CN110246072B - Review method executed by machine equipment - Google Patents
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
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- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
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- G06—COMPUTING; CALCULATING OR COUNTING
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Abstract
The application relates to a review method executed by machine equipment, which is characterized in that weak knowledge points of students are found more effectively in a knowledge map mode, the mastering conditions of the students on the knowledge points are evaluated in a capacity value mode, the consolidated memory of the knowledge points is enhanced according to a forgetting curve, and an individualized comprehensive review scheme is formulated for the students, so that the students can review more pertinently, the review effect is effectively enhanced, and the review efficiency is improved.
Description
Technical Field
The application relates to the field of education, in particular to a review method executed by machine equipment.
Background
In the conventional teaching, a teacher generally gives a lecture or reviews the content of the lecture or does not review the content of the lecture, which is different according to the content of the lecture and the characteristics of the teacher, and even if the teacher does review, the teacher generally gives a lecture or reviews questions according to the important content of the lecture. Teachers cannot specifically review weak points of students according to the mastery degree of each student, cannot judge the mastery degree of the students on knowledge points according to the mastery condition of the students and the homeward review condition to specifically review required points of the students, cannot review the weak points one by one according to the mastery degree of the students on the knowledge points, cannot judge which knowledge points need to be reviewed according to the previous degree of the students, and does not need to review the knowledge points. Therefore, personalized review cannot be achieved in the traditional teaching.
Disclosure of Invention
The inventor finds out through long-term observation and experiments that personalized review cannot be achieved in the past teaching, on one hand, due to the influence of domestic education environment and education concept for a long time, an educator and an education institution cannot realize the importance of the personalized review, and tend to use a standard learning or reviewing mode, or tend to use a uniform education mode to cultivate and educate students and adopt uniform standards to evaluate the students.
On the other hand, personalized review requires different learning schemes to be formulated according to different students, and also requires dynamic tracking of the learning conditions of the students and corresponding adjustment and update of the learning schemes. In the past, due to the lack of support and application of big data and artificial intelligence technology, individual learning or review is not performed on a large number of students.
In view of the above defects in the prior art, the present application provides a review method based on artificial intelligence, which finds weak knowledge points of students more effectively in a knowledge map manner, evaluates the mastering conditions of the students on the knowledge points in an ability value manner, strengthens the consolidated memory of the knowledge points according to a forgetting curve, and formulates an individualized comprehensive review scheme for the students, so that the students can review more pertinently, thereby effectively strengthening the review effect and improving the review efficiency.
Knowledge graph: all knowledge points of the current learning stage and the previous learning stage are put together to be made into the knowledge graph with the preposed follow-up relationship. Pre-successor relationship: the meaning that the knowledge point b is not known because the knowledge point a is not known, and a can be called a front knowledge point of b and b is a subsequent knowledge point of a. The knowledge graph is a knowledge structure for marking the preposed follow-up relation between knowledge points clearly. Knowledge points of all subjects in all learning stages are integrated together in a big data mode, each knowledge point is marked with a front follow-up relation, and the formed knowledge graph can remarkably promote learning, understanding and memory of students. The knowledge graph generation mode by utilizing big data is difficult to imagine and cannot be realized in the traditional education field. Or in the traditional education field, due to the influence of the overall environment and the restriction of objective conditions, no one wants to associate and mark all related knowledge points in the way.
Capacity value: the student has an overall mastery of a certain knowledge point. Project response theory assumes that a "potential trait," which is a statistical idea proposed based on observing analytical test responses, is sought, and in tests, the potential trait generally refers to potential ability, and the total score of the test is often used as an estimate of this potential. Project response theory considers that the responses and achievements tested on test projects have a special relationship with their underlying traits. The potential traits are our ability values. The learning process of a large number of students to each knowledge point is collected and analyzed by using an artificial intelligence technology, so that a corresponding ability value is set and dynamically adjusted for each knowledge point, and the learning condition of the students can be effectively and accurately evaluated. Based on the quantitative evaluation mode, the method is beneficial to intelligently pushing personalized learning resources to students and intelligently adjusting the learning process of the students. The mode of evaluating students by using the artificial intelligence technology is difficult to imagine and cannot be realized in the traditional education field. Or in the traditional education field, due to the influence of the whole environment and the restriction of objective conditions, people do not want to adjust the mastery degree of each knowledge point for each student in the way.
