Intelligent classroom management system based on AI

Abstract

The invention discloses an intelligent classroom management system based on AI, relates to the technical field of intelligent classrooms, and mainly solves the problems of inaccurate teaching resource matching, inaccurate student performance mastering and unobvious feedback effect. Comprising a student information management module, a teaching resource library, an intelligent teaching auxiliary module, an interactive communication platform, a student behavior analysis module and a family-school communication module, and student demands are analyzed through a collaborative filtering algorithm to improve accurate matching of teaching resources; student behaviors are analyzed and evaluated through a regression analysis prediction model, and comprehensive mastering of students is improved; teaching experience is continuously optimized through feedback circulation, and the feedback effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart classrooms, and in particular to an AI-based smart classroom management system. Background Art

[0002] The AI-based smart classroom management system is a product of the combination of educational technology and artificial intelligence. It uses advanced artificial intelligence technology to transform traditional classroom teaching to improve teaching quality and realize personalized teaching. With the advancement of AI technology, the smart classroom management system will pay more attention to the generation of personalized learning paths. Students can conduct experiments, visit historical scenes, and create art in a virtual environment. At the same time, the smart classroom management system will allow teachers to transform from traditional information transmitters to learning guides and supervisors. They will participate more in the students' learning process and provide guidance and feedback. However, the traditional smart classroom management system still has problems such as inaccurate matching of teaching resources, inaccurate grasp of student performance, and unclear feedback results.

[0003] Therefore, the present invention provides an AI-based intelligent classroom management system, which analyzes student needs through a collaborative filtering algorithm to improve the precise matching of teaching resources; analyzes and evaluates student behavior through a regression analysis predictive model to improve the comprehensive grasp of students; and continuously optimizes the teaching experience through a feedback loop to improve the effectiveness of feedback. Summary of the invention

[0004] The purpose of the present invention is to solve the problems of inaccurate teaching resource matching, inaccurate grasp of student performance and unclear feedback effectiveness in the prior art, and to propose an AI-based intelligent classroom management system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An AI-based intelligent classroom management system, including student information management module, teaching resource library, intelligent teaching auxiliary module, interactive communication platform, student behavior analysis module and home-school communication module.

[0007] The student information management module is used to collect and update student information and provide student background data for the teaching resource library, intelligent teaching auxiliary module, interactive communication platform, student behavior analysis module and home-school communication module;

[0008] The teaching resource library provides personalized resource information for the intelligent teaching auxiliary module according to the data information provided by the student information management module;

[0009] The intelligent teaching auxiliary module designs a personalized teaching plan based on the data of the student information management module and the teaching resource library, and integrates the teaching plan into the interactive communication platform;

[0010] The interactive communication platform is used to promote interaction between teachers and students, and collects interactive information and transmits it to the student behavior analysis module for analysis;

[0011] The student behavior analysis module analyzes the comprehensive student data, adjusts the strategy of the intelligent teaching auxiliary module according to the analysis results, and transmits the analysis results to the home-school communication module to strengthen the connection between home and school;

[0012] The home-school communication module contacts the student information management module and the student behavior analysis module to synchronize student information to the students' parents, and transmits parent feedback information to the intelligent teaching auxiliary module to provide intelligent teaching strategy support.

[0013] As a further description of the above technical solution, the student information management module includes a data management unit, a report generation unit, a data update unit and a security unit. The data management unit uses a MySQL database to store and organize student data information; the report generation unit uses Power BI to convert data into charts to facilitate the intuitive display of student information, including student personal information, student grades, learning progress and student attendance statistics; the data update unit receives external input data through an API interface and a Cron Job scheduled task, and automatically updates student data information; the security unit uses an RSA encryption algorithm, an access control list ACL and identity authentication to ensure the security of data transmission and storage.

[0014] As a further description of the above technical solution, the teaching resource library includes a resource classification unit, a collaborative sharing unit and a statistical unit. The resource classification unit uses a label system and a directory structure to classify and store teaching resources. Resource types include subjects, grades and resource formats; the collaborative sharing unit uses cloud storage services and Trello tools to achieve resource sharing and cooperative editing among teachers, and promotes the co-construction and sharing of high-quality resources; the statistical unit uses the MySQL database to count resource usage, including resource views, downloads and collections.

