Personalized education system and method based on artificial intelligence

Abstract

The present invention relates to the field of interactive education technology, and more particularly to an artificial intelligence-based personalized education system and method. The method comprises: obtaining two progress groups that have completed teaching in a target teaching phase during live teaching; obtaining interaction data for each interaction in the target teaching phase corresponding to each progress group; obtaining an interaction positivity index for each interaction in each progress group based on the interaction data; obtaining an interaction rhythm adjustment coefficient for the target teaching phase based on the interaction positivity index for each interaction in each progress group; and adjusting the interaction moments of other progress groups that have not yet started the target teaching phase based on the interaction rhythm adjustment coefficient. The present invention can improve the interactive quality of live teaching.

Description

Technical Field

[0001] The present invention relates to the field of interactive education technology, and in particular to a personalized education system and method based on artificial intelligence. Background Art

[0002] As a new educational model emerging through internet technology and platforms, online interactive education enables real-time or non-real-time interaction between teachers and students. It successfully transcends the dual geographical and temporal constraints of traditional education, enabling a more balanced and rational distribution of educational resources while providing students with more flexible and diverse learning paths.

[0003] Currently, mainstream education platforms primarily focus on analyzing data such as student learning progress and homework completion, providing personalized instructional recommendations based on this data. However, these platforms largely neglect the interactive experience of live teaching. This lack of intuitive interaction makes it difficult for teachers to quickly and effectively assess teaching effectiveness and understand students' learning status.

[0004] Existing technologies typically use a method to determine interactive time periods by counting the number of participants and setting a threshold for the duration of the interaction. However, given the high mobility of online education participants, this pre-defined time period selection method is difficult to adapt to the actual needs of participants of different levels, resulting in poor interactive quality in live teaching. Summary of the Invention

[0005] In order to solve the technical problem of poor interactive quality in live teaching, the purpose of the present invention is to provide a personalized education system and method based on artificial intelligence. The technical solutions adopted are as follows:

[0006] In a first aspect of the present invention, a personalized education method based on artificial intelligence is provided, the method comprising:

[0007] Get the two progress groups that have completed the target teaching phase in the live teaching;

[0008] Obtaining interaction data for each interaction in the target teaching stage corresponding to each progress group;

[0009] Obtaining an interaction positivity index for each interaction in each progress group according to the interaction data;

[0010] Obtaining an interaction rhythm adjustment coefficient for the target teaching stage according to the interaction positivity index of each interaction in each progress group;

[0011] The interaction moments of other progress groups that have not started the target teaching stage are adjusted according to the interaction rhythm adjustment coefficient.

[0012] In one embodiment, the interaction data includes: the total number of students, interaction duration, and actual answering duration of each student who actually participated in the interaction; obtaining the interaction positivity index of each interaction in each progress group based on the interaction data includes:

[0013] The following steps are performed on the interaction data of each interaction in each progress group:

[0014] Obtaining answer status parameters based on the actual answering time of the current interaction and the total number of students;

[0015] Obtaining an interference coefficient according to the interaction duration of the current interaction;

[0016] An interaction positivity index of the current interaction is obtained according to the answering state parameter and the interference coefficient.

[0017] In one embodiment, the interaction data further includes: the actual number of students who actually participated in the interaction; and obtaining the answer status parameter based on the actual answering time of the current interaction and the total number of students includes:

[0018] Obtaining the number of first people whose actual answering time is less than or equal to the first preset time;

[0019] Obtain the number of second people whose actual answering time is greater than the first preset time;

[0020] Obtaining a first ratio based on the first number of personnel and the total number of students;

[0021] Obtaining a second ratio based on the second number of personnel and the total number of students;

[0022] Obtaining a participant ratio coefficient based on the actual number of participants and the total number of participants;

[0023] Obtain the total answering time according to the actual answering time;

[0024] The answer status parameter is obtained according to the participant number ratio coefficient, the total answering time, the actual number of people, the first ratio and the second ratio.

