning progress, such as chapter completion, study time, homework submission and correction status, etc., help teachers understand students' real-time learning dynamics.

[0022] Learning preferences and abilities information Subject interests and hobbies, such as the degree of preference for different subjects such as Chinese, mathematics, English, etc., can help teachers teach students in accordance with their aptitude; learning styles, such as visual (prefer learning materials such as images, colors, and charts), auditory (better at absorbing explanations and audio materials), kinesthetic (like to learn through physical activities), etc., can help teachers adjust teaching methods and resource presentation forms.

[0023] The results of learning ability assessment, such as analysis reports of previous test scores and learning ability test scores, can be used to determine students' learning strengths and weaknesses and provide a reference for personalized tutoring.

[0024] After completing the information entry, a progress information division signal is generated and sent to the progress information division unit. After receiving the progress information division signal, the progress information division unit divides the student progress information, determines the learning progress of each student through data analysis, and accurately locates each student's progress information, so as to divide the current online education into targeted courses, improve the efficiency of online education, and ensure that each student can improve the speed and quality of learning progress completion through online education; The real-time offline course progress of the participating online students is obtained, and the current progress is marked as the surface progress; the ratio of the continuous decrease time of the repetition error rate of the knowledge point questions within the surface progress of each participating online student to the total learning time is obtained, and the increase span of the frequency of use of the solution of the knowledge point questions outside the surface progress of each participating online student is obtained, and the ratio of the continuous decrease time of the repetition error rate of the knowledge point questions within the surface progress of the participating online students to the total learning time and the increase span of the frequency of use of the solution of the knowledge point questions outside the surface progress of the participating online students are marked as in-progress information and out-of-progress information, respectively, and compared with the time ratio threshold and the frequency span threshold, respectively: If the ratio of the continuous decrease time of the repeated error rate of the knowledge point test questions within the explicit progress of the participating online students to the total learning time exceeds the time ratio threshold, and the increase span of the use frequency of the solution method of the knowledge point test questions outside the explicit progress of the participating online students exceeds the frequency span threshold, it is inferred that the current explicit progress of the participating online students can be extended, and a progress extension signal is generated and sent to the information management server. After receiving the progress extension signal, the information management server partially extends the set progress of the corresponding participating online students with the explicit progress as the starting point. If there is an increase in the error rate during the extension process, the extension speed is reduced; If the ratio of the continuous decrease time of the repeated error rate of the knowledge point test questions within the corresponding surface progress of the online students to the total learning time exceeds the time ratio threshold, and the increase span of the use frequency of the solution of the knowledge point test questions outside the corresponding surface progress of the online students does not exceed the frequency span threshold, it is inferred that the surface progress of the current online students does not need to be extended, and a progress unchanged signal is generated and sent to the information management server. After receiving the progress unchanged signal, the information management server sets the progress of the current online students to the current surface progress; If the ratio of the continuous decrease time of the repeated error rate of the knowledge point test questions within the surface progress of the online students to the total learning time does not exceed the time ratio threshold, and the increase span of the frequency of using the solution of the knowledge point test questions outside the surface progress of the online students does not exceed the frequency span threshold, it is inferred that there is a deviation in the surface progress of the current online students, and a progress shortening signal is generated and sent to the information management server. After receiving the progress shortening signal, the information management server sets the progress of the current online students to the current surface progress, and at the same time controls the progress speed, focusing on reviewing the content of the current surface progress; The information management server divides online channels according to the progress control requirements of the corresponding online students; After the progress information is divided, a regional information analysis signal is generated and sent to the regional information analysis unit. After receiving the regional information analysis signal, the regional information analysis unit conducts regional impact analysis based on the participating online students in different regions, and infers whether the different educational methods and strategies in different regions have an impact on the current learning progress, so as to avoid that the participating online students in multiple regions cannot adapt to the same educational model when conducting online teaching on the same channel, resulting in the gradual difference in the progress of the participating online students in the current channel, thereby reducing the effectiveness of online education; The information of different online students participating in different regions is analyzed to obtain the cumulative number of non-overlapping knowledge point types covered by the same grade teaching tasks of the corresponding online students in different regions, and the corresponding progress deviation values ​​of the teaching task nodes where the covered knowledge points are located in the same grade teaching tasks of the corresponding online students in different regions are obtained. The progress deviation value corresponds to the time point when the knowledge point appears in the current teaching cycle, and the cumulative number of non-overlapping knowledge point types covered by the same grade teaching tasks of the corresponding online students in different regions and the corresponding progress deviation values ​​of the teaching task nodes where the covered knowledge points are located in the same grade teaching tasks of the corresponding online students in different regions are marked as knowledge type data and knowledge progress data, respectively, and compared with the type cumulative number threshold and the progress deviation threshold, respectively: If the cumulative number of non-overlapping knowledge point types covered by the same grade teaching tasks for online students in different regions exceeds the cumulative number threshold, or the progress deviation value of the teaching task node where the covered knowledge points are located in the same grade teaching tasks for online students in different regions exceeds the progress deviation threshold, it is inferred that the regional information analysis of the corresponding online students is abnormal, and a regional differentiation signal is generated and sent to the information management server. After receiving the regional differentiation signal, the information management server will not treat the online participating students with the corresponding regional information analysis abnormality as the same channel teaching; If the cumulative number of non-overlapping knowledge point types covered by the same grade teaching tasks for online students in different regions does not exceed the cumulative number threshold of the types, and the corresponding progress deviation value of the teaching task node where the covered knowledge points are located in the same grade teaching tasks for online students in different regions does not exceed the progress deviation threshold, it is inferred that the regional information analysis of the corresponding online students is normal, and a regional no-influence signal is generated and sent to the information management server. After receiving the regional no-influence signal, the information management server will give priority to the online participating students whose corresponding regional information analysis is normal as the same channel teaching; After completing the regional information analysis, a preference ability assessment signal is generated and sent to the preference ability assessment unit. After receiving the preference ability assessment signal, the preference ability assessment unit conducts a learning preference ability assessment on the online participating students. After completing the teaching channel classification through progress information and regional information analysis, further channels are distinguished according to the learning preference ability assessment of the students participating in the online teaching, so as to facilitate more accurate classroom channel division in online teaching and promote online teaching efficiency. After completing the progress information and regional information analysis, the differentiated online participating students are set as pre-planned same-channel students, and the deviation duration of the error rate control cycle of knowledge points of different subjects corresponding to the pre-planned same-channel students is obtained. At the same time, the peak value of the continuous rising span of the error frequency of any subject knowledge point of the pre-planned same-channel students is obtained, and the deviation duration of the error rate control cycle of knowledge points of different subjects corresponding to the pre-planned same-channel students and the peak value of the continuous rising span of the error frequency of any subject knowledge point of the pre-planned same-channel students are marked as ZPC and SKF respectively; Obtain the cumulative time deviation of the preparation time for different subject knowledge points of the students in the same channel according to the preset plan, and mark the cumulative time deviation of the preparation time for different subject knowledge points of the students in the same channel according to the preset plan as HSL; The above collected data are uniformly marked as preference evaluation information, and substituted into the formula to obtain the preference ability evaluation coefficient of the students in the same channel of the preset plan. The formula is: , where F is the preference ability evaluation coefficient, er1, er2 and er3 are preset proportional coefficients, e is a natural constant, and β is an error correction factor, which is 0.97; Compare the preference ability evaluation coefficient of the preset same-channel students with the preference ability evaluation coefficient threshold: If the preference ability evaluation coefficient of the preset plan same channel student exceeds the preference ability evaluation coefficient threshold, it is inferred that the preference ability evaluation of the preset plan same channel student is abnormal, a channel distinction signal is generated and the channel distinction signal is sent to the information management server, and after the information management server receives the channel distinction signal, the current preset plan same channel students are distinguished, and the subjects of the preferred knowledge points are used as the distinction criteria for division; If the preference ability evaluation coefficient of the preset same-channel students does not exceed the preference ability evaluation coefficient threshold, it is inferred that the preference ability evaluation of the preset same-channel students is normal, and a channel fitting signal is generated and sent to the information management server; after the information management server receives the channel fitting signal, it will use the current preset same-channel students as the frequency class division standard.