[0015] As a further description of the above technical solution, the intelligent teaching auxiliary module includes a personalized teaching strategy unit, a resource matching unit, an intelligent question-answering unit and an adaptive difficulty adjustment unit. The personalized teaching strategy unit generates corresponding teaching strategies through AI algorithms to improve learning efficiency; the resource matching unit analyzes student needs through a collaborative filtering algorithm based on the generated teaching strategies, and accurately matches learning materials from the teaching resource library; the intelligent question-answering unit understands student questions and provides answers based on the questions in the learning materials through natural language processing NLP and deep learning, thereby realizing instant online question-answering; the adaptive difficulty adjustment unit dynamically adjusts the difficulty of learning tasks through question-answering intensity and reinforcement learning to ensure that learning challenges match abilities.

[0016] As a further description of the above technical solution, the implementation method of the collaborative filtering algorithm in the process of analyzing needs is: first, organize the students' learning behavior data, including the video courses watched, the documents read, the exercises completed, and the feedback information of the ratings and comments; then, organize the collected student data into a student-project rating matrix, each row represents a student, each column represents a project, and the project includes multimedia resource materials. When the student completes watching and downloading the project, fill in 1 in the corresponding grid to indicate completion, otherwise fill in 0; then, calculate the similarity between students through the similarity calculation function, and the formula expression of the similarity calculation function is:

[0017]

[0018] In formula (1), represents the similarity calculation function, m represents the recommended student vector, n represents the target student vector, v m represents the average rating of recommended students, v n represents the average score of the target students, i represents the order of the project data of the function calculation, p represents the order of the termination project of the function calculation, and m i Indicates the score of the recommended student's corresponding project, n i Indicates the score of the target student for the corresponding item;

[0019] The target student's rating for the unrated items is predicted based on the behavior of similar students. The ratings of adjacent students are weighted and summed according to the similarity. The project resources with the highest predicted rating are selected and recommended to the target student. Finally, the click-through rate and conversion rate of the recommendation results are evaluated according to the evaluation function, and the algorithm parameters are adjusted to continuously optimize the recommendation effect. The formula expression of the evaluation function is:

[0020]

[0021] In formulas (2)-(3), H represents the calculation of the click-through rate by the evaluation function, ξ represents the click-through rate coefficient, and α i represents the target project data, i represents the current calculation order, n represents the calculation termination order, ξe -i α i Indicates the number of clicks on an item. represents the number of recommendations for an item, T represents the calculation of the conversion rate by the evaluation function, κ represents the conversion rate coefficient, κsin -1 (π / 2+α i ) indicates the number of times a specific action has been completed.

[0022] As a further description of the above technical solution, the interactive communication platform includes a scenario simulation unit, a multimedia sharing unit and a game and test unit. The scenario simulation unit creates actual scenes in real life through virtual reality VR to realize students' ability to solve problems in a specific environment; the multimedia sharing unit optimizes teaching quality through multimedia teaching resources to assist teaching content, including audio, video and 3D decomposition pictures; the game and test unit enhances student participation through educational games and interactive tests to stimulate learning interest.

[0023] As a further description of the above technical solution, the student behavior analysis module includes a preference analysis unit, an effect evaluation unit, a feedback unit and a decision adjustment unit. The preference analysis unit analyzes students' learning preferences, including favorite subjects, study time and learning motivation, through data mining and association rule learning; the effect evaluation unit quantifies students' learning outcomes through analysis results and regression analysis prediction models, and provides an objective evaluation of learning effectiveness; the feedback unit feeds back students' learning preferences and learning effects to the home-school communication module through WebSockets; the decision adjustment unit combines the feedback information, comprehensively integrates learning behavior, effect evaluation and feedback through the intelligent decision support system IDSS, and dynamically adjusts the teaching plan, and transmits the adjustment information to the intelligent teaching auxiliary module to optimize the teaching methods and content.

