Learning method and device for learning materials

Abstract

The embodiment of the invention provides a learning method and device for learning materials. The method comprises the steps that a user interaction interface is provided; wherein the user interaction interface is used for guiding a user to input a learning target for learning materials in at least one round in combination with a preset language processing model; generating an intelligent learning plan for the learning material according to the learning target; and in the process that the user learns the learning materials according to the intelligent learning plan, adjusting the intelligent learning plan according to a preset forgetting curve rule. According to the embodiment of the invention, an interactive learning environment is provided for the user, the user obtains intelligent guidance, a personalized intelligent learning plan and other services through the user interaction interface, the intelligent learning plan can be dynamically adjusted according to the forgetting curve rule, the user is prompted to review in time, the memory effect is prolonged to the greatest extent, and the user experience is improved. Therefore, the learning efficiency of the user on the learning materials can be improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of computer technology, and in particular, to a learning method for learning learning materials, a learning device for learning learning materials, an electronic device, and a computer-readable storage medium. Background Art

[0002] In the study of traditional learning materials, users acquire knowledge through reading, but due to various reasons, the learning effect of the learning materials is often not ideal.

[0003] Therefore, it is urgent to propose a learning method for learning materials to improve users' learning effects. Summary of the invention

[0004] In view of the above problems, a method and device for learning learning materials are proposed to overcome the above problems or at least partially solve the above problems. The specific technical solution is as follows:

[0005] In a first aspect of the present invention, a method for learning learning materials is provided, the method comprising:

[0006] Providing a user interaction interface; wherein the user interaction interface is used to guide the user to input a learning goal for the learning material at least once in combination with a preset language processing model;

[0007] Generating an intelligent learning plan for the learning material according to the learning goal;

[0008] When the user studies the learning material according to the intelligent learning plan, the intelligent learning plan is adjusted according to a preset forgetting curve rule.

[0009] In one embodiment of the present invention, when the user learns the learning material according to the intelligent learning plan, adjusting the intelligent learning plan according to a preset forgetting curve rule includes:

[0010] During the process of the user learning the learning material according to the intelligent learning plan, monitoring the learning progress information of the user based on the intelligent learning plan;

[0011] The intelligent learning plan is adjusted according to a preset forgetting curve rule and / or the learning progress information; the intelligent learning plan at least includes a learning plan for reviewing the learning material.

[0012] In one embodiment of the present invention, the method further comprises:

[0013] The learning data is input into the language processing model; the language processing model is used to output key information corresponding to the learning data; the language processing model is trained according to the training learning data;

[0014] The key information corresponding to the learning material is displayed in the user interaction interface to guide the user to learn the learning material.

[0015] In one embodiment of the present invention, the method further comprises:

[0016] The learning material is input into the language processing model; the language processing model is used to output intelligent questions and answers corresponding to the learning material, and the language processing model is trained according to the training learning material and the interactive feedback information of the user on the training learning material;

[0017] The intelligent questions and answers corresponding to the learning materials are displayed in the user interaction interface to guide the user to learn the learning materials.

[0018] In one embodiment of the present invention, the method further comprises:

[0019] Generating test questions for the user according to the learning materials and the learning progress information;

[0020] The test questions are displayed in the user interaction interface to guide the user to review the learning materials.

[0021] In one embodiment of the present invention, the method further comprises:

[0022] The learning material is input into the language processing model, and the language processing model is used to output the extended knowledge points corresponding to the learning material. The language processing model is trained according to the training learning material.

[0023] In an embodiment of the present invention, the learning materials include at least documents, videos and images.

[0024] The embodiment of the present invention further discloses a learning device for learning learning materials, the device comprising:

[0025] A user interaction interface display module, used to provide a user interaction interface; wherein the user interaction interface is used to guide the user to input a learning goal for the learning material at least once in combination with a preset language processing model;

[0026] An intelligent learning plan generation module, used to generate an intelligent learning plan for the learning material according to the learning goal;

[0027] The intelligent learning plan adjustment module is used to adjust the intelligent learning plan according to a preset forgetting curve rule when the user learns the learning material according to the intelligent learning plan.

