Course content auxiliary learning method and device, medium and product

Abstract

The invention discloses a course content auxiliary learning method and device, a medium and a product. The method comprises the steps that a search request input by a user through a user interface is received, and the search request carries a course question of the user; in response to the search request, obtaining the content of a direct knowledge node and the content of a related knowledge node corresponding to the user course question, the first structured knowledge data being used for reflecting the relationship between the direct knowledge node and the related knowledge node; visually processing the contents of the direct knowledge points, the contents of the related knowledge points and the first structured knowledge data to obtain a visual knowledge relationship graph; and sending the visual knowledge relationship graph to a terminal, so that the terminal presents the visual knowledge relationship graph through the user interface, thereby assisting a user in learning. The learning efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of educational technology, and in particular to a course content-assisted learning method, device, medium and product. Background Art

[0002] With the rapid development of globalization and informatization, the field of education is facing unprecedented challenges and opportunities. Modern education not only needs to provide rich knowledge, but also needs to cultivate students' creativity, critical thinking and problem-solving skills. The realization of these goals urgently depends on the effective use and management of educational resources. Traditional curriculum construction methods often rely on teachers' personal experience and static teaching materials, which greatly limits students' learning content.

[0003] In recent years, artificial intelligence technology has continued to penetrate into the field of education. From intelligent tutoring systems to adaptive learning platforms, various AI-based applications have emerged one after another. Machine learning and data mining technologies can extract valuable information from large amounts of educational data to provide support for course design and learning path optimization.

[0004] However, most of the existing AI applications are still in an independent running state. For example, in the intelligent question-answering technology in the existing technology, the user inputs a question through the user interface. For example, the question is "What is a triangle?" After receiving the question, the intelligent question-answering searches for the answer to the question and then returns the answer to the user interface. For example, the returned answer is "A triangle is a closed figure composed of three line segments in the same plane that are not on the same straight line and are connected in sequence from head to tail."

[0005] From the above content, it can be seen that the existing technology has at least the following technical problems: intelligent question answering only answers questions raised by users. It is very likely that after users get the answer to the current question, they will have new questions. Obviously, they cannot fully understand the problem, and the learning efficiency is low. Summary of the invention

[0006] The purpose of this application is to provide a course content assisted learning method, device, medium and product to solve the problems described in the background technology.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a course content assisted learning method, comprising:

[0009] Receiving a search request input by a user through a user interface, wherein the search request carries a user course question;

[0010] In response to the search request, the content of the direct knowledge node corresponding to the user course question, the content of the related knowledge node and the first structured knowledge data are obtained, where the first structured knowledge data is used to reflect the relationship between the direct knowledge node and the related knowledge node;

[0011] Visually processing the content of the direct knowledge point, the content of the related knowledge point, and the first structured knowledge data to obtain a visualized knowledge relationship graph;

[0012] The visualized knowledge relationship graph is sent to a terminal so that the terminal presents the visualized knowledge relationship graph through the user interface, thereby assisting user learning.

[0013] Optionally, the user course question includes a chapter identifier, and the search request is further used to request to return an auxiliary course corresponding to the chapter identifier, and the method further includes:

[0014] Obtaining the auxiliary course corresponding to the chapter identifier, wherein the auxiliary course includes a visualized knowledge relationship graph corresponding to the chapter identifier, or an explanation video of the course content corresponding to the chapter identifier;

[0015] The auxiliary course is returned to the terminal.

[0016] The auxiliary course includes a visualized knowledge relationship map corresponding to the chapter identifier, or an explanation video of the course content corresponding to the chapter identifier.

[0017] Optionally, in response to the search request, the content of the direct knowledge node corresponding to the user course question, the content of the related knowledge node and the first structured knowledge data are obtained, and the first structured knowledge data is used to reflect the relationship between the direct knowledge node and the related knowledge node, including:

[0018] Extracting keywords from the user's course question, searching for knowledge nodes identical to the keywords from pre-constructed second structured knowledge data, and obtaining direct knowledge nodes, wherein the pre-constructed second structured knowledge data includes knowledge nodes of the original course and the relationships between the knowledge nodes, and the original course is a course for which an auxiliary course needs to be constructed;

[0019] Querying and acquiring relevant knowledge nodes that are associated with the direct knowledge nodes from the pre-constructed second structured knowledge data;

[0020] Acquire the content of the direct knowledge node and the content of the related knowledge node from a knowledge node database;

[0021] The first structured knowledge data between the direct knowledge node and the related knowledge node is obtained from the pre-constructed second structured knowledge data.

