Knowledge point state determination method and device based on AI intelligent agent

Abstract

The invention discloses a knowledge point state determination method and device based on an AI agent, and relates to the technical field of education, and the method comprises the steps: obtaining the learning behavior data of a student for learning knowledge points in a knowledge point learning grid, and the mastery degree identification of the knowledge points in the knowledge point learning grid; determining first mastery degree data of the students on knowledge points in the knowledge point learning grids based on the mastery degree identifiers, and evaluating first knowledge point state data according to the first mastery degree data; determining second mastering degree data of the students on knowledge points in the knowledge point learning grid based on the learning behavior data, and evaluating second knowledge point state data according to the second mastering degree data; and according to the first knowledge point state data and the second knowledge point state data, determining state abnormal knowledge points from the knowledge point learning grid, and according to the state abnormal knowledge points, updating a knowledge graph corresponding to the knowledge point learning grid. The learning effect can be improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and apparatus for determining the state of knowledge points based on an AI agent. Background Technology

[0002] In existing intelligent learning systems, dynamic knowledge networks built on knowledge graphs have become the core infrastructure supporting personalized learning path planning. This system continuously tracks students' learning behavior, labels the mastery status of each node in the knowledge grid, and thus provides students with precise learning resources and path recommendations.

[0003] However, existing intelligent learning systems exhibit significant discrepancies between the knowledge point mastery status indicators maintained by these systems and students' actual cognition. This leads to inaccurate content recommendations from these systems. Existing intelligent learning systems assume a stable and singular mapping between learning behavior data and knowledge mastery levels, ignoring potential noise, randomness, and ambiguity in the behavior data. Furthermore, these systems lack the ability to proactively verify and calibrate the consistency of knowledge point statuses. They struggle to identify and address the differences between the mastery statuses recorded in the knowledge grid based on historical evaluation and rule-based reasoning, and the behavior-derived statuses implicit in the real-time learning behavior data stream. Consequently, they fail to recognize students' needs for updating the knowledge graph, impacting student learning outcomes. Summary of the Invention

[0004] In view of this, this application provides a method and apparatus for determining the state of knowledge points based on AI intelligent agents. The main purpose is to improve the technical problem that the existing technology is unable to discover and process the difference between the mastery state recorded in the knowledge grid based on historical evaluation and rule reasoning and the behavior inference state implicit in the real-time learning behavior data stream, which leads to the inability to identify students' needs for updating the knowledge graph and affects students' learning effectiveness.

[0005] Firstly, this application provides a method for determining the state of knowledge points based on an AI agent, including: The system obtains learning behavior data of students learning knowledge points in the knowledge point learning grid, as well as the mastery level of knowledge points in the knowledge point learning grid. Based on the mastery level identifier, the student's first mastery level data for the knowledge points in the knowledge point learning grid is determined, and the first knowledge point status data of the knowledge points in the knowledge point learning grid is evaluated based on the first mastery level data. Based on the learning behavior data, the student's second mastery level data for the knowledge points in the knowledge point learning grid is determined, and the second knowledge point status data of the knowledge points in the knowledge point learning grid is evaluated based on the second mastery level data. Based on the first knowledge point status data and the second knowledge point status data, abnormal knowledge points are determined from the knowledge point learning grid. 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. The target knowledge graph is used to generate the target knowledge point learning grid.

[0006] Secondly, this application provides a knowledge point state determination device based on an AI agent, comprising: The acquisition module is configured to acquire learning behavior data of students learning knowledge points in the knowledge point learning grid from the learning companion system, as well as the mastery level identifier of the knowledge points in the knowledge point learning grid. The determination module is configured to determine the student's first mastery level data for the knowledge points in the knowledge point learning grid based on the mastery level identifier, and to evaluate the first knowledge point status data of the knowledge points in the knowledge point learning grid based on the first mastery level data. The evaluation module is configured to determine the student's second mastery level data for knowledge points in the knowledge point learning grid based on the learning behavior data, and evaluate the second knowledge point status data of the knowledge points in the knowledge point learning grid based on the second mastery level data. The update module is configured to determine abnormal knowledge points from the knowledge point learning grid based on the first knowledge point status 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, and obtain the updated target knowledge graph, which is used to generate the target knowledge point learning grid.

[0007] 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 knowledge point state determination method based on AI intelligent agents as described in the first aspect.

[0008] 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 knowledge point state determination method based on AI intelligent agents as described in the first aspect.

[0009] Using the above technical solution, this application provides a method and apparatus for determining the state of knowledge points based on an AI intelligent agent, comprising: acquiring learning behavior data of students learning knowledge points in a knowledge point learning grid from a learning companion system, and mastery level identifiers of knowledge points in the knowledge point learning grid; determining first mastery level data of students on knowledge points in the knowledge point learning grid based on the mastery level identifiers, and evaluating first knowledge point state data of knowledge points in the knowledge point learning grid based on the first mastery level data; determining second mastery level data of students on knowledge points in the knowledge point learning grid based on the learning behavior data, and evaluating second knowledge point state data of knowledge points in the knowledge point learning grid based on the second mastery level data; determining abnormal knowledge points from the knowledge point learning grid based on the first and second knowledge point state data, and updating the knowledge graph corresponding to the knowledge point learning grid based on the abnormal knowledge points to obtain an updated target knowledge graph, wherein the target knowledge graph is used to generate a target knowledge point learning grid. Compared with existing technologies, this application obtains learning behavior data of students learning knowledge points in the knowledge point learning grid from the learning companion system, as well as the mastery level indicators of the knowledge points in the knowledge point learning grid. It can determine the first mastery level data and the first knowledge point status based on the mastery level indicators. It can also analyze the second mastery level data and the second knowledge point status of students based on learning behavior. By analyzing the difference information between the two, it can identify abnormal knowledge points with abnormal knowledge point status markings. Based on the abnormal knowledge points, the target knowledge graph is updated, and the target knowledge point learning grid is updated. This application can actively verify and calibrate the consistency of knowledge point status through two different knowledge point statuses. In this way, it can accurately identify the difference between the mastery status in the knowledge grid and the behavior inference status implicit in the real-time learning behavior data stream. This clarifies the student's need for updating the knowledge graph, allowing students to learn based on the updated knowledge graph and improve learning effectiveness. Attached Figure Description

[0010] 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.

[0011] 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.

[0012] Figure 1 A flowchart illustrating a knowledge point state determination method based on an AI agent, provided in an embodiment of this application, is shown. Figure 2 This illustration shows a schematic diagram of a knowledge point learning grid provided in an embodiment of this application; Figure 3 A flowchart illustrating a knowledge point state determination method based on an AI agent, provided in an embodiment of this application, is shown. Figure 4 This illustration shows a schematic diagram of a knowledge point state determination device based on an AI agent, according to an embodiment of this application. Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0013] 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.

[0014] To address the technical problem of existing technologies failing to detect and process the discrepancy between the mastery status recorded in the knowledge grid based on historical evaluation and rule-based reasoning and the behavioral inference status implicit in the real-time learning behavior data stream, thus hindering the identification of students' knowledge graph update needs and impacting student learning outcomes, this embodiment provides a knowledge point state determination method based on an AI agent, such as... Figure 1 As shown, the method includes: Step 101: Obtain learning behavior data of students learning knowledge points in the knowledge point learning grid from the learning companion system, as well as the mastery level indicators of knowledge points in the knowledge point learning grid.

[0015] In this embodiment, the learning companion system can be the backend system corresponding to the AI ​​learning assistant installed on the learning tablet. The learning companion system can store students' basic information, learning records, course materials, and other data. The basic information such as class, grade, name, age, and semester filled in when logging into the student's learning companion system can be used to generate a user ID, which can be associated with all of the student's learning data.

[0016] In this embodiment of the application, the knowledge point learning grid can be a two-dimensional grid formed by mapping the learning content of the student's current semester's courses according to logical relationships (such as chapter order, knowledge dependencies), such as... Figure 2 As shown, in Figure 2 Each grid cell represents a knowledge point. The level of mastery of different knowledge points can be marked within the cell. For example, different circles can be used to mark the proficiency of knowledge points, different symbols can be used to mark different circles, and different shapes can be used to mark the proficiency of knowledge points. The specific marking form of knowledge point proficiency is not limited in this embodiment.