[0025] In one embodiment, obtaining the interference coefficient according to the interaction duration of the current interaction includes:

[0026] Get the reference time required to complete each question in the current interaction;

[0027] The interference coefficient is obtained according to the interaction duration and all the reference times.

[0028] In one embodiment, obtaining the interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positivity index of each interaction in each progress group includes:

[0029] Obtaining an interaction conversion coefficient corresponding to each of two adjacent interactions of each progress group according to the interaction positivity index of each of two adjacent interactions of each progress group;

[0030] Obtaining a peer participation index for each progress group according to the interaction conversion coefficient;

[0031] The interaction rhythm adjustment coefficient is obtained according to the same-order participation index.

[0032] In one embodiment, obtaining the interaction conversion coefficient corresponding to each two adjacent interactions of each progress group according to the interaction positivity index of each two adjacent interactions of each progress group includes:

[0033] The following steps are performed for the interaction positivity index corresponding to each two adjacent interactions:

[0034] Obtain the minimum value of the interaction positivity index of two adjacent interactions;

[0035] Obtaining the absolute value of the difference between the interaction positivity indexes of the two current adjacent interactions;

[0036] The interaction conversion coefficient of the two current adjacent interactions is obtained according to the absolute value of the difference and the minimum value.

[0037] In one embodiment, obtaining the peer participation index of each progress group according to the interaction conversion coefficient includes:

[0038] The following steps are performed for the interaction conversion coefficient of each progress group:

[0039] Obtain the mean of all interaction conversion coefficients of the current progress group;

[0040] Obtaining a maximum interactive conversion coefficient value among the interactive conversion coefficients;

[0041] Obtaining a target interaction positive index corresponding to the maximum interaction conversion coefficient value;

[0042] The peer participation index of the current progress group is obtained according to the mean, the maximum interaction conversion coefficient value and the target interaction positivity index.

[0043] In one embodiment, obtaining the interaction rhythm adjustment coefficient according to the peer participation index includes:

[0044] Obtaining the maximum value among the same-order participation indexes;

[0045] The interaction rhythm adjustment coefficient is obtained according to the maximum value of all interaction durations corresponding to each of the progress groups and the same-level participation index.

[0046] In one embodiment, adjusting the interaction time of other progress groups that have not started the target teaching stage according to the interaction rhythm adjustment coefficient includes:

[0047] For each additional progress group, perform the following steps:

[0048] Get the time of the teaching stage that other progress groups have completed;

[0049] The interaction moment at which the other current progress groups start the target teaching stage is obtained according to the time and the interaction rhythm adjustment coefficient.

[0050] In a second aspect of the present invention, there is provided a personalized education system based on artificial intelligence, the system comprising:

[0051] The progress group acquisition module is used to obtain the two progress groups that have completed the target teaching stage in the live teaching;

[0052] An interactive data acquisition module, configured to acquire interactive data of each interaction in the target teaching stage corresponding to each progress group;

[0053] An interaction positivity index acquisition module, configured to acquire the interaction positivity index of each interaction in each progress group according to the interaction data;

[0054] An interaction rhythm adjustment coefficient acquisition module, configured to acquire the interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positivity index of each interaction in each progress group;

[0055] The adjustment module is used to adjust the interaction time of other progress groups that have not started the target teaching stage according to the interaction rhythm adjustment coefficient.

[0056] In one embodiment, the interaction data includes: the total number of students, interaction time, and the actual answering time of each student who actually participated in the interaction; the interaction positivity index acquisition module is specifically used to:

[0057] The following steps are performed on the interaction data of each interaction in each progress group:

[0058] Obtaining answer status parameters based on the actual answering time of the current interaction and the total number of students;

[0059] Obtaining an interference coefficient according to the interaction duration of the current interaction;

[0060] An interaction positivity index of the current interaction is obtained according to the answering state parameter and the interference coefficient.

