An AI agent-based learning path generation method and device

Abstract

The application discloses an AI intelligent agent-based learning path generation method and device, relates to the technical field of education, and comprises the following steps: acquiring learning position information of a student in a target knowledge point learning grid from a companion learning system, and determining a target learning course; dividing learning behavior data of the student in the target learning course into at least one learning behavior data set, and generating at least one behavior feature set; analyzing the mastering degree of the student to knowledge points in the target learning course based on the at least one behavior feature set, and determining a set of unmastered knowledge points of the student; matching the set of unmastered knowledge points with a target knowledge graph corresponding to the target knowledge point learning grid, and obtaining a target knowledge point network; and generating a target learning path of the student in the target knowledge point learning grid according to the target knowledge point network. The application can improve the reliability of learning path generation and enhance the pertinence of assisting the student to master the unmastered knowledge points.

Description

Technical Field

[0001] This application relates to the field of educational technology, and in particular to a learning path generation method and apparatus based on AI intelligent agents. Background Technology

[0002] The knowledge graphs used in current systems mostly rely on expert subjective annotation or automated data mining methods for construction. Because knowledge graphs are affected by the cognitive boundaries and subjective judgments of experts, they are difficult to fully cover the complex implicit relationships between knowledge points. Furthermore, knowledge graphs are also affected by data noise and sample bias, resulting in quality problems such as insufficient confidence and weak logical support in the extracted relationships, which affects the reliability of subsequent learning path generation.

[0003] The relation weights in existing knowledge graphs are often based on static rules or historical data presets, making it impossible to adjust them in real time according to students' actual learning behavior and mastery levels. When students learn according to the learning path recommended by the system, the system cannot accurately determine whether the mastery of prior knowledge truly meets the learning requirements of subsequent content, nor can it identify knowledge jumps or path redundancy caused by inaccurate relationships. This rigid knowledge representation method severely restricts the personalization of learning paths.

[0004] Traditional systems can only process single-dimensional learning data and lack the ability to deeply integrate and analyze multi-source heterogeneous learning behavior data. Multimodal data generated by students during the learning process, such as video viewing records, exercise answering behavior, and participation in interactive discussions, are often analyzed in isolation, making it impossible to form a comprehensive and accurate assessment of students' knowledge mastery status. This makes it difficult for the system to accurately identify the set of knowledge points that students have not mastered. Summary of the Invention

[0005] In view of this, this application provides a learning path generation method and apparatus based on AI intelligent agents. The main purpose is to improve the technical problem in the current technology that students cannot receive timely replies from other students after leaving messages in the comment area, or even receive no replies at all. This results in students not being able to fully understand the knowledge points tested in the questions even if they have done them, which affects students' learning efficiency and experience.

[0006] Firstly, this application provides a learning path generation method based on an AI agent, including:

[0007] The learning location information of students in the learning grid of target knowledge points is obtained from the learning companion system, and the target learning course corresponding to the learning location information is determined.

[0008] The student's learning behavior data in the target learning course is divided into at least one learning behavior data set, and at least one set of behavioral features corresponding to the at least one learning behavior data set is generated;

[0009] Based on the analysis of the at least one set of behavioral characteristics, the student’s mastery of the knowledge points in the target learning course is determined, and the set of knowledge points that the student has not mastered in the target learning course is determined.

[0010] The set of unmastered knowledge points is matched with the target knowledge graph corresponding to the target knowledge point learning grid to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph;

[0011] Based on the target knowledge point network, a target learning path is generated for the student in the target knowledge point learning grid. The target learning path is used to assist the student in mastering the set of unmastered knowledge points.

[0012] Optionally, the step of analyzing the student's mastery of knowledge points in the target learning course based on the at least one set of behavioral features, and determining the set of knowledge points the student has not mastered in the target learning course, includes:

[0013] Determine the time information corresponding to each behavioral feature in the at least one set of behavioral features, and evaluate the effectiveness of each behavioral feature in the at least one set of behavioral features based on the time information to obtain the effective value data of each behavioral feature in the at least one set of behavioral features;

[0014] Based on the effective value data, the behavioral features in the at least one set of behavioral features are filtered to obtain the target behavioral features that meet the effective conditions in the at least one set of behavioral features, and the target behavioral features are combined into at least one set of target behavioral features.

[0015] Based on the analysis of the at least one set of target behavioral features, the student's mastery of the knowledge points in the target learning course is determined, thereby identifying the set of knowledge points that the student has not mastered in the target learning course.

[0016] Optionally, the step of analyzing the student's mastery of knowledge points in the target learning course based on the at least one set of target behavioral features, and determining the set of knowledge points the student has not mastered in the target learning course, includes:

[0017] The at least one set of target behavior features is matched with each knowledge point in the target learning course to determine at least one target behavior feature corresponding to each knowledge point in the target learning course;

[0018] The target model identifies the importance 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. The target model is trained based on the student's historical behavioral characteristics and the degree of mastery of historical knowledge points.

[0019] Based on at least one target behavioral feature and importance data corresponding to each knowledge point, determine the student's mastery data of each knowledge point in the target learning course;

[0020] Based on the mastery data, determine the set of knowledge points that the students have not mastered in the target learning course.

[0021] Optionally, determining the student's mastery data for each knowledge point in the target learning course based on at least one target behavioral feature and importance data corresponding to each knowledge point includes:

[0022] Based on the 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;

[0023] Based on the influence coefficient, the at least one behavioral feature is analyzed to generate data on the student's mastery of each knowledge point in the target learning course.

[0024] Optionally, determining the set of unmastered knowledge points for the student in the target learning course based on the mastery data includes:

[0025] By mapping the mastery data of each knowledge point to the objective function, the probability data of the student's mastery of each knowledge point is obtained.

[0026] Based on the mastery probability data, knowledge points that meet the unmastered conditions are selected from the knowledge corresponding to the target learning course, and the selected knowledge points are combined to form the unmastered knowledge point set.

[0027] Optionally, the step of matching the set of unmastered knowledge points with the target knowledge graph corresponding to the target knowledge point learning grid to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph includes:

[0028] Generate a sequence of unmastered knowledge points based on the aforementioned set of unmastered knowledge points;

[0029] The sequence of unmastered knowledge points is marked with an unmastered identifier in the target knowledge point learning grid, and the path of the unmastered knowledge point corresponding to the sequence of unmastered knowledge points is extracted from the target knowledge point learning grid.

[0030] Based on the unmastered identifier, the paths of the unmastered knowledge points are matched with the target knowledge graph to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph.

[0031] Optionally, the step of matching the paths of the unmastered knowledge points with the target knowledge graph based on the unmastered identifier to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph includes:

[0032] Based on the unknown identifier, the paths of the unknown knowledge points are matched with the target knowledge graph to obtain the network of knowledge points to be screened corresponding to the set of unknown knowledge points in the target knowledge graph;

[0033] Based on the prerequisite relationships and relevance information of the unmastered knowledge point paths in the network of knowledge points to be screened, the target knowledge point network is determined from the network of knowledge points to be screened.