[0024] As a further description of the above technical solution, the home-school communication module includes an information synchronization unit, a progress reporting unit and a two-way feedback unit. The information synchronization unit transmits the data information in the student information management module through a cloud synchronization service, and updates the student's school performance and school notifications in real time; the progress reporting unit provides information feedback to the students' parents through a client APP and e-mail based on the student data received by the information synchronization unit; after receiving the student feedback information, the two-way feedback unit transmits the parent feedback information to the intelligent teaching assistance module in the form of a questionnaire, thereby realizing an effective communication cycle.

[0025] Other advantages, objectives and features of the present invention will be described in part in the following description; and in part, will be apparent to those skilled in the art based on an examination of the following; or, may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to understand and grasp the technical solution more intuitively and clearly, when describing the embodiments of the present invention or the prior art, drawings are often used to supplement and illustrate. It should be noted that the drawings are only one way of expressing the embodiments of the present invention or the prior art. In fact, the technical solution can also have other implementation methods and changes, which all fall within the protection scope of the present invention. Therefore, technicians can design other drawings as needed to implement the technical solution of the present invention, among which,

[0027] Figure 1 It is the overall structural framework diagram of the present invention.

[0028] Figure 2 It is a structural schematic diagram of the teaching resource library of the present invention.

[0029] Figure 3 It is a structural schematic diagram of the intelligent teaching auxiliary module of the present invention.

[0030] Figure 4 It is a structural schematic diagram of the interactive communication platform of the present invention.

[0031] Figure 5 It is a structural diagram of the student behavior analysis module of the present invention.

[0032] Figure 6 It is a structural schematic diagram of the home-school communication module of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of this invention through the accompanying drawings in the embodiments of this invention. Obviously, the described embodiments are only part of the embodiments of this invention, not all of the embodiments. At the same time, the description of well-known structures and technologies is omitted in the following description to avoid unnecessary confusion of the concepts of the present invention.

[0034] like Figure 1-Figure 6 As shown, an AI-based intelligent classroom management system includes a student information management module, a teaching resource library, an intelligent teaching auxiliary module, an interactive communication platform, a student behavior analysis module and a home-school communication module.

[0035] The student information management module is used to collect and update student information and provide student background data for the teaching resource library, intelligent teaching auxiliary module, interactive communication platform, student behavior analysis module and home-school communication module;

[0036] The teaching resource library provides personalized resource information for the intelligent teaching auxiliary module according to the data information provided by the student information management module;

[0037] The intelligent teaching auxiliary module designs a personalized teaching plan based on the data of the student information management module and the teaching resource library, and integrates the teaching plan into the interactive communication platform;

[0038] The interactive communication platform is used to promote interaction between teachers and students, and collects interactive information and transmits it to the student behavior analysis module for analysis;

[0039] The student behavior analysis module analyzes the comprehensive student data, adjusts the strategy of the intelligent teaching auxiliary module according to the analysis results, and transmits the analysis results to the home-school communication module to strengthen the connection between home and school;

[0040] The home-school communication module contacts the student information management module and the student behavior analysis module to synchronize student information to the students' parents, and transmits parent feedback information to the intelligent teaching auxiliary module to provide intelligent teaching strategy support.