[0028] In another aspect of the present invention, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, and when the computer-readable storage medium is run on a computer, the computer executes any of the above-mentioned learning methods for the learning materials.

[0029] In another aspect of the present invention, a computer program product comprising instructions is provided, which, when executed on a computer, enables the computer to execute any of the above-mentioned learning methods for learning learning materials.

[0030] Compared with the related art, the embodiments of the present invention have at least the following advantages:

[0031] In an embodiment of the present invention, a user interaction interface is provided, wherein the user interaction interface is used to guide the user to input a learning goal for the learning material in combination with a preset language processing model for at least one round, and generate an intelligent learning plan for the learning material according to the learning goal. Then, in the process of the user learning the learning material according to the intelligent learning plan, the intelligent learning plan can be adjusted according to the preset forgetting curve rule for the user. The embodiment of the present invention provides an interactive learning environment for users, and users obtain services such as intelligent guidance and personalized intelligent learning plans through the user interaction interface. In addition, the intelligent learning plan can be dynamically adjusted according to the forgetting curve rule, and the user can be reminded to review in time to maximize the memory effect, thereby improving the user's learning efficiency of the learning material. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art.

[0033] Figure 1 A schematic diagram of an application environment provided in an embodiment of the present invention;

[0034] Figure 2 A flowchart of a method for learning learning materials provided in an embodiment of the present invention;

[0035] Figure 3 A schematic diagram of a flow chart of interactive intelligent document learning provided in an embodiment of the present invention;

[0036] Figure 4 A structural block diagram of a learning device for learning learning materials provided in an embodiment of the present invention;

[0037] Figure 5 The present invention is a block diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0039] Currently, intelligent document software can combine search and knowledge graph extraction functions to support users in finding targeted learning materials. However, when users fail to identify knowledge gaps or are unclear about how to search for learning materials in a targeted manner, their learning efficiency is low.

[0040] In view of the above problems, the embodiment of the present invention provides an intelligent, interactive and personalized document learning method, which makes full use of agent technologies such as artificial intelligence (AI), natural language processing (NLP) and large language model (LLM), and combines agent technology and forgetting curve theory to guide user learning. The agent module based on agent technology in the embodiment of the present invention can be carefully designed based on user needs and the characteristics of learning materials, covering key features such as language expression ability and guidance methods. By designing an intelligent agent module, the system optimizes the user's learning experience of learning materials, provides accurate learning guidance and personalized learning plans, so as to effectively improve learning effects and knowledge retention. Therefore, the embodiment of the present invention can promote the learning of learning materials to develop in a more intelligent and personalized direction, meet the differentiated learning needs of users, and bring new development opportunities to the field of intelligent education technology.

[0041] The learning method of learning materials provided by the embodiment of the present invention can be applied to Figure 1 In the application environment shown. Among them, the client 101 communicates with the server 102 through the network through the network. Among them, the client 101 can be but not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices, and the server 102 can be implemented with an independent server or a server cluster composed of multiple servers. Among them, the client 101 can be a user interaction interface to guide the user to input learning goals for the learning materials. It should be supplemented that the embodiment of the present invention can be implemented in the client 101 alone, or in a system composed of the client 101 and the server 102, and the embodiment of the present invention does not need to be limited to this.

[0042] Reference Figure 2 , is a flow chart of the steps of a learning method for learning learning materials provided in an embodiment of the present invention, such as Figure 1 As shown, the method may specifically include the following steps:

[0043] Step 201, providing a user interaction interface; wherein the user interaction interface is used to guide the user to input a learning goal for the learning material at least once in combination with a preset language processing model;

[0044] In an embodiment of the present invention, the learning materials (materials) may at least include documents, videos, images, etc. For ease of description, the following mainly uses documents as an example for description.

[0045] The learning goal of the learning material may be a specific goal that the user hopes to achieve when studying the document or other learning material, wherein the learning goal may be personalized according to the user's needs. For example, assuming that the document that the user needs to study is a textbook, the learning goal may be the specific chapter of the textbook that the user hopes to master, the time to complete the study, the time the user can study, the degree of mastery, the content to be mastered, the expected score for the exam, the learning method (such as doing exercises, reading, discussion or practice), etc.