[0022] Optionally, the second structured knowledge data is constructed in advance by the following method:

[0023] Acquire a knowledge node of an original course, where the original course is a course for which second structured knowledge data needs to be constructed;

[0024] The relationship between the knowledge nodes of each of the original courses is determined and represented in the form of second structured knowledge data.

[0025] Optionally, determining the relationship between the knowledge nodes of each of the original courses and expressing it in the form of second structured knowledge data includes:

[0026] Representing each knowledge node of the original course based on symbols or vectors;

[0027] Establishing a knowledge node model for each knowledge node of the original course;

[0028] The second structured knowledge data is obtained by extracting, integrating, and reasoning and calculating each knowledge node and knowledge node model of the original course.

[0029] Optionally, the extraction includes extracting the concept of each knowledge node, the attributes of each knowledge node, and the relationship between each knowledge node; the fusion includes fusion of the concept layer and fusion of the data layer; the calculation includes determining the position of each knowledge node in the structured graph to form the second structured knowledge data.

[0030] Optionally, after obtaining the knowledge nodes of the original course, the method further includes:

[0031] The acquired knowledge nodes of the original course are stored in a table structure storage method or a graph structure storage method.

[0032] Optionally, after returning the auxiliary course to the terminal, the method further comprises: generating a recommended learning path based on the chapter identifier and the auxiliary course corresponding to the chapter identifier.

[0033] Optionally, after obtaining the content of the direct knowledge node corresponding to the user course question, the content of the related knowledge node and the first structured knowledge data, the method further includes:

[0034] Auralize the content of the direct knowledge point, the content of the related knowledge point, and the first structured knowledge data to obtain an auralized knowledge relationship graph;

[0035] The sonifiable knowledge relationship graph is sent to a terminal so that the terminal plays the sonifiable knowledge relationship graph through an audio player, thereby assisting user learning.

[0036] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the methods described in the first aspect.

[0037] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect.

[0038] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0039] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0040] The course content assisted learning method provided by the embodiment of the present application, when replying to the user's course questions, not only returns the direct knowledge node corresponding to the user's course question, but also includes the related knowledge nodes related to the direct knowledge node, as well as the structured relationship between the direct knowledge node and the related knowledge node, so as to systematically explain the course question, so that the user can fully understand the user's course question and improve learning efficiency. With the existing AI learning technology, each knowledge point runs independently; therefore, compared with the existing technology, the present application improves the systematic display of knowledge nodes. When the user is unclear about a certain knowledge node, the relevant knowledge nodes of the unclear knowledge and the relationship between each knowledge node can be obtained at the same time, so as to systematically understand the unclear knowledge node and the related knowledge nodes, thereby improving learning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] 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 will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 This is an application environment diagram of a course content assisted learning method in one embodiment of the present application;

[0043] Figure 2 A flow chart of a course content assisted learning method provided in one embodiment of the present application;

[0044] Figure 3 A schematic diagram of a returned visualized knowledge relationship graph provided in an embodiment of the present application;

[0045] Figure 4 A flowchart of a recommended learning path provided in another embodiment of the present application;

[0046] Figure 5 A schematic diagram of a process for obtaining first structured knowledge data provided by an embodiment of the present application;

[0047] Figure 6 A schematic diagram of a process for generating second structured knowledge data provided by another embodiment of the present application;

[0048] Figure 7 A schematic diagram of a process for generating second structured knowledge data provided by another embodiment of the present application;

[0049] Figure 8 A logical schematic diagram for generating second structured knowledge data provided by another embodiment of the present application;