In the application, for a certain specific student, a corresponding ability value is set for each knowledge point, and the mastering condition (for example, standard reaching or not standard reaching) of the knowledge point by the student can be judged according to the ability value. In the process of learning or reviewing by students, the ability value can be dynamically adjusted according to the learning or reviewing condition of the students.
Forgetting curve: after the information is input into the brain, forgetting is started. The forgetting rate is fast before slow along with the lapse of time, and particularly in the short time of just being learned, the forgetting is fastest, so that a forgetting curve is formed. Assuming that x hours have elapsed after the initial memory, the memory rate (or remaining memory amount) y approximately satisfies the condition that y is 1-0.56x0.06. In some embodiments, the approximate estimate may also be made according to the following table:
| time interval | Residual memory capacity |
| Just after recording | 100％ |
| After 20 minutes | 58.2％ |
| After 1 hour | 44.2％ |
| After 8-9 hours | 35.8％ |
| After 1 day | 33.7％ |
| After 2 days | 27.8％ |
| After 6 days | 25.4％ |
The application provides a review method based on artificial intelligence, which comprises the following steps: acquiring knowledge points to be reviewed, wherein the knowledge points to be reviewed correspond to the ability values, and the ability values reflect the mastering conditions of students on the knowledge points to be reviewed; pushing corresponding review resources to the students for review according to the knowledge points to be reviewed and the corresponding ability values thereof, wherein the knowledge points to be reviewed correspond to the content of the review resources, and the ability values corresponding to the knowledge points to be reviewed correspond to the difficulty of the review resources; adjusting the capability value corresponding to the knowledge point to be reviewed according to the review condition of the student; judging whether the knowledge points to be reviewed reach the standard or not according to the adjusted capability values corresponding to the knowledge points to be reviewed; if the knowledge points to be reviewed do not reach the standard, continuously pushing corresponding review resources to the students to continuously review according to the knowledge points to be reviewed and the current corresponding capability values of the knowledge points to be reviewed; and completing the review of the knowledge points to be reviewed if the knowledge points to be reviewed reach the standard.
In some embodiments, optionally, the step of obtaining knowledge points to be reviewed further comprises the steps of: obtaining a weak knowledge point list according to the test result of the student; pushing corresponding learning resources to the students for learning according to the weak knowledge point list; obtaining an unknown knowledge point list according to the learning condition of the student on the learning resources; according to the non-learning knowledge point list, making and pushing personalized homework to students; obtaining an unowned knowledge point list according to the condition that the student completes the personalized homework; and selecting the knowledge points to be reviewed from the list of the unconscious knowledge points.
In some embodiments, optionally, the review method further includes the following steps: the student is tested according to a knowledge graph, wherein the knowledge graph comprises knowledge points marked with the preposed follow-up relationship.
In some embodiments, optionally, the step of obtaining knowledge points to be reviewed further comprises the steps of: calculating the residual memory capacity of the learned knowledge points according to the time and the learning times of the learned knowledge points of the first learning of the students; and selecting the knowledge points to be reviewed from one or more learned knowledge points with the least residual memory.
In some embodiments, optionally, the review method further includes the following steps: and selecting the knowledge points to be reviewed from the 3 learned knowledge points with the least residual memory.
In some embodiments, optionally, the step of calculating the remaining memory of the learned knowledge point comprises the steps of: and calculating the residual memory amount according to the forgetting curve.
In some embodiments, optionally, the step of calculating the remaining memory of the learned knowledge point comprises the steps of: the remaining memory was calculated according to the following formula: y is 1-0.56x0.06Wherein x represents the number of hours from the time of the first learning knowledge point to the current time, and y represents the remaining memory amount.
In some embodiments, optionally, if the knowledge point to be reviewed does not reach the standard, determining whether the review frequency of the knowledge point to be reviewed reaches a threshold; if the review times do not reach the threshold value, pushing corresponding review resources to the students according to the knowledge points to be reviewed and the current corresponding ability values of the knowledge points to be reviewed so as to continue to review; and if the review times reach the threshold value, marking the knowledge points needing to be reviewed, and stopping the review of the knowledge points needing to be reviewed.
