Review knowledge point recommendation method and device based on AI intelligent agent

Abstract

The invention discloses a review knowledge point recommendation method and device based on an AI agent, and relates to the technical field of education, and the method comprises the steps: obtaining a knowledge point review path for a student to review learned knowledge points in a target knowledge point learning grid from a accompanying learning system, and determining to-be-reviewed knowledge points indicated by the knowledge point review path; extracting a to-be-reviewed knowledge point network corresponding to the to-be-reviewed knowledge points from a knowledge graph corresponding to the target knowledge point learning grid; updating the knowledge point review path to obtain a target knowledge point review path according to the mastering degrees of the students on the knowledge point to be reviewed, the at least one first reviewed knowledge point and the at least one related knowledge point; and determining a target to-be-reviewed knowledge point of the student based on the target knowledge point review path, and generating recommendation information of the target to-be-reviewed knowledge point to review the target to-be-reviewed knowledge point to the student. The accuracy of review knowledge point recommendation can be improved, and students are helped to focus on core review content.

Description

Technical Field

[0001] This application relates to the field of educational technology, and in particular to a method and apparatus for recommending review knowledge points based on an AI intelligent agent. Background Technology

[0002] In current intelligent education systems, personalization and precision in the review process have become key to improving learning efficiency. However, existing learning support systems fail to generate review paths that truly match each student's individual cognitive state, resulting in low review efficiency and negatively impacting students' learning motivation.

[0003] Existing systems recommend review points based solely on students' recent learning records or simple quiz accuracy rates, failing to consider the complex prerequisites and relationships between knowledge points and lacking the ability to dynamically extract and analyze these networks from knowledge graphs. As a result, the recommended review paths are often linear and point-like, unable to help students build a systematic knowledge framework.

[0004] The review paths initially generated by existing systems are usually fixed and cannot be updated according to students' real-time mastery of the knowledge points in the path. They lack real-time path update logic based on multi-dimensional mastery assessment, which makes the review plan inflexible and difficult to adapt to students' rapidly changing cognitive states.

[0005] Existing technologies often rely on pre-set rules or a single difficulty level, failing to comprehensively consider both the individual student's probability of mastery and the inherent attributes of the knowledge points. This coarse-grained prioritization method struggles to accurately recommend the most critical and urgent knowledge points to students within a limited review time, thus failing to maximize review efficiency. Summary of the Invention

[0006] In view of this, this application provides a method and apparatus for recommending review knowledge points based on an AI intelligent agent. The main purpose is to improve upon existing technologies that rely heavily on preset rules or a single difficulty level, failing to comprehensively consider the individual mastery probability of students and the inherent attributes of knowledge points. This coarse-grained prioritization method struggles to accurately recommend the most critical and urgent knowledge points to students within a limited review time, thus failing to maximize review efficiency.

[0007] Firstly, this application provides a method for recommending review knowledge points based on an AI intelligent agent, including: The learning companion system retrieves the knowledge point review path of the student in the target knowledge point learning grid, and determines the knowledge points to be reviewed indicated by the knowledge point review path. Extract the network of knowledge points to be reviewed corresponding to the knowledge point to be reviewed from the knowledge graph corresponding to the learning grid of the target knowledge point. 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. 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, the knowledge point review path is updated to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed. Based on the review path for the target knowledge points, the student's target knowledge points to be reviewed are determined, and recommendation information for the target knowledge points to be reviewed is generated to recommend the student to review the target knowledge points.

[0008] Optionally, updating the review path for the knowledge point to be reviewed 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 includes: Determine the knowledge mastery level indicator of the knowledge points to be reviewed from the network of knowledge points to be reviewed; 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 corresponding to the knowledge point to be reviewed in the knowledge point network, and the second knowledge point mastery level indicator of the at least one related knowledge point. Based on the mastery level identifier of the first knowledge point and the mastery level identifier of the second knowledge point, the review path of the knowledge point is updated to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed.

[0009] Optionally, the step of updating the knowledge point review path based on the first knowledge point mastery level identifier and the second knowledge point mastery level identifier to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed includes: Based on the mastery level indicator of the first knowledge point and the mastery level indicator of the second knowledge point, the set of first knowledge points that the student has mastered is determined from the at least one prerequisite knowledge point and the at least one related knowledge point; Remove the first set of 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, thereby obtaining the target knowledge point review path corresponding to the knowledge points to be reviewed.

[0010] Optionally, the step of updating the knowledge point review path based on the first knowledge point mastery level identifier and the second knowledge point mastery level identifier to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed includes: Based on the mastery level indicator of the first knowledge point and the mastery level indicator of the second knowledge point, determine the set of second knowledge points that the student has not mastered from the at least one prerequisite knowledge point and the at least one related knowledge point; Determine the learning priority information of the knowledge points in the second knowledge point set, and based on the learning priority information, select the first target knowledge point from the second knowledge point set that meets the priority condition; Using the first target knowledge point as the starting point of the review path, the network of knowledge points to be reviewed is updated to obtain the review path of the target knowledge point.

[0011] Optionally, determining the learning priority information of knowledge points in the second knowledge point set, and selecting a first target knowledge point from the second knowledge point set that meets the priority condition based on the learning priority information, includes: Determine the mastery probability data and knowledge point difficulty data for each knowledge point in the second knowledge point set; The learning priority of each knowledge point in the second knowledge point set is evaluated based on the mastery probability data and the knowledge point difficulty data to obtain the learning priority information of each knowledge point in the second knowledge point set. The knowledge point with the highest learning priority in the second set of knowledge points is determined as the first target knowledge point.

[0012] Optionally, after determining the knowledge point mastery level indicator of the knowledge point to be reviewed from the network of knowledge points to be reviewed, the method further includes: If the knowledge point mastery level indicator 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. Determine the second target knowledge point that meets the priority condition from the third knowledge point set, the prerequisite knowledge point set, and the related knowledge point set; Remove the knowledge points to be reviewed from the network of knowledge points to be reviewed, and use the second target knowledge point as the starting point of the review path to update the network of knowledge points to be reviewed, thereby obtaining the review path of the target knowledge point.

[0013] Optionally, the step of obtaining the knowledge point review path from the learning support system where students review learned knowledge points in the target knowledge point learning grid, and determining the knowledge points to be reviewed indicated by the knowledge point review path, includes: The student's learning behavior data in the target knowledge point learning grid is divided into at least one learning behavior data set, and a set of behavioral features corresponding to the at least one learning behavior data set is generated. 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 to obtain the effective value data of each behavioral feature in the behavioral feature set; Based on the effective value data, the behavioral features in the behavioral feature set are filtered to obtain the target behavioral features that meet the effective conditions in the behavioral feature set, and the target behavioral features are combined into a target behavioral feature set. Based on the target behavior feature set, the student's mastery of the knowledge points in the target knowledge point learning grid is analyzed, the student's unmastered knowledge points in the target knowledge point learning grid are determined, and the unmastered knowledge points are identified as the knowledge points to be reviewed indicated by the knowledge point review path.

