Knowledge point cluster-based learning path generation method and apparatus, and electronic device

Abstract

The invention discloses a learning path generation method and device based on a knowledge point cluster, a storage medium and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: carrying out the extraction of knowledge points based on a textbook text, and obtaining a basic knowledge point cluster; determining a difficulty level corresponding to each knowledge point in the basic knowledge point cluster according to the refining degree of the knowledge points in the basic knowledge point cluster; 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; and based on the basic knowledge point cluster and the important knowledge point cluster, generating question banks corresponding to the questions with different difficulty levels and mapping the question banks on a learning map so as to generate a learning path of the student in the learning process through the learning map. The important knowledge point cluster is analyzed and extracted from the textbook, so that the screening accuracy of the important knowledge points is improved, and personalized learning paths and key learning guidance are provided for students.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, storage medium and electronic device for generating a learning path based on knowledge point clusters. Background Art

[0002] When students are doing questions, 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 knowledge systems, and assist teaching through question banks and visualization tools has become an urgent problem to be solved in the field of education.

[0003] At present, 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 between important knowledge points and basic knowledge points, which affects 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. The main purpose is to improve the technical problem that the knowledge point clusters extracted in the current prior art do not effectively distinguish between important knowledge points and basic knowledge points, affecting the accuracy of important knowledge point screening.

[0006] In a first aspect, the present application provides a method for generating a learning path based on a knowledge point cluster, comprising: Extract knowledge points based on textbook texts to obtain basic knowledge point clusters; 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; Evaluating the importance of 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; 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.

[0007] In a second aspect, the present application provides a learning path generation device based on knowledge point clusters, comprising: An extraction module is configured to extract knowledge points based on the textbook text to obtain basic knowledge point clusters; 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; An evaluation module, configured to evaluate the importance of 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; 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.

[0008] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for generating a learning path based on knowledge point clusters described in the first aspect is implemented.

[0009] In a fourth aspect, the present application provides an electronic device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, the learning path generation method based on knowledge point clusters described in the first aspect is implemented.

[0010] By means of the above technical scheme, the present application provides a learning path generation method, device, storage medium and electronic device based on knowledge point clusters. Compared with the current existing technology, the present application first extracts knowledge points based on the textbook text to obtain basic knowledge point clusters; determines 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; evaluates the importance of each knowledge point in the basic knowledge point cluster based on the difficulty level, and extracts important knowledge point clusters from the basic knowledge point cluster according to the importance; based on the basic knowledge point cluster and the important knowledge point cluster, generates a question bank corresponding to questions of different difficulty levels and maps them on a learning map, so as to generate a learning path for students in the learning process through the learning map, wherein the learning map includes one or more key learning grids, and the key learning grids are grids corresponding to the important knowledge point clusters. By analyzing and processing text information, we can obtain basic knowledge point clusters from textbooks and further extract important knowledge point clusters, thereby effectively distinguishing important knowledge points from basic knowledge points, improving the accuracy of important knowledge point screening, and generating corresponding question banks and learning maps based on important knowledge point clusters, thereby providing students with personalized learning paths and key learning guidance, and also providing scientific basis and auxiliary tools for teachers' teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0012] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0013] Figure 1 A flow chart of a method for generating a learning path based on knowledge point clusters provided in an embodiment of the present application is shown; Figure 2 A flow chart of a method for generating a learning path based on knowledge point clusters provided in an embodiment of the present application is shown; Figure 3 A schematic diagram of an example process provided by an embodiment of the present application is shown; Figure 4 A flow chart of a method for generating a learning path based on knowledge point clusters provided in an embodiment of the present application is shown; Figure 5 A schematic diagram of an example process provided by an embodiment of the present application is shown; Figure 6A flow chart of a method for generating a learning path based on knowledge point clusters provided in an embodiment of the present application is shown; Figure 7 A schematic diagram of an example process provided by an embodiment of the present application is shown; Figure 8 A flow chart of a method for generating a learning path based on knowledge point clusters provided in an embodiment of the present application is shown; Fig. 9 A schematic diagram of an example process provided by an embodiment of the present application is shown; Fig.10 A schematic diagram of the structure of a learning path generation device based on knowledge point clusters provided in an embodiment of the present application is shown; Fig.11 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0014] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0015] In order to improve the technical problem that the knowledge point clusters extracted in the current prior art do not effectively distinguish between important knowledge points and basic knowledge points, which affects the accuracy of important knowledge point screening. This embodiment provides a learning path generation method based on knowledge point clusters, such as Figure 1 As shown, the method includes: Step 101: Extract knowledge points based on the textbook text to obtain basic knowledge point clusters.