[0046] In a specific example of the present invention, the Agent module implemented by Agent technology helps users set clear learning goals through intelligent guidance, deeply understand the content of documents, and provide personalized intelligent learning plans (personalized learning schedules), making the learning process more accurate and targeted. Among them, the Agent module can include a language processing model (large language model).

[0047] In an embodiment of the present invention, when a user needs learning materials, a highly optimized interface for interaction between the user and the system (server and client), namely a user interaction interface (real-time interaction interface), is provided. The user interaction interface has functions such as learning goal setting and learning material browsing, wherein the user interaction interface is used to guide the user to input learning goals for the learning materials in combination with a preset language processing model (NLP model) for at least one round. Specifically, in an embodiment of the present invention, on the user interaction interface, the user can conduct multiple rounds of dialogues to fill in the slots of the large language model to guide the user to set personalized learning goals, thereby improving user participation. Of course, the learning goals of the embodiment of the present invention can also be generated automatically based on the user's registered user information, and the embodiment of the present invention does not need to be limited to this.

[0048] Step 202: Generate an intelligent learning plan for the learning material according to the learning goal;

[0049] Among them, the intelligent learning plan is based on the learning goals set by users based on the user interaction interface, combined with the content of the learning materials and other information such as user data, to generate a personalized learning path (the learning path may include the order of learning the content of the learning materials), time arrangement, question-solving method, questions, etc. Based on the intelligent learning plan, users can achieve their learning goals efficiently and improve their learning effects on the learning materials.

[0050] Step 203: When the user is learning the learning material according to the intelligent learning plan, the intelligent learning plan is adjusted according to a preset forgetting curve rule.

[0051] Among them, the preset forgetting curve rule can be generated for user personalization based on the forgetting curve theory. The forgetting curve theory describes the law of human brain forgetting new things, and the human brain forgets new things step by step. People can master the forgetting law from the forgetting curve and use it to improve their self-memory ability. Specifically, the forgetting curve theory tells people that forgetting in learning is regular, the process of forgetting is very fast, and it is fast at first and then slow. Through multiple observations, it is found that if the learned learning materials are not reviewed in time after one day, only 33.7% of the original ones will be left. As time goes by, the speed of forgetting slows down, and the amount of forgetting is reduced. Therefore, in the embodiment of the present invention, the characteristics of the forgetting curve theory can be used to formulate and adjust the user's intelligent learning plan for learning materials, thereby improving the user's learning efficiency for learning materials. For example, the first review is arranged within 24 hours after the user's first learning of the material to consolidate the memory of the learning material that has been learned, and the subsequent review of the learning material is reminded at key time points such as 1 day, 3 days, and 7 days to prevent short-term forgetting and prolong the persistence of the memory of the content of the learned learning material.

[0052] In the above-mentioned learning method of learning materials, a user interaction interface is provided, wherein the user interaction interface is used to guide the user to input the learning goal for the learning material in combination with the preset language processing model for at least one round, and generate an intelligent learning plan for the learning material according to the learning goal, and then, in the process of the user learning the learning material according to the intelligent learning plan, the intelligent learning plan can be adjusted according to the preset forgetting curve rule for the user. The embodiment of the present invention provides an interactive learning environment for users, and users obtain services such as intelligent guidance and personalized intelligent learning plans through the user interaction interface. In addition, the intelligent learning plan can be dynamically adjusted according to the forgetting curve rule, and the user can be reminded to review in time to maximize the memory effect, thereby improving the user's learning efficiency of the learning material.

[0053] In one embodiment of the present invention, when the user learns the learning material according to the intelligent learning plan, adjusting the intelligent learning plan according to a preset forgetting curve rule includes:

[0054] During the process of the user learning the learning material according to the intelligent learning plan, monitoring the learning progress information of the user based on the intelligent learning plan;

[0055] The intelligent learning plan is adjusted according to a preset forgetting curve rule and / or the learning progress information; the intelligent learning plan at least includes a learning plan for reviewing the learning material.