[0014] Secondly, this application provides a review knowledge point recommendation device based on an AI intelligent agent, including: The module is configured to obtain the knowledge point review path of the student in the target knowledge point learning grid for reviewing the learned knowledge points from the learning companion system, and determine the knowledge points to be reviewed indicated by the knowledge point review path; The extraction module is configured to extract the network of knowledge points to be reviewed corresponding to the knowledge point to be reviewed from the knowledge graph corresponding to the learning grid of the target knowledge point. 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. The update module is configured to update the knowledge point 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. The determination module is configured to 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 student to review the target knowledge points.

[0015] 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 AI-based intelligent agent-based method for recommending review knowledge points as described in the first aspect.

[0016] 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 method for recommending review knowledge points as described in the first aspect.

[0017] By employing the above technical solutions, this application provides a method and apparatus for recommending review knowledge points based on AI intelligent agents. Compared with existing technologies, this application extracts a network of review knowledge points from a knowledge graph, consisting of prerequisite knowledge points and related knowledge points corresponding to the knowledge points to be reviewed. It then updates the review path based on the student's mastery of these knowledge points, thereby adapting the review path to the student's cognitive state. By generating recommendation information based on the updated review path for the target knowledge points, the accuracy of the review knowledge point recommendations is improved, helping students focus on core review content. Attached Figure Description

[0018] 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.

[0019] 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.

[0020] Figure 1 The illustration shows a flowchart of a method for recommending review knowledge points 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 recommending review knowledge points based on an AI agent, as provided in an embodiment of this application. Figure 4 This illustration shows a structural schematic diagram of a knowledge point recommendation device based on an AI agent provided in 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

[0021] 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.

[0022] To improve existing technologies, many rely heavily on preset rules or a single difficulty level, failing to comprehensively consider the individual student's probability of mastery and the inherent attributes of knowledge points. This coarse-grained prioritization method struggles to accurately recommend the most critical and urgent knowledge points to students within a limited review time, thus failing to maximize review efficiency. This embodiment provides a review knowledge point recommendation method based on an AI agent, such as... Figure 1 As shown, the method includes: Step 101: Obtain the knowledge point review path of the student in the target knowledge point learning grid from the learning companion system, and determine the knowledge points to be reviewed indicated by the knowledge point review path.

[0023] 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.

[0024] In this embodiment of the application, the target knowledge point learning grid can be a two-dimensional grid formed by mapping the student's current semester's course content 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.

[0025] 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.

[0026] In this embodiment, the knowledge point review path can be the knowledge point sequence path that students follow when reviewing learned knowledge points in the target knowledge point learning grid. The knowledge point review path can be pre-generated or dynamically adjusted based on the student's previous learning trajectory, the knowledge logic of the course, the teaching syllabus requirements, etc. The knowledge point review path can be used to guide students to systematically review the learned content.

[0027] In the embodiments of this application, the knowledge points to be reviewed can be the knowledge points indicated in the knowledge point review path that students need to focus on reviewing. The knowledge points to be reviewed can include knowledge points that students have not mastered to the preset standard, have knowledge gaps, or need to be strengthened and consolidated.

[0028] In this embodiment of the application, the system obtains the knowledge point review path of the student in the target knowledge point learning grid, which is used to review the knowledge points already learned by the student. The knowledge point to be reviewed indicated by the knowledge point review path can be determined by the system first retrieving the student's learning record in the target knowledge point learning grid through the student's user ID, including the knowledge points already learned, the learning time of each knowledge point, the practice questions (accuracy rate, number of wrong questions, number of redo questions), the online course learning progress (video completion rate, number of repeated viewings, dwell time), the number of notes, the frequency of asking questions, and other data. Then, the system extracts the knowledge point review path of the student reviewing the knowledge points already learned. Subsequently, the system combines the student's mastery of each knowledge point with the data to select the knowledge points that need to be reviewed and determine them as the knowledge points to be reviewed indicated by the knowledge point review path.

[0029] Step 102: 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.

[0030] 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.

[0031] 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.

[0032] In the embodiments of this application, the knowledge point network to be reviewed can be a sub-network in a knowledge graph that is directly associated with the knowledge point to be reviewed. The knowledge point network to be reviewed can include the knowledge point to be reviewed itself, as well as at least one prerequisite knowledge point that has a prerequisite relationship with the knowledge point to be reviewed and at least one related knowledge point that has a correlation relationship. The knowledge point network to be reviewed can be used to present the knowledge dependencies and associations of the knowledge point to be reviewed.

[0033] In the embodiments of this application, the prerequisite knowledge points can be the knowledge points that need to be mastered in advance before learning the knowledge points to be reviewed, and mastering the prerequisite knowledge points can be the basis for understanding and mastering the knowledge points to be reviewed.

[0034] In this embodiment of the application, the relevant knowledge points can be those that are related to or complementary to the knowledge points to be reviewed in terms of content, but do not need to strictly follow the order of learning.

[0035] In this embodiment of the application, the extraction of the network of knowledge points to be reviewed corresponding to the knowledge points to be reviewed from the knowledge graph corresponding to the learning grid of the target knowledge points can be achieved by the system first locating the knowledge graph corresponding to the learning grid of the target knowledge points. The knowledge graph corresponding to the learning grid of the target knowledge points is constructed based on the course content of the student in the current semester. The knowledge graph corresponding to the learning grid of the target knowledge points can include all knowledge points in the course and the relationships between knowledge points. Then, the system extracts all nodes and edges related to the knowledge points to be reviewed from the knowledge graph corresponding to the learning grid of the target knowledge points to form the network of knowledge points to be reviewed.

[0036] For example, extracting all nodes and edges related to the knowledge point to be reviewed from the knowledge graph corresponding to the learning grid of the target knowledge point can specifically include: the system can search for nodes corresponding to directed edges pointing to the knowledge point to be reviewed in the knowledge graph, and the nodes corresponding to directed edges of the knowledge point to be reviewed can be the prerequisite knowledge points of the knowledge point to be reviewed; the system can search for nodes connected to the knowledge point to be reviewed through undirected edges, and the nodes connected to the knowledge point to be reviewed through undirected edges can be the related knowledge points of the knowledge point to be reviewed.

[0037] Step 103: Based on the student's mastery of the knowledge points to be reviewed, at least one prerequisite knowledge point, and at least one related knowledge point, update the knowledge point review path to obtain the target knowledge point review path corresponding to the knowledge points to be reviewed.

[0038] In this embodiment of the application, the degree of mastery can be a quantitative indicator of a student's understanding and application of knowledge points. The degree of mastery can be calculated through students' learning behavior data (such as the accuracy rate of answering questions, the progress of online learning, the number of notes, the frequency of asking questions, etc.).

[0039] For example, in the embodiments of this application, the degree of mastery can be represented by the mastery probability, which can be in the range of [0,1]. The degree of mastery can have different mastery indicators, such as no mastery indicator, mastery but not proficient indicator, and mastery indicator (i.e., proficiency indicator), to provide data support for adjusting the review path.

[0040] In this embodiment, the target knowledge point review path can be a review path that is more in line with the student's actual learning situation, obtained by adjusting the initial knowledge point review path based on the student's mastery of the relevant knowledge points. The target knowledge point review path can be used to help students make up for knowledge gaps in a targeted manner and improve review efficiency.