[0016] Exemplarily, the basic knowledge point cluster is a set of knowledge units consisting of all basic concepts, terms, facts, rules, etc. identified after text analysis and processing of teaching materials or learning materials. First, the teaching material content is cleaned and preprocessed, including removing irrelevant characters (such as punctuation marks), converting to lowercase, stemming or word form restoration, etc., to ensure the effectiveness of subsequent analysis. The text is divided into words or phrases, and each word is annotated 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 vector form, which can capture the semantic relationship between words to form an identification vector.

[0017] In some examples, the generated identification vectors are grouped through clustering algorithms (such as K-means, hierarchical clustering, etc.) to construct clusters composed of similar knowledge points, namely basic knowledge point clusters, thereby clarifying the subject structure and association patterns in the textbook content.

[0018] Step 102: Determine 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.

[0019] Exemplarily, by classifying the knowledge points in the basic knowledge point cluster in detail, and determining the difficulty level according to the degree of refinement of the knowledge points, for example, the degree of refinement corresponding to the knowledge points can be determined according to the chapter, section, or unit to which the knowledge points belong in the textbook text; or the degree of refinement corresponding to the knowledge points can be determined according to the form of the identification vector corresponding to the knowledge points (in the form of a single unit or a combination of multiple units); and then the difficulty level is determined according to the degree of refinement of the knowledge points, and the higher the degree of refinement of the knowledge points, the higher the corresponding difficulty level. By applying the method of this embodiment, the systematicness and hierarchy of the knowledge points are ensured, and a basis is provided for the subsequent generation of questions of different difficulty levels.

[0020] Step 103: 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.

[0021] In some examples, different difficulty levels correspond to different weights of the importance of indicators of each knowledge point. Based on different difficulty levels and weights, each knowledge point can be evaluated to obtain an importance score. According to the importance score, the important knowledge point cluster can be further screened from the basic knowledge point cluster to form a collection of core knowledge points that are essential for understanding the target subject or completing 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 are often the basis for building complex concepts. Mastering these core points helps to deeply understand the structure and logic of the subject, thereby better solving complex problems.

[0022] Step 104: Based on the basic knowledge point clusters and the important knowledge point clusters, 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.

[0023] Among them, the learning map includes one or more key learning grids, which are grids corresponding to important knowledge point clusters. The learning map can be used to reflect the learning grid 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. Through the combination of knowledge graph and learning path, key knowledge points are identified and monitored when students enter the learning scope of these knowledge points. When students encounter difficulties in their learning progress, the system will match users with similar learning paths to promote interaction and mutual assistance between students, thereby improving the learning experience.

[0024] For example, by classifying the knowledge points in the basic knowledge point clusters and important knowledge point clusters in detail and determining their difficulty levels according to the degree of refinement of the knowledge points, the systematic and hierarchical nature of the knowledge points is ensured, providing a basis for subsequent question generation. The question bank contains multiple-choice questions, fill-in-the-blank questions, short-answer questions, and essay questions. The questions in the question bank correspond to the important knowledge points in the textbook content. Using text analysis and data mining technology, important knowledge point clusters are 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.