[0017] As an optional method, when marking the proficiency of knowledge points by filling different circles, red can be used to indicate not mastered, yellow can be used to indicate mastered but not proficient, and green can be used to indicate proficient; when marking the proficiency of knowledge points by filling different shapes, 1 can be used to indicate not mastered, 2 can be used to indicate mastered but not proficient, and 3 can be used to indicate proficient; when marking the proficiency of knowledge points by filling different shapes, circles can be used to indicate not mastered, triangles can be used to indicate mastered but not proficient, squares can be used to indicate proficient, and so on, without further examples.

[0018] In the embodiments of this application, the mastery level identifier can be a mark indicating the student's mastery of the target knowledge point. The mastery level identifier can be used to reflect the student's knowledge mastery status and can be determined based on the mastery probability of the target knowledge point.

[0019] For example, the mastery level indicator in the embodiments of this application may include a not mastered indicator, a mastered but not proficient indicator, a mastered indicator, etc. The presentation form of the first mastery level indicator may include color marking (such as red corresponding to not mastering the knowledge point, yellow corresponding to mastering the knowledge point but not proficient, and green corresponding to mastering the knowledge point), text annotation, symbol marking, etc.

[0020] In this embodiment of the application, behavioral data may include multi-dimensional data such as students' completion rate of online learning of target knowledge points, accuracy rate of practice questions, redoing of wrong questions, learning time, supplementary notes, and frequency of asking questions.

[0021] In this embodiment of the application, the system can monitor the learning operations of students in the learning companion system in real time. When the system detects that a student is learning a knowledge point, it can obtain the corresponding learning behavior data.

[0022] Step 102: Determine the student's first mastery level data for the knowledge points in the knowledge point learning grid based on the mastery level identifier, and evaluate the first knowledge point status data of the knowledge points in the knowledge point learning grid based on the first mastery level data.

[0023] In the embodiments of this application, the first mastery level identifier can be the mastery level identifier currently displayed in the knowledge point learning grid. For example, in the embodiments of this application, the first mastery level identifier can be used to reflect the student's mastery of the knowledge points as perceived by the current learning support system.

[0024] For example, the first mastery level indicator in the embodiments of this application may include a not mastered indicator, a mastered but not proficient indicator, a mastered indicator, etc. The presentation form of the first mastery level indicator may include color marking (such as red corresponding to not mastering the knowledge point, yellow corresponding to mastering the knowledge point but not proficient, and green corresponding to mastering the knowledge point), text annotation, symbol marking, etc. The embodiments of this application can determine the mastery level indicator of different knowledge points by presenting different knowledge points in the knowledge point learning grid.

[0025] In some examples, the knowledge point status data can be the data on the student's mastery of different knowledge points as perceived by the learning companion system. It should be noted that there is a mapping relationship between the mastery level identifier and the knowledge point status data in the embodiments of this application. That is, the embodiments of this application can determine the mastery level identifier of different knowledge points by the presentation of different knowledge points in the knowledge point learning grid, and can also determine the knowledge point status data of different knowledge points by the mastery level identifier of different knowledge points marked in the knowledge point learning grid.

[0026] Step 103: Determine the student's second level of mastery of knowledge points in the knowledge point learning grid based on the learning behavior data, and evaluate the second knowledge point status data of knowledge points in the knowledge point learning grid based on the second level of mastery data.

[0027] In this application embodiment, the second mastery level identifier can be an identifier of the student's actual mastery level of knowledge points obtained based on the analysis of the student's current learning behavior data. For example, in this application embodiment, the second mastery level identifier can reflect the student's mastery of knowledge points as analyzed by the current learning companion system based on the student's learning behavior data.

[0028] For example, the second mastery level indicator in the embodiments of this application may include a not mastered indicator, a mastered but not proficient indicator, a mastered indicator, etc. The presentation form of the second mastery level indicator may include color markings (such as red corresponding to not mastering the knowledge point, yellow corresponding to mastering the knowledge point but not proficient, and green corresponding to mastering the knowledge point), text annotations, symbol markings, etc. The embodiments of this application can determine the mastery level indicator of different knowledge points by presenting different knowledge points in the knowledge point learning grid.

[0029] In some examples, the second knowledge point status data can be the actual mastery status data of students on different knowledge points. It should be noted that the learning companion system in this application embodiment has the ability to analyze mastery status data. Specifically, through learning behavior data, the learning companion system can identify the actual mastery of knowledge points in the knowledge point learning grid, and then determine the actual mastery level of knowledge points in the knowledge point learning grid, thereby determining the actual status data of knowledge points in the knowledge point learning grid.

[0030] Step 104: Based on the state data of the first knowledge point and the state data of the second knowledge point, identify the knowledge points with abnormal states from the knowledge point learning grid. Update the knowledge graph corresponding to the knowledge point learning grid based on the knowledge points with abnormal states to obtain the updated target knowledge graph. The target knowledge graph is used to generate the target knowledge point learning grid.

[0031] In this embodiment of the application, a knowledge graph can be a graph that displays knowledge points and the relationships between them in a graphical structure. Nodes in the knowledge graph can correspond to knowledge points, and edges in the knowledge graph can correspond to the relationships between knowledge points. Edges in the knowledge graph can include directed edges (representing prerequisite relationships) and undirected edges (representing relevance relationships), and the weights of the edges in the knowledge graph can represent the strength of the relationships. For example, the knowledge graph in this embodiment of the application can be constructed based on the course textbook catalog, teaching syllabus, and course standards, combined with NLP technology for structured analysis. The knowledge graph can include all knowledge points in the course and the inherent logical relationships between them.

[0032] In this application, by comparing the first knowledge point status data and the second knowledge point status data, that is, by using the knowledge point learning grid to identify the mastery level of different knowledge points and comparing it with the actual status data of the knowledge points in the knowledge point learning grid based on learning behavior data analysis, abnormal knowledge points with inconsistent statuses can be identified. Then, 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, and thus the updated target knowledge point learning grid is generated.

[0033] In this embodiment of the application, the target knowledge point learning grid can be a knowledge point learning grid that reflects the student's latest and most accurate knowledge mastery status after updating the mastery level label of abnormal knowledge points. The target knowledge point learning grid can be used to present the student's current knowledge gaps and mastery status in real time and accurately.

[0034] Compared with existing technologies, this embodiment obtains learning behavior data of students learning knowledge points in the knowledge point learning grid from the learning companion system, as well as the mastery level indicators of the knowledge points in the knowledge point learning grid. It can determine the first mastery level data and the first knowledge point status based on the mastery level indicators. It can also analyze the second mastery level data and the second knowledge point status of students based on learning behavior. By analyzing the difference information between the two, abnormal knowledge points with abnormal knowledge point status markings can be identified. The target knowledge graph is then updated based on the abnormal knowledge points, and the target knowledge point learning grid is updated. This embodiment can actively verify and calibrate the consistency of knowledge point status through two different knowledge point statuses. This allows this embodiment to accurately identify the difference between the mastery status in the knowledge grid and the behavior inference status implicit in the real-time learning behavior data stream, thereby clarifying the student's need for updating the knowledge graph. This allows students to learn based on the updated knowledge graph, improving learning effectiveness.

[0035] As a refinement and extension of the above embodiments, when performing the action of "determining abnormal knowledge points from the knowledge point learning grid based on the first knowledge point state data and the second knowledge point state data, updating the knowledge graph corresponding to the knowledge point learning grid based on the abnormal knowledge points, and obtaining the updated target knowledge graph", the following methods can be used, but are not limited to: Figure 2 As shown, the method includes: Step 201: Perform a status data difference analysis based on the first knowledge point status data and the second knowledge point status data 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.

[0036] In this embodiment, State Data Difference Analysis can be a process of comparing, identifying changes, locating anomalies, and deriving causes for the state data of the same system, entity, or user at different points in time or under different conditions. For example, in this embodiment, State Data Difference Analysis can be an analysis of the state data of the first knowledge point and the state data of the second knowledge point. That is, it can be an analysis of the state data of the knowledge points that are identified by the mastery level of different knowledge points marked by the knowledge point learning grid and the actual state data of the knowledge points in the knowledge point learning grid based on the analysis of learning behavior data, so as to evaluate the degree of difference between the mastery status of the knowledge points displayed in the knowledge point learning grid and the mastery status of the knowledge points in the knowledge point learning grid by the student.

