Online education live broadcast interaction optimization system

Abstract

The invention provides an online education live broadcast interaction optimization system, and belongs to the technical field of live broadcast systems, the system comprises a multi-mode interaction module, a real-time response module, a learning condition analysis module and a personalized recommendation module, the multi-mode interaction module provides video, voice and expression interaction sub-modules, and the interaction experience is enhanced; the real-time response module ensures timely and accurate interaction and comprises an intelligent question and answer sub-module and an interaction reminding sub-module; the learning condition analysis module is used for generating a student learning report and a teacher teaching report; the personalized recommendation module recommends personalized courses and learning resources for students according to the conditions of the students, and the system calculates a multi-modal interaction satisfaction index, a system comprehensive evaluation index and the like through related formulas. The invention aims to solve the problems of single interaction mode, untimely response, difficulty in learning of students by teachers, insufficient personalized recommendation and the like in the existing online education live broadcast interaction system, and improve the interaction effect, teaching quality and student learning experience of online education live broadcast.

Description

Technical Field

[0001] The present invention relates to the technical field of live broadcast systems, and in particular to an online education live broadcast interactive optimization system. Background Art

[0002] Online education live broadcast is an educational method that conducts real-time teaching through the Internet platform. In online education live broadcast, teachers transmit teaching content to students in real time in the form of video and audio through devices such as cameras and microphones. Students can watch live courses remotely through computers, mobile phones, tablets and other devices, and interact with teachers in real time, such as asking questions, answering questions, and participating in discussions.

[0003] Online education live broadcast has the advantages of real-time, strong interactivity, and high convenience. It can break the limitations of time and space and allow students to receive high-quality educational resources anytime and anywhere. At the same time, online education live broadcast also provides teachers with a broader teaching platform to better demonstrate their teaching ability and teaching style. With the development of Internet technology, online education is becoming more and more popular. As an important form of online education, online education live broadcast has the advantages of real-time and strong interactivity. However, there are still some problems in the current online education live broadcast interactive system.

[0004] In the current field of online education live broadcast, the live broadcast interactive system plays a vital role. However, the existing online education live broadcast interactive system has some shortcomings. First, the interactive mode is relatively single, mainly based on text chat, lacking diversified modes such as video interaction, voice interaction and expression interaction, which makes the communication between students and teachers, and between students and students not rich and real enough, affecting students' learning experience and participation. Secondly, the interactive response is not timely enough, and the intelligent question and answer submodule (1021) is slow to answer students' questions. When encountering unanswerable questions, there may be a delay in the process of forwarding to teachers, resulting in students' problems not being solved in time, affecting learning efficiency. In addition, it is difficult for teachers to accurately understand students' learning situation. The system has deficiencies in data collection and analysis, and cannot comprehensively and accurately collect students' learning behavior data and teachers' teaching behavior data, making it difficult for teachers to carry out targeted teaching according to students' actual conditions, affecting teaching effects. Finally, there are deficiencies in personalized recommendations. The accuracy and relevance of course recommendations and learning resource recommendations are not high, which cannot meet students' personalized learning needs and are not conducive to students' personalized development. Summary of the invention

[0005] The purpose of the present invention is to provide an online education live interactive optimization system, which aims to solve the problem that the interactive system in the prior art is mainly based on text chat, lacks diversified methods such as video interaction, voice interaction and expression interaction, resulting in insufficient richness and authenticity of communication between students and teachers, and between students, affecting students' learning experience and participation. This single interactive method cannot meet students' needs for multiple ways of expression during the learning process, and it is difficult to create an active classroom atmosphere, which is not conducive to improving students' learning effects.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] An online education live interactive optimization system, including a multimodal interactive module, a real-time response module, a learning situation analysis module and a personalized recommendation module;

[0008] The multimodal interaction module includes a video interaction submodule, a voice interaction submodule and an expression interaction submodule, which are used to provide a variety of interaction methods;

[0009] The real-time response module includes an intelligent question-answering submodule and an interactive reminder submodule to ensure the timeliness and accuracy of the interaction;

[0010] The learning situation analysis module includes a data collection submodule and a data analysis submodule, which are used to generate a student's learning report and a teacher's teaching report;

[0011] The personalized recommendation module includes a course recommendation submodule and a learning resource recommendation submodule, which provide students with personalized course and learning resource recommendations.

