AI agent-based review content determination method and device

Abstract

The invention discloses a review content determination method and device based on an AI agent, and relates to the technical field of education, and the method comprises the steps: responding to behavior data, obtained from a knowledge point learning grid of a learning accompanying system, of reviewing a target knowledge point by a student, and updating a first mastery degree identifier of the target knowledge point; determining an update influence range of the mastery degree of a target knowledge point in a knowledge graph corresponding to the knowledge point learning grid, and forming a to-be-updated knowledge point set based on knowledge points covered by the update influence range; updating the mastering degree data of the knowledge points in the to-be-updated knowledge point set, and updating the second mastering degree identifier according to the updated mastering degree data; and updating the knowledge point learning grid into a target knowledge point learning grid based on the second mastering degree identifier, and determining target review content of the student according to the target knowledge point learning grid. The matching degree of the review content and the actual cognitive state of the student can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of education, and in particular to a review content determination method and device based on an AI agent. BACKGROUND

[0002] In the field of intelligent education, a companion system based on a knowledge graph has become an important tool for realizing personalized learning. Such a system can plan a learning path and recommend review content for each student by constructing an association network between knowledge points. However, the evaluation of the knowledge mastery state of students in existing systems is usually static and isolated, and cannot capture the influence of changes in the mastery state of individual knowledge points in the entire knowledge network in real time, resulting in a lag between the recommended review content and the actual cognitive development of students, making it difficult to achieve truly adaptive learning.

[0003] After the existing system detects that a student has reviewed a certain knowledge point, it usually only updates the mastery state identifier of the isolated knowledge point. This updating mechanism ignores the inherent complex dependency relationships in the knowledge graph, leading to a gradual deviation between the representation of knowledge mastery state and the actual cognitive situation.

[0004] When updating the mastery degree of associated knowledge points, the existing system lacks a quantitative basis to accurately calculate the degree of influence, and mostly uses simple rules or threshold judgments, which are not accurate enough. Moreover, the existing system also has the problem of asynchronous updating, which leads to inconsistencies between the knowledge point learning grid and the actual knowledge mastery network, so that the review recommendation content generated based on this grid may contain knowledge that the student has indirectly mastered through associated review, or miss new weak links caused by shaking of prerequisite knowledge. SUMMARY

[0005] Therefore, the present application provides a review content determination method and device based on an AI agent, which aims to improve the technical problems in the prior art that the existing system lacks a quantitative basis to accurately calculate the degree of influence when updating the mastery degree of associated knowledge points, mostly uses simple rules or threshold judgments, which are not accurate enough, and the existing system also has the problem of asynchronous updating, leading to inconsistencies between the knowledge point learning grid and the actual knowledge mastery network, so that the review recommendation content generated based on this grid may contain knowledge that the student has indirectly mastered through associated review, or miss new weak links caused by shaking of prerequisite knowledge.

[0006] In a first aspect, the present application provides a review content determination method based on an AI agent, comprising: In response to obtaining behavior data of a student reviewing a target knowledge point in a review knowledge point set from a knowledge point learning grid of a companion system, updating a first mastery degree identifier of the target knowledge point; determine an update influence range of the mastery degree of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid, and generate a knowledge point set to be updated based on knowledge points covered by the update influence range; update the mastery degree data of the knowledge points in the knowledge point set to be updated, and update the second mastery degree identifiers of the knowledge points in the knowledge point set to be updated according to the updated mastery degree data; update the knowledge point learning grid to a target knowledge point learning grid based on the second mastery degree identifiers, and determine the target review content of the student according to the target knowledge point learning grid.

[0007] Optionally, the determination of the update influence range of the mastery degree of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid, and the generation of the knowledge point set to be updated based on knowledge points covered by the update influence range, comprises: determine a direct dependency relationship of the target knowledge point based on a target prerequisite relationship and a target correlation relationship of the target knowledge point in the knowledge graph, and determine a direct influence range based on the direct dependency relationship; determine an indirect dependency relationship of the target knowledge point based on an indirect connection relationship between the target knowledge point and other knowledge points in the knowledge graph, and determine an indirect influence range based on the indirect dependency relationship; determine a direct influence knowledge point set covered by the direct influence range, and an indirect influence knowledge point set covered by the indirect influence range; generate the knowledge point set to be updated according to the direct influence knowledge point set and the indirect influence knowledge point set.

[0008] Optionally, the determination of the indirect dependency relationship of the target knowledge point based on the indirect connection relationship between the target knowledge point and other knowledge points in the knowledge graph, and the determination of the indirect influence range based on the indirect dependency relationship, comprises: determine a first indirect dependency relationship of the target knowledge point through one indirect knowledge point in the knowledge graph, and a second indirect dependency relationship of the target knowledge point connected through multiple indirect knowledge points; determine a first influence range of the target knowledge point based on the first indirect dependency relationship from the knowledge graph based on a first influence attenuation degree corresponding to the first indirect dependency relationship; determine a second influence range of the target knowledge point based on the second indirect dependency relationship from the knowledge graph based on a second influence attenuation degree corresponding to the second indirect dependency relationship; determine the indirect influence range according to the first influence range and the second influence range.

[0009] Optionally, the mastery degree data of the knowledge points in the to-be-updated knowledge point set is updated, and the second mastery degree identifier of the knowledge points in the to-be-updated knowledge point set is updated according to the updated mastery degree data, including: According to the direct dependency relationship and the indirect dependency relationship, dependency strength data of the knowledge points in the to-be-updated knowledge point set and the target knowledge point is determined; The importance data of the knowledge points in the to-be-updated knowledge point set is identified through a target model, and the target model is obtained by training based on historical behavior characteristics and historical knowledge point mastery degrees of students; Based on the dependency strength data, the importance data and the mastery degree data, update priority information of the knowledge points in the to-be-updated knowledge point set is determined; The mastery degree data is updated according to the update priority information, and the second mastery degree identifier of the knowledge points in the to-be-updated knowledge point set is updated according to the updated mastery degree data.

[0010] Optionally, the mastery degree data of the knowledge points in the to-be-updated knowledge point set is updated according to the update priority information, and the second mastery degree identifier of the knowledge points in the to-be-updated knowledge point set is updated according to the updated mastery degree data, including: According to the update priority information, a knowledge point update sequence list corresponding to the to-be-updated knowledge point set is determined to determine the update sequence of the knowledge points in the to-be-updated knowledge point set; For a first target knowledge point in the to-be-updated knowledge point set, a centerality adjustment coefficient corresponding to the first target knowledge point is determined according to the connection strength data of the first target knowledge point in the knowledge graph, the distance data between the first target knowledge point and other knowledge points, and the degree centrality coefficient, and the first target knowledge point is any one of the knowledge points in the to-be-updated knowledge point set; According to the centerality adjustment coefficient and the change amount of the mastery degree data of the target knowledge point, the mastery degree data of the first target knowledge point is updated; According to the updated mastery degree data of the knowledge points in the to-be-updated knowledge point set, the second mastery degree identifier of the knowledge points in the to-be-updated knowledge point set is updated.

[0011] Optionally, the mastery degree data of the first target knowledge point is updated according to the centerality adjustment coefficient and the change amount of the mastery degree data of the target knowledge point, including: If the connection strength data in the centrality adjustment coefficient is greater than the connection strength threshold and the change in the mastery data is less than the first change threshold, the first target knowledge point is identified as a key knowledge point, and the second mastery indicator of the first target knowledge point is marked as a not mastered indicator. If the degree centrality coefficient in the centrality adjustment coefficient is greater than the degree centrality threshold and the mastery level data is greater than the second mastery level threshold, the first target knowledge point is determined as a basic knowledge point, and the second mastery level identifier of the first target knowledge point is marked as a mastered identifier.

[0012] Optionally, updating the knowledge point learning grid to the target knowledge point learning grid based on the second mastery level identifier, and determining the student's target review content based on the target knowledge point learning grid, includes: Determine at least one second mastery level identifier corresponding to the second target knowledge point in the set of knowledge points to be updated, wherein the second target knowledge point is any one of the knowledge points in the set of knowledge points to be updated; The at least one second mastery level identifier is decided based on the direct dependency relationship and the indirect dependency relationship, so as to determine the target mastery level identifier with the highest dependency relationship from the at least one second mastery level identifier; Determine the confidence level data of the target mastery level identifier corresponding to each knowledge point in the set of knowledge points to be updated, and update the knowledge point learning grid to the target knowledge point learning grid based on the target mastery level identifier that meets the confidence level conditions. The target review content for the student is determined based on the learning grid of the target knowledge points.

[0013] Secondly, this application provides a review content determination device based on an AI intelligent agent, comprising: The update module is configured to update the first mastery level identifier of the target knowledge point in response to obtaining behavioral data of students reviewing target knowledge points in the set of review knowledge points from the knowledge point learning grid of the learning companion system. The determination module is configured to determine the update influence range of the mastery level of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid, and to form a set of knowledge points to be updated based on the knowledge points covered by the update influence range. The update module is configured to update the mastery level data of the knowledge points in the set of knowledge points to be updated, and update the second mastery level identifier of the knowledge points in the set of knowledge points to be updated based on the updated mastery level data. The determination module is configured to update the knowledge point learning grid to a target knowledge point learning grid based on the second mastery level identifier, and determine the student's target review content based on the target knowledge point learning grid.

[0014] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for determining review content based on an AI intelligent agent as described in the first aspect.

[0015] Fourthly, this application provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement the AI-based intelligent agent-based review content determination method described in the first aspect.

[0016] By utilizing the above technical solutions, this application provides a method and apparatus for determining review content based on AI intelligent agents. Compared with existing technologies, this application updates the first mastery level identifier of students in response to their review behavior data of target knowledge points, and determines the update influence range of target knowledge points in the knowledge graph to form a set of knowledge points to be updated, thereby realizing dynamic linkage update of knowledge point mastery status. By updating the mastery level data and second mastery level identifier of the set of knowledge points to be updated, the knowledge point learning grid is updated and the target review content is determined, thereby improving the matching degree between review content and students' actual cognitive status. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 The illustration shows a flowchart of a method for determining review content based on an AI agent, as provided in an embodiment of this application. Figure 2 This illustration shows a schematic diagram of a knowledge point learning grid provided in an embodiment of this application; Figure 3 The illustration shows a flowchart of a method for determining review content based on an AI agent, as provided in an embodiment of this application. Figure 4This illustration shows a structural schematic diagram of a review content determination device based on an AI agent, according to an embodiment of this application. Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0020] The embodiments of this application will now be described in more detail with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0021] To improve existing technical systems' ability to accurately calculate the impact of updating the mastery of related knowledge points, they often rely on simple rules or thresholds, resulting in insufficient accuracy. Furthermore, existing systems suffer from asynchronous updates, leading to inconsistencies between the knowledge point learning grid and the actual knowledge mastery network. This results in review recommendations generated based on this grid potentially including knowledge already indirectly acquired by students through related reviews, or overlooking new weaknesses arising from eroded prior knowledge. This embodiment provides a method for determining review content based on an AI agent, such as... Figure 1 As shown, the method includes: Step 101: In response to the behavioral data of students reviewing target knowledge points in the set of review knowledge points obtained from the knowledge point learning grid of the learning companion system, update the first mastery level label of the target knowledge points.

[0022] In this embodiment, the learning companion system can be the backend system corresponding to the AI ​​learning assistant installed on the learning tablet. The learning companion system can store students' basic information, learning records, course materials, and other data. The basic information such as class, grade, name, age, and semester filled in when logging into the student's learning companion system can be used to generate a user ID, which can be associated with all of the student's learning data.