[0025] In some examples, a learning map is a visualization tool that can be presented in the form of a chart, network diagram, or flowchart to help students or learners understand and navigate a complex knowledge system, where each knowledge point cluster is represented as a grid, and the relationship between the knowledge points can be represented by connecting lines or other forms of links.

[0026] For example, in the knowledge graph, important knowledge point clusters that are core concepts or principles with wide applications can be marked. Different colors or symbols can be used to distinguish between common knowledge points and important knowledge points. Design a learning map based on the knowledge graph to ensure that the relationship between knowledge points is clearly visible and highlight important knowledge point clusters.

[0027] Specifically, knowledge points can be distributed on a two-dimensional grid, with each knowledge point cluster occupying a cell. The size of the cell can be adjusted according to the complexity and importance of the knowledge point. For cells where important knowledge point clusters are located, special identifiers (such as colors, etc.) are used to mark them 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 to better plan their learning paths. The setting of a key learning grid not only highlights key knowledge points, but also provides students with a clear learning path, thereby improving learning efficiency and effectiveness.

[0028] Compared with the current existing technology, this embodiment first extracts knowledge points based on the textbook text to obtain basic knowledge point clusters; according to the refinement degree of knowledge points in the basic knowledge point clusters, the difficulty level corresponding to each knowledge point in the basic knowledge point clusters is determined; based on the difficulty level, the importance of each knowledge point in the basic knowledge point clusters is evaluated, and the important knowledge point clusters are extracted from the basic knowledge point clusters according to the importance; based on the basic knowledge point clusters and the important knowledge point clusters, 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 a learning map, wherein 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 the textbooks and important knowledge point clusters can be further extracted, thereby effectively distinguishing important knowledge points from basic knowledge points, improving the accuracy of important knowledge point screening, and generating corresponding question banks and learning maps based on important knowledge point clusters, thereby providing students with personalized learning paths and key learning guidance, and also providing scientific basis and auxiliary tools for teachers' teaching.

[0029] As a refinement and extension of the above embodiment, when extracting knowledge points based on the textbook text to obtain basic knowledge point clusters, the following methods may be used but are not limited to: Figure 2 As shown, the method includes: Step 201: Analyze the textbook text to extract knowledge points in the textbook text, and divide the extracted knowledge points into classification nodes at various levels according to the association relationship between the knowledge points.

[0030] In some examples, the teaching material content can be uploaded to the first knowledge point analysis module, and the knowledge points in the text can be extracted through text analysis technology and graded. Graded identification refers to giving specific marks to knowledge points or nodes at each level in a multi-level classification structure for easy distinction and management. Through graded identification, the hierarchical relationship of knowledge points can be clearly displayed, which helps to build the entire knowledge network.

[0031] For example, the first-level classification node, as the main node, can be classified by chapter; the second-level classification node, as the slave node of the first-level classification node, can be classified by section, representing the various subsections in each chapter; the third-level classification node, as the slave node of the second-level classification node, can be classified by unit results, further subdividing the specific knowledge points or units under each section, and so on.

[0032] Step 202: Encode the hierarchical identifications of the classification nodes at each level to map the classification nodes at each level into identification vector sets.

[0033] Among them, 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.

[0034] For example, the knowledge points in the textbook are extracted and graded. The graded labels are divided into n levels, for example, n=3, then U=[An, Bm, Ck], such as Figure 3 As shown, among them: the first-level classification An is taken as the main node, and is classified by chapter, and An=[A1, A2...An] is obtained; the second-level classification Bm is taken as the slave node of An, and is classified by section, and Bm=[A1b1, A1b2, A2b1...Anbm] is obtained; the third-level classification Ck is taken as the slave node of Bm, and is classified by unit results, and Ck=[A1b1c1, A1b2c2, A1b2c3...Anbmck] is obtained.