[0056] In an embodiment of the present invention, in the process of a user learning materials based on an intelligent learning plan through a user interaction interface, the user's learning progress information based on the intelligent learning plan can be monitored in real time, for example, the chapters of the textbook that the user has learned, the number of times the chapter of the textbook has been studied, the learning time, etc. Then, the intelligent learning plan can be adjusted according to the preset forgetting curve rules, or the intelligent learning plan can be adjusted according to the learning progress information, or the intelligent learning plan can be adjusted according to the preset forgetting curve rules and the learning progress information, so as to continuously optimize the user's learning experience during the user's learning process, and promptly remind the user to review, so as to maximize the memory effect.

[0057] In one embodiment of the present invention, the method may further include:

[0058] The learning data is input into the language processing model; the language processing model is used to output key information corresponding to the learning data; the language processing model is trained according to the training learning data;

[0059] The key information corresponding to the learning material is displayed in the user interaction interface to guide the user to learn the learning material.

[0060] Among them, a large language model (language processing model) can be obtained by training with a large amount of training learning materials as samples.

[0061] In an embodiment of the present invention, based on a large language model, in-depth understanding of learning materials and information extraction can be achieved. Specifically, the user's learning materials can be input into a large language model. The learning materials can be text, pictures, audio or video, etc. The large language model can output the key information extracted from the learning materials and display it to the user in the user interaction interface for the user's reference and learning. Specifically, the output key information can be displayed in the form of text, charts, formulas, etc., which is convenient for users to quickly understand. Furthermore, when the user views the key information of the learning materials through the user interaction interface, the user can also further search for the key information and filter the key information with a smaller range, thereby improving the user's learning efficiency of the learning materials.

[0062] In one embodiment of the present invention, the method may further include:

[0063] The learning material is input into the language processing model; the language processing model is used to output intelligent questions and answers corresponding to the learning material, and the language processing model is trained according to the training learning material and the interactive feedback information of the user on the training learning material;

[0064] The intelligent questions and answers corresponding to the learning materials are displayed in the user interaction interface to guide the user to learn the learning materials.

[0065] Among them, the large language model (language processing model) can be obtained by obtaining a large amount of training learning materials and user interactive feedback information on the training learning materials as sample training. The interactive feedback information can be the results of the user's learning of the training learning materials, or the confusion of the learning materials fed back by the user during the learning process, or the learning experience of the learning materials, or the user information obtained based on the intelligent question-answering method, etc. In the embodiment of the present invention, more accurate and personalized learning support is provided by collecting user feedback and performing HFRL (human feedback reinforcement learning) iterative training. Specifically, HFRL is a technology that combines reinforcement learning with human feedback, in which human preferences are used as reward signals to guide the model to generate high-quality language output. RLHF can use the interactive feedback information of diversified users to help the model learn to generate texts that better represent different viewpoints, making it more general and effective in various contexts.

[0066] In an embodiment of the present invention, intelligent question and answer of learning materials can be realized based on a large language model. Specifically, the user's learning materials can be input into a large language model, and the learning materials can be text, pictures, audio or video, etc. The large language model can extract key information from the learning materials to generate intelligent questions and answers for the learning materials, and display them to the user in the user interaction interface, so that the user can learn based on the question and answer method, further improving the user's participation, and then better obtaining other information such as the user's learning progress information.

[0067] In one embodiment of the present invention, the method may further include:

[0068] Generating test questions for the user according to the learning materials and the learning progress information;

[0069] The test questions are displayed in the user interaction interface to guide the user to review the learning materials.

[0070] In an embodiment of the present invention, targeted test questions can be designed based on the content of the learning materials and the user's learning progress information on the learning materials, thereby implementing targeted Quiz, guiding the user to think deeply and review, and strengthen knowledge points. Specifically, Quiz is a form of test, which is usually used to evaluate the user's mastery of the learning materials (for example, based on the score obtained after all the test questions). Quiz can be composed of a series of test questions, and the user needs to answer these questions within a specified time. In addition, Quiz can be implemented in a variety of forms, such as multiple-choice questions, fill-in-the-blank questions, short-answer questions, true-or-false questions, etc. The specific form can be generated based on the learning goals input by the user in advance, or generated for a specific type of learning material.

