Method and system for measuring mastering degree of knowledge points

Abstract

The invention provides a method and system for measuring the mastering degree of knowledge points. The system comprises a to-be-tested knowledge point determination module, a master degree preliminary judgment module, an accurate proficiency calculation module, a state transition easiness calculation module, a review consolidation progress calculation module, a reference duration determination module, a multi-dimensional data integration module, a data standardization processing module, a personalized push module, a system interface module and a user feedback module. The system also comprises a data security module, a system maintenance module, a learning effect evaluation module and a system extension module. According to the method and the system for measuring the mastering degree of the knowledge points, the system can push personalized review plans and learning materials for students based on the measurement result of the firmness of the knowledge points, so that the students are helped to consolidate mastered knowledge points more effectively; according to the method, data of multiple dimensions are comprehensively considered, so that the mastering degree of students on knowledge points can be more accurately quantified.

Description

Technical Field

[0001] The present invention relates to the field of education and training, and in particular to a method and system for measuring the degree of mastery of knowledge points. Background Art

[0002] In the existing education and training process, tests are usually used to judge students' mastery of knowledge points. For knowledge points that have not been mastered, students will enter the learning or review stage and use learning materials such as videos, handouts, exercises, etc. to consolidate. However, for knowledge points that have been mastered, most of the existing education and training methods have reduced or no longer paid attention, lacking personalized design for knowledge points that have been mastered. Although some products will conduct regular reviews based on the principle of the Ebbinghaus forgetting curve, they do not make more detailed distinctions in terms of mastery.

[0003] Therefore, a method and system for measuring the degree of mastery of knowledge points are proposed. Summary of the invention

[0004] The present invention aims to solve the problems raised in the background technology and provides a method and system for measuring the degree of mastery of knowledge points, thereby providing later measurement indicators for the mastered knowledge points to further provide personalized push for students.

[0005] The specific technical solutions are as follows:

[0006] A system for measuring the degree of mastery of knowledge points, comprising:

[0007] The module for determining knowledge points to be tested is used to determine the knowledge points that students have mastered after learning as the knowledge points to be tested;

[0008] The mastery level preliminary judgment module is used to judge whether the student has mastered the knowledge point by the number and / or score of questions related to the knowledge point to be tested answered by the student; when the number and / or score exceeds a threshold, it is judged that the student has mastered the knowledge point to be tested;

[0009] The accuracy and proficiency calculation module is used to calculate the accuracy and proficiency based on the student's score rate and answering time on the knowledge point to be tested; the higher the score rate, the higher the accuracy and proficiency; the longer the answering time, the lower the accuracy and proficiency; among them, the score rate has a higher priority than the answering time;

[0010] The state transition ease calculation module is used to collect the learning time that students have experienced in changing from not mastering the knowledge point to mastering it, and calculate the state transition ease according to the learning time; the longer the learning time, the lower the state transition ease;

[0011] The review and consolidation progress calculation module is used to collect the number of review rounds that students have completed on the knowledge points to be tested, and calculate the review and consolidation progress based on the number of review rounds; the more review rounds, the higher the review and consolidation progress;

[0012] A reference time determination module is used to calculate the accuracy proficiency and determine the reference time, that is, the median time of all students answering the questions;

[0013] The multi-dimensional data integration module is used to integrate the accuracy, state transition ease and review and consolidation progress into the knowledge point firmness through the multi-dimensional vector modeling method, so as to measure the students' mastery of the knowledge points to be tested;

[0014] The data standardization processing module is used to standardize the value ranges of accuracy proficiency, state transition ease, and review and consolidation progress so that they are all greater than or equal to 0 and less than or equal to 1;

[0015] Personalized push module, used to push personalized information to students based on the solidity of knowledge points, including review plans and learning materials;

[0016] The system interface module is used to provide a friendly user interface to display students' knowledge mastery and firmness information;

[0017] User feedback module, used to collect user feedback on the system for system optimization and improvement;

[0018] Data security module, used to ensure the security and privacy of student data and prevent data leakage;

[0019] System maintenance module, used to provide daily maintenance functions of the system, including data backup and system update;

[0020] The learning effect evaluation module is used to evaluate students' learning effects through the learning data recorded by the system and provide feedback to teachers and students;

[0021] System expansion module is used to support system expansion and upgrade to adapt to the development of future educational technology and changes in demand.

[0022] As a preferred solution of the present invention, the specific working methods of the module for determining the knowledge points to be tested, the module for initially judging the mastery level, the module for calculating the accuracy and proficiency, the module for calculating the ease of state transition, the module for calculating the review and consolidation progress, the module for determining the reference duration, the multi-dimensional data integration module and the data standardization processing module are as follows:

[0023] The module for determining knowledge points to be tested receives data from the learning management system LMS or the online course platform, identifies the knowledge points that the students have completed learning, and uses them as the knowledge points to be tested. At the same time, the module for determining knowledge points to be tested shares data with the module for preliminary determination of mastery through an internal database or an API interface;

[0024] The mastery level preliminary judgment module obtains questions related to the knowledge point to be tested from the question bank and generates a test paper. After the student answers the questions, the mastery level preliminary judgment module collects the answer data and compares it with the correct answer bank to determine whether the student has mastered the knowledge point. In addition, the mastery level preliminary judgment module transmits the judgment result and the answer data to the accurate proficiency calculation module;

[0025] The accurate proficiency calculation module receives the answer data and the judgment result transmitted by the mastery degree preliminary judgment module, and calculates the accurate proficiency of the student in combination with the reference time provided by the reference time determination module. After the calculation is completed, the accurate proficiency data is transmitted to the multi-dimensional data integration module;

[0026] The state transition ease calculation module obtains the learning time that the student has experienced from not mastering the knowledge point to mastering it through the internal database or LMS, calculates the state transition ease based on the learning time, and transmits the result to the multi-dimensional data integration module;

[0027] The review and consolidation progress calculation module obtains the number of review rounds completed by the student on the knowledge point to be tested from the LMS or the review system, calculates the review and consolidation progress according to the number of review rounds, and after the calculation is completed, passes the review and consolidation progress data to the multi-dimensional data integration module;

[0028] The reference time determination module obtains statistical information of the answering time of all students on the knowledge points to be tested from the database, calculates the median as the reference time, and transmits the reference time data to the accurate proficiency calculation module through the internal interface;

[0029] The multi-dimensional data integration module receives the data transmitted by the accuracy proficiency calculation module, the state transition ease calculation module and the review and consolidation progress calculation module, integrates them into the knowledge point firmness by means of multi-dimensional vector modeling, and transmits the integrated data to the personalized push module and the learning effect evaluation module;

[0030] The data standardization processing module performs standardization processing on the data before or after the multi-dimensional data integration module to ensure that the value range of each dimensional data is greater than or equal to 0 and less than or equal to 1, and the processed data is transmitted to the multi-dimensional data integration module through the internal interface.