[0041] In this embodiment, the student's mastery of the knowledge point to be reviewed, at least one prerequisite knowledge point, and at least one related knowledge point can be determined by the system first acquiring the student's mastery data on the knowledge point not yet reviewed, at least one prerequisite knowledge point corresponding to the knowledge point to be reviewed, and at least one related knowledge point. The mastery data can be obtained by analyzing the student's learning behavior data, which may include data such as the accuracy rate of completing practice questions, the accuracy rate of redoing incorrect questions, the completion rate of watching online course videos, the number of times repeated viewing, the number of notes, and the frequency of asking questions. The mastery probability of the knowledge point to be reviewed, at least one prerequisite knowledge point, and at least one related knowledge point can be calculated using models such as logistic regression model and softmax regression model, thereby determining the mastery level identifier corresponding to the knowledge point to be reviewed, at least one prerequisite knowledge point, and at least one related knowledge point.

[0042] In this embodiment of the application, the system can adjust and update the initial knowledge point review path based on the mastery level indicators of the knowledge point to be reviewed, at least one prerequisite knowledge point, and at least one related knowledge point. For example, if the mastery level indicator of prerequisite knowledge point A is "not mastered," then prerequisite knowledge point A can be added to the review path and prioritized for review; if the mastery level indicator of related knowledge point B is "mastered," then the review content of related knowledge point B can be appropriately simplified or skipped in the review path; if the mastery level of knowledge point C to be reviewed is low, then the review time and practice intensity of knowledge point C can be increased in the path, and finally, the target knowledge point review path corresponding to the knowledge point to be reviewed can be obtained.

[0043] Step 104: Based on the review path of the target knowledge points, determine the target knowledge points to be reviewed for students, generate recommendation information for the target knowledge points to be reviewed, and recommend the target knowledge points to be reviewed to students.

[0044] In this embodiment of the application, the target knowledge point to be reviewed can be the knowledge point that the student currently needs to review most, as indicated in the target knowledge point review path. For example, the target knowledge point to be reviewed in this embodiment of the application can specifically be a high-priority knowledge point selected based on factors such as the degree of mastery, the importance of the knowledge point, and the strength of the correlation.

[0045] For the embodiments of this application, the recommended information may include learning suggestions, learning resources, learning time planning, testing methods, etc. for the target knowledge points to be reviewed. The recommended information can be used to intuitively guide students to efficiently review the target knowledge points to be reviewed, clarify the review direction and specific requirements.

[0046] In this embodiment of the application, determining the student's target knowledge points to be reviewed based on the target knowledge point review path can be achieved by prioritizing all knowledge points in the target knowledge point review path. The criteria for prioritization can include the degree of mastery of the knowledge points (the lower the degree of mastery, the higher the priority), the importance of the knowledge points (based on the centrality indicators of nodes in the knowledge graph, such as betweenness centrality, degree centrality, proximity centrality, etc.; the higher the centrality, the more critical the knowledge point is in the knowledge network, and the higher the priority), and the strength of the association between the knowledge points and the knowledge points to be reviewed (the stronger the association, the greater the support for mastering the knowledge points to be reviewed, and the higher the priority). The system can select one or more knowledge points with the highest priority as the student's target knowledge points to be reviewed and generate corresponding recommendation information.

[0047] For the embodiments of this application, the recommended information may include the specific learning content of the target knowledge points to be reviewed (such as key concepts, core theorems, and common mistakes), recommended learning resources (such as targeted online course videos, special practice question sets, knowledge point explanation documents, and interactive learning tools), suggested learning time, learning order (such as reviewing basic concepts first, then analyzing example problems, and finally completing practice questions), and testing methods.

[0048] In this embodiment of the application, recommending review targets and knowledge points to students can be achieved by the system displaying the recommendation information to students through the learning tablet's learning companion system in the form of pop-ups, message pushes, or path annotations, thereby guiding students to review the target knowledge points according to the recommended content.

[0049] Compared with existing technologies, this embodiment extracts a network of prerequisite and related knowledge points from a knowledge graph to form the knowledge points to be reviewed. It then updates the review path based on the student's mastery of these knowledge points, thus adapting the review path to the student's cognitive state. By generating recommendation information based on the updated target knowledge point review path, the accuracy of the review knowledge point recommendations is improved, helping students focus on core review content.

[0050] As an optional approach, when executing the task of "updating the review path of the knowledge point to be reviewed based on the student's mastery of the knowledge point to be reviewed, at least one prerequisite knowledge point, and at least one related knowledge point," the following methods can be used, but are not limited to: Figure 3 As shown, the method includes: Step 201: Determine the knowledge mastery level indicators of the knowledge points to be reviewed from the network of knowledge points to be reviewed.

[0051] In the embodiments of this application, the knowledge point mastery level indicator can be a marker representing a student's mastery of a knowledge point. This indicator can reflect a student's knowledge mastery status and can be determined based on the probability of mastering the knowledge point. For example, the presentation format of the knowledge point mastery level indicator in the embodiments of this application can include color markings (e.g., red corresponds to not mastering the knowledge point, yellow corresponds to mastering the knowledge point but not being proficient, and green corresponds to mastering the knowledge point), text annotations, symbol markings, etc.

[0052] In the embodiments of this application, the knowledge point mastery level identifier of the knowledge point to be reviewed can be determined from the knowledge point network to be reviewed by the system extracting the mastery probability data of the knowledge point to be reviewed from the knowledge point network to be reviewed. The mastery probability data is calculated based on the student's previous learning behavior data (such as practice data, online course learning data, interaction data, etc.) through a preset model. The system can determine the knowledge point mastery level identifier corresponding to the knowledge point to be reviewed according to the preset probability threshold range.

[0053] Step 202: If 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.

[0054] In the embodiments of this application, the mastery level indicator of the first knowledge point can be the mastery level indicator of the prerequisite knowledge point of the knowledge point to be reviewed, and the mastery level indicator of the first knowledge point can be used to reflect the student's mastery level of the prerequisite knowledge.

[0055] In the embodiments of this application, the mastery level indicator of the second knowledge point can be the mastery level indicator corresponding to the related knowledge points of the knowledge point to be reviewed, and the mastery level indicator of the second knowledge point can be used to reflect the student's mastery of related knowledge.

[0056] In this embodiment of the application, if the knowledge point mastery level indicator of the knowledge point to be reviewed is determined to be "not mastered", it indicates that the student has significant gaps in their mastery of the knowledge point to be reviewed. The student can adjust their review path based on their mastery of prerequisite knowledge points and related knowledge points. The system can further search for all prerequisite knowledge points in the network of knowledge points to be reviewed that have a prerequisite relationship with the knowledge point to be reviewed, as well as related knowledge points that have a correlation relationship. Then, it can obtain the mastery probability data of prerequisite knowledge points and related knowledge points one by one (the mastery probability data can be calculated based on the student's past learning behavior data). After that, the system can determine the first knowledge point mastery level indicator for each prerequisite knowledge point and the second knowledge point mastery level indicator for each related knowledge point according to the threshold range of the mastery probability data.

[0057] Step 203: Based on the mastery level indicators of the first and second knowledge points, update the knowledge point review path to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed.