[0071] In one embodiment of the present invention, the method may further include:

[0072] The learning material is input into the language processing model, and the language processing model is used to output the extended knowledge points corresponding to the learning material. The language processing model is trained according to the training learning material.

[0073] Among them, a large language model (language processing model) can be obtained by training with a large amount of training learning materials as samples.

[0074] In an embodiment of the present invention, based on a large language model, extended learning of learning materials can be achieved. Specifically, the user's learning materials can be input into a large language model. The learning materials can be text, pictures, audio or video, etc. The large language model can extract key information from the learning materials to generate extended knowledge points, and display them to the user in the user interaction interface for the user to study in combination with the learning materials. Specifically, the output extended knowledge points can be displayed in the form of text, charts, formulas, etc., which is convenient for users to quickly understand. Furthermore, when the user views the extended knowledge points of the learning materials through the user interaction interface, the user can also further search for the extended knowledge points and filter the extended knowledge points with a smaller range, thereby improving the user's learning efficiency of the extended knowledge points of the learning materials.

[0075] For example, refer to Figure 3 , is a flowchart of an interactive intelligent document learning provided in an embodiment of the present invention, 1. The user interacts with the real-time interactive interface provided by the system; 2. The NLU big model understands the user's needs and sets the Agent learning goals; 3. The multi-round dialogue big model guides the setting of learning goals and generates a personalized learning log (intelligent learning plan); 4. The algorithm and machine learning model are used to monitor the user's learning progress in real time; 5. The big language model collects learning feedback and adjusts the learning plan; 6. The forgetting curve theory is integrated to adjust the review time points in the learning plan.

[0076] In summary, the interactive intelligent document learning method based on Agent technology and forgetting curve provided by the embodiment of the present invention can have at least the following advantages:

[0077] 1) Intelligent guided learning: Using Agent technology, the system helps users set clear learning goals through intelligent guidance, deeply understand the content of learning materials such as documents, and provide personalized intelligent learning plans (personalized learning schedules), making the learning process more accurate and targeted.

[0078] 2) Forgetting curve management: Based on the forgetting curve theory, the system dynamically adjusts the learning plan, reminds users to review in time, and strengthens the persistent memory of knowledge points, thereby significantly improving learning results.

[0079] 2) Application of large language models: Combined with large language model technology, the system deeply understands the content of learning materials such as documents, achieves highly accurate information extraction and intelligent question answering, provides excellent learning support, and enables users to acquire high-quality knowledge.

[0080] 4) Personalized learning experience: Through an interactive learning environment, the system continuously optimizes the guidance method based on user feedback to achieve a personalized learning experience, meet the learning needs of different users, and improve user satisfaction in the learning process.

[0081] 5) Reinforcement learning optimization: The system collects interactive feedback information between users and the system. The system uses reinforcement learning and other technologies to continuously iterate and optimize the design of the Agent, improve the system's adaptability to user learning needs, and make the learning process more intelligent and flexible.

[0082] 6) Interactive question generation: Through the intelligent question generation agent, the system generates targeted questions according to the user's learning progress to achieve intelligent question and answer, guide users to think deeply and consolidate knowledge points, and improve learning efficiency and depth.

[0083] The advantages of the embodiments of the present invention described above enable the embodiments of the present invention to have more advanced and efficient technical features in the field of intelligent learning, provide users with a more intelligent and personalized learning experience, and further promote the development of intelligent education technology.

[0084] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0085] Reference Figure 4, is a structural block diagram of a learning device for learning materials provided in an embodiment of the present invention, such as Figure 4 As shown, the device may specifically include the following modules:

[0086] A user interaction interface display module 401 is used to provide a user interaction interface; wherein the user interaction interface is used to guide the user to input a learning goal for the learning material at least once in combination with a preset language processing model;

[0087] An intelligent learning plan generating module 402, configured to generate an intelligent learning plan for the learning material according to the learning goal;

[0088] The intelligent learning plan adjustment module 403 is used to adjust the intelligent learning plan according to a preset forgetting curve rule when the user learns the learning material according to the intelligent learning plan.