[0035] In some examples, identification codes are used to reflect the hierarchical structure of knowledge points (such as chapters, sections, and units). Each knowledge point corresponds to a unique identification code. The specific forms of identification code values ​​may include digital numbers, letter numbers, mixed numbers, etc. These identification code values ​​can be designed according to the hierarchical structure of the knowledge points and converted into identification vectors through one-hot encoding or other methods for further processing and analysis.

[0036] Step 203: Generate a basic knowledge point cluster based on the identification vector set.

[0037] In some examples, by encoding the hierarchical identification, a unique identification code is matched for each knowledge point, and a corresponding identification vector is generated according to the identification code. A knowledge point network is constructed based on the identification vector, and the knowledge point network is used to represent the relationship between knowledge points, and then multiple knowledge point networks are combined into a comprehensive identification vector set Uori (i.e., basic knowledge point cluster).

[0038] Exemplarily, when constructing a knowledge point network, the first-level classification An exists in the form of a single-unit identification vector, and the second-level and third-level classifications exist in the form of a combination of multiple units. The identification vectors are superimposed in sequence, and multiple knowledge point networks form a knowledge point cluster, covering the combination of all knowledge points. The general expression of the knowledge point network is: U=(k, β), where k is the knowledge point set and β is the edge set.

[0039] In some examples, the course video can also be recorded and stored through IoT devices (cameras, smart blackboards, etc.); then the audio is converted into text through a pre-trained speech-to-text model. The speech-to-text model can use the OpenAI open source model Whisper to construct an identification vector set Z corresponding to the knowledge point identification and generate a knowledge point network; based on the identification vector set (Dn, Em, Fk), the basic knowledge point cluster L=[Dn, Em, Fk] is obtained.

[0040] In some examples, knowledge point network coverage refers to grading knowledge points according to their importance and complexity, and ensuring that all possible combinations of knowledge points are covered. Through multi-level classification identification and identification vector construction, the knowledge system of the textbook content can be systematically represented to form a complete knowledge framework.

[0041] For example, questions can also be pushed based on the importance level of knowledge points. This push mechanism based on the level and difficulty level of knowledge points 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 apply multiple knowledge points, etc. The relationship and combination 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 when pushing questions, you should first ensure that students have mastered A.

[0042] Further, when evaluating the importance of each knowledge point in the basic knowledge point cluster based on the difficulty level and extracting the important knowledge point cluster from the basic knowledge point cluster according to the importance, the following steps may be adopted but are not limited to: Figure 4 As shown, including: Step 301: Use a pre-trained evaluation model to evaluate the importance of each knowledge point in the basic knowledge point cluster.

[0043] Specifically, the importance score of each knowledge point in the basic knowledge point cluster is obtained, wherein the importance score G is calculated by using a pre-trained evaluation model (Formula 1); according to the importance score, the importance of each knowledge point is determined. Formula 1 is specifically as follows: (Formula 1) In formula 1, 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. , , , are the weight coefficients corresponding to the importance of the first-level, second-level, third-level, and n-level identification vectors, is the weight score corresponding to the importance of the n-level identification vector, and < < , because as the knowledge points become more refined, their importance accounts for a larger proportion.

[0044] In some examples, the weight coefficient should reflect the importance and complexity of each level of knowledge points. 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 and have moderate weights, and the knowledge points corresponding to the third-level classification are relatively detailed knowledge points with the highest weights. Adjust the weight coefficient 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, the weight coefficient may need to be re-evaluated and adjusted. In addition to the degree of refinement of the knowledge points, other factors (such as the frequency of application of the knowledge points, difficulty, etc.) can also be considered to adjust the weight coefficient.

[0045] For example, a basic knowledge point cluster Zori=[Wn, Rm, Hk] can be generated by obtaining a historical question bank according to the identification vector set (Wn, Rm, Hk); and then the basic knowledge point cluster is screened by a pre-trained key knowledge point evaluation model (Formula 2), wherein Formula 2 is specifically as follows: (Formula 2) Among them, Gj is the number of times the test point knowledge point appears; Gq is the number of times the error frequency of the test point knowledge point appears; Gr is the number of test point knowledge point branches; α is the weight score of the corresponding indicator. Set the scoring threshold T2, when yG>T2, select the key knowledge point combination to form the second important knowledge point cluster Znew.