[0041] In a specific embodiment, the student information management module includes a data management unit, a report generation unit, a data update unit and a security unit. The data management unit uses an efficient and stable MySQL relational database to store and organize student data. The MySQL ensures high reliability and consistency of the data. The data covers multi-dimensional information such as student personal information, grade records, learning progress and attendance, laying a solid foundation for subsequent data analysis and application; the report generation unit introduces the Power BI analysis tool to effectively convert complex data into intuitive and easy-to-understand charts. Using its rich graphics library and custom dashboard, it can not only show the profile of student personal information, but also present the trend of student grades, learning progress dynamics and attendance statistics in a refined manner, so that education managers can quickly grasp the overall teaching situation, adjust teaching strategies in time, and improve teaching effectiveness; the data update unit realizes automatic update and real-time synchronization of student data information by integrating API interface and Cron Job timing task mechanism. The API interface supports seamless connection with external systems, receives new data from various teaching platforms, examination systems and other sources, and the Cron Job regularly performs data refresh tasks to ensure that system data is always up to date. This mechanism greatly improves the timeliness and accuracy of data, meeting the demand for rapid response to information in education; the security unit builds a comprehensive data protection system, combining RSA asymmetric encryption algorithm, access control list ACL and strong identity authentication mechanism to protect the security of information from data transmission to storage. The RSA algorithm ensures that data is not stolen during transmission, the ACL strategy accurately controls data access rights, and identity authentication prevents unauthorized access, forming an indestructible security line of defense, providing strong protection for the integrity and privacy of student information.

[0042] In a specific embodiment, the teaching resource library includes a resource classification unit, a collaborative sharing unit and a statistical unit. The resource classification unit uses a label system and a directory structure to scientifically classify and manage teaching resources. The label system is a flexible classification method. Multiple labels can be added according to the attributes of the resource, such as subject, grade, resource format, etc., so that the search and retrieval of resources are more convenient. The directory structure provides a clear hierarchical structure to help users quickly locate the required resources; the collaborative sharing unit uses the cloud storage service Google Drive and the project management tool Trello to provide a platform for resource sharing and collaborative editing between teachers. Teachers can upload and download resources, and use Trello to create cards, lists and boards to jointly plan and track the status of resource projects and improve the efficiency of resource co-construction; the statistical unit records and analyzes the use of resources through the MySQL database, including the number of views, downloads and collections of resources. These data help to understand the popularity and user preferences of resources, provide a basis for the optimization and updating of resources, and also allow teachers to more intuitively grasp the dynamics of resource usage.

[0043] In a specific embodiment, the intelligent teaching auxiliary module includes a personalized teaching strategy unit, a resource matching unit, an intelligent question-answering unit, and an adaptive difficulty adjustment unit. The personalized teaching strategy unit generates a personalized teaching strategy for each student by analyzing the student's learning habits, ability level, and progress trajectory using an AI algorithm. This strategy can dynamically adjust the teaching content and methods to promote the student's learning effect in the most optimized way; the resource matching unit combines the student's needs and learning goals through a collaborative filtering algorithm to accurately match the learning materials in the teaching resource library. The collaborative filtering algorithm analyzes the preferences of students and other similar learners, recommends the most relevant and valuable resources, and ensures that the learning materials can meet the learning needs and are attractive and challenging; the intelligent question-answering unit combines natural language processing NLP and deep learning technology to understand the complex questions raised by students and provide accurate and timely answers. The NLP enables the system to parse the semantics of the problem, and deep learning helps the system learn the patterns and laws of problem solving, thereby achieving high-quality instant online question answering; the adaptive difficulty adjustment unit introduces a reinforcement learning algorithm to dynamically adjust the difficulty of the learning task according to the student's learning performance. The system will automatically fine-tune the difficulty of subsequent tasks based on the students' task completion, ensuring that the challenge matches the students' ability level, avoiding being too simple to cause boredom or too difficult to cause frustration, and maintaining optimal learning motivation.

[0044] In a specific embodiment, the collaborative filtering algorithm is implemented in the process of analyzing needs as follows: first, the learning behavior data of students is sorted, including the video courses watched, the documents read, the exercises completed, and the feedback information of ratings and comments; then, the collected student data is sorted into a student-project rating matrix, where each row represents a student and each column represents a project. The project includes multimedia resource materials. When the student completes watching and downloading the project, 1 is filled in the corresponding grid to indicate completion, otherwise 0 is filled; then, the similarity between students is calculated by the similarity calculation function, and the formula expression of the similarity calculation function is:

[0045]

[0046] In formula (1), represents the similarity calculation function, m represents the recommended student vector, n represents the target student vector, ν m represents the average rating of recommended students, ν n represents the average score of the target students, i represents the order of the project data of the function calculation, p represents the order of the termination project of the function calculation, and m i Indicates the score of the recommended student's corresponding project, n i Indicates the score of the target student for the corresponding item;

[0047] The target student's rating for the unrated items is predicted based on the behavior of similar students. The ratings of adjacent students are weighted and summed according to the similarity. The project resources with the highest predicted rating are selected and recommended to the target student. Finally, the click-through rate and conversion rate of the recommendation results are evaluated according to the evaluation function, and the algorithm parameters are adjusted to continuously optimize the recommendation effect. The formula expression of the evaluation function is:

[0048]

[0049] In formulas (2)-(3), H represents the calculation of the click-through rate by the evaluation function, ξ represents the click-through rate coefficient, and α i represents the target project data, i represents the current calculation order, n represents the calculation termination order, ξe -i α i Indicates the number of clicks on an item. represents the number of recommendations for an item, T represents the calculation of the conversion rate by the evaluation function, κ represents the conversion rate coefficient, κsin -1 (π / 2+α i ) indicates the number of times a specific action has been completed.

[0050] The role of the collaborative filtering algorithm in the process of analyzing needs is to be used for personalized recommendations, especially in intelligent education platforms, to help understand students' learning preferences, predict the learning resources they may be interested in, and thus improve learning efficiency and satisfaction; the problems solved include: helping students find the resources that best suit their interests and needs among a large amount of educational content, reducing information overload, avoiding students from wasting time on a large amount of irrelevant resources, improving the user stickiness of the platform, and increasing students' learning motivation. Through the above method, relying on the comprehensive use of the education platform's servers, cloud services, and mobile application carriers, the collaborative filtering algorithm not only improves the accuracy of resource recommendations, but also promotes the formation of personalized learning paths for students, providing strong support for creating a smart education environment. The difference in effect between the collaborative filtering algorithm and the traditional matching algorithm is shown in Table 1:

[0051] Table 1 Differences between collaborative filtering algorithms and traditional algorithms

[0052]

[0053] In summary, the collaborative filtering algorithm is superior to the traditional algorithm in terms of both accuracy and matching efficiency. Therefore, it can be seen that the collaborative filtering algorithm is the best choice for this algorithm.

[0054] In a specific embodiment, the interactive communication platform includes a scenario simulation unit, a multimedia sharing unit, and a game and test unit. The scenario simulation unit creates a realistic scene through virtual reality VR, allowing students to solve specific problems. This immersive learning method can exercise students' practical ability and adaptability, for example, applying the learned knowledge in chemical experiments, historical events or workplace situations to deepen understanding and memory; the multimedia sharing unit integrates audio, video, 3D decomposition pictures and other multimedia resources to make the teaching content vivid and easy to understand. Multimedia resources not only enrich teaching methods, but also take care of students with different learning styles, provide diversified learning methods, and improve teaching effects; the game and test unit integrates education into games, stimulates students' interest in learning through interactive tests and educational games, and improves participation. The gamified learning method can reduce the boredom of learning and enhance students' sense of accomplishment and spirit of exploration.

[0055] In a specific embodiment, the student behavior analysis module includes a preference analysis unit, an effect evaluation unit, a feedback unit and a decision adjustment unit. The preference analysis unit deeply analyzes the student's learning preferences through data mining technology and association rule learning algorithms, including favorite subjects, learning time preferences and learning motivation sources, which helps teachers understand each student's unique learning needs and habits, and lays the foundation for providing personalized guidance; the effect evaluation unit uses regression analysis and prediction models to quantitatively evaluate learning outcomes and give objective learning effect evaluation. This not only allows students and teachers to intuitively understand learning outcomes, but also reveals deficiencies and potential in the learning process, providing a basis for subsequent teaching improvements; the feedback unit, through the home-school communication module, feeds back students' learning preferences and effects to parents and teachers, promotes communication between home and school, ensures that families, schools and students can pay attention to and participate in the learning process together, and form an educational synergy; the decision adjustment unit integrates the intelligent decision support system IDSS, combines learning behavior, effect evaluation and feedback information, and dynamically adjusts the teaching plan. The system will intelligently recommend teaching strategies based on students' performance and needs, optimize teaching methods and content, ensure that the teaching process is more in line with students' actual situation, and improve teaching efficiency and satisfaction.

[0056] In a specific embodiment, the home-school communication module includes an information synchronization unit, a progress report unit and a two-way feedback unit. The home-school communication module includes an information synchronization unit, a progress report unit and a two-way feedback unit. The information synchronization unit transmits the data in the student information management module in real time through the cloud synchronization service, including important information such as student performance in school, grade changes and school notifications. Such instant synchronization ensures that parents can understand their children's learning situation and school dynamics at the first time; the progress report unit generates and sends detailed student learning progress reports in the form of client APP or email based on the data received in real time by the information synchronization unit. These reports cover comprehensive information such as student performance dynamics and behavioral performance, so that parents have a comprehensive understanding of their children's learning situation; the two-way feedback unit collects parents' feedback on student learning and school teaching. When parents submit feedback through the client, the system organizes and transmits these feedback information to the intelligent teaching auxiliary module in the form of questionnaires. An effective communication cycle is formed to ensure that parents' opinions and suggestions can be reflected in teaching practice in a timely manner, promoting continuous optimization and improvement of teaching.

[0057] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An AI-based intelligent classroom management system, including a student information management module, a teaching resource library, an intelligent teaching auxiliary module, an interactive communication platform, a student behavior analysis module and a home-school communication module, characterized in that: The student information management module is used to collect and update student information and provide student background data for the teaching resource library, intelligent teaching auxiliary module, interactive communication platform, student behavior analysis module and home-school communication module; The teaching resource library provides personalized resource information for the intelligent teaching auxiliary module according to the data information provided by the student information management module; The intelligent teaching auxiliary module designs a personalized teaching plan based on the data of the student information management module and the teaching resource library, and integrates the teaching plan into the interactive communication platform; The interactive communication platform is used to promote interaction between teachers and students, and collects interactive information and transmits it to the student behavior analysis module for analysis; The student behavior analysis module analyzes the comprehensive student data, adjusts the strategy of the intelligent teaching auxiliary module according to the analysis results, and transmits the analysis results to the home-school communication module to strengthen the connection between home and school; The home-school communication module contacts the student information management module and the student behavior analysis module to synchronize student information to the students' parents, and transmits parent feedback information to the intelligent teaching auxiliary module to provide intelligent teaching strategy support.

2. According to claim 1, an AI-based intelligent classroom management system is characterized in that: The student information management module includes a data management unit, a report generation unit, a data update unit and a security unit. The data management unit uses a MySQL database to store and organize student data information; the report generation unit uses Power BI to convert data into charts to intuitively display student information, including student personal information, student grades, learning progress and student attendance statistics; the data update unit receives external input data through an API interface and a Cron Job scheduled task, and automatically updates student data information; the security unit uses an RSA encryption algorithm, an access control list ACL and identity authentication to ensure the security of data transmission and storage.

3. According to claim 1, an AI-based intelligent classroom management system is characterized in that: The teaching resource library includes a resource classification unit, a collaborative sharing unit and a statistical unit. The resource classification unit uses a label system and a directory structure to classify and store teaching resources. Resource types include subjects, grades and resource formats; the collaborative sharing unit uses cloud storage services and Trello tools to achieve resource sharing and cooperative editing among teachers, and promotes the co-construction and sharing of high-quality resources; the statistical unit uses the MySQL database to count resource usage, including resource views, downloads and collections.

4. According to claim 1, an AI-based intelligent classroom management system is characterized in that: The intelligent teaching auxiliary module includes a personalized teaching strategy unit, a resource matching unit, an intelligent question-answering unit and an adaptive difficulty adjustment unit. The personalized teaching strategy unit generates corresponding teaching strategies through an AI algorithm to improve learning efficiency; the resource matching unit analyzes student needs through a collaborative filtering algorithm based on the generated teaching strategy, and accurately matches learning materials from the teaching resource library; the intelligent question-answering unit understands student questions and provides answers based on the questions in the learning materials through natural language processing NLP and deep learning, thereby realizing instant online question-answering; the adaptive difficulty adjustment unit dynamically adjusts the difficulty of learning tasks through question-answering intensity and reinforcement learning to ensure that learning challenges match abilities.

5. According to claim 4, an AI-based intelligent classroom management system is characterized in that: The collaborative filtering algorithm is implemented in the process of analyzing needs as follows: first, students’ learning behavior data is sorted, including video courses watched, documents read, exercises completed, and feedback information of ratings and comments; then, the collected student data is sorted into a student-project rating matrix, where each row represents a student and each column represents a project. The project includes multimedia resource materials. When a student has completed watching and downloading a project, 1 is filled in the corresponding grid to indicate completion, otherwise 0 is filled; then, the similarity between students is calculated by a similarity calculation function, and the formula expression of the similarity calculation function is: In formula (1), represents the similarity calculation function, m represents the recommended student vector, n represents the target student vector, v m represents the average rating of recommended students, v n represents the average score of the target students, i represents the order of the project data of the function calculation, p represents the order of the termination project of the function calculation, and m i Indicates the score of the recommended student's corresponding project, n i Indicates the score of the target student for the corresponding item; Predict the target student's rating of the unrated items based on the behavior of similar students, perform weighted summation of the ratings of adjacent students based on similarity, and select the project resource with the highest predicted rating to recommend to the target student; Finally, the click-through rate and conversion rate of the recommendation results are evaluated according to the evaluation function, and the algorithm parameters are adjusted to continuously optimize the recommendation effect. The formula expression of the evaluation function is: In formulas (2)-(3), H represents the calculation of the click-through rate by the evaluation function, ξ represents the click-through rate coefficient, and α i represents the target project data, i represents the current calculation order, n represents the calculation termination order, ξe -i α i Indicates the number of clicks on an item. represents the number of recommendations for an item, T represents the calculation of the conversion rate by the evaluation function, κ represents the conversion rate coefficient, κsin -1 (π / 2+α i ) indicates the number of times a specific action has been completed.

6. The AI-based intelligent classroom management system according to claim 1, characterized in that: The interactive communication platform includes a scenario simulation unit, a multimedia sharing unit and a game and test unit. The scenario simulation unit creates actual scenes in real life through virtual reality VR to enable students to solve problems in a specific environment; the multimedia sharing unit optimizes teaching quality through multimedia teaching resources to assist teaching content, including audio, video and 3D decomposition pictures; the game and test unit improves student participation and stimulates learning interest through educational games and interactive tests.

7. The AI-based intelligent classroom management system according to claim 1, characterized in that: The student behavior analysis module includes a preference analysis unit, an effect evaluation unit, a feedback unit and a decision adjustment unit. The preference analysis unit analyzes students' learning preferences, including favorite subjects, study time and learning motivation, through data mining and association rule learning; the effect evaluation unit quantifies students' learning outcomes through analysis results and regression analysis prediction models, and provides an objective evaluation of learning effectiveness; the feedback unit feeds back students' learning preferences and learning effects to the home-school communication module through WebSockets; the decision adjustment unit combines feedback information, integrates learning behavior, effect evaluation and feedback through the intelligent decision support system IDSS, and dynamically adjusts the teaching plan, and transmits the adjustment information to the intelligent teaching auxiliary module to optimize teaching methods and content.

8. The AI-based intelligent classroom management system according to claim 1, characterized in that: The home-school communication module includes an information synchronization unit, a progress reporting unit and a two-way feedback unit. The information synchronization unit transmits the data information in the student information management module through a cloud synchronization service to update the student's performance in school and school notifications in real time; the progress reporting unit provides information feedback to the student's parents through a client APP and email based on the student data received by the information synchronization unit; after receiving the student feedback information, the two-way feedback unit transmits the parent feedback information to the intelligent teaching auxiliary module in the form of a questionnaire survey, thereby realizing an effective communication cycle.

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