Learning assistance system

Abstract

The present disclosure provides a learning assistance system. The system includes: a teacher terminal, a student terminal, and a server; the teacher terminal is used to upload questions to be updated to the server; the student terminal is used to upload review requirements to the server; the server is used to update the questions to be updated to the corresponding question bank based on a preset question bank knowledge graph to obtain an updated question bank, and screen out questions corresponding to the review knowledge point categories from the updated question bank based on the review knowledge point categories in the review requirements, and send the questions to the student terminal as review exercises; the preset question bank knowledge graph is constructed through a preset knowledge tree, and the preset knowledge tree is extracted from the content of the review textbook. By integrating the teacher terminal, the student terminal, and the server, the real-time dynamic update of the question bank and the precise personalized screening of questions are realized, which not only ensures the synchronization of the question bank content with the latest teaching content, but also meets the personalized review needs of students, and effectively improves the review effect of students.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular, to a learning assistance system. Background Art

[0002] In the field of education, with the rapid development of information technology, the application of learning assistance systems has become increasingly common. Learning assistance systems aim to optimize the learning process and improve students' learning efficiency and effectiveness through technical means such as data analysis and artificial intelligence.

[0003] Currently, most learning assistance systems in the related art adopt a pre-set question bank, and determine the difficulty level of knowledge points by statistically analyzing the frequency of appearance and error rate of questions, etc., and recommend practice questions to students accordingly, so that students can achieve the purpose of reviewing and consolidating knowledge through these practice questions.

[0004] However, since the question bank is fixed and the determination of the difficulty level depends on big data statistics, the question bank in the related technology cannot reflect the latest teaching content and review needs in a timely manner, resulting in the questions that students review may be out of touch with the actual teaching content, so that when the system screens questions, it cannot provide targeted review exercises for students, affecting the review effect. Summary of the Invention

[0005] The present disclosure provides a learning assistance system.

[0006] According to a first aspect of the present disclosure, there is provided a learning assistance system, which includes: a teacher terminal, a student terminal, and a server; the teacher terminal is used to upload questions to be updated to the server; the student terminal is used to upload review requirements to the server; the server is used to update the questions to be updated to the corresponding question bank based on a pre-set question bank knowledge graph to obtain an updated question bank, and screen out questions corresponding to the review knowledge point categories from the updated question bank based on the review knowledge point categories in the review requirements, and send the questions to the student terminal as review exercises; the pre-set question bank knowledge graph is constructed through a pre-set knowledge tree, and the pre-set knowledge tree is extracted from the content of the review textbook.

[0007] In some embodiments of the present disclosure, the server is used to label the question knowledge points from the text of the questions to be updated, and determine the level corresponding to the question knowledge points in the pre-set question bank knowledge graph based on the knowledge point hierarchical structure in the pre-set question bank knowledge graph, and obtain the difficulty level of the question knowledge points based on the mapping relationship between the level and the difficulty level, so as to update the questions to be updated to the question bank with the same difficulty level to obtain an updated question bank.

[0008] In some embodiments of the present disclosure, the server is used to determine the review knowledge point categories and review difficulty levels corresponding to the review requirements, and based on the review knowledge point categories and review difficulty levels, search for the corresponding review knowledge points in the preset question bank knowledge graph, so as to screen out the questions matching the review knowledge points from the updated question bank and send them to the student terminal. The review knowledge point categories include basic knowledge points and important knowledge points.

[0009] In some embodiments of the present disclosure, the server is also used to perform semantic analysis on the question bank text, extract the question bank questions and the associated knowledge points of the question bank questions in the preset knowledge tree, analyze the association degree between the questions and the associated knowledge points based on the knowledge point hierarchical structure of the preset knowledge tree, and construct a question bank knowledge graph based on the association degree.

[0010] In some embodiments of the present disclosure, the server is used to select the first associated knowledge point and the second associated knowledge point at different levels based on the knowledge point hierarchical structure and determine the association degree between the question and the first associated knowledge point and the second associated knowledge point, so as to associate the question with the first associated knowledge point or the second associated knowledge point according to the comparison result of the association degree and the preset association threshold to obtain the question bank knowledge graph.

[0011] In some embodiments of the present disclosure, the server is used to determine the association degree between the question and the first associated knowledge point and the second associated knowledge point according to the following formula;

[0012]

[0013] where is the association degree between the i-th associated knowledge point and the j-th associated knowledge point, is the first question set corresponding to the i-th knowledge point, is the second question set corresponding to the j-th knowledge point, is the number of questions in the first question set, is the number of questions in the second question set, is the number of questions in the intersection of the first question set and the second question set.

[0014] In some embodiments of the present disclosure, the server is also used to obtain the review teaching material content uploaded by the student terminal or the teacher terminal, and perform hierarchical processing on the textbook text knowledge points extracted from the review teaching material content to obtain a basic knowledge tree, so as to screen out a knowledge tree by using the knowledge point distribution in the basic knowledge tree.

[0015] In some embodiments of the present disclosure, the server is configured to construct a first important knowledge tree and a second important knowledge tree based on a historical question bank and wrong-question correlation features, and optimize the knowledge tree based on the basic knowledge tree, the first important knowledge tree, and the second important knowledge tree to obtain an optimized knowledge tree.

[0016] In some embodiments of the present disclosure, the server is configured to use a preset knowledge point screening model to screen eligible knowledge points from all knowledge points corresponding to the historical question bank to obtain a first important knowledge tree; use a hierarchical clustering algorithm and a correlation analysis algorithm to screen wrong-question features under a target feature category from the wrong-question correlation features, and obtain a second important knowledge tree based on all knowledge points corresponding to the wrong-question features; perform matching verification on the knowledge tree based on the basic knowledge tree, the first important knowledge tree, and the second important knowledge tree, and optimize the knowledge tree based on the matching verification result to obtain an optimized knowledge tree.