[0025] Embodiment 2 See also Figure 2 As shown, this application adds an online quality analysis unit; The online quality analysis unit analyzes the online teaching quality of online students, infers whether the current online teaching can promote students' learning efficiency and avoid obstacles to students' learning progress. Through real-time quality analysis, it can reasonably control the quality of online teaching and conduct rectification and control in time to ensure the feasibility of online teaching; The average rising span of the error rate of the corresponding teaching knowledge points of the online students in the same channel is obtained, and the continuous expansion span of the error rate mean deviation of the corresponding teaching knowledge points of the online students of the same grade who are not in the same channel is obtained. The average rising span of the error rate of the corresponding teaching knowledge points of the online students in the same channel and the continuous expansion span of the error rate mean deviation of the corresponding teaching knowledge points of the online students of the same grade who are not in the same channel are compared with the mean span threshold and the expansion span threshold respectively: If the average error rate of the corresponding teaching knowledge points of the online students in the same channel increases by more than the average span threshold, or the average deviation of the error rate of the corresponding teaching knowledge points of the online students of the same grade who are not in the same channel continues to expand by more than the expansion span threshold, it is inferred that the efficiency of the corresponding online quality analysis at the current moment is low, and an online education regulation signal is generated and sent to the information management server. After receiving the online education regulation signal, the information management server re-divides the channels or re-plans the courses for the online students. If the rising span of the average error rate of the corresponding teaching knowledge points of the online students who are in the same channel does not exceed the mean span threshold, and the continuous expansion span of the mean deviation of the error rate of the corresponding teaching knowledge points of the online students of the same grade who are not in the same channel does not exceed the expansion span threshold, it is inferred that the corresponding online quality analysis efficiency at the current moment is high, and an online education qualification signal is generated and sent to the information management server.