[0050] Fig. 9 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0053] The course content assisted learning method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the application environment includes a terminal and a server. The terminal communicates with the server through a network. The data storage system can store data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. The terminal is installed with a client corresponding to the course-assisted learning method. After starting the client, a search request can be sent to the server. After receiving the search request, the server obtains the user course question carried by the search request, and searches for the direct knowledge nodes, related knowledge nodes, and structured relationships between the direct knowledge nodes and related knowledge nodes corresponding to the user course question, and visualizes the direct knowledge nodes, related knowledge nodes, and structured relationships, and returns them to the terminal. After receiving the visualized reply, the terminal presents the visualized knowledge relationship map to assist user learning.

[0054] In addition, in some embodiments, the course content assisted learning method can also be implemented by a server or a terminal alone.

[0055] The terminals may be, but are not limited to, various desktop computers, laptops, smart phones, tablet computers, IoT devices and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server may be implemented as an independent server or a server cluster consisting of multiple servers, or a cloud server.

[0056] In an exemplary embodiment, Figure 2 As shown, a course content assisted learning method is provided, which is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server in the example is used to illustrate, including the following steps 201 to 204:

[0057] Step 201: receiving a search request input by a user through a user interface, wherein the search request carries a user course question.

[0058] Among them, the search request is used to request the server to answer the user's course questions.

[0059] Among them, user course problems are problems encountered by users in the process of learning course content. Furthermore, the course content to be learned can be a textbook, such as Advanced Mathematics (Part 1), and of course it can also be a textbook for primary and secondary school students. This application does not make any restrictions here.

[0060] The search request may be a semantic search, a knowledge question, an evolutionary analysis, a dialogue understanding or other search requests.

[0061] Step 202, in response to the search request, obtain the content of the direct knowledge node corresponding to the user course question, the content of the related knowledge node and the first structured knowledge data, wherein the first structured knowledge data is used to reflect the relationship between the direct knowledge node and the related knowledge node.

[0062] Among them, the knowledge node is the name of the knowledge point obtained from the existing knowledge. For example, the knowledge node can be a proper noun such as triangle, derivative, inverse, etc., and of course it can also be a custom name determined by human experience. Among them, the direct knowledge node is a knowledge node used to directly answer the user's course questions, and the direct knowledge node can include multiple knowledge nodes. The related knowledge node is a knowledge node that has an associated relationship with the direct knowledge node, and can also include multiple knowledge nodes.

[0063] For example, Figure 3 As shown, derivatives and differentials can be regarded as direct knowledge nodes, while other knowledge nodes distributed around them can be regarded as related knowledge nodes. Figure 3 Derivatives, differentials, applications of derivatives, etc.

[0064] The first structured knowledge data may be relationship data between any two or more knowledge nodes that have any association relationship.

[0065] Step 203: Visualize the content of the direct knowledge point, the content of the related knowledge point, and the first structured knowledge data to obtain a visualized knowledge relationship graph.

[0066] The visualization processing may be video processing. After the structured relationship is visualized, the user can directly watch the reply returned by the server, so that the user can understand the knowledge more intuitively.

[0067] Step 204: sending the visualized knowledge relationship graph to a terminal so that the terminal presents the visualized knowledge relationship graph through the user interface, thereby assisting user learning.

[0068] When the reply content is presented through the user interface, it can include three parts. The first part is the content of the direct knowledge node, the second part is the related knowledge node, and the third part is the first structured knowledge data, so that the user can have a deeper understanding of the user's course questions.

[0069] After the terminal presents the visualized knowledge relationship map through the user interface, the user can view the visualized knowledge relationship map to understand the reply content.

[0070] From the above content, we can see that semantic search, knowledge questions, evolutionary analysis, dialogue understanding, etc. can be performed through the user interface.

[0071] Furthermore, when the user's search request is derivative and differential, the visual knowledge relationship graph is Figure 3 As shown, we can clearly get the relationship between the derivative and the differential. Figure 3 For an introduction to each knowledge node in Figure 3 That’s it, and when you click on each knowledge node, the content of each knowledge node can be displayed.