[0017] In some embodiments of the present disclosure, the server is configured to, based on a same-level matching rule, use the first important knowledge tree and the second important knowledge tree to perform first matching verification on all knowledge points at each level in the knowledge tree in sequence; if the first matching verification fails, perform cross-matching verification on each first important knowledge point in the first important knowledge tree and each second important knowledge point in the second important knowledge tree; if the cross-matching verification is successful, determine the cross-matching knowledge points with successful cross-matching, and based on the same-level matching rule, use the cross-matching knowledge points to perform second matching verification on all basic knowledge points at each level in the basic knowledge tree in sequence; if the second matching verification is successful, integrate the matching knowledge points corresponding to the successful second matching into the knowledge tree.

[0018] The learning assistance system provided by the present disclosure includes a teacher terminal, a student terminal, and a server; the teacher terminal is configured to upload questions to be updated to the server; the student terminal is configured to upload review requirements to the server; the server is configured to update the questions to be updated to the corresponding question bank based on a preset question bank knowledge graph to obtain an updated question bank, and screen questions corresponding to the review knowledge point category from the updated question bank based on the review knowledge point category in the review requirements, and send the questions to the student terminal as review exercises; the preset question bank knowledge graph is constructed through a preset knowledge tree, and the preset knowledge tree is extracted from the content of the review textbook. By integrating the teacher terminal, the student terminal, and the server, real-time dynamic update of the question bank and accurate personalized screening of questions are realized, which not only ensures the synchronization of the question bank content with the latest teaching content, but also meets the personalized review needs of students, effectively improving the review effect of students.

[0019] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understandable through the following description. Brief Description of the Drawings

[0020] The drawings are used to better understand the present solution and do not constitute a limitation to the present disclosure. Among them:

[0021] Figure 1 is a schematic diagram of a learning assistance system provided by an embodiment of the present disclosure;

[0022] Figure 2 is a schematic flowchart of a learning assistance method provided by an embodiment of the present disclosure;

[0023] Figure 3 is a schematic structural diagram of a learning assistance device provided by an embodiment of the present disclosure;

[0024] Figure 4 is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present disclosure. Detailed Embodiments

[0025] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted below.

[0026] In the field of education, with the rapid development of information technology, the application of learning assistance systems has become increasingly common. The learning assistance system aims to optimize the learning process and improve students' learning efficiency and effectiveness through technical means such as data analysis and artificial intelligence.

[0027] Currently, most of the learning assistance systems in the related art adopt a pre-set question bank, and determine the difficulty level of knowledge points by statistically analyzing the frequency and error rate of questions, etc., and recommend practice questions to students accordingly, so that students can achieve the purpose of reviewing and consolidating knowledge through these practice questions.

[0028] However, since the question bank is fixed and the determination of the difficulty level depends on big data statistics, the system has a certain lag in coping with the changes in teaching content and examination trends, resulting in the questions that students review may be out of touch with the actual teaching content, so that the system cannot provide targeted review exercises for students when screening questions, affecting the review effect.

[0029] To solve the problems in the related art, the learning assistance system proposed by the present disclosure realizes the dynamic update of the question bank and the accurate screening of questions by introducing a teacher terminal, a student terminal, a server, and a preset question bank knowledge graph. The teacher terminal can upload the questions to be updated in real time to ensure that the content of the question bank is consistent with the actual teaching content. At the same time, the student terminal can upload the review requirements, and the server, based on the preset question bank knowledge graph, screens out the questions corresponding to the review knowledge point categories from the updated question bank and provides personalized review exercises for the students. This not only improves the update efficiency of the question bank but also enhances the accuracy of question screening, thus effectively improving the students' review effect.

[0030] The learning assistance system, learning assistance method, device, electronic device, and storage medium according to the embodiments of the present disclosure will be described below with reference to the accompanying drawings.

[0031] Figure 1 It is a schematic diagram of a learning assistance system provided by an embodiment of the present disclosure. As Figure 1 shown, the system includes:

[0032] A teacher terminal 1, a student terminal 2, and a server 3;

[0033] The teacher terminal 1 is used to upload the questions to be updated to the server 3;

[0034] The student terminal 2 is used to upload the review requirements to the server 3;

[0035] The server 3 is used to update the questions to be updated to the corresponding question bank based on the preset question bank knowledge graph to obtain an updated question bank, and to screen out the questions corresponding to the review knowledge point categories from the updated question bank based on the review knowledge point categories in the review requirements, and send the questions to the student terminal as review exercises; the preset question bank knowledge graph is constructed by a preset knowledge tree, and the preset knowledge tree is extracted from the review textbook content.

[0036] Through the close cooperation of the teacher terminal, the student terminal, and the server, and the intelligent application of the preset question bank knowledge graph, the present disclosure provides a highly efficient, personalized, and interactive learning environment for students.

[0037] Among them, the teacher terminal is a user-friendly platform that allows teachers to upload new or revised questions to be updated to the server. The questions to be updated may be designed based on the latest teaching content, exam trends, or student feedback, and are not limited in the embodiments of the present disclosure. The teachers of the present disclosure upload the questions to be updated in real time through the teacher terminal, ensuring the timeliness and accuracy of the content of the question bank, and keeping the students' learning materials always consistent with the current teaching requirements.

[0038] The student terminal is the main interface for students to interact with the system. Students can upload their review requirements through the interface of the student terminal. The review requirements may include the knowledge points they hope to review, the expected exercise difficulty, or question type preferences, etc. Through the student terminal, students in this disclosure can customize their review plans according to their actual situations and learning goals, thereby improving learning efficiency.

[0039] The server is the core of the entire learning assistance system. It is responsible for receiving the questions to be updated uploaded by the teacher terminal and updating these questions to the corresponding question banks based on the preset question bank knowledge graph to form an updated question bank. At the same time, the server is also responsible for receiving the review requirements uploaded by the student terminal and accurately screening based on these review requirements and the questions in the updated question bank, and sending the screened questions to the student terminal as review exercises.