[0058] In this embodiment of the application, the system can combine the mastery level indicators of the first knowledge point and the mastery level indicators of the second knowledge point to analyze the impact of prerequisite knowledge points and related knowledge points on mastering the knowledge points to be reviewed, and make targeted adjustments and optimizations to the initial knowledge point review path, thereby obtaining the target knowledge point review path.

[0059] For example, if the mastery level of the first knowledge point corresponding to the prerequisite knowledge point is marked as "not mastered," it indicates that the prerequisite knowledge point is the main obstacle for students to master the knowledge point to be reviewed. The prerequisite knowledge point can be prioritized in the review path and can be scheduled for focused review before the knowledge point to be reviewed. If the mastery level of the first knowledge point corresponding to the prerequisite knowledge point is marked as "mastered but not proficient," the review path can include consolidation review content for the prerequisite knowledge point to strengthen students' understanding and application of the prerequisite knowledge before advancing the learning of the knowledge point to be reviewed. If the mastery level of the first knowledge point is marked as "mastered," it indicates that students have a solid grasp of the prerequisite knowledge point and do not need focused review. The content of the prerequisite knowledge point can be appropriately simplified in the review path, with only a brief review or direct skipping, thereby saving review time.

[0060] For example, if the mastery level of the second knowledge point corresponding to the relevant knowledge point is marked as "not mastered" or "mastered but not proficient", the relevant knowledge point can be arranged for review after the knowledge point to be reviewed. By reviewing the relevant knowledge point, the content of the knowledge point to be reviewed can be further consolidated, thereby forming a knowledge connection. If the mastery level of the second knowledge point corresponding to the relevant knowledge point is marked as "mastered", the review of the relevant knowledge point can be skipped in the review path, or the related content can be briefly mentioned when reviewing the knowledge point to be reviewed, thereby avoiding repetitive learning.

[0061] As an optional approach, when performing the task of "updating the review path of knowledge points based on the mastery level indicators of the first and second knowledge points to obtain the review path of the target knowledge point corresponding to the knowledge point to be reviewed", the following methods can be used, but are not limited to: determining 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 based on the mastery level indicators of the first and second knowledge points; removing the set of first knowledge points from the network of knowledge points to be reviewed, and using the knowledge point to be reviewed as the starting point of the review path to update the network of knowledge points to be reviewed, thereby obtaining the review path of the target knowledge point corresponding to the knowledge point to be reviewed.

[0062] In this embodiment of the application, the system can analyze the mastery level indicator of the first knowledge point for each prerequisite knowledge point and the mastery level indicator of the second knowledge point for each related knowledge point, filter out the knowledge points marked as mastered, and form a first knowledge point set. The knowledge points in the first knowledge point set can be knowledge points that students have already mastered, so students do not need to spend a lot of time on key review. The knowledge points in the first knowledge point set can be removed from the network of knowledge points to be reviewed.

[0063] In this embodiment of the application, if the mastery level of the knowledge point to be reviewed is marked as not mastered, then the knowledge point to be reviewed can be used as the starting point of the review path and the review can be arranged first. The system can adjust the original review path based on the remaining network of knowledge points to be reviewed (i.e. the network after removing the first set of knowledge points) and the relationship between knowledge points, so as to form a review path for the target knowledge point.

[0064] As an optional approach, when performing the task of "updating the review path of knowledge points based on the mastery level indicators of the first and second knowledge points to obtain the review path of the target knowledge point corresponding to the knowledge point to be reviewed," the following methods can be used, but are not limited to: determining a 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 based on the mastery level indicators of the first and second knowledge points; determining the learning priority information of the knowledge points in the second knowledge point set; selecting the first target knowledge point that meets the priority condition from the second knowledge point set based on the learning priority information; and using the first target knowledge point as the starting point of the review path to update the network of knowledge points to be reviewed, thereby obtaining the review path of the target knowledge point.

[0065] In this embodiment of the application, the system can filter out the prerequisite knowledge points marked as "not mastered" or "mastered but not proficient" for the first knowledge point mastery level, and the related knowledge points marked as "not mastered" or "mastered but not proficient" for the second knowledge point mastery level. The prerequisite knowledge points marked as "not mastered" or "mastered but not proficient" and the related knowledge points marked as "not mastered" or "mastered but not proficient" can form the second knowledge point set.

[0066] In this embodiment of the application, the knowledge points in the second set of knowledge points can be knowledge points where students have knowledge gaps or need to consolidate their knowledge.

[0067] In this embodiment of the application, learning priority information can be indicator information used to quantify the priority of knowledge point review. Knowledge points with higher learning priority information can be scheduled for review more frequently. The basis for determining learning priority information may include the degree of mastery of the knowledge point (the lower the degree of mastery, the higher the priority), the importance of the knowledge point (based on the centrality indicator of the knowledge graph, the higher the centrality, the higher the priority), the strength of the association between the knowledge point and the knowledge point to be reviewed (the stronger the association, the greater the support for mastering the knowledge point to be reviewed, and the higher the priority), and the difficulty of the knowledge point (the higher the difficulty, the more review time and effort are required, and the priority can be appropriately increased), etc.

[0068] In the embodiments of this application, the first target knowledge point can be the knowledge point that students need to review most at the moment. The first target knowledge point can be the starting point of the review path and can be used to help students fill in the most critical knowledge gaps first.

[0069] In this embodiment of the application, the system can evaluate the priority of each knowledge point in the second knowledge point set to obtain the corresponding learning priority information (such as priority score, priority level, etc.); then the system can set priority conditions (such as the highest priority score, priority level one, etc.) and select the knowledge points that meet the conditions from the second knowledge point set as the first target knowledge points.

[0070] In this embodiment of the application, the system can update the original network of knowledge points to be reviewed based on the first target knowledge point, the knowledge points to be reviewed, and other knowledge points in the second set of knowledge points, and combine the relationships between the knowledge points to generate a review path for the target knowledge point.

[0071] As an optional approach, when performing the step of "determining the learning priority information of knowledge points in the second set of knowledge points, and selecting the first target knowledge point from the second set of knowledge points that meets the priority conditions based on the learning priority information", the following methods may be used, but are not limited to: determining the mastery probability data and knowledge point difficulty data of each knowledge point in the second set of knowledge points; evaluating the learning priority of each knowledge point in the second set of knowledge points based on the mastery probability data and knowledge point difficulty data to obtain the learning priority information of each knowledge point in the second set of knowledge points; and determining the knowledge point with the highest learning priority information in the second set of knowledge points as the first target knowledge point.

[0072] In this embodiment of the application, the mastery probability data can be the mastery probability of each knowledge point in the second knowledge point set. The value range of the mastery probability can be [0,1]. The mastery probability data can be calculated based on the student's learning behavior data (such as the accuracy of answering questions, the progress of online learning, the number of notes, etc.) through a preset model (such as a logistic regression model). The mastery probability data can be used to quantify the degree of mastery of the knowledge points by the students.

[0073] In this embodiment of the application, the knowledge point difficulty data can be data used to represent the difficulty level of the knowledge point. The value range of the knowledge point difficulty data can be [0,1]. The knowledge point difficulty data can be determined by combining the complexity of the knowledge point content (such as the abstractness of the concept, the difficulty of the derivation of the theorem), the mastery level required by the teaching syllabus (such as understanding, comprehending, mastering, and applying), the average mastery time and average accuracy rate of the student group (the longer the average mastery time and the lower the average accuracy rate, the higher the difficulty data).

