Online teaching programming system and teaching recommendation method based on large language model assistance

Abstract

The invention discloses an online programming system based on large language model assistance and a teaching recommendation method, the system comprises a base model acquisition module, a problem decomposition module, an abstract modeling module, an algorithm design module, a code analysis and assistance module and an evaluation module, and is used for helping students to finish a programming task of teaching design online. And performing problem decomposition, abstract modeling and algorithm design on actual problems by using a large language model, and solving the problems encountered by students during programming. Based on the system, the invention further designs a teaching recommendation method, multi-modal data in the information classroom teaching process is collected, a dynamic three-level teaching interaction diagram of the space-time environment, knowledge resources and cognitive behaviors is established through man-machine, life and teacher-student interaction behaviors, and accurate teaching recommendation and intervention strategies of the human-in-the-loop are provided. According to the invention, a three-dimensional comprehensive teaching field for learning environment intellectual computing is established in a programming scene, and a guarantee is provided for high-quality modern education development.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence and recommendation algorithms, and in particular relates to an online programming teaching system and a teaching recommendation method based on the assistance of a large language model. Background Art

[0002] With the advent of the era of artificial intelligence, the "New Generation Artificial Intelligence Development Plan" proposes to establish a learner-centered education environment, provide accurately pushed education services, and realize the customization of daily education and lifelong education. What followed was the hot development of the intersection of AI + education. AI-driven tools are a major trend in programming education, such as chatbots and automatic code evaluation tools. Skalka et al. proposed a conceptual framework that combines micro-learning and automatic source code evaluation to provide students with instant feedback. Malik et al. introduced a specially designed chatbot to highlight problem-solving strategies, common programming errors, syntax and semantics, with the ultimate goal of helping inexperienced learners master multiple skills at the same time.

[0003] However, although the above methods have achieved good results in improving students' programming ability, they often do not focus on the cultivation of students' comprehensive quality and thinking ability. At the same time, these tools often focus on a specific stage of programming skills and cannot track the multimodal learning data of students throughout the whole process, resulting in insufficient continuity and comprehensiveness of teaching support. In addition, existing methods fail to fully consider the differences between individual learners and lack a mechanism for adjusting teaching and recommending resources based on individual learning status, which makes the allocation of educational resources and teaching intervention strategies fail to achieve true personalization and precision.

[0004] Therefore, there is an urgent need to develop an integrated online programming teaching system that can comprehensively track students' learning progress, provide continuous support from entry to advanced stages, and generate personalized learning paths and teaching recommendations through dynamic data analysis, truly realizing a learner-centered education model and promoting programming education to develop in a higher quality and modern direction. Summary of the invention

[0005] The purpose of the present invention is to solve the problems existing in the existing information classroom teaching in primary and secondary schools, and to provide an online teaching programming system and a teaching recommendation method assisted by a large language model. The method of the present invention can promptly solve the problems encountered by students in programming, and provide students with accurate teaching recommendations and intervention strategies.

[0006] In order to achieve the above-mentioned invention object, the present invention specifically adopts the following technical solutions:

[0007] In a first aspect, the present invention provides an online teaching programming system based on a large language model, comprising:

[0008] Base model acquisition module: used to obtain a general large language model and fine-tune it to make it a dedicated base model for the field of programming teaching;

[0009] Problem decomposition module: used for obtaining the first user input and converting it into the designed first prompt word, inputting the question to be decomposed and the first prompt word into the exclusive base model, and the exclusive base model outputs the problem decomposition result and displays it on the user interface of the online teaching programming system;

[0010] Abstract modeling module: used for obtaining the second user input and converting it into the designed second prompt word, inputting the topic to be abstract modeled and the second prompt word into the exclusive base model, and the exclusive base model outputs the abstract modeling result and displays it on the user interface;

[0011] Algorithm design module: used for obtaining the third user input and converting it into the designed third prompt word, inputting the problem to be algorithmically designed and the third prompt word into the exclusive base model, and the exclusive base model outputs the algorithm design result and displays it on the user interface;

[0012] Code analysis and auxiliary module: used for intelligently analyzing the code completed by the user, obtaining the error information that appears in the console, and inputting the code completed by the user and the error information into the exclusive base model, and the exclusive base model outputs the analysis results and correction suggestions and displays them on the user interface;

[0013] Evaluation module: used for intelligently evaluating the code submitted by the user, inputting the code submitted by the user together with the standard answer and scoring rules of the programming question into the exclusive base model, and the exclusive base model outputs the evaluation result of the code submitted by the user and displays it on the user interface.