[0023] In this embodiment of the application, the knowledge point learning grid can be a two-dimensional grid formed by mapping the learning content of the student's current semester's courses according to logical relationships (such as chapter order, knowledge dependencies), such as... Figure 2 As shown, in Figure 2 Each grid cell represents a knowledge point. The proficiency level of a knowledge point can be marked by filling the cell with different circles, different symbols on different circles, or different shapes. The specific marking method for the proficiency level of a knowledge point is not limited in this embodiment.

[0024] As an optional method, when marking the proficiency of knowledge points by filling different circles, red can be used to indicate not mastered, yellow can be used to indicate mastered but not proficient, and green can be used to indicate proficient; when marking the proficiency of knowledge points by filling different shapes, 1 can be used to indicate not mastered, 2 can be used to indicate mastered but not proficient, and 3 can be used to indicate proficient; when marking the proficiency of knowledge points by filling different shapes, circles can be used to indicate not mastered, triangles can be used to indicate mastered but not proficient, squares can be used to indicate proficient, and so on, without further examples.

[0025] In this embodiment of the application, the set of knowledge points to be reviewed can be the set of all knowledge points that the student has learned and needs to review.

[0026] In the embodiments of this application, the target knowledge point can be the knowledge point that the student is currently reviewing in the set of review knowledge points.

[0027] In the embodiments of this application, the first mastery level identifier can be a mark indicating the student's mastery of the target knowledge point. The first mastery level identifier can be used to reflect the student's knowledge mastery status. The first mastery level identifier can be determined based on the mastery probability of the target knowledge point.

[0028] For example, the first mastery level indicator in the embodiments of this application may include a not mastered indicator, a mastered but not proficient indicator, a mastered indicator, etc. The presentation form of the first mastery level indicator may include color marking (such as red corresponding to not mastering the knowledge point, yellow corresponding to mastering the knowledge point but not proficient, and green corresponding to mastering the knowledge point), text annotation, symbol marking, etc.

[0029] In this embodiment of the application, behavioral data may include multi-dimensional data such as the completion rate of online course review for the target knowledge point, the accuracy rate of practice questions, the redoing of wrong questions, the review time, the supplementary content of notes, and the frequency of asking questions.

[0030] In this embodiment of the application, the system can monitor the students' review operations in the learning companion system in real time. When the system detects that the students are reviewing the target knowledge points in the set of review knowledge points, it can obtain the corresponding review behavior data. Based on the behavior data, the system can calculate the mastery probability of the target knowledge points through a preset model (including but not limited to logistic regression model and softmax regression model), and then update the first mastery level label of the target knowledge points according to the threshold rule.

[0031] For example, updating the first mastery level indicator of the target knowledge point in the embodiments of this application may specifically include: if the initial first mastery level indicator is a mastery level indicator, and the mastery probability after review reaches a threshold, the first mastery level indicator can be updated to a mastery indicator; if the mastery probability after review does not reach the threshold, the first mastery level indicator can be maintained as a mastery indicator.

[0032] Step 102: Determine the update influence range of the mastery level of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid, and form a set of knowledge points to be updated based on the knowledge points covered by the update influence range.

[0033] In this embodiment of the application, a knowledge graph can be a graph that displays knowledge points and the relationships between them in a graphical structure. Nodes in the knowledge graph can correspond to knowledge points, and edges in the knowledge graph can correspond to the relationships between knowledge points. Edges in the knowledge graph can include directed edges (representing prerequisite relationships) and undirected edges (representing relevance relationships), and the weights of the edges in the knowledge graph can represent the strength of the relationships. For example, the knowledge graph in this embodiment of the application can be constructed based on the course textbook catalog, teaching syllabus, and course standards, combined with NLP technology for structured analysis. The knowledge graph can include all knowledge points in the course and the inherent logical relationships between them.

[0034] In this embodiment of the application, the system can retrieve the knowledge graph corresponding to the knowledge point learning grid. Specifically, the knowledge graph corresponding to the knowledge point learning grid can be constructed based on the course content of the current semester and can include all knowledge points and their inherent logical relationships. The system can analyze the relationship between the target knowledge point in the knowledge graph, including prerequisite relationships and relevance relationships. Combining the association strength weight and centrality index, the system can determine the update impact range caused by the change in the mastery level of the target knowledge point. The update impact range can include the direct impact range and the indirect impact range. The direct impact range can cover knowledge points that have a direct prerequisite or relevance relationship with the target knowledge point, and the indirect impact range can cover knowledge points that are associated with the target knowledge point through one or more intermediate knowledge points. The system filters out all knowledge points that are covered by both the direct and indirect impact ranges to form a set of knowledge points to be updated.

[0035] Step 103: Update the mastery level data of the knowledge points in the set of knowledge points to be updated, and update the second mastery level label of the knowledge points in the set of knowledge points to be updated based on the updated mastery level data.

[0036] In the embodiments of this application, the mastery level data can be used to quantify the student's mastery level of knowledge points. The value range of the mastery level data can be [0,1]. The mastery level data can be calculated from learning behavior data (such as the accuracy rate of answering questions, the frequency of review, the completion rate of online courses, etc.). The mastery level data can be used to reflect the mastery status.

[0037] In the embodiments of this application, the second mastery level identifier can be a mastery status marker corresponding to the knowledge point in the knowledge point set to be updated after the mastery level data is updated. For example, the second mastery level identifier in the embodiments of this application may specifically include a not mastered identifier, a mastered but not proficient identifier, a mastered identifier, etc. The presentation form of the second mastery level identifier may include color markings (such as red corresponding to not mastering the knowledge point, yellow corresponding to mastering the knowledge point but not proficient, and green corresponding to mastering the knowledge point), text annotations, symbol markings, etc.

[0038] In this embodiment of the application, the system can update the original mastery data by combining factors such as changes in the mastery level of the target knowledge point, the association type (prerequisite or related) between the knowledge point to be updated and the target knowledge point, and the association strength weight.

[0039] For example, if the mastery of the target knowledge point is significantly improved and there is a strong prerequisite relationship between the target knowledge point and the knowledge point to be updated, the mastery data of the knowledge point to be updated can be improved accordingly; if there is a weak correlation between the target knowledge point and the knowledge point to be updated, the change in the mastery data of the knowledge point to be updated can be relatively small.

[0040] In this embodiment, the system can determine the second mastery level identifier for each knowledge point based on the updated mastery level data and threshold rules. For example, if the updated mastery probability of knowledge point A is lower than the basic threshold, the second mastery level identifier for knowledge point A can be marked as "not mastered"; if the updated mastery probability of knowledge point A is between the basic threshold and the proficiency threshold, the second mastery level identifier for knowledge point A can be marked as "mastered but not proficient"; if the updated mastery probability of knowledge point A is higher than the proficiency threshold, the second mastery level identifier for knowledge point A can be marked as "mastered". Simultaneously, the system can optimize and adjust the second mastery level identifier by combining special rules such as key knowledge point protection and basic knowledge point acceleration to ensure that the second mastery level identifier accurately reflects the actual mastery status of the knowledge point.

[0041] Step 104: Update the knowledge point learning grid to the target knowledge point learning grid based on the second mastery level identifier, and determine the student's target review content based on the target knowledge point learning grid.

[0042] In this embodiment of the application, the target knowledge point learning grid can be a knowledge point learning grid that reflects the student's latest knowledge mastery status after the second mastery level identifier of the knowledge points in the set of knowledge points to be updated is updated synchronously. The target knowledge point learning grid can be used to present the student's current knowledge gaps and mastery status in real time and accurately.

[0043] In this embodiment of the application, the target review content can be the knowledge points that students need to focus on reviewing, as well as the corresponding learning resources and learning arrangements, selected based on the mastery status of knowledge points in the target knowledge point learning grid.

[0044] In this embodiment, the system can synchronize the second mastery level identifiers of all knowledge points in the set of knowledge points to be updated to the original knowledge point learning grid and replace the original identifiers to complete the update of the target knowledge point learning grid. The system can analyze the second mastery level identifiers of knowledge points in the target knowledge point learning grid, prioritize the selection of knowledge points marked as not mastered and mastered but not proficient, and determine the target review content that needs to be focused on by combining the importance of knowledge points (based on the centrality index of the knowledge graph), correlation, teaching progress, exam focus and other factors.

[0045] Compared with existing technologies, this embodiment updates the first mastery level identifier of students in response to their review behavior data of target knowledge points, and determines the update influence range of target knowledge points in the knowledge graph to form a set of knowledge points to be updated, thereby realizing dynamic linkage update of knowledge point mastery status; by updating the mastery level data and second mastery level identifier of the set of knowledge points to be updated, the knowledge point learning grid is updated and the target review content is determined, thereby improving the matching degree between review content and students' actual cognitive status.

[0046] As an optional approach, when performing the task of "determining the update influence range of the mastery level of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid, and forming a set of knowledge points to be updated based on the knowledge points covered by the update influence range," the following methods can be used, but are not limited to: Figure 3 As shown, the method includes: Step 201: Based on the target prerequisite relationships and target relevance relationships of the target knowledge points in the knowledge graph, determine the direct dependencies of the target knowledge points, and determine the scope of direct influence based on the direct dependencies.

[0047] In the embodiments of this application, the target prerequisite relationship can be the dependency relationship between other knowledge points that need to be mastered in advance before learning the target knowledge point and the target knowledge point. The target prerequisite relationship can correspond to the directed edge in the knowledge graph, and the dependency weight of the target prerequisite relationship can be set to a specific range.

[0048] In the embodiments of this application, the target relevance relationship can be the association or complementarity between the target knowledge point and other knowledge points in terms of content. The target relevance relationship can correspond to the undirected edge in the knowledge graph, and the dependency weight of the target relevance relationship can also be set to another specific range.

[0049] In the embodiments of this application, a direct dependency can be a direct dependency between a target knowledge point and other knowledge points. For example, the direct dependencies in the embodiments of this application may include direct prerequisite relationships and direct relevance relationships.

[0050] For the embodiments of this application, the scope of direct influence can be the range covered by the knowledge points directly affected by the change in the mastery of the target knowledge points, that is, the range of knowledge points corresponding to the direct dependency set. The knowledge points corresponding to the direct dependency set can be directly associated with the target knowledge points.

[0051] In this embodiment, the system can extract the target prerequisite relationship and target relevance relationship of the target knowledge point from the knowledge graph, and filter out the knowledge points that have a direct dependency relationship with the target knowledge point by combining the dependency weights to form a direct dependency set. The direct influence range can be defined based on the direct dependency set. The direct influence range can be the range covered by all knowledge points belonging to Ddirect(u). The expression of the direct dependency set can be as shown in Formula 1, where u can represent the target knowledge point, v can represent the set of all knowledge points in the knowledge graph, and w(u,v) can represent the dependency weight between the target knowledge point u and the knowledge point v.

[0052] (Formula 1) Step 202: Based on the indirect connection relationship between the target knowledge point and other knowledge points in the knowledge graph, determine the indirect dependency relationship of the target knowledge point, and determine the scope of indirect influence based on the indirect dependency relationship.

[0053] In the embodiments of this application, the indirect connection relationship can be a connection relationship between the target knowledge point and other knowledge points established through one or more intermediate knowledge points. That is, there can be intermediate knowledge points that enable the target knowledge point to form an association with other knowledge points, and the other knowledge points do not belong to the direct dependency set or the target knowledge point itself.