[0074] In this embodiment of the application, the system can evaluate the learning priority of each knowledge point in the second knowledge point set based on the evaluation rules and the mastery probability data and knowledge point difficulty data, and obtain the corresponding learning priority information; the system can compare the learning priority information of all knowledge points in the second knowledge point set and determine the knowledge point with the highest learning priority information as the first target knowledge point.

[0075] For example, the learning priority of each knowledge point in the second knowledge point set can be evaluated by weighted summation. The calculation formula for weighted summation is shown in Formula 1, where weight 1 and weight 2 are weight coefficients (e.g., weight 1 = 0.6, weight 2 = 0.4). If the learning priority score is higher, it indicates that the learning priority is higher.

[0076] Learning priority score = (1 - Probability of mastery data) × Weight 1 + Difficulty data of knowledge point × Weight 2 (Formula 1) For example, the learning priority of each knowledge point in the second knowledge point set can also be evaluated by a graded evaluation method. Specifically, the graded evaluation method can divide different level intervals according to the mastery probability data and the knowledge point difficulty data to determine the priority level of each knowledge point (such as level one, level two, level three). If the priority level is higher, it means that the learning priority is higher.

[0077] As an optional approach, after performing the step of "determining the knowledge point mastery level indicator of the knowledge points to be reviewed from the knowledge point network to be reviewed", the following methods can be used, but are not limited to: if the knowledge point mastery level indicator of the knowledge points to be reviewed is a mastered indicator, determine the knowledge point traversal range based on the knowledge points to be reviewed; traverse the knowledge points within the knowledge point traversal range to obtain a third set of knowledge points that the student has not mastered, and determine the set of prerequisite knowledge points and related knowledge points corresponding to the third set of knowledge points in the knowledge graph; determine the second target knowledge point that meets the priority condition from the third set of knowledge points, the set of prerequisite knowledge points, and the set of related knowledge points; remove the knowledge points to be reviewed from the knowledge point network to be reviewed, and use the second target knowledge point as the starting point of the review path to update the knowledge point network to obtain the target knowledge point review path.

[0078] For the embodiments of this application, the knowledge point traversal range can be the range of knowledge points in the knowledge graph that have a direct or indirect relationship with the knowledge point to be reviewed. The relationship can include subsequent relationships (the knowledge point to be reviewed is a prerequisite knowledge point for other knowledge points), correlation relationships, etc.

[0079] In the embodiments of this application, the basis for determining the traversal range can be the strength of association. For example, in the embodiments of this application, the basis for determining the traversal range can be to include knowledge points whose association strength with the knowledge points to be reviewed is greater than a threshold in the traversal range.

[0080] In this embodiment of the application, if the knowledge point mastery level of the knowledge point to be reviewed is marked as "mastered", it indicates that the student has a solid grasp of the knowledge point to be reviewed and there is no need to focus on it for further review. The system can determine the knowledge point traversal range based on the knowledge point to be reviewed. The system can traverse each knowledge point within the knowledge point traversal range, analyze the student's mastery level of each knowledge point, and filter out the knowledge points marked as "not mastered" to form a third knowledge point set.

[0081] In this embodiment of the application, the system can search for the prerequisite knowledge points and related knowledge points corresponding to each knowledge point in the third knowledge point set in the knowledge graph. The prerequisite knowledge points corresponding to each knowledge point in the third knowledge point set can form a prerequisite knowledge point set, and the related knowledge points corresponding to each knowledge point in the third knowledge point set can form a related knowledge point set.

[0082] In this embodiment of the application, the system can determine a second target knowledge point that meets the priority condition from the third knowledge point set, the prerequisite knowledge point set, and the related knowledge point set based on factors such as the importance, relevance, and mastery of the knowledge point. The priority condition can be that the learning priority score is higher than a preset score threshold, or that the priority level is level one.

[0083] In the embodiments of this application, the system can remove mastered knowledge points from the network of knowledge points to be reviewed, take the second target knowledge point as the starting point of the review path, and update the original network of knowledge points to be reviewed by combining the knowledge points that have not been mastered or need to be consolidated in the set of prerequisite knowledge points and the set of related knowledge points, so as to obtain the target knowledge point review path.

[0084] As an optional approach, when performing the task of "obtaining the knowledge point review path for students to review learned knowledge points in the target knowledge point learning grid from the learning support system, and determining the knowledge points to be reviewed indicated by the knowledge point review path," the following methods can be used, but are not limited to: dividing the students' learning behavior data in the target knowledge point learning grid into at least one learning behavior data set, and generating a behavioral feature set corresponding to at least one learning behavior data set; determining the time information corresponding to each behavioral feature in the behavioral feature set, and evaluating the effectiveness of each behavioral feature in the behavioral feature set based on the time information, to obtain the effective value data of each behavioral feature in the behavioral feature set; filtering the behavioral features in the behavioral feature set based on the effective value data, obtaining the target behavioral features that meet the effective conditions in the behavioral feature set, and forming a target behavioral feature set; analyzing the students' mastery of knowledge points in the target knowledge point learning grid based on the target behavioral feature set, determining the students' unmastered knowledge points in the target knowledge point learning grid, and determining the unmastered knowledge points as the knowledge points to be reviewed indicated by the knowledge point review path.

[0085] In this embodiment, learning behavior data can be various operational data generated by students when learning within a learning grid for target knowledge points. This data can reflect the student's learning process and learning status. For example, the learning behavior data in this embodiment may include, but is not limited to, online course learning data (such as video completion rate, number of repeated views, dwell time, fast-forward / rewind operation records), practice question data (such as answer accuracy rate, answer speed, number of incorrect questions, correct rate of redoing incorrect questions, number of questions completed, and question difficulty level), and interaction data (such as question frequency, question content, number of notes, detail of notes, number of comments, and activity level in discussions).

[0086] In this embodiment, the system can divide learning behavior data into at least one learning behavior data set according to the scenario or data type in which the data is generated. For example, it can be divided into online course learning data set, question-solving data set, and interaction data set according to the scenario, or into progress data set, accuracy data set, and interaction frequency data set according to the data type. The system can extract features that reflect the student's learning status and mastery of knowledge points for each learning behavior data set and generate a corresponding behavioral feature set.

[0087] For example, the behavioral feature set corresponding to the online course learning dataset may include features such as video completion rate, number of repeated views, average dwell time, and percentage of dwell time on key chapters; the behavioral feature set corresponding to the question-solving dataset may include features such as answer accuracy, answering speed, correct answering rate for incorrect questions, and correct answering rate for high-difficulty questions; the behavioral feature set corresponding to the interaction dataset may include features such as question frequency, number of notes, and level of detail in notes.

[0088] In this embodiment of the application, the time information corresponding to the behavioral feature can be the specific timestamp when each behavioral feature is generated. For example, the time information corresponding to the behavioral feature in this embodiment of the application can include the time corresponding to the video viewing completion rate (the time when the student finishes watching the video), the time corresponding to the answer accuracy rate (the time when the student finishes answering the questions), etc.