[0014] As a preferred embodiment of the above-mentioned first aspect, in the base model acquisition module, the specific process of fine-tuning the large language model is: use the LoRA model to fine-tune the large language model, and use pre-designed restrictive words to constrain the output content and language style of the large language model, obtain a pre-built information technology course knowledge base, and use the information technology course knowledge base and the large language model fine-tuned by LoRA as inputs to the large model knowledge chain framework, obtain the large language model with reduced hallucinations and use it as an exclusive base model.

[0015] As a preferred embodiment of the first aspect, in the problem decomposition module, the first prompt word includes the output format requirements and word limit of the exclusive base model, and indicates that the exclusive base model is required to complete the problem decomposition.

[0016] As a preferred embodiment of the above-mentioned first aspect, in the abstract modeling module, the second prompt word includes that the output of the exclusive base model is a calculation model and a word limit, and indicates that the exclusive base model is required to provide an explanation of the modeling process.

[0017] As a preferred embodiment of the above-mentioned first aspect, in the algorithm design module, the third prompt word includes that the output of the exclusive base model is an algorithm flow chart and flow chart format requirements, and indicates that the exclusive base model is required to provide an explanation of the algorithm design process.

[0018] As a preferred embodiment of the first aspect, the code analysis and auxiliary module includes a code analysis module and an auxiliary debugging module;

[0019] The code analysis module is used to obtain the code completed by the user, input the code completed by the user into the exclusive base model, and the exclusive base model outputs the analysis results of the code from the whole to the line by line, and displays the analysis results on the user interface;

[0020] The auxiliary debugging module is used to obtain the error information output by the console, input the error information into the exclusive base model, and the exclusive base model outputs the error explanation and correction suggestions of the error information, and displays the error explanation and correction suggestions of the error information on the user interface.

[0021] As a preferred embodiment of the above-mentioned first aspect, in the evaluation module, the exclusive base model weights the execution time and code analysis results of the user-submitted code to comprehensively evaluate the efficiency and quality of the user-submitted code, and finally outputs the evaluation results of the user-submitted code.

[0022] In a second aspect, the present invention provides a teaching recommendation method based on the online teaching programming system described in the first aspect, which comprises the following steps:

[0023] S1. Obtain multimodal data during online classroom teaching, including human-computer interaction data, student-student interaction data, and teacher-student interaction data;

[0024] S2. Clean the multimodal data and annotate the cleaned multimodal data, annotate the human-computer interaction behavior, the student-student interaction behavior and the teacher-student interaction behavior, and obtain the processed multimodal data;

[0025] S3, construct a dynamic three-level teaching interaction diagram of coupled knowledge resources and interactive behaviors based on the processed multimodal data;

[0026] S4. Conduct knowledge blind spot distribution and learning pattern evolution analysis on the dynamic three-level teaching interaction diagram, input the analysis results into the trained intelligent agent, and the intelligent agent selects teaching resources corresponding to the knowledge points that need to be recommended for teaching from the pre-built teaching resource recommendation library and pushes the teaching resources to student users in a preset manner.