[0034] Secondly, this application provides a learning path generation device based on an AI agent, comprising:

[0035] The determination module is configured to obtain the student's learning location information in the target knowledge point learning grid from the learning companion system, and determine the target learning course corresponding to the learning location information;

[0036] The generation module is configured to divide the student's learning behavior data in the target learning course into at least one learning behavior data set, and generate at least one set of behavioral features corresponding to the at least one learning behavior data set;

[0037] The analysis module is also configured to analyze the student's mastery of knowledge points in the target learning course based on the at least one set of behavioral features, and to determine the set of knowledge points that the student has not mastered in the target learning course;

[0038] The matching module is configured to match the set of unmastered knowledge points with the target knowledge graph corresponding to the target knowledge point learning grid, so as to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph;

[0039] The generation module is also configured to generate a target learning path for the student in the target knowledge point learning grid based on the target knowledge point network, the target learning path being used to assist the student in mastering the set of unmastered knowledge points.

[0040] 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 ​​agent-based learning path generation method described in the first aspect.

[0041] 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 ​​agent-based learning path generation method described in the first aspect.

[0042] By employing the above technical solutions, this application provides a learning path generation method and apparatus based on AI intelligent agents. Compared with existing technologies, this application achieves precise positioning of the student's current learning focus by obtaining the student's learning location information in the target knowledge point learning grid from the learning companion system and determining the corresponding target learning course; it achieves structured processing of multi-source heterogeneous learning behavior data by dividing the student's learning behavior data in the target learning course into at least one learning behavior dataset and merging it to generate at least one corresponding behavioral feature set; it improves the accuracy of identifying unmastered knowledge points by analyzing the student's mastery of knowledge points in the target learning course based on at least one behavioral feature set and determining the set of unmastered knowledge points; it improves the confidence of knowledge graph association by matching the set of unmastered knowledge points with the target knowledge graph corresponding to the target knowledge point learning grid and obtaining the target knowledge point network; and it improves the reliability of learning path generation and enhances the targeted assistance to students in mastering unmastered knowledge points by generating the student's target learning path in the target knowledge point learning grid based on the target knowledge point network. Attached Figure Description

[0043] 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.

[0044] 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.

[0045] Figure 1 A flowchart illustrating a learning path generation method based on an AI agent provided in an embodiment of this application is shown.

[0046] Figure 2 A schematic diagram of a directed edge knowledge graph provided in an embodiment of this application is shown;

[0047] Figure 3 A schematic diagram of an undirected edge knowledge graph provided in an embodiment of this application is shown;

[0048] Figure 4A schematic diagram of a centrality analysis provided in an embodiment of this application is shown;

[0049] Figure 5 A flowchart illustrating a learning path generation method based on an AI agent provided in an embodiment of this application is shown.

[0050] Figure 6 This illustration shows a schematic diagram of a learning path generation device based on an AI agent provided in an embodiment of this application.

[0051] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0052] 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.

[0053] To address the limitations of existing technologies that can only process single-dimensional learning data and lack the ability to deeply integrate and analyze multi-source heterogeneous learning behavior data, the multimodal data generated by students during the learning process, such as video viewing records, exercise responses, and participation in interactive discussions, is often analyzed in isolation. This fails to provide a comprehensive and accurate assessment of students' knowledge mastery, leading to the system's difficulty in accurately identifying the set of knowledge points that students have not mastered. This embodiment provides a learning path generation method based on AI intelligent agents, such as... Figure 1 As shown, the method includes:

[0054] Step 101: Obtain the student's learning location information in the target knowledge point learning grid from the learning companion system, and determine the target learning course corresponding to the learning location information.

[0055] In this embodiment of the application, 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 data such as students' basic information, learning records, and course materials.

[0056] In this embodiment of the application, the target knowledge point learning grid can be a two-dimensional grid formed by mapping the knowledge points of the student's target learning course in the current semester according to logical relationships (such as chapter order, knowledge dependence). Each cell of the target knowledge point learning grid can correspond to a specific knowledge point or course unit, and each cell can mark the proficiency of the knowledge point (three levels of proficiency: not mastered, mastered but not proficient, and proficient) with different colored circles according to the student's learning record.

[0057] In this embodiment, the learning location information can be the location data corresponding to the cell that the student is currently focusing on learning in the target knowledge point learning grid, such as grid coordinates (xi, yi). The learning location information can be associated with the corresponding knowledge point or course content in the learning grid.

[0058] In the embodiments of this application, the target learning course can be the subject course that the student needs to study in the current semester, which is determined based on the student's learning location information and the semester information pre-filled by the student.

[0059] In this embodiment of the application, the learning companion system can first generate a student identification ID based on the basic information such as class, grade, name, age, and semester filled in by the student during login. The student identification ID can be used to associate all the student's learning data. The system can retrieve the full range of courses corresponding to the semester from the resource library based on the semester information entered by the student, and then break down these course contents into knowledge points and map them to the target knowledge point learning grid. Each course's knowledge point corresponds to one or more cells in the grid. The system can determine the student's specific learning location information in the target knowledge point learning grid by recording the student's learning operations on the tablet (such as clicking on the course module to be learned, watching the online course chapter, and answering the exercises to the knowledge point), and then determine the corresponding target learning course based on the location information.

[0060] Step 102: Divide the students' learning behavior data in the target learning course into at least one learning behavior data set, and generate at least one set of behavioral features corresponding to at least one learning behavior data set.

[0061] In this application embodiment, learning behavior data can be various types of operational data generated by students when learning questions in the tutoring system. For example, the learning behavior data in this application implementation may specifically include question answering data (such as answer results, answering time, and incorrect question markings), note data (such as note text and the corresponding learning position of the notes), and learning interaction data (such as online class dwell time and number of repeated views).

[0062] In this embodiment of the application, the behavioral feature set can be a combination of feature data that can reflect the student's learning status extracted from each learning behavior data set. Each behavioral feature can be quantified and normalized through specific numerical values ​​to ensure data consistency.

[0063] In this embodiment of the application, the learning companion system can first classify all the learning behavior data of students in the target learning course. The classification dimension can be set as the scenario in which the data is generated (online class learning, doing exercises, note-taking, questioning and interaction), and the learning behavior data in the same scenario can be grouped into a learning behavior data set.

[0064] In this embodiment of the application, the system can extract features from each learning behavior dataset to generate a corresponding behavior feature set. For example, video viewing completion rate can be extracted from the online course learning behavior dataset. ), number of times viewed repeatedly ( ), length of stay ( ) as features, forming a set of behavioral features [ =0.8, =2, =45]; the accuracy rate can be extracted from the dataset of problem-solving behavior data ( ), answering speed ( ), Correctness rate of redoing incorrect questions ( ) as features, forming a set of behavioral features [ =0.7, =1.5, =0.6]; the frequency of questions can be extracted from the interactive behavior data set ( ), Number of notes ( ) are used as features to form a set of behavioral features [fq=0.1,nn=2].

[0065] Step 103: Analyze students' mastery of knowledge points in the target learning course based on at least one set of behavioral characteristics, and determine the set of knowledge points that students have not mastered in the target learning course.

[0066] In the embodiments of this application, the degree of mastery can be the level of understanding and application of each knowledge point in the target learning course by the student. The degree of mastery can be reflected by scores or probabilities and can be used to reflect the student's mastery of knowledge points.