[0031] As a preferred solution of the present invention, the specific working method of the personalized push module is as follows:

[0032] The personalized push module receives the knowledge point firmness data transmitted by the multi-dimensional data integration module, and performs personalized push for students based on the firmness data, including at least review plans and learning materials. The pushed content is displayed to students through LMS or online course platform.

[0033] As a preferred solution of the present invention, the specific working method of the system interface module is as follows:

[0034] The system interface module provides a friendly user interface to display students' knowledge mastery and firmness information. The interface data comes from the multi-dimensional data integration module and the personalized push module. At the same time, the system interface module collects user operation feedback and transmits it to the user feedback module.

[0035] As a preferred solution of the present invention, the specific working method of the user feedback module is as follows:

[0036] The user feedback module collects user feedback on the use of the system, including at least opinions on interface operation and pushed content. The feedback data is transmitted to the system maintenance module through the internal interface for system optimization and improvement.

[0037] As a preferred solution of the present invention, the specific working method of the data security module is as follows:

[0038] The data security module is responsible for ensuring the security and privacy protection of student data. During data transmission and storage, encryption technology and access control strategies are used to prevent data leakage. At the same time, the data security module is responsible for communicating with all other modules through a secure interface.

[0039] As a preferred solution of the present invention, the specific working method of the system maintenance module is as follows:

[0040] The system maintenance module provides daily maintenance functions for the system, including at least data backup and system update. During the maintenance process, the system maintenance module interacts with all other modules through the internal database or API interface for data exchange and status synchronization.

[0041] As a preferred solution of the present invention, the specific working method of the learning effect evaluation module is as follows:

[0042] The learning effect evaluation module evaluates the learning effect of students through the learning data recorded by the system. The evaluation results are used to provide learning suggestions for students and are also transmitted to teachers or educational institutions through internal interfaces for monitoring and improving teaching quality.

[0043] As a preferred solution of the present invention, the specific working method of the system expansion module is as follows:

[0044] The system expansion module supports the expansion and upgrading of the system to adapt to the development of future educational technology and changes in demand. During the expansion process, the system expansion module and all other modules are seamlessly connected and flexibly configured through modular design.

[0045] The present invention also provides a method for measuring the degree of mastery of knowledge points, using the above-mentioned system for measuring the degree of mastery of knowledge points, comprising the following steps:

[0046] S1. Determination of knowledge points to be tested

[0047] The module for determining knowledge points to be tested receives data from the learning management system LMS or the online course platform, identifies the knowledge points that students have completed learning, and uses them as knowledge points to be tested. At the same time, it shares data with the module for preliminary judgment of mastery through the internal database or API interface;

[0048] S2. Preliminary judgment of mastery level

[0049] The mastery level preliminary judgment module obtains questions related to the knowledge point to be tested from the question bank, generates a test paper, and the student completes the test paper. After the student answers the question, the mastery level preliminary judgment module collects the student's answer data and compares it with the correct answer bank to determine whether the student has mastered the knowledge point, and passes the judgment result and answer data to the accurate proficiency calculation module;

[0050] S3. Accurate proficiency calculation

[0051] The accurate proficiency calculation module receives the answer data and judgment results transmitted by the mastery level preliminary judgment module, combines the reference time provided by the reference time determination module (the median of all students' answering time), calculates the students' accurate proficiency according to the score rate and answering time, and transmits the accurate proficiency data to the multi-dimensional data integration module;

[0052] S4. Ease of state transition and calculation of review and consolidation progress

[0053] The state transition ease calculation module obtains the learning time that students have experienced from not mastering the knowledge point to mastering it through the internal database or LMS, calculates the state transition ease based on the learning time, and passes the result to the multi-dimensional data integration module. The review and consolidation progress calculation module obtains the number of review rounds that students have completed on the knowledge point to be tested from the LMS or review system, calculates the review and consolidation progress based on the number of review rounds, and after the calculation is completed, passes the review and consolidation progress data to the multi-dimensional data integration module;

[0054] S5. Determine by reference duration

[0055] The reference time determination module obtains the statistical information of the answering time of all students on the knowledge points to be tested from the database, calculates the median as the reference time, and transmits the reference time data to the accurate proficiency calculation module through the internal interface;

[0056] S6. Multi-dimensional data integration

[0057] The multi-dimensional data integration module receives the data transmitted by the accuracy proficiency calculation module, the state transition ease calculation module and the review and consolidation progress calculation module, and integrates them into the knowledge point firmness through the multi-dimensional vector modeling method. The integrated data is transmitted to the personalized push module and the learning effect evaluation module;

[0058] S7. Data standardization

[0059] The data standardization processing module performs standardization processing on the data before or after the multi-dimensional data integration module to ensure that the value range of each dimension of data is greater than or equal to 0 and less than or equal to 1. The processed data is transmitted to the multi-dimensional data integration module through the internal interface;

[0060] S8. Personalized push notifications

[0061] The personalized push module receives the knowledge point firmness data transmitted by the multi-dimensional data integration module, and pushes personalized content to students based on the firmness data, including at least review plans and learning materials. The pushed content is displayed to students through the LMS or online course platform;

[0062] S9. System interface

[0063] The system interface module provides a friendly user interface to display students' knowledge mastery and firmness information. The interface data comes from the multi-dimensional data integration module and the personalized push module. At the same time, the system interface module collects user operation feedback and transmits it to the user feedback module.

[0064] S10.User Feedback

[0065] The user feedback module collects user feedback on the system, including at least opinions on interface operation and pushed content. The feedback data is transmitted to the system maintenance module through the internal interface for system optimization and improvement;

[0066] S11. Data Security

[0067] The data security module is responsible for ensuring the security and privacy of student data. During data transmission and storage, encryption technology and access control strategies are used to prevent data leakage. At the same time, the data security module is responsible for communicating with all other modules through a secure interface.