[0037] In some examples, the degree of difference can specifically refer to the degree of difference in the mastery status of knowledge points. For instance, if the mastery status data of knowledge point 1 is determined to be 0.3 (if the mastery data range is 0-1) by the mastery level identifier of different knowledge points marked by the knowledge point learning grid, and the actual status data of knowledge point 1 in the knowledge point learning grid based on learning behavior data analysis is 0.7 (if the mastery data range is 0-1), then after performing difference data analysis on the knowledge point status data of knowledge point 1 determined by the mastery level identifier of knowledge point 1 marked by the knowledge point learning grid and the actual status data of knowledge point 1 in the knowledge point learning grid based on learning behavior data analysis, the degree of difference between the mastery status of knowledge point 1 displayed in the knowledge point learning grid and the student's mastery status of knowledge point 1 in the knowledge point learning grid can be evaluated, that is, the data difference can be 0.4.

[0038] As an optional approach, if the knowledge point status data of knowledge point 2 is determined to be level 1 (if the mastery data includes levels 1-5) by identifying the mastery level of different knowledge point markers through the knowledge point learning grid, and if the actual status data of knowledge point 2 in the knowledge point learning grid based on learning behavior data analysis is level 4 (if the mastery data includes levels 1-5), then after performing a difference data analysis on the knowledge point status data of knowledge point 2 determined by the mastery level markers of knowledge point 2 through the knowledge point learning grid and the actual status data of knowledge point 2 in the knowledge point learning grid based on learning behavior data analysis, the degree of difference between the knowledge point mastery status of knowledge point 2 displayed in the knowledge point learning grid and the student's mastery status of knowledge point 2 in the knowledge point learning grid can be evaluated. That is, the data difference can be 3 levels.

[0039] Step 202: Based on learning behavior data, determine the question information and note information of students learning knowledge points in the knowledge point learning grid, and determine the students' learning status information of knowledge points in the knowledge point learning grid based on question information, note information and first mastery level data.

[0040] Optionally, when performing the task of "determining the question information and note information of students learning knowledge points in the knowledge point learning grid based on learning behavior data", the following methods can be used, but are not limited to these: dividing the students' learning behavior data in the target knowledge point learning grid into at least one learning behavior data set, and generating a behavioral feature set corresponding to at least one learning behavior data set; determining the time information corresponding to each behavioral feature in the behavioral feature set, and evaluating the effectiveness of each behavioral feature in the behavioral feature set based on the time information, to obtain the effective value data of each behavioral feature in the behavioral feature set; filtering the behavioral features in the behavioral feature set based on the effective value data, to obtain the target behavioral features that meet the effective conditions in the behavioral feature set, and forming a target behavioral feature set from the target behavioral feature set; identifying the box-selection behavior from the target behavioral feature set, and classifying 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.

[0041] In this embodiment, learning behavior data can be various operational data generated by students when learning within a learning grid for target knowledge points. This data can reflect the student's learning process and learning status. For example, the learning behavior data in this embodiment may include, but is not limited to, online course learning data (such as video completion rate, number of repeated views, dwell time, fast-forward / rewind operation records), practice question data (such as answer accuracy rate, answer speed, number of incorrect questions, correct rate of redoing incorrect questions, number of questions completed, and question difficulty level), and interaction data (such as question frequency, question content, number of notes, detail of notes, number of comments, and activity level in discussions).

[0042] In this embodiment, the system can divide learning behavior data into at least one learning behavior data set according to the scenario or data type in which the data is generated. For example, it can be divided into online course learning data set, question-solving data set, and interaction data set according to the scenario, or into progress data set, accuracy data set, and interaction frequency data set according to the data type. The system can extract features that reflect the student's learning status and mastery of knowledge points for each learning behavior data set and generate a corresponding behavioral feature set.

[0043] For example, the behavioral feature set corresponding to the online course learning dataset may include features such as video completion rate, number of repeated views, average dwell time, and percentage of dwell time on key chapters; the behavioral feature set corresponding to the question-solving dataset may include features such as answer accuracy, answering speed, correct answering rate for incorrect questions, and correct answering rate for high-difficulty questions; the behavioral feature set corresponding to the interaction dataset may include features such as question frequency, number of notes, and level of detail in notes.

[0044] In this embodiment of the application, the time information corresponding to the behavioral feature can be the specific timestamp when each behavioral feature is generated. For example, the time information corresponding to the behavioral feature in this embodiment of the application can include the time corresponding to the video viewing completion rate (the time when the student finishes watching the video), the time corresponding to the answer accuracy rate (the time when the student finishes answering the questions), etc.

[0045] 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.

[0046] In the embodiments of this application, the system can evaluate the effectiveness of each behavioral feature based on time information to obtain the corresponding effective value data; the system can introduce a time decay factor to evaluate the effectiveness of each behavioral feature. The time decay factor can be determined based on the interval between the time when the behavioral feature is generated and the current time. If the interval is longer, the time decay factor is smaller.

[0047] For example, the time decay factor in this application embodiment can be specifically expressed as: ,in, It can represent the time taken to answer the question for the i-th time. It can indicate the current time for answering the question. It can represent the attenuation coefficient.

[0048] In this embodiment, the system can filter behavioral features in the behavioral feature set by setting valid conditions. The valid condition can be that the valid value data is greater than the valid threshold. The valid threshold can be determined based on experience or data statistics. The valid threshold can be used to filter out behavioral features that have practical reference value for assessing the current level of mastery. The system can filter out the behavioral features that meet the valid conditions to form a target behavioral feature set.

[0049] In this embodiment, analyzing students' mastery of knowledge points based on a set of target behavioral features can be achieved by the system calculating the mastery level of each knowledge point using behavioral feature data. Specifically, this calculation can be performed by constructing feature vectors, incorporating online learning data (video viewing completion rate, etc.). Repeat viewing count Duration of stay ), practice data (accuracy rate) Answering speed Correctness rate of redoing incorrect questions Interaction data (frequency of questions) Number of notes Features such as ) are included, and the feature vector is shown in Formula 1, 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 component to the range of [0,1].

[0050] (Formula 1) (Formula 2) In this embodiment, the system can use a model (such as a logistic regression model, a softmax regression model, etc.) to calculate the mastery level of knowledge points. For example, the system can use a logistic regression model to calculate the mastery level of knowledge points. Specifically, the system can first use normalized target behavioral features as model input. The model's weight vector (representing the importance of each feature to the mastery level) is obtained based on historical student data. Specifically, the model's weight vector can be obtained by collecting a large amount of historical students' behavioral feature data and corresponding actual knowledge point mastery level labels (such as determined through exam scores and teacher evaluations). Furthermore, the model parameters can be optimized using a gradient descent algorithm to ensure the model accurately outputs the mastery level assessment results. The bias term in the model can be used to adjust the overall assessment benchmark.

[0051] For example, the system can calculate the mastery level of knowledge points using models (such as logistic regression, softmax regression, etc.) by first calculating the mastery score si, as shown in Formula 3, 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.

[0052] (Formula 3) For example, a score can be determined through an objective function. Mapping knowledge points to mastery probability data, the objective function can include the sigmoid function, softmax function, etc.; calculating the probability of students mastering knowledge points can be done by mapping si to mastery probability using the sigmoid function. Mastering probability The expression is shown in Formula 4; 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 Then you can mark the knowledge point as a knowledge point you have mastered.

[0053] (Formula 4) Optionally, when performing the step of "determining the student's learning status information for knowledge points in the knowledge point learning grid based on question information, note information, and first mastery level data," the following methods may be used, but are not limited to these: determining the frequency of student updates to notes within the target time range based on note information, and evaluating 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; determining the frequency of student questions about knowledge points in the knowledge point learning grid based on note information and question information, and evaluating the student's difficulty in understanding knowledge points in the knowledge point learning grid based on the question frequency; and determining the student's learning status information for knowledge points in the knowledge point learning grid as abnormal learning status information 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 first mastery level data is less than the mastery level threshold.

[0054] For the embodiments of this application, the target time range can be a fixed period of time used to assess learning activity. The target time range can be set to the most recent 7 days, or it can be flexibly adjusted according to the characteristics of the subject and the learning progress.

[0055] In this embodiment of the application, the note update frequency can be the total number of times a student adds, modifies, or supplements notes on knowledge points with abnormal status within a target time range. For example, the note update frequency in this embodiment can include the number of updates to note formats such as text notes, annotated notes, and mind maps.

[0056] In this embodiment of the application, the learning activity data can be numerical data on the degree of active learning engagement of students in knowledge points with abnormal status. The value range of the learning activity data can be [0,1]. The learning activity data can be used to reflect the students' attention and active participation in knowledge points.