[0067] In the embodiments of this application, the set of unmastered knowledge points can be a set of knowledge points in the target learning course whose mastery level has not reached the preset qualification standard, and the knowledge points in the set of unmastered knowledge points can be the content that the learning path planning focuses on.

[0068] In the embodiments of this application, the analysis of students' mastery of knowledge points in the target learning course based on at least one set of behavioral features can first associate all sets of behavioral features corresponding to each knowledge point in the target learning course; then calculate the mastery score for each knowledge point; and then map the mastery score to the mastery probability through a function. The mastery probability can be used as a quantitative indicator of the degree of mastery. The value range of the mastery probability can be [0,1]. The larger the value of the mastery probability, the higher the degree of mastery of the student.

[0069] In this embodiment of the application, the set of knowledge points that students have not mastered in the target learning course can be determined by setting a threshold for the probability of mastery. The threshold for the probability of mastery can include a low threshold for the probability of mastery and a high threshold for the probability of mastery. For example, if the probability of mastering knowledge point k1 is less than the low threshold for the probability of mastery, then knowledge point k1 can be determined as a knowledge point that has not been mastered; if the probability of mastering knowledge point k1 is greater than or equal to the low threshold for the probability of mastery and less than the high threshold for the probability of mastery, then knowledge point k1 can be determined as a knowledge point that has been mastered but not proficient; if the probability of mastering knowledge point k1 is greater than the high threshold for the probability of mastery, then knowledge point k1 can be determined as a knowledge point that has been mastered.

[0070] In this embodiment of the application, all knowledge points in the target learning course that are determined to be unmastered can be filtered out to form a set of unmastered knowledge points.

[0071] Step 104: Match the set of unmastered knowledge points with the target knowledge graph corresponding to the target knowledge point learning grid to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph.

[0072] In this embodiment, the target knowledge graph can be a knowledge structure graph formed by extracting knowledge points, constructing relationships, and performing network analysis on the target learning courses corresponding to the target knowledge point learning grid. The nodes in the target knowledge graph can be knowledge points, the edges in the target knowledge graph can be the relationships between knowledge points (including directed prerequisite relationships and undirected correlation relationships), and the weights of the edges in the target knowledge graph can be the relationship strength. The importance of knowledge points can be evaluated by degree centrality, betweenness centrality, and proximity centrality.

[0073] In this embodiment of the application, the target knowledge point network can be a sub-network composed of knowledge points directly related to the set of unmastered knowledge points in the target knowledge graph and the relationships between the knowledge points. The target knowledge point network can be used to reflect the learning dependency logic of unmastered knowledge points.

[0074] In this embodiment of the application, matching the set of unmastered knowledge points with the target knowledge graph corresponding to the learning grid of the target knowledge points can begin with knowledge point extraction. This involves using Natural Language Processing (NLP) technology to perform structured analysis on the textbook catalog, syllabus, and curriculum standards of the target learning course, identifying core concepts, theorems, and skill points, and establishing a hierarchical knowledge structure. For example, in this embodiment of the application, the extracted knowledge points can be specifically represented as knowledge point k1, knowledge point k2, knowledge point k3, and knowledge point k4.

[0075] In the embodiments of this application, the relationship between knowledge points can be constructed based on the acquired knowledge points. Specifically, the relationship can be determined by using the attribute information of the knowledge points. The attribute information can be a comprehensive description of various features of the knowledge points. In addition to including basic learning information, prerequisite knowledge information and relevance information, it can also include related information such as the type of association and the strength of association derived from these information. It can comprehensively reflect the features of the knowledge points and the relationship between the knowledge points.

[0076] In this embodiment, attribute information of knowledge points can be generated based on the acquired basic learning information, prerequisite knowledge information, and relevance information. The difficulty, importance, and learning time in the basic learning information, the specific prerequisite knowledge points corresponding to the prerequisite knowledge information, and the related knowledge points and degree of association corresponding to the relevance information are integrated to form unique attribute information for each knowledge point. Based on the generated attribute information, a graph model is constructed with knowledge points as nodes, relationships as edges, and association strength as edge weights. The centrality index of the nodes is calculated to construct the target knowledge graph.

[0077] In the embodiments of this application, the matching of the set of unknowable knowledge points with the target knowledge graph can be based on locating the corresponding node in the target knowledge graph for each knowledge point in the set of unknowable knowledge points, extracting all associated nodes (including pre-requisite nodes, subsequent nodes, and related nodes) and associated edges of the node to form a preliminary matching network; the preliminary matching network can be filtered, and the filtering criteria can be an association strength threshold. Specifically, the filtering can be to retain edges whose association strength meets the threshold requirements and to remove edges whose association strength does not meet the threshold conditions. After filtering the preliminary matching network, a target knowledge point grid is generated.

[0078] Optionally, in the embodiments of this application, the directed edges of the knowledge graph can maintain direction information, such as a directed edge knowledge graph. Figure 2 As shown, Figure 2 The numbers (0, 1, 2, etc.) in the graph can represent knowledge point identifiers, and each identifier corresponds to one knowledge point. Arrows can indicate the direction of edges; undirected edges do not need to have a direction set. An undirected edge knowledge graph is shown below. Figure 3 As shown, the corresponding target association strength can be used as the weight of the graph edge and labeled on the graph edge; when constructing graph nodes, each knowledge point corresponds to a graph node, and the attribute information of the knowledge point (including basic learning information, prerequisite knowledge information, relevance information, etc.) can be associated with the corresponding graph node.

[0079] In this embodiment, graph nodes can be connected by graph edges according to the relationships between knowledge points to form a complete knowledge graph. During the construction process, graph algorithms can be used to calculate the network centrality indices of nodes, including degree centrality, betweenness centrality, and proximity centrality. Network centrality analysis can be performed as follows: Figure 4 As shown, degree centrality represents the number of other nodes directly connected to a node, exhibiting local influence; betweenness centrality represents the frequency with which a node is a necessary node on the shortest path between other nodes in the network, exhibiting bridging control; and proximity centrality represents the reciprocal of the average distance from a node to all other nodes in the network, exhibiting global accessibility. For example, knowledge points with high degree centrality can be basic knowledge points, those with high betweenness centrality can be key transit knowledge points, and those with high proximity centrality can be core knowledge points.

[0080] Step 105: Generate the student's target learning path in the target knowledge point learning grid based on the target knowledge point network.

[0081] The target learning path is used to help students master the set of knowledge points they have not yet mastered.

[0082] In the embodiments of this application, the target learning path can be the sequence of learning knowledge points planned for students in the target knowledge point learning grid according to the logical relationship of knowledge points and the students' lack of mastery. The target learning path can be used to guide students to gradually master the knowledge points in the set of unmastered knowledge points. The target learning path can be mapped to a grid coordinate sequence for display.

[0083] In this embodiment of the application, the student's target learning path in the target knowledge point learning grid can be generated based on the target knowledge point network. The knowledge points in the target knowledge point network can be prioritized first. The priority ranking can be based on: the mastery status of the knowledge points (knowledge points that have not been mastered have higher priority than knowledge points that have been mastered but not mastered), prerequisite relationship (prerequisite knowledge points have higher priority than subsequent knowledge points), importance (based on betweenness centrality and degree centrality, knowledge points with higher centrality have higher priority), and time decay (knowledge points that have been studied for a longer time have higher priority).