[0068] S12. System maintenance

[0069] The system maintenance module provides daily maintenance functions for the system, including at least data backup and system update. During the maintenance process, the system maintenance module interacts with all other modules through the internal database or API interface for data exchange and status synchronization;

[0070] S13. Learning effect evaluation

[0071] The learning effect evaluation module evaluates students' learning effects through the learning data recorded by the system. The evaluation results are used to provide learning suggestions for students and are also passed to teachers or educational institutions through internal interfaces for monitoring and improving teaching quality.

[0072] S14. System expansion

[0073] The system expansion module supports the expansion and upgrade of the system to adapt to the development of future educational technology and changes in demand. During the expansion process, the system expansion module and all other modules achieve seamless docking and flexible configuration through modular design.

[0074] The present invention has the following beneficial effects:

[0075] 1. The system for measuring the degree of mastery of knowledge points provided by the present invention realizes a comprehensive and detailed quantitative evaluation of the degree of mastery of knowledge points by integrating data of multiple dimensions (including accuracy and proficiency, ease of state transition, review and consolidation progress, etc.). The system has the following significant technical effects:

[0076] 1. Personalized push optimization: Based on the measurement results of the solidity of knowledge points, the system can provide students with personalized review plans and push learning materials, thereby helping students to more effectively consolidate the knowledge points they have mastered and improve their learning efficiency.

[0077] 2. Multi-dimensional assessment accuracy: By considering multiple aspects such as accurate proficiency, ease of state transition, and review and consolidation progress, the system can more accurately assess students' mastery of knowledge points, avoiding the one-sidedness of single-dimensional assessment.

[0078] 3. Easy to use and versatile: The system's calculation and measurement methods are relatively simple, relying on a certain amount of test data, learning sessions and review sessions. Therefore, it is easy to use in various education and training scenarios and has good versatility.

[0079] 4. Data security and privacy protection: The system has a built-in data security module to ensure the security and privacy protection of student data, prevent data leakage, and enhance user trust.

[0080] 5. System maintenance and scalability: The system provides daily maintenance functions and expansion and upgrade capabilities, which can adapt to the development of future educational technology and changes in demand, and maintain the continuous availability and advancement of the system.

[0081] 2. The method for measuring the degree of mastery of knowledge points proposed in the present invention realizes the quantitative evaluation of the degree of mastery of knowledge points through a series of steps. The method has the following significant technical effects:

[0082] 1. Accuracy of quantitative assessment: This method can more accurately quantify students' mastery of knowledge points by comprehensively considering data from multiple dimensions (such as accuracy and proficiency, ease of state transition, review and consolidation progress, etc.), providing strong support for personalized teaching.

[0083] 2. Easy operation and strong practicality: The method has clear steps and is easy to operate. It is easy to apply in education and training practice, and can flexibly adjust the evaluation parameters according to the learning situation of different students, which improves the practicality and applicability of the method.

[0084] 3. Promote the improvement of teaching effectiveness: Through quantitative evaluation of students’ mastery of knowledge points, teachers can understand students’ learning situation more accurately, so as to formulate more targeted teaching strategies and promote the improvement of teaching effectiveness.

[0085] 4. Support personalized learning: The method can provide students with personalized learning suggestions and review plans, helping students to more effectively consolidate the knowledge points they have mastered and improve learning efficiency and learning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 A schematic diagram of a system architecture for measuring the degree of mastery of knowledge points provided by an embodiment of the present invention;

[0087] Figure 2 A flow chart of a method for measuring the degree of mastery of knowledge points provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0088] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0089] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on this patent; in order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0090] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inner", "outer", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as a limitation on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0091] In the description of the present invention, unless otherwise clearly specified and limited, if the term "connection" or the like appears to indicate the connection relationship between components, the term should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two components or the interaction relationship between two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0092] Example 1

[0093] The system for measuring the degree of mastery of knowledge points provided in this embodiment is as follows: Figure 1 As shown, it includes: a module for determining knowledge points to be tested, a module for initially judging the degree of mastery, a module for calculating accurate proficiency, a module for calculating the ease of state transition, a module for calculating the progress of review and consolidation, a module for determining reference duration, a module for integrating multi-dimensional data, a module for processing data standardization, a module for personalized push notification, a system interface module, a user feedback module, a data security module, a system maintenance module, a learning effect evaluation module, and a system expansion module, among which:

[0094] The module for determining knowledge points to be tested is used to determine the knowledge points that students have mastered after learning as the knowledge points to be tested;

[0095] The mastery level preliminary judgment module is used to judge whether the student has mastered the knowledge point by the number and / or score of questions related to the knowledge point to be tested answered by the student; when the number and / or score exceeds a threshold, it is judged that the student has mastered the knowledge point to be tested;

[0096] The accuracy and proficiency calculation module is used to calculate the accuracy and proficiency according to the student's score rate and answering time on the knowledge point to be tested; the higher the score rate, the higher the accuracy and proficiency; the longer the answering time, the lower the accuracy and proficiency; among them, the score rate has a higher priority than the answering time;

[0097] The calculation formula for accurate proficiency is as follows:

[0098] Let S be the scoring rate of the student, and T be the answering time of the student. T ref is the reference duration (the median of the answering times of all students);

[0099] The formula for calculating the accurate proficiency A is as follows:

[0100]

[0101] where α and β are weight coefficients used to adjust the influence of the scoring rate and answering time on the accurate proficiency. Usually, α is set to be greater than β to reflect that the scoring rate has a higher priority than the answering time.

[0102] Constraint:

[0103] 0 ≤ A ≤ 1

[0104] That is, the value range of the accurate proficiency is normalized to between [0, 1].

[0105] The state transition ease calculation module is used to collect the learning time experienced by the student from not mastering to mastering the knowledge point to be tested, and calculate the state transition ease according to the learning time; the longer the learning time, the lower the state transition ease;

[0106] The formula for calculating the state transition ease is as follows:

[0107] Let L be the learning time experienced by the student from not mastering to mastering the knowledge point to be tested. The formula for calculating the state transition ease E is: E = 1 / (L + 1), where adding 1 is to avoid the denominator being 0.

[0108] Constraint: 0 < E ≤ 1, that is, the value range of the state transition ease is normalized to between [0, 1].