[0027] As a preferred embodiment of the second aspect, the specific process of step S3 is as follows:

[0028] S31, taking the student to be recommended for teaching as a student node, obtaining student information from the online teaching programming system, taking the obtained student information as a student node feature, taking the knowledge point corresponding to the programming question on the online teaching programming system as a knowledge point node, taking the teaching teacher as a teacher node, taking the obtained human-computer interaction behavior as an edge between the student node and the knowledge point node, taking the obtained student-student interaction behavior as an edge between two student nodes, taking the obtained teacher-student interaction behavior as an edge between the student node and the teacher node, and obtaining an original three-level teaching interaction graph;

[0029] S32. Input the original three-level teaching interaction graph into the trained graph neural network, update the nodes and edges in the original three-level teaching interaction graph, and form a dynamic three-level teaching interaction graph.

[0030] In a third aspect, the present invention provides a computer electronic device comprising a memory and a processor;

[0031] The memory is used to store computer programs;

[0032] The processor is used to implement the teaching recommendation method as described in any scheme of the second aspect when executing the computer program.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention proposes an online teaching programming system and teaching recommendation method based on the assistance of a large language model, which effectively solves the problem of insufficient teaching resources for information courses in primary and secondary schools. With the support of a large language model, students can independently complete programming learning tasks without relying on teachers, enhancing their programming ability and independent problem-solving ability. At the same time, the present invention provides students with customized teaching support by building a full-scale learning community, accurately pushes teaching resources and intervention strategies, solves the problem of insufficient personalized teaching recommendations in existing online teaching programming systems, and has the advantages of high efficiency and personalization. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of specific modules included in the online teaching programming system of the present invention;

[0036] Figure 2 A flowchart of the steps of the teaching recommendation method of the present invention;

[0037] Figure 3 It is a structural schematic diagram of the dynamic three-level teaching interaction diagram of the present invention;

[0038] Figure 4 This is a schematic diagram of the proportion of data obtained in this embodiment in each stage of study; DETAILED DESCRIPTION

[0039] In order to make the above-mentioned purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation mode of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0040] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for the purpose of distinguishing descriptions, and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0041] The technical problems to be solved by the present invention are: 1) Most existing programming education tools are modules with single functions, lacking integrated support throughout the entire learning process, and unable to effectively integrate teaching, evaluation and feedback functions; 2) Insufficient personalized support for students in the learning process, and unable to adaptively adjust the teaching content when there are differences in individual student levels; 3) Existing systems usually only focus on data collection of programming behavior itself, ignoring multimodal information in the teaching process, such as teacher-student interaction, environmental information, etc., and unable to form a comprehensive perception of students' learning status. Based on this, the present invention proposes an online teaching programming system and teaching recommendation method based on the assistance of a large language model.

[0042] like Figure 1 As shown, in a preferred implementation of the present invention, the online teaching programming system based on the assistance of a large language model includes the following modules:

[0043] Base model acquisition module: used to obtain a general large language model and fine-tune it to make it a dedicated base model for the field of programming teaching;

[0044] Problem decomposition module: used for obtaining the first user input and converting it into the designed first prompt word, inputting the question to be decomposed and the first prompt word into the exclusive base model, and the exclusive base model outputs the problem decomposition result and displays it on the user interface of the online teaching programming system;

[0045] Abstract modeling module: used for obtaining the second user input and converting it into the designed second prompt word, inputting the topic to be abstract modeled and the second prompt word into the exclusive base model, and the exclusive base model outputs the abstract modeling result and displays it on the user interface;

[0046] Algorithm design module: used for obtaining the third user input and converting it into the designed third prompt word, inputting the problem to be algorithmically designed and the third prompt word into the exclusive base model, and the exclusive base model outputs the algorithm design result and displays it on the user interface;

[0047] Code analysis and auxiliary module: used for intelligently analyzing the code completed by the user, obtaining the error information that appears in the console, and inputting the code completed by the user and the error information into the exclusive base model, and the exclusive base model outputs the analysis results and correction suggestions and displays them on the user interface;

[0048] Evaluation module: used for intelligently evaluating the code submitted by the user, inputting the code submitted by the user together with the standard answer and scoring rules of the programming question into the exclusive base model, and the exclusive base model outputs the evaluation result of the code submitted by the user and displays it on the user interface.