[0084] In this embodiment, a learning path can be generated based on priority ranking and the dependency relationship (prerequisite relationship) of knowledge points. Specifically, the learning path can be generated using a graph traversal algorithm (such as breadth-first search (BFS) or depth-first search (DFS)). After the learning path is generated, it can be adjusted according to the student's learning ability. The learning ability can be evaluated by the student's learning speed, knowledge retention, and knowledge application ability.

[0085] In this embodiment, obtaining the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph can be achieved by mapping the target learning path to the target knowledge point learning grid. The target learning path can be displayed to students in the form of a grid coordinate sequence. For example, if knowledge point k2 in the set of unmastered knowledge points corresponds to the target knowledge point learning grid coordinates (1,2), and knowledge point k4 in the set of unmastered knowledge points corresponds to the target knowledge point learning grid coordinates (2,3), then the target learning path can be displayed in the target knowledge point learning grid as (1,2) → (2,3). This can guide students to learn sequentially according to the target knowledge point learning grid positions, helping students gradually master k2 and k4 in the set of unmastered knowledge points.

[0086] Compared with existing technologies, this embodiment achieves precise positioning of the student's current learning focus by obtaining the student's learning location information in the target knowledge point learning grid from the learning companion system and determining the corresponding target learning course; it achieves structured processing of multi-source heterogeneous learning behavior data by dividing the student's learning behavior data in the target learning course into at least one learning behavior dataset and merging it to generate at least one corresponding behavioral feature set; it improves the accuracy of identifying unmastered knowledge points by analyzing the student's mastery of knowledge points in the target learning course based on at least one behavioral feature set and determining the set of unmastered knowledge points; it improves the confidence of knowledge graph association by matching the set of unmastered knowledge points with the target knowledge graph corresponding to the target knowledge point learning grid to obtain the target knowledge point network; and it improves the reliability of learning path generation and enhances the targeted approach to helping students master unmastered knowledge points by generating the student's target learning path in the target knowledge point learning grid based on the target knowledge point network.

[0087] As an optional approach, when performing the task of "analyzing students' mastery of knowledge points in the target learning course based on at least one set of behavioral characteristics, and determining the set of knowledge points that students have not mastered in the target learning course," the following methods can be used, but are not limited to: Figure 3 As shown, the method includes:

[0088] Step 201: 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, so as to obtain the effective value data of each behavioral feature in at least one set of behavioral features.

[0089] 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), and so on.

[0090] 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.

[0091] Optionally, the behavioral features in this application embodiment may include online course learning progress (such as video completion rate, number of repeated views, etc.), practice questions (such as accuracy rate, number of questions answered, time spent answering questions, etc.), and interaction data (such as frequency of asking questions, number of notes, etc.). The system can use this data to calculate the mastery level of each knowledge point. Specifically, the calculation can be performed by constructing a feature vector, incorporating online course learning data (video completion rate, etc.) into the model. 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 2, normalizing each feature vector to the range of [0,1].

[0092] (Formula 1)

[0093] (Formula 2)

[0094] In this embodiment of the application, the time decay calculation formula is shown in Formula 3, where It can represent whether the i-th answer is correct or incorrect (1 or 0). 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.

[0095] (Formula 3)

[0096] In the embodiments of this application, obtaining the effective value data of each behavioral feature in at least one behavioral feature set can be achieved by multiplying the normalized value of the behavioral feature by the corresponding time decay factor.

[0097] Step 202: Based on the valid value data, filter the behavioral features in at least one set of behavioral features to obtain target behavioral features that meet the valid conditions in at least one set of behavioral features, and form at least one set of target behavioral features.

[0098] In this embodiment, the valid condition can be a condition used to determine whether a behavioral feature has reference value. The valid condition can include valid value data conditions and time information conditions. For example, in this embodiment, the valid condition can be that the valid value data is greater than or equal to a valid value data threshold and the time information condition is less than or equal to a time information threshold. For example, the valid value is greater than or equal to the valid value data threshold of 0.3 and the time of generation of the behavioral feature is within 7 days of the time information threshold.

[0099] In the embodiments of this application, the target behavioral feature can be a behavioral feature that meets the valid conditions from at least one set of behavioral features.

[0100] In this embodiment of the application, the target behavior feature set can be a set formed by aggregating the target behavior features selected from each learning behavior data set.

[0101] Step 203: Analyze the students' mastery of knowledge points in the target learning course based on at least one set of target behavioral characteristics, and determine the set of knowledge points that students have not mastered in the target learning course.

[0102] In the embodiments of this application, the mastery probability can be used as a quantitative indicator of the degree of mastery. The value range of the mastery probability can be [0,1]. The larger the value of the mastery probability, the higher the degree of mastery of the knowledge point by the student.

[0103] In this embodiment of the application, the mastery probability can be calculated using a logistic regression or softmax regression model. After calculating the mastery probability, a mastery probability threshold can be set to classify the knowledge points using a three-color system. Specifically, the mastery probability can be calculated first by calculating the mastery score, 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.

[0104] (Formula 4)

[0105] Optionally, when performing the task of "analyzing students' mastery of knowledge points in the target learning course based on at least one set of target behavioral features, and determining the set of knowledge points that students have not mastered in the target learning course," the following methods may be used, but are not limited to: matching 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; identifying 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, wherein the target model is trained based on students' historical behavioral features and historical mastery of knowledge points; determining the 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; and determining the set of knowledge points that students have not mastered in the target learning course based on the mastery data.

[0106] In the embodiments of this application, matching at least one set of target behavioral features with each knowledge point in the target learning course can first involve retrieving the historical student dataset corresponding to the target learning course from the learning companion system database. This dataset contains a set of behavioral features of historical students (such as online course completion rate, correct answer rate for practice questions, etc.) and corresponding knowledge point mastery labels. The target model can be trained using a logistic regression model or a softmax regression model. Specifically, this can include using the set of behavioral features of historical students as input features and the knowledge point mastery labels as output labels. The model parameters can be optimized using a gradient descent algorithm so that the model can output a prediction result of the knowledge point mastery based on the behavioral features. During the model training process, cross-validation can be used to adjust hyperparameters (such as regularization coefficients).

[0107] In the embodiments of this application, determining at least one target behavioral feature corresponding to each knowledge point in the target learning course can be achieved by splitting the set of at least one target behavioral feature of the current student according to the knowledge point dimension, determining at least one target behavioral feature corresponding to each knowledge point in the target learning course, and then calling the target model to output importance data. Specifically, calling the target model to output importance data can be achieved by inputting the target behavioral feature corresponding to each knowledge point into the trained target model, and the model will output the importance data of each behavioral feature to the knowledge point.

[0108] Optionally, when performing the task of “determining the 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”, the following methods may be used, but are not limited to: determining the influence coefficient of at least one behavioral feature corresponding to each knowledge point on the mastery of each knowledge point based on the importance data; analyzing at least one behavioral feature based on the influence coefficient to generate the students’ mastery data of each knowledge point in the target learning course.