[0089] In this embodiment of the application, the effective value data can be the data obtained by adjusting the original value of the behavioral feature in combination with the time decay effect, and the effective value data can be used to reflect the student's current mastery status.

[0090] In the embodiments of this application, the system can evaluate the effectiveness of each behavioral feature based on time information to obtain the corresponding effective value data; the system can introduce a time decay factor to evaluate the effectiveness of each behavioral feature. The time decay factor can be determined based on the interval between the time when the behavioral feature is generated and the current time. If the interval is longer, the time decay factor is smaller.

[0091] For example, the time decay factor in this application embodiment can be specifically expressed as: ,in, It can represent the time taken to answer the question for the i-th time. It can indicate the current time for answering the question. It can represent the attenuation coefficient.

[0092] In this embodiment, the system can filter behavioral features in the behavioral feature set by setting valid conditions. The valid condition can be that the valid value data is greater than the valid threshold. The valid threshold can be determined based on experience or data statistics. The valid threshold can be used to filter out behavioral features that have practical reference value for assessing the current level of mastery. The system can filter out the behavioral features that meet the valid conditions to form a target behavioral feature set.

[0093] In this embodiment, analyzing students' mastery of knowledge points based on a set of target behavioral features can be achieved by the system calculating the mastery level of each knowledge point using behavioral feature data. Specifically, this calculation can be performed by constructing feature vectors, incorporating online learning data (video viewing completion rate, etc.). Repeat viewing count Duration of stay ), practice data (accuracy rate) Answering speed Correctness rate of redoing incorrect questions Interaction data (frequency of questions) Number of notes Features such as ) are included, and the feature vector is shown in Formula 2, where, This can be expressed as video completion rate, This can be expressed as the number of times the video was viewed. It can be expressed as the length of stay, It can represent the accuracy rate of solving problems, It can indicate the speed of answering questions, It can represent the accuracy rate of redoing incorrect questions. It can indicate the frequency of questions asked. It can represent the number of notes; and perform normalization processing, the expression for which is shown in Formula 3, normalizing each feature vector to the range of [0,1].

[0094] (Formula 2) (Formula 3) In this embodiment, the system can use a model (such as a logistic regression model, a softmax regression model, etc.) to calculate the mastery level of knowledge points. For example, the system can use a logistic regression model to calculate the mastery level of knowledge points. Specifically, the system can first use normalized target behavioral features as model input. The model's weight vector (representing the importance of each feature to the mastery level) is obtained based on historical student data. Specifically, the model's weight vector can be obtained by collecting a large amount of historical students' behavioral feature data and corresponding actual knowledge point mastery level labels (such as determined through exam scores and teacher evaluations). Furthermore, the model parameters can be optimized using a gradient descent algorithm to ensure the model accurately outputs the mastery level assessment results. The bias term in the model can be used to adjust the overall assessment benchmark.

[0095] For example, the system can first calculate the mastery score when using a model (such as logistic regression model, softmax regression model, etc.) to calculate the mastery level of knowledge points. As shown in Formula 4, where, It can represent a weight vector, which can represent the importance of each feature to the degree of mastery, and b is the bias term.

[0096] (Formula 4) For example, a score can be determined through an objective function. Mapping to mastery probability data, the objective function can include the sigmoid function, softmax function, etc.; calculating the probability of students mastering knowledge points can be done using the sigmoid function. Mapping to mastery probability Mastering probability The expression is shown in Formula 5; a low threshold for the probability of mastery can be set. and mastering the high probability threshold (For example, setting a threshold) =0.4 and =0.7), if the probability is known Less than the low threshold of mastery probability If so, the knowledge point can be marked as one that meets the condition of not being mastered; if the probability of mastery is high... Greater than or equal to the low threshold of mastery probability And less than the high probability of mastery threshold If so, the knowledge point can be marked as knowledge point that you have mastered but are not proficient in; if Greater than or equal to the high probability threshold If you do not master a knowledge point, you can mark it as a knowledge point you have mastered; the system can identify knowledge points that you have not mastered as knowledge points to be reviewed in the knowledge point review path.

[0097] (Formula 5) As an optional approach, this application embodiment also provides an example of a learning path recommendation for consolidating knowledge points. First, a knowledge point is randomly selected as the starting point for learning; this knowledge point can be one of red, green, or yellow. Second, a graph traversal algorithm (such as Breadth-First Search (BFS) or Depth-First Search (DFS)) can be used to search for unmastered (red or yellow) knowledge points starting from the current knowledge point, while respecting the dependencies of directed edges. Specific steps may include: using C(k) to represent the color of knowledge point k; if the student is currently at knowledge point k and C(k) is green, knowledge point k can be marked as "mastered," and subsequent learning content (such as additional exercises) for knowledge point k can be skipped; traversing the neighboring knowledge points of knowledge point k (connected by directed or undirected edges), prioritizing knowledge points with a red or yellow color as candidates; checking whether all prerequisites (knowledge points pointed to by directed edges) of the candidate knowledge point have been mastered (green). If all prerequisites of the candidate knowledge point have been mastered, the candidate knowledge point is recommended; otherwise, it is recommended to learn the unmastered prerequisites first.

[0098] Optionally, if there are multiple candidate knowledge points, they can be sorted according to the probability of mastery pi (prioritizing the one with the lowest probability of learning) or other priorities (such as the difficulty of the knowledge point).

[0099] Optional, we recommend the next knowledge point. The calculation formula can be shown in Formula 6, where N(k) can represent the neighbor set of knowledge point k. C(k) can represent the set of prerequisites for knowledge point k', and C(k) can represent the color of knowledge point k. If there is no knowledge point k' that satisfies the conditions, backtracking or choosing another path is possible.

[0100] (Formula 6) Optionally, after a student completes the learning of a knowledge point, their mastery probability can be recalculated and the color updated, and a new path recommended.

[0101] For example, given a knowledge graph with knowledge points A, B, and C, and edges A→B and A→C (where A is a prerequisite for B and C), if a student starts learning from knowledge point A and calculates C(A) = green, then the subsequent content of knowledge point A can be skipped, and knowledge points B and C can be checked. If C(B) = red, C(C) = yellow, and the prerequisite knowledge point A for knowledge points B and C has been mastered, then the system may prioritize recommending knowledge point B (because red indicates that it has not been mastered and needs to be learned more). After the student learns knowledge point B, C(B) is updated; if it turns green, then knowledge point C can be recommended next.

[0102] Compared with existing technologies, this embodiment first determines the mastery level indicator of the knowledge point to be reviewed, and then updates the path by combining the first mastery level indicator of its prerequisite knowledge points and the second mastery level indicator of related knowledge points, thus achieving targeted updating of the review path; by removing the set of mastered first knowledge points and updating the path with the knowledge point to be reviewed as the starting point, it improves the efficiency of the review path; by determining the set of unmastered second knowledge points and selecting the first target knowledge point as the starting point based on learning priority, it achieves the rationality of the starting point selection of the review path; by evaluating learning priority and determining the first target knowledge point based on mastery probability data and knowledge point difficulty data, it improves the objectivity of priority evaluation; and by traversing and determining the set of unmastered third knowledge points and the corresponding prerequisite and related knowledge point sets when the knowledge point to be reviewed has been mastered, and selecting the second target knowledge point as the starting point to update the path, it achieves the dynamic adjustment and adaptability of the review path.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 6: In response to obtaining the fourth learning behavior data of the student reviewing the target knowledge point to be reviewed, the first mastery level identifier of the target knowledge point to be reviewed is updated; the update influence range of the mastery level of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid is determined, and a set of knowledge points to be updated is formed 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 according to 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 of the student is determined according to the target knowledge point learning grid.