[0061] In one embodiment, the interaction data further includes: the actual number of students who actually participated in the interaction; the interaction positivity index acquisition module is specifically used to:

[0062] Obtaining the number of first people whose actual answering time is less than or equal to the first preset time;

[0063] Obtain the number of second people whose actual answering time is greater than the first preset time;

[0064] Obtaining a first ratio based on the first number of personnel and the total number of students;

[0065] Obtaining a second ratio based on the second number of personnel and the total number of students;

[0066] Obtaining a participant ratio coefficient based on the actual number of participants and the total number of participants;

[0067] Obtain the total answering time according to the actual answering time;

[0068] The answer status parameter is obtained according to the participant number ratio coefficient, the total answering time, the actual number of people, the first ratio and the second ratio.

[0069] In one embodiment, the interaction positive index acquisition module is specifically used to:

[0070] Get the reference time required to complete each question in the current interaction;

[0071] The interference coefficient is obtained according to the interaction duration and all the reference times.

[0072] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically used to:

[0073] Obtaining an interaction conversion coefficient corresponding to each of two adjacent interactions of each progress group according to the interaction positivity index of each of two adjacent interactions of each progress group;

[0074] Obtaining a peer participation index for each progress group according to the interaction conversion coefficient;

[0075] The interaction rhythm adjustment coefficient is obtained according to the same-order participation index.

[0076] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically used to:

[0077] The following steps are performed for the interaction positivity index corresponding to each two adjacent interactions:

[0078] Obtain the minimum value of the interaction positivity index of two adjacent interactions;

[0079] Obtaining the absolute value of the difference between the interaction positivity indexes of the two current adjacent interactions;

[0080] The interaction conversion coefficient of the two current adjacent interactions is obtained according to the absolute value of the difference and the minimum value.

[0081] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically used to:

[0082] The following steps are performed for the interaction conversion coefficient of each progress group:

[0083] Obtain the mean of all interaction conversion coefficients of the current progress group;

[0084] Obtaining a maximum interactive conversion coefficient value among the interactive conversion coefficients;

[0085] Obtaining a target interaction positive index corresponding to the maximum interaction conversion coefficient value;

[0086] The peer participation index of the current progress group is obtained according to the mean, the maximum interaction conversion coefficient value and the target interaction positivity index.

[0087] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically used to:

[0088] Obtaining the maximum value among the same-order participation indexes;

[0089] The interaction rhythm adjustment coefficient is obtained according to the maximum value of all interaction durations corresponding to each of the progress groups and the same-level participation index.

[0090] In one embodiment, the adjustment module is specifically configured to:

[0091] For each additional progress group, perform the following steps:

[0092] Get the time of the teaching stage that other progress groups have completed;

[0093] The interaction moment at which the other current progress groups start the target teaching stage is obtained according to the time and the interaction rhythm adjustment coefficient.

[0094] The present invention has the following beneficial effects:

[0095] The present invention analyzes the interactive data of live interactive teaching of students participating in the target teaching stage during the live teaching process to obtain the interactive positive index of each interaction. Based on this index, the participation willingness of the participating students can be accurately determined, and the interactive rhythm adjustment coefficient of the target teaching stage can be obtained according to the interactive positive index of each interaction. With this interactive rhythm adjustment coefficient, the interactive time of the target teaching stage of other progress groups that have not yet entered the target teaching stage is adaptively adjusted. Since the interactive data of the previous participating students are taken into account when adjusting the interactive time, the obtained interactive rhythm adjustment coefficient is more accurate, which can improve the student adaptability in the teaching process and improve the teaching quality. That is, since the coefficient is calculated based on the rich and real interactive data of the previous participating students, it fully covers the behavioral characteristics and feedback of the students in different interactive scenarios, and thus can comprehensively and accurately reflect the ideal rhythm and pattern of interaction in the teaching stage. With this accurate interactive rhythm adjustment coefficient, the present invention is able to adaptively adjust the interactive time of the target teaching stage of other progress groups that have not yet entered the target teaching stage. During the adjustment process, by consistently referencing the interaction data of previously participating students, the interaction moments are closely aligned with the students' actual participation patterns and learning needs, further optimizing the accuracy of the interaction rhythm adjustment coefficient. A more accurate interaction rhythm adjustment coefficient allows the teaching process to more precisely adapt to students' learning pace and willingness to participate. Teachers can flexibly adjust teaching methods and interaction sessions based on students' actual status, ensuring a close fit between teaching activities and student needs. This significantly improves student adaptability during the teaching process and ultimately leads to a significant improvement in teaching quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0097] Figure 1 A process of a personalized education method based on artificial intelligence provided by an embodiment of the present invention Figure 1 ;