[0109] In the embodiments of this application, determining the influence coefficient based on importance data can be achieved by normalizing the data by dividing the importance data of each behavioral feature by the sum of the importance data of all behavioral features, thus obtaining the influence coefficient corresponding to the behavioral feature, and the sum of the influence coefficients of all behavioral features is 1. It should be noted that if there is an initial bias in the importance data (such as the importance data of a certain behavioral feature being labeled too high, resulting in a sum that is not 1), the initial importance data can be calibrated first.

[0110] In the embodiments of this application, the mastery data can be generated by analyzing at least one behavioral feature based on the influence coefficient. This can be done by first normalizing the original data of each behavioral feature and mapping the feature components to the range of [0,1]; then, by weighting and summing each behavioral feature based on the influence coefficient, mastery data can be generated, as shown in Formula 5.

[0111] Mastery data = (Normalized value of behavioral characteristic × Influence coefficient) (Formula 5)

[0112] Optionally, when performing the task of “determining the set of unmastered knowledge points for students in the target learning course based on mastery data”, the following methods may be used, but are not limited to: mapping the mastery data of each knowledge point to the objective function to obtain the mastery probability data of each knowledge point for students; selecting knowledge points that meet the unmastered condition from the knowledge corresponding to the target learning course based on the mastery probability data, and forming the set of unmastered knowledge points from the knowledge corresponding to the target learning course.

[0113] In this embodiment, mastery data can be mapped to mastery probability data using an objective function, which may include a sigmoid function, a softmax function, etc. The probability of a student mastering a knowledge point can be calculated using the sigmoid function. Mapping to mastery probability Mastering probability The expression is shown in Formula 6; 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 so, the knowledge point can be marked as mastered; the system can select knowledge points that meet the conditions of not mastering from the knowledge corresponding to the target learning course to form a set of unmastered knowledge points.

[0114] (Formula 6)

[0115] Optionally, when performing the step of "matching the set of unmastered knowledge points with the target knowledge graph corresponding to the target knowledge point learning grid to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph", the following methods may be used, but are not limited to: generating a sequence of unmastered knowledge points based on the set of unmastered knowledge points; marking the sequence of unmastered knowledge points with unmastered identifiers in the target knowledge point learning grid, and extracting the paths of unmastered knowledge points corresponding to the sequence of unmastered knowledge points from the target knowledge point learning grid; matching the paths of unmastered knowledge points with the target knowledge graph based on the unmastered identifiers to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph.

[0116] In the embodiments of this application, the "unknown" identifier can be the marking information corresponding to the unknown knowledge point in the target knowledge point learning grid. For example, the "unknown" identifier in the embodiments of this application may specifically include a specific color mark, a unique symbol, or a text label, and each "unknown" identifier can correspond one-to-one with an unknown knowledge point.

[0117] In the embodiments of this application, the path of unmastered knowledge points can be an ordered path extracted from the target knowledge point learning grid and composed of unmastered knowledge points according to the grid connection logic. Each node in the path of unmastered knowledge points can carry the corresponding unmastered identifier and basic information of the knowledge points.

[0118] In this embodiment, a sequence of unmastered knowledge points (e.g., [k2,k4]) can be generated based on the set of unmastered knowledge points according to chapter order or importance. In the target knowledge point learning grid, the cells corresponding to the knowledge points in the sequence are marked as unmastered identifiers, and the unmastered knowledge point paths are extracted by connecting the cells according to the sequence (e.g., (1,2)→(2,3)). Based on the unmastered identifiers, the knowledge points in the path are matched with the nodes in the target knowledge graph, and the associated nodes and edges are extracted to obtain the target knowledge point network. For example, if the unmastered knowledge point path is (1,2)→(2,3), the unmastered knowledge point path can correspond to knowledge point k2→k4. The knowledge point k2→k4 in the path is matched with the nodes in the target knowledge graph and the extracted knowledge points k1, k2, k4, and edges k1→k2, k1→k4, and k2→k4 are used to form the target knowledge point network.

[0119] In this embodiment of the application, generating a sequence of unmastered knowledge points based on a set of unmastered knowledge points can be based on generating at least two sets of unmastered knowledge point sequence schemes according to different rules. Specifically, the at least two sets of unmastered knowledge point sequence schemes can include sorting by mastery probability from low to high; sorting by knowledge dependency relationship (refer to the prerequisite relationship in the knowledge graph), etc.

[0120] In this embodiment of the application, marking the unknown identifier and initially extracting the path may include marking the cell corresponding to the knowledge point in each set of unknown knowledge point sequences in the target knowledge point learning grid; based on each set of unknown knowledge point sequences, the depth-first search algorithm (DFS) can be used to extract the path of unknown knowledge points, ensuring that the knowledge points in the path are connected in the grid (i.e., the coordinate distance between adjacent knowledge points in the grid is less than or equal to 1).

[0121] In the embodiments of this application, optimizing the path of unmastered knowledge points can be done by combining the topology of the knowledge graph (mapped to a grid through a force-directed algorithm) to verify the consistency between the path of unmastered knowledge points and the knowledge graph; matching the path of unmastered knowledge points with the target knowledge graph based on the unmastered identifier can be done by matching each knowledge point in the path of unmastered knowledge points with a node in the target knowledge graph to obtain a target knowledge point network that matches the path of unmastered knowledge points.

[0122] Optionally, when performing the step of "matching the paths of unmastered knowledge points with the target knowledge graph based on the unmastered identifier to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph", the following methods may be used, but are not limited to: matching the paths of unmastered knowledge points with the target knowledge graph based on the unmastered identifier to obtain the network of knowledge points to be screened corresponding to the set of unmastered knowledge points in the target knowledge graph; and determining 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.

[0123] In this embodiment, matching the path of the unknown knowledge point with the target knowledge graph based on the unknown identifier can be achieved by first locating the corresponding node in the target knowledge graph for each node in the path of the unknown knowledge point according to the knowledge point identifier; extracting all associated nodes and relationships of each located node, including all directed prerequisite relationship nodes, subsequent relationship nodes, and undirected relevance relationship nodes, and retaining the association strength data corresponding to the relationship; and forming a network of knowledge points to be screened by combining the unknown knowledge point nodes, associated nodes, and prerequisite, subsequent, and relevance relationships between nodes, which can completely cover the direct and indirect associated nodes of the unknown knowledge points in the knowledge graph.

[0124] In the embodiments of this application, the prerequisite relationship can be a dependency relationship between knowledge points. The prerequisite relationship can be expressed as mastering one knowledge point (prerequisite knowledge point) is a prerequisite for understanding or mastering another knowledge point (subsequent knowledge point).

[0125] In the embodiments of this application, the relevance information can be the association information between knowledge points. The relevance information can indicate that two knowledge points are related in semantic description, application scenario or learning logic, but there is no strict order of mastery.

[0126] In this embodiment of the application, the process of determining the target knowledge point network from the network of knowledge points to be screened based on the prior relationship and relevance information of the knowledge point paths that are not known in the network of knowledge points to be screened can be based on the priority retention rule of prior relationship or the screening rule based on the relevance strength threshold to retain or remove nodes in the network of knowledge points to be screened, so as to obtain the target knowledge point network.