[0046] Step 302: Screening is performed in the basic knowledge point cluster based on the importance of each knowledge point and a preset screening threshold, and the screened knowledge point set is determined as an important knowledge point cluster.

[0047] The preset screening threshold is an initial value set according to the importance of the identification vector.

[0048] Exemplarily, a screening threshold T1 is set, and when G>T1, the screened knowledge point identification vector is used as the important knowledge point cluster Unew, such as Figure 5 As shown, the initial threshold can be set by customizing, averaging, median and other methods, which are not limited in this embodiment.

[0049] By applying the method of this embodiment, important knowledge point clusters can be effectively screened out. According to the actual situation, a suitable screening threshold setting method is selected to ensure that the screening result can accurately reflect the importance of the knowledge point.

[0050] Optionally, the knowledge points extracted from the textbook content are converted into identification vectors, and the knowledge points are divided into classification nodes at various levels by sequentially superimposing the identification vectors, and the identification vector form corresponding to the classification nodes at each level is determined, where the first-level classification appears in the form of a single-unit identification vector, and the multi-level classification appears in the form of a combination of identification vectors of multiple units.

[0051] 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 based on the identification code, and then classify the knowledge points according to the level (first level, second level, third level, etc.): for example, the chapter is "Algebra Foundation", 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 "Polynomial Operations". In this knowledge point cluster, the first level knowledge point is the knowledge point corresponding to "Algebra Foundation", the second level knowledge point is the knowledge point corresponding to "Basic Concepts" and "Polynomials", and the third level knowledge point is the knowledge point corresponding to "Variables and Constants", "Expressions and Equations", "Definition of Polynomials" and "Polynomial Operations". Further, each knowledge point is assigned a unique identification code to build a knowledge network and question bank.

[0052] Furthermore, when combining knowledge points of different difficulty levels in the basic knowledge point cluster and the important knowledge point cluster to generate a question bank and a learning map corresponding to the textbook content, the following methods may also be used but are not limited to: Figure 6 As shown, the method includes: Step 401: Combine knowledge points of different difficulty levels in the basic knowledge point cluster and the important knowledge point cluster respectively to generate a question bank corresponding to questions of different difficulty levels.

[0053] In some examples, the classification node level corresponding to the knowledge point can be obtained according to the identification vector form corresponding to the knowledge point, and then the comprehensive difficulty level of the question can be calculated by combining the degree of refinement of the knowledge point and other factors (such as application frequency, complexity, etc.). Then, questions of different difficulty levels can be appropriately mixed in the same set of questions, and the question bank can be regularly updated to add new knowledge points and question types as educational goals change and technology develops. The question bank includes questions corresponding to the first, second, 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.

[0054] Step 402: Based on the information identifiers of the basic knowledge point clusters and the important knowledge point clusters in the question bank, a learning map containing knowledge point distribution information is generated according to the preset relationship between the knowledge points.

[0055] In some examples, according to the information identification and preset relationship arrangement of the basic knowledge point clusters and the important knowledge point clusters, the learning grids corresponding to all the knowledge point clusters contained in a chapter of the textbook content are arranged to form a learning area. The preset relationship between knowledge points may include hierarchical relationship, causal relationship, etc. Each learning grid contains all relevant information of the knowledge point cluster, such as the knowledge point name, description, difficulty level, etc.

[0056] In some examples, a learning map is generated based on the learning area, and each learning grid in the learning map corresponds to all the questions corresponding to a knowledge point cluster in the question bank. Each learning grid in the learning map corresponds to all the questions corresponding to a knowledge point cluster in the question bank. The learning map can be a graphical representation or a structured list, which can provide personalized learning paths and improve learning efficiency based on the student's learning progress and knowledge mastery.