[0040] Through the intelligent screening and personalized recommendation functions of the server, this disclosure reduces the learning burden on students, enabling them to focus on the knowledge points that truly need to be reviewed. In addition, through the application of the preset question bank knowledge graph, the server can ensure that the screened questions highly match the learning needs of students, thereby improving the review effect. The preset question bank knowledge graph enables the server to accurately understand the meaning, location of each knowledge point and its relationship with other knowledge points, so that more wise and accurate decisions can be made when screening and recommending questions.

[0041] In some embodiments, the server is used to label the question knowledge points from the text of the questions to be updated, and based on the knowledge point hierarchical structure in the preset question bank knowledge graph, determine the level corresponding to the question knowledge points in the preset question bank knowledge graph, and based on the mapping relationship between the level and the difficulty level, obtain the difficulty level of the question knowledge points, so as to update the questions to be updated to the question bank with the same difficulty level to obtain an updated question bank.

[0042] In the learning assistance system, the server is not only responsible for data storage and transmission, but also undertakes the core tasks of knowledge processing and intelligent recommendation.

[0043] When the teacher terminal uploads the questions to be updated to the server, the server will first parse these question texts. That is, through natural language processing (NLP) technologies (such as entity recognition, keyword extraction, etc.), identify and label the knowledge points involved in the questions (i.e., question knowledge points), and then match the question knowledge points with the knowledge points in the preset question bank knowledge graph to ensure the consistency of the knowledge points.

[0044] The knowledge points in the preset question bank knowledge graph have a hierarchical structure (such as a tree structure), and each knowledge point has its corresponding level (such as first-level knowledge points, second-level knowledge points, etc.). The server will search for the corresponding level of the extracted question knowledge points in the knowledge graph. There is a mapping relationship between the level of knowledge points and the difficulty level. For example, the first-level knowledge points correspond to the primary difficulty, the second-level knowledge points correspond to the intermediate difficulty, and so on. The server determines the corresponding difficulty level according to the level of the question knowledge points. According to the difficulty level of the question knowledge points, the server updates the question to the question bank with the same difficulty level. The updated question bank will contain new questions, and the difficulty level of the questions will be consistent with the difficulty level of the question bank.

[0045] Specifically, in an optional embodiment of the present disclosure, assume that the question text of the question to be updated is: "Please explain Newton's first law." The server can extract the question knowledge point "Newton's first law" from the question to be updated and search in the preset question bank knowledge graph to determine that "Newton's first law" belongs to the subclass of "Newton's laws of motion" under "mechanics" and the level is a second-level knowledge point. According to the mapping relationship, it is determined that the second-level knowledge point corresponds to the intermediate difficulty. At this time, the server updates the question to be updated, "Please explain Newton's first law.", to the question bank with the intermediate difficulty level to ensure a reasonable distribution of the question difficulty in the question bank and facilitate students to select appropriate questions for practice according to their own levels.

[0046] In some implementation manners, for the review scenario of students, the server can also determine the review knowledge point category and review difficulty level corresponding to the review requirements, and based on the review knowledge point category and review difficulty level, search for the corresponding review knowledge points in the preset question bank knowledge graph, so as to screen out the questions that match the review knowledge points from the updated question bank as review exercises and send them to the student side. The review knowledge point category includes basic knowledge points and important knowledge points.

[0047] In the learning assistance system of the present disclosure, the server receives the review requirements of students, which may include the review scope (such as a certain subject, chapter) and the review goal (such as consolidating the foundation, increasing the difficulty). The server parses the review requirements through natural language processing (NLP) or predefined rules to determine the knowledge point category (basic knowledge points or important knowledge points) and review difficulty level (such as primary, intermediate, advanced) that need to be reviewed. The server searches for the corresponding review knowledge points in the preset question bank knowledge graph based on the parsed review knowledge point category and review difficulty level, and screens out the questions that match the review knowledge points from the updated question bank based on the knowledge point category and difficulty level to which the questions belong, and sends the screened review questions to the student side for the students to practice.

[0048] In an alternative embodiment of the present disclosure, assume that the review requirement of a student is: "Review the basic knowledge points in the mechanics part, with a primary difficulty level." At this time, the server can use natural language processing (NLP) to parse and obtain that the review scope in the review requirement is the mechanics part, the review knowledge point category is basic knowledge points, and the review difficulty level is primary. Then, based on the parsed information, the server can search for the basic knowledge points in the "mechanics" part from the knowledge graph, such as "Newton's First Law", "Composition and Decomposition of Forces", etc., and further confirm whether the difficulty level of these basic knowledge points is primary. If it is determined that the difficulty level is primary, then the server can screen out the primary-difficulty questions that match the knowledge points such as "Newton's First Law" and "Composition and Decomposition of Forces" from the updated question bank, and send the primary-difficulty questions that match the knowledge points such as "Newton's First Law" and "Composition and Decomposition of Forces" to the student terminal as review exercises. The student can conduct targeted reviews through these questions to improve the learning effect.

[0049] In some embodiments, the server is also used to perform semantic analysis on the question bank text, extract the question bank questions and the associated knowledge points of the question bank questions in the preset knowledge tree, analyze the correlation degree between the questions and the associated knowledge points based on the knowledge point hierarchical structure of the preset knowledge tree, and construct a question bank knowledge graph based on the correlation degree.

[0050] Specifically, the server can use the BERT model to perform semantic analysis on the question bank text, extract the key information in the questions, and identify the question knowledge points involved in the questions through natural language processing (NLP) technology. Based on the preset knowledge tree, the server matches the extracted question knowledge points with the nodes in the knowledge tree to determine the associated knowledge points of the questions in the knowledge tree.

[0051] For example, the question "Please explain Newton's First Law" will be associated with "Mechanics", "Newton's Laws of Motion", and "Newton's First Law" in the preset knowledge tree.

[0052] After that, based on the hierarchical structure of the preset knowledge tree, further determine the levels of the associated knowledge points that the questions may be associated with, so as to construct a question bank knowledge graph according to the correlation degree between every two associated knowledge points at different levels.