[0012] Preferably, the multimodal interaction satisfaction index M in the multimodal interaction module can be calculated by the following formula:

[0013] M=αV+βA+γS,

[0014] Where: M is the multimodal interaction satisfaction, indexes α, β, and γ are weight coefficients, V is the video interaction satisfaction value, A is the voice interaction satisfaction value, and S is the expression interaction satisfaction value.

[0015] Preferably, the personalized recommendation module is provided with a system comprehensive evaluation index Z, and the calculation formula of the system comprehensive evaluation index Z is as follows:

[0016] Z=ωM+ηR+θP,

[0017] Where: ω, η, θ are weight coefficients and = 1, R is the personalized recommendation satisfaction value, and P is the real-time response satisfaction value.

[0018] Preferably, the sum of the weight coefficients of α, β and γ is equal to 1, and the sum of the weight coefficients of ω, η and θ is equal to 1.

[0019] Preferably, the video interaction submodule allows students to turn on the camera to interact with teachers and other classmates via video, and teachers can view students' learning status and give feedback. The voice interaction submodule provides a clear and smooth voice interaction function, and has voice recognition technology to convert students' voice into text for teachers to view and record. The expression interaction submodule allows students to send expressions to express emotions and attitudes.

[0020] Preferably, the intelligent question-answering submodule uses artificial intelligence technology to answer students' questions in real time, and forwards them to teachers when they cannot be answered. The teachers reply in time, and the response time T satisfies T≤t0, where t0 is the preset maximum response time. The calculation method of the real-time response satisfaction value P is as follows:

[0021] P=λPA+μAT,

[0022] Wherein: λ, μ are weight coefficients and λ+μ=1.

[0023] Preferably, the interactive reminder submodule sends a reminder to the teacher when a student asks a question, answers a question or participates in an interaction, and sets a reminder time interval I, I≤i0, i0 being a preset maximum time interval.

[0024] Preferably, the data collection submodule automatically collects student learning behavior data and teacher teaching behavior data, and the data analysis submodule uses big data analysis technology to analyze the collected data to generate student learning reports and teacher teaching reports.

[0025] Preferably, the course recommendation submodule recommends suitable courses to students based on their learning history, interests and hobbies, and learning goals, and the recommendation algorithm takes into account course difficulty, content, and evaluation factors.

[0026] Preferably, the learning resource recommendation submodule is used to recommend relevant learning resources to students, and the relevance of the recommendation can be measured by a correlation coefficient r, r≥r0, r0 is a preset minimum correlation coefficient.

[0027] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0028] 1. The present invention can provide diversified interactive methods. Through the multimodal interactive module, including the video interactive submodule, the voice interactive submodule and the expression interactive submodule, it provides students with diversified interactive methods such as video, voice and expression, enhances the communication between students and teachers, and between students, improves students' participation and learning interest, and makes the learning experience richer and more real.

[0029] 2. The present invention can ensure that the interaction is timely and accurate. The intelligent question and answer submodule in the real-time response module uses artificial intelligence technology to provide real-time answers to students' questions. When the questions cannot be answered, they are forwarded to the teacher in a timely manner and the teacher is ensured to reply to the students within the preset maximum response time. The interactive reminder submodule sends reminders to the teacher in a timely manner when the students ask questions, answer questions or participate in the interaction, ensuring that the teacher will not miss the students' interaction and can pay attention and respond in time, thereby improving the timeliness and accuracy of the interaction and improving learning efficiency.

[0030] 3. The present invention accurately understands the students' learning situation. The data collection submodule in the learning situation analysis module automatically collects the students' learning behavior data and the teachers' teaching behavior data. The data analysis submodule uses big data analysis technology to conduct in-depth analysis of these data to generate accurate and reliable student learning reports and teacher teaching reports, helping students and teachers understand the learning and teaching situation so as to make targeted improvements, so that teachers can teach more effectively according to the students' actual situation and improve teaching effectiveness.

[0031] 4. The present invention can meet personalized learning needs. The course recommendation submodule in the personalized recommendation module recommends suitable courses to students based on their learning history, interests and hobbies, and learning goals. The learning resource recommendation submodule recommends relevant learning resources to students, and the relevance of the recommendations can be measured by the correlation coefficient, ensuring that the recommended resources are closely related to students' learning, which can effectively assist students' learning, meet students' personalized learning needs, and promote students' personalized development. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a system block diagram of the online education live interactive optimization system of the present invention;

[0033] Figure 2 This is an architectural block diagram of the modules in the online education live interactive optimization system of the present invention.