[0098] Figure 2 A process of a personalized education method based on artificial intelligence provided by an embodiment of the present invention Figure 2 ;

[0099] Figure 3 A process of a personalized education method based on artificial intelligence provided by an embodiment of the present invention Figure 3 ;

[0100] Figure 4 A process of a personalized education method based on artificial intelligence provided by an embodiment of the present invention Figure 4 ;

[0101] Figure 5 A schematic diagram of the interaction interval duration provided by one embodiment of the present invention;

[0102] Figure 6 A schematic diagram of interaction time for different classes provided by one embodiment of the present invention;

[0103] Figure 7 A process of a personalized education method based on artificial intelligence provided by an embodiment of the present invention Figure 5 ;

[0104] Figure 8 A schematic diagram of the functional modules of an artificial intelligence-based personalized education system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0105] To further illustrate the technical means and effects of the present invention to achieve the intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effects of an artificial intelligence-based personalized education system and method proposed by the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable form.

[0106] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0107] The following describes in detail a specific scheme of an artificial intelligence-based personalized education system and method provided by the present invention with reference to the accompanying drawings.

[0108] Existing education platforms mainly focus on analyzing data such as students' learning progress and homework completion status to provide personalized teaching recommendations, but ignore the interactive experience during live teaching, resulting in a lack of intuitive interactive links. This neglect makes it difficult for teachers to evaluate teaching effectiveness in a timely and effective manner during the teaching process, and it is also impossible to fully understand the learning status of students. The personalized education system and method based on artificial intelligence provided by the present invention can be applied to existing education platforms. By analyzing the interactive data of each stage of live teaching, the student's interactive positivity index is evaluated, and the interactive rhythm adjustment coefficient is obtained based on the interactive positivity index. The teaching rhythm is dynamically adjusted based on the interactive rhythm adjustment coefficient, thereby effectively improving learning enthusiasm and assisting teachers to accurately grasp the learning dynamics of students.

[0109] See also Figure 1 , which shows a flow chart of a personalized education method based on artificial intelligence provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes the following steps S101-S105:

[0110] Step S101: Acquire two progress groups of completed teaching in the target teaching stage of the live teaching.

[0111] The progress group in the present invention can be a group of students with the same or similar learning progress, for example, a class, where one class is one progress group.

[0112] Then in step S101 , it is necessary to obtain the classes that have completed the teaching of a certain teaching stage at the same time.

[0113] For example, if class 1 has completed teaching phase 1, class 2 has also completed teaching phase 1, and class 3 has also completed teaching phase 1, then step 101 can select any two classes from class 1, class 2, and class 3 to execute S102-S105.

[0114] Step S102: Obtain interaction data of each interaction in the target teaching stage corresponding to each progress group.

[0115] The interaction data includes: the total number of students, the preset interaction time, the actual number of students who actually participated in the interaction, and the actual answering time of each student who actually participated in the interaction.

[0116] This invention is based on online interactive education based on artificial intelligence. During online education, all interactive data generated by users (teachers and students) will be recorded, including various status data such as the teacher's live broadcast content, questions, homework, and student responses, as well as student emotions, attention, and expression data obtained using AI. Therefore, for the research on online interactive education methods, the overall interaction positivity index can be analyzed by obtaining interactive data during the teacher's teaching process. The following technologies can be used to obtain data using AI:

[0117] Facial Expression Recognition: Analyze students' facial expressions to determine whether they are focused, confused, or interested.