[0072] Among them, the visualized knowledge relationship map can be a visualized knowledge relationship map, which can display a complete and clear knowledge system, so that students can easily view the position of the current knowledge nodes in the entire knowledge system, and effectively help students to conduct serial learning of knowledge nodes and fill in the gaps.

[0073] The course content assisted learning method provided by the embodiment of the present application, when replying to the user's course questions, not only returns the direct knowledge node corresponding to the user's course question, but also includes the related knowledge nodes related to the direct knowledge node, as well as the structured relationship between the direct knowledge node and the related knowledge node, so as to systematically explain the course question, so that the user can fully understand the user's course question and improve learning efficiency. With the existing AI learning technology, each knowledge point runs independently; therefore, compared with the existing technology, the present application improves the systematic display of knowledge nodes. When the user is unclear about a certain knowledge node, the relevant knowledge nodes of the unclear knowledge and the relationship between each knowledge node can be obtained at the same time, so as to systematically understand the unclear knowledge node and the related knowledge nodes, thereby improving learning efficiency.

[0074] In addition, for the prior art, only replies are given to user course questions. Then, when new questions arise after reading the replies, the user course questions need to be input again, which is a cumbersome process and inefficient. However, for the present application, not only the direct knowledge nodes of the current user course questions are given, but also the related knowledge nodes, as well as the structured relationship between the direct knowledge nodes and the related knowledge nodes, so that the user can systematically learn the user course questions at one time. Even if new user course questions arise, they can be answered through the structured relationship, so that not only the user customer questions can be deeply understood, but also the number of times the user inputs the user course questions can be reduced, thereby improving learning efficiency.

[0075] Optionally, the user course question includes a chapter identifier, and the search request is further used to request to return an auxiliary course corresponding to the chapter identifier. The method further includes:

[0076] Obtaining the auxiliary course corresponding to the chapter identifier, wherein the auxiliary course includes a visualized knowledge relationship graph corresponding to the chapter identifier, or an explanation video of the course content corresponding to the chapter identifier;

[0077] The auxiliary course is returned to the terminal.

[0078] For each chapter, a visual knowledge relationship map is generated in advance based on the content of the chapter, and explanation videos are provided, which can be obtained in a timely manner when needed.

[0079] Through the above method, users can directly watch the visual knowledge relationship map or explanation video corresponding to the chapter mark, making it easier for users to understand the course content.

[0080] Furthermore, if Figure 4 As shown, after the user actively watches the content of a certain chapter, the next chapter mark for recommended learning can be generated based on the chapter mark and the auxiliary course corresponding to the chapter mark, and then the next chapter mark for recommended learning can be generated based on the next chapter mark and the auxiliary course, thereby forming a recommended learning path step by step to form feedback and gradually guide the user to complete the learning content. Therefore, the method also includes: generating a recommended learning path based on the chapter mark and the auxiliary course corresponding to the chapter mark.

[0081] Furthermore, after the user starts learning, the user's learning status can also be recorded, for example, see Figure 4 This application is equipped with a knowledge graph statistical analysis, which includes an average completion rate of 73.8%, ranking, the highest completion rate of 50%, and the lowest completion rate of 23.8%; further, it also shows the user's learning path and the learning progress of each path, for example, the progress of arrays and generalized tables is 70%. The other completion rate data shown in the figure are not explained one by one.

[0082] From the above content, it can be seen that this application can be a hybrid learning of chapter video mode and graph mode. Users can switch freely between chapter videos and course graphs to choose different learning paths. The chapter mode allows students to learn the course videos of each chapter coherently and complete the chapter test; while the graph mode focuses on starting from the overall structure, exploring the relationship between each knowledge point and learning the resources corresponding to the knowledge point, so as to deepen the understanding of knowledge and improve learning efficiency.

[0083] Optionally, after step 202, the method further includes the following steps:

[0084] Step 1: sonification processing the content of the direct knowledge point, the content of the related knowledge point, and the first structured knowledge data to obtain an sonification knowledge relationship graph;

[0085] Step 2: Send the sonifiable knowledge relationship graph to the terminal, so that the terminal plays the sonifiable knowledge relationship graph through an audio player, thereby assisting the user in learning.