[0057] For example, the learning map can provide personalized learning paths and improve learning efficiency based on students' learning progress and knowledge mastery. Specifically, the learning map can record students' completion status in each learning grid, evaluate students' mastery of each knowledge point through tests or exercises, and then recommend learning paths and questions suitable for them based on the students' evaluation results. Suppose a student has mastered the knowledge point of "variables and constants" but has encountered 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.

[0058] Furthermore, when generating a learning map containing knowledge point distribution information, the following steps may be adopted but are not limited to: Figure 8 As shown, including: Step 501: 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.

[0059] For example, Fig. 9 As shown, the basic knowledge point clusters in the question bank are marked with colors as the first information identifier (white), and the key knowledge point clusters are marked with colors as the second information identifier (black).

[0060] Step 502: Map the questions corresponding to the basic knowledge point cluster and the questions corresponding to the important knowledge point cluster onto the learning map according to the sequence of the chapters in the textbook based on the first identifier and the second identifier in the question bank.

[0061] Optionally, the method of this embodiment may further include: marking the learning grid corresponding to the question marked with the second identifier on the learning map as a key learning grid; and determining the area composed of multiple adjacent key learning grids in each learning area as a key monitoring area in the learning area.

[0062] Exemplarily, the shape of a single knowledge point mapped in the learning map can be arranged in rectangles, squares, etc., and the second information identifiers with preset distances and adjacent to each other are circled to obtain multiple key monitoring areas. When the user enters the monitoring range of the key knowledge point, it is monitored based on 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 student's learning trajectory and learning situation can be monitored in real time, reducing the processing volume of the cloud server and improving the operating efficiency of the system.

[0063] In some examples, the question bank distribution can also be mapped to a virtual learning map through information interaction between the user terminal and the cloud server. The user terminal acts as a signal point to obtain the student's learning trajectory through the cloud server. In this embodiment, the cloud-based learning system tracks and recommends students' learning progress through information interaction between multiple user terminals and cloud servers. The learning system allows multiple user terminals (such as tablets, computers, etc.) to exchange data with the cloud server in real time. This enables the system to recommend learning content or progress based on each user's learning behavior and promote a personalized learning experience. Mapping the question bank distribution to the learning map: knowledge points or question types are distributed on the learning map according to certain rules, and each user terminal is used as a "signal point" whose position represents the user's current knowledge mastery or learning progress. This visualization method helps students intuitively understand their learning status and find areas that need to be strengthened.

[0064] For example, the question types of key knowledge points are necessary to master. The key monitoring areas are used as the trigger conditions of the system. The learning situation of users in the key monitoring areas is monitored in real time. Data analysis tools are used to track the learning trajectory of each student, provide personalized learning suggestions and topic recommendations, and collect learning information after students complete the questions to continuously optimize the knowledge point network coverage and topic push strategy. For the key monitoring areas set for important knowledge points, when users enter these areas, the system will collect and analyze the student's learning data more intensively to provide timely feedback and support. Among them, learning information includes but is not limited to user ID, learning progress (represented by the location on the learning map), notes, learning honors, learning trajectory, and learning time. These rich data support a comprehensive analysis of students' learning situation and help teachers and parents better understand and support students' learning needs. This method not only focuses on the overall learning trajectory of students, but also places special emphasis on the mastery of key knowledge, thereby improving learning efficiency.

[0065] In some examples, by automatically extracting knowledge point clusters, generating question banks, and building virtual learning maps, powerful learning and teaching tools are provided for students and teachers, which has broad application prospects and important educational significance.