[0118] Attention analysis: By analyzing the student's eye gaze direction, head posture, etc., we can determine whether they are paying attention to the screen content.

[0119] Sentiment classification: Analyze the emotional tendencies (positive, negative, neutral) in students' answers or comments in discussion forums to understand students' learning status and participation enthusiasm.

[0120] Attention analysis, expression recognition, and emotion classification are common analysis tasks in artificial intelligence, and their technical principles will not be elaborated here.

[0121] During the live teaching process, the teacher sends the answers to the students (total number of students) through the system's internal answer database. ) Questionnaires or pop-up windows are distributed on the computer terminal. Each time an interactive interface is sent, the preset interactive time is displayed on the student's terminal. , that is, the duration of the interactive interface (pop-up window) of the student terminal is The system will record each interaction and mark it as , that is, Indicates the interactions, and you can also count the total number of interactions .

[0122] It is worth noting that one interaction refers to a pop-up window, and multiple questions can be asked in one interaction.

[0123] Students answer questions asked by teachers through computer terminals. The system will record the operation results and actual answering time of each student according to the operation of different students. , (If the student's actual answer time exceeds the specified answer time ,but And always ).

[0124] For the parameters of students' answers to questions, it is necessary to count the initiative of different students in answering the teacher. If students participate in answering, the number of students' interaction records will be increased by 1. If students answer within the specified time, If you do not participate in answering the questions, no record will be made.

[0125] It is also necessary to count the actual number of students who actually participated in the interaction, that is, the number of students who actually participated in answering questions needs to be counted, which is recorded as .

[0126] The present invention is a personalized education system and method based on artificial intelligence. During live teaching within the system, teachers may ask students questions at any time during the lecture to ensure their attentiveness and increase their participation. Students then answer the questions based on their knowledge. The system then analyzes students' learning status based on the data parameters of their classroom questions and the completion status of their homework.

[0127] Step S103: Obtaining an interaction positivity index for each interaction in each progress group according to the interaction data.

[0128] In one embodiment, Figure 2As shown, the step S103 of obtaining the interaction positivity index of each interaction in each progress group according to the interaction data includes the following sub-steps S1031-S1033 for the interaction data of each interaction in each progress group:

[0129] S1031. Obtain answer status parameters based on the actual answering time of the current interaction and the total number of students.

[0130] During online interactive teaching, teachers ask students questions. Due to differences in students' understanding of the questions, some students participate in the answers (including complete answers and incomplete answers) while others do not. Therefore, when analyzing the teaching response status, it is necessary to obtain the response status parameters for each response.

[0131] In one embodiment, step S1031 obtains the answer status parameter based on the actual answering time of the current interaction and the total number of students, including the following sub-steps A1-A7:

[0132] A1. Obtain the first number of people whose actual answering time is less than or equal to the first preset time.

[0133] A2. Obtain the number of second people whose actual answering time is greater than the first preset time.

[0134] For each student who has participated in the question-answering process, the more timely and accurate the student's responses, the better the interaction. Therefore, when analyzing student participation, it is necessary to analyze the efficiency of different students in answering questions.

[0135] In the process of completing an answer, according to the time required for each student to answer, the first preset time is set to , when the actual time for students who actually participated in the interaction to answer Reasonable time ,when When (when If the answering time is exceeded, it will not be counted as participation).

[0136] Obtain the first number of personnel corresponding to the reasonable time and the second number of personnel corresponding to the general time respectively.

[0137] A3. Obtain a first ratio based on the first number of personnel and the total number of students. The formula is:

[0138] ;

[0139] in, is the first ratio, is the number of first personnel, It indicates a reasonable time, which refers to the time when the actual answering time of the students who actually participated in the interaction is less than or equal to the first preset time. The total number of students.

[0140] A4. Obtain a second ratio based on the second number of personnel and the total number of students.

[0141] ;

[0142] in, For the second ratio, is the number of the second person, Indicates the general time, which refers to the time when the actual answering time of the students who actually participated in the interaction is longer than the first preset time. The total number of students.