[0109] The review and consolidation progress calculation module is used to collect the number of review rounds completed by the student on the knowledge point to be tested, and calculate the review and consolidation progress according to the number of review rounds; the more review rounds, the higher the review and consolidation progress;

[0110] The formula for calculating the review and consolidation progress is as follows:

[0111] Let R be the number of review rounds completed by the student on the knowledge point to be tested. The formula for calculating the review and consolidation progress P is: P = R / Rmax, where Rmax is the preset maximum number of review rounds used to normalize the review and consolidation progress to between [0, 1]. If R exceeds Rmax, then P = 1.

[0112] The reference duration determination module is used to calculate the accurate proficiency and determine the reference duration, that is, the median of the answering times of all students who answered the questions;

[0113] The multi-dimensional data integration module is used to integrate the accuracy, state transition ease and review and consolidation progress into the knowledge point firmness through the multi-dimensional vector modeling method, so as to measure the students' mastery of the knowledge points to be tested;

[0114] By adopting the multi-dimensional vector modulus method, the accuracy proficiency A, the ease of state transition E and the review and consolidation progress P are integrated into the knowledge point firmness K. The formula for multi-dimensional data integration is as follows:

[0115]

[0116] Alternatively, to take into account the weights of different dimensions, a weighted modulus approach can be used:

[0117]

[0118] Among them, ωA, ωE and ωP are the weight coefficients of accurate proficiency, ease of state transition and review and consolidation progress respectively, and ωA+ωE+ωP=1.

[0119] In order to further improve the accuracy of measuring the degree of mastery of knowledge points, this embodiment adds a dynamic adjustment mechanism for weight coefficients based on the original system. This mechanism will automatically or manually adjust the weight coefficients of dimensions such as accuracy, proficiency, ease of state transition, and review and consolidation progress according to the student's learning stage, learning goals, and subject characteristics, so as to more accurately reflect the student's performance in different dimensions. The specific plan is as follows:

[0120] 1. Design of Dynamic Adjustment Mechanism of Weight Coefficient

[0121] Principles for determining weight coefficients:

[0122] Set the initial weight coefficient according to the requirements of teaching, examination or subject.

[0123] Consider the students' learning stages. For example, the initial learning stage may focus more on accuracy and proficiency, while the review stage may pay more attention to review and consolidation progress.

[0124] In combination with learning goals, such as preparing for a specific exam, the weights of each dimension can be adjusted accordingly.

[0125] Dynamic adjustment strategy:

[0126] Automatic adjustment: The system uses machine learning algorithms to automatically adjust weight coefficients based on students’ learning data (such as answering status, learning time, number of review rounds, etc.). For example, if a student’s accuracy and proficiency in a certain knowledge point is continuously high, the system can automatically reduce the weight of that dimension and increase attention to the ease of state transition or review and consolidation progress.

[0127] Manual adjustment: Teachers or students can manually adjust the weight coefficient through the system interface according to actual conditions. This allows teachers to make personalized settings based on teaching experience or student learning characteristics.

[0128] Adjust frequency and range:

[0129] The system can set the adjustment frequency of the weight coefficient, such as daily, weekly or monthly.

[0130] The adjustment range should be set within a reasonable range to ensure that the sum of the weight coefficients of each dimension is always 1 and that the weight coefficient of each dimension is not lower than a preset minimum value to avoid extreme situations.

[0131] The specific implementation steps are as follows:

[0132] Data collection and preprocessing:

[0133] The system continuously collects students’ learning data, including answer status, study time, number of review rounds, etc.

[0134] Preprocess the data, such as denoising and normalization, to improve data quality.

[0135] Weight coefficient calculation and adjustment:

[0136] According to the preset weight coefficient determination principle and dynamic adjustment strategy, the weight coefficient of each dimension in the current learning stage is calculated.

[0137] If the automatic adjustment method is adopted, the machine learning algorithm is used to analyze the students' learning data to obtain new weight coefficients.

[0138] If manual adjustment is adopted, the teacher or student inputs the new weight coefficient through the system interface.

[0139] Knowledge point firmness calculation:

[0140] According to the adjusted weight coefficient, the firmness of the knowledge point is calculated using the multi-dimensional vector modulus method.

[0141] The calculation results are displayed on the system interface for teachers and students to view.

[0142] Personalized push and optimization:

[0143] Based on the solidity of knowledge points and the adjusted weight coefficients, personalized push is provided to students, including review plans, learning materials, etc.

[0144] The system continuously collects students' feedback data to optimize the weight coefficient adjustment strategy and personalized push content.

[0145] Technological innovation and advantages

[0146] A dynamic adjustment mechanism for weight coefficients is added to make personalized settings based on students’ learning stages, learning goals and subject characteristics.

[0147] The machine learning algorithm is used to automatically adjust the weight coefficients to improve the accuracy and efficiency of the measurement.

[0148] More accurately reflect students' performance in different dimensions and improve measurement accuracy.

[0149] Adapt to the learning characteristics and needs of different students, and provide personalized learning suggestions and push content.

[0150] By continuously collecting and optimizing feedback data, we continuously improve system performance and user experience.

[0151] In summary, by adding a dynamic adjustment mechanism for the weight coefficient, the accuracy of measuring the degree of mastery of knowledge points can be further improved.

[0152] The data standardization processing module is used to standardize the value ranges of accuracy proficiency, state transition ease, and review and consolidation progress so that they are all greater than or equal to 0 and less than or equal to 1;

[0153] For accuracy proficiency A, state transition ease E, and review and consolidation progress P, if their original value range is not between [0,1], they need to be standardized.

[0154] The calculation formula of the standardized value X norm is:

[0155]

[0156] Where X is the original value, X min and X max are the minimum and maximum values ​​of the dimension respectively.

[0157] The personalized push module is used to provide personalized push for students based on the solidity of knowledge points, including review plans and learning materials;

[0158] The system interface module is used to provide a friendly user interface to display students' knowledge mastery and firmness information;

[0159] The user feedback module is used to collect user feedback on the system for system optimization and improvement;

[0160] The data security module is used to ensure the security and privacy of student data and prevent data leakage;

[0161] The system maintenance module is used to provide daily maintenance functions for the system, including data backup and system updates;

[0162] The learning effect evaluation module is used to evaluate students' learning effects through the learning data recorded by the system and provide feedback to teachers and students;

[0163] The system expansion module is used to support the expansion and upgrade of the system to adapt to the development of future educational technology and changes in demand.