[0049] It should be noted that in the base model acquisition module of the present invention, the specific process of fine-tuning the large language model is: use the LoRA model to fine-tune the large language model, and use pre-designed restrictive words to constrain the output content and language style of the large language model, obtain a pre-constructed information technology course knowledge base, and use the information technology course knowledge base and the large language model fine-tuned by LoRA as inputs to the large model knowledge chain framework to obtain a large language model with reduced hallucinations and use it as an exclusive base model.

[0050] It should be noted that, in the problem decomposition module of the present invention, the first prompt word includes the output format requirements and word limit of the exclusive base model, and indicates that the exclusive base model is required to complete the problem decomposition.

[0051] It should be noted that, in the abstract modeling module of the present invention, the second prompt word includes that the output of the exclusive base model is a calculation model and a word limit, and indicates that the exclusive base model is required to provide an explanation of the modeling process.

[0052] It should be noted that, in the algorithm design module of the present invention, the third prompt word includes that the output of the exclusive base model is an algorithm flow chart and flow chart format requirements, and indicates that the exclusive base model is required to provide an explanation of the algorithm design process.

[0053] It should be noted that the code analysis and auxiliary module of the present invention includes a code analysis module and an auxiliary debugging module;

[0054] The code analysis module is used to obtain the code completed by the user, input the code completed by the user into the exclusive base model, and the exclusive base model outputs the analysis results of the code from the whole to the line by line, and displays the analysis results on the user interface;

[0055] The auxiliary debugging module is used to obtain the error information output by the console, input the error information into the exclusive base model, and the exclusive base model outputs the error explanation and correction suggestions of the error information, and displays the error explanation and correction suggestions of the error information on the user interface.

[0056] It should be noted that in the evaluation module of the present invention, the exclusive base model weights the execution time and code analysis results of the user-submitted code to comprehensively evaluate the efficiency and quality of the user-submitted code, and finally outputs the evaluation results of the user-submitted code.

[0057] like Figure 2 As shown, in a preferred implementation of the present invention, based on the above-mentioned online teaching programming system, the present invention also provides a teaching recommendation method, including steps S1 to S4, and its implementation process is described below.

[0058] S1. Obtain multimodal data in the online classroom teaching process, including human-computer interaction data, student-student interaction data, and teacher-student interaction data.

[0059] It should be noted that in step S1 of the present invention, the online teaching programming system obtains the human-computer interaction data generated by the students, such as the code submitted by the students, the questions asked by the students to the exclusive base model, etc.; the IoT devices in the smart classroom are used to collect the voice data and video data of the students during class. These two types of data can come from the interaction between students, such as communication between students, and can also come from the interaction between students and teachers, such as questions asked by students to teachers.

[0060] S2. Clean the multimodal data and annotate the cleaned multimodal data, annotate the human-computer interaction behaviors, student-student interaction behaviors, and teacher-student interaction behaviors, and obtain processed multimodal data.

[0061] It should be noted that in step S2 of the present invention, the missing and incomplete data in the collected multimodal data such as text, voice and video are cleaned, and then the cleaned multimodal data are standardized and labeled to mark the human-computer interaction behavior, student-student interaction behavior and teacher-student interaction behavior.

[0062] S3, construct a dynamic three-level teaching interaction diagram of coupled knowledge resources and interactive behaviors based on the processed multimodal data;

[0063] It should be noted that the specific process of step S3 of the present invention is as follows:

[0064] S31, taking the student to be recommended for teaching as the student node, obtaining the student information (such as the student's gender, grade, etc.) from the online teaching programming system, taking the obtained student information as the student node feature, taking the knowledge point corresponding to the programming question on the online teaching programming system as the knowledge point node, taking the teaching teacher as the teacher node, taking the obtained human-computer interaction behavior as the edge between the student node and the knowledge point node, taking the obtained student-student interaction behavior as the edge between two student nodes, taking the obtained teacher-student interaction behavior as the edge between the student node and the teacher node, and obtaining the original three-level teaching interaction graph;

[0065] S32. Input the original three-level teaching interaction graph into the trained graph neural network, update the nodes and edges in the original three-level teaching interaction graph, and form a dynamic three-level teaching interaction graph.