[0054] In this embodiment, indirect dependency can be a dependency between knowledge points formed based on indirect connection. Indirect dependency can correspond to an indirect dependency set. Indirect dependency can be used to reflect the influence association between knowledge points transmitted through intermediate nodes.

[0055] In this embodiment of the application, the indirect influence range can be the range covered by the knowledge points indirectly affected by the change in the mastery of the target knowledge points. That is, the indirect influence range can be the range of knowledge points corresponding to the indirect dependency set. The indirect influence range can be attenuated as intermediate knowledge points are transmitted. The degree of attenuation of the indirect influence range can be controlled by the propagation attenuation coefficient.

[0056] In this embodiment of the application, the system can filter out the indirect connections of target knowledge points in the knowledge graph and determine the indirect dependency set based on the indirect connections. The expression of the indirect dependency set can be as shown in Formula 2, where u can represent the target knowledge point, v can represent the intermediate knowledge point in the direct dependency set, and w(v,w) can represent the dependency weight between the intermediate knowledge point v and the knowledge point w; the system can combine the propagation attenuation coefficient Assess the impact on transmission effectiveness. The value can be 0.6, which includes knowledge points with an impact value greater than the minimum impact threshold in the scope of indirect influence. The minimum impact threshold can be 0.05.

[0057] (Formula 2) For example, if the direct dependency of the target knowledge point u is knowledge point v, and the direct dependency of knowledge point v is knowledge point w, and... Then, it can be determined that knowledge point w is under indirect influence. Combined with D_direct(u), knowledge point w can be included in the scope of indirect influence.

[0058] Step 203: Determine the set of directly affected knowledge points covered by the scope of direct impact, and the set of indirectly affected knowledge points covered by the scope of indirect impact.

[0059] In this embodiment, the set of directly affected knowledge points can be a set of direct dependencies composed of all knowledge points within the scope of direct influence. The knowledge points in the set of direct dependencies are directly related to the target knowledge point and are directly affected by changes in the mastery level of the target knowledge point. For example, the set of direct dependencies in this embodiment can be specifically represented by D_direct(u).

[0060] In this embodiment, the set of indirectly affected knowledge points can be an indirect dependency set consisting of all knowledge points within the scope of indirect influence. Knowledge points in the indirect dependency set can be associated with the target knowledge point through intermediate knowledge points and are indirectly affected by changes in the mastery level of the target knowledge point. For example, the indirect dependency set in this embodiment can be specifically represented by D_indirect(u).

[0061] In this embodiment of the application, the system can perform a full traversal of the direct impact range and extract all knowledge points within the direct impact range to form a set of directly impacted knowledge points D_direct(u); the system can also traverse the indirect impact range and extract all knowledge points within the indirect impact range to form a set of indirect impacted knowledge points D_indirect(u).

[0062] It should be noted that in the process of forming the set of directly affected knowledge points from all knowledge points within the direct impact range and forming the set of indirectly affected knowledge points from all knowledge points within the indirect impact range, it is necessary to ensure that the set of directly affected knowledge points D_direct(u) and the set of indirectly affected knowledge points D_indirect(u) do not contain duplicate entries, so as to avoid the same knowledge point being included repeatedly in the scope to be updated.

[0063] Step 204: Generate a set of knowledge points to be updated based on the set of directly affected knowledge points and the set of indirectly affected knowledge points.

[0064] In this embodiment, generating the set of knowledge points to be updated based on the set of directly affected knowledge points and the set of indirectly affected knowledge points can be achieved by the system merging the set of directly affected knowledge points D_direct(u) and the set of indirectly affected knowledge points D_indirect(u), and removing duplicate knowledge point elements from the two sets through deduplication to obtain S=D_direct(u). D_indirect(u); then calculate the priority of each knowledge point in S. The formula for calculating the priority can be shown in Formula 3, where w(u,v) can represent the dependence strength between the target knowledge point u and the knowledge point v (the value of w(u,v) in D_direct(u) can be the original value of w(u,v), and the value of w(u,v) in D_indirect(u) can be w(v,w). C_B(v) can represent the betweenness centrality of knowledge point v (betweenness centrality can be used to reflect the importance of knowledge points), and P_old(v) can represent the original mastery probability of knowledge point v before this update. The system can sort the knowledge points from high to low priority and filter out the knowledge points with an impact value greater than the minimum impact threshold. The filtered knowledge points can form a set of knowledge points to be updated.

[0065] (Formula 3) As an optional approach, when performing the task of "determining the indirect dependencies of a target knowledge point based on the indirect connections between the target knowledge point and other knowledge points in the knowledge graph, and determining the scope of indirect influence based on the indirect dependencies," the following methods may be used, but are not limited to: determining a first indirect dependency established by the target knowledge point through one indirect knowledge point in the knowledge graph, and a second indirect dependency connected by multiple indirect knowledge points; determining a first scope of influence of the target knowledge point based on the first scope of influence based on the first indirect dependency; determining a second scope of influence of the target knowledge point based on the second indirect dependency; and determining the scope of indirect influence based on the first scope of influence and the second scope of influence.

[0066] In the embodiments of this application, the first indirect dependency relationship can be an indirect connection relationship between the target knowledge point and other knowledge points established through an indirect knowledge point. The influence transmission path between the target knowledge point and other knowledge points can have only one indirect knowledge point between the target knowledge point and the affected knowledge point, without any other additional intermediate knowledge points.

[0067] In the embodiments of this application, the second indirect dependency relationship can be an indirect connection relationship between the target knowledge point and other knowledge points established through two or more indirect knowledge points. The influence transmission path between the target knowledge point and other knowledge points can be transmitted step by step through multiple indirect knowledge points, and the path length can be greater than 1.

[0068] In this embodiment of the application, the first influence attenuation degree can correspond to the influence transmission attenuation degree of the first indirect dependency relationship. Since the influence transmission path only passes through one indirect knowledge point, the attenuation degree is small. Therefore, the value of the first influence attenuation degree can be consistent with the propagation attenuation coefficient preset by the system.

[0069] In this embodiment, the second influence attenuation degree can correspond to the influence transmission attenuation degree of the second indirect dependency relationship. Since the influence transmission path passes through multiple indirect knowledge points, the attenuation degree is large. Therefore, the value of the second influence attenuation degree can be the square of the propagation attenuation coefficient (simulating the superimposed attenuation effect of multi-path propagation).

[0070] In this embodiment of the application, the first scope of influence can be the range of knowledge points affected by the change in the mastery of the target knowledge point under the first indirect dependency relationship. The first scope of influence can include knowledge points that are affected by the first indirect dependency relationship and whose influence value meets the requirements.

[0071] In this embodiment of the application, the second scope of influence can be the range of knowledge points affected by the change in the mastery of the target knowledge point under the second indirect dependency relationship. The second scope of influence can include knowledge points that are affected by the second indirect dependency relationship and whose influence value meets the requirements.

[0072] In the embodiments of this application, determining the first influence range of a target knowledge point based on the first influence decay degree corresponding to the first indirect dependency from the knowledge graph can be achieved by calculating the influence value of each knowledge point w in the first indirect dependency based on the first influence decay degree. The formula for calculating the influence value can be as shown in Formula 4, where, P_direct(v) can represent the change in the degree of mastery of indirect knowledge point v. It can represent the first influence decay degree, w(v,w) can represent the dependence strength between target knowledge point u and knowledge point v, and Factor_centrality (w) can represent the centrality adjustment factor; The formula for calculating P_direct(v) is shown in Formula 5, where P_new(u) - P_old(u) represents the change in the mastery level of knowledge point u, P_new(u) represents the updated mastery level of knowledge point u, P_old(u) represents the original mastery level of knowledge point u, and w(u,v) represents the dependence strength between the target knowledge point u and knowledge point v; the formula for calculating Factor_centrality(w) is shown in Formula 6, where... It can represent the betweenness centrality adjustment coefficient. The value can be 0.2. It can represent the proximity centrality adjustment coefficient. The value of can be 0.1, C_B(w) can represent betweenness centrality, and C_C(w) can represent proximity centrality; the system can filter out the knowledge points whose influence value of each knowledge point w in the first indirect dependency is greater than the minimum influence threshold, and determine the first influence range.

[0073] For the embodiments of this application, determining the second influence range of a target knowledge point based on the second influence decay degree corresponding to the second indirect dependency from the knowledge graph can be achieved by calculating the influence value of each knowledge point w in the second indirect dependency based on the second influence decay degree. The formula for calculating the influence value can also be as shown in Formula 4, where, P_direct(v) can represent the change in the level of mastery of the last indirect knowledge point v. The second influence decay can be represented by w(v,w), which can represent the dependence strength between the target knowledge point u and the knowledge point v. Factor_centrality(w) can represent the centrality adjustment factor. The system can filter out the knowledge points in the second indirect dependency relationship whose influence value of each knowledge point w is greater than the minimum influence threshold, and determine the range of the second influence.

[0074] (Formula 4) (Formula 5) (Formula 6) In the embodiments of this application, determining the indirect influence range based on the first influence range and the second influence range can be achieved by merging the first influence range and the second influence range and removing duplicate knowledge points to obtain the indirect influence range.

[0075] As an optional approach, when performing the action of "updating the mastery level data of knowledge points in the set of knowledge points to be updated, and updating the second mastery level identifier of knowledge points in the set of knowledge points to be updated based on the updated mastery level data," the following methods may be used, but are not limited to: determining the dependency strength data between knowledge points in the set of knowledge points to be updated and the target knowledge point based on direct and indirect dependencies; identifying the importance data of knowledge points in the set of knowledge points to be updated through a target model, which is trained based on students' historical behavioral characteristics and historical mastery levels of knowledge points; determining the update priority information for updating knowledge points in the set of knowledge points to be updated based on dependency strength data, importance data, and mastery level data; updating the mastery level data according to the update priority information, and updating the second mastery level identifier of knowledge points in the set of knowledge points to be updated based on the updated mastery level data.

[0076] In the embodiments of this application, the dependency strength data between the knowledge points in the set of knowledge points to be updated and the target knowledge point can be determined based on direct and indirect dependencies by extracting the corresponding dependency weights w(u,v) (direct dependency) and w(v,w) from the direct and indirect dependencies recorded in the knowledge graph. (Indirect dependency relationship, k is the indirect path length), the dependency weights w(u,v) and w(v,w) can be used. This serves as the dependency strength data between each knowledge point in the set of knowledge points to be updated and the target knowledge point.

[0077] For the embodiments of this application, the target model can be a machine learning model trained based on a large number of students' historical behavioral characteristics (such as study time, accuracy of practice questions, frequency of review, percentage of exam scores, etc.) and their mastery of historical knowledge points.

[0078] In the embodiments of this application, the importance data of knowledge points in the set of knowledge points to be updated are associated with the centrality index of the knowledge points. The centrality index may include betweenness centrality, proximity centrality, and degree centrality. Betweenness centrality can be represented by C_B(v), proximity centrality can be represented by C_C(v), and degree centrality can be represented by C_D(v). The centrality index can be normalized to the range [0,1].

[0079] In the embodiments of this application, the update priority information for updating knowledge points in the set of knowledge points to be updated can be determined by the system combining the dependency strength data, importance data, and the original mastery data of the knowledge points, and using a priority formula to determine the update priority information for each knowledge point. The priority formula is shown in Formula 7, where P_old(v) can represent the original mastery data of knowledge point v. Knowledge points with higher priority scores can be updated with higher priority.