[0164] Specifically, in this embodiment, the specific working method of the module for determining the knowledge point to be tested is as follows:

[0165] The module for determining knowledge points to be tested receives data from the learning management system LMS or the online course platform, identifies the knowledge points that students have completed learning, and uses them as knowledge points to be tested. At the same time, the module for determining knowledge points to be tested shares data with the module for initially judging the degree of mastery through an internal database or an API interface;

[0166] In this embodiment, the specific working method of the mastery degree preliminary judgment module is as follows:

[0167] The mastery level preliminary judgment module obtains questions related to the knowledge point to be tested from the question bank and generates a test paper. After the student answers the questions, the mastery level preliminary judgment module collects the answer data and compares it with the correct answer bank to determine whether the student has mastered the knowledge point. In addition, the mastery level preliminary judgment module passes the judgment result and answer data to the accurate proficiency calculation module;

[0168] In this embodiment, the specific working method of the accurate proficiency calculation module is as follows:

[0169] The accurate proficiency calculation module receives the answer data and judgment results transmitted by the mastery level preliminary judgment module, and calculates the student's accurate proficiency in combination with the reference time provided by the reference time determination module. After the calculation is completed, the accurate proficiency data is transmitted to the multi-dimensional data integration module;

[0170] In this embodiment, the specific working method of the state transition ease calculation module is as follows:

[0171] The state transition ease calculation module obtains the learning time that students have experienced in changing from not mastering to mastering the knowledge point to be tested through the internal database or LMS, calculates the state transition ease based on the learning time, and passes the result to the multi-dimensional data integration module;

[0172] In this embodiment, the specific working methods of the review and consolidation progress calculation module, the reference duration determination module, the multi-dimensional data integration module and the data standardization processing module are as follows:

[0173] The review and consolidation progress calculation module obtains the number of review rounds that students have completed on the knowledge points to be tested from the LMS or review system, calculates the review and consolidation progress based on the number of review rounds, and after the calculation is completed, passes the review and consolidation progress data to the multi-dimensional data integration module;

[0174] In this embodiment, the specific working methods of the reference duration determination module, the multi-dimensional data integration module and the data standardization processing module are as follows:

[0175] The reference time determination module obtains the statistical information of the answering time of all students on the knowledge points to be tested from the database, calculates the median as the reference time, and transmits the reference time data to the accurate proficiency calculation module through the internal interface;

[0176] In this embodiment, the specific working methods of the multi-dimensional data integration module and the data standardization processing module are as follows:

[0177] The multi-dimensional data integration module receives the data transmitted by the accuracy proficiency calculation module, the state transition ease calculation module and the review and consolidation progress calculation module, and integrates them into the knowledge point firmness through the multi-dimensional vector modeling method. The integrated data is transmitted to the personalized push module and the learning effect evaluation module;

[0178] In this embodiment, the specific working method of the data standardization processing module is as follows:

[0179] The data standardization processing module performs standardization processing on the data before or after the multi-dimensional data integration module to ensure that the value range of each dimensional data is greater than or equal to 0 and less than or equal to 1. The processed data is passed to the multi-dimensional data integration module through the internal interface.

[0180] Specifically, in this embodiment, the specific working method of the personalized push module is as follows:

[0181] The personalized push module receives the knowledge point firmness data transmitted by the multi-dimensional data integration module, and makes personalized push for students based on the firmness data, including at least review plans and learning materials. The push content is displayed to students through LMS or online course platform.

[0182] Specifically, in this embodiment, the specific working method of the system interface module is as follows:

[0183] The system interface module provides a friendly user interface to display students' knowledge mastery and firmness information. The interface data comes from the multi-dimensional data integration module and the personalized push module. At the same time, the system interface module collects user operation feedback and passes it to the user feedback module.

[0184] Specifically, in this embodiment, the specific working method of the user feedback module is as follows:

[0185] The user feedback module collects user feedback on the system, including at least opinions on interface operation and pushed content. The feedback data is transmitted to the system maintenance module through the internal interface for system optimization and improvement.

[0186] Specifically, in this embodiment, the specific working method of the data security module is as follows:

[0187] The data security module is responsible for ensuring the security and privacy of student data. During data transmission and storage, encryption technology and access control strategies are used to prevent data leakage. At the same time, the data security module is responsible for communicating with all other modules through a secure interface.

[0188] Specifically, in this embodiment, the specific working method of the system maintenance module is as follows:

[0189] The system maintenance module provides daily maintenance functions for the system, including at least data backup and system update. During the maintenance process, the system maintenance module interacts with all other modules and synchronizes data and status through the internal database or API interface.

[0190] Specifically, in this embodiment, the specific working method of the learning effect evaluation module is as follows:

[0191] The learning effectiveness evaluation module evaluates students' learning effectiveness through the learning data recorded by the system. The evaluation results are used to provide learning suggestions for students and are also passed to teachers or educational institutions through internal interfaces for monitoring and improving teaching quality.

[0192] Specifically, in this embodiment, the specific working method of the system expansion module is as follows:

[0193] The system expansion module supports the expansion and upgrade of the system to adapt to the development of future educational technology and changes in demand. During the expansion process, the system expansion module and all other modules achieve seamless docking and flexible configuration through modular design.

[0194] Specifically, in this embodiment, in addition to the above-mentioned accuracy proficiency, state transition ease and review consolidation progress, more dimensions of measurement can be added, such as learning engagement, learning interest, etc. These new dimensions can be obtained by analyzing students' learning behavior, learning time, learning feedback and other data.

[0195] Specifically, in this embodiment, in the personalized push module, the results of multi-dimensional data integration can be used to provide students with more personalized learning resources and review plans. For example, for students with low accuracy and proficiency in a certain knowledge point, the system can recommend more related exercises; for students with high ease of state transition, the system can recommend more extended learning resources;

[0196] Specifically, in this embodiment, in order to further improve the accuracy of personalized push, machine learning algorithms such as collaborative filtering and content recommendation can be introduced to deeply mine and analyze students' learning data, thereby providing students with more accurate learning suggestions.

[0197] Specifically, in this embodiment, in order to help students better understand their learning situation, a learning progress tracking module can be added. The learning progress tracking module can record students' learning progress, learning time, learning results and other data in real time, and display them to students and teachers in the form of charts, reports, etc.