[0066] S4. Conduct knowledge blind spot distribution and learning pattern evolution analysis on the dynamic three-level teaching interaction diagram, input the analysis results into the trained intelligent agent, and the intelligent agent selects teaching resources corresponding to the knowledge points that need to be recommended for teaching from the pre-built teaching resource recommendation library and pushes the teaching resources to student users in a preset manner.

[0067] In order to better demonstrate the specific implementation and technical effect of the present invention, the teaching recommendation method shown in steps S1 to S4 in the above preferred implementation is applied to a specific example.

[0068] Example

[0069] The specific implementation process of the teaching recommendation method used in this embodiment is as described above and will not be repeated here.

[0070] The online teaching programming system based on the large language model in this embodiment has been tried out in many primary and secondary schools in Zhejiang Province, with nearly 5,000 registered users and 70,407 human-computer interaction data collected. At present, 12,183 multimodal data from more than 200 schools in all stages of primary school, junior high school, high school, and university have been collected through various survey methods such as questionnaires and interviews. Figure 4shown.

[0071] In order to demonstrate the technical effect of this embodiment, 831 pieces of background data generated by 50 students in a class of a middle school in an information class were analyzed, and they were divided into a high-efficiency group, a medium-efficiency group and a low-efficiency group according to the number of successful question submissions and the accuracy rate. After excluding some useless records (such as logins), the experimental results obtained based on the acquired data using the method of the present invention are shown in Table 1.

[0072] Table 1. Experimental results of improving learning efficiency of a class using the method of the present invention

[0073] Ask for large model Run error Run successfully Ask about the proportion of large models High efficiency group 76 97 107 37.25％ Medium effect group 42 92 47 30.21％ Inefficient group 19 110 5 16.52％

[0074] As shown in Table 1, the efficient group can use the online teaching programming system to assist programming more skillfully and effectively, while the inefficient group rarely asks questions to the large language model. It can be seen that the online teaching programming system developed by the present invention can well improve the learning efficiency of programming learners.

[0075] It is understandable that the teaching recommendation method described in S1 to S4 above can be implemented by a computer program. Therefore, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer electronic device corresponding to the teaching recommendation method provided in the above embodiment, which includes a memory and a processor;

[0076] The memory is used to store computer programs;

[0077] The processor is used to implement the teaching recommendation method in the above embodiment when executing the computer program.

[0078] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0079] It is understandable that 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.

[0080] The above-described embodiment is only a preferred solution of the present invention, but it is not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. An online teaching programming system based on a large language model, characterized in that: include: Base model acquisition module: used to obtain a general large language model and fine-tune it to make it a dedicated base model for the field of programming teaching; Problem decomposition module: used for obtaining the first user input and converting it into the designed first prompt word, inputting the question to be decomposed and the first prompt word into the exclusive base model, and the exclusive base model outputs the problem decomposition result and displays it on the user interface of the online teaching programming system; Abstract modeling module: used for obtaining the second user input and converting it into the designed second prompt word, inputting the topic to be abstract modeled and the second prompt word into the exclusive base model, and the exclusive base model outputs the abstract modeling result and displays it on the user interface; Algorithm design module: used for obtaining the third user input and converting it into the designed third prompt word, inputting the problem to be algorithmically designed and the third prompt word into the exclusive base model, and the exclusive base model outputs the algorithm design result and displays it on the user interface; Code analysis and auxiliary module: used for intelligently analyzing the code completed by the user, obtaining the error information that appears in the console, and inputting the code completed by the user and the error information into the exclusive base model, and the exclusive base model outputs the analysis results and correction suggestions and displays them on the user interface; Evaluation module: used for intelligently evaluating the code submitted by the user, inputting the code submitted by the user together with the standard answer and scoring rules of the programming question into the exclusive base model, and the exclusive base model outputs the evaluation result of the code submitted by the user and displays it on the user interface.