[0127] For example, determining the target knowledge point network from the network of knowledge points to be screened based on the priority retention rule of prior relationship may specifically include: if a node in the network of knowledge points to be screened is a direct prior node of a knowledge point in a path that has not been mastered, then the direct prior node can be retained first; if a node in the network of knowledge points to be screened is an indirect prior node, then the mastery status of the corresponding direct prior node can be determined. If the direct prior node has been mastered, the indirect prior node can be eliminated; if the direct prior node has not been mastered, the indirect prior node can be retained.

[0128] For example, determining the target knowledge point network from the network of knowledge points to be screened based on the relevance strength threshold screening rule may specifically include: setting a relevance strength threshold for relevance nodes that are not prerequisites; if the relevance strength of a node is greater than or equal to the relevance strength threshold, the node and its corresponding relationship can be retained; if the relevance strength of a node is less than the relevance strength threshold, the node and its corresponding relationship can be removed.

[0129] Compared with existing technologies, the embodiments of this application improve the effectiveness of behavioral feature data by determining the time information corresponding to each behavioral feature in at least one set of behavioral features and evaluating the effectiveness of each behavioral feature based on the time information, thereby filtering out target behavioral features that meet the effectiveness conditions and forming a set of target behavioral features. Furthermore, by matching at least one set of target behavioral features with each knowledge point in the target learning course, the importance data of the target behavioral features corresponding to each knowledge point is identified using a target model, and the student's mastery of each knowledge point is determined by combining the importance data, thus improving the accuracy of determining the set of unmastered knowledge points. Finally, by determining the mastery of each knowledge point by using at least one behavioral feature corresponding to each knowledge point based on the importance data, the accuracy of determining the mastery of that knowledge point is improved. The influence coefficient of the degree is used to analyze behavioral characteristics and generate mastery data, improving the alignment between mastery data and students' actual mastery. Mastery probability data is obtained by mapping the mastery data of each knowledge point to an objective function. Based on this probability data, knowledge points that meet the criteria for not mastering the knowledge are selected to form a set of unmastered knowledge points, improving the standardization of the selection process. An unmastered knowledge point sequence is generated based on this set. Unmastered identifiers are marked in the target knowledge point learning grid, and corresponding paths are extracted. These paths are then matched with the target knowledge graph to obtain the target knowledge point network, achieving greater accuracy in associating unmastered knowledge points with the knowledge graph and improving the targeting of knowledge point network extraction.

[0130] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0147] Example 3: Obtain second 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 of the student learning knowledge points in the set of unmastered knowledge points based on the second 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 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.

[0148] Example 31: 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.

[0149] Example 32: Based on the second 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.

[0150] Example 33: Based on the second question information and the second note information, generate student learning content data and corresponding behavior sequence data for the second 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.

[0151] Example 34: 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 threshold for required understanding, 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 threshold for required understanding and greater than the second threshold for required understanding, 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 threshold for required understanding is less than the first threshold for required understanding; if the required level of understanding data is less than the second threshold for required understanding, 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.

[0152] Example 35: Generate student learning content data based on the second question information and the second note information; map the second 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.

[0153] Example 36: Obtain second learning behavior data of students learning knowledge points in the set of unmastered knowledge points within the knowledge point learning grid from the learning companion system; divide the second 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 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 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 to 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.

[0154] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0155] Example 4: The second 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 location information of the set of unmastered knowledge points.

[0156] Example 41: 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.

[0157] Example 42: 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.

[0158] Example 43: 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.

[0159] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0160] Example 5: Based on the student's review path for already learned knowledge points within the target knowledge point learning grid, determine the knowledge points to be reviewed indicated by the review path; extract the network of knowledge points to be reviewed corresponding to the knowledge points to be reviewed from the knowledge graph corresponding to the target knowledge point learning grid, wherein the network of knowledge points to be reviewed includes at least one prerequisite knowledge point and at least one related knowledge point corresponding to the knowledge point to be reviewed; update the review path based on the student's mastery of the knowledge point to be reviewed, the at least one prerequisite knowledge point, and the at least one related knowledge point to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed; determine the student's target knowledge points to be reviewed based on the target knowledge point review path, generate recommendation information for the target knowledge points to be reviewed, and recommend the review of the target knowledge points to the student.

[0161] Example 51: Determine the knowledge point mastery level indicator of the knowledge point to be reviewed from the knowledge point network; when the knowledge point mastery level indicator of the knowledge point to be reviewed is not mastered, determine the first knowledge point mastery level indicator of at least one prerequisite knowledge point and the second knowledge point mastery level indicator of at least one related knowledge point in the knowledge point network to be reviewed; based on the first knowledge point mastery level indicator and the second knowledge point mastery level indicator, update the knowledge point review path to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed.

[0162] Example 52: Based on the mastery level identifier of the first knowledge point and the mastery level identifier of the second knowledge point, determine the set of first knowledge points that the student has mastered from at least one prerequisite knowledge point and at least one related knowledge point; remove the set of first knowledge points from the network of knowledge points to be reviewed, and use the knowledge points to be reviewed as the starting point of the review path to update the network of knowledge points to be reviewed, so as to obtain the target knowledge point review path corresponding to the knowledge points to be reviewed.

[0163] Example 53: Based on the mastery level indicators of the first and second knowledge points, determine the set of second knowledge points that the student has not mastered from at least one prerequisite knowledge point and at least one related knowledge point; determine the learning priority information of the knowledge points in the second knowledge point set; based on the learning priority information, select the first target knowledge point that meets the priority condition from the second knowledge point set; use the first target knowledge point as the starting point of the review path, update the network of knowledge points to be reviewed, and obtain the review path of the target knowledge point.

[0164] Example 54: Determine the mastery probability data and knowledge point difficulty data for each knowledge point in the second knowledge point set; evaluate the learning priority of each knowledge point in the second knowledge point set based on the mastery probability data and knowledge point difficulty data to obtain the learning priority information of each knowledge point in the second knowledge point set; determine the knowledge point with the highest learning priority information in the second knowledge point set as the first target knowledge point.

[0165] Example 55: When 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; the second target knowledge point that meets the priority condition is determined from the third set of knowledge points, the set of prerequisite knowledge points, and the set of related knowledge points; the knowledge point to be reviewed is removed from the knowledge point network to be reviewed, and the second target knowledge point is used as the starting point of the review path to update the knowledge point network to obtain the target knowledge point review path.

[0166] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0167] Example 6: In response to obtaining the third learning behavior data of the student reviewing the target knowledge point, the first mastery level identifier of the target knowledge point 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0174] Example 7: Obtain fourth learning behavior data of students learning knowledge points in the knowledge point learning grid, and obtain third mastery level identifiers of knowledge points in the knowledge point learning grid; determine second mastery level data of students learning knowledge points in the knowledge point learning grid based on the fourth learning behavior data, and evaluate second knowledge point status data of knowledge points in the knowledge point learning grid based on the second mastery level data; determine abnormal knowledge points from the knowledge point learning grid based on the first knowledge point status data corresponding to the second mastery level data and the second knowledge point status data; update the knowledge graph corresponding to the knowledge point learning grid based on the abnormal knowledge points to obtain an updated target knowledge graph, which is used to generate the target knowledge point learning grid.