In some embodiments, the threshold is optionally 2 times.
In some embodiments, optionally, the review method further includes: after completing or stopping the review of the point of knowledge to be reviewed, the review method according to any of the preceding claims is repeated for other points of knowledge to be reviewed.
Compared with the prior art, the technical scheme of the application at least comprises the following improvement points and beneficial effects:
firstly, the learned knowledge points are combined with the ability value of each knowledge point student according to a forgetting curve to push the knowledge points which are forgotten by the student and can be learned at the fastest speed (combined with the ability of the student, the selected student does not learn but has a certain degree of grasp but does not have the qualified knowledge points, so that the student can learn or review in a short time) to review and consolidate the knowledge points for the student.
Secondly, the knowledge points which are not mastered during learning are pushed to the students according to the ability values, so that the students can learn the knowledge points at the fastest speed (combining the abilities of the students, selecting the knowledge points which are not mastered by the students but are mastered to a certain extent but not reach the standard yet, so that the students can learn the knowledge points or review the knowledge points in a short time).
Thirdly, the students can study the unknowingly knowledge points again after finishing learning, and the learning chances of the students are increased. The knowledge points learned by students can be forgotten along with the time, so the timely review can strengthen the forgotten knowledge points until the knowledge points are completely mastered by the students.
The conception, specific structure and technical effects of the present application will be further described in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present application.
Drawings
The present application will become more readily understood from the following detailed description when read in conjunction with the accompanying drawings, wherein like reference numerals designate like parts throughout the figures, and in which:
fig. 1 is a flowchart of an embodiment of a review method of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments that can be derived by a person skilled in the art from the embodiments given herein without making any creative effort shall fall within the protection scope of the present application.
The application provides a review method based on artificial intelligence, which comprises the following steps:
firstly, knowledge points to be reviewed are obtained and correspond to ability values, wherein the ability values reflect the mastering conditions of students on the knowledge points to be reviewed.
In some embodiments, if the student has not previously learned related content, such as for the first time a new subject, the student is first tested against the knowledge-graph. The knowledge graph comprises knowledge points marked with the preposed follow-up relation, weak knowledge points of students can be tested through the test, and the weak knowledge points can be analyzed to find out which related knowledge points can not lead to the weak knowledge points, so that the symptom nodes which are not learned by the students can be found more accurately, and the students can be guided to study according to symptoms.
Obtaining a weak knowledge point list according to the test result of the student; pushing corresponding learning resources to the students for learning according to the weak knowledge point list; obtaining an unknown knowledge point list according to the learning condition of the student on the learning resources; according to the non-learning knowledge point list, making and pushing personalized homework to students; obtaining an unowned knowledge point list according to the condition that the student completes the personalized homework; and selecting the knowledge points to be reviewed from the list of the unconscious knowledge points. Through the mode, targeted review can be performed according to different learning conditions of each student.
In some embodiments, the student has previously performed learning of the relevant content. The residual memory capacity of the learned knowledge points can be calculated according to the time and the learning times of the learnt knowledge points of the first time of the students; and selecting the knowledge points to be reviewed from one or more learned knowledge points (for example, 2, 3, 4, 5, 6, 7, 8, 9 and 10) with the least residual memory, so as to review the knowledge points which are about to be forgotten in a targeted manner. The number of the knowledge points to be reviewed selected according to the remaining memory amount can be flexibly adjusted, for example, when other knowledge points to be reviewed are more or the review time is less, some knowledge points can be selected less correspondingly; when other knowledge points needing to be reviewed are fewer, or the review time is longer, more knowledge points can be selected accordingly.
The amount of memory (or degree of memory) a student remembers for a certain knowledge point can be set in percentage form. In some embodiments, the amount of remaining memory may be calculated or estimated from a forgetting curve. For example, the remaining memory amount may be calculated according to the following formula: y is 1-0.56x0.06Wherein x represents the number of hours from the time of the first learning knowledge point to the current time, and y represents the remaining memory amount. Through the evaluation or quantification of the memory amount, corresponding review contents can be arranged for students in a more targeted manner, so that the memory of knowledge points is further deepened.