[0080] Priority(v) = Dependency strength data · Importance data · (1 - P_old(v)) (Formula 7) In this embodiment of the application, updating the mastery level data according to the update priority information may include updating the mastery level of directly dependent knowledge points and updating the mastery level of indirectly dependent knowledge points; the calculation formula for updating the mastery level of directly dependent knowledge points can be as shown in Formula 8, wherein, P_direct(v) can represent the change in the mastery level of indirect knowledge point v, and P_old(v) can represent the original mastery level data of knowledge point v; the calculation formula for updating the mastery level of indirect dependent knowledge points can be shown in Formula 9. P_indirect(w) can represent the change in the mastery of indirect knowledge point w, while P_old(w) can represent the original mastery level of knowledge point w.

[0081] (Formula 8) (Formula 9) In this embodiment of the application, updating the second mastery level identifier of the knowledge points in the set of knowledge points to be updated based on the updated mastery level data can be done by updating the second mastery level identifier of each knowledge point based on the updated mastery level data P_updated(v) and the color mapping rules, combined with special rules.

[0082] For example, updating the color mapping rules in the second mastery level identifier of each knowledge point based on the updated mastery level data P_updated(v) and the color mapping rules, combined with special rules, may include setting a low mastery level threshold and a high mastery level threshold (the low mastery level threshold can be 0.4, and the high mastery level threshold can be 0.7). If the mastery level P(v) of knowledge point v is less than the low mastery level threshold, the knowledge point can be marked as a knowledge point that meets the condition of not mastering and marked in red; if the mastery level P(v) of knowledge point v is greater than or equal to the low mastery level threshold and less than the high mastery level threshold, the knowledge point v can be marked as a knowledge point that is mastered but not proficient and marked in yellow; if the mastery level P(v) of knowledge point v is greater than or equal to the high mastery level threshold, the knowledge point v can be marked as a knowledge point that is proficient and marked in green.

[0083] For example, the special rules in updating the second mastery level identifier of each knowledge point based on the updated mastery level data P_updated(v) and color mapping rules, combined with special rules, may include key knowledge point protection and basic knowledge point acceleration. Key knowledge point protection can mark knowledge point v as yellow if the betweenness centrality C_B(v) of knowledge point v is greater than 0.8 and the updated mastery level is less than 0.6. Basic knowledge point protection can mark knowledge point v as green if the betweenness centrality C_D(v) of knowledge point v is greater than 0.7 and the updated mastery level is greater than or equal to 0.65.

[0084] As an optional approach, when performing the action of "updating the mastery data according to the update priority information and updating the second mastery identifier of the knowledge points in the set of knowledge points to be updated based on the updated mastery data," the following methods may be used, but are not limited to: determining the knowledge point update order list corresponding to the set of knowledge points to be updated according to the update priority information, so as to determine the update order of the knowledge points in the set of knowledge points to be updated; for the first target knowledge point in the set of knowledge points to be updated, determining the centrality adjustment coefficient corresponding to the first target knowledge point based on the connection strength data of the first target knowledge point in the knowledge graph, the distance data between the first target knowledge point and other knowledge points, and the degree centrality coefficient, wherein the first target knowledge point is any knowledge point in the set of knowledge points to be updated; updating the mastery data of the first target knowledge point based on the centrality adjustment coefficient and the change in the mastery data of the target knowledge point; and updating the second mastery identifier of the knowledge points in the set of knowledge points to be updated based on the updated mastery data of the knowledge points in the set of knowledge points to be updated.

[0085] In this embodiment of the application, the knowledge point update order list corresponding to the set of knowledge points to be updated is determined according to the update priority information. To determine the update order of knowledge points in the set of knowledge points to be updated, the system can sort the knowledge points in the set of knowledge points to be updated from high to low according to the priority score based on the update priority information Priority(v), forming a knowledge point update order list to clarify the update order of each knowledge point. For example, the knowledge point with the highest priority score is placed first and updated first.

[0086] In this embodiment of the application, for any first target knowledge point v in the set of knowledge points to be updated, the system can extract the connection strength data (i.e., betweenness centrality C_B(v)), distance data (i.e., the reciprocal of proximity centrality C_C(v)) and degree centrality coefficient C_D(v) of the first target knowledge point in the knowledge graph to determine the centrality adjustment coefficient corresponding to the first target knowledge point. The centrality adjustment coefficient is consistent with the centrality adjustment factor Factor_centrality(v). The calculation formula of the centrality adjustment coefficient can be as shown in Formula 6. If the connection strength data C_B(v) is larger and the distance data C_C(v) is smaller, then the centrality adjustment coefficient can be larger.

[0087] In this embodiment, updating the mastery data of the first target knowledge point based on the centrality adjustment coefficient and the change in the mastery data of the target knowledge point can be achieved by the system calculating the change in the mastery data of the target knowledge point, P_new(u)-P_old(u), and extracting the corresponding dependency weights w(u,v) (direct dependency) and w(v,w) by combining the direct and indirect dependencies recorded in the knowledge graph. (Indirect dependency relationship, k is the indirect path length), update the mastery level data of the first target knowledge point. The update formula for directly dependent knowledge points can be shown as Formula 8, and the update formula for indirectly dependent knowledge points can be shown as Formula 9.

[0088] In this embodiment of the application, the second mastery level identifier of the knowledge points in the knowledge point set to be updated can be updated by the system based on the updated mastery level data of the knowledge points in the knowledge point set to be updated after the update. The system updates the second mastery level identifier of each knowledge point based on the updated P_updated(v) and color mapping rules, combined with special rules (key knowledge point protection, basic knowledge point acceleration).

[0089] As an optional approach, when performing the action of "updating the mastery data of the first target knowledge point based on the change in the mastery data of the target knowledge point according to the centrality adjustment coefficient and the change in the mastery data of the target knowledge point", the following methods may be used, but are not limited to: if the connection strength data in the centrality adjustment coefficient is greater than the connection strength threshold and the change in the mastery data is less than the first change threshold, the first target knowledge point is identified as a key knowledge point and the second mastery identifier of the first target knowledge point is marked as a not mastered identifier; if the degree centrality coefficient in the centrality adjustment coefficient is greater than the degree centrality threshold and the mastery data is greater than the second mastery threshold, the first target knowledge point is identified as a basic knowledge point and the second mastery identifier of the first target knowledge point is marked as a mastered identifier.

[0090] In the embodiments of this application, the connection strength data C_B(v) in the centrality adjustment coefficient of knowledge point v is greater than the connection strength threshold and the change in the mastery level data of knowledge point v is... If P_updated(v) is less than the first change threshold, it can be said that the improvement in the mastery of the knowledge point affected by the target knowledge point is limited. Therefore, knowledge point v can be identified as a key knowledge point, and the second mastery level indicator of knowledge point v can be marked as a not mastered indicator (red).

[0091] In the embodiments of this application, if the degree centrality coefficient C_D(v) in the centrality adjustment coefficient of knowledge point v is greater than the degree centrality threshold and the mastery level data P_updated(v) of knowledge point v is greater than the second mastery level threshold, it can be said that the student has a good mastery level. Therefore, knowledge point v can be identified as a basic knowledge point, and the second mastery level identifier of knowledge point v is marked as a mastery identifier (green).

[0092] As an optional approach, when performing the task of "updating the knowledge point learning grid to the target knowledge point learning grid based on the second mastery level identifier, and determining the student's target review content based on the target knowledge point learning grid," the following methods may be used, but are not limited to: determining at least one second mastery level identifier corresponding to the second target knowledge point in the set of knowledge points to be updated, where the second target knowledge point is any knowledge point in the set of knowledge points to be updated; making a decision on at least one second mastery level identifier based on direct and indirect dependencies, so as to determine the target mastery level identifier with the highest dependency from at least one second mastery level identifier; determining the confidence level data of the target mastery level identifier corresponding to each knowledge point in the set of knowledge points to be updated, and updating the knowledge point learning grid to the target knowledge point learning grid based on the target mastery level identifier whose confidence level data meets the confidence conditions; and determining the student's target review content based on the target knowledge point learning grid.

[0093] In this embodiment of the application, any second target knowledge point in the set of knowledge points to be updated may have multiple indirect influence paths, and the second target knowledge point may correspond to multiple temporary second mastery level indicators (color indicators calculated based on different paths). For example, the color of the second mastery level indicator calculated based on the path u→v1→w is yellow, and the color of the second mastery level indicator calculated based on the path u→v2→w is red.

[0094] In this embodiment of the application, the system makes a decision on at least one second mastery level identifier based on direct and indirect dependencies, so as to select the target mastery level identifier with the highest dependency from at least one second mastery level identifier. This can be achieved by the system making a decision on temporary second mastery level identifiers based on the dependency strength data of direct and indirect dependencies. The higher the dependency strength data, the higher the priority of the second mastery level identifier corresponding to the path. The second mastery level identifier with the highest dependency is then selected as the target mastery level identifier.

[0095] In this embodiment of the application, the confidence level data for determining the target mastery level identifier corresponding to each knowledge point in the set of knowledge points to be updated can be determined by using a confidence level formula, as shown in Formula 10, where... This can represent updated mastery level data. It can represent betweenness centrality. It can represent proximity centrality; the system can filter out target mastery level indicators that meet the pre-set confidence conditions (such as confidence data greater than or equal to 0.5) and synchronize the target mastery level indicators that meet the conditions to the knowledge point learning grid to complete the update of the target knowledge point learning grid.

[0096] (Formula 10) In the embodiments of this application, determining the student's target review content based on the target knowledge point learning grid can be based on the target mastery level indicators of each knowledge point in the target knowledge point learning grid. The system prioritizes filtering out the knowledge points corresponding to red (not mastered) and yellow (mastered but not proficient) indicators, and determines the student's target review content by combining the importance data of the knowledge points, the correlation, the teaching progress, the exam focus, and other factors.

[0097] Compared with existing technologies, this embodiment determines the direct and indirect influence range based on the prerequisite relationships, relevance relationships, and indirect connection relationships of target knowledge points in the knowledge graph, thus achieving precise screening of the set of knowledge points to be updated; it improves the accuracy of indirect influence range division by distinguishing different indirect dependencies and combining them with the corresponding influence decay degree to determine the indirect influence range; it achieves the orderliness of updating the mastery level data of the knowledge points to be updated by determining the update priority based on dependency strength data and importance data identified by the target model; it improves the accuracy of mastery level data update by determining the update order based on the update priority and combining the centrality adjustment coefficient and the change in the mastery level of the target knowledge points; and it achieves accurate differentiation of the mastery level indicators of key knowledge points and basic knowledge points by marking the mastery level indicators based on the comparison results of the relevant data in the centrality adjustment coefficient and the corresponding threshold.

[0098] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 1: Based on the knowledge point set corresponding to the course material information, determine the basic learning information of the knowledge points in the knowledge point set, as well as the prerequisite knowledge information and correlation information between the knowledge points; generate attribute information of the knowledge points based on the basic learning information, the prerequisite knowledge information, and the correlation information, and construct a knowledge graph corresponding to the knowledge point set based on the attribute information; map the knowledge graph into the knowledge point learning grid corresponding to the course material information to obtain the target knowledge point learning grid; generate the student's learning plan for the target time period based on the knowledge point learning grid in the knowledge point learning grid module, and generate the student's learning path based on the learning plan; the knowledge point learning grid module is used to display the learning path to the student through the knowledge point learning grid; wherein, the target knowledge point learning grid is a learning grid that includes the attribute information of the knowledge points and the correlation information between the knowledge points.