[0198] Specifically, in this embodiment, in order to collect students' learning feedback and provide targeted suggestions, a learning feedback suggestion module can be added. The learning feedback suggestion module can allow students to submit their learning experience, difficulties and suggestions, and the system can provide students with corresponding solutions and learning suggestions based on these feedbacks.

[0199] By introducing a dynamic adjustment mechanism for weight coefficients and adding more dimensions of measurement, this application will be able to more accurately reflect students' performance in different dimensions and provide students with more personalized learning resources and review plans;

[0200] Through quantitative evaluation of students' mastery of knowledge points, teachers can more accurately understand students' learning situation, thus formulating more targeted teaching strategies and promoting the improvement of teaching effectiveness. At the same time, students can also adjust their learning strategies in time according to their own learning situation to improve their learning efficiency and learning effect.

[0201] The system for measuring the degree of mastery of knowledge points provided in this embodiment realizes a comprehensive and detailed quantitative evaluation of the degree of mastery of knowledge points by integrating data from multiple dimensions (including accuracy and proficiency, ease of state transition, review and consolidation progress, etc.). The system has the following significant technical effects:

[0202] 1. Personalized push optimization: Based on the measurement results of the solidity of knowledge points, the system can provide students with personalized review plans and push learning materials, thereby helping students to more effectively consolidate the knowledge points they have mastered and improve their learning efficiency.

[0203] 2. Multi-dimensional assessment accuracy: By considering multiple aspects such as accurate proficiency, ease of state transition, and review and consolidation progress, the system can more accurately assess students' mastery of knowledge points, avoiding the one-sidedness of single-dimensional assessment.

[0204] 3. Easy to use and versatile: The system's calculation and measurement methods are relatively simple, relying on a certain amount of test data, learning sessions and review sessions. Therefore, it is easy to use in various education and training scenarios and has good versatility.

[0205] 4. Data security and privacy protection: The system has a built-in data security module to ensure the security and privacy protection of student data, prevent data leakage, and enhance user trust.

[0206] 5. System maintenance and scalability: The system provides daily maintenance functions and expansion and upgrade capabilities, which can adapt to the development of future educational technology and changes in demand, and maintain the continuous availability and advancement of the system.

[0207] Example 2

[0208] This embodiment provides a method for measuring the degree of mastery of knowledge points, such as Figure 2 As shown, the system for measuring the degree of mastery of knowledge points in Example 1 is used to measure the degree of mastery of knowledge points of students, including the following steps:

[0209] S1. Determination of knowledge points to be tested

[0210] The module for determining knowledge points to be tested receives data from the learning management system LMS or the online course platform, identifies the knowledge points that students have completed learning, and uses them as knowledge points to be tested. At the same time, it shares data with the module for preliminary judgment of mastery through the internal database or API interface;

[0211] S2. Preliminary judgment of mastery level

[0212] The mastery level preliminary judgment module obtains questions related to the knowledge point to be tested from the question bank, generates a test paper, and the student completes the test paper. After the student answers the question, the mastery level preliminary judgment module collects the student's answer data and compares it with the correct answer bank to determine whether the student has mastered the knowledge point, and passes the judgment result and answer data to the accurate proficiency calculation module;

[0213] S3. Accurate proficiency calculation

[0214] The accurate proficiency calculation module receives the answer data and judgment results transmitted by the mastery level preliminary judgment module, combines the reference time provided by the reference time determination module (the median of all students' answering time), calculates the students' accurate proficiency according to the score rate and answering time, and transmits the accurate proficiency data to the multi-dimensional data integration module;

[0215] S4. Ease of state transition and calculation of review and consolidation progress

[0216] The state transition ease calculation module obtains the learning time that students have experienced from not mastering the knowledge point to mastering it through the internal database or LMS, calculates the state transition ease based on the learning time, and passes the result to the multi-dimensional data integration module. The review and consolidation progress calculation module obtains the number of review rounds that students have completed on the knowledge point to be tested from the LMS or review system, calculates the review and consolidation progress based on the number of review rounds, and after the calculation is completed, passes the review and consolidation progress data to the multi-dimensional data integration module;

[0217] S5. Determine by reference duration

[0218] The reference time determination module obtains the statistical information of the answering time of all students on the knowledge points to be tested from the database, calculates the median as the reference time, and transmits the reference time data to the accurate proficiency calculation module through the internal interface;

[0219] S6. Multi-dimensional data integration

[0220] The multi-dimensional data integration module receives the data transmitted by the accuracy proficiency calculation module, the state transition ease calculation module and the review and consolidation progress calculation module, and integrates them into the knowledge point firmness through the multi-dimensional vector modeling method. The integrated data is transmitted to the personalized push module and the learning effect evaluation module;

[0221] S7. Data standardization

[0222] The data standardization processing module performs standardization processing on the data before or after the multi-dimensional data integration module to ensure that the value range of each dimension of data is greater than or equal to 0 and less than or equal to 1. The processed data is transmitted to the multi-dimensional data integration module through the internal interface;

[0223] S8. Personalized push notifications

[0224] The personalized push module receives the knowledge point firmness data transmitted by the multi-dimensional data integration module, and pushes personalized content to students based on the firmness data, including at least review plans and learning materials. The pushed content is displayed to students through the LMS or online course platform;

[0225] S9. System interface

[0226] The system interface module provides a friendly user interface to display students' knowledge mastery and firmness information. The interface data comes from the multi-dimensional data integration module and the personalized push module. At the same time, the system interface module collects user operation feedback and transmits it to the user feedback module.

[0227] S10. User Feedback

[0228] The user feedback module collects user feedback on the system, including at least opinions on interface operation and pushed content. The feedback data is transmitted to the system maintenance module through the internal interface for system optimization and improvement;

[0229] S11. Data Security

[0230] The data security module is responsible for ensuring the security and privacy of student data. During data transmission and storage, encryption technology and access control strategies are used to prevent data leakage. At the same time, the data security module is responsible for communicating with all other modules through a secure interface.

[0231] S12. System maintenance

[0232] The system maintenance module provides daily maintenance functions for the system, including at least data backup and system update. During the maintenance process, the system maintenance module interacts with all other modules through the internal database or API interface for data exchange and status synchronization;

[0233] S13. Learning effect evaluation

[0234] The learning effect evaluation module evaluates students' learning effects through the learning data recorded by the system. The evaluation results are used to provide learning suggestions for students and are also passed to teachers or educational institutions through internal interfaces for monitoring and improving teaching quality.