Secondly, pushing corresponding review resources to the students for review according to the knowledge points to be reviewed and the corresponding ability values thereof, wherein the knowledge points to be reviewed correspond to the content of the review resources, and the ability values corresponding to the knowledge points to be reviewed correspond to the difficulty of reviewing the resources.
A large number of learning resources are collected in a big data mode, the resources are associated with related knowledge points, different difficulties are marked for different resources, and a huge learning resource library is formed corresponding to different capacity values. In the review process, corresponding learning resources can be selected from the learning resource library as personalized review resources to be pushed to students according to relevant factors such as learning progress, correlation of knowledge points, capability values and the like.
And thirdly, adjusting the corresponding ability value of the knowledge point to be reviewed according to the review condition of the student.
And in the review process of the students, dynamically adjusting the ability values corresponding to the knowledge points according to the review conditions of the students so as to synchronously adjust the evaluation of the review conditions of the students. In some embodiments, a higher ability value corresponding to a knowledge point indicates a better mastery of the knowledge point. If the student goes forward with respect to the previous one after review, the ability value is correspondingly improved.
Fourthly, judging whether the knowledge points needing to be reviewed reach the standard or not according to the adjusted capability values corresponding to the knowledge points needing to be reviewed; if the knowledge points to be reviewed do not reach the standard, continuously pushing corresponding review resources to the students to continuously review according to the knowledge points to be reviewed and the current corresponding capability values of the knowledge points to be reviewed; and completing the review of the knowledge points to be reviewed if the knowledge points to be reviewed reach the standard.
The standard of standard can be preset, if the ability value reaches a certain value, the student can learn the knowledge point, or the learning of the knowledge point can be completed if the mastering of the knowledge point reaches the standard.
In some embodiments, if the knowledge point to be reviewed does not reach the standard, whether the review times of the knowledge point to be reviewed reach a threshold value (for example, review 2, 3, 4, 5 times) is judged; if the review times do not reach the threshold value, pushing corresponding review resources to the students according to the knowledge points to be reviewed and the current corresponding ability values of the knowledge points to be reviewed so as to continue to review; and if the review times reach the threshold value, marking the knowledge points needing to be reviewed, and stopping the review of the knowledge points needing to be reviewed.
After the review of the knowledge points needing to be reviewed is finished or stopped, the steps can be repeated for other knowledge points needing to be reviewed to continue reviewing other knowledge points until all review tasks are finished.
In some embodiments, the present application provides an artificial intelligence based review system.
The system tests according to the input knowledge graph to obtain a weak knowledge point list A which needs to be learned by each student. The system learns the weak knowledge points to obtain an un-learned knowledge point list B. The system can perform personalized operation to obtain an unowned knowledge point list C. The system firstly records the time and the learning times of a knowledge point of the first learning meeting of the student, and calculates the residual memory of the knowledge points to obtain a list D of 3 knowledge points needing review with the least memory. The system combines the knowledge point list C which is not mastered and the knowledge point list D which is obtained according to the forgetting curve and needs to be reviewed into a knowledge point list E which needs to be reviewed.
The system can push the knowledge points a to the knowledge points needing to be reviewed according to the ability value of each knowledge point, the system can push review resources suitable for the difficulty of students according to the ability value of the knowledge points a, and the students review the knowledge points. Adjusting the ability value of the knowledge point a according to the review condition of the student, and then judging whether the ability value of the knowledge point a reaches the standard:
if the ability value of the knowledge point a does not reach the standard, the system judges whether to review for 2 times at most, if not, the system pushes the review resources with proper difficulty according to the new ability value until reaching the standard or reaching the maximum. If the number of the knowledge points reaches 2 times, the system marks the weakness of the knowledge points, deletes the knowledge points in the review knowledge point list, and then continues to push the knowledge points in the review knowledge point list according to the capability values until the knowledge points do not need to be reviewed.