[0057] In this embodiment of the application, determining the update frequency of students updating notes within a target time range based on note information can be achieved by first extracting the note update record corresponding to each knowledge point with an abnormal status from the note information. The note update record can include information such as the timestamp of each update and the update type (addition, modification, supplement). Then, the update records within the target time range can be filtered out, the total number of updates can be counted, and the note update frequency of knowledge points with abnormal status can be obtained.

[0058] In this embodiment of the application, evaluating students' learning activity data within a target time range based on update frequency can be achieved by mapping the note update frequency to learning activity data. The mapping method can be to normalize the update frequency based on the maximum possible update frequency within the target time range, converting the update frequency into a value within the range of [0,1]. If the note update frequency is closer to the upper limit, it indicates that the learning activity data is closer to 1; if there are no note update records within the target time range, the learning activity data is 0.

[0059] In this embodiment of the application, the frequency of questions can be the total number of times a student expresses doubts about a knowledge point through answering questions, taking notes, or other means while learning about the knowledge point in an abnormal learning state.

[0060] In this embodiment of the application, the comprehension difficulty data can be numerical data of the degree of comprehension obstacles encountered by students when their learning state is abnormal. The comprehension difficulty data can take values ​​in the range of [0,1]. The larger the value of the comprehension difficulty data, the higher the difficulty for students to understand the knowledge points and the more obstacles they encounter.

[0061] In this embodiment of the application, determining the frequency of questions a student has about knowledge points with abnormal status based on note information and question information can be achieved by the system comprehensively extracting records related to the questions from the question information and note information and counting the frequency of questions; it can also be achieved by identifying questions and counting the number of times from the question information through the student's answer results (such as conceptual misunderstandings or logical confusion errors reflected in wrong questions), hesitant behavior during the answering process (such as taking too long to answer questions or repeatedly modifying answers), and question-related question records; or it can be achieved by identifying questions and counting the number of times from the note information through question symbols marked by the student, confused content recorded in the note text, and supplementary questions about knowledge points.

[0062] In this embodiment of the application, the system can map the frequency of questions to the difficulty of understanding data based on the update frequency. During the mapping, the system can refer to the maximum reasonable frequency of questions within the target time range for normalization processing, and convert the frequency of questions into a value in the range of [0,1]. If the frequency of questions is higher, it can indicate that the difficulty of understanding data is greater.

[0063] As an optional approach, this application embodiment can use Formula 5 to determine that a student's learning status information for knowledge points in the knowledge point learning grid is abnormal learning status information when the learning activity data is greater than the activity threshold and / or the comprehension difficulty data is greater than the difficulty threshold and / or the first mastery level data is less than the mastery level threshold. Specifically, the three abnormal conditions can correspond to excessively high note frequency, excessively high question density, and excessively low system assessment mastery, respectively. Formula 5 is as follows: (Formula 5) Step 203: 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 according to the knowledge points with abnormal status to obtain the updated target knowledge graph.

[0064] Optionally, when performing the action of "identifying anomalous knowledge points from the knowledge point learning grid based on learning state information and degree of difference, and updating the knowledge graph corresponding to the knowledge point learning grid based on the anomalous knowledge points to obtain the updated target knowledge graph," the following methods can be used, but are not limited to these: when the student's learning state information for knowledge points in the knowledge point learning grid is anomalous, evaluate the stability data of knowledge points in the knowledge point learning grid in the knowledge graph based on the betweenness centrality data and proximity centrality data of knowledge points in the knowledge point learning grid; determine the credibility data of anomalous learning state information based on the stability data and degree of difference; identify anomalous knowledge points from the knowledge point learning grid based on the credibility data, learning state information, and degree of difference; and update the knowledge graph corresponding to the knowledge point learning grid based on the anomalous knowledge points to obtain the updated target knowledge graph.

[0065] It should be noted that the knowledge graph in this embodiment connects graph nodes according to the relationships between knowledge points through graph edges to form a complete knowledge graph. During the construction process, graph algorithms can be used to calculate the centrality indicators of nodes, including degree centrality, betweenness centrality, and proximity centrality. Degree centrality measures the number of direct connections a node has, betweenness centrality measures the degree to which a node acts as a bridge, and proximity centrality measures the reciprocal of the average distance from a node to other nodes. 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.

[0066] For example, embodiments of this application can evaluate the stability of knowledge points in the knowledge graph within the knowledge point learning grid using betweenness centrality data and proximity centrality data. That is, betweenness centrality data can be used to evaluate whether a knowledge point is a key transit knowledge point, and proximity centrality data can be used to evaluate whether the knowledge point is a core knowledge point. By evaluating whether a knowledge point is a key transit knowledge point and whether it is a core knowledge point, the stability of the knowledge point in the knowledge graph can be comprehensively evaluated.

[0067] For example, if knowledge point 1 has high betweenness centrality and proximity centrality in the knowledge graph, then knowledge point 1 can be identified as a key transit knowledge point and core knowledge point in the knowledge graph, and thus knowledge point 1 can be comprehensively evaluated as having high stability in the knowledge graph.

[0068] For example, if knowledge point 2 has a high betweenness centrality but a low proximity centrality in the knowledge graph, then knowledge point 2 can be identified as a key transit knowledge point in the knowledge graph but not a core knowledge point. Thus, the stability of knowledge point 2 in the knowledge graph can be comprehensively evaluated as being in the middle range.

[0069] For example, if knowledge point 3 has a low betweenness centrality but a high proximity centrality in the knowledge graph, then knowledge point 3 can be determined to be a core knowledge point in the knowledge graph but not a key transit knowledge point. Thus, the stability of knowledge point 3 in the knowledge graph can be comprehensively evaluated as being in the middle range.

[0070] For example, if knowledge point 4 has low betweenness centrality and proximity centrality in the knowledge graph, it can be determined that knowledge point 4 is neither a key transit knowledge point nor a core knowledge point in the knowledge graph. Therefore, it can be comprehensively evaluated that knowledge point 4 has low stability in the knowledge graph.

[0071] As an optional approach, embodiments of this application can further determine the credibility data of abnormal learning state information based on stability data and the degree of difference. Specifically, the stability data and the degree of difference can be weighted using Formula Six to perform a state credibility assessment. It should be noted that in embodiments of this application, a larger state credibility assessment value indicates a more reliable system assessment. Formula Six is ​​shown below: (Formula 6) In Formula Six, C_B(v) can represent the structural stability of a knowledge point in a network, C_C(v) can represent the betweenness centrality of a knowledge point in a knowledge graph, and C_C(v) can represent the proximity centrality of a knowledge point in a knowledge graph.

[0072] As an optional approach, embodiments of this application can further determine anomalous knowledge points from the knowledge point learning grid based on credibility data, learning state information, and degree of difference. Specifically, the credibility data, learning state information, and degree of difference can be judged using Formula 7 to determine anomalous knowledge points. Formula 7 is as follows: (Formula 7) Optionally, when performing the task of “determining the student’s first mastery level data for knowledge points in the knowledge point learning grid based on the mastery level identifier, and evaluating the first knowledge point status data of knowledge points in the knowledge point learning grid based on the first mastery level data”, the following methods may be used, but are not limited to these: determining the baseline mastery level data corresponding to the mastery level identifier, determining the baseline mastery level data as the first mastery level data; and evaluating the first knowledge point status data of knowledge points in the knowledge point learning grid based on the first mastery level data.

[0073] As an optional approach, this embodiment of the application can determine the baseline mastery level data corresponding to the mastery level identifier using Formula Nine, and then determine the baseline mastery level data as the first mastery level data. The first knowledge point state data of the knowledge points in the knowledge point learning grid is then evaluated based on the first mastery level data. This is how the knowledge point state data of different knowledge points is determined by using the mastery level identifiers of different knowledge points marked by the knowledge point learning grid. Formula Eight is specifically shown below: (Formula 8) For example, Formula 8 can be used to determine that when the mastery level indicator is green, which corresponds to the indicator that the student has mastered it, the baseline mastery level data corresponding to the mastery level indicator can be determined to be 1. Then, the baseline mastery level data 1 can be determined as the first mastery level data, and then the first knowledge point status data in the knowledge point learning grid can be evaluated based on the baseline mastery level data 1.

[0074] For example, Formula 8 can be used to determine that when the mastery level is marked with a yellow mark, which corresponds to the mark that the student has mastered but is not proficient, the baseline mastery level data corresponding to the mastery level mark can be determined to be 0.5. Then, the baseline mastery level data of 0.5 can be determined as the first mastery level data, and then the first knowledge point status data in the knowledge point learning grid can be evaluated based on the baseline mastery level data of 0.5.