[0099] Example 11: Based on attribute information, determine the frequency data of knowledge points appearing in the same historical learning sessions, and generate a first association strength corresponding to the knowledge points in the knowledge point set based on the frequency data; based on attribute information, determine the mastery level data of knowledge points, and generate a second association strength corresponding to the knowledge points in the knowledge point set based on the mastery level data; based on attribute information, determine the proximity of the learning order of knowledge points, and generate a third association strength corresponding to the knowledge points in the knowledge point set based on the proximity of the learning order; based on attribute information, determine the probability data of incorrect answers to knowledge points, and generate a fourth association strength corresponding to the knowledge points in the knowledge point set based on the probability data of incorrect answers; generate a target association strength corresponding to the knowledge points in the knowledge point set based on the first association strength, second association strength, third association strength, and fourth association strength.

[0100] Example 12: Determine the first association type between knowledge points based on basic learning information, the second association type between knowledge points based on prerequisite knowledge information, and the third association type between knowledge points based on relevance information; generate directed edges corresponding to the knowledge point set based on the first association type between knowledge points corresponding to basic learning information and the second association type between knowledge points corresponding to prerequisite knowledge information, and generate undirected edges corresponding to the knowledge point set based on the first association type between knowledge points corresponding to the first association type and the third association type between knowledge points corresponding to relevance information; determine the target association strength corresponding to the knowledge points in the knowledge point set based on attribute information, and generate an association matrix based on the knowledge point set, directed edges, undirected edges, and association strength; construct graph edges based on the association matrix, and construct graph nodes based on the knowledge point set and attribute information, and combine graph nodes and graph edges to generate a knowledge graph.

[0101] Example 13: Quality assessment of the knowledge graph. In the case of isolated nodes in the knowledge graph, the knowledge point set is refined, and the knowledge graph is adjusted based on the refined knowledge point set. In the case of node density in the knowledge graph that is greater than the density threshold, the knowledge point set is merged, and the knowledge graph is adjusted based on the merged knowledge point set.

[0102] Example 14: In response to acquiring learning behavior data of students learning based on the learning plan, the system determines the students' question information and note information during the learning process based on the learning behavior data; identifies implicit knowledge points in the question information and note information, and determines the implicit attribute information of the implicit knowledge points, including implicit prerequisite knowledge information and implicit relevance information; when the implicit attribute information meets the knowledge graph update conditions, the system updates the knowledge graph based on the implicit knowledge points and implicit attribute information to obtain the target knowledge graph, which is used to generate a learning grid that includes implicit knowledge points and their attribute information.

[0103] Example 15: Determine the prerequisite knowledge points corresponding to the implicit knowledge points based on implicit prerequisite knowledge information; determine the students' mastery of the implicit knowledge points and prerequisite knowledge points based on the question information, as well as the answer results data of the questions containing the implicit knowledge points and prerequisite knowledge points; determine the confidence level of the implicit prerequisite knowledge information based on the mastery level and answer results data; in response to the confidence level of the implicit prerequisite knowledge information being greater than the confidence level threshold and the number of times the implicit knowledge points are identified being greater than the number of times threshold, update the graph nodes in the knowledge graph based on the implicit knowledge points, and update the directed edges in the knowledge graph based on the implicit prerequisite knowledge information.

[0104] Example 16: Determine the relevant knowledge points corresponding to the implicit knowledge points based on implicit correlation information; determine the student's mastery of the implicit knowledge points and relevant knowledge points based on note information, as well as the information contribution data of note information to implicit correlation information; determine the confidence level of implicit correlation information based on the mastery level and information contribution data; in response to the confidence level of implicit correlation information being greater than the confidence level threshold and the number of times implicit knowledge points are identified being greater than the number of times threshold, update the graph nodes in the knowledge graph based on the implicit knowledge points, and update the undirected edges in the knowledge graph based on implicit prerequisite knowledge information.

[0105] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 2: In response to obtaining first learning behavior data of a student learning questions based on the learning plan, the system determines first question information and first note information of the student's learning target questions based on the first learning behavior data, and sends the first question information and first note information to the knowledge point learning grid module; based on the implicit knowledge point set in the knowledge point set corresponding to the first question information and first note information; according to the prerequisite relationship and relevance information between the implicit knowledge point set and the first knowledge point set, the system determines the target implicit knowledge point set corresponding to the learning plan from the implicit knowledge point set; the system updates the target implicit knowledge point set in the target knowledge point learning grid, and sends the updated target knowledge point learning grid to the learning path generation module; the system updates the learning plan to include the target implicit knowledge point set.

[0106] Example 21: Extract knowledge points from the first question information and the first note information respectively to obtain the second knowledge point set corresponding to the first question information and the third knowledge point set corresponding to the note information; determine the knowledge points in the second knowledge point set that are not included in the first knowledge point set as the first implicit knowledge point subset corresponding to the target question; determine the knowledge points in the third knowledge point set that are not included in the first knowledge point set as the second implicit knowledge point subset corresponding to the target question; determine the implicit knowledge point set based on the first and second implicit knowledge point subsets.

[0107] Example 22: Establish multiple prerequisite relationships for knowledge points based on a first subset of implicit knowledge points and a first set of knowledge points, so as to determine the knowledge points in the first subset of implicit knowledge points as prerequisite knowledge points for the knowledge points in the first set of knowledge points; establish multiple correlation relationships for knowledge points based on a second subset of implicit knowledge points and a first set of knowledge points, so as to associate the knowledge points in the second subset of implicit knowledge points with the knowledge points in the first set of knowledge points; determine the first confidence level of using the multiple prerequisite relationships for knowledge points as prerequisite relationships for target knowledge points corresponding to the learning plan and the second confidence level of using the multiple correlation relationships for knowledge points as correlation relationships for target knowledge points corresponding to the learning plan; determine the target implicit knowledge point set corresponding to the learning plan from the implicit knowledge point set based on the first confidence level and the second confidence level.

[0108] Example 23: Determine the student's first mastery data on the first subset of implicit knowledge points, and determine the student's answer to the questions based on the student's answer data in the question information; determine the first support data for each knowledge point prerequisite relationship in multiple knowledge point prerequisite relationships based on the first mastery data and the answer results; update the first confidence of each knowledge point prerequisite relationship based on the historical confidence of the first support data to obtain the first confidence of using multiple knowledge point prerequisite relationships as the target knowledge point prerequisite relationship.

[0109] Example 24: Determine the second mastery data of students on the second subset of implicit knowledge points, and the contribution data of note information to the correlation relationship of multiple knowledge points; determine the second support data of each knowledge point correlation relationship in the multiple knowledge point correlation relationships based on the mastery data and contribution data; update the data according to the historical confidence of each knowledge point correlation relationship based on the second support data, and obtain the second confidence of using the current correlation relationship of multiple knowledge points as the correlation relationship of the target knowledge point.

[0110] Example 25: Multiple prerequisite relationships and multiple correlation relationships of knowledge points are stored in a candidate relation library corresponding to the learning plan, so as to monitor the first confidence level and the second confidence level through the candidate relation library; in response to the first confidence level of the prerequisite relationship of the first knowledge point being greater than the prerequisite confidence threshold and the number of times the first implicit knowledge point corresponding to the prerequisite relationship of the first knowledge point being identified being greater than the number of times threshold, the first implicit knowledge point is determined as the first target implicit knowledge point corresponding to the learning plan; in response to the first confidence level of the correlation relationship of the first knowledge point being greater than the correlation confidence threshold and the number of times the first implicit knowledge point corresponding to the correlation relationship of the first knowledge point being identified being greater than the number of times threshold, the first implicit knowledge point is determined as the first target implicit knowledge point corresponding to the learning plan; the target implicit knowledge point set corresponding to the learning plan is determined based on the first target implicit knowledge point and the second target implicit knowledge point.

[0111] Example 26: Determine the monitoring period for the first confidence level and the second confidence level of the candidate relation library; within the monitoring period, in response to the first confidence level of the second knowledge point prerequisite relation among multiple knowledge point prerequisite relations and / or the second confidence level of the second knowledge point prerequisite relation among multiple knowledge point correlation relations being lower than the monitoring threshold, remove the second knowledge point prerequisite relation and / or the second knowledge point prerequisite relation from the candidate relation library; in response to the end of the monitoring period, remove multiple knowledge point prerequisite relations and multiple knowledge point correlation relations from the candidate relation library.

[0112] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 3: Based on the student's learning location information in the target knowledge point learning grid, determine the target learning course corresponding to the learning location information; divide the student's second learning behavior data in the target learning course into at least one learning behavior data set, and generate at least one behavioral feature set corresponding to the at least one learning behavior data set; analyze the student's mastery of knowledge points in the target learning course based on the at least one behavioral feature set, and determine the student's unmastered knowledge point set in the target learning course; match the unmastered knowledge point set with the target knowledge graph corresponding to the target knowledge point learning grid to obtain the target knowledge point network corresponding to the unmastered knowledge point set in the target knowledge graph; generate the student's target learning path in the target knowledge point learning grid based on the target knowledge point network, and the target learning path is used to assist the student in mastering the unmastered knowledge point set.

[0113] Example 31: Determine the time information corresponding to each behavioral feature in at least one set of behavioral features, and evaluate the effectiveness of each behavioral feature in at least one set of behavioral features based on the time information to obtain effective value data for each behavioral feature in at least one set of behavioral features; filter the behavioral features in at least one set of behavioral features based on the effective value data to obtain target behavioral features that meet the effective conditions in at least one set of behavioral features, and form at least one set of target behavioral features; analyze the students' mastery of knowledge points in the target learning course based on at least one set of target behavioral features to determine the set of knowledge points that students have not mastered in the target learning course.

[0114] Example 32: Match at least one set of target behavioral features with each knowledge point in the target learning course to determine at least one target behavioral feature corresponding to each knowledge point in the target learning course; identify the importance data of at least one target behavioral feature corresponding to each knowledge point in the target learning course to each knowledge point in the target learning course through a target model, which is trained based on students' historical behavioral features and historical knowledge point mastery levels; determine students' mastery data of each knowledge point in the target learning course based on at least one target behavioral feature and importance data corresponding to each knowledge point; determine the set of unmastered knowledge points in the target learning course based on the mastery data.

[0115] Example 33: Based on importance data, determine the influence coefficient of at least one behavioral feature corresponding to each knowledge point on the degree of mastery of each knowledge point; analyze at least one behavioral feature based on the influence coefficient to generate data on the student's mastery of each knowledge point in the target learning course.

[0116] Example 34: Map the mastery data of each knowledge point to the objective function to obtain the mastery probability data of each knowledge point; select knowledge points that meet the unmastered conditions from the knowledge corresponding to the target learning course based on the mastery probability data, and form a set of unmastered knowledge points from the knowledge points selected from the knowledge corresponding to the target learning course.

[0117] Example 35: Generate a sequence of unmastered knowledge points based on the set of unmastered knowledge points; mark the sequence of unmastered knowledge points with unmastered identifiers in the target learning grid, and extract the paths of unmastered knowledge points corresponding to the sequence of unmastered knowledge points from the target learning grid; match the paths of unmastered knowledge points with the target knowledge graph according to the unmastered identifiers to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph.

[0118] Example 36: This method is used to match the paths of unmastered knowledge points with the target knowledge graph based on the unmastered identifier, thereby obtaining the network of knowledge points to be screened corresponding to the set of unmastered knowledge points in the target knowledge graph; and to determine the target knowledge point network from the network of knowledge points to be screened based on the prerequisite relationships and relevance information of the paths of unmastered knowledge points in the network of knowledge points to be screened.