[0235] S14. System expansion

[0236] The system expansion module supports the expansion and upgrade of the system to adapt to the development of future educational technology and changes in demand. During the expansion process, the system expansion module and all other modules achieve seamless docking and flexible configuration through modular design.

[0237] The method for measuring the degree of mastery of knowledge points proposed in this embodiment realizes quantitative evaluation of the degree of mastery of knowledge points through a series of steps. The method has the following significant technical effects:

[0238] 1. Accuracy of quantitative assessment: This method can more accurately quantify students' mastery of knowledge points by comprehensively considering data from multiple dimensions (such as accuracy and proficiency, ease of state transition, review and consolidation progress, etc.), providing strong support for personalized teaching.

[0239] 2. Easy operation and strong practicality: The method has clear steps and is easy to operate. It is easy to apply in education and training practice, and can flexibly adjust the evaluation parameters according to the learning situation of different students, which improves the practicality and applicability of the method.

[0240] 3. Promote the improvement of teaching effectiveness: Through quantitative evaluation of students’ mastery of knowledge points, teachers can understand students’ learning situation more accurately, so as to formulate more targeted teaching strategies and promote the improvement of teaching effectiveness.

[0241] 4. Support personalized learning: The method can provide students with personalized learning suggestions and review plans, helping students to more effectively consolidate the knowledge points they have mastered and improve learning efficiency and learning results.

[0242] The above are only preferred embodiments of the present invention, and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. A system for measuring the degree of mastery of knowledge points, characterized in that: include: The module for determining knowledge points to be tested is used to determine the knowledge points that students have mastered after learning as the knowledge points to be tested; The mastery level preliminary judgment module is used to judge whether the student has mastered the knowledge point by the number and / or score of questions related to the knowledge point to be tested answered by the student; when the number and / or score exceeds a threshold, it is judged that the student has mastered the knowledge point to be tested; The accuracy and proficiency calculation module is used to calculate the accuracy and proficiency based on the student's score rate and answering time on the knowledge point to be tested; the higher the score rate, the higher the accuracy and proficiency; the longer the answering time, the lower the accuracy and proficiency; among them, the score rate has a higher priority than the answering time; The state transition ease calculation module is used to collect the learning time that students have experienced in changing from not mastering the knowledge point to mastering it, and calculate the state transition ease according to the learning time; the longer the learning time, the lower the state transition ease; The review and consolidation progress calculation module is used to collect the number of review rounds that students have completed on the knowledge points to be tested, and calculate the review and consolidation progress based on the number of review rounds; the more review rounds, the higher the review and consolidation progress; A reference time determination module is used to calculate the accuracy proficiency and determine the reference time, that is, the median time of all students answering the questions; The multi-dimensional data integration module is used to integrate the accuracy, state transition ease and review and consolidation progress into the knowledge point firmness through the multi-dimensional vector modeling method, so as to measure the students' mastery of the knowledge points to be tested; The data standardization processing module is used to standardize the value ranges of accuracy proficiency, state transition ease, and review and consolidation progress so that they are all greater than or equal to 0 and less than or equal to 1; Personalized push module, used to push personalized information to students based on the solidity of knowledge points, including review plans and learning materials; The system interface module is used to provide a friendly user interface to display students' knowledge mastery and firmness information; User feedback module, used to collect user feedback on the system for system optimization and improvement; Data security module, used to ensure the security and privacy of student data and prevent data leakage; System maintenance module, used to provide daily maintenance functions of the system, including data backup and system update; The learning effect evaluation module is used to evaluate students' learning effects through the learning data recorded by the system and provide feedback to teachers and students; System expansion module is used to support system expansion and upgrade to adapt to the development of future educational technology and changes in demand.

2. The system for measuring the degree of mastery of knowledge points according to claim 1, characterized in that: The specific working methods of the module for determining the knowledge points to be tested, the module for initially judging the mastery level, the module for calculating the accuracy and proficiency, the module for calculating the ease of state transition, the module for calculating the review and consolidation progress, the module for determining the reference duration, the module for integrating multi-dimensional data, and the module for processing data standardization are as follows: The module for determining knowledge points to be tested receives data from the learning management system LMS or the online course platform, identifies the knowledge points that the students have completed learning, and uses them as the knowledge points to be tested. At the same time, the module for determining knowledge points to be tested shares data with the module for preliminary determination of mastery through an internal database or an API interface; The mastery level preliminary judgment module obtains questions related to the knowledge point to be tested from the question bank and generates a test paper. After the student answers the questions, the mastery level preliminary judgment module collects the answer data and compares it with the correct answer bank to determine whether the student has mastered the knowledge point. In addition, the mastery level preliminary judgment module transmits the judgment result and the answer data to the accurate proficiency calculation module; The accurate proficiency calculation module receives the answer data and the judgment result transmitted by the mastery degree preliminary judgment module, and calculates the accurate proficiency of the student in combination with the reference time provided by the reference time determination module. After the calculation is completed, the accurate proficiency data is transmitted to the multi-dimensional data integration module; The state transition ease calculation module obtains the learning time that the student has experienced from not mastering the knowledge point to mastering it through the internal database or LMS, calculates the state transition ease based on the learning time, and transmits the result to the multi-dimensional data integration module; The review and consolidation progress calculation module obtains the number of review rounds completed by the student on the knowledge point to be tested from the LMS or the review system, calculates the review and consolidation progress according to the number of review rounds, and after the calculation is completed, passes the review and consolidation progress data to the multi-dimensional data integration module; The reference time determination module obtains statistical information of the answering time of all students on the knowledge points to be tested from the database, calculates the median as the reference time, and transmits the reference time data to the accurate proficiency calculation module through the internal interface; The multi-dimensional data integration module receives the data transmitted by the accuracy proficiency calculation module, the state transition ease calculation module and the review and consolidation progress calculation module, integrates them into the knowledge point firmness by means of multi-dimensional vector modeling, and transmits the integrated data to the personalized push module and the learning effect evaluation module; The data standardization processing module performs standardization processing on the data before or after the multi-dimensional data integration module to ensure that the value range of each dimensional data is greater than or equal to 0 and less than or equal to 1, and the processed data is transmitted to the multi-dimensional data integration module through the internal interface.