[0075] For example, Formula 9 can be used to determine that when the mastery level is marked in red, which corresponds to the student not mastering the knowledge, the baseline mastery level data corresponding to the mastery level can be determined to be 0. Then, the baseline mastery level data of 0 can be determined as the first mastery level data, and the first knowledge point status data in the knowledge point learning grid can be evaluated based on the baseline mastery level data of 0.

[0076] Optionally, when performing the task of "determining the student's second level of mastery data for knowledge points in the knowledge point learning grid based on learning behavior data, and evaluating the second knowledge point status data of knowledge points in the knowledge point learning grid based on the second level of mastery data," the following methods can be used, but are not limited to these: matching the set of behavioral features corresponding to the learning behavior data with the knowledge points in the knowledge point learning grid to determine at least one target behavioral feature corresponding to the knowledge point in the knowledge point learning grid; identifying the importance data of at least one target behavioral feature corresponding to the knowledge point in the knowledge point learning grid to the knowledge point in the knowledge point learning grid through a target model, wherein the target model is trained based on the student's historical behavioral features and historical knowledge point mastery; and determining the student's second level of mastery data for the knowledge point in the knowledge point learning grid based on at least one target behavioral feature corresponding to the knowledge point in the knowledge point learning grid and the importance data.

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

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

[0079] Compared with existing technologies, this embodiment obtains learning behavior data of students learning knowledge points in the knowledge point learning grid from the learning companion system, as well as the mastery level indicators of the knowledge points in the knowledge point learning grid. It can determine the first mastery level data and the first knowledge point status based on the mastery level indicators. It can also analyze the second mastery level data and the second knowledge point status of students based on learning behavior. By analyzing the difference information between the two, abnormal knowledge points with abnormal knowledge point status markings can be identified. The target knowledge graph is then updated based on the abnormal knowledge points, and the target knowledge point learning grid is updated. This embodiment can actively verify and calibrate the consistency of knowledge point status through two different knowledge point statuses. This allows this embodiment to accurately identify the difference between the mastery status in the knowledge grid and the behavior inference status implicit in the real-time learning behavior data stream, thereby clarifying the student's need for updating the knowledge graph. This allows students to learn based on the updated knowledge graph, improving learning effectiveness.

[0080] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 1: Based on the knowledge point set corresponding to the course material information, determine the basic learning information of the knowledge points in the knowledge point set, as well as the prerequisite knowledge information and correlation information between the knowledge points; generate attribute information of the knowledge points based on the basic learning information, the prerequisite knowledge information, and the correlation information, and construct a knowledge graph corresponding to the knowledge point set based on the attribute information; map the knowledge graph into the knowledge point learning grid corresponding to the course material information to obtain the target knowledge point learning grid; generate the student's learning plan for the target time period based on the knowledge point learning grid in the knowledge point learning grid module, and generate the student's learning path based on the learning plan; the knowledge point learning grid module is used to display the learning path to the student through the knowledge point learning grid; wherein, the target knowledge point learning grid is a learning grid that includes the attribute information of the knowledge points and the correlation information between the knowledge points.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 2: In response to obtaining first learning behavior data of a student learning questions based on the learning plan, the system determines first question information and first note information of the student's learning target questions based on the first learning behavior data, and sends the first question information and first note information to the knowledge point learning grid module; based on the implicit knowledge point set in the knowledge point set corresponding to the first question information and first note information; according to the prerequisite relationship and relevance information between the implicit knowledge point set and the first knowledge point set, the system determines the target implicit knowledge point set corresponding to the learning plan from the implicit knowledge point set; the system updates the target implicit knowledge point set in the target knowledge point learning grid, and sends the updated target knowledge point learning grid to the learning path generation module; the system updates the learning plan to include the target implicit knowledge point set.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 3: Based on the student's learning location information in the target knowledge point learning grid, determine the target learning course corresponding to the learning location information; divide the student's second learning behavior data in the target learning course into at least one learning behavior data set, and generate at least one behavioral feature set corresponding to the at least one learning behavior data set; analyze the student's mastery of knowledge points in the target learning course based on the at least one behavioral feature set, and determine the student's unmastered knowledge point set in the target learning course; match the unmastered knowledge point set with the target knowledge graph corresponding to the target knowledge point learning grid to obtain the target knowledge point network corresponding to the unmastered knowledge point set in the target knowledge graph; generate the student's target learning path in the target knowledge point learning grid based on the target knowledge point network, and the target learning path is used to assist the student in mastering the unmastered knowledge point set.

[0095] Example 31: Determine the time information corresponding to each behavioral feature in at least one set of behavioral features, and evaluate the effectiveness of each behavioral feature in at least one set of behavioral features based on the time information to obtain effective value data for each behavioral feature in at least one set of behavioral features; filter the behavioral features in at least one set of behavioral features based on the effective value data to obtain target behavioral features that meet the effective conditions in at least one set of behavioral features, and form at least one set of target behavioral features; analyze the students' mastery of knowledge points in the target learning course based on at least one set of target behavioral features to determine the set of knowledge points that students have not mastered in the target learning course.

[0096] Example 32: Match at least one set of target behavioral features with each knowledge point in the target learning course to determine at least one target behavioral feature corresponding to each knowledge point in the target learning course; identify the importance data of at least one target behavioral feature corresponding to each knowledge point in the target learning course to each knowledge point in the target learning course through a target model, which is trained based on students' historical behavioral features and historical knowledge point mastery levels; determine students' mastery data of each knowledge point in the target learning course based on at least one target behavioral feature and importance data corresponding to each knowledge point; determine the set of unmastered knowledge points in the target learning course based on the mastery data.

[0097] Example 33: Based on importance data, determine the influence coefficient of at least one behavioral feature corresponding to each knowledge point on the degree of mastery of each knowledge point; analyze at least one behavioral feature based on the influence coefficient to generate data on the student's mastery of each knowledge point in the target learning course.

[0098] Example 34: Map the mastery data of each knowledge point to the objective function to obtain the mastery probability data of each knowledge point; select knowledge points that meet the unmastered conditions from the knowledge corresponding to the target learning course based on the mastery probability data, and form a set of unmastered knowledge points from the knowledge points selected from the knowledge corresponding to the target learning course.

[0099] Example 35: Generate a sequence of unmastered knowledge points based on the set of unmastered knowledge points; mark the sequence of unmastered knowledge points with unmastered identifiers in the target learning grid, and extract the paths of unmastered knowledge points corresponding to the sequence of unmastered knowledge points from the target learning grid; match the paths of unmastered knowledge points with the target knowledge graph according to the unmastered identifiers to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph.

[0100] Example 36: This method is used to match the paths of unmastered knowledge points with the target knowledge graph based on the unmastered identifier, thereby obtaining the network of knowledge points to be screened corresponding to the set of unmastered knowledge points in the target knowledge graph; and to determine the target knowledge point network from the network of knowledge points to be screened based on the prerequisite relationships and relevance information of the paths of unmastered knowledge points in the network of knowledge points to be screened.

[0101] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 4: Obtain third learning behavior data of the student learning knowledge points in the set of unmastered knowledge points within the knowledge point learning grid; determine second question information and second note information based on the third learning behavior data; evaluate the student's learning needs for knowledge points in the set of unmastered knowledge points based on the second question information and the second note information, and obtain learning intention intensity data corresponding to the knowledge points in the knowledge point learning grid; identify the student's target learning intention for knowledge points in the set of unmastered knowledge points based on the learning intention intensity data, the second question information, and the second note information; update the set of unmastered knowledge points based on the target learning intention and the knowledge point location information of the set of unmastered knowledge points in the knowledge point learning grid to obtain a target set of unmastered knowledge points, and recommend the target set of unmastered knowledge points to the student.

[0102] Example 41: Based on note information, determine the update frequency of students' notes within the target time range, and evaluate students' learning activity data within the target time range based on the update frequency; based on note information and question information, determine the question frequency of students' doubts about knowledge points in the set of unmastered knowledge points, and evaluate students' difficulty in understanding knowledge points in the set of unmastered knowledge points based on the question frequency; evaluate students' learning needs for knowledge points in the set of unmastered knowledge points based on learning activity data and difficulty in understanding data, and obtain the learning intention intensity data corresponding to knowledge points in the knowledge point learning grid.