[0026] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technicians in this field according to actual conditions; When the present invention is used, the information input unit is used to input information of students participating in online education; The progress information division unit is used to divide student progress information, collect information within the progress and outside the progress, and reasonably plan the progress setting of online students based on information analysis; the regional information analysis unit is used to conduct regional impact analysis based on online students participating in different regions, collect knowledge type data and knowledge progress data, and infer whether online students participating in the same channel based on data analysis; the preference ability assessment unit is used to conduct learning preference ability assessment on online participating students, collect preference assessment information, and obtain preference ability assessment coefficients, and further conduct channel division based on coefficient threshold comparison.

[0027] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An online education student information management system based on artificial intelligence, characterized by: Including information management server, the information management server communication connections are: An information entry unit, used to enter information about students participating in online education; The progress information division unit is used to divide the student progress information, collect the progress information and the out-of-progress information, and reasonably plan the progress setting of the online students based on the information analysis; A regional information analysis unit is used to conduct regional impact analysis based on online students participating in different regions, collect knowledge type data and knowledge progress data, and infer whether the online students participating in the same channel are divided into the same channel based on data analysis; The preference ability assessment unit is used to conduct learning preference ability assessment on online participating students, collect preference assessment information, and obtain preference ability assessment coefficients, and further divide channels according to coefficient threshold comparison.

2. The online education student information management system based on artificial intelligence according to claim 1 is characterized in that: The on-progress information and off-progress information are respectively the ratio of the continuous decline time of the repeated error rate of knowledge point questions within the corresponding explicit progress of the online students to the total learning time, and the increase span of the frequency of use of solutions to knowledge point questions outside the explicit progress of the online students.

3. The online education student information management system based on artificial intelligence according to claim 2 is characterized in that: If the information within the progress exceeds the duration ratio threshold, and the information outside the progress exceeds the frequency span threshold, a progress extension signal is generated; If the information within the progress exceeds the duration ratio threshold, and the information outside the progress does not exceed the frequency span threshold, a progress unchanged signal is generated; If the information within the schedule does not exceed the duration ratio threshold, and the information outside the schedule does not exceed the frequency span threshold, a schedule shortening signal is generated.

4. The online education student information management system based on artificial intelligence according to claim 1 is characterized in that: The knowledge type data and knowledge progress data are respectively the cumulative number of non-overlapping knowledge point types covered by the teaching tasks of the same grade for students participating in online teaching in different regions, and the progress deviation values ​​corresponding to the teaching task nodes where the knowledge points are covered in the teaching tasks of the same grade for students participating in online teaching in different regions.

5. The online education student information management system based on artificial intelligence according to claim 4 is characterized in that: If the knowledge type data exceeds the type cumulative quantity threshold, or the knowledge progress data exceeds the progress deviation threshold, a regional differentiation signal is generated; If the knowledge type data does not exceed the type cumulative quantity threshold, and the knowledge progress data does not exceed the progress deviation threshold, a regional no-impact signal is generated.

6. The online education student information management system based on artificial intelligence according to claim 1 is characterized in that: The preference evaluation information includes the deviation duration of the error rate control cycle of the knowledge points of different subjects for the pre-planned same-channel students, the peak value of the continuous increase span of the error frequency of any subject knowledge points for the pre-planned same-channel students, and the cumulative time deviation of the preparation time for different subject knowledge points for the pre-planned same-channel students.

7. The online education student information management system based on artificial intelligence according to claim 6 is characterized in that: If the preference ability evaluation coefficient of the students in the same channel of the preset plan exceeds the preference ability evaluation coefficient threshold, a channel differentiation signal is generated and sent to the information management server. After receiving the channel differentiation signal, the information management server differentiates the students in the same channel of the current preset plan and divides them according to the subject of the preferred knowledge point as the differentiation standard; If the preference ability evaluation coefficient of the preset same-channel students does not exceed the preference ability evaluation coefficient threshold, a channel fitting signal is generated and sent to the information management server; After receiving the channel matching signal, the information management server will use the students in the same channel as the frequency division standard.

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