[0066] Compared with the current existing technology, this embodiment can obtain basic knowledge point clusters from textbooks and further extract important knowledge point clusters through analysis and processing of text information, thereby effectively distinguishing important knowledge points from basic knowledge points, improving the accuracy of important knowledge point screening, and generating corresponding question banks and learning maps based on important knowledge point clusters, thereby providing students with personalized learning paths and key learning guidance, and also providing scientific basis and auxiliary tools for teachers' teaching, which can effectively solve the problems of unclear knowledge system and low learning efficiency in traditional teaching.

[0067] Further, as Figures 1 to 8 The specific implementation of the method shown in the embodiment provides a learning path generation device based on knowledge point clusters, such as Fig.10 As shown, the device includes: an extraction module 61, a determination module 62, an evaluation module 63, and a generation module 64.

[0068] An extraction module 61 is configured to extract knowledge points based on the textbook text to obtain basic knowledge point clusters; A 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 point in the basic knowledge point cluster; An evaluation module 63 is configured to evaluate the importance of 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; The generation module 64 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, wherein the learning map includes one or more key learning grids, and the key learning grids are grids corresponding to the important knowledge point clusters.

[0069] In some examples of the present embodiment, the extraction module 61 is specifically configured to analyze the textbook text to extract the knowledge points in the textbook text, and divide the extracted knowledge points into classification nodes at various levels according to the association relationship between the knowledge points; by encoding the hierarchical identification of the classification nodes at various levels, the classification nodes at various levels are mapped 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; the basic knowledge point cluster is generated based on the identification vector set.

[0070] In some examples of this embodiment, the evaluation module 63 is specifically configured to use a pre-trained evaluation model to evaluate the importance of each knowledge point in the basic knowledge point cluster; based on the importance of each knowledge point and a preset filtering threshold, the basic knowledge point cluster is filtered, and the filtered knowledge point set is determined as the important knowledge point cluster, wherein the preset filtering threshold is an initial value set for the importance of each knowledge point.

[0071] In some examples of this embodiment, the determination module 62 is specifically configured to obtain the importance score of each knowledge point in the basic knowledge point cluster, wherein the importance score G is calculated by using a pre-trained evaluation model:

[0072] Among them, 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. , , , They are the weight coefficients corresponding to the first-level, second-level, third-level, and n-level knowledge points respectively; The importance of each knowledge point is determined according to the importance score.

[0073] In some examples of this embodiment, the generating module 64 is specifically configured to respectively combine 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.

[0074] In some examples of the present embodiment, the generation 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 to mark the questions corresponding to the key knowledge point cluster with a second identifier; based on 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 in the order of the chapters in the textbook.

[0075] In some examples of this embodiment, the generation module 64 is further configured to mark the learning grid corresponding to the question marked with the second identifier on the learning map as a key learning grid; and determine the area in each learning area composed of multiple adjacent key learning grids as the key monitoring area in the learning area.

[0076] It should be noted that for other corresponding descriptions of the functional units involved in the learning path generation device based on knowledge point clusters provided in this embodiment, please refer to Figures 1 to 8 The corresponding description in will not be repeated here.

[0077] Based on the above Figures 1 to 8 The method shown in the embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Figures 1 to 8 The method shown.

[0078] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0079] like Fig.11 The figure shows a hardware structure diagram of an electronic device of the present invention, including: at least one processor 701; and, A memory 702 that is communicatively connected to at least one of the processors 701; wherein, The memory 702 stores instructions that can be executed by at least one of the processors. The instructions are executed by at least one of the processors to enable the at least one of the processors to execute the learning path generation method based on knowledge point clusters as described above.

[0080] Fig.11 A processor 701 is taken as an example.

[0081] The electronic device may further include: an input device 703 and a display device 704 .

[0082] The processor 701, the memory 702, the input device 703 and the display device 704 may be connected via a bus or other means. Fig.11 The example of connecting through bus is taken in the following.

[0083] The memory 702 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the learning path generation method based on knowledge point clusters in the embodiment of the present application, for example, Figures 1 to 8 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, the learning path generation method based on knowledge point clusters in the above embodiment is implemented.

[0084] The memory 702 may include a program storage area and a data storage area, wherein 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 a learning path generation method based on knowledge point clusters, etc. In addition, the memory 702 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 702 may optionally include a memory remotely arranged relative to the processor 701, and these remote memories may be connected to a device for executing a learning path generation method based on knowledge point clusters via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0085] The input device 703 can receive user clicks and generate signal inputs related to user settings and function control of the learning path generation method based on knowledge point clusters. The display device 704 can include display devices such as a display screen.

[0086] The one or more modules are stored in the memory 702 , and when executed by the one or more processors 701 , the learning path generation method based on knowledge point clusters in any of the above method embodiments is executed.

[0087] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also 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.

[0088] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0089] 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 above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0090] Through the description of the above implementation methods, the technical personnel in this field can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the scheme of this embodiment, compared with the current prior art, this embodiment first extracts knowledge points based on the textbook text to obtain a basic knowledge point cluster; according to the degree of refinement of the knowledge points in the basic knowledge point cluster, the difficulty level corresponding to each knowledge point in the basic knowledge point cluster is determined; based on the difficulty level, the importance of each knowledge point in the basic knowledge point cluster is evaluated, and the important knowledge point cluster is extracted from the basic knowledge point cluster according to the importance; 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 a learning map, wherein the learning map includes one or more key learning grids, and the key learning grids are grids corresponding to the important knowledge point clusters. By analyzing and processing text information, we can obtain basic knowledge point clusters from textbooks and further extract important knowledge point clusters, thereby effectively distinguishing important knowledge points from basic knowledge points, improving the accuracy of important knowledge point screening, and generating corresponding question banks and learning maps based on important knowledge point clusters, thereby providing students with personalized learning paths and key learning guidance, and also providing scientific basis and auxiliary tools for teachers' teaching, which can effectively solve the problems of unclear knowledge system and low learning efficiency in traditional teaching.

[0091] 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 such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0092] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent 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 the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features applied for herein.

Claims

1. A learning path generation method based on knowledge point clusters, characterized in that: include: Extract knowledge points based on textbook texts to obtain basic knowledge point clusters; 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; Evaluating the importance of 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; 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 extraction of knowledge points based on the textbook text to obtain basic knowledge point clusters includes: Analyze the textbook text to extract knowledge points in the textbook text, and divide the extracted knowledge points into classification nodes at various levels according to the association relationship between the knowledge points; By encoding the hierarchical identifications of the classification nodes at each level, the classification nodes at each level are mapped into 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; The basic knowledge point cluster is generated according to the identification vector set.

3. The method according to claim 1, characterized in that The step of evaluating the importance of 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 level, includes: Using a pre-trained evaluation model to evaluate the importance of each knowledge point in the basic knowledge point cluster; The basic knowledge point cluster is screened based on the importance of each knowledge point and a preset screening threshold, and the screened knowledge point set is determined as the important knowledge point cluster, wherein the preset screening threshold is an initial value set for the importance of each knowledge point.

4. The method according to claim 3, characterized in that: The using of the pre-trained evaluation model to determine the importance of each knowledge point in the basic knowledge point cluster includes: Obtain the importance score of each knowledge point in the basic knowledge point cluster, wherein the importance score G is calculated by using a pre-trained evaluation model: Among them, 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. , , , They are the weight coefficients corresponding to the first-level, second-level, third-level, and n-level knowledge points respectively; The importance of each knowledge point is determined according to the importance score.

5. 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.

6. The method according to claim 5, 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 key 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.

7. The method according to claim 6, 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.

8. A learning path generation device based on knowledge point clusters, characterized in that: include: An extraction module is configured to extract knowledge points based on the textbook text to obtain basic knowledge point clusters; 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; An evaluation module, configured to evaluate the importance of 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; 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.

9. 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 7 is implemented.

10. 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 7 is implemented.

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