[0103] Example 42: Based on the third learning behavior data, determine the learning end time for students to learn the knowledge points in the set of unmastered knowledge points; determine the decay factor of learning intention intensity over time based on the learning end time and time decay coefficient; evaluate the urgency data of students learning the knowledge points in the set of unmastered knowledge points based on the decay factor; perform weighted analysis on learning activity data, comprehension difficulty data and urgency data to evaluate students' learning needs for the knowledge points in the set of unmastered knowledge points, and obtain the learning intention intensity data corresponding to the knowledge points in the knowledge point learning grid.

[0104] Example 43: Based on the second question information and the second note information, generate student learning content data and corresponding behavior sequence data for the third learning behavior data; evaluate the student's learning intention for the knowledge points in the set of unmastered knowledge points as a first probability data of deep understanding intention, a second probability data of basic consolidation intention, and a third probability data of conceptual questioning intention based on the learning intention intensity data, learning content data, and behavior sequence data; determine the target learning intention from deep understanding intention, basic consolidation intention, and conceptual questioning intention based on the first probability data, the second probability data, and the third probability data.

[0105] Example 44: Evaluate the student's required level of understanding of the knowledge points in the set of unmastered knowledge points based on the first probability data, the second probability data, and the third probability data; if the required level of understanding data is greater than the first required level of understanding threshold, determine the student's intention to deepen understanding as the target learning intention for the knowledge points in the set of unmastered knowledge points; if the required level of understanding data is less than or equal to the first required level of understanding threshold and greater than the second required level of understanding threshold, determine the student's intention to consolidate basic knowledge as the target learning intention for the knowledge points in the set of unmastered knowledge points, wherein the second required level of understanding threshold is less than the first required level of understanding threshold; if the required level of understanding data is less than the second required level of understanding threshold, determine the student's intention to raise conceptual questions as the target learning intention for the knowledge points in the set of unmastered knowledge points.

[0106] Example 45: Generate student learning content data based on the second question information and the second note information; map the third learning behavior data into the knowledge graph corresponding to the knowledge point learning grid to obtain the student's learning knowledge point data in the knowledge graph; perform multi-dimensional information matching between the learning content data and the learning knowledge point data to generate knowledge point location information for the set of unmastered knowledge points; update the set of unmastered knowledge points according to the target learning intention and the knowledge point location information to obtain the target set of unmastered knowledge points, generate the target set of unmastered knowledge points and the target learning path corresponding to the target set of unmastered knowledge points; generate recommendation information for the target set of unmastered knowledge points and the target learning path so that students can master the knowledge points in the target set of unmastered knowledge points based on the target set of unmastered knowledge points and the target learning path.

[0107] Example 46: Obtain third learning behavior data from the learning support system, showing students learning knowledge points in the set of unmastered knowledge points within the knowledge point learning grid; divide the third learning behavior data of students in the target knowledge point learning grid into at least one learning behavior data set, and generate a set of behavioral features corresponding to at least one learning behavior data set; determine the time information corresponding to each behavioral feature in the behavioral feature set, and evaluate the effectiveness of each behavioral feature in the behavioral feature set based on the time information, obtaining the effective value data of each behavioral feature in the behavioral feature set; filter the behavioral features in the behavioral feature set based on the effective value data, obtain the target behavioral features that meet the effective conditions in the behavioral feature set, and form a target behavioral feature set; analyze the students' mastery of knowledge points in the target knowledge point learning grid based on the target behavioral feature set, and determine the set of unmastered knowledge points in the target knowledge point learning grid; identify the box-selection behavior from the target behavioral feature set, and classify the learning content selected by the box-selection behavior to obtain the second question information and second note information of students learning knowledge points in the set of unmastered knowledge points.

[0108] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 5: The third learning behavior data is mapped into the knowledge graph corresponding to the knowledge point learning grid to obtain the student's learning knowledge point data in the knowledge graph; the student's learning content data is generated based on the question information and the note information, and the learning content data is matched with the learning knowledge point data in multiple dimensions to generate the knowledge point positioning information of the set of unmastered knowledge points.

[0109] Example 51: Generate student learning content data based on question information and note information; perform similarity matching based on the first semantic embedding vector corresponding to the learning content data and the second semantic embedding vector corresponding to the learning knowledge point data to match the content similarity between the learning content data and the learning knowledge point data, and obtain a first similarity matching result; perform similarity matching based on the context data corresponding to the learning content data and the adjacent knowledge point data corresponding to the learning knowledge point data to match the overall similarity between the learning content data and the learning knowledge point data in the knowledge graph, and obtain a second similarity matching result; perform similarity matching based on the first position data of the learning content data in the textbook and the second position data of the learning knowledge point data in the textbook to match the position similarity between the learning content data and the learning knowledge point data, and obtain a third similarity matching result; based on the first similarity matching result, the second similarity matching result, and the third similarity matching result, generate knowledge point location information for the set of unmastered knowledge points.

[0110] Example 52: A fusion analysis is performed based on the first similarity matching result, the second similarity matching result, and the third similarity matching result to evaluate the accuracy of the location of knowledge points in the set of unmastered knowledge points, and to obtain the location reliability data of the learning content data and the learning knowledge point data; based on the location reliability data, unmastered knowledge points that meet the confidence conditions are selected from the set of unmastered knowledge points to form a location knowledge point set; based on the prerequisite relationships and relevance relationships of the knowledge points in the location knowledge point set in the knowledge graph, the knowledge point location information of the set of unmastered knowledge points is generated.

[0111] Example 53: Based on the prerequisite relationships of knowledge points in the knowledge point set located in the knowledge graph, determine the first knowledge point set consisting of the prerequisite knowledge points corresponding to the knowledge point set located in the knowledge graph, and the second knowledge point set with the knowledge points in the knowledge point set as prerequisite knowledge points; extract the knowledge point dependency relationship chain corresponding to the unmastered knowledge point set from the knowledge graph based on the knowledge point set located, the first knowledge point set, and the second knowledge point set; determine the third knowledge point set related to the knowledge point set located in the knowledge graph based on the correlation relationships of knowledge points in the knowledge point set located in the knowledge graph, and extract the knowledge point related relationship chain corresponding to the unmastered knowledge point set based on the knowledge point dependency relationship chain and the knowledge point related relationship chain; generate the knowledge point location information of the unmastered knowledge point set based on the knowledge point dependency relationship chain and the knowledge point related relationship chain.

[0112] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 6: Based on the student's review path for already learned knowledge points within the target knowledge point learning grid, determine the knowledge points to be reviewed indicated by the review path; extract the network of knowledge points to be reviewed corresponding to the knowledge points to be reviewed from the knowledge graph corresponding to the target knowledge point learning grid, wherein the network of knowledge points to be reviewed includes at least one prerequisite knowledge point and at least one related knowledge point corresponding to the knowledge point to be reviewed; update the review path based on the student's mastery of the knowledge point to be reviewed, the at least one prerequisite knowledge point, and the at least one related knowledge point to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed; determine the student's target knowledge points to be reviewed based on the target knowledge point review path, generate recommendation information for the target knowledge points to be reviewed, and recommend the review of the target knowledge points to the student.

[0113] Example 61: Determine the knowledge point mastery level indicator of the knowledge point to be reviewed from the knowledge point network; when the knowledge point mastery level indicator of the knowledge point to be reviewed is not mastered, determine the first knowledge point mastery level indicator of at least one prerequisite knowledge point and the second knowledge point mastery level indicator of at least one related knowledge point in the knowledge point network to be reviewed; based on the first knowledge point mastery level indicator and the second knowledge point mastery level indicator, update the knowledge point review path to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed.

[0114] Example 62: Based on the mastery level identifier of the first knowledge point and the mastery level identifier of the second knowledge point, determine the set of first knowledge points that the student has mastered from at least one prerequisite knowledge point and at least one related knowledge point; remove the set of first knowledge points from the network of knowledge points to be reviewed, and use the knowledge points to be reviewed as the starting point of the review path to update the network of knowledge points to be reviewed, so as to obtain the target knowledge point review path corresponding to the knowledge points to be reviewed.

[0115] Example 63: Based on the mastery level indicators of the first and second knowledge points, determine the set of second knowledge points that the student has not mastered from at least one prerequisite knowledge point and at least one related knowledge point; determine the learning priority information of the knowledge points in the second knowledge point set; based on the learning priority information, select the first target knowledge point that meets the priority condition from the second knowledge point set; use the first target knowledge point as the starting point of the review path, update the network of knowledge points to be reviewed, and obtain the review path of the target knowledge point.