[0143] A5. Obtain a participant ratio coefficient based on the actual number of participants and the total number of participants.

[0144] According to the degree of participation of different students in the teacher's questions, after each question is completed, the proportion coefficient of the number of participants is obtained ( is the actual number of students who actually participated in the interaction, is the total number of students), and then the ratio coefficient of the number of people who obtain each question number i is recorded.

[0145] A6. Obtain the total answering time based on the actual answering time.

[0146] A7. Obtain the answer status parameter according to the participant ratio coefficient, the total answering time, the actual number of participants, the first ratio, and the second ratio.

[0147] Analyze the current interactive answer status parameters based on the participant ratio coefficient, the total answer time, the actual number of participants, the first ratio, and the second ratio. .

[0148]

[0149] Where, is the interaction number, For the The coefficient of the proportion of participants in the interaction, is the sum of the actual answering time. is the actual number of students who actually participated in the interaction, It is the ratio of the sum of the actual answering time and the actual number of people, indicating the average answering time. Is the contrast coefficient between the first ratio and the second ratio. The larger the value, the better the answer. The function performs normalization.

[0150] The above formula is based on the difference between the number of people participating in answering questions and the number of times they spend answering questions. The greater the number of people answering questions and the shorter the time required to answer questions, the better the interactive status of this question.

[0151] In another embodiment, the answer status parameter of the current interaction can be reflected by the product of the proportion of the student's concentration state and the proportion of the positive emotional tendency of the answer.

[0152] S1032. Obtain an interference coefficient according to the interaction duration of the current interaction.

[0153] In one embodiment, obtaining the interference coefficient according to the interaction duration of the current interaction in step S1032 includes the following sub-steps B1-B2:

[0154] B1. Obtain the reference time required to complete each question in the current interaction.

[0155] B2. Obtain the interference coefficient according to the interaction duration and all the reference times.

[0156] For random questions from the system question bank that correspond to the current knowledge point, there is a discrepancy between the expected time for each question and the student's interactive time. The greater the discrepancy, the worse the overall student interaction effect. Therefore, when directly testing the interactive process of live teaching, it is necessary to analyze the interference coefficient of question thinking on overall answering efficiency.

[0157] Based on the issues in the current interaction (question number), get each question in the question bank Reference time required for completion .

[0158] The difference in reference time required for different questions in the current interaction and the preset interaction duration of the current interaction , get the interference coefficient during each interaction , reflecting the adaptability between the questions asked and the student users, as follows:

[0159] ;

[0160] Where, is the interaction number, n represents the number of questions in one interaction, For the The reference time required for each question in the interaction is the same as the The closer the value is to 1, the more suitable the knowledge points corresponding to the keywords in the currently extracted questions are for the current batch of participating users. Indicates the preset duration for each interactive interface to be displayed on the student's terminal, that is, the duration of the interactive interface (pop-up window) on the student's terminal.

[0161] Due to the existence Phenomenon, use Function pair Perform normalization so that The value range is .

[0162] S1033. Obtain an interaction positivity index of the current interaction according to the answering state parameter and the interference coefficient.

[0163] Furthermore, combined with the interference coefficient , using it as an interference factor to obtain the students’ positive interaction index for the current interaction during the live teaching interaction :

[0164] ;

[0165] Where, Indicates the The interaction positivity index of interactions, Indicates the The response status parameter of the interaction, Indicates the The interference coefficient of the interaction is calculated using The function normalizes the result. This index characterizes the compatibility between questions and learners. For each interaction, the compatibility between questions and learners is analyzed by analyzing the response status and the student's response time, comprehensively reflecting the positivity of each interaction. A higher value for the Interaction Positivity Index indicates a better student engagement, a better interaction effect, and a more engaged learner. A lower value for the Interaction Positivity Index indicates a worse student engagement, a poorer interaction effect, and a less engaged learner.

[0166] Step S104: obtaining an interaction rhythm adjustment coefficient for the target teaching stage according to the interaction positivity index of each interaction in each progress group.