[0053] Specifically, based on the knowledge point hierarchical structure, the server selects the first associated knowledge point and the second associated knowledge point at different levels and determines the correlation degree between the questions and the first associated knowledge point and the second associated knowledge point, so as to associate the questions with the first associated knowledge point or the second associated knowledge point according to the comparison result of the correlation degree with the preset correlation threshold to obtain the question bank knowledge graph.

[0054] The server selects the two most relevant associated knowledge points to the questions from the knowledge tree, that is, the second associated knowledge point is the main knowledge point directly related to the questions; the first associated knowledge point is the secondary knowledge point indirectly related to the questions.

[0055] For example, for the question "Please explain Newton's First Law", the associated knowledge points extracted are the first-level knowledge points (such as "Mechanics"), the second-level knowledge points (such as "Newton's Laws of Motion"), and the third-level knowledge points (such as "Newton's First Law"). The second associated knowledge point is Newton's First Law (third-level knowledge point); the first associated knowledge point: Newton's Laws of Motion (second-level knowledge point).

[0056] In some embodiments, the server may determine the correlation degree between the question and the first associated knowledge point and the second associated knowledge point according to the following formula;

[0057]

[0058] where, is the correlation degree between the i-th associated knowledge point and the j-th associated knowledge point, is the first question set corresponding to the i-th knowledge point, is the second question set corresponding to the j-th knowledge point, is the number of questions in the first question set, is the number of questions in the second question set, is the number of questions in the intersection of the first question set and the second question set. Wherein, i < j, that is, the i-th associated knowledge point is the first associated knowledge point, and the j-th associated knowledge point is the second associated knowledge point.

[0059] After calculating the correlation degree between the first associated knowledge point and the second associated knowledge point through the above formula 1, the server of the present disclosure may further calculate the obtained correlation degree and compare it with a preset correlation threshold. If the correlation degree is greater than or equal to the preset correlation threshold, the question is associated with the second associated knowledge point; if the correlation degree is less than the preset correlation threshold, the question is associated with the second associated knowledge point.

[0060] This is because the knowledge points in the present disclosure are divided by a hierarchical structure. When the correlation degree between a question and a knowledge point is relatively high (greater than or equal to the preset correlation threshold), it indicates that the question is directly related to the knowledge point. Therefore, it is associated with the first associated knowledge point (usually a more specific and directly related knowledge point); on the contrary, when the correlation degree is relatively low (less than the preset correlation threshold), it indicates that the direct relevance between the question and the knowledge point is weak. Therefore, it is associated with the second associated knowledge point (usually a more general and indirectly related knowledge point). This logic ensures that the association relationship between the question and the knowledge point is more accurate and reasonable, and at the same time makes full use of the characteristics of the knowledge point hierarchical structure to optimize the construction and management of the question bank knowledge graph.

[0061] Through the above method, the present disclosure calculates the correlation degree for each question, so as to associate each question with its corresponding knowledge point, thereby constructing a knowledge graph of the question bank. The nodes of the knowledge graph of the question bank include questions and knowledge points, and the edges represent the association relationship between the questions and the knowledge points. The weight of the edge is determined by the correlation degree.

[0062] In some embodiments, since the knowledge graph of the question bank is constructed according to a preset knowledge tree, the server also needs to obtain the review material content uploaded by the student side or the teacher side, and perform hierarchical processing on the textbook text knowledge points extracted from the review material content to obtain a basic knowledge tree, so as to filter out the knowledge tree by using the knowledge point distribution in the basic knowledge tree.

[0063] Specifically, the server can use text analysis technologies such as text mining, natural language, and semantic analysis to extract the textbook knowledge points in the review textbook text of the review material content, and perform hierarchical identification on the textbook knowledge points (for example, hierarchical classification according to chapters, difficulty levels, learning stages, etc.), and perform coding processing on the identification of each textbook knowledge point to obtain the identification coding value corresponding to each textbook knowledge point, so as to construct a set of identification vectors corresponding to the textbook knowledge point identification according to the identification coding value, generate a textbook knowledge point network U, and form a basic knowledge tree Uori = [An, Bm, Ck] by combining all textbook knowledge point networks.

[0064] In the present disclosure, the hierarchical identification can be divided into n levels. For example, when n = 3, the textbook knowledge point network U = [An, Bm, Ck], and the general expression is: U = (k, β), where k is the set of knowledge points and β is the set of edges.

[0065] When n = 3, the textbook knowledge point network U in the present disclosure can be a three-level classification system: the first-level classification An, the second-level classification Bm, and the third-level classification Ck. Each level of classification represents a level in the textbook knowledge point network, where the first-level classification is used as the main node, the second-level classification is used as the subordinate node of the first-level classification, and the third-level classification is used as the subordinate node of the second-level classification. That is, the first-level classification An is used as the main node and classified by chapter, and An = [A1, A2... An] can be obtained; the second-level classification Bm is used as the subordinate node of An and classified by section, and Bm = [A1b1, A1b2, A2b1.. Anbm] can be obtained; the third-level classification Ck is used as the subordinate node of Bm and classified by unit result, and Ck = [A1b1c1, A1b2c2, A1b2c3.. Anbmck] can be obtained.

[0066] Since textbook knowledge points can appear in the form of a single unit or a combination of multiple units according to their difficulty levels and learning depths, when constructing the network of textbook knowledge points, it is determined that the existence form of the first-level classification is the identification vector of a single unit, and the existence forms of the second-level and third-level classifications are in the form of combinations of multiple units, and the identification vectors are superimposed in turn.

[0067] The present disclosure combines multiple textbook knowledge point networks corresponding to the identification coding values of textbook knowledge points to form the final basic knowledge tree. The basic knowledge tree of the present disclosure can cover all possible combination forms of textbook knowledge points, providing a rich material library for the push of questions. In practical applications, the server can also further intelligently select textbook knowledge points of different difficulties for pushing according to the learning situation and needs of students. For example, for beginners or students who need to consolidate the foundation, the system can push knowledge points of the first-level classification; for students who hope to study deeply, knowledge points of the second-level or third-level classification can be pushed. This personalized push method not only improves learning efficiency but also enhances students' learning interest and motivation.