[0119] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 4: Obtain third learning behavior data of the student learning knowledge points in the set of unmastered knowledge points within the knowledge point learning grid; determine second question information and second note information based on the third learning behavior data; evaluate the student's learning needs for knowledge points in the set of unmastered knowledge points based on the second question information and the second note information, and obtain learning intention intensity data corresponding to the knowledge points in the knowledge point learning grid; identify the student's target learning intention for knowledge points in the set of unmastered knowledge points based on the learning intention intensity data, the second question information, and the second note information; update the set of unmastered knowledge points based on the target learning intention and the knowledge point location information of the set of unmastered knowledge points in the knowledge point learning grid to obtain a target set of unmastered knowledge points, and recommend the target set of unmastered knowledge points to the student.

[0120] Example 41: Based on note information, determine the update frequency of students' notes within the target time range, and evaluate students' learning activity data within the target time range based on the update frequency; based on note information and question information, determine the question frequency of students' doubts about knowledge points in the set of unmastered knowledge points, and evaluate students' difficulty in understanding knowledge points in the set of unmastered knowledge points based on the question frequency; evaluate students' learning needs for knowledge points in the set of unmastered knowledge points based on learning activity data and difficulty in understanding data, and obtain the learning intention intensity data corresponding to knowledge points in the knowledge point learning grid.

[0121] Example 42: Based on the third learning behavior data, determine the learning end time for students to learn the knowledge points in the set of unmastered knowledge points; determine the decay factor of learning intention intensity over time based on the learning end time and time decay coefficient; evaluate the urgency data of students learning the knowledge points in the set of unmastered knowledge points based on the decay factor; perform weighted analysis on learning activity data, comprehension difficulty data and urgency data to evaluate students' learning needs for the knowledge points in the set of unmastered knowledge points, and obtain the learning intention intensity data corresponding to the knowledge points in the knowledge point learning grid.

[0122] Example 43: Based on the second question information and the second note information, generate student learning content data and corresponding behavior sequence data for the third learning behavior data; evaluate the student's learning intention for the knowledge points in the set of unmastered knowledge points as a first probability data of deep understanding intention, a second probability data of basic consolidation intention, and a third probability data of conceptual questioning intention based on the learning intention intensity data, learning content data, and behavior sequence data; determine the target learning intention from deep understanding intention, basic consolidation intention, and conceptual questioning intention based on the first probability data, the second probability data, and the third probability data.

[0123] Example 44: Evaluate the student's required level of understanding of the knowledge points in the set of unmastered knowledge points based on the first probability data, the second probability data, and the third probability data; if the required level of understanding data is greater than the first required level of understanding threshold, determine the student's intention to deepen understanding as the target learning intention for the knowledge points in the set of unmastered knowledge points; if the required level of understanding data is less than or equal to the first required level of understanding threshold and greater than the second required level of understanding threshold, determine the student's intention to consolidate basic knowledge as the target learning intention for the knowledge points in the set of unmastered knowledge points, wherein the second required level of understanding threshold is less than the first required level of understanding threshold; if the required level of understanding data is less than the second required level of understanding threshold, determine the student's intention to raise conceptual questions as the target learning intention for the knowledge points in the set of unmastered knowledge points.

[0124] Example 45: Generate student learning content data based on the second question information and the second note information; map the third learning behavior data into the knowledge graph corresponding to the knowledge point learning grid to obtain the student's learning knowledge point data in the knowledge graph; perform multi-dimensional information matching between the learning content data and the learning knowledge point data to generate knowledge point location information for the set of unmastered knowledge points; update the set of unmastered knowledge points according to the target learning intention and the knowledge point location information to obtain the target set of unmastered knowledge points, generate the target set of unmastered knowledge points and the target learning path corresponding to the target set of unmastered knowledge points; generate recommendation information for the target set of unmastered knowledge points and the target learning path so that students can master the knowledge points in the target set of unmastered knowledge points based on the target set of unmastered knowledge points and the target learning path.

[0125] Example 46: Obtain third learning behavior data from the learning support system, showing students learning knowledge points in the set of unmastered knowledge points within the knowledge point learning grid; divide the third learning behavior data of students in the target knowledge point learning grid into at least one learning behavior data set, and generate a set of behavioral features corresponding to at least one learning behavior data set; determine the time information corresponding to each behavioral feature in the behavioral feature set, and evaluate the effectiveness of each behavioral feature in the behavioral feature set based on the time information, obtaining the effective value data of each behavioral feature in the behavioral feature set; filter the behavioral features in the behavioral feature set based on the effective value data, obtain the target behavioral features that meet the effective conditions in the behavioral feature set, and form a target behavioral feature set; analyze the students' mastery of knowledge points in the target knowledge point learning grid based on the target behavioral feature set, and determine the set of unmastered knowledge points in the target knowledge point learning grid; identify the box-selection behavior from the target behavioral feature set, and classify the learning content selected by the box-selection behavior to obtain the second question information and second note information of students learning knowledge points in the set of unmastered knowledge points.

[0126] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 5: The third learning behavior data is mapped into the knowledge graph corresponding to the knowledge point learning grid to obtain the student's learning knowledge point data in the knowledge graph; the student's learning content data is generated based on the question information and the note information, and the learning content data is matched with the learning knowledge point data in multiple dimensions to generate the knowledge point positioning information of the set of unmastered knowledge points.

[0127] Example 51: Generate student learning content data based on question information and note information; perform similarity matching based on the first semantic embedding vector corresponding to the learning content data and the second semantic embedding vector corresponding to the learning knowledge point data to match the content similarity between the learning content data and the learning knowledge point data, and obtain a first similarity matching result; perform similarity matching based on the context data corresponding to the learning content data and the adjacent knowledge point data corresponding to the learning knowledge point data to match the overall similarity between the learning content data and the learning knowledge point data in the knowledge graph, and obtain a second similarity matching result; perform similarity matching based on the first position data of the learning content data in the textbook and the second position data of the learning knowledge point data in the textbook to match the position similarity between the learning content data and the learning knowledge point data, and obtain a third similarity matching result; based on the first similarity matching result, the second similarity matching result, and the third similarity matching result, generate knowledge point location information for the set of unmastered knowledge points.

[0128] Example 52: A fusion analysis is performed based on the first similarity matching result, the second similarity matching result, and the third similarity matching result to evaluate the accuracy of the location of knowledge points in the set of unmastered knowledge points, and to obtain the location reliability data of the learning content data and the learning knowledge point data; based on the location reliability data, unmastered knowledge points that meet the confidence conditions are selected from the set of unmastered knowledge points to form a location knowledge point set; based on the prerequisite relationships and relevance relationships of the knowledge points in the location knowledge point set in the knowledge graph, the knowledge point location information of the set of unmastered knowledge points is generated.

[0129] Example 53: Based on the prerequisite relationships of knowledge points in the knowledge point set located in the knowledge graph, determine the first knowledge point set consisting of the prerequisite knowledge points corresponding to the knowledge point set located in the knowledge graph, and the second knowledge point set with the knowledge points in the knowledge point set as prerequisite knowledge points; extract the knowledge point dependency relationship chain corresponding to the unmastered knowledge point set from the knowledge graph based on the knowledge point set located, the first knowledge point set, and the second knowledge point set; determine the third knowledge point set related to the knowledge point set located in the knowledge graph based on the correlation relationships of knowledge points in the knowledge point set located in the knowledge graph, and extract the knowledge point related relationship chain corresponding to the unmastered knowledge point set based on the knowledge point dependency relationship chain and the knowledge point related relationship chain; generate the knowledge point location information of the unmastered knowledge point set based on the knowledge point dependency relationship chain and the knowledge point related relationship chain.

[0130] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 6: Based on the student's review path for already learned knowledge points within the target knowledge point learning grid, determine the knowledge points to be reviewed indicated by the review path; extract the network of knowledge points to be reviewed corresponding to the knowledge points to be reviewed from the knowledge graph corresponding to the target knowledge point learning grid, wherein the network of knowledge points to be reviewed includes at least one prerequisite knowledge point and at least one related knowledge point corresponding to the knowledge point to be reviewed; update the review path based on the student's mastery of the knowledge point to be reviewed, the at least one prerequisite knowledge point, and the at least one related knowledge point to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed; determine the student's target knowledge points to be reviewed based on the target knowledge point review path, generate recommendation information for the target knowledge points to be reviewed, and recommend the review of the target knowledge points to the student.

[0131] Example 61: Determine the knowledge point mastery level indicator of the knowledge point to be reviewed from the knowledge point network; when the knowledge point mastery level indicator of the knowledge point to be reviewed is not mastered, determine the first knowledge point mastery level indicator of at least one prerequisite knowledge point and the second knowledge point mastery level indicator of at least one related knowledge point in the knowledge point network to be reviewed; based on the first knowledge point mastery level indicator and the second knowledge point mastery level indicator, update the knowledge point review path to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed.

[0132] Example 62: Based on the mastery level identifier of the first knowledge point and the mastery level identifier of the second knowledge point, determine the set of first knowledge points that the student has mastered from at least one prerequisite knowledge point and at least one related knowledge point; remove the set of first knowledge points from the network of knowledge points to be reviewed, and use the knowledge points to be reviewed as the starting point of the review path to update the network of knowledge points to be reviewed, so as to obtain the target knowledge point review path corresponding to the knowledge points to be reviewed.

[0133] Example 63: Based on the mastery level indicators of the first and second knowledge points, determine the set of second knowledge points that the student has not mastered from at least one prerequisite knowledge point and at least one related knowledge point; determine the learning priority information of the knowledge points in the second knowledge point set; based on the learning priority information, select the first target knowledge point that meets the priority condition from the second knowledge point set; use the first target knowledge point as the starting point of the review path, update the network of knowledge points to be reviewed, and obtain the review path of the target knowledge point.

[0134] Example 64: Determine the mastery probability data and knowledge point difficulty data for each knowledge point in the second knowledge point set; evaluate the learning priority of each knowledge point in the second knowledge point set based on the mastery probability data and knowledge point difficulty data to obtain the learning priority information of each knowledge point in the second knowledge point set; determine the knowledge point with the highest learning priority information in the second knowledge point set as the first target knowledge point.

[0135] Example 65: When the knowledge point mastery level of the knowledge point to be reviewed is marked as "mastered", the knowledge point traversal range is determined based on the knowledge point to be reviewed; the knowledge points are traversed within the knowledge point traversal range to obtain the third set of knowledge points that the student has not mastered; the set of prerequisite knowledge points and the set of related knowledge points corresponding to the third set of knowledge points are determined in the knowledge graph; the second target knowledge point that meets the priority condition is determined from the third set of knowledge points, the set of prerequisite knowledge points, and the set of related knowledge points; the knowledge point to be reviewed is removed from the knowledge point network to be reviewed, and the second target knowledge point is used as the starting point of the review path to update the knowledge point network to obtain the target knowledge point review path.

[0136] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 7: Obtain fourth learning behavior data of students learning knowledge points in the knowledge point learning grid, and obtain third mastery level identifiers of knowledge points in the knowledge point learning grid; determine second mastery level data of students learning knowledge points in the knowledge point learning grid based on the fourth learning behavior data, and evaluate second knowledge point status data of knowledge points in the knowledge point learning grid based on the second mastery level data; determine abnormal knowledge points from the knowledge point learning grid based on the first knowledge point status data corresponding to the second mastery level data and the second knowledge point status data; update the knowledge graph corresponding to the knowledge point learning grid based on the abnormal knowledge points to obtain an updated target knowledge graph, which is used to generate the target knowledge point learning grid.