[0175] Example 71: Based on the first knowledge point status data and the second knowledge point status data, perform a status data difference analysis to evaluate the degree of difference between the knowledge point mastery status displayed in the knowledge point learning grid and the student's mastery status of the knowledge points in the knowledge point learning grid; based on the fourth learning behavior data, determine the question information and note information of the student's learning of the knowledge points in the knowledge point learning grid, and determine the student's learning status information of the knowledge points in the knowledge point learning grid based on the question information, note information, and the second mastery level data; based on the learning status information and the degree of difference, identify the knowledge points with abnormal status from the knowledge point learning grid, and update the knowledge graph corresponding to the knowledge point learning grid based on the knowledge points with abnormal status to obtain the updated target knowledge graph.

[0176] 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.

[0177] 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.

[0178] Example 74: Divide the fourth learning behavior data of students in the target knowledge point learning grid into at least one learning behavior data set, and generate a behavior feature set corresponding to at least one learning behavior data set; determine the time information corresponding to each behavior feature in the behavior feature set, and evaluate the effectiveness of each behavior feature in the behavior feature set based on the time information to obtain the effective value data of each behavior feature in the behavior feature set; filter the behavior features in the behavior feature set based on the effective value data to obtain the target behavior features in the behavior feature set that meet the effective conditions, and form a target behavior feature set; identify the box selection behavior from the target behavior feature set, and classify the learning content selected by the box selection behavior to obtain the question information and note information of students learning knowledge points in the abnormal knowledge point set.

[0179] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0180] 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.

[0181] 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.

[0182] 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.

[0183] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Furthermore, as Figure 1 and Figure 5 The specific implementation of the method shown in this embodiment provides a learning aid device, such as... Figure 6 As shown, the device includes: a determination module 31, a generation module 32, an analysis module 33, and a matching module 34.

[0192] Module 31 is configured to obtain the student's learning location information in the target knowledge point learning grid from the learning companion system and determine the target learning course corresponding to the learning location information.

[0193] The generation module 32 is configured to divide the students' learning behavior data in the target learning course into at least one learning behavior data set, and generate at least one set of behavioral features corresponding to at least one learning behavior data set;

[0194] Analysis module 33 is configured to analyze students’ mastery of knowledge points in the target learning course based on at least one set of behavioral features, and to determine the set of knowledge points that students have not mastered in the target learning course.

[0195] Matching module 34 is configured to match the set of unmastered knowledge points with the target knowledge graph corresponding to the target knowledge point learning grid, so as to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph;

[0196] The generation module 32 is also configured to generate a target learning path for students in the target knowledge point learning grid based on the target knowledge point network. The target learning path is used to help students master the set of knowledge points they have not yet mastered.

[0197] In some examples of this embodiment, the determining module 31 is configured to 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 in at least one set of behavioral features that meet the effective conditions, 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.

[0198] In some examples of this embodiment, the determining module 31 is further configured to 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, wherein the target model is trained based on the student's historical behavioral features and historical knowledge point mastery level; determine the student's mastery level data for each knowledge point in the target learning course based on at least one target behavioral feature and importance data corresponding to each knowledge point; and determine the set of unmastered knowledge points in the target learning course based on the mastery level data.

[0199] In some examples of this embodiment, the determining module 31 is further configured to determine the influence coefficient of at least one behavioral feature corresponding to each knowledge point on the mastery of each knowledge point based on the importance data; and to 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.

[0200] In some examples of this embodiment, the determining module 31 is further configured to map the mastery data of each knowledge point through an objective function to obtain the mastery probability data of each knowledge point by the student; based on the mastery probability data, knowledge points that meet the unmastered conditions are selected from the knowledge corresponding to the target learning course, and the knowledge points selected from the knowledge corresponding to the target learning course are combined into a set of unmastered knowledge points.

[0201] In some examples of this embodiment, the matching module 34 is specifically configured to 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 knowledge point learning grid, and extract the unmastered knowledge point paths corresponding to the sequence of unmastered knowledge points from the target knowledge point learning grid; match the unmastered knowledge point paths 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.

[0202] In some examples of this embodiment, the matching module 34 is further configured 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.

[0203] It should be noted that other corresponding descriptions of the functional units involved in the learning assistance device provided in this embodiment can be found in [reference]. Figure 1 and Figure 5 The corresponding descriptions in [the document] will not be repeated here.

[0204] Based on the above, Figure 1 and Figure 5 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 5 The method shown.

[0205] 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.

[0206] like Figure 7 The diagram shown is a hardware structure schematic of an electronic device according to the present invention, comprising:

[0207] At least one processor 401; and,

[0208] Memory 402 is communicatively connected to at least one processor 401; wherein,

[0209] The memory 402 stores instructions that can be executed by at least one processor to enable the at least one processor to perform the learning path generation method based on the AI ​​agent as described above.

[0210] Figure 7 Take a processor 401 as an example.

[0211] The electronic device may also include an input device 403 and a display device 404.

[0212] The processor 401, memory 402, input device 403, and display device 404 can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0213] 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 ​​agent-based learning path generation method in this application embodiment, for example, Figure 1 and Figure 5 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 ​​agent-based learning path generation method in the above embodiments.

[0214] 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 learning path generation method. Furthermore, memory 402 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 402 may optionally include memory remotely located relative to processor 401, and these remote memories may be connected via a network to means of performing the AI ​​agent-based learning path generation method. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0215] The input device 403 can receive user clicks and generate signal inputs related to user settings and function control of the AI ​​agent-based learning path generation method. The display device 404 may include a display screen or other display device.

[0216] When one or more modules are stored in the memory 402, and are run by one or more processors 401, the learning path generation method based on AI agents in any of the above method embodiments is executed.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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 platform, or it can be implemented by hardware. Compared with existing technologies, this embodiment obtains the student's learning location information in the target knowledge point learning grid from the learning companion system and determines the corresponding target learning course, achieving precise positioning of the student's current learning focus; it achieves structured processing of multi-source heterogeneous learning behavior data by dividing the student's learning behavior data in the target learning course into at least one learning behavior dataset and merging it to generate at least one corresponding behavior feature set; it improves the accuracy of identifying unmastered knowledge points by analyzing the student's mastery of knowledge points in the target learning course based on at least one behavior feature set; it improves the confidence of knowledge graph association by matching the unmastered knowledge point set with the target knowledge graph corresponding to the target knowledge point learning grid and obtaining the target knowledge point network; it improves the reliability of learning path generation and enhances the targeted approach to helping students master unmastered knowledge points by generating the student's target learning path in the target knowledge point learning grid based on the target knowledge point network; and it selects those that meet the effective conditions by determining the time information corresponding to each behavior feature in at least one behavior feature set and evaluating the effectiveness of each behavior feature based on the time information. The study focuses on identifying target behavioral characteristics and forming a set of target behavioral characteristics to improve the effectiveness of behavioral characteristic data. By matching at least one set of target behavioral characteristics with each knowledge point in the target learning course, the study utilizes a target model to identify the importance data of the target behavioral characteristics corresponding to each knowledge point. This importance data is then combined with the determination of students' mastery of each knowledge point, improving the accuracy of identifying the set of unmastered knowledge points. Furthermore, by determining the influence coefficient of at least one behavioral characteristic corresponding to each knowledge point on the degree of mastery of that knowledge point based on the importance data, the study analyzes the behavioral characteristics based on the influence coefficient and generates mastery data, improving the alignment between mastery data and students' actual mastery. A mastery probability data is obtained by mapping the mastery data of each knowledge point to an objective function. Based on this probability data, knowledge points that meet the conditions for not mastering the knowledge point are selected to form a set of unmastered knowledge points, improving the standardization of unmastered knowledge point selection. Finally, an unmastered knowledge point sequence is generated based on the set of unmastered knowledge points. Unmastered identifiers are marked in the target knowledge point learning grid, and corresponding unmastered knowledge point paths are extracted. These paths are then matched with the target knowledge graph based on the identifiers to obtain the target knowledge point network, achieving accurate association between unmastered knowledge points and the knowledge graph, and improving the targeting of knowledge point network extraction.