[0086] Alternatively, see Figure 5 In another exemplary embodiment of the present application, step 202 may be replaced by the following steps 301 to 304:

[0087] Step 301, extract the keywords in the user course question, query the knowledge nodes identical to the keywords from the pre-constructed second structured knowledge data, and obtain direct knowledge nodes. The pre-constructed second structured knowledge data includes the knowledge nodes of the original course and the relationship between the knowledge nodes. The original course is a course for which an auxiliary course needs to be constructed.

[0088] Each knowledge node is configured in the second structured knowledge data according to the content of the knowledge node.

[0089] Exemplarily, the user's course question is "What is a triangle?", and the extracted keywords are "what" and "triangle", then a knowledge node is found in the second structured knowledge data, and the name of the knowledge node is "triangle", and the content corresponding to the knowledge node is returned to the terminal.

[0090] Step 302: query and obtain relevant knowledge nodes that are associated with the direct knowledge nodes from the pre-constructed second structured knowledge data.

[0091] For example, relevant knowledge nodes associated with "triangle" include "line segment", "plane" and "non-collinear".

[0092] Step 303: Acquire the content of the direct knowledge node and the content of the related knowledge node from the knowledge node database.

[0093] The knowledge node data stores the content of each knowledge node, and of course also stores the content of the above-mentioned direct knowledge nodes and the content of related knowledge nodes.

[0094] Step 304: Acquire the first structured knowledge data between the direct knowledge node and the related knowledge node from the pre-constructed second structured knowledge data.

[0095] From the above content, it can be seen that the present application embodies knowledge through the second structured knowledge data, which improves the systematic nature of knowledge. When the user is unclear about a certain knowledge, he can simultaneously obtain the related knowledge of the unclear knowledge and the relationship between the knowledge nodes, so as to systematically understand the related knowledge of the unclear knowledge, thereby improving learning efficiency.

[0096] Alternatively, see Figure 6 , and combined with Figure 8In another exemplary embodiment of the present application, the second structured knowledge data is constructed in advance through the following steps 401 to 402:

[0097] Step 401, obtaining knowledge nodes of an original course, where the original course is a course for which second structured knowledge data needs to be constructed.

[0098] The original course may be a textbook, for example, Advanced Mathematics (Volume 1).

[0099] The original course can be an electronic version of the textbook stored in the database, or it can be a course manually input into the server by the staff.

[0100] Furthermore, knowledge nodes can also be obtained through the following data acquisition tools: knowledge representation tools, knowledge storage tools, ontology construction tools, knowledge calculation tools and basic development platforms. For how each acquisition tool acquires knowledge nodes, please refer to the relevant prior art, and this application will not elaborate on this.

[0101] Furthermore, each acquired knowledge node needs to be tested by a first test evaluation tool to determine whether the acquired knowledge node has any error.

[0102] Exemplarily, the acquired knowledge node is "one variable two squares", which is modified into "one variable quadratic equation" through the first test evaluation tool.

[0103] The knowledge nodes of the original course include every knowledge node in the original course.

[0104] Step 402: determine the relationship between the knowledge nodes of each of the original courses and represent it in the form of second structured knowledge data.

[0105] It can be seen from the above content that the constructed second structured knowledge data includes the knowledge nodes of the original course and the relationship between the knowledge nodes.

[0106] Furthermore, the newly added knowledge nodes can be timely acquired through the above-mentioned knowledge acquisition tool, and step 402 is re-executed to update the second structured knowledge data.

[0107] Similarly, for chapter videos, after adding new knowledge nodes, the relevant videos of the newly added knowledge nodes can be updated in a timely manner to maintain the timeliness and relevance of the course content.

[0108] In the process of building the second structured knowledge data, a graph database (such as Neo4j) can be used to build the second structured knowledge data, define the types of knowledge nodes (such as the concepts, themes, skills, courses, key points, test points, difficult points, ideological and political points, easy-to-make mistakes, cases, etc.) and their relationships (such as "sub-level", "association", "pre-knowledge", "pre- and post-position", etc.). Furthermore, each knowledge node can be accompanied by attributes, such as knowledge point description, label, difficulty, suitable grade, etc.