[0034] Among them: 101, multimodal interaction module; 1011, video interaction sub-module; 1012, voice interaction sub-module; 1013, expression interaction sub-module; 102, real-time response module; 1021, intelligent question and answer sub-module; 1022, interactive reminder sub-module; 103, learning situation analysis module; 1031, data collection sub-module; 1032, data analysis sub-module; 104, personalized recommendation module; 1041, course recommendation sub-module; 1042, learning resource recommendation sub-module. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] See also Figure 1-Figure 2 , an online education live interactive optimization system, comprising a multimodal interactive module 101, a real-time response module 102, a learning situation analysis module 103 and a personalized recommendation module 104;

[0037] The multimodal interaction module 101 includes a video interaction submodule 1011, a voice interaction submodule 1012 and an expression interaction submodule 1013, which are used to provide a variety of interaction methods;

[0038] The real-time response module 102 includes an intelligent question-answering submodule 1021 and an interactive reminder submodule 1022 to ensure the timeliness and accuracy of the interaction;

[0039] The learning situation analysis module 103 includes a data collection submodule 1031 and a data analysis submodule 1032, which are used to generate a student's learning report and a teacher's teaching report;

[0040] The personalized recommendation module 104 includes a course recommendation submodule 1041 and a learning resource recommendation submodule 1042, which provide personalized course and learning resource recommendations for students.

[0041] In a specific embodiment of the present invention, the multimodal interaction module 101 provides a variety of interactive methods, enhances the interactive experience between students and teachers and classmates, and improves students' participation and learning interest. During the live broadcast, students can choose to use the video interaction submodule 1011, the voice interaction submodule 1012 or the expression interaction submodule 1013 to interact with teachers and other classmates as needed. The video interaction submodule 1011 allows students to turn on the camera and communicate with teachers and other classmates in real time via video; teachers can view students' learning status, such as expressions, movements, etc., through video, understand students' learning situation in a timely manner, and give feedback and guidance; the voice interaction submodule 1012 provides a clear and fluent voice function, and students can ask questions, answer questions or participate in discussions through voice; at the same time, voice recognition technology converts students' voice into text, which is convenient for teachers to view and record, and helps teachers better understand students' needs; the expression interaction submodule 1013 allows students to send expressions to express emotions and attitudes, increasing the fun and liveliness of the interaction;

[0042] The real-time response module 102 can ensure the timeliness and accuracy of interaction and improve teaching efficiency and quality. The intelligent question-answering submodule 1021 will automatically answer students' questions in real time. When the questions cannot be answered, they will be forwarded to the teacher, and the teacher will reply in time; the interactive reminder submodule 1022 will send reminders to the teacher when students ask questions, answer questions or participate in interactions; the intelligent question-answering submodule 1021 uses artificial intelligence technology to quickly respond to students' questions and provide answers in time; if there are questions that cannot be answered, they will be forwarded to the teacher in time, and the teacher will reply to the student within the preset maximum response time t0 to ensure that the student's problem is solved in time; the interactive reminder submodule 1022 will send reminders to the teacher when there is student interaction, and the reminder time interval I does not exceed the preset maximum time interval i0 to ensure that the teacher will not miss the student's interaction and can pay attention and respond in time;

[0043] The learning situation analysis module 103 is used to generate students' learning reports and teachers' teaching reports to help students and teachers understand the learning and teaching situation so as to make targeted improvements. The data acquisition submodule 1031 will automatically collect students' learning behavior data and teachers' teaching behavior data, and the data analysis submodule 1032 will analyze these data; the data acquisition submodule 1031 will comprehensively collect students' learning behavior data, such as learning time, courses participated in, answering status, etc., and teachers' teaching behavior data, such as teaching content, teaching methods, questioning status, etc. The data analysis submodule 1032 uses big data analysis technology to conduct in-depth analysis of these data to dig out students' learning characteristics, advantages and disadvantages, as well as teachers' advantages and areas for improvement in teaching; the generated student learning reports and teacher teaching reports are accurate and reliable, and can provide valuable references for students and teachers, helping them to formulate personalized learning and teaching plans;