[0089] In one embodiment of the present invention, the intelligent learning plan adjustment module 403 is used to:

[0090] During the process of the user learning the learning material according to the intelligent learning plan, monitoring the learning progress information of the user based on the intelligent learning plan;

[0091] The intelligent learning plan is adjusted according to a preset forgetting curve rule and / or the learning progress information; the intelligent learning plan at least includes a learning plan for reviewing the learning material.

[0092] In one embodiment of the present invention, the device further comprises:

[0093] The learning data is input into the language processing model; the language processing model is used to output key information corresponding to the learning data; the language processing model is trained according to the training learning data;

[0094] The key information corresponding to the learning material is displayed in the user interaction interface to guide the user to learn the learning material.

[0095] In one embodiment of the present invention, the device further comprises: inputting the learning material into the language processing model; the language processing model is used to output intelligent questions and answers corresponding to the learning material, and the language processing model is trained according to the training learning material and the user's interactive feedback information on the training learning material;

[0096] The intelligent questions and answers corresponding to the learning materials are displayed in the user interaction interface to guide the user to learn the learning materials.

[0097] In one embodiment of the present invention, the device further comprises: generating a test question for the user according to the learning material and the learning progress information;

[0098] The test questions are displayed in the user interaction interface to guide the user to review the learning materials.

[0099] In one embodiment of the present invention, the device further includes: inputting the learning material into the language processing model, the language processing model is used to output extended knowledge points corresponding to the learning material, and the language processing model is trained according to the training learning material.

[0100] In an embodiment of the present invention, the learning materials include at least documents, videos and images.

[0101] In an embodiment of the present invention, a user interaction interface is provided, wherein the user interaction interface is used to guide the user to input a learning goal for the learning material in combination with a preset language processing model for at least one round, and generate an intelligent learning plan for the learning material according to the learning goal. Then, in the process of the user learning the learning material according to the intelligent learning plan, the intelligent learning plan can be adjusted according to the preset forgetting curve rule for the user. The embodiment of the present invention provides an interactive learning environment for users, and users obtain services such as intelligent guidance and personalized intelligent learning plans through the user interaction interface. In addition, the intelligent learning plan can be dynamically adjusted according to the forgetting curve rule, and the user can be reminded to review in time to maximize the memory effect, thereby improving the user's learning efficiency of the learning material.

[0102] As for the above-mentioned device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0103] It should be noted that the embodiments of the present invention may involve the use of user data. In actual applications, user-specific personal data can be used in the scheme described herein within the scope permitted by applicable laws and regulations, subject to the requirements of applicable laws and regulations of the country where the user is located (for example, with the user's explicit consent, effective notification to the user, etc.).

[0104] The embodiment of the present invention further provides an electronic device, such as Figure 5 As shown, it includes a processor 501, a communication interface 502, a memory 503 and a communication bus 504, wherein the processor 501, the communication interface 502, and the memory 503 communicate with each other through the communication bus 504.

[0105] Memory 503, used for storing computer programs;

[0106] The processor 501 is used to implement the learning method of any one of the learning materials described in the above embodiments when executing the program stored in the memory 503:

[0107] Providing a user interaction interface; wherein the user interaction interface is used to guide the user to input a learning goal for the learning material at least once in combination with a preset language processing model;

[0108] Generating an intelligent learning plan for the learning material according to the learning goal;

[0109] When the user studies the learning material according to the intelligent learning plan, the intelligent learning plan is adjusted according to a preset forgetting curve rule.

[0110] Optionally, in the process of the user learning the learning material according to the intelligent learning plan, adjusting the intelligent learning plan according to a preset forgetting curve rule includes:

[0111] During the process of the user learning the learning material according to the intelligent learning plan, monitoring the learning progress information of the user based on the intelligent learning plan;

[0112] The intelligent learning plan is adjusted according to a preset forgetting curve rule and / or the learning progress information; the intelligent learning plan at least includes a learning plan for reviewing the learning material.