[0116] Example 64: Determine the mastery probability data and knowledge point difficulty data for each knowledge point in the second knowledge point set; evaluate the learning priority of each knowledge point in the second knowledge point set based on the mastery probability data and knowledge point difficulty data to obtain the learning priority information of each knowledge point in the second knowledge point set; determine the knowledge point with the highest learning priority information in the second knowledge point set as the first target knowledge point.

[0117] Example 65: When the knowledge point mastery level of the knowledge point to be reviewed is marked as "mastered", the knowledge point traversal range is determined based on the knowledge point to be reviewed; the knowledge points are traversed within the knowledge point traversal range to obtain the third set of knowledge points that the student has not mastered; the set of prerequisite knowledge points and the set of related knowledge points corresponding to the third set of knowledge points are determined in the knowledge graph; the second target knowledge point that meets the priority condition is determined from the third set of knowledge points, the set of prerequisite knowledge points, and the set of related knowledge points; the knowledge point to be reviewed is removed from the knowledge point network to be reviewed, and the second target knowledge point is used as the starting point of the review path to update the knowledge point network to obtain the target knowledge point review path.

[0118] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 7: In response to the fourth learning behavior data of the student reviewing the target knowledge point to be reviewed, the first mastery level identifier of the target knowledge point to be reviewed is updated; the update influence range of the mastery level of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid is determined, and a set of knowledge points to be updated is formed based on the knowledge points covered by the update influence range; the mastery level data of the knowledge points in the set of knowledge points to be updated is updated, and the second mastery level identifier of the knowledge points in the set of knowledge points to be updated is updated according to the updated mastery level data; the knowledge point learning grid is updated to the target knowledge point learning grid based on the second mastery level identifier, and the target review content of the student is determined according to the target knowledge point learning grid.

[0119] Example 71: 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.

[0120] Example 72: 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.

[0121] Example 73: 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 identifier of knowledge points in the set of knowledge points to be updated based on the updated mastery level data.

[0122] Example 74: 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 level data of the first target knowledge point based on the centrality adjustment coefficient and the change in the mastery level data of the target knowledge point; update the second mastery level label of the knowledge points in the knowledge point set to be updated based on the updated mastery level data of the knowledge points in the knowledge point set to be updated.

[0123] Example 75: 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.

[0124] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 8: Determine the state correction content corresponding to the abnormal knowledge point; based on the correction content, perform state correction on the abnormal knowledge point in the knowledge point learning grid, and determine the mastery level impact data after the abnormal knowledge point has been state corrected; determine the influence range of the mastery level impact data in the knowledge graph corresponding to the knowledge point learning grid based on the mastery level impact data, and update the knowledge graph within the influence range.

[0125] 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.

[0126] 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.

[0127] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including: Example 9: The learning behavior collection module acquires the student's historical learning data. Based on this data, it determines the student's learning ability data and learning status information for the knowledge points in the knowledge point learning grid. The learning path generation module determines the student's learning plan type based on the learning ability data and learning status information, and determines the set of knowledge points to be learned in the knowledge point learning grid according to the learning plan type. It generates the student's learning path in the knowledge point learning grid based on the set of knowledge points to be learned. The learning material recommendation module determines a set of learning materials matching the learning path from the learning materials, recommending that the student learn the set of knowledge points based on the set of learning materials.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Furthermore, as Figure 1 and Figure 3 The specific implementation of the method shown in this embodiment provides a knowledge point state determination device based on an AI agent, such as... Figure 4 As shown, the device includes: an acquisition module 31, a determination module 32, an evaluation module 33, and an update module 34.

[0135] The acquisition module 31 is configured to acquire learning behavior data of students learning knowledge points in the knowledge point learning grid from the learning companion system, as well as the mastery level identifier of the knowledge points in the knowledge point learning grid. The determination module 32 is configured to determine the student's first mastery level data for the knowledge points in the knowledge point learning grid based on the mastery level identifier, and evaluate the first knowledge point status data of the knowledge points in the knowledge point learning grid based on the first mastery level data. Evaluation module 33 is configured to determine the student's second mastery level data of knowledge points in the knowledge point learning grid based on the learning behavior data, and evaluate the second knowledge point status data of knowledge points in the knowledge point learning grid based on the second mastery level data; The update module 34 is configured to determine abnormal knowledge points from the knowledge point learning grid based on the first knowledge point status 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, and obtain the updated target knowledge graph, which is used to generate the target knowledge point learning grid.

[0136] In some examples of this embodiment, the update module 34 is specifically configured to perform a state data difference analysis based on the first knowledge point state data and the second knowledge point state data 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; determine the question information and note information of the student's learning of the knowledge points in the knowledge point learning grid based on the learning behavior data; determine the student's learning state information of the knowledge points in the knowledge point learning grid based on the question information, the note information, and the first mastery level data; determine the abnormal state knowledge points from the knowledge point learning grid based on the learning state information and the degree of difference; update the knowledge graph corresponding to the knowledge point learning grid based on the abnormal state knowledge points to obtain the updated target knowledge graph.

[0137] In some examples of this embodiment, the update module 34 is further configured to: determine the update frequency of the student updating the notes within a target time range based on the note information; evaluate the student's learning activity data for the knowledge points in the knowledge point learning grid within the target time range based on the update frequency; determine the frequency of the student's questions about the knowledge points in the knowledge point learning grid based on the note information and the question information; evaluate the student's difficulty in understanding the knowledge points in the knowledge point learning grid based on the question frequency; and determine the student's learning status information for the knowledge points in the knowledge point learning grid as abnormal learning status information if the learning activity data is greater than an activity threshold and / or the difficulty in understanding data is greater than a difficulty threshold and / or the first mastery level data is less than a mastery level threshold.

[0138] In some examples of this embodiment, the update module 34 is further configured to, when the student's learning status information for knowledge points in the knowledge point learning grid is abnormal learning status information, evaluate the stability data of the knowledge points in the knowledge point learning grid in the knowledge graph based on the betweenness centrality data and proximity centrality data of the knowledge points in the knowledge point learning grid; determine the credibility data of the abnormal learning status information based on the stability data and the degree of difference; determine the abnormal knowledge points from the knowledge point learning grid based on the credibility data, the learning status information, and the degree of difference; and update the knowledge graph corresponding to the knowledge point learning grid based on the abnormal knowledge points to obtain the updated target knowledge graph.

[0139] In some examples of this embodiment, the update module 34 is further configured to divide the student's learning behavior data in the target knowledge point learning grid into at least one learning behavior data set, and generate a set of behavioral features corresponding to the at least one learning behavior data set; determine the time information corresponding to each behavioral feature in the set of behavioral features, and evaluate the effectiveness of each behavioral feature in the set of behavioral features based on the time information to obtain effective value data for each behavioral feature in the set of behavioral features; filter the behavioral features in the set of behavioral features based on the effective value data to obtain target behavioral features that meet the effective conditions in the set of behavioral features, and form a target behavioral feature set from the target behavioral feature set; identify box selection behavior from the target behavioral feature set, and classify the learning content selected by the box selection behavior to obtain the question information and note information of the student learning the knowledge points in the set of abnormal state knowledge points.

[0140] In some examples of this embodiment, the determining module 32 is specifically configured to determine the baseline mastery level data corresponding to the mastery level identifier, determine the baseline mastery level data as the first mastery level data, and evaluate the first knowledge point status data of the knowledge points in the knowledge point learning grid based on the first mastery level data.

[0141] In some examples of this embodiment, the evaluation module 33 is specifically configured to match the set of behavioral features corresponding to the learning behavior data with the knowledge points in the knowledge point learning grid to determine at least one target behavioral feature corresponding to the knowledge points in the knowledge point learning grid; identify the importance data of at least one target behavioral feature corresponding to the knowledge points in the knowledge point learning grid to the knowledge points in the knowledge point learning grid through a target model, wherein the target model is trained based on the student's historical behavioral features and historical knowledge point mastery level; and determine the student's second mastery level data of the knowledge points in the knowledge point learning grid based on at least one target behavioral feature corresponding to the knowledge points in the knowledge point learning grid and the importance data.

[0142] It should be noted that other corresponding descriptions of the functional units involved in the knowledge point state determination device based on AI intelligent agents provided in this embodiment can be found in [reference]. Figure 1 and Figure 3 The corresponding descriptions in [the document] will not be repeated here.

[0143] Based on the above, Figure 1 and Figure 3 Accordingly, this embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figure 1 and Figure 3 The method shown.