[0221] 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.

[0222] 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 learning path generation method based on AI intelligent agents, characterized in that, include: The learning location information of students in the learning grid of target knowledge points is obtained from the learning companion system, and the target learning course corresponding to the learning location information is determined. The student's learning behavior data in the target learning course is divided into at least one learning behavior data set, and at least one set of behavioral features corresponding to the at least one learning behavior data set is generated; Based on the analysis of the at least one set of behavioral characteristics, the student’s mastery of the knowledge points in the target learning course is determined, and the set of knowledge points that the student has not mastered in the target learning course is determined. The set of unmastered knowledge points is matched with the target knowledge graph corresponding to the target knowledge point learning grid to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph; Based on the target knowledge point network, a target learning path for the student in the target knowledge point learning grid is generated, wherein the target learning path is mapped to a grid coordinate sequence in the target knowledge point learning grid for display, and the target learning path is used to assist the student in mastering the set of unmastered knowledge points; The step of matching the set of unmastered knowledge points with the target knowledge graph corresponding to the target knowledge point learning grid to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph includes: Generate a sequence of unmastered knowledge points based on the aforementioned set of unmastered knowledge points; The sequence of unmastered knowledge points is marked with an unmastered identifier in the target knowledge point learning grid, and the path of the unmastered knowledge point corresponding to the sequence of unmastered knowledge points is extracted from the target knowledge point learning grid. Based on the unknown identifier, the paths of the unknown knowledge points are matched with the target knowledge graph to obtain the target knowledge point network corresponding to the set of unknown knowledge points in the target knowledge graph; The step of matching the paths of the unmastered knowledge points with the target knowledge graph based on the unmastered identifier to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph includes: Based on the unknown identifier, the paths of the unknown knowledge points are matched with the target knowledge graph to obtain the network of knowledge points to be screened corresponding to the set of unknown knowledge points in the target knowledge graph; Based on the prerequisite relationships and relevance information of the unmastered knowledge point paths in the network of knowledge points to be screened, the target knowledge point network is determined from the network of knowledge points to be screened.

2. The method according to claim 1, characterized in that, The step of analyzing the student's mastery of knowledge points in the target learning course based on the at least one set of behavioral features, and determining the set of knowledge points that the student has not mastered in the target learning course, includes: Determine the time information corresponding to each behavioral feature in the at least one set of behavioral features, and evaluate the effectiveness of each behavioral feature in the at least one set of behavioral features based on the time information to obtain the effective value data of each behavioral feature in the at least one set of behavioral features; Based on the effective value data, the behavioral features in the at least one set of behavioral features are filtered to obtain the target behavioral features that meet the effective conditions in the at least one set of behavioral features, and the target behavioral features are combined into at least one set of target behavioral features; Based on the analysis of the at least one set of target behavioral features, the student's mastery of the knowledge points in the target learning course is determined, thereby identifying the set of knowledge points that the student has not mastered in the target learning course.

3. The method according to claim 2, characterized in that, The step of analyzing the student's mastery of knowledge points in the target learning course based on the at least one set of target behavioral features, and determining the set of knowledge points that the student has not mastered in the target learning course, includes: The at least one set of target behavior features is matched with each knowledge point in the target learning course to determine at least one target behavior feature corresponding to each knowledge point in the target learning course; The target model identifies the importance 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. The target model is trained based on the student's historical behavioral characteristics and the degree of mastery of historical knowledge points. Based on at least one target behavioral feature and importance data corresponding to each knowledge point, determine the student's mastery data of each knowledge point in the target learning course; Based on the mastery data, determine the set of knowledge points that the students have not mastered in the target learning course.

4. The method according to claim 3, characterized in that, The method of determining the student's 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 includes: Based on the 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; Based on the influence coefficient, the at least one behavioral feature is analyzed to generate data on the student's mastery of each knowledge point in the target learning course.

5. The method according to claim 3, characterized in that, The process of determining the set of unmastered knowledge points for students in the target learning course based on the mastery data includes: By mapping the mastery data of each knowledge point to the objective function, the probability data of the student's mastery of each knowledge point is obtained. Based on the mastery probability data, knowledge points that meet the unmastered conditions are selected from the knowledge corresponding to the target learning course, and the selected knowledge points are combined to form the unmastered knowledge point set.

6. A learning path generation device based on an AI intelligent agent, characterized in that, include: The determination module is configured to obtain the student's learning location information in the target knowledge point learning grid from the learning companion system, and determine the target learning course corresponding to the learning location information; The generation module is configured to divide the student's learning behavior data in the target learning course into at least one learning behavior data set, and generate at least one set of behavioral features corresponding to the at least one learning behavior data set; The analysis module is configured to analyze the student's mastery of knowledge points in the target learning course based on the at least one set of behavioral features, and to determine the set of knowledge points that the student has not mastered in the target learning course. The matching module is configured to match the set of unmastered knowledge points with the target knowledge graph corresponding to the target knowledge point learning grid, so as to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph; The generation module is configured to generate a target learning path for the student in the target knowledge point learning grid based on the target knowledge point network, wherein the target learning path is mapped to a grid coordinate sequence in the target knowledge point learning grid for display, and the target learning path is used to assist the student in mastering the set of unmastered knowledge points; The step of matching the set of unmastered knowledge points with the target knowledge graph corresponding to the target knowledge point learning grid to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph includes: Generate a sequence of unmastered knowledge points based on the aforementioned set of unmastered knowledge points; The sequence of unmastered knowledge points is marked with an unmastered identifier in the target knowledge point learning grid, and the path of the unmastered knowledge point corresponding to the sequence of unmastered knowledge points is extracted from the target knowledge point learning grid. Based on the unknown identifier, the paths of the unknown knowledge points are matched with the target knowledge graph to obtain the target knowledge point network corresponding to the set of unknown knowledge points in the target knowledge graph; The step of matching the paths of the unmastered knowledge points with the target knowledge graph based on the unmastered identifier to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph includes: Based on the unknown identifier, the paths of the unknown knowledge points are matched with the target knowledge graph to obtain the network of knowledge points to be screened corresponding to the set of unknown knowledge points in the target knowledge graph; Based on the prerequisite relationships and relevance information of the unmastered knowledge point paths in the network of knowledge points to be screened, the target knowledge point network is determined from the network of knowledge points to be screened.

7. 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 5.

8. 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 5.

Images