[0044] The personalized recommendation module 104 provides students with personalized course and learning resource recommendations to meet their personalized learning needs. The course recommendation submodule 1041 recommends suitable courses to students based on their learning history, interests and hobbies, and learning goals. The learning resource recommendation submodule 1042 recommends relevant learning resources to students. The course recommendation submodule 1041 will comprehensively consider factors such as students' learning history, interests and hobbies, and learning goals, and recommend suitable courses to students through a specific recommendation algorithm. The recommendation algorithm will consider factors such as course difficulty, content, and evaluation to ensure that the recommended courses meet the students' actual conditions and needs. The learning resource recommendation submodule 1042 will recommend relevant learning resources to students, such as books, articles, videos, etc., based on the students' learning conditions and needs. The relevance of the recommendation can be measured by the correlation coefficient r, which will be greater than or equal to the preset minimum correlation coefficient r0, to ensure that the recommended resources are closely related to the students' learning and can effectively assist the students' learning.

[0045] It should be noted that the multimodal interaction satisfaction index M in the multimodal interaction module 101 can be calculated by the following formula:

[0046] M=αV+βA+γS,

[0047] Where: M is the multimodal interaction satisfaction, indexes α, β, and γ are weight coefficients, V is the video interaction satisfaction value, A is the voice interaction satisfaction value, and S is the expression interaction satisfaction value.

[0048] It should be noted that: the personalized recommendation module 104 is provided with a system comprehensive evaluation index Z, and the calculation formula of the system comprehensive evaluation index Z is as follows:

[0049] Z=ωM+ηR+θP,

[0050] Where: ω, η, θ are weight coefficients and = 1, R is the personalized recommendation satisfaction value, and P is the real-time response satisfaction value.

[0051] It should be noted that the sum of the weight coefficients of α, β and γ is equal to 1, and the sum of the weight coefficients of ω, η and θ is equal to 1.

[0052] It should be noted that: the video interaction submodule 1011 allows students to turn on the camera to interact with teachers and other classmates via video. Teachers can view students' learning status and give feedback, and the contribution value V of this submodule to the multimodal interaction satisfaction index M can be comprehensively determined by factors such as the clarity, fluency, and interaction frequency of the video interaction; the voice interaction submodule 1012 provides a clear and smooth voice interaction function, and has voice recognition technology to convert students' voice into text for teachers to view and record. The contribution value A of the voice interaction submodule 1012 to the multimodal interaction satisfaction index M can be comprehensively determined by factors such as voice clarity, response speed, and interaction effect. The expression interaction submodule 1013 allows students to send expressions to express their emotions and attitudes, thereby increasing the fun of the interaction. The contribution value S of the expression interaction submodule 1013 to the multimodal interaction satisfaction index M can be comprehensively determined by factors such as the richness of the expression and frequency of use.

[0053] In this embodiment: the video interaction submodule 1011 allows students to interact with teachers and other students through video by turning on the camera, increasing the realism and intimacy of the interaction. The teacher can check the students' learning status at any time and give feedback in time. The voice interaction submodule 1012 can provide a clear and smooth voice interaction function. Students can ask questions, answer questions or participate in discussions through the microphone. The voice recognition technology can convert the students' voice into text, which is convenient for teachers to view and record; the expression interaction submodule 1013 allows students to express their emotions and attitudes by sending expressions, increasing the fun of the interaction. The teacher can adjust the teaching rhythm and method according to the students' expression feedback.

[0054] It should be noted that the intelligent question-answering submodule 1021 uses artificial intelligence technology to answer students' questions in real time. If the questions cannot be answered, they are forwarded to the teacher, who replies in time. The response time T satisfies T≤t0, where t0 is the preset maximum response time. The calculation method of the real-time response satisfaction value P is as follows:

[0055] P=λPA+μAT,

[0056] Wherein: λ, μ are weight coefficients and λ+μ=1.

[0057] In this embodiment: the intelligent question and answer submodule 1021 can use artificial intelligence technology to provide real-time answers to students' questions. The system can automatically search the knowledge base based on the students' questions and provide accurate and detailed answers. If the question cannot be answered, the system will forward the question to the teacher, who can respond in time.

[0058] It should be noted that the interactive reminder submodule 1022 sends a reminder to the teacher when a student asks a question, answers a question or participates in an interaction, and sets a reminder time interval I, I≤i0, i0 is a preset maximum time interval, and the contribution of the interactive reminder submodule 1022 to the real-time response satisfaction value P is determined by the reminder accuracy CA and the average reminder time AT', and the calculation formula is as follows:

[0059] P=νCA+ξAT',

[0060] Wherein: ν, ξ are weight coefficients and ν+ξ=1.