3. The system for measuring the degree of mastery of knowledge points according to claim 2, characterized in that: The specific working method of the personalized push module is as follows: The personalized push module receives the knowledge point firmness data transmitted by the multi-dimensional data integration module, and performs personalized push for students based on the firmness data, including at least review plans and learning materials. The pushed content is displayed to students through LMS or online course platform.

4. The system for measuring the degree of mastery of knowledge points according to claim 3, characterized in that: The specific working method of the system interface module is as follows: The system interface module provides a friendly user interface to display students' knowledge mastery and firmness information. The interface data comes from the multi-dimensional data integration module and the personalized push module. At the same time, the system interface module collects user operation feedback and transmits it to the user feedback module.

5. The system for measuring the degree of mastery of knowledge points according to claim 4, characterized in that: The specific working method of the user feedback module is as follows: The user feedback module collects user feedback on the use of the system, including at least opinions on interface operation and pushed content. The feedback data is transmitted to the system maintenance module through the internal interface for system optimization and improvement.

6. The system for measuring the degree of mastery of knowledge points according to claim 5, characterized in that: The specific working method of the data security module is as follows: The data security module is responsible for ensuring the security and privacy protection of student data. During data transmission and storage, encryption technology and access control strategies are used to prevent data leakage. At the same time, the data security module is responsible for communicating with all other modules through a secure interface.

7. The system for measuring the degree of mastery of knowledge points according to claim 6, characterized in that: The specific working method of the system maintenance module is as follows: The system maintenance module provides daily maintenance functions for the system, including at least data backup and system update. During the maintenance process, the system maintenance module interacts with all other modules through the internal database or API interface for data exchange and status synchronization.

8. The system for measuring the degree of mastery of knowledge points according to claim 7, characterized in that: The specific working method of the learning effect evaluation module is as follows: The learning effect evaluation module evaluates the learning effect of students through the learning data recorded by the system. The evaluation results are used to provide learning suggestions for students and are also transmitted to teachers or educational institutions through internal interfaces for monitoring and improving teaching quality.

9. The system for measuring the degree of mastery of knowledge points according to claim 8, characterized in that: The specific working method of the system expansion module is as follows: The system expansion module supports the expansion and upgrading of the system to adapt to the development of future educational technology and changes in demand. During the expansion process, the system expansion module and all other modules are seamlessly connected and flexibly configured through modular design.

10. A method for measuring the degree of mastery of knowledge points, characterized in that: The system for measuring the degree of mastery of knowledge points according to claim 9 comprises the following steps: S1. Determination of knowledge points to be tested The module for determining knowledge points to be tested receives data from the learning management system LMS or the online course platform, identifies the knowledge points that students have completed learning, and uses them as knowledge points to be tested. At the same time, it shares data with the module for preliminary judgment of mastery through the internal database or API interface; S2. Preliminary judgment of mastery level The mastery level preliminary judgment module obtains questions related to the knowledge point to be tested from the question bank, generates a test paper, and the student completes the test paper. After the student answers the question, the mastery level preliminary judgment module collects the student's answer data and compares it with the correct answer bank to determine whether the student has mastered the knowledge point, and passes the judgment result and answer data to the accurate proficiency calculation module; S3. Accurate proficiency calculation The accurate proficiency calculation module receives the answer data and judgment results transmitted by the mastery level preliminary judgment module, combines the reference time provided by the reference time determination module (the median of all students' answering time), calculates the students' accurate proficiency according to the score rate and answering time, and transmits the accurate proficiency data to the multi-dimensional data integration module; S4. Ease of state transition and calculation of review and consolidation progress The state transition ease calculation module obtains the learning time that students have experienced from not mastering the knowledge point to mastering it through the internal database or LMS, calculates the state transition ease based on the learning time, and passes the result to the multi-dimensional data integration module. The review and consolidation progress calculation module obtains the number of review rounds that students have completed on the knowledge point to be tested from the LMS or review system, calculates the review and consolidation progress based on the number of review rounds, and after the calculation is completed, passes the review and consolidation progress data to the multi-dimensional data integration module; S5. Determine by reference duration The reference time determination module obtains the statistical information of the answering time of all students on the knowledge points to be tested from the database, calculates the median as the reference time, and transmits the reference time data to the accurate proficiency calculation module through the internal interface; S6. Multi-dimensional data integration The multi-dimensional data integration module receives the data transmitted by the accuracy proficiency calculation module, the state transition ease calculation module and the review and consolidation progress calculation module, and integrates them into the knowledge point firmness through the multi-dimensional vector modeling method. The integrated data is transmitted to the personalized push module and the learning effect evaluation module; S7. Data standardization The data standardization processing module performs standardization processing on the data before or after the multi-dimensional data integration module to ensure that the value range of each dimension of data is greater than or equal to 0 and less than or equal to 1. The processed data is transmitted to the multi-dimensional data integration module through the internal interface; S8. Personalized push notifications The personalized push module receives the knowledge point firmness data transmitted by the multi-dimensional data integration module, and pushes personalized content to students based on the firmness data, including at least review plans and learning materials. The pushed content is displayed to students through the LMS or online course platform; S9. System interface The system interface module provides a friendly user interface to display students' knowledge mastery and firmness information. The interface data comes from the multi-dimensional data integration module and the personalized push module. At the same time, the system interface module collects user operation feedback and transmits it to the user feedback module. S10. User Feedback The user feedback module collects user feedback on the system, including at least opinions on interface operation and pushed content. The feedback data is transmitted to the system maintenance module through the internal interface for system optimization and improvement; S11. Data Security The data security module is responsible for ensuring the security and privacy of student data. During data transmission and storage, encryption technology and access control strategies are used to prevent data leakage. At the same time, the data security module is responsible for communicating with all other modules through a secure interface. S12. System maintenance The system maintenance module provides daily maintenance functions for the system, including at least data backup and system update. During the maintenance process, the system maintenance module interacts with all other modules through the internal database or API interface for data exchange and status synchronization; S13. Learning effect evaluation The learning effect evaluation module evaluates students' learning effects through the learning data recorded by the system. The evaluation results are used to provide learning suggestions for students and are also passed to teachers or educational institutions through internal interfaces for monitoring and improving teaching quality. S14. System expansion The system expansion module supports the expansion and upgrade of the system to adapt to the development of future educational technology and changes in demand. During the expansion process, the system expansion module and all other modules achieve seamless docking and flexible configuration through modular design.

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