[0137] Example 71: Based on the first knowledge point status data and the second knowledge point status data, perform a status data difference analysis to evaluate the degree of difference between the knowledge point mastery status displayed in the knowledge point learning grid and the student's mastery status of the knowledge points in the knowledge point learning grid; based on the fourth learning behavior data, determine the question information and note information of the student's learning of the knowledge points in the knowledge point learning grid, and determine the student's learning status information of the knowledge points in the knowledge point learning grid based on the question information, note information, and the second mastery level data; based on the learning status information and the degree of difference, identify the knowledge points with abnormal status from the knowledge point learning grid, and update the knowledge graph corresponding to the knowledge point learning grid based on the knowledge points with abnormal status to obtain the updated target knowledge graph.

[0138] Example 72: Based on note information, determine the frequency of student note updates within a target time range, and evaluate the student's learning activity data for knowledge points in the knowledge point learning grid within the target time range based on the update frequency; based on note information and question information, determine the frequency of student questions about knowledge points in the knowledge point learning grid, and evaluate the student's difficulty in understanding knowledge points in the knowledge point learning grid based on the question frequency; if the learning activity data is greater than the activity threshold and / or the difficulty in understanding data is greater than the difficulty threshold and / or the second mastery level data is less than the mastery level threshold, determine the student's learning status information for knowledge points in the knowledge point learning grid as abnormal learning status information.

[0139] Example 73: When the student's learning status information for knowledge points in the knowledge point learning grid is abnormal, the stability data of knowledge points in the knowledge point learning grid in the knowledge graph is evaluated based on the betweenness centrality data and proximity centrality data of knowledge points in the knowledge point learning grid; the credibility data of abnormal learning status information is determined based on the stability data and the degree of difference; abnormal knowledge points are identified from the knowledge point learning grid based on the credibility data, learning status information, and degree of difference; the knowledge graph corresponding to the knowledge point learning grid is updated based on the abnormal knowledge points to obtain the updated target knowledge graph.

[0140] Example 74: Divide the fourth learning behavior data of students in the target knowledge point learning grid into at least one learning behavior data set, and generate a behavior feature set corresponding to at least one learning behavior data set; determine the time information corresponding to each behavior feature in the behavior feature set, and evaluate the effectiveness of each behavior feature in the behavior feature set based on the time information to obtain the effective value data of each behavior feature in the behavior feature set; filter the behavior features in the behavior feature set based on the effective value data to obtain the target behavior features in the behavior feature set that meet the effective conditions, and form a target behavior feature set; identify the box selection behavior from the target behavior feature set, and classify the learning content selected by the box selection behavior to obtain the question information and note information of students learning knowledge points in the abnormal knowledge point set.

[0141] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 8: Determine the state correction content corresponding to the abnormal knowledge point; based on the correction content, perform state correction on the abnormal knowledge point in the knowledge point learning grid, and determine the mastery level impact data after the abnormal knowledge point has been state corrected; determine the influence range of the mastery level impact data in the knowledge graph corresponding to the knowledge point learning grid based on the mastery level impact data, and update the knowledge graph within the influence range.

[0142] Example 81: When the first mastery level identifier is a mastery identifier, determine the question information and note information of the student's learning of knowledge points in the knowledge point learning grid based on learning behavior data; evaluate the student's learning needs for knowledge points with abnormal status based on question information and note information, and obtain the learning intention intensity data corresponding to the knowledge points with abnormal status; determine the first mastery level data corresponding to the knowledge points with abnormal status based on the first mastery level identifier; when the learning intention intensity data is greater than the learning intention intensity threshold and the first mastery level data is less than the mastery level threshold, determine the first non-mastery identifier as the second mastery level identifier; determine the first state correction content corresponding to the knowledge points with abnormal status based on the mastery identifier and the first non-mastery identifier.

[0143] Example 82: When the first mastery level identifier is the first non-mastery identifier, the student's question information and note information for learning knowledge points in the knowledge point learning grid are determined based on learning behavior data; the frequency of questions the student has about knowledge points with abnormal status is determined based on the note information and question information, and the student's difficulty in understanding knowledge points with abnormal status is evaluated based on the question frequency; the student's score data for the questions corresponding to the knowledge points with abnormal status is determined; when the difficulty in understanding data is greater than the difficulty in understanding threshold and the score data is less than the score threshold, the second non-mastery identifier is determined as the second mastery level identifier; based on the first non-mastery identifier and the second non-mastery identifier, the second status correction content corresponding to the knowledge points with abnormal status is determined.

[0144] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 9: Obtain the student's historical learning data; based on the student's historical learning data, determine the student's learning ability data and the student's learning status information for the knowledge points in the knowledge point learning grid; based on the learning ability data and the learning status information, determine the student's learning plan type, and determine the set of knowledge points to be learned in the knowledge point learning grid according to the learning plan type; generate the student's learning path in the knowledge point learning grid based on the set of knowledge points to be learned; determine the set of learning materials that match the learning path from the learning materials, so as to recommend that the student learn the set of knowledge points to be learned based on the set of learning materials.

[0145] Example 91: Extract students' learning characteristics from their historical learning data, and determine their historical learning status for the corresponding historical knowledge points based on these characteristics. The learning characteristics include at least one of learning speed, learning memory, and answering characteristics. Determine students' learning ability data based on their historical learning status. Determine the mastery probability data and centrality data of knowledge points in the knowledge point learning grid, and determine the intensity of students' learning intention for the knowledge points in the knowledge point learning grid based on their historical learning data. Construct a learning status matrix for students' knowledge points in the knowledge point learning grid based on the mastery probability data, centrality data, and intensity of learning intention data. This learning status matrix represents the students' learning status information for the knowledge points in the knowledge point learning grid.

[0146] Example 92: Based on learning ability data and learning status information, determine the time limit of the student's learning plan, and generate at least one learning task information corresponding to the student according to the learning plan time limit; match the at least one learning task information with the knowledge graph corresponding to the knowledge point learning grid to map the at least one learning task information to at least one candidate knowledge point set in the knowledge point learning grid; determine the priority data of the knowledge points in the at least one candidate knowledge point set based on mastery probability data, centrality data, and learning intention intensity data; and form a learning knowledge point set based on the priority data and the knowledge points in the at least one candidate knowledge point set that meet the priority conditions.

[0147] Example 93: Based on the dependencies of knowledge points in the knowledge graph corresponding to the knowledge point learning grid, generate a first knowledge point sequence corresponding to the knowledge points in the set of knowledge points to be learned. The dependencies include prerequisite relationships and relevance relationships. Determine the learning speed information corresponding to the first knowledge point sequence. If the learning speed is greater than the speed threshold, determine the parallel knowledge points corresponding to the first knowledge point sequence, and generate a learning path based on the parallel knowledge points and the first knowledge point sequence. If the learning speed is less than or equal to the speed threshold, generate a learning path based on the first knowledge point sequence.

[0148] Example 94: Based on the prerequisite relationships of knowledge points in the set of knowledge points to be learned in the knowledge graph, determine the set of prerequisite knowledge points corresponding to the set of knowledge points to be learned; generate an initial knowledge point sequence based on the set of knowledge points to be learned, and insert the knowledge points in the set of prerequisite knowledge points into the preceding positions of the knowledge points in the set of knowledge points to be learned in the initial knowledge point sequence to obtain the first knowledge point sequence.

[0149] Example 95: Based on at least one first material feature in the content dimension and at least one second material feature in the question dimension, a feature vector corresponding to the learning material is constructed; the feature vector and student ability data are subjected to correlation matching and similarity matching to obtain a comprehensive matching result of the feature vector and student ability data, and candidate learning materials suitable for the student are selected from the learning materials according to the comprehensive matching result; the student's learning duration is determined based on the student's ability data, and at least one target learning plan is generated for the student according to the learning duration and the learning path to be learned; a set of learning materials matching each of the at least one target learning plan is determined from the learning materials.

[0150] Example 96: Determine the students' already-learned plans in at least one target learning plan, and the set of questions that the students have already learned in the already-learned plans; based on the students' learning of the set of questions they have already learned, analyze the students' mastery data of the knowledge points corresponding to the set of questions they have already learned; update the students' unlearned plans and the set of learning materials corresponding to the unlearned plans in at least one target learning plan based on the mastery data.

[0151] Furthermore, as Figure 1 and Figure 3 The specific implementation of the method shown in this embodiment provides a review content determination device based on an AI intelligent agent, such as... Figure 4 As shown, the device includes: an update module 31 and a determination module 32.

[0152] Update module 31 is configured to update the first mastery level identifier of the target knowledge point in response to the behavioral data of students reviewing the target knowledge point in the set of review knowledge points obtained from the knowledge point learning grid of the learning companion system. The determination module 32 is configured to determine the update influence range of the mastery level of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid, and to form a set of knowledge points to be updated based on the knowledge points covered by the update influence range; The update module 31 is also configured to update the mastery level data of the knowledge points in the set of knowledge points to be updated, and update the second mastery level identifier of the knowledge points in the set of knowledge points to be updated based on the updated mastery level data. The determination module 32 is also configured to update the knowledge point learning grid to the target knowledge point learning grid based on the second mastery level identifier, and determine the student's target review content based on the target knowledge point learning grid.

[0153] In some examples of this embodiment, the determining module 32 is specifically configured to: determine the direct dependencies of the target knowledge points based on the target prerequisite relationships and target relevance relationships in the knowledge graph, and determine the direct impact range based on the direct dependencies; determine the indirect dependencies of the target knowledge points based on the indirect connection relationships between the target knowledge points and other knowledge points in the knowledge graph, and determine the indirect impact range based on the indirect dependencies; determine the set of directly impacted knowledge points covered by the direct impact range, and the set of indirect impacted knowledge points covered by the indirect impact range; and generate a set of knowledge points to be updated based on the set of directly impacted knowledge points and the set of indirect impacted knowledge points.

[0154] In some examples of this embodiment, the determining module 32 is further configured to: determine a first indirect dependency relationship established by a target knowledge point through an indirect knowledge point in the knowledge graph, and a second indirect dependency relationship connected by multiple indirect knowledge points; determine a first influence range of the target knowledge point based on the first influence decay degree corresponding to the first indirect dependency relationship from the knowledge graph; determine a second influence range of the target knowledge point based on the second influence decay degree corresponding to the second indirect dependency relationship from the knowledge graph; and determine the indirect influence range based on the first influence range and the second influence range.

[0155] In some examples of this embodiment, the update module 31 is specifically configured to: determine the dependency strength data between the knowledge points in the knowledge point set to be updated and the target knowledge point based on direct and indirect dependencies; identify the importance data of the knowledge points in the knowledge point set to be updated through a target model, which is trained based on students' historical behavioral characteristics and their mastery of historical knowledge points; determine the update priority information for updating the knowledge points in the knowledge point set to be updated based on the dependency strength data, importance data, and mastery level data; update the mastery level data according to the update priority information; and update the second mastery level identifier of the knowledge points in the knowledge point set to be updated based on the updated mastery level data.