If the ability value of the knowledge point a reaches the standard, the system marks the knowledge point mastery, deletes the knowledge point in the knowledge point list needing to be learned, and then continues to push the knowledge point in the knowledge point list needing to be reviewed according to the ability value until the knowledge point needing to be reviewed does not exist.
The method and system provided herein are described in more detail below in a specific embodiment. The lower graph is a knowledge graph of knowledge points associated with a quadratic root, marking the pre-successor relationships between knowledge points. The system applies a method of preamble learning based on such a graph of the preceding successor relationships, which makes learning very efficient.
The system determines weak knowledge points of a student through testing to be c090301, c090302, c090303, c090304 and c090305, and the weak knowledge points form a weak knowledge point list A.
c090101, c090102, c090103, c090201, c090202, c090203, c090204 and c090205 are known knowledge points.
Through system learning, unknown knowledge points c090303, c090304, c090305 are obtained.
Through system personalization, the points c090304, c090305 of the unknown knowledge are obtained.
The system calculates the memory amount based on the learning time and the learning frequency, and pushes the knowledge points c090201, c090202, and c090203 with the minimum remaining memory amount based on the memory amount.
Thus c090304, c090305, c090201, c090202, c090203 form a list of knowledge points that need to be reviewed.
The system pushes the knowledge points c090201 to the knowledge points needing to be reviewed according to the ability value of each knowledge point, and pushes review resources suitable for the difficulty of students according to the ability value of the knowledge points c090201, so that the students can learn.
Adjusting the ability value of the knowledge point c090201 according to the learning condition of the student, and then judging whether the ability value of the knowledge point c090201 reaches the standard:
if the ability value of the knowledge point c090201 does not reach the standard, the system will judge whether the maximum number of times is 2, if not, the system will push the appropriate review resources according to the new ability value until the ability value reaches the standard or the maximum number of times is reached. If learning is reached 2 times, the system marks the weak point of the knowledge c090201, deletes the knowledge point c090201 in the unknown knowledge point list, and then continues to push the knowledge point c090304 in the unknown knowledge point list according to the capability value until no unknown knowledge point exists.
If the ability value reaches the standard, the system marks that the knowledge point c090201 is mastered, deletes the knowledge point c090201 in the list of the knowledge points needing to be reviewed, and then continues to push the knowledge point c090304 in the list of the unknown knowledge points according to the ability value until no unknown knowledge points exist.
In some embodiments, the various methods, modules, devices, or systems described above may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices that perform some or all of the operations of a method in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for performing one or more operations of a method. The above description is only for the preferred embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present application, and equivalent alternatives or modifications according to the technical solutions and the inventive concepts of the present application, and all such alternatives or modifications are encompassed in the scope of the present application.
Embodiments of the present application may be implemented in hardware, firmware, software, or various combinations thereof. The present application may also be implemented as instructions stored on a machine-readable medium, which may be read and executed using one or more processing devices. In one implementation, a machine-readable medium may include various mechanisms for storing and/or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable storage medium may include read-only memory, random-access memory, magnetic disk storage media, optical storage media, flash-memory devices, and other media for storing information, and a machine-readable transmission medium may include various forms of propagated signals (including carrier waves, infrared signals, digital signals), and other media for transmitting information. While firmware, software, routines, or instructions may be described in the above disclosure in terms of performing certain exemplary aspects and embodiments of certain actions, it will be apparent that such descriptions are merely for convenience and that such actions in fact result from computing devices, processing devices, processors, controllers, or other devices or machines executing the firmware, software, routines, or instructions.
This specification discloses the application using examples in which one or more examples are described or illustrated in the specification and drawings. Each example is provided by way of explanation of the application, not limitation of the application. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope or spirit of the application. For instance, features illustrated or described as part of one embodiment, can be used with another embodiment to yield a still further embodiment. It is therefore intended that the present application cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.