[0167] In one embodiment, Figure 3As shown, in step S104, the interaction rhythm adjustment coefficient of the target teaching stage is obtained according to the interaction positivity index of each interaction in each progress group, including the following sub-steps S1041-S1043:

[0168] S1041. Obtain an interaction conversion coefficient corresponding to each two adjacent interactions of each progress group according to the interaction positivity index of each two adjacent interactions of each progress group.

[0169] In one embodiment, Figure 4 As shown, in step S1041, the interaction conversion coefficient corresponding to each two adjacent interactions of each progress group is obtained according to the interaction positivity index of each two adjacent interactions of each progress group, including performing the following steps S10411-S10413 on the interaction positivity index corresponding to each two adjacent interactions:

[0170] S10411. Obtain the minimum value of the interaction positivity index of two adjacent interactions.

[0171] S10412. Obtain the absolute value of the difference between the interaction positivity indexes of the two current adjacent interactions.

[0172] S10413. Obtain the interaction conversion coefficient of the two current adjacent interactions according to the absolute value of the difference and the minimum value.

[0173] In one embodiment, the present invention further provides a method for dividing teaching stages:

[0174] During the live teaching process, the frequency and specific stages of teacher interaction in different time periods will affect the interaction effect. Therefore, it is necessary to update and adjust the overall division of the data to obtain better live teaching interaction effects in different teaching stages, thereby improving the interaction state parameters.

[0175] During the teacher's live teaching, the students' seriousness in the live learning varies in different time periods, and the teacher cannot monitor the participants at all times. As a result, during the interactive teaching process, multiple interactions cause changes in the adaptability of the preset interaction plan to the participants as the teaching staff changes.

[0176] Therefore, based on the adaptation deviation between the preset interaction plans of each teacher and the students during the teaching process at different times, and based on the changes and fluctuations in the students' overall interaction positivity index during the interactive questioning process at different moments, the relevance of the knowledge point content keywords of the same course in the teaching process is screened out.

[0177] The teaching stage pointers in the present invention are different from the knowledge points, such as Figure 5As shown in the figure, when explaining the same knowledge point, the interval between interactions is shorter, that is, the interaction interval in the same teaching stage is shorter. However, when explaining different knowledge points, due to the length of time that the knowledge points occupy in the brief description process, there will be a longer time interval between the interaction of the new knowledge point and the previous knowledge point, that is, the interaction interval in different teaching stages is longer.

[0178] First, during the explanation process, teachers will ask questions about the knowledge points after explaining them, so as to analyze the relevance of students' participation in the preset interactive plan content. For the explanation of knowledge points, the keywords of the knowledge point content and the scale of the preset interactive plan reflect the importance of the knowledge points. Therefore, the teaching stages of knowledge explanation are divided according to the deviation of adjacent explanation time.

[0179] ;

[0180] in, represents the time deviation between adjacent interactions, is the interval between adjacent interactions, Represents the mean interaction time of adjacent interactions.

[0181] The number of questions for the mark will be adjacent and are divided into the same teaching stage.

[0182] During the teaching process, the teacher will give different Teaching is conducted based on the same knowledge subject for the same major (profession). During the explanation process, different progress groups will have different reactions to the same knowledge subject. That is, in different classes, during the teacher's interactive explanation, the students' interactive positive index may be different, such as Figure 6 As shown, the interaction time of different classes is different. Therefore, in order to achieve better interactive explanation effects, it is best to choose to ask questions and interact during the time period (stage) when students are more active.