[0156] In some examples of this embodiment, the update module 31 is further configured to: determine the knowledge point update order list corresponding to the knowledge point set to be updated according to the update priority information, so as to determine the update order of the knowledge points in the knowledge point set to be updated; for the first target knowledge point in the knowledge point set to be updated, determine the centrality adjustment coefficient corresponding to the first target knowledge point based on the connection strength data of the first target knowledge point in the knowledge graph, the distance data between the first target knowledge point and other knowledge points, and the degree centrality coefficient, wherein the first target knowledge point is any knowledge point in the knowledge point set to be updated; update the mastery level data of the first target knowledge point based on the centrality adjustment coefficient and the change in the mastery level data of the target knowledge point; and update the second mastery level identifier of the knowledge points in the knowledge point set to be updated based on the updated mastery level data of the knowledge points in the knowledge point set to be updated.

[0157] In some examples of this embodiment, the updating module 31 is further configured to, when the connection strength data in the centrality adjustment coefficient is greater than the connection strength threshold and the change in the mastery level data is less than the first change threshold, determine the first target knowledge point as a key knowledge point and mark the second mastery level identifier of the first target knowledge point as an unmastered identifier; when the degree centrality coefficient in the centrality adjustment coefficient is greater than the degree centrality threshold and the mastery level data is greater than the second mastery level threshold, determine the first target knowledge point as a basic knowledge point and mark the second mastery level identifier of the first target knowledge point as a mastered identifier.

[0158] In some examples of this embodiment, the determining module 32 is further configured to: determine at least one second mastery level identifier corresponding to a second target knowledge point in the set of knowledge points to be updated, wherein the second target knowledge point is any knowledge point in the set of knowledge points to be updated; make a decision on at least one second mastery level identifier based on direct and indirect dependencies, so as to determine the target mastery level identifier with the highest dependency from at least one second mastery level identifier; determine the confidence level data of the target mastery level identifier corresponding to each knowledge point in the set of knowledge points to be updated, and update the knowledge point learning grid to the target knowledge point learning grid based on the target mastery level identifier that meets the confidence level conditions based on the confidence level data; and determine the student's target review content based on the target knowledge point learning grid.

[0159] It should be noted that other corresponding descriptions of the functional units involved in the AI-based intelligent agent-based review content determination device provided in this embodiment can be found in [reference]. Figure 1 and Figure 3 The corresponding description in [the document] will not be repeated here.

[0160] Based on the above, Figure 1 and Figure 3Accordingly, this embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figure 1 and Figure 3 The method shown.

[0161] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0162] like Figure 5 The diagram shown is a hardware structure schematic of an electronic device according to the present invention, comprising: At least one processor 401; and, Memory 402 is communicatively connected to at least one processor 401; wherein, The memory 402 stores instructions that can be executed by at least one processor to enable the at least one processor to perform the aforementioned AI agent-based review content determination method.

[0163] Figure 5 Take a processor 401 as an example.

[0164] The electronic device may also include an input device 403 and a display device 404.

[0165] The processor 401, memory 402, input device 403, and display device 404 can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0166] Memory 402, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the AI-based intelligent agent-based review content determination method in the embodiments of this application, for example, Figure 1 and Figure 3 The method flow is shown. The processor 401 executes various functional applications and data processing by running non-volatile software programs, instructions, and modules stored in the memory 402, thereby realizing the AI-based intelligent agent-based review content determination method in the above embodiments.

[0167] Memory 402 may include a program storage area and a data storage area. The program storage area may store an operating system and applications required for at least one function. The data storage area may store data created based on the use of the AI ​​agent-based review content determination method. Furthermore, memory 402 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 402 may optionally include memory remotely located relative to processor 401, and these remote memories may be connected via a network to the apparatus performing the AI ​​agent-based review content determination method. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0168] The input device 403 can receive user clicks and generate signal inputs related to user settings and function control of the AI-based intelligent agent's review content determination method. The display device 404 may include a display screen or other display device.

[0169] One or more modules are stored in memory 402, and when run by one or more processors 401, the method for determining review content based on AI intelligent agents in any of the above method embodiments is executed.

[0170] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0171] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0172] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0173] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms, or it can be implemented by hardware. By applying the solution of this embodiment, compared with the prior art, this embodiment updates the first mastery level identifier of students in response to their review behavior data of target knowledge points, and determines the update influence range of the target knowledge point in the knowledge graph to form a set of knowledge points to be updated, thereby realizing the dynamic linkage update of the mastery status of knowledge points; by updating the mastery level data and the second mastery level identifier of the set of knowledge points to be updated, the knowledge point learning grid is updated and the target review content is determined, thereby improving the matching degree between the review content and the actual cognitive state of students; by determining the direct and indirect influence range based on the prerequisite relationship, relevance relationship and indirect connection relationship of the target knowledge point in the knowledge graph, the update of the knowledge point learning grid and the determination of the target review content are realized. The system achieves precise selection of point sets; improves the accuracy of indirect influence range division by distinguishing different indirect dependencies and determining the indirect influence range based on the corresponding influence decay; ensures orderly updating of mastery data of knowledge points by determining update priority based on dependency strength data and importance data identified by the target model; improves the accuracy of mastery data update by determining update order based on update priority and updating data by combining centrality adjustment coefficient and change in mastery of target knowledge points; and accurately distinguishes mastery status indicators of key knowledge points and basic knowledge points by marking mastery indicators based on the comparison results of relevant data in the centrality adjustment coefficient and corresponding threshold.

[0174] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0175] The above are merely specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for determining review content based on an AI intelligent agent, characterized in that, include: In response to obtaining behavioral data from the knowledge point learning grid of the learning companion system on students reviewing target knowledge points in the set of review knowledge points, the first mastery level identifier of the target knowledge point is updated. Determine the update influence range of the mastery level of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid, and form a set of knowledge points to be updated based on the knowledge points covered by the update influence range; The mastery level data of the knowledge points in the set of knowledge points to be updated is updated, and the second mastery level identifier of the knowledge points in the set of knowledge points to be updated is updated based on the updated mastery level data; The knowledge point learning grid is updated to the target knowledge point learning grid based on the second mastery level identifier, and the target review content for the student is determined based on the target knowledge point learning grid.

2. The method according to claim 1, characterized in that, The step of determining the update influence range of the mastery level of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid, and forming a set of knowledge points to be updated based on the knowledge points covered by the update influence range, includes: Based on the target prerequisite relationships and target relevance relationships of the target knowledge points in the knowledge graph, the direct dependencies of the target knowledge points are determined, and the direct impact range is determined based on the direct dependencies. Based on the indirect connection relationship between the target knowledge point and other knowledge points in the knowledge graph, the indirect dependency relationship of the target knowledge point is determined, and the scope of indirect influence is determined based on the indirect dependency relationship; Determine the set of directly affected knowledge points covered by the direct impact scope, and the set of indirectly affected knowledge points covered by the indirect impact scope; Based on the set of directly affected knowledge points and the set of indirectly affected knowledge points, the set of knowledge points to be updated is generated.

3. The method according to claim 2, characterized in that, The process of determining the indirect dependencies of the target knowledge point based on the indirect connections between the target knowledge point and other knowledge points in the knowledge graph, and determining the scope of indirect influence based on the indirect dependencies, includes: Determine the first indirect dependency relationship established by the target knowledge point in the knowledge graph through an indirect knowledge point, and the second indirect dependency relationship connected by the target knowledge point through multiple indirect knowledge points; Based on the first influence decay degree corresponding to the first indirect dependency, the first influence range of the target knowledge point based on the first indirect dependency is determined from the knowledge graph; Based on the second influence decay degree corresponding to the second indirect dependency, the second influence range of the target knowledge point based on the second indirect dependency is determined from the knowledge graph; The indirect influence range is determined based on the first influence range and the second influence range.

4. The method according to claim 2, characterized in that, The step of updating the mastery level data of the knowledge points in the set of knowledge points to be updated, and updating the second mastery level identifier of the knowledge points in the set of knowledge points to be updated based on the updated mastery level data, includes: Based on the direct and indirect dependencies, determine the dependency strength data between the knowledge points in the set of knowledge points to be updated and the target knowledge point; The importance data of knowledge points in the set of knowledge points to be updated is identified by the target model, which is trained based on students' historical behavioral characteristics and their mastery of historical knowledge points. Based on the dependency strength data, the importance data, and the mastery level data, the update priority information for updating the knowledge points in the set of knowledge points to be updated is determined; The mastery level data is updated according to the update priority information, and the second mastery level identifier of the knowledge points in the set of knowledge points to be updated is updated based on the updated mastery level data.

5. The method according to claim 4, characterized in that, The step of updating the mastery level data according to the update priority information, and updating the second mastery level identifier of the knowledge points in the set of knowledge points to be updated according to the updated mastery level data, includes: The update priority information is used to determine the knowledge point update order list corresponding to the set of knowledge points to be updated, so as to determine the update order of the knowledge points in the set of knowledge points to be updated. For the first target knowledge point in the set of knowledge points to be updated, the centrality adjustment coefficient corresponding to the first target knowledge point is determined based on the connection strength data of the first target knowledge point in the knowledge graph, the distance data between the first target knowledge point and other knowledge points, and the degree centrality coefficient. The first target knowledge point is any knowledge point in the set of knowledge points to be updated. The mastery data of the first target knowledge point is updated based on the centrality adjustment coefficient and the change in the mastery data of the target knowledge point. Based on the updated mastery level data of the knowledge points in the set of knowledge points to be updated, the second mastery level identifier of the knowledge points in the set of knowledge points to be updated is updated.

6. The method according to claim 5, characterized in that, The step of updating the mastery data of the first target knowledge point based on the centrality adjustment coefficient and the change in the mastery data of the target knowledge point includes: If the connection strength data in the centrality adjustment coefficient is greater than the connection strength threshold and the change in the mastery data is less than the first change threshold, the first target knowledge point is identified as a key knowledge point, and the second mastery indicator of the first target knowledge point is marked as a not mastered indicator. If the degree centrality coefficient in the centrality adjustment coefficient is greater than the degree centrality threshold and the mastery level data is greater than the second mastery level threshold, the first target knowledge point is determined as a basic knowledge point, and the second mastery level identifier of the first target knowledge point is marked as a mastered identifier.

7. The method according to claim 2, characterized in that, The step of updating the knowledge point learning grid to the target knowledge point learning grid based on the second mastery level identifier, and determining the student's target review content based on the target knowledge point learning grid, includes: Determine at least one second mastery level identifier corresponding to the second target knowledge point in the set of knowledge points to be updated, wherein the second target knowledge point is any one of the knowledge points in the set of knowledge points to be updated; The at least one second mastery level identifier is decided based on the direct dependency relationship and the indirect dependency relationship, so as to determine the target mastery level identifier with the highest dependency relationship from the at least one second mastery level identifier; Determine the confidence level data of the target mastery level identifier corresponding to each knowledge point in the set of knowledge points to be updated, and update the knowledge point learning grid to the target knowledge point learning grid based on the target mastery level identifier that meets the confidence level conditions. The target review content for the student is determined based on the learning grid of the target knowledge points.

8. A device for determining review content based on an AI intelligent agent, characterized in that, include: The update module is configured to update the first mastery level identifier of the target knowledge point in response to obtaining behavioral data of students reviewing target knowledge points in the set of review knowledge points from the knowledge point learning grid of the learning companion system. The determination module is configured to determine the update influence range of the mastery level of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid, and to form a set of knowledge points to be updated based on the knowledge points covered by the update influence range. The update module is configured to update the mastery level data of the knowledge points in the set of knowledge points to be updated, and update the second mastery level identifier of the knowledge points in the set of knowledge points to be updated based on the updated mastery level data. The determination module is configured to update the knowledge point learning grid to a target knowledge point learning grid based on the second mastery level identifier, and determine the student's target review content based on the target knowledge point learning grid.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. An electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

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