[0113] Optionally, the method further comprises:

[0114] The learning data is input into the language processing model; the language processing model is used to output key information corresponding to the learning data; the language processing model is trained according to the training learning data;

[0115] The key information corresponding to the learning material is displayed in the user interaction interface to guide the user to learn the learning material.

[0116] Optionally, the method further comprises:

[0117] The learning material is input into the language processing model; the language processing model is used to output intelligent questions and answers corresponding to the learning material, and the language processing model is trained according to the training learning material and the interactive feedback information of the user on the training learning material;

[0118] The intelligent questions and answers corresponding to the learning materials are displayed in the user interaction interface to guide the user to learn the learning materials.

[0119] Optionally, the method further comprises:

[0120] Generating test questions for the user according to the learning materials and the learning progress information;

[0121] The test questions are displayed in the user interaction interface to guide the user to review the learning materials.

[0122] Optionally, the method further comprises:

[0123] The learning material is input into the language processing model, and the language processing model is used to output the extended knowledge points corresponding to the learning material. The language processing model is trained according to the training learning material.

[0124] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0125] The communication interface is used for communication between the above terminal and other devices.

[0126] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0127] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0128] In another embodiment provided by the present invention, a computer-readable storage medium is provided, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the learning method of the learning material described in any of the above embodiments.

[0129] In another embodiment provided by the present invention, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute the learning method of the learning material described in any one of the above embodiments.

[0130] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0131] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0132] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0133] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method for learning learning materials, characterized in that: The method comprises: Providing a user interaction interface; wherein the user interaction interface is used to guide the user to input a learning goal for the learning material at least once in combination with a preset language processing model; Generating an intelligent learning plan for the learning material according to the learning goal; When the user studies the learning material according to the intelligent learning plan, the intelligent learning plan is adjusted according to a preset forgetting curve rule.

2. The method according to claim 1, characterized in that: When the user learns the learning material according to the intelligent learning plan, adjusting the intelligent learning plan according to a preset forgetting curve rule includes: During the process of the user learning the learning material according to the intelligent learning plan, monitoring the learning progress information of the user based on the intelligent learning plan; The intelligent learning plan is adjusted according to a preset forgetting curve rule and / or the learning progress information; the intelligent learning plan at least includes a learning plan for reviewing the learning material.

3. The method according to claim 1, characterized in that The method further comprises: The learning data is input into the language processing model; the language processing model is used to output key information corresponding to the learning data; the language processing model is trained according to the training learning data; The key information corresponding to the learning material is displayed in the user interaction interface to guide the user to learn the learning material.

4. The method according to claim 1, characterized in that The method further comprises: The learning material is input into the language processing model; the language processing model is used to output intelligent questions and answers corresponding to the learning material, and the language processing model is trained according to the training learning material and the interactive feedback information of the user on the training learning material; The intelligent questions and answers corresponding to the learning materials are displayed in the user interaction interface to guide the user to learn the learning materials.

5. The method according to claim 2, characterized in that: The method further comprises: Generating test questions for the user according to the learning materials and the learning progress information; The test questions are displayed in the user interaction interface to guide the user to review the learning materials.

6. The method according to claim 1, characterized in that The method further comprises: The learning material is input into the language processing model, and the language processing model is used to output the extended knowledge points corresponding to the learning material. The language processing model is trained according to the training learning material.

7. The method according to claim 1, characterized in that The learning materials include at least documents, videos and images.

8. A learning device for learning materials, characterized in that: The device comprises: A user interaction interface display module, used to provide a user interaction interface; wherein the user interaction interface is used to guide the user to input a learning goal for the learning material at least once in combination with a preset language processing model; An intelligent learning plan generation module, used to generate an intelligent learning plan for the learning material according to the learning goal; The intelligent learning plan adjustment module is used to adjust the intelligent learning plan according to a preset forgetting curve rule when the user learns the learning material according to the intelligent learning plan.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing the method steps described in any one of claims 1 to 7 when executing a program stored in a memory.

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

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