[0109] Alternatively, see Figure 7 , and combined with Figure 8 In another exemplary embodiment of the present application, step 402 includes the following steps 501-503

[0110] Step 501, representing each knowledge node of the original course based on symbols or vectors.

[0111] For example, for the knowledge node of "inverse", it can be represented by a symbol, for example, the inverse of n is 1 / n; and for knowledge nodes with learning order requirements, they can be represented by vectors, such as line segments and triangles. Since the premise of understanding triangles is to understand line segments, the vector composed of the two knowledge nodes of line segment and triangle is the line segment pointing to the triangle.

[0112] Step 502: Establish a knowledge node model for each knowledge node of the original course.

[0113] The knowledge node model includes the relationship between knowledge nodes, event model, rule model and space-time model.

[0114] Exemplarily, for two knowledge nodes that have event models, an event model can be established, for example, knowledge node n and knowledge node 1 / n, the established event model is: n, 1 / n, inverse relationship.

[0115] For other models, the parameters are related to the existing technology, which will not be described in detail in this application.

[0116] Step 503, obtaining the second structured knowledge data by extracting, integrating, and reasoning and calculating each knowledge node and knowledge node model of the original course.

[0117] Optionally, the extraction includes extracting the concept of each knowledge node, the attributes of each knowledge node, and the relationship between each knowledge node; the fusion includes the fusion of the concept layer and the fusion of the data layer; the calculation includes determining the position of each knowledge node in the structured graph to form the second structured knowledge data.

[0118] Among them, calculation can be understood as reasoning, statistics, graph mining and other processing on each knowledge node. For related content, please refer to the existing technology, and this application will not elaborate on it here.

[0119] Optionally, in another exemplary embodiment of the present application, after step 401, the method further includes the following step 601:

[0120] Step 601: store the acquired knowledge nodes of the original course in a table structure storage manner or a graph structure storage manner.

[0121] The storage method used for a knowledge node can be determined based on the properties of the knowledge node itself.

[0122] Furthermore, after generating the second structured knowledge data, the method further includes the following method:

[0123] Based on the second structured knowledge data and combined with the course outline to analyze the composition content of the knowledge nodes and their interrelationships, the course outline and module structure are automatically generated.

[0124] The method also includes developing a web-based visualization tool that allows teachers to intuitively view the second structured knowledge data and edit and adjust the specific content of the second structured knowledge data. For example, teachers can add, delete and link knowledge nodes to ensure the logic and coherence of the course.

[0125] The method also includes a personalized recommendation algorithm: by performing a profile analysis on the user, such as the user's interests, basic knowledge level and learning habits, combined with the relationship in the second structured knowledge data, a combination of collaborative filtering and content recommendation is used to recommend personalized learning paths for users.

[0126] The method also includes a real-time feedback mechanism: during the learning process, the system will adjust the recommended path in real time based on the user's performance, such as test scores, study time, etc., to achieve dynamic personalized learning.

[0127] The method also includes a feedback and evaluation module:

[0128] Second, dynamic update of structured knowledge data: Based on feedback data and evaluation results, the knowledge nodes and relationships in the knowledge graph are dynamically adjusted to ensure that the knowledge graph can reflect the latest educational needs and trends.

[0129] Effectiveness evaluation report: Generate a course effectiveness evaluation report to help educators understand the effectiveness of the course and provide data support for the optimization of subsequent courses.

[0130] The solution proposed in this application brings an innovative course construction and application method to the education field by combining the second structured knowledge data technology and AI algorithm. The core advantage of this method is that it can provide a highly personalized learning experience while keeping the course content dynamically updated and optimized to adapt to the rapidly changing educational needs and knowledge updates, and to meet the needs of modern education.

[0131] Furthermore, for each of the above operations, a test evaluation tool is provided to test whether the result obtained after the evaluation operation is correct, such as the first test evaluation tool and Figure 8 The second test in the evaluation tool.