[0136] Example 61: Based on the target prerequisite relationships and target relevance relationships of target knowledge points in the knowledge graph, determine the direct dependencies of target knowledge points, and determine the direct impact range based on the direct dependencies; based on the indirect connection relationships between target knowledge points and other knowledge points in the knowledge graph, determine the indirect dependencies of target knowledge points, 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; 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.

[0137] Example 62: Determine the first indirect dependency relationship established by a target knowledge point through an indirect knowledge point in the knowledge graph, and the second indirect dependency relationship connected by multiple indirect knowledge points; based on the first influence decay degree corresponding to the first indirect dependency relationship, determine the first influence range of the target knowledge point based on the first indirect dependency relationship in the knowledge graph; based on the second influence decay degree corresponding to the second indirect dependency relationship, determine the second influence range of the target knowledge point based on the second indirect dependency relationship in the knowledge graph; determine the indirect influence range based on the first influence range and the second influence range.

[0138] Example 63: Based on direct and indirect dependencies, determine the dependency strength data between knowledge points in the set of knowledge points to be updated and the target knowledge points; identify the importance data of knowledge points in the set of knowledge points to be updated through the 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 knowledge points in the set of knowledge points to be updated based on 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 label of the knowledge points in the set of knowledge points to be updated based on the updated mastery level data.

[0139] Example 64: 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 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. The first target knowledge point is any knowledge point in the knowledge point set to be updated; update 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; update the second mastery label of the knowledge points in the knowledge point set to be updated based on the updated mastery data of the knowledge points in the knowledge point set to be updated.

[0140] Example 65: 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, the first target knowledge point is identified as a key knowledge point, and the second mastery level identifier of the first target knowledge point is marked 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, the first target knowledge point is identified as a basic knowledge point, and the second mastery level identifier of the first target knowledge point is marked as a mastered identifier.

[0141] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 7: Obtain fifth 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 fifth 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.

[0142] 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 fifth 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; based on the question information, note information, and the second mastery level data, determine the student's learning status information of the knowledge points in the knowledge point learning grid; based on the learning status information and the degree of difference, identify the knowledge points with abnormal status from the knowledge point learning grid; 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.

[0143] 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.

[0144] 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.

[0145] Example 74: Divide the fifth 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Furthermore, as Figure 1 and Figure 3 The specific implementation of the method shown in this embodiment provides a review knowledge point recommendation device based on an AI intelligent agent, such as... Figure 4 As shown, the device includes: a determination module 31, an extraction module 32, and an update module 33.

[0157] Module 31 is configured to obtain the knowledge point review path for students to review the learned knowledge points in the target knowledge point learning grid from the learning companion system, and determine the knowledge points to be reviewed indicated by the knowledge point review path. The extraction module 32 is configured to extract the network of knowledge points to be reviewed corresponding to the knowledge points to be reviewed from the knowledge graph corresponding to the learning grid of the target knowledge points. 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 points to be reviewed. The update module 33 is configured to update the knowledge point review path based on the student's mastery of the knowledge point to be reviewed, at least one prerequisite knowledge point, and at least one related knowledge point, to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed. The determination module 31 is also configured to determine the student's target knowledge points to be reviewed based on the target knowledge point review path, and generate recommendation information for the target knowledge points to be reviewed, so as to recommend the target knowledge points to be reviewed to the student.

[0158] In some examples of this embodiment, the determining module 31 is specifically configured to: determine the knowledge point mastery level identifier of the knowledge point to be reviewed from the knowledge point network to be reviewed; when the knowledge point mastery level identifier of the knowledge point to be reviewed is an unmastered identifier, determine the first knowledge point mastery level identifier of at least one prerequisite knowledge point corresponding to the knowledge point to be reviewed in the knowledge point network to be reviewed, and the second knowledge point mastery level identifier of at least one related knowledge point; based on the first knowledge point mastery level identifier and the second knowledge point mastery level identifier, update the knowledge point review path to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed.

[0159] In some examples of this embodiment, the update module 33 is specifically configured to 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 based on the first knowledge point mastery level identifier and the second knowledge point mastery level identifier; remove the set of first knowledge points from the knowledge point network to be reviewed, and use the knowledge point to be reviewed as the starting point of the review path to update the knowledge point network to be reviewed, thereby obtaining the target knowledge point review path corresponding to the knowledge point to be reviewed.

[0160] In some examples of this embodiment, the update module 33 is further configured to: determine a second set of knowledge points that the student has not mastered from at least one prerequisite knowledge point and at least one related knowledge point based on the first knowledge point mastery level identifier and the second knowledge point mastery level identifier; determine the learning priority information of the knowledge points in the second knowledge point set; select a first target knowledge point from the second knowledge point set that meets the priority condition based on the learning priority information; and update the network of knowledge points to be reviewed by using the first target knowledge point as the starting point of the review path to obtain the target knowledge point review path.

[0161] In some examples of this embodiment, the determining module 31 is further configured to determine the mastery probability data and knowledge point difficulty data of 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; and determine the knowledge point with the highest learning priority information in the second knowledge point set as the first target knowledge point.

[0162] In some examples of this embodiment, the determining module 31 is further configured to: determine the knowledge point traversal range based on the knowledge point to be reviewed when the knowledge point mastery level indicator is marked as "mastered"; traverse the knowledge points within the knowledge point traversal range to obtain a third set of knowledge points that the student has not mastered; determine the set of prerequisite knowledge points and the set of related knowledge points corresponding to the third set of knowledge points in the knowledge graph; determine a second target knowledge point that meets the priority condition from the third set of knowledge points, the set of prerequisite knowledge points, and the set of related knowledge points; remove the knowledge point to be reviewed from the knowledge point network to be reviewed, and use the second target knowledge point as the starting point of the review path to update the knowledge point network to obtain the target knowledge point review path.

[0163] In some examples of this embodiment, the determining module 31 is further configured to divide the student's learning behavior data in the target knowledge point learning grid into at least one learning behavior data set, and generate a behavioral feature set 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 to obtain 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 to obtain the target behavioral features in the behavioral feature set that meet the effective conditions, and form a target behavioral feature set; analyze the student's mastery of the knowledge points in the target knowledge point learning grid based on the target behavioral feature set, determine the student's unmastered knowledge points in the target knowledge point learning grid, and determine the unmastered knowledge points as the knowledge points to be reviewed indicated by the knowledge point review path.

[0164] It should be noted that other corresponding descriptions of the functional units involved in the AI-based intelligent agent-based review knowledge point recommendation 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.

[0165] Based on the above, Figure 1 and Figure 3 Accordingly, 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.

[0166] 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.