[0183] First, analyze the differences in the students' interaction positivity index at each adjacent interaction time in each progress group during the target teaching stage to obtain the interaction conversion coefficient. :

[0184] ;

[0185] ;

[0186] Where, Indicates the first progress group The interaction conversion coefficient at the interaction time, For the Interactions, Conduct the first progress group The positive interaction index at the time of interaction, Conduct the first progress group -1 interaction positive index, For use The function obtains the smaller value of the interaction positive index of the adjacent interactions of the first progress group. It is the ratio of the smaller value of the interaction positivity index difference of adjacent interactions of the first progress group to the interaction positivity index of adjacent interactions; Indicates the second progress group The corresponding interaction conversion coefficient for interactions, For the Interactions, Conduct the second progress group -1 interaction positive index, Conduct the second progress group The positive interaction index at the time of interaction, For use The function obtains the smaller value of the interaction positive index of the adjacent interactions of the second progress group. is the ratio of the interaction positive index difference of adjacent interactions of the second progress group to the smaller value of the interaction positive index of adjacent interactions. It is worth noting that when obtaining the interaction conversion coefficient, is an integer greater than 1.

[0187] It should be noted that the first progress group and the second progress group represent progress groups corresponding to the same teaching stage, that is, both represent progress groups that have completed the target teaching stage, and are closest to the next progress group that will complete the target teaching stage.

[0188] S1042. Obtain the same-level participation index of each progress group according to the interaction conversion coefficient.

[0189] In one embodiment, Figure 7 As shown, in step S1042, obtaining the same-level participation index of each progress group according to the interaction conversion coefficient includes executing the following steps S10421-S10424 for the interaction conversion coefficient of each progress group:

[0190] S10421. Obtain the mean value of all the interaction conversion coefficients of the current progress group.

[0191] S10422. Obtain a maximum interactive conversion coefficient value among the interactive conversion coefficients.

[0192] S10423. Obtain a target interaction positivity index corresponding to the maximum interaction conversion coefficient value.

[0193] S10424. Obtain the peer participation index of the current progress group according to the mean, the maximum interaction conversion coefficient value, and the target interaction positivity index.

[0194] For the students' positive interaction index with the teacher during the target teaching stage, by analyzing the changes in the positive interaction shown by the questions asked during the target teaching stage, the same-level participation index of each progress group during the target teaching stage is obtained. :

[0195] ;

[0196] ;

[0197] Where, It represents the peer participation index of the first progress group within the target teaching stage. is the mean of all interaction conversion coefficients of the first progress group within the target teaching period, , represents the mean of the sum of all interaction conversion coefficients of the first progress group within the target teaching stage, Indicates the Interactions, Indicates the number of interactions. Indicates that the first progress group is in The interaction conversion coefficient corresponding to the interaction, , represents the maximum value of the interaction conversion coefficient of the first progress group within the target teaching stage, Indicates the interaction conversion coefficient of the second interaction of the first progress group. It represents the interaction conversion coefficient of the third interaction of the first progress group. Indicates the first progress group The interaction conversion coefficient at the interaction time, It is the ratio of the difference between the maximum value of the interaction conversion coefficient of the first progress group in the target education stage and the mean value of the interaction conversion coefficient to the mean value of the interaction conversion coefficient, indicating the overall interaction effect of the target teaching stage. The smaller the value, the better the overall effect.

[0198] In the first progress group within the target teaching stage, one interaction corresponds to an interaction conversion coefficient, and one interaction also corresponds to an interaction positivity index. The number of interactions corresponding to the maximum value of the interaction conversion coefficient of the first progress group within the target teaching stage is obtained, and the interaction positivity index corresponding to this interaction is calculated. The maximum interaction conversion coefficient The corresponding target interaction positive index. Based on this, for The corresponding target interaction positive index represents the actual participation index. For its ( The corresponding target interaction positive index) is inversely proportional to the result. Use The function normalizes the result so that The larger the value, the better the participation effect in the target teaching stage. It represents the peer participation index of the second progress group within the target teaching stage. is the mean of all interaction conversion coefficients of the second progress group within the target teaching period, , represents the mean of the sum of all interaction conversion coefficients of the second progress group within the target teaching stage, Indicates the Interactions, Indicates the number of interactions. Indicates that the second progress group is in The interaction conversion coefficient corresponding to the interaction, It represents the maximum value of the interaction conversion coefficient of the second progress group within the target teaching stage. Indicates