[0132] Furthermore, for each of the above operations, safety and protection tools are provided to ensure that each operation is safe.

[0133] Furthermore, for each of the above operations, an operation and maintenance tool is provided to provide timely maintenance when an operation fails.

[0134] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data for course content-assisted learning. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a course content-assisted learning method is implemented.

[0135] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0136] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0137] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0138] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0139] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0140] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0141] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0142] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0143] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A course content-assisted learning method, characterized in that: include: Receiving a search request input by a user through a user interface, wherein the search request carries a user course question; In response to the search request, the content of the direct knowledge node corresponding to the user course question, the content of the related knowledge node and the first structured knowledge data are obtained, where the first structured knowledge data is used to reflect the relationship between the direct knowledge node and the related knowledge node; Visually processing the content of the direct knowledge point, the content of the related knowledge point, and the first structured knowledge data to obtain a visualized knowledge relationship graph; The visualized knowledge relationship graph is sent to a terminal so that the terminal presents the visualized knowledge relationship graph through the user interface, thereby assisting user learning.

2. The course content-assisted learning method according to claim 1, characterized in that: The user course question includes a chapter identifier, and the search request is also used to request to return the auxiliary course corresponding to the chapter identifier. The method further includes: Obtaining the auxiliary course corresponding to the chapter identifier, wherein the auxiliary course includes a visualized knowledge relationship graph corresponding to the chapter identifier, or an explanation video of the course content corresponding to the chapter identifier; The auxiliary course is returned to the terminal.

3. The course content-assisted learning method according to claim 1, characterized in that: In response to the search request, the content of the direct knowledge node corresponding to the user course question, the content of the related knowledge node and the first structured knowledge data are obtained, and the first structured knowledge data is used to reflect the relationship between the direct knowledge node and the related knowledge node, including: Extracting keywords from the user's course question, searching for knowledge nodes identical to the keywords from pre-constructed second structured knowledge data, and obtaining direct knowledge nodes, wherein the pre-constructed second structured knowledge data includes knowledge nodes of the original course and the relationships between the knowledge nodes, and the original course is a course for which an auxiliary course needs to be constructed; Querying and acquiring relevant knowledge nodes that are associated with the direct knowledge nodes from the pre-constructed second structured knowledge data; Acquire the content of the direct knowledge node and the content of the related knowledge node from a knowledge node database; The first structured knowledge data between the direct knowledge node and the related knowledge node is obtained from the pre-constructed second structured knowledge data.

4. The course content-assisted learning method according to claim 3, characterized in that: The second structured knowledge data is constructed in advance by the following method: Acquire a knowledge node of an original course, where the original course is a course for which second structured knowledge data needs to be constructed; The relationship between the knowledge nodes of each of the original courses is determined and represented in the form of second structured knowledge data.

5. The course content-assisted learning method according to claim 4, characterized in that: The determining of the relationship between the knowledge nodes of each of the original courses and expressing it in the form of second structured knowledge data includes: Representing each knowledge node of the original course based on symbols or vectors; Establishing a knowledge node model for each knowledge node of the original course; The second structured knowledge data is obtained by extracting, integrating, and reasoning and calculating each knowledge node and knowledge node model of the original course.

6. The course content-assisted learning method according to claim 2, characterized in that: After returning the auxiliary course to the terminal, the method further includes: generating a recommended learning path based on the chapter identifier and the auxiliary course corresponding to the chapter identifier.

7. The course content-assisted learning method according to any one of claims 1 to 6, characterized in that: After obtaining the content of the direct knowledge node corresponding to the user course question, the content of the related knowledge node and the first structured knowledge data, the method further includes: Auralize the content of the direct knowledge point, the content of the related knowledge point, and the first structured knowledge data to obtain an auralized knowledge relationship graph; The sonifiable knowledge relationship graph is sent to a terminal so that the terminal plays the sonifiable knowledge relationship graph through an audio player, thereby assisting user learning.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the course content assisted learning method described in any one of claims 1 to 7.

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 course content-assisted learning method described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the course content-assisted learning method described in any one of claims 1 to 7 is implemented.

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