[0144] 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.

[0145] like Figure 5 The diagram shown is a hardware structure schematic of an electronic device according to the present invention, comprising: At least one processor 401; and, A memory 402 is communicatively connected to at least one of the processors 401; wherein, The memory 402 stores instructions that can be executed by at least one of the processors, which enable the at least one processor to perform the knowledge point state determination method based on the AI ​​agent as described above.

[0146] Figure 5 Take a processor 401 as an example.

[0147] The electronic device may also include an input device 403 and a display device 404.

[0148] The processor 401, memory 402, input device 403, and display device 404 can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0149] 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 knowledge point state determination method based on AI intelligent agents in the embodiments of this application, for example, Figure 1 and Figure 3 The method flow is shown. The processor 401 executes various functional applications and data processing by running non-volatile software programs, instructions, and modules stored in the memory 402, thereby realizing the knowledge point state determination method based on AI intelligent agents in the above embodiments.

[0150] 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 knowledge point state determination method. Furthermore, memory 402 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 402 may optionally include memory remotely located relative to processor 401, and these remote memories may be connected via a network to means of performing the AI ​​agent-based knowledge point state determination method. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0151] The input device 403 can receive user clicks and generate signal inputs related to user settings and function control of the knowledge point state determination method based on the AI ​​intelligent agent. The display device 404 may include a display screen or other display device.

[0152] When one or more modules are stored in the memory 402, and are run by one or more processors 401, the knowledge point state determination method based on AI intelligent agents in any of the above method embodiments is executed.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms, or it can be implemented by hardware. By applying the solution of this embodiment, compared with the existing technology, this embodiment obtains the learning behavior data of students learning knowledge points in the knowledge point learning grid from the learning companion system, as well as the mastery level identifier of the knowledge points in the knowledge point learning grid. It can determine the first mastery level data and the first knowledge point state based on the mastery level identifier. It can also analyze the second mastery level data and the second knowledge point state of students based on learning behavior. By analyzing the difference information between the two, abnormal knowledge points with abnormal knowledge point state markings can be identified. Based on the abnormal knowledge points, the target knowledge graph is updated, and the target knowledge point learning grid is updated. This embodiment can actively verify and calibrate the consistency of knowledge point state through two different knowledge point states. This allows this embodiment to accurately identify the difference between the mastery state in the knowledge grid and the behavior inference state implicit in the real-time learning behavior data stream, thereby clarifying the student's need for updating the knowledge graph. This allows students to learn based on the updated knowledge graph, improving learning effectiveness.

[0157] 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.

[0158] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for determining the state of knowledge points based on an AI agent, characterized in that, include: The system obtains learning behavior data of students learning knowledge points in the knowledge point learning grid, as well as the mastery level of knowledge points in the knowledge point learning grid. Based on the mastery level identifier, the student's first mastery level data for the knowledge points in the knowledge point learning grid is determined, and the first knowledge point status data of the knowledge points in the knowledge point learning grid is evaluated based on the first mastery level data. Based on the learning behavior data, the student's second mastery level data for the knowledge points in the knowledge point learning grid is determined, and the second knowledge point status data of the knowledge points in the knowledge point learning grid is evaluated based on the second mastery level data. Based on the first knowledge point status data and the second knowledge point status data, abnormal knowledge points are determined from the knowledge point learning grid. 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. The target knowledge graph is used to generate the target knowledge point learning grid.

2. The method according to claim 1, characterized in that, The step of determining abnormal knowledge points from the knowledge point learning grid based on the first knowledge point state data and the second knowledge point state data, and updating the knowledge graph corresponding to the knowledge point learning grid based on the abnormal knowledge points to obtain the updated target knowledge graph includes: Based on the first knowledge point status data and the second knowledge point status data, a status data difference analysis is performed 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 learning behavior data, determine the question information and note information of the student learning 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, the note information and the first mastery level data. Based on the learning state information and the degree of difference, abnormal knowledge points are determined from the knowledge point learning grid. 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.

3. The method according to claim 2, characterized in that, The step of determining the student's learning status information for the knowledge points in the knowledge point learning grid based on the question information, the note information, and the first mastery level data includes: Based on the note information, determine the update frequency of the student's notes within the target time range, and evaluate the student's learning activity data for the knowledge points in the knowledge point learning grid within the target time range based on the update frequency; Based on the notes and questions, the frequency of questions the student has about the knowledge points in the knowledge point learning grid is determined, and the difficulty of the student's understanding of the knowledge points in the knowledge point learning grid is evaluated based on the frequency of questions. If the learning activity data is greater than the activity threshold and / or the comprehension difficulty data is greater than the difficulty threshold and / or the first mastery level data is less than the mastery level threshold, the student's learning status information for the knowledge points in the knowledge point learning grid is determined to be abnormal learning status information.

4. The method according to claim 3, characterized in that, The process involves determining abnormal knowledge points from the knowledge point learning grid based on the learning state information and the degree of difference, and updating the knowledge graph corresponding to the knowledge point learning grid based on the abnormal knowledge points to obtain an updated target knowledge graph, including: When the student's learning status information for the knowledge points in the knowledge point learning grid is abnormal, the stability data of the 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 the knowledge points in the knowledge point learning grid. The credibility data of the abnormal learning state information is determined based on the stability data and the degree of difference. The abnormal knowledge points are then determined from the knowledge point learning grid based on the credibility data, the learning state information, and the degree of difference. The knowledge graph corresponding to the knowledge point learning grid is updated based on the abnormal state knowledge points to obtain the updated target knowledge graph.

5. The method according to claim 2, characterized in that, The step of determining the question information and note information of the student's learning of knowledge points in the knowledge point learning grid based on the learning behavior data includes: The student's learning behavior data in the target knowledge point learning grid is divided into at least one learning behavior data set, and a set of behavioral features corresponding to the at least one learning behavior data set is generated. Determine the time information corresponding to each behavioral feature in the behavioral feature set, and evaluate the effectiveness of each behavioral feature in the behavioral feature set based on the time information to obtain the effective value data of each behavioral feature in the behavioral feature set; Based on the effective value data, the behavioral features in the behavioral feature set are filtered to obtain the target behavioral features that meet the effective conditions in the behavioral feature set, and the target behavioral features are combined into a target behavioral feature set. Identify 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 the student learning the knowledge points in the abnormal state knowledge point set.

6. The method according to claim 1, characterized in that, The step of determining the student's first mastery level data for the knowledge points in the knowledge point learning grid based on the mastery level identifier, and evaluating the first knowledge point status data of the knowledge points in the knowledge point learning grid based on the first mastery level data, includes: Determine the baseline mastery level data corresponding to the mastery level identifier, and determine the baseline mastery level data as the first mastery level data; The first knowledge point status data in the knowledge point learning grid is evaluated based on the first mastery level data.

7. The method according to claim 1, characterized in that, The step of determining the student's second level of mastery data for knowledge points in the knowledge point learning grid based on the learning behavior data, and evaluating the second knowledge point status data of knowledge points in the knowledge point learning grid based on the second level of mastery data, includes: The behavioral feature set corresponding to the learning behavior data is matched with the knowledge points in the knowledge point learning grid to determine at least one target behavioral feature corresponding to the knowledge points in the knowledge point learning grid. The target model identifies the importance of at least one target behavioral feature corresponding to a knowledge point in the knowledge point learning grid to the knowledge point in the knowledge point learning grid. 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 behavior feature and importance data corresponding to the knowledge points in the knowledge point learning grid, the student's second mastery data of the knowledge points in the knowledge point learning grid is determined.

8. A knowledge point state determination device based on an AI intelligent agent, characterized in that, include: The acquisition module is configured to acquire learning behavior data of students learning knowledge points in the knowledge point learning grid from the learning companion system, as well as the mastery level identifier of the knowledge points in the knowledge point learning grid. The determination module is configured to determine the student's first mastery level data for the knowledge points in the knowledge point learning grid based on the mastery level identifier, and to evaluate the first knowledge point status data of the knowledge points in the knowledge point learning grid based on the first mastery level data. The evaluation module is configured to determine the student's second mastery level data for knowledge points in the knowledge point learning grid based on the learning behavior data, and evaluate the second knowledge point status data of the knowledge points in the knowledge point learning grid based on the second mastery level data. The update module is configured to determine abnormal knowledge points from the knowledge point learning grid based on the first knowledge point status 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, and obtain the updated target knowledge graph, which is used to generate the target knowledge point learning grid.

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

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

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