[0167] 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, such that the at least one processor can perform the aforementioned AI agent-based method for recommending review points.

[0168] Figure 5 Take a processor 401 as an example.

[0169] The electronic device may also include an input device 403 and a display device 404.

[0170] 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.

[0171] 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 knowledge point recommendation method in this application embodiment. 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 method for recommending review knowledge points in the above embodiments.

[0172] 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 knowledge point recommendation 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, which can be connected via a network to means of executing the AI ​​agent-based review knowledge point recommendation method. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0173] Input device 403 can receive user clicks and generate signal inputs related to user settings and function control of the AI-based intelligent agent-based review knowledge point recommendation method. Display device 404 may include display devices such as a display screen.

[0174] One or more modules are stored in memory 402, and when run by one or more processors 401, the AI-based intelligent agent-based method for recommending review knowledge points is executed in any of the above method embodiments.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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 extracts a network of knowledge points to be reviewed from a knowledge graph, consisting of prerequisite knowledge points and related knowledge points, and updates the review path based on the student's mastery of these knowledge points, thus achieving adaptation of the review path to the student's cognitive state; by generating recommendation information based on the updated target knowledge point review path, the accuracy of the review knowledge point recommendation is improved, helping students focus on core review content; by first determining the mastery level indicator of the knowledge point to be reviewed, and then combining it with the first mastery level indicator of its prerequisite knowledge points and the second mastery level indicator of related knowledge points to update the path, the review path is realized. The updates are targeted; the efficiency of the review path is improved by removing the first set of mastered knowledge points and updating the path with the knowledge points to be reviewed as the starting point; the rationality of the starting point selection of the review path is achieved by identifying the second set of unmastered knowledge points and selecting the first target knowledge point as the starting point based on learning priority; the objectivity of priority evaluation is improved by evaluating learning priority and determining the first target knowledge point based on mastery probability data and knowledge point difficulty data; and the dynamic adjustment and adaptability of the review path is achieved by traversing and identifying the third set of unmastered knowledge points and the corresponding prerequisite and related knowledge point sets when the knowledge points to be reviewed have been mastered, and selecting the second target knowledge point as the starting point to update the path.

[0179] 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. Without further limitations, 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 said element.

[0180] The above description is merely a specific embodiment 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 the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for recommending review knowledge points based on AI intelligent agents, characterized in that, include: The system retrieves the knowledge point review path of the student in the target knowledge point learning grid, and determines the knowledge points to be reviewed indicated by the knowledge point review path. Extract the network of knowledge points to be reviewed corresponding to the knowledge point to be reviewed from the knowledge graph corresponding to the learning grid of the target knowledge point. 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. 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, the knowledge point review path is updated to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed. Based on the review path for the target knowledge points, the student's target knowledge points to be reviewed are determined, and recommendation information for the target knowledge points to be reviewed is generated to recommend the student to review the target knowledge points.

2. The method according to claim 1, characterized in that, The method of updating the review path of the knowledge point 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 includes: Determine the knowledge mastery level indicator of the knowledge points to be reviewed from the network of knowledge points to be reviewed; 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 corresponding to the knowledge point to be reviewed in the knowledge point network, and the second knowledge point mastery level indicator of the at least one related knowledge point. Based on the mastery level identifier of the first knowledge point and the mastery level identifier of the second knowledge point, the review path of the knowledge point is updated to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed.

3. The method according to claim 2, characterized in that, The step of updating the review path for the knowledge point based on the mastery level identifier of the first knowledge point and the mastery level identifier of the second knowledge point to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed includes: Based on the mastery level indicator of the first knowledge point and the mastery level indicator of the second knowledge point, the set of first knowledge points that the student has mastered is determined from the at least one prerequisite knowledge point and the at least one related knowledge point; Remove the first set of 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, thereby obtaining the target knowledge point review path corresponding to the knowledge points to be reviewed.

4. The method according to claim 2, characterized in that, The step of updating the review path for the knowledge point based on the mastery level identifier of the first knowledge point and the mastery level identifier of the second knowledge point to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed includes: Based on the mastery level indicator of the first knowledge point and the mastery level indicator of the second knowledge point, determine the set of second knowledge points that the student has not mastered from the at least one prerequisite knowledge point and the at least one related knowledge point; Determine the learning priority information of the knowledge points in the second knowledge point set, and based on the learning priority information, select the first target knowledge point from the second knowledge point set that meets the priority condition; Using the first target knowledge point as the starting point of the review path, the network of knowledge points to be reviewed is updated to obtain the review path of the target knowledge point.

5. The method according to claim 4, characterized in that, The step of determining the learning priority information of knowledge points in the second knowledge point set, and selecting a first target knowledge point from the second knowledge point set that meets the priority conditions based on the learning priority information, includes: Determine the mastery probability data and knowledge point difficulty data for each knowledge point in the second knowledge point set; The learning priority of each knowledge point in the second knowledge point set is evaluated based on the mastery probability data and the knowledge point difficulty data to obtain the learning priority information of each knowledge point in the second knowledge point set. The knowledge point with the highest learning priority in the second set of knowledge points is determined as the first target knowledge point.

6. The method according to claim 2, characterized in that, After determining the knowledge point mastery level indicator of the knowledge point to be reviewed from the network of knowledge points to be reviewed, the method further includes: If the knowledge point mastery level indicator 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. Determine the second target knowledge point that meets the priority condition from the third knowledge point set, the prerequisite knowledge point set, and the related knowledge point set; Remove the knowledge points to be reviewed from the network of knowledge points to be reviewed, and use the second target knowledge point as the starting point of the review path to update the network of knowledge points to be reviewed, thereby obtaining the review path of the target knowledge point.

7. The method according to claim 1, characterized in that, The step of obtaining the knowledge point review path from the learning support system, in which the student reviews the learned knowledge points within the target knowledge point learning grid, and determining the knowledge points to be reviewed indicated by the knowledge point review path, includes: The student's learning behavior data in the target knowledge point learning grid is divided into at least one learning behavior data set, and a set of behavioral features corresponding to the at least one learning behavior data set is generated. 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 to obtain the effective value data of each behavioral feature in the behavioral feature set; Based on the effective value data, the behavioral features in the behavioral feature set are filtered to obtain the target behavioral features that meet the effective conditions in the behavioral feature set, and the target behavioral features are combined into a target behavioral feature set. Based on the target behavior feature set, the student's mastery of the knowledge points in the target knowledge point learning grid is analyzed, the student's unmastered knowledge points in the target knowledge point learning grid are determined, and the unmastered knowledge points are identified as the knowledge points to be reviewed indicated by the knowledge point review path.

8. A review knowledge point recommendation device based on an AI intelligent agent, characterized in that, include: The module is configured to obtain the knowledge point review path of the student in the target knowledge point learning grid for reviewing the learned knowledge points from the learning companion system, and determine the knowledge points to be reviewed indicated by the knowledge point review path; The extraction module is configured to extract the network of knowledge points to be reviewed corresponding to the knowledge point to be reviewed from the knowledge graph corresponding to the learning grid of the target knowledge point. 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. The update module is configured to update the knowledge point 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. The determination module is configured to 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 student to review the target knowledge points.

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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