Claims (9)
1. A review method performed by a machine device, comprising the steps of:
testing students according to a preposed follow-up relation among knowledge points in a knowledge graph to obtain a weak knowledge point list, wherein the knowledge graph comprises the preposed knowledge points and follow-up knowledge points, and the preposed follow-up relation is formed according to a relation that the follow-up knowledge points do not learn because the preposed knowledge points do not learn;
pushing corresponding learning resources to the students for learning according to the weak knowledge point list; obtaining an unknown knowledge point list according to the learning condition of the student on the learning resource; according to the non-learning knowledge point list, making and pushing personalized homework to students; obtaining an unowned knowledge point list according to the condition that the student completes the personalized homework; selecting to-be-reviewed knowledge points from the list of the unordled knowledge points, wherein the to-be-reviewed knowledge points correspond to capacity values, and the capacity values reflect the mastering conditions of students on the to-be-reviewed knowledge points;
pushing corresponding review resources to students for review according to the knowledge points to be reviewed and the corresponding capability values thereof, wherein the knowledge points to be reviewed correspond to the content of the review resources, and the capability values corresponding to the knowledge points to be reviewed correspond to the difficulty of the review resources;
adjusting the corresponding ability value of the knowledge point to be reviewed according to the review condition of the student; and
judging whether the knowledge points to be reviewed reach the standard or not according to the adjusted capability values corresponding to the knowledge points to be reviewed;
if the knowledge point to be reviewed does not reach the standard, continuously pushing corresponding review resources to the students to continue reviewing according to the knowledge point to be reviewed and the current corresponding capability value of the knowledge point to be reviewed; and
and if the knowledge points to be reviewed reach the standard, reviewing the knowledge points to be reviewed is completed.
2. The review method of claim 1, further comprising the steps of:
the student is tested according to a knowledge graph, wherein the knowledge graph comprises knowledge points marked with a preposed follow-up relationship.
3. The review method of claim 1, wherein the step of obtaining knowledge points to be reviewed further comprises the steps of:
calculating the residual memory capacity of the learned knowledge points according to the time and the learning times of the learned knowledge points of the first time of the students; and
and selecting and obtaining the knowledge points to be reviewed from one or more learned knowledge points with the least residual memory.
4. A review method according to claim 3, further comprising the steps of:
and selecting the knowledge points to be reviewed from the 3 learned knowledge points with the least residual memory.
5. A review method according to claim 3, wherein the step of calculating the remaining memory of the learned knowledge points comprises the steps of:
and calculating the residual memory amount according to the forgetting curve.
6. A review method according to claim 3, wherein the step of calculating the remaining memory of the learned knowledge points comprises the steps of:
calculating the remaining memory amount according to the following formula: ,
wherein x represents the number of hours from the time of the first learning knowledge point to the current time, and y represents the remaining memory amount.
7. A review method according to claim 1, characterized in that:
if the knowledge point needing to be reviewed does not reach the standard, judging whether the review frequency of the knowledge point needing to be reviewed reaches a threshold value;
if the review times do not reach the threshold value, pushing corresponding review resources to the students according to the knowledge points to be reviewed and the current corresponding ability values of the knowledge points to be reviewed so as to continue to review; and
and if the review times reach the threshold value, marking the knowledge points needing to be reviewed, and stopping the review of the knowledge points needing to be reviewed.
8. The review method of claim 7, wherein:
the threshold is 2 times.
9. A review method according to any of the preceding claims, further comprising:
after completing or stopping the review of the point of knowledge to be reviewed, repeating the review method according to any one of the preceding claims for other points of knowledge to be reviewed.
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| CN111563156B (en) * | 2020-04-30 | 2023-10-13 | 广东小天才科技有限公司 | Knowledge point intelligent diagnosis method and device, electronic equipment and storage medium |
| CN111815267B (en) * | 2020-06-19 | 2024-01-19 | 北京国音红杉树教育科技有限公司 | Foreign language learning and review method and device |
| CN112015991B (en) * | 2020-08-31 | 2021-07-20 | 上海松鼠课堂人工智能科技有限公司 | Student learning reminding method |
| CN112115274A (en) * | 2020-09-16 | 2020-12-22 | 上海松鼠课堂人工智能科技有限公司 | Knowledge graph generation system considering time influence and block chain naming system |
| CN112331306A (en) * | 2020-11-02 | 2021-02-05 | 潍坊学院 | Method and system for improving mathematical cognition structure diagram based on wrong questions |
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