[0061] In this embodiment: When a student asks a question, answers a question or participates in an interaction, the interactive reminder submodule 1022 will automatically send a reminder to the teacher to ensure that the teacher can respond in time. At the same time, the system will also send reminders to other students to encourage more students to participate in the interaction.

[0062] It should be noted that: the data acquisition submodule 1031 automatically collects student learning behavior data and teacher teaching behavior data, and the collected data volume D should satisfy D≥d0, d0 is the preset minimum data volume. The data analysis submodule 1032 uses big data analysis technology to analyze the collected data to generate student learning reports and teacher teaching reports. The accuracy of the report can be measured by the accuracy rate P, P≥p0, p0 is the preset minimum accuracy rate.

[0063] In this embodiment: Data collection submodule 1031: The system will automatically collect students' learning behavior data, such as the length of time they watch live broadcasts, the number of questions they ask, the status of their answers, etc.; at the same time, the system will also collect teachers' teaching behavior data, such as the length of classes, the number of interactions, evaluations, etc. Data analysis submodule 1032: Use big data analysis technology to analyze the collected data and generate students' learning reports and teachers' teaching reports; learning reports can help students understand their own learning situation, identify deficiencies, and adjust their learning methods in a timely manner; teaching reports can help teachers understand students' learning needs and problems, and optimize teaching content and methods.

[0064] Specifically, the course recommendation submodule 1041 recommends suitable courses for students based on their learning history, interests and hobbies, and learning goals. The recommendation algorithm considers factors such as course difficulty C, content Q, and evaluation E. The recommendation index R = f(C, Q, E), where f is a specific functional relationship. The contribution of the course recommendation submodule 1041 to the personalized recommendation satisfaction value R is determined by the recommendation accuracy RA and the recommendation coverage RC, and the calculation formula is as follows:

[0065] R=σRA+τRC,

[0066] Where: σ, τ are weight coefficients and σ+τ=1.

[0067] In this embodiment: the course recommendation submodule 1041 can recommend suitable courses to students based on their learning history, interests and hobbies, and learning goals. The recommendation algorithm will consider factors such as the difficulty, content, and evaluation of the course to ensure that the recommended courses meet the needs of students.

[0068] It should be noted that the learning resource recommendation submodule 1042 is used to recommend relevant learning resources to students. The relevance of the recommendation can be measured by the correlation coefficient r, r≥r0, r0 is the preset minimum correlation coefficient, and the contribution of this unit to the personalized recommendation satisfaction value R is determined by the resource recommendation accuracy RRA and the resource recommendation diversity RD, and the calculation formula is as follows:

[0069] R=ω1RRA+ω2RD,

[0070] In the formula: ω1, ω2 are weight coefficients and ω1+ω2=1.

[0071] In this embodiment: the learning resource recommendation submodule 1042 can recommend relevant learning resources to students, such as books, papers, videos, etc. The recommendation algorithm will provide students with personalized learning resource recommendations based on the students' learning situation and needs.

[0072] The present invention also provides the working principle of the online education live interactive optimization system, which is as follows:

[0073] Working principle:

[0074] S1. Multimodal interaction:

[0075] During the live broadcast, students can choose to use the video interaction submodule 1011, the voice interaction submodule 1012 or the expression interaction submodule 1013 to interact with the teacher and other classmates as needed.

[0076] During video interaction, students turn on their cameras and communicate with teachers and other students in real time. Teachers can view students' learning status through video and give feedback.

[0077] During voice interaction, students use microphones to ask questions, answer questions or participate in discussions, and voice recognition technology converts students' voices into text for teachers to view and record.

[0078] Students can also send expressions to express emotions and attitudes through the expression interaction submodule 1013;

[0079] S2. Real-time response:

[0080] The intelligent question-answering submodule 1021 uses artificial intelligence technology to answer students' questions in real time. If the questions cannot be answered, they are forwarded to the teacher, who replies to the students within the preset maximum response time t0.

[0081] When a student asks a question, answers a question or participates in an interaction, the interaction reminder submodule 1022 sends a reminder to the teacher, and the reminder time interval I does not exceed the preset maximum time interval i0;

[0082] S3. Analysis of learning situation:

[0083] The data collection submodule 1031 automatically and comprehensively collects students' learning behavior data (such as learning time, courses participated, answering status, etc.) and teachers' teaching behavior data (such as teaching content, teaching methods, questioning status, etc.).