2. The online teaching programming system as claimed in claim 1, characterized in that: In the base model acquisition module, the specific process of fine-tuning the large language model is: use the LoRA model to fine-tune the large language model, and use pre-designed restrictive words to constrain the output content and language style of the large language model, obtain a pre-built information technology course knowledge base, and use the information technology course knowledge base and the large language model fine-tuned by LoRA as inputs to the large model knowledge chain framework, obtain the large language model with reduced hallucinations and use it as an exclusive base model.

3. The online teaching programming system as claimed in claim 1, characterized in that: In the problem decomposition module, the first prompt word includes the output format requirements and word limit of the exclusive base model, and indicates that the exclusive base model is required to complete the problem decomposition.

4. The online teaching programming system as claimed in claim 1, characterized in that: In the abstract modeling module, the second prompt word includes that the output of the exclusive base model is a computational model and a word limit, and indicates that the exclusive base model is required to provide an explanation of the modeling process.

5. The online teaching programming system as claimed in claim 1, characterized in that: In the algorithm design module, the third prompt word includes that the output of the exclusive base model is an algorithm flow chart and flow chart format requirements, and indicates that the exclusive base model is required to provide an explanation of the algorithm design process.

6. The online teaching programming system as claimed in claim 1, characterized in that: The code analysis and auxiliary module includes a code analysis module and an auxiliary debugging module; The code analysis module is used to obtain the code completed by the user, input the code completed by the user into the exclusive base model, and the exclusive base model outputs the analysis results of the code from the whole to the line by line, and displays the analysis results on the user interface; The auxiliary debugging module is used to obtain the error information output by the console, input the error information into the exclusive base model, and the exclusive base model outputs the error explanation and correction suggestions of the error information, and displays the error explanation and correction suggestions of the error information on the user interface.

7. The online teaching programming system as claimed in claim 1, characterized in that: In the evaluation module, the exclusive base model weights the execution time and code analysis results of the user-submitted code to comprehensively evaluate the efficiency and quality of the user-submitted code, and finally outputs the evaluation results of the user-submitted code.

8. A teaching recommendation method based on the online teaching programming system according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Obtain multimodal data during online classroom teaching, including human-computer interaction data, student-student interaction data, and teacher-student interaction data; S2. Clean the multimodal data and annotate the cleaned multimodal data, annotate the human-computer interaction behavior, the student-student interaction behavior and the teacher-student interaction behavior, and obtain the processed multimodal data; S3, construct a dynamic three-level teaching interaction diagram of coupled knowledge resources and interactive behaviors based on the processed multimodal data; S4. Conduct knowledge blind spot distribution and learning pattern evolution analysis on the dynamic three-level teaching interaction diagram, input the analysis results into the trained intelligent agent, and the intelligent agent selects teaching resources corresponding to the knowledge points that need to be recommended for teaching from the pre-built teaching resource recommendation library and pushes the teaching resources to student users in a preset manner.

9. The teaching recommendation method according to claim 8, characterized in that: The specific process of step S3 is as follows: S31, taking the student to be recommended for teaching as a student node, obtaining student information from the online teaching programming system, taking the obtained student information as a student node feature, taking the knowledge point corresponding to the programming question on the online teaching programming system as a knowledge point node, taking the teaching teacher as a teacher node, taking the obtained human-computer interaction behavior as an edge between the student node and the knowledge point node, taking the obtained student-student interaction behavior as an edge between two student nodes, taking the obtained teacher-student interaction behavior as an edge between the student node and the teacher node, and obtaining an original three-level teaching interaction graph; S32. Input the original three-level teaching interaction graph into the trained graph neural network, update the nodes and edges in the original three-level teaching interaction graph, and form a dynamic three-level teaching interaction graph.

10. A computer electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the teaching recommendation method according to any one of claims 8 to 9 when executing the computer program.

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