[0068] After obtaining the basic knowledge tree, the server in the present disclosure can count the number of basic knowledge points at each level in the basic knowledge tree, and according to the following formula, determine the basic knowledge tree score based on the number of basic knowledge points at each level, so as to screen the knowledge tree according to the basic knowledge tree score result, that is, the preset knowledge tree of the present disclosure;

[0069]

[0070] Among them, is the basic knowledge tree score result; Z is the number of basic knowledge points at the first level in the basic knowledge tree; X is the number of basic knowledge points at the second level in the basic knowledge tree; V is the number of basic knowledge points at the third level in the basic knowledge tree; M is the number of basic knowledge points at the nth level in the basic knowledge tree; is the weight of the number of basic knowledge points at the first level, is the weight of the number of basic knowledge points at the second level, is the weight of the number of basic knowledge points at the third level; is the weight of the number of basic knowledge points at the nth level. < < , because as the degree of refinement of knowledge points becomes deeper, the proportion of importance becomes larger.

[0071] The server of the present disclosure can compare the size of the basic knowledge tree scoring result Q with the preset screening threshold T1. When Q is greater than or equal to T1, the knowledge point identification vectors in the screened basic knowledge tree are used as the preset knowledge tree in the present disclosure. Among them, the preset screening threshold can set the initial threshold by means of custom, average method, median, etc., which is not limited in the embodiments of the present disclosure.

[0072] In some embodiments, the server in the present disclosure can also optimize the above-obtained preset knowledge tree, construct the first important knowledge tree and the second important knowledge tree based on the historical question bank and the wrong question correlation features, and optimize the above-obtained knowledge tree based on the basic knowledge tree, the first important knowledge tree and the second important knowledge tree to obtain the optimized knowledge tree, and the optimized knowledge tree is the preset knowledge tree of the present disclosure.

[0073] Among them, the server in the present disclosure can use the preset knowledge point screening model to screen the eligible knowledge points from all the knowledge points corresponding to the historical question bank to obtain the first important knowledge tree; use the hierarchical clustering algorithm and the correlation analysis algorithm to screen the wrong question features under the target feature category from the wrong question correlation features, and obtain the second important knowledge tree based on all the knowledge points corresponding to the wrong question features; match and verify the knowledge tree based on the basic knowledge tree, the first important knowledge tree and the second important knowledge tree, and optimize the knowledge tree based on the matching verification result to obtain the optimized knowledge tree.

[0074] Specifically, the process of obtaining the first important knowledge tree is as follows:

[0075] The server classifies all the knowledge points extracted from the historical question bank text to obtain the historical question bank knowledge tree, and determines the knowledge point scores of each knowledge point in the historical question bank knowledge tree according to the preset knowledge point screening model of the following formula, so as to screen the eligible knowledge points from the knowledge point scores of each knowledge point to obtain the first important knowledge tree;

[0076]

[0077] Among them, is the knowledge point score of the knowledge point P in the historical question bank knowledge tree, is the number of occurrences of the knowledge point P, is the number of errors in which the knowledge point P appears, is the number of knowledge points at the corresponding level of the knowledge point P, is the weight corresponding to the number of occurrences, the weight corresponding to the number of errors, is the weight corresponding to the number of knowledge points.

[0078] After obtaining the knowledge point scores of each knowledge point, the present disclosure can compare the knowledge point scores of each knowledge point and the preset scoring threshold T2. When it is greater than or equal to T2, important knowledge point combinations are screened out to form the first important knowledge tree Znew.

[0079] The process for obtaining the second important knowledge tree is as follows:

[0080] The server of the present disclosure can obtain the wrong - question correlation features of subjective and objective factors that affect students' wrong - answering in the teaching management system, and use clustering analysis and correlation evaluation models to screen out the wrong - question features caused by objective factors, and then determine the most influential feature group or features, and accordingly construct the second important knowledge tree Onew focusing on students' knowledge point mastery.

[0081] The present disclosure collects factors affecting wrong - answering through the teaching management system, including emotions, concentration, problem - solving styles, knowledge point mastery, problem - solving efficiency, question difficulty levels, etc., and simulates exam scenarios through the teaching management system to collect students' problem - solving video images. The subjective factor feature dataset can be obtained through Single Shot Multibox Detector (SSD) facial expression recognition technology and eye - movement tracking technology; the objective factor feature dataset can be obtained by the teaching management system acquiring the students' problem - solving situations collected by the computer. After obtaining these wrong - question correlation features, the teaching management system in the present disclosure can feedback the obtained wrong - question correlation features to the server.

[0082] Among them, the subjective factors specifically include: emotion features (such as positive, plain, negative, etc.), concentration features (such as concentrated, distracted, vulnerable to external factors, etc.), problem - solving style features (such as careful, careless, impatient, etc.). The objective factors specifically include: knowledge point mastery features (such as proficient, skilled, unskilled, etc.), problem - solving efficiency features (such as fast, medium, slow, etc.), question difficulty level features (such as easy, ordinary, difficult, etc.).

[0083] The server of the present disclosure can use the clustered wrong - question correlation feature groups to analyze the influence relationship of different groups of wrong - question correlation features on wrong - answering. That is, the present disclosure can randomly combine all wrong - question correlation features to find out the objective factors that truly affect wrong - answering. This is because there is data interference in subjective factors and it is impossible to accurately judge whether students have mastered the corresponding knowledge points. Therefore, the present disclosure needs to screen out the wrong - question feature dataset caused by objective factors from the wrong - question correlation feature dataset, thereby improving the accuracy of the second important knowledge tree.

[0084] Among them, when performing clustering analysis, the present disclosure needs to first perform data preprocessing on the obtained wrong-question associated features, such as dealing with missing values and outliers in the wrong-question associated features to ensure data quality, and performing Z-score standardization processing on the wrong-question associated features.

[0085] After that, because there are difficult questions, resulting in slow problem-solving efficiency, which is a normal phenomenon, the server of the present disclosure needs to first use the hierarchical clustering algorithm to group the preprocessed wrong-question associated features, and use the Pearson correlation coefficient distance algorithm of the following formula to calculate the distance matrix between each wrong-question associated feature, and obtain the first correlation analysis result.

[0086]

[0087] Among them, is the first correlation analysis result, , is the average value of wrong-question associated feature a and wrong-question associated feature b, where a and b are wrong-question associated features, The value range is [0, 1], and the closer the value is to 1, the less closely the linear correlation between the wrong-question associated feature and the wrong-question feature is.

[0088] Based on the above first correlation analysis result, select hierarchical clustering, gradually merge or split clusters to generate a dendrogram, and obtain the correlation distribution of wrong-question associated features.

[0089] For example, the present disclosure can select the shortest distance link method:

[0090] d(Ccluster,Dcluster)=min{d(c,d):c∈C,d∈D}, where d(Ccluster,Dcluster) represents the distance between cluster C and cluster D, and d(Ccluster,Dcluster) represents the distance between a point c in cluster C and a point d in cluster D. Determine the number of groups k of wrong-question associated features, and assign the wrong-question associated features to the corresponding groups to obtain the correlation distribution of wrong-question associated features.

[0091] After that, the server analyzes each group of wrong-question associated features, re-evaluates their relationship with the wrong-question features, and obtains the second correlation analysis result through a pre-trained correlation evaluation model (that is, based on the second correlation analysis result, generate the correlation distribution of wrong-question associated feature sets, and obtain whether there is a correlation between wrong-question associated feature sets):

[0092] The correlation evaluation model is as follows:

[0093]

[0094] Among them, y is the result of the second correlation analysis, is the intercept, is the partial regression coefficient, and x1, x2... xn are the wrong-question related features.

[0095] Through the first correlation analysis result and the second correlation analysis result, the present disclosure screens the wrong-question related feature group or features that have the greatest impact on wrong questions (that is, selects the wrong-question related feature with the maximum mean value of the first correlation analysis result and the second correlation analysis result of each wrong-question related feature, or selects the wrong-question related feature with the maximum value of the weighted fusion of the first correlation analysis and the second correlation analysis results of each wrong-question related feature), and determines the feature category of each selected wrong-question related feature (the feature category includes the subjective category and the objective category). The present disclosure uses the wrong-question related feature with the feature category being the target feature category (that is, the objective category) as the wrong-question feature, thereby constructing the second important knowledge tree Onew.

[0096] Among them, the subjective category reflects the reasons on the student's own psychology that do not directly lead to wrong questions due to unfamiliar knowledge points, and what needs to be improved is the student's own psychological quality; the objective category reflects that the student makes wrong questions because of insufficient mastery of knowledge points. Since the subjective category requires the student to adjust himself or be assisted by a third party for counseling, etc., and only the objective category is the essential reason for making wrong questions due to insufficient mastery of knowledge points, the objective category is used as the target feature category.

[0097] Through the above content, after obtaining the first important knowledge tree and the second important knowledge tree, the present disclosure can perform matching verification on the initially obtained knowledge tree based on the basic knowledge tree, the first important knowledge tree, and the second important knowledge tree, and optimize the knowledge tree based on the matching verification result to obtain the optimized knowledge tree.

[0098] Specifically, based on the same-level matching rule, the server uses the first important knowledge tree and the second important knowledge tree to perform the first matching verification on all knowledge points at each level in the knowledge tree in turn; if the first matching verification fails, cross-matching verification is performed on each first important knowledge point in the first important knowledge tree and each second important knowledge point in the second important knowledge tree; if the cross-matching verification is successful, the cross-matching knowledge points with successful cross-matching are determined, and based on the same-level matching rule, the cross-matching knowledge points are used to perform the second matching verification on all basic knowledge points at each level in the basic knowledge tree in turn; if the second matching verification is successful, the matching knowledge points corresponding to the successful second matching are integrated into the knowledge tree.

[0099] It should be noted that for the matching verification process in the present disclosure, if the matching verification at the current level fails, the matching verification of the next level will be automatically performed. After all current levels have been subjected to the matching verification, different operations will be carried out according to the matching verification results. The cross-matching in the present disclosure refers to, after the first matching verification of the knowledge tree fails between the first important knowledge tree and the second important knowledge tree, a matching verification of the same level is separately carried out between the first important knowledge tree and the second important knowledge tree to determine whether there are matching knowledge points in the first important knowledge tree and the second important knowledge tree, so as to avoid the situation of incorrect identification of knowledge points caused by systematic errors.

[0100] If the first matching verification is successful, the successfully matched knowledge points will be bound to the question bank and imported into the knowledge tree. If the cross-matching verification fails or the second matching verification fails, the knowledge tree currently undergoing the matching verification will be stored in the alternative library.

[0101] Among them, the server can comprehensively obtain the matching verification result of each matching verification based on the first matching result and the second matching result obtained according to the following formula;

[0102]

[0103] Among them, is the first matching result between knowledge tree G and knowledge tree H. Knowledge tree G and knowledge tree H are any two knowledge trees among the basic knowledge tree, the first important knowledge tree, the second important knowledge tree, and the knowledge tree that need to be subjected to the matching verification, represents the number of knowledge points in the intersection of knowledge tree G and knowledge tree H, represents the number of knowledge points in the union of knowledge tree G and knowledge tree H;

[0104]

[0105] Among them, is the second matching result between knowledge tree G and knowledge tree H. n is the nth knowledge point, N is the total number of aligned knowledge points of knowledge tree G and knowledge tree H, is the nth knowledge point vector in knowledge tree G, is the average value of the nth knowledge point vector after alignment of knowledge tree G and knowledge tree H, is the nth knowledge point vector in knowledge tree H.

[0106] In the present disclosure, when the first matching verification result is greater than or equal to the first matching verification threshold and the second matching verification result is greater than the second matching verification threshold, it is determined that the current matching verification result is a successful match.

[0107] It can be understood that since the verification matching in this disclosure adopts the principle of peer matching, if only relying on the matching verification algorithm of the first formula (for example, the Jaccard similarity algorithm), a situation may occur: even if the matching degrees of two objects seem similar overall (such as the output result like q(1, 0, 1)), there may be a mismatch at a more detailed secondary dimension (such as the secondary dimension value being 0), which does not meet the consistency standard. Therefore, to make up for this deficiency, this disclosure further combines the matching verification algorithm of the second formula (for example, the improved cosine similarity algorithm) for verification. The improved cosine similarity algorithm has an improvement in measuring the differences of each dimension value compared with other algorithms, but its accuracy still has certain limitations. Therefore, by combining these two algorithms, this disclosure can achieve complementary advantages, thereby improving the accuracy and reliability of matching.

[0108] In summary, the learning assistance system provided by this disclosure includes a teacher terminal, a student terminal, and a server; the teacher terminal is used to upload the questions to be updated to the server; the student terminal is used to upload the review requirements to the server; the server is used to update the questions to be updated to the corresponding question bank based on the preset question bank knowledge graph to obtain an updated question bank, and screen out the questions corresponding to the review knowledge point categories from the updated question bank based on the review knowledge point categories in the review requirements, and send the questions to the student terminal as review exercises; the preset question bank knowledge graph is constructed through a preset knowledge tree, and the preset knowledge tree is extracted from the content of the review textbook. By integrating the teacher terminal, the student terminal, and the server, the real-time dynamic update of the question bank and the accurate personalized screening of questions are realized, which not only ensures the synchronization of the question bank content with the latest teaching content, but also meets the personalized review needs of students, effectively improving the review effect of students.

[0109] Based on Figure 1 the learning assistance system shown Figure 2 is a schematic flowchart of a learning assistance method provided by an embodiment of this disclosure. As Figure 2 shown, this method is mainly applied to the server in the learning assistance system, and this method includes:

[0110] Step 101: Update the questions to be updated received from the teacher terminal to the corresponding question bank based on the preset question bank knowledge graph to obtain an updated question bank. The preset question bank knowledge graph is constructed through a preset knowledge tree, and the preset knowledge tree is extracted from the content of the review textbook.

[0111] Step 102: Screen out the questions corresponding to the review knowledge point categories from the updated question bank based on the review knowledge point categories in the review requirements received from the student terminal, and send the questions to the student terminal as review exercises, so that the student terminal can conduct consolidated learning based on the review questions.

[0112] In some embodiments, the principles in the embodiments of steps 101 and 102 are the same as those in the related embodiments Figure 1 shown above. For the relevant descriptions of the shown embodiments, reference can be made to Figure 1 the relevant descriptions of the shown embodiments, which will not be elaborated here.

[0113] In summary, for the technical solution provided by the present disclosure, by updating the questions to be updated received from the teacher terminal to the corresponding question bank based on the preset question bank knowledge graph, an updated question bank is obtained. The preset question bank knowledge graph is constructed through a preset knowledge tree, and the preset knowledge tree is extracted from the content of the review teaching materials; based on the review knowledge point categories in the review requirements received from the student terminal, questions corresponding to the review knowledge point categories are screened out from the updated question bank, and the questions are sent to the student terminal as review exercises, so that the student terminal can conduct consolidated learning based on the review questions, realizing the real-time dynamic update of the question bank and the accurate personalized screening of questions. This not only ensures the synchronization of the question bank content with the latest teaching content but also meets the personalized review needs of students, not only improving the efficiency of question bank management but also providing intelligent support for teaching and learning, ultimately improving the teaching quality and learning effect.

[0114] Corresponding to the above learning assistance method, the present invention also proposes a learning assistance device. Since the device embodiments of the present invention correspond to the above method embodiments, for the details not disclosed in the device embodiments, reference can be made to the above method embodiments, which will not be elaborated in the present invention.

[0115] Figure 3 As shown in Figure 3 for the structural schematic diagram of a learning assistance device provided by an embodiment of the present disclosure, the device includes:

[0116] A question bank processing unit 310, configured to update the questions to be updated received from the teacher terminal to the corresponding question bank based on the preset question bank knowledge graph, to obtain an updated question bank. The preset question bank knowledge graph is constructed through a preset knowledge tree, and the preset knowledge tree is extracted from the content of the review teaching materials.

[0117] A review assistance unit 320, configured to screen out questions corresponding to the review knowledge point categories from the updated question bank based on the review knowledge point categories in the review requirements received from the student terminal, and send the questions to the student terminal as review exercises, so that the student terminal can conduct consolidated learning based on the review questions.

[0118] It should be noted that the above explanations of the method embodiments also apply to the device in this embodiment, with the same principle, which will not be limited in this embodiment.

[0119] Based on the above as Figure 2The method described above, correspondingly, this embodiment also provides a computer program product, including a computer program, which when executed by a processor implements the method as described above Figure 2 as shown.

[0120] Based on the method as described above Figure 2 as shown, correspondingly, this embodiment also 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 method as described above Figure 2 as shown.

[0121] Based on such an understanding, the technical solution of this application can be embodied in the form of a software product, and this 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.), including 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.

[0122] As Figure 4 shown is a schematic hardware structure diagram of an electronic device according to the present invention, including:

[0123] at least one processor 401; and,

[0124] a memory 402 communicatively connected to at least one of the processors 401; wherein,

[0125] the memory 402 stores instructions executable by at least one of the processors, and when the instructions are executed by at least one of the processors, at least one of the processors can execute the learning assistance method as described above.

[0126] Figure 4 Taking one processor 401 as an example in

[0127] The electronic device may further include: an input device 403 and a display device 404.

[0128] The processor 401, the memory 402, the input device 403, and the display device 404 may be connected through a bus or other means. In the figure, it is taken as an example of being connected through a bus.

[0129] The memory 402, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the review content generation method in the embodiments of this application. For example, Figure 2 the method flow as shown. The processor 401 executes various functional applications and data processing by running the non-volatile software programs, instructions, and modules stored in the memory 402, that is, implements the learning assistance method in the above embodiments.

[0130] The memory 402 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 review content generation method, etc. In addition, the memory 402 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 402 may optionally include a memory remotely provided with respect to the processor 401, and these remote memories may be connected to the device executing the review content generation method through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0131] The input device 403 may receive input user clicks and generate signal inputs related to user settings and function controls of the review content generation method. The display device 404 may include a display screen and other display devices.

[0132] When the one or more modules are stored in the memory 402 and run by the one or more processors 401, the learning assistance method in any of the above method embodiments is executed.

[0133] Optionally, the above 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.

[0134] Those skilled in the art can understand that the above physical device structure provided in this embodiment does not limit the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0135] 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 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.

[0136] 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 is based on a preset question bank knowledge graph, updates the questions to be updated received from the teacher side to the corresponding question bank to obtain an updated question bank. The preset question bank knowledge graph is constructed through a preset knowledge tree, and the preset knowledge tree is extracted from the content of the review textbook; based on the review knowledge point categories in the review requirements received from the student side, questions corresponding to the review knowledge point categories are screened out from the updated question bank, and the questions are sent to the student side as review exercises, so that the student side can conduct consolidated learning based on the review questions, realizing the real-time dynamic update of the question bank and the accurate personalized screening of questions, which not only ensures the synchronization of the question bank content with the latest teaching content, but also meets the personalized review needs of students, not only improving the efficiency of question bank management, but also providing intelligent support for teaching and learning, and ultimately improving the teaching quality and learning effect.

[0137] 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, so 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 also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of another identical element in the process, method, article or device comprising the element.

[0138] 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 conform to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. A learning assistance system, characterized in that, The system includes: a teacher terminal, a student terminal, and a server; The teacher terminal is used to upload the questions to be updated to the server; The student terminal is used to upload the review requirements to the server; The server is used to update the questions to be updated to the corresponding question bank based on a preset question bank knowledge graph to obtain an updated question bank, and to screen out questions corresponding to the review knowledge point categories from the updated question bank based on the review knowledge point categories in the review requirements, and send the questions to the student terminal as review exercises; the preset question bank knowledge graph is constructed through a preset knowledge tree, and the preset knowledge tree is extracted from the content of the review textbook; Among them, the server is used to label the question knowledge points from the text of the questions to be updated, and based on the knowledge point hierarchical structure in the preset question bank knowledge graph, determine the level corresponding to the question knowledge points in the preset question bank knowledge graph, and based on the mapping relationship between the level and the difficulty level, obtain the difficulty level of the question knowledge points, so as to update the questions to be updated to the question bank with the same difficulty level to obtain an updated question bank; Among them, the server is also used to perform semantic analysis on the question bank text, extract the question bank questions and the associated knowledge points of the question bank questions in the preset knowledge tree, analyze the association degree between the questions and the associated knowledge points based on the knowledge point hierarchical structure of the preset knowledge tree, and construct a question bank knowledge graph based on the association degree; Among them, the server is used to select the first associated knowledge point and the second associated knowledge point at different levels based on the knowledge point hierarchical structure and determine the association degree between the question and the first associated knowledge point and the second associated knowledge point, so as to associate the question with the first associated knowledge point or the second associated knowledge point according to the comparison result of the association degree and the preset association threshold to obtain a question bank knowledge graph.

2. The system according to claim 1, wherein The server is used to determine the review knowledge point categories and review difficulty levels corresponding to the review requirements, and based on the review knowledge point categories and review difficulty levels, search for corresponding review knowledge points in the preset question bank knowledge graph, so as to screen out questions matching the review knowledge points from the updated question bank as the review exercises and send them to the student terminal, and the review knowledge point categories include basic knowledge points and important knowledge points.

3. The system according to claim 1, characterized in that, The server is used to determine the association degree between the question and the first associated knowledge point and the second associated knowledge point according to the following formula; Among them, is the correlation degree between the i-th associated knowledge point and the j-th associated knowledge point, is the first question set corresponding to the i-th knowledge point, is the second question set corresponding to the j-th knowledge point, is the number of questions in the first question set, is the number of questions in the second question set, is the number of questions in the intersection of the first question set and the second question set.

4. The system according to claim 1, wherein The server is also used to obtain the content of the review textbook uploaded by the student terminal or the teacher terminal, and perform hierarchical processing on the textbook text knowledge points extracted from the content of the review textbook to obtain a basic knowledge tree, so as to screen out the knowledge tree by using the knowledge point distribution in the basic knowledge tree.

5. The system according to claim 4, characterized in that, The server is used to construct a first important knowledge tree and a second important knowledge tree based on the historical question bank and the wrong question association characteristics, and optimize the knowledge tree based on the basic knowledge tree, the first important knowledge tree, and the second important knowledge tree to obtain an optimized knowledge tree.

6. The system according to claim 5, wherein The server is used to screen out eligible knowledge points from all the knowledge points corresponding to the historical question bank by using a preset knowledge point screening model to obtain the first important knowledge tree; screen out the wrong-question features under the target feature category from the wrong-question correlation features by using a hierarchical clustering algorithm and a correlation analysis algorithm, and obtain the second important knowledge tree based on all the knowledge points corresponding to the wrong-question features; Match and verify the knowledge tree based on the basic knowledge tree, the first important knowledge tree, and the second important knowledge tree, and optimize the knowledge tree based on the match verification result to obtain the optimized knowledge tree.

7. The system according to claim 6, characterized in that, The server is used to perform a first match verification on all the knowledge points at each level in the knowledge tree in sequence by using the first important knowledge tree and the second important knowledge tree based on the same-level matching rule; If the first match verification fails, perform a cross-match verification on each first important knowledge point in the first important knowledge tree and each second important knowledge point in the second important knowledge tree; if the cross-match verification is successful, determine the cross-match knowledge points that are successfully cross-matched, and based on the same-level matching rule, use the cross-match knowledge points to perform a second match verification on all the basic knowledge points at each level in the basic knowledge tree in sequence; If the second match verification is successful, integrate the matching knowledge points corresponding to the successful second match into the knowledge tree.

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