[0084] The data analysis submodule 1032 uses big data analysis technology to analyze the collected data, dig out the learning characteristics, strengths and weaknesses of students, as well as the advantages and areas for improvement in teachers' teaching, and generate student learning reports and teacher teaching reports;

[0085] S4. Personalized recommendation:

[0086] The course recommendation submodule 1041 recommends suitable courses to students based on their learning history, interests and learning goals through a specific recommendation algorithm (taking into account factors such as course difficulty, content and evaluation).

[0087] The learning resource recommendation submodule 1042 recommends relevant learning resources (such as books, articles, videos, etc.) to students based on their learning situations and needs. The relevance of the recommendations is measured by the correlation coefficient r (r≥r0).

[0088] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit thereof, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Online education live interactive optimization system, characterized by: It includes a multimodal interaction module (101), a real-time response module (102), a learning situation analysis module (103) and a personalized recommendation module (104); The multimodal interaction module (101) includes a video interaction submodule (1011), a voice interaction submodule (1012) and an expression interaction submodule (1013), and is used to provide a variety of interaction methods; The real-time response module (102) includes an intelligent question-answering submodule (1021) and an interactive reminder submodule (1022) to ensure the timeliness and accuracy of the interaction; The learning situation analysis module (103) includes a data collection submodule (1031) and a data analysis submodule (1032), which are used to generate a student's learning report and a teacher's teaching report; The personalized recommendation module (104) includes a course recommendation submodule (1041) and a learning resource recommendation submodule (1042), which provide personalized course and learning resource recommendations for students.

2. The online education live interactive optimization system according to claim 1, characterized in that: The multimodal interaction satisfaction index M in the multimodal interaction module (101) can be calculated by the following formula: M=αV+βA+γS, Where: M is the multimodal interaction satisfaction, indexes α, β, and γ are weight coefficients, V is the video interaction satisfaction value, A is the voice interaction satisfaction value, and S is the expression interaction satisfaction value.

3. The online education live interactive optimization system according to claim 2 is characterized by: The personalized recommendation module (104) is provided with a system comprehensive evaluation index Z, and the calculation formula of the system comprehensive evaluation index Z is as follows: Z=ωM+ηR+θP, Where: ω, η, θ are weight coefficients and = 1, R is the personalized recommendation satisfaction value, and P is the real-time response satisfaction value.

4. The online education live interactive optimization system according to claim 3 is characterized by: The sum of the weight coefficients of α, β and γ is equal to 1, and the sum of the weight coefficients of ω, η and θ is equal to 1.

5. The online education live interactive optimization system according to claim 1 is characterized by: The video interaction submodule (1011) allows students to turn on the camera to interact with teachers and other classmates via video, and teachers can view students' learning status and give feedback. The voice interaction submodule (1012) provides a clear and smooth voice interaction function, and has voice recognition technology to convert students' voice into text for teachers to view and record. The expression interaction submodule (1013) allows students to send expressions to express their emotions and attitudes.

6. The online education live interactive optimization system according to claim 1, characterized in that: The intelligent question-answering submodule (1021) uses artificial intelligence technology to answer students' questions in real time, and its response time T satisfies T≤t0, where t0 is the preset maximum response time. The calculation method of the real-time response satisfaction value P is as follows: P=λPA+μAT, Wherein: λ, μ are weight coefficients and λ+μ=1.

7. The online education live interactive optimization system according to claim 1, characterized in that: The interactive reminder submodule (1022) sends a reminder to the teacher when a student asks a question, answers a question or participates in an interaction, and sets a reminder time interval I, where I≤i0, i0 is a preset maximum time interval.

8. The online education live interactive optimization system according to claim 1, characterized in that: The data collection submodule (1031) automatically collects student learning behavior data and teacher teaching behavior data, and the data analysis submodule (1032) uses big data analysis technology to analyze the collected data to generate student learning reports and teacher teaching reports.

9. The online education live interactive optimization system according to claim 1, characterized in that: The course recommendation submodule (1041) recommends suitable courses to students based on their learning history, interests and learning goals.

10. The online education live interactive optimization system according to claim 1, characterized in that: The learning resource recommendation submodule (1042) is used to recommend relevant learning resources to students, and the recommendation relevance is measured by the correlation coefficient r, r≥r0, r0 is the preset minimum correlation coefficient.

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