Knowledge Point Cluster-Based Learning Path Generation Method, Device and Electronic Device

Abstract

The present application discloses a learning path generation method, device, storage medium and electronic device based on knowledge point clusters, which relates to the technical field of data processing, and includes: extracting knowledge points from teaching material texts to obtain basic knowledge point clusters; determining the difficulty levels corresponding to each knowledge point in the basic knowledge point clusters according to the refinement degree of the knowledge points in the basic knowledge point clusters; evaluating the importance levels corresponding to each knowledge point in the basic knowledge point clusters based on the difficulty levels, and extracting important knowledge point clusters from the basic knowledge point clusters according to the importance levels; generating question banks corresponding to questions of different difficulty levels based on the basic knowledge point clusters and the important knowledge point clusters and mapping them on a learning map, so as to generate the learning path of students during the learning process through the learning map. By analyzing and extracting important knowledge point clusters from teaching materials, the accuracy of screening important knowledge points is improved, and thus personalized learning paths and key learning guidance are provided for students.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and particularly to a learning path generation method, device, storage medium and electronic device based on knowledge point clusters. Background Art

[0002] During the process of students doing exercises, they usually encounter a large number of knowledge points. With the continuous development of educational technology, how to efficiently extract knowledge points from textbooks, construct a knowledge system, and assist teaching through question banks and visualization tools has become an urgent problem in the education field.

[0003] Currently, the difficulty level of knowledge points is usually determined by statistically analyzing the frequency of question appearance and the error rate of students. For example, questions that appear frequently and have a high error rate are often considered to correspond to more difficult knowledge points, while questions that appear infrequently and have a low error rate are considered to correspond to easier knowledge points.

[0004] However, the data collected using this knowledge point cluster extraction method does not effectively distinguish important knowledge points and basic knowledge points, affecting the accuracy of screening important knowledge points. Summary of the Invention

[0005] In view of this, the present application provides a learning path generation method, device, storage medium and electronic device based on knowledge point clusters, mainly aiming to improve the technical problem that the extracted knowledge point clusters in the existing technology at present do not effectively distinguish important knowledge points and basic knowledge points, affecting the accuracy of screening important knowledge points.

[0006] In a first aspect, the present application provides a learning path generation method based on knowledge point clusters, including:

[0007] Extracting knowledge points from textbook texts to obtain a basic knowledge point cluster;

[0008] Determining the difficulty level corresponding to each knowledge point in the basic knowledge point cluster according to the refinement degree of the knowledge points in the basic knowledge point cluster;

[0009] Evaluating the importance degree corresponding to each knowledge point in the basic knowledge point cluster based on the difficulty level, and extracting an important knowledge point cluster from the basic knowledge point cluster according to the importance degree;

[0010] Generating question banks corresponding to questions of different difficulty levels based on the basic knowledge point cluster and the important knowledge point cluster and mapping them on a learning map, so as to generate a learning path for students during the learning process through the learning map, where the learning map includes one or more key learning grids, and the key learning grids are grids corresponding to the important knowledge point cluster.

[0011] Second aspect, the present application provides a learning path generation device based on knowledge point clusters, including:

[0012] An extraction module, configured to extract knowledge points based on teaching material texts to obtain a basic knowledge point cluster;

[0013] A determination module, configured to determine the difficulty level corresponding to each knowledge point in the basic knowledge point cluster according to the refinement degree of the knowledge points in the basic knowledge point cluster;

[0014] An evaluation module, configured to evaluate the importance degree corresponding to each knowledge point in the basic knowledge point cluster based on the difficulty level, and extract an important knowledge point cluster from the basic knowledge point cluster according to the importance degree;

[0015] A generation module, configured to generate question banks corresponding to questions of different difficulty levels based on the basic knowledge point cluster and the important knowledge point cluster and map them on a learning map, so as to generate a learning path of a student during the learning process through the learning map, where the learning map includes one or more key learning grids, and the key learning grids are grids corresponding to the important knowledge point cluster.

[0016] Third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the learning path generation method based on knowledge point clusters described in the first aspect.

[0017] Fourth aspect, the present 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, and when the processor executes the computer program, it implements the learning path generation method based on knowledge point clusters described in the first aspect.

[0018] With the above technical solutions, a method, apparatus, storage medium, and electronic device for generating a learning path based on knowledge point clusters provided by the present application, compared with the current existing technologies, first extracts knowledge points from teaching materials texts to obtain basic knowledge point clusters; determines the difficulty levels corresponding to each knowledge point in the basic knowledge point clusters according to the refinement degree of the knowledge points in the basic knowledge point clusters; evaluates the importance levels corresponding to each knowledge point in the basic knowledge point clusters based on the difficulty levels, and extracts important knowledge point clusters from the basic knowledge point clusters according to the importance levels; generates question banks corresponding to questions of different difficulty levels based on the basic knowledge point clusters and the important knowledge point clusters and maps them on a learning map, so as to generate a learning path for students during the learning process through the learning map, where the learning map includes one or more key learning grids, and the key learning grids are grids corresponding to the important knowledge point clusters. Through the analysis and processing of text information, basic knowledge point clusters can be obtained from teaching materials and further important knowledge point clusters can be extracted, thereby effectively distinguishing important knowledge points and basic knowledge points, improving the accuracy rate of screening important knowledge points, and generating corresponding question banks and learning maps based on the important knowledge point clusters. Furthermore, personalized learning paths and key learning guidance can be provided for students, and at the same time, scientific bases and auxiliary tools can be provided for teachers' teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application.

[0020] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0021] Figure 1 Shows a schematic flowchart of a method for generating a learning path based on knowledge point clusters provided by an embodiment of the present application;

[0022] Figure 2 Shows a schematic flowchart of a method for generating a learning path based on knowledge point clusters provided by an embodiment of the present application;

[0023] Figure 3 Shows a schematic flowchart of an example provided by an embodiment of the present application;

[0024] Figure 4 Shows a schematic flowchart of a method for generating a learning path based on knowledge point clusters provided by an embodiment of the present application;

[0025] Figure 5Shows a schematic flowchart of an example provided by an embodiment of the present application;

[0026] Figure 6 Shows a schematic flowchart of a learning path generation method based on a knowledge point cluster provided by an embodiment of the present application;

[0027] Figure 7 Shows a schematic flowchart of an example provided by an embodiment of the present application;

[0028] Figure 8 Shows a schematic flowchart of a learning path generation method based on a knowledge point cluster provided by an embodiment of the present application;

[0029] Figure 9 Shows a schematic flowchart of an example provided by an embodiment of the present application;

[0030] Figure 10 Shows a schematic structural diagram of a learning path generation device based on a knowledge point cluster provided by an embodiment of the present application;

[0031] Figure 11 Shows a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0032] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0033] In order to improve the technical problem that the knowledge point clusters extracted in the current prior art do not effectively distinguish important knowledge points and basic knowledge points, which affects the accuracy of screening important knowledge points. This embodiment provides a learning path generation method based on a knowledge point cluster, as Figure 1 shown, the method includes:

[0034] Step 101: Extract knowledge points based on the textbook text to obtain a basic knowledge point cluster.

[0035] Exemplarily, the basic knowledge point cluster is a set of knowledge units composed of all basic concepts, terms, facts, rules, etc. identified after text analysis and processing of textbooks or learning materials. First, the textbook content is cleaned and preprocessed, including operations such as removing irrelevant characters (such as punctuation marks), converting to lowercase, stemming or lemmatization, etc., to ensure the effectiveness of subsequent analysis. The text is segmented into words or phrases, and each vocabulary is labeled using text analysis techniques, such as text mining, natural language processing, semantic analysis, etc., and based on the extracted keywords and context information, named entity recognition or other machine learning models are applied to identify specific knowledge points. For each identified knowledge point, models such as Word2Vec, GloVe, or BERT can be used to convert it into a vector form, and these vectors can capture the semantic relationships between words, thus forming identification vectors.

[0036] In some examples, the generated identification vectors are grouped through clustering algorithms (such as K-means, hierarchical clustering, etc.), and the cluster composed of similar knowledge points thus constructed is the basic knowledge point cluster, thereby clarifying the theme structure and association pattern in the textbook content.

[0037] Step 102: Determine the difficulty level corresponding to each knowledge point in the basic knowledge point cluster according to the refinement degree of the knowledge points in the basic knowledge point cluster.

[0038] Exemplarily, by classifying the knowledge points in the basic knowledge point cluster in detail and determining their difficulty levels according to the refinement degree of the knowledge points. For example, the refinement degree corresponding to a knowledge point can be determined according to the chapter, section, and unit to which the knowledge point belongs in the textbook text; or the refinement degree corresponding to a knowledge point can also be determined according to the form of the identification vector corresponding to the knowledge point (the form of a single unit or a combination of multiple units); and then, based on the refinement degree of the knowledge point, the difficulty level is determined. The higher the refinement degree of the knowledge point, the higher the corresponding difficulty level. By applying the method of this embodiment, the systematicness and hierarchy of the knowledge points are ensured, providing a basis for the generation of questions with different difficulty levels in the future.

[0039] Step 103: Evaluate the importance degree corresponding to each knowledge point in the basic knowledge point cluster based on the difficulty level, and extract the important knowledge point cluster from the basic knowledge point cluster according to the importance degree.

[0040] In some examples, different difficulty levels correspond to different weight scores for the importance of the indicators of each knowledge point. Based on different difficulty levels and weight scores, an importance score can be obtained for each knowledge point through evaluation. According to the importance score, the cluster of important knowledge points can be further screened out from the basic knowledge point cluster, which is a set of core knowledge points crucial for understanding the target subject or achieving specific learning goals. By identifying and extracting important knowledge points, students can focus on learning key information and avoid wasting time on minor details. Important knowledge points often form the basis for constructing complex concepts. Mastering these core points helps in deeply understanding the structure and logic of the subject, thus better solving complex problems.

[0041] Step 104: Based on the basic knowledge point cluster and the important knowledge point cluster, generate question banks corresponding to questions of different difficulty levels and map them on the learning map, so as to generate the learning path of the student during the learning process through the learning map.

[0042] Among them, the learning map includes one or more key learning grids, and the key learning grids are the grids corresponding to the important knowledge point cluster. The learning map can be used to reflect the learning grids corresponding to the current user, and the learning grids are linked in sequence to obtain the learning path of the current user. The learning path is generated based on the learning map according to the student's learning goals, current knowledge level, and learning progress. By combining the knowledge graph and the learning path, key knowledge points are identified and key monitoring is carried out when the student enters the learning scope of these knowledge points. When the student encounters difficulties in the learning progress, the system will match users with similar learning paths to promote interaction and mutual assistance among students, thereby enhancing the learning experience.

[0043] Exemplarily, by classifying the knowledge points in the basic knowledge point cluster and the important knowledge point cluster in detail and determining their difficulty levels according to the refinement degree of the knowledge points, the systematicness and hierarchy of the knowledge points are ensured, providing a basis for subsequent question generation. The question bank contains questions of types such as multiple-choice questions, fill-in-the-blank questions, short-answer questions, and essay questions, and the questions in the question bank correspond to the important knowledge points in the textbook content. Using text analysis and data mining technologies, the important knowledge point cluster is automatically extracted and the corresponding question bank is generated, avoiding the subjectivity and inefficiency of manual screening and improving the accuracy and efficiency of knowledge extraction.

[0044] In some examples, the learning map is a visualization tool that can be presented in the form of a chart, network diagram, or flow chart, and is used to help students or learners understand and navigate complex knowledge systems. Each knowledge point cluster is represented as a grid, and the relationships between knowledge points can be represented by connecting lines or other forms of links.

[0045] For example, in a knowledge graph, mark important clusters of knowledge points that are core concepts or principles with wide applications. Different colors or symbols can be used to distinguish ordinary knowledge points from important ones during the marking process. Design a learning map based on the knowledge graph to ensure that the relationships between knowledge points are clearly visible and highlight important clusters of knowledge points.

[0046] Specifically, knowledge points can be distributed on a two-dimensional grid, with each cluster of knowledge points occupying a cell. The size of the cell can be adjusted according to the complexity and importance of the knowledge point. For the cell where an important cluster of knowledge points is located, special markings (such as colors, etc.) are used for annotation to form a key learning grid. Navigation paths can also be added to the learning map to indicate the learning order from basic knowledge points to advanced knowledge points, helping students better plan their learning paths. The setting of the key learning grid not only highlights key knowledge points but also provides students with a clear learning path, thereby improving learning efficiency and effectiveness.

[0047] Compared with the current existing technologies, in this embodiment, knowledge points are first extracted based on textbook texts to obtain a basic cluster of knowledge points; according to the refinement degree of the knowledge points in the basic cluster of knowledge points, the difficulty levels corresponding to each knowledge point in the basic cluster of knowledge points are determined; based on the difficulty levels, the importance degrees corresponding to each knowledge point in the basic cluster of knowledge points are evaluated, and important clusters of knowledge points are extracted from the basic cluster of knowledge points according to the importance degrees; based on the basic cluster of knowledge points and the important clusters of knowledge points, question banks corresponding to questions of different difficulty levels are generated and mapped on the learning map to generate the learning path of students during the learning process, where the learning map includes one or more key learning grids, and the key learning grids are grids corresponding to important clusters of knowledge points. Through the analysis and processing of text information, a basic cluster of knowledge points can be obtained from the textbook and further important clusters of knowledge points can be extracted, thus effectively distinguishing important knowledge points from basic knowledge points, improving the accuracy of screening important knowledge points, and generating corresponding question banks and learning maps based on the important clusters of knowledge points, and then personalized learning paths and key learning guidance can be provided for students, and at the same time, scientific bases and auxiliary tools can be provided for teachers' teaching.

[0048] As a refinement and extension of the above embodiment, when extracting knowledge points based on textbook texts to obtain a basic cluster of knowledge points, the following methods can be used but are not limited to, such as Figure 2 As shown, this method includes:

[0049] Step 201: Analyze the textbook text to extract knowledge points in the textbook text, and divide the extracted knowledge points into classification nodes at all levels according to the association relationships between the knowledge points.

[0050] In some examples, the teaching material content can be uploaded to the first knowledge point analysis module, and the knowledge points in the text are extracted through text analysis technology and hierarchically labeled. Hierarchical labeling means assigning specific labels to the knowledge points or nodes at each level in a multi-level classification structure for easy distinction and management. Through hierarchical labeling, the hierarchical relationship of knowledge points can be clearly shown, which helps to construct the entire knowledge network.

[0051] For example, the first-level classification node, as the main node, can be classified by chapter; the second-level classification node, as the subordinate node of the first-level classification node, can be classified by section, representing each subsection in each chapter; the third-level classification node, as the subordinate node of the second-level classification node, can be classified by unit results to further subdivide the specific knowledge points or units under each section, and so on.

[0052] Step 202: Through encoding the hierarchical labels of each level of classification nodes, map each level of classification nodes to a set of label vectors.

[0053] Among them, the first-level classification node appears in the form of a single unit's label vector, and the multi-level classification node appears in the form of a combination of multiple units' label vectors.

[0054] Exemplarily, extract the knowledge points in the teaching material and hierarchically label them. Divide the hierarchical labels into n levels. For example, when n = 3, U = [An, Bm, Ck], as Figure 3 shown, where: the first-level classification An, as the main node, is classified by chapter to obtain An = [A1, A2... An]; the second-level classification Bm, as the subordinate node of An, is classified by section to obtain Bm = [A1b1, A1b2, A2b1... Anbm]; the third-level classification Ck, as the subordinate node of Bm, is classified by unit results to obtain Ck = [A1b1c1, A1b2c2, A1b2c3... Anbmck].

[0055] In some examples, the label encoding is used to reflect the hierarchical structure of knowledge points (such as chapters, sections, units). Each knowledge point corresponds to a unique label encoding. The specific form of the label encoding value can include numerical numbers, letter numbers, mixed numbers, etc. These label encoding values can be designed according to the hierarchical structure of knowledge points and converted into label vectors through one-hot encoding or other methods for further processing and analysis.

[0056] Step 203: Generate a basic knowledge point cluster based on the set of label vectors.

[0057] In some examples, by encoding the hierarchical identifiers, a unique identifier code is matched for each knowledge point, and a corresponding identifier vector is generated according to the identifier code. A knowledge point network is constructed based on the identifier vector. The knowledge point network is used to represent the relationships between knowledge points, and then multiple knowledge point networks are combined into a comprehensive set of identifier vectors Uori (i.e., the basic knowledge point cluster).

[0058] Exemplarily, when constructing the knowledge point network, the existence form of the first-level classification An is a single-unit identifier vector, and the existence forms of the second-level and third-level classifications are in the form of a combination of multiple units. The identifier vectors are stacked in sequence, and multiple knowledge point networks form a knowledge point cluster, covering all possible combination forms of knowledge points. The general expression of the knowledge point network is: U=(k, β), where k is the set of knowledge points and β is the set of edges.

[0059] In some examples, the course video can also be recorded and stored through Internet of Things devices (such as cameras, smart blackboards, etc.); then, audio-to-text processing is performed through a pre-trained speech-to-text model. The speech-to-text model can adopt the OpenAI open-source model Whisper to construct a set of identifier vectors Z corresponding to the knowledge point identifiers, generate a knowledge point network; based on the set of identifier vectors (Dn, Em, Fk), the basic knowledge point cluster L = [Dn, Em, Fk] is obtained.

[0060] In some examples, the coverage of the knowledge point network means that the knowledge points are classified and graded according to their importance and complexity, and all possible combination forms of knowledge points are ensured to be covered. Through the construction of multi-level classification identifiers and identifier vectors, the knowledge system of the teaching material content can be systematically represented, forming a complete knowledge framework.

[0061] For example, questions can also be pushed according to the importance level of knowledge points. This push mechanism based on the knowledge point hierarchy and difficulty level can effectively help students gradually master knowledge. First, the difficulty of knowledge points is classified and graded, which can be divided into different levels such as basic, intermediate, and advanced. The basic difficulty corresponds to the first-level knowledge points, which are used to help students consolidate basic knowledge and understand basic concepts; the intermediate difficulty corresponds to the second-level knowledge points, which are used to help students apply basic knowledge to more complex problem-solving; the advanced difficulty corresponds to the third-level knowledge points, which are used to help students improve their comprehensive application ability, requiring students to be able to integrate and flexibly use multiple knowledge points, etc. The mutual relationships and combination situations between knowledge points can also be considered to ensure that all combinations of knowledge points are covered. For example, if a certain knowledge point A is the basis of another knowledge point B, then it should be ensured that students have mastered A when pushing questions.

[0062] Further, when evaluating the importance level of each knowledge point in the basic knowledge point cluster and extracting the important knowledge point cluster from the basic knowledge point cluster according to the importance level, the following steps can be adopted but are not limited to, as Figure 4 shown, including:

[0063] Step 301: Use a pre-trained evaluation model to evaluate the importance level of each knowledge point in the basic knowledge point cluster.

[0064] Specifically, obtain the importance level scores of each knowledge point in the basic knowledge point cluster. Among them, the importance level score G is calculated by using a pre-trained evaluation model (Formula 1); according to the importance level scores, determine the importance level corresponding to each knowledge point. Among them, Formula 1 is specifically as follows:

[0065] (Formula 1)

[0066] In Formula 1, Z, X, V, and Y are respectively the numbers of identification vectors corresponding to the first-level, second-level, third-level, and n-level knowledge points in the basic knowledge point cluster, , , , are respectively the weight coefficients corresponding to the importance levels of the first-level, second-level, third-level, and n-level identification vectors, is the weight score corresponding to the importance level of the n-level identification vector, and < < , because as the refinement degree of the knowledge point gets deeper, the proportion of importance gets larger.

[0067] In some examples, the weight coefficients should reflect the importance and complexity of the knowledge points at each level. For example: The knowledge points corresponding to the first-level classification are usually relatively broad concepts with lower weights, the knowledge points corresponding to the second-level classification further refine the content with moderate weights, and the knowledge points corresponding to the third-level classification are relatively detailed knowledge points with the highest weights. Adjust the weight coefficients according to the actual situation to ensure that the scoring model can accurately reflect the actual importance of the knowledge points. As time goes by and new knowledge is introduced, it may be necessary to re-evaluate and adjust the weight coefficients. In addition to the refinement degree of the knowledge points, other factors (such as the application frequency and difficulty of the knowledge points) can also be considered to adjust the weight coefficients.

[0068] Exemplarily, it is also possible to obtain a historical question bank, generate a basic knowledge point cluster Zori = [Wn, Rm, Hk] according to the set of identification vectors (Wn, Rm, Hk); then screen the basic knowledge point cluster through a pre-trained key knowledge point evaluation model (Formula 2), where Formula 2 is specifically as follows:

[0069] (Formula 2)

[0070] Among them, Gj is the number of occurrences of the test point knowledge points; Gq is the number of error frequency occurrences of the test point knowledge points; Gr is the number of branches of the test point knowledge points; α is the weight score of the corresponding index. Set the scoring threshold T2. When yG > T2, screen out the key knowledge point combinations to form the second most important knowledge point cluster Znew.

[0071] Step 302: Screen in the basic knowledge point cluster based on the importance degree corresponding to each knowledge point and the preset screening threshold, and determine the set of screened knowledge points as the important knowledge point cluster.

[0072] Among them, the preset screening threshold is the initial value set for the importance degree of the identification vector.

[0073] Exemplarily, set the screening threshold T1. When G > T1, take the screened knowledge point identification vector as the important knowledge point cluster Unew, as Figure 5 shown. Among them, methods such as custom, average method, median, etc. can be used to set the initial threshold, and this embodiment does not limit this.

[0074] By applying the method of this embodiment, the important knowledge point cluster can be effectively screened out. Select a suitable method for setting the screening threshold according to the actual situation to ensure that the screening result can accurately reflect the importance of the knowledge points.

[0075] Optionally, convert the knowledge points extracted from the textbook content into identification vectors, divide the knowledge points into classification nodes at all levels by successively superimposing the identification vectors, and determine the form of the identification vectors corresponding to the classification nodes at all levels. Among them, the first-level classification appears in the form of the identification vector of a single unit, and the multi-level classification appears in the form of the combination of the identification vectors of multiple units.

[0076] For example, first extract each knowledge point from the textbook content, assign a unique identification code to each knowledge point, then generate the corresponding identification vector according to the identification code, and then classify the knowledge points according to the levels (first level, second level, third level, etc.): For example, the chapter is "Algebra Basics", the first section is "Basic Concepts", the second section is "Polynomials", the first unit in the first section is "Variables and Constants", and the second unit is "Expressions and Equations"; the first unit in the second section is "Definition of Polynomials", and the second unit is "Operations of Polynomials". Then in this knowledge point cluster, the first-level knowledge points are the knowledge points corresponding to "Algebra Basics", the second-level knowledge points are the knowledge points corresponding to "Basic Concepts" and "Polynomials", and the third-level knowledge points are the knowledge points corresponding to "Variables and Constants", "Expressions and Equations", "Definition of Polynomials", and "Operations of Polynomials". Further, assign a unique identification code to each knowledge point to facilitate the construction of the knowledge network and the question bank.

[0077] Furthermore, when combining knowledge points of different difficulty levels in the basic knowledge point cluster and the important knowledge point cluster to generate question banks and learning maps corresponding to teaching materials content, the following methods can be used but are not limited to, such as Figure 6 As shown, this method includes:

[0078] Step 401: Combine knowledge points of different difficulty levels in the basic knowledge point cluster and the important knowledge point cluster respectively to generate question banks corresponding to questions of different difficulty levels.

[0079] In some examples, according to the form of the identification vector corresponding to the knowledge points, the classification node level corresponding to the knowledge points can be obtained. Furthermore, the comprehensive difficulty level of the questions can be calculated by combining the refinement degree of the knowledge points and other factors (such as application frequency, complexity, etc.). Furthermore, questions of different difficulty levels can be appropriately mixed in the same set of questions, and the question bank can be updated regularly with the change of educational goals and the development of technology, adding new knowledge points and question types. The question bank includes questions corresponding to first-level, second-level, and third-level knowledge points. The question bank is stored in the cloud server for use by the learning tutoring system, such as Figure 7 As shown, the learning tutoring system includes a cloud server and multiple user terminals.

[0080] Step 402: According to the information identifiers of the basic knowledge point cluster and the important knowledge point cluster in the question bank, arrange them according to the preset relationships between the knowledge points to generate a learning map containing knowledge point distribution information.

[0081] In some examples, according to the information identifiers and preset relationships of the basic knowledge point cluster and the important knowledge point cluster, the learning grids corresponding to all knowledge point clusters included in a chapter of the teaching materials content are arranged to form a learning area. The preset relationships between the knowledge points can include hierarchical relationships, causal relationships, etc. Each learning grid contains all relevant information of the knowledge point cluster, such as knowledge point name, description, difficulty level, etc.

[0082] In some examples, a learning map is generated based on the learning area. Each learning grid in the learning map corresponds to all the questions of a knowledge point cluster in the question bank. Each learning grid in the learning map corresponds to all the questions of a knowledge point cluster in the question bank. The learning map can be a graphical representation or a structured list, and can provide personalized learning paths according to the student's learning progress and knowledge mastery, improving learning efficiency.

[0083] For example, a learning map can provide personalized learning paths based on students' learning progress and knowledge mastery, improving learning efficiency. Specifically, it can record students' completion status in each learning grid through the learning map, evaluate students' mastery of each knowledge point through tests or exercises, and then recommend suitable learning paths and questions according to the students' evaluation results. Suppose a student has mastered the knowledge point of "variables and constants" but encounters difficulties in "expressions and equations". The learning map can suggest that the student review the relevant basic knowledge first and then practice more complex questions.

[0084] Furthermore, when generating a learning map containing knowledge point distribution information, the following steps can be adopted but are not limited to, such as Figure 8 shown, including:

[0085] Step 501: Mark the questions corresponding to the basic knowledge point clusters in the question bank with a first identifier, and mark the questions corresponding to the key knowledge point clusters with a second identifier.

[0086] Exemplarily, as Figure 9 shown, the basic knowledge point clusters in the question bank are marked with a first information identifier (white) through color marking, and the key knowledge point clusters are marked with a second information identifier (black) through color marking.

[0087] Step 502: Map the questions corresponding to the basic knowledge point clusters and the questions corresponding to the important knowledge point clusters on the learning map according to the order of the chapters in the textbook based on the first identifier and the second identifier in the question bank.

[0088] Optionally, the method of this embodiment may further include: marking the learning grids corresponding to the questions marked with the second identifier on the learning map as key learning grids; determining the area composed of multiple adjacent key learning grids in each learning area as the key monitoring area in the learning area.

[0089] Exemplarily, the shapes mapped by a single knowledge point in the learning map can be arranged in rectangles, squares, etc., and the second information identifiers with a preset distance and adjacent to each other are circled to obtain multiple key monitoring areas. When the user enters the monitoring range of the key knowledge points, the user is monitored according to the user's learning path. The purpose is to help students master the key knowledge points. When the learning progress is blocked, users with similar learning paths will be matched to help each other and improve the user interaction experience. Through the interaction between the user terminal and the cloud server, the learning trajectory and learning situation of students are monitored in real time, reducing the processing volume of the cloud server and improving the operation efficiency of the system.

[0090] In some examples, the question bank distribution can also be mapped onto a virtual learning map through the information interaction between the user terminal and the cloud server. The user terminal serves as a signal point to obtain the learning trajectory of the student through the cloud server. In this embodiment, the cloud-based learning system tracks and recommends the learning progress of the student through the information interaction between multiple user terminals and the cloud server. The learning system allows multiple user terminals (such as tablets, computers, etc.) to exchange real-time data with the cloud server. This enables the system to recommend learning content or progress according to the learning behavior of each user, promoting a personalized learning experience. Mapping the question bank distribution to the learning map: The knowledge points or question types are distributed on the learning map according to certain rules. Each user terminal serves as a "signal point", and its position represents the user's current knowledge mastery level or learning progress. This visual way helps students intuitively understand their learning status and discover areas that need to be strengthened.

[0091] For example, the question types of key knowledge points belong to the content that must be mastered. Taking the key monitoring area as the trigger condition of the system, the learning situation of users in the key monitoring area is monitored in real time. Data analysis tools are used to track the learning trajectory of each student, provide personalized learning suggestions and question recommendations, and collect the learning information of students after they complete the questions to continuously optimize the knowledge point network coverage and question push strategy. For the key monitoring areas set for important knowledge points, when users enter these areas, the system will collect and analyze the learning data of this student more intensively to provide feedback and support in a timely manner. Among them, the learning information includes but is not limited to user ID, learning progress (represented by the position on the learning map), notes, learning honors, learning trajectory, and learning duration, etc. These rich data support the comprehensive analysis of the learning situation of students, helping teachers and parents better understand and support the learning needs of students. This method not only pays attention to the overall learning trajectory of students, but also particularly emphasizes the mastery of key knowledge, thus improving learning efficiency.

[0092] In some examples, by automatically extracting knowledge point clusters, generating question banks, and constructing virtual learning maps, powerful learning and teaching tools are provided for students and teachers, with broad application prospects and important educational significance.

[0093] Compared with the current existing technologies, in this embodiment, through the analysis and processing of text information, the basic knowledge point clusters can be obtained from the textbooks and further important knowledge point clusters can be extracted, thus effectively distinguishing important knowledge points from basic knowledge points, improving the accuracy rate of screening important knowledge points, generating corresponding question banks and learning maps based on the important knowledge point clusters, and then providing personalized learning paths and key learning guidance for students. At the same time, it also provides a scientific basis and auxiliary tools for teachers' teaching, and can effectively solve the problems of unclear knowledge system and low learning efficiency in traditional teaching.

[0094] Further, as Figures 1 to 8 a specific implementation of the method shown, this embodiment provides a learning path generation device based on knowledge point clusters, as Figure 10 shown. The device includes: an extraction module 61, a determination module 62, an evaluation module 63, and a generation module 64.

[0095] The extraction module 61 is configured to extract knowledge points based on the textbook text to obtain a basic knowledge point cluster;

[0096] The determination module 62 is configured to determine the difficulty level corresponding to each knowledge point in the basic knowledge point cluster according to the refinement degree of the knowledge points in the basic knowledge point cluster;

[0097] The evaluation module 63 is configured to evaluate the importance degree corresponding to each knowledge point in the basic knowledge point cluster based on the difficulty level, and extract an important knowledge point cluster from the basic knowledge point cluster according to the importance degree;

[0098] The generation module 64 is configured to generate question banks corresponding to questions of different difficulty levels based on the basic knowledge point cluster and the important knowledge point cluster and map them on a learning map, so as to generate a learning path for students during the learning process through the learning map. The learning map includes one or more key learning grids, and the key learning grids are grids corresponding to the important knowledge point cluster.

[0099] In some examples of this embodiment, the extraction module 61 is specifically configured to analyze the textbook text to extract knowledge points in the textbook text, and classify the extracted knowledge points into classification nodes at all levels according to the association relationship between the knowledge points; by encoding the hierarchical identifiers of the classification nodes at all levels, map the classification nodes at all levels to a set of identifier vectors, where the first-level classification nodes appear in the form of a single-unit identifier vector, and the multi-level classification nodes appear in the form of a combination of multiple-unit identifier vectors; generate the basic knowledge point cluster according to the set of identifier vectors.

[0100] In some examples of this embodiment, the evaluation module 63 is specifically configured to evaluate the importance degree corresponding to each knowledge point in the basic knowledge point cluster by using a pre-trained evaluation model; perform screening in the basic knowledge point cluster based on the importance degree corresponding to each knowledge point and a preset screening threshold, and determine the set of screened knowledge points as the important knowledge point cluster, where the preset screening threshold is an initial value set for the importance degree corresponding to each knowledge point.

[0101] In some examples of this embodiment, the determining module 62 is specifically configured to obtain the importance score of each knowledge point in the basic knowledge point cluster, where the importance score G is calculated by using a pre-trained evaluation model:

[0102]

[0103] where Z, X, V, and Y are the numbers of the identification vectors corresponding to the first-level, second-level, third-level, and n-level knowledge points in the basic knowledge point cluster, respectively. 、 、 、 are the weight coefficients corresponding to the first-level, second-level, third-level, and n-level knowledge points, respectively;

[0104] Determine the importance level corresponding to each knowledge point according to the importance score.

[0105] In some examples of this embodiment, the generating module 64 is specifically configured to combine the knowledge points of different difficulty levels in the basic knowledge point cluster and the important knowledge point cluster respectively to generate question banks corresponding to questions of different difficulty levels;

[0106] Generate a learning map including knowledge point distribution information according to the information identifiers of the basic knowledge point cluster and the important knowledge point cluster in the question bank and arranging them according to the preset relationship between knowledge points.

[0107] In some examples of this embodiment, the generating module 64 is further specifically configured to mark the questions corresponding to the basic knowledge point cluster in the question bank with a first identifier, and mark the questions corresponding to the key knowledge point cluster with a second identifier; map the questions corresponding to the basic knowledge point cluster and the questions corresponding to the important knowledge point cluster on the learning map according to the order of the chapters in the textbook based on the first identifier and the second identifier in the question bank.

[0108] In some examples of this embodiment, the generating module 64 is further specifically configured to label the learning grids corresponding to the questions marked with the second identifier on the learning map as key learning grids; determine the area composed of multiple adjacent key learning grids in each learning area as the key monitoring area in the learning area.

[0109] It should be noted that for other corresponding descriptions of each functional unit involved in a learning path generation device based on knowledge point clusters provided in this embodiment, reference can be made to the corresponding description in Figures 1 to 8 and will not be elaborated here.

[0110] Based on the above as Figures 1 to 8The method described above, correspondingly, this embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method as described above Figures 1 to 8 as shown.

[0111] Based on such an understanding, the technical solution of this application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various implementation scenarios of this application.

[0112] As Figure 11 shown is a schematic diagram of the hardware structure of an electronic device according to the present invention, including:

[0113] At least one processor 701; and,

[0114] A memory 702 communicatively connected to at least one of the processors 701; wherein,

[0115] The memory 702 stores instructions executable by at least one of the processors. The instructions are executed by at least one of the processors so that at least one of the processors can execute the method for generating a learning path based on knowledge point clusters as described above.

[0116] Figure 11 Taking one processor 701 as an example in

[0117] The electronic device may further include: an input device 703 and a display device 704.

[0118] The processor 701, the memory 702, the input device 703, and the display device 704 may be connected through a bus or other means. Figure 11 Taking the connection through a bus as an example in

[0119] The memory 702, 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 method for generating a learning path based on knowledge point clusters in the embodiments of this application. For example, Figures 1 to 8 the method flow shown. The processor 701 executes various functional applications and data processing by running the non-volatile software programs, instructions, and modules stored in the memory 702, that is, implements the method for generating a learning path based on knowledge point clusters in the above embodiments.

[0120] The memory 702 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created according to the use of the method for generating a learning path based on knowledge point clusters, etc. In addition, the memory 702 may include high-speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 702 may optionally include a memory remotely provided with respect to the processor 701, and these remote memories may be connected to the device for executing the method for generating a learning path based on knowledge point clusters through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0121] The input device 703 may receive input user clicks and generate signal inputs related to user settings and function controls of the method for generating a learning path based on knowledge point clusters. The display device 704 may include a display screen and other display devices.

[0122] When the one or more modules are stored in the memory 702 and run by the one or more processors 701, the method for generating a learning path based on knowledge point clusters in any of the above method embodiments is executed.

[0123] Optionally, the above-mentioned physical device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, sensors, an audio circuit, a WI-FI module, etc. The user interface may include a display screen and an input unit such as a keyboard, etc. Optionally, the user interface may further include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0124] Those skilled in the art can understand that the above-mentioned physical device structure provided in this embodiment does not limit the physical device, and it may include more or fewer components, or combine certain components, or have different component arrangements.

[0125] The storage medium may further include an operating system and a network communication module. The operating system is a program for managing the hardware and software resources of the above-mentioned physical device and supports the operation of information processing programs and other software and / or programs. The network communication module is used to implement communication between components inside the storage medium and communication between other hardware and software in the information processing physical device.

[0126] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or can also be implemented by hardware. By applying the solution of this embodiment, compared with the current existing technologies, this embodiment first extracts knowledge points based on textbook texts to obtain a basic knowledge point cluster; determines the difficulty level corresponding to each knowledge point in the basic knowledge point cluster according to the refinement degree of the knowledge points in the basic knowledge point cluster; evaluates the importance degree corresponding to each knowledge point in the basic knowledge point cluster based on the difficulty level, and extracts an important knowledge point cluster from the basic knowledge point cluster according to the importance degree; generates question banks corresponding to questions of different difficulty levels based on the basic knowledge point cluster and the important knowledge point cluster and maps them on the learning map, so as to generate the learning path of students during the learning process through the learning map, wherein the learning map includes one or more key learning grids, and the key learning grids are the grids corresponding to the important knowledge point cluster. Through the analysis and processing of text information, a basic knowledge point cluster can be obtained from the textbook and an important knowledge point cluster can be further extracted, thereby effectively distinguishing important knowledge points from basic knowledge points, improving the accuracy rate of screening important knowledge points, and generating corresponding question banks and learning maps based on the important knowledge point cluster. Furthermore, personalized learning paths and key learning guidance can be provided for students, and at the same time, scientific basis and auxiliary tools can be provided for teachers' teaching, effectively solving the problems of unclear knowledge system and low learning efficiency in traditional teaching.

[0127] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0128] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments described herein, but will be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A learning path generation method based on knowledge point clusters, characterized in that: include: Extracting knowledge points based on textbook text to obtain basic knowledge point clusters includes: analyzing the textbook text to extract knowledge points in the textbook text, and dividing the extracted knowledge points into classification nodes at various levels according to the association relationship between the knowledge points; encoding the hierarchical identifications of the classification nodes at various levels to map the classification nodes at various levels into identification vector sets, wherein the first-level classification node appears in the form of a single-unit identification vector, and the multi-level classification node appears in the form of a combination of identification vectors of multiple units; generating the basic knowledge point clusters according to the identification vector set; Determining the difficulty level corresponding to each knowledge point in the basic knowledge point cluster according to the degree of refinement of the knowledge points in the basic knowledge point cluster; The importance of each knowledge point in the basic knowledge point cluster is evaluated based on the difficulty level, and an important knowledge point cluster is extracted from the basic knowledge point cluster according to the importance, including: obtaining an importance score of each knowledge point in the basic knowledge point cluster; determining the importance of each knowledge point according to the importance score; screening the basic knowledge point cluster based on the importance of each knowledge point and a preset screening threshold, and determining the screened knowledge point set as the important knowledge point cluster, wherein the preset screening threshold is an initial value set for the importance of each knowledge point, and the importance score G is calculated by using a pre-trained evaluation model: Among them, Z, X, V, and Y are the number of identification vectors corresponding to the first-level, second-level, third-level, and n-level knowledge points in the basic knowledge point cluster, respectively. , , , They are the weight coefficients corresponding to the first-level, second-level, third-level, and n-level knowledge points respectively; Based on the basic knowledge point cluster and the important knowledge point cluster, a question bank corresponding to questions of different difficulty levels is generated and mapped on a learning map, so as to generate a learning path for students in the learning process through the learning map. The learning map includes one or more key learning grids, and the key learning grids are grids corresponding to the important knowledge point clusters.

2. The method according to claim 1, characterized in that: The step of generating question banks corresponding to questions of different difficulty levels based on the basic knowledge point clusters and the important knowledge point clusters and mapping them on a learning map includes: Respectively combining knowledge points of different difficulty levels in the basic knowledge point cluster and the important knowledge point cluster to generate question banks corresponding to questions of different difficulty levels; According to the information identifiers of the basic knowledge point cluster and the important knowledge point cluster in the question bank, a learning map containing knowledge point distribution information is generated by arranging the knowledge points according to the preset relationship between the knowledge points.

3. The method according to claim 2, characterized in that The step of generating a learning map containing knowledge point distribution information according to the information identifiers of the basic knowledge point cluster and the important knowledge point cluster in the question bank and arranging the knowledge points according to the preset relationship between the knowledge points includes: Marking the questions corresponding to the basic knowledge point cluster in the question bank with a first identifier, and marking the questions corresponding to the important knowledge point cluster with a second identifier; According to the first identifier and the second identifier in the question bank, the questions corresponding to the basic knowledge point cluster and the questions corresponding to the important knowledge point cluster are mapped on the learning map according to the sequence of chapters in the textbook.

4. The method according to claim 3, characterized in that The method further comprises: Mark the learning grid corresponding to the question marked with the second identifier on the learning map as a key learning grid; An area in each learning area consisting of a plurality of adjacent key learning grids is determined as a key monitoring area in the learning area.

5. A learning path generation device based on knowledge point clusters, characterized in that: include: The extraction module is configured to extract knowledge points based on the textbook text to obtain a basic knowledge point cluster, including: analyzing the textbook text to extract the knowledge points in the textbook text, and dividing the extracted knowledge points into classification nodes at various levels according to the association relationship between the knowledge points; encoding the hierarchical identifications of the classification nodes at various levels to map the classification nodes at various levels to a set of identification vectors, wherein the first-level classification node appears in the form of a single-unit identification vector, and the multi-level classification node appears in the form of a combination of multiple-unit identification vectors; generating the basic knowledge point cluster according to the set of identification vectors; A determination module configured to determine the difficulty level corresponding to each knowledge point in the basic knowledge point cluster according to the refinement degree of the knowledge point in the basic knowledge point cluster; The evaluation module is configured to evaluate the importance of each knowledge point in the basic knowledge point cluster based on the difficulty level, and extract the important knowledge point cluster from the basic knowledge point cluster according to the importance, including: obtaining the importance score of each knowledge point in the basic knowledge point cluster; determining the importance of each knowledge point according to the importance score; screening the basic knowledge point cluster based on the importance of each knowledge point and a preset screening threshold, and determining the screened knowledge point set as the important knowledge point cluster, wherein the preset screening threshold is an initial value set for the importance of each knowledge point, and the importance score G is calculated by using a pre-trained evaluation model: Among them, Z, X, V, and Y are the number of identification vectors corresponding to the first-level, second-level, third-level, and n-level knowledge points in the basic knowledge point cluster, respectively. , , , They are the weight coefficients corresponding to the first-level, second-level, third-level, and n-level knowledge points respectively; A generation module is configured to generate a question bank corresponding to questions of different difficulty levels based on the basic knowledge point cluster and the important knowledge point cluster and map them on a learning map, so as to generate a learning path for students in the learning process through the learning map. The learning map includes one or more key learning grids, and the key learning grids are grids corresponding to the important knowledge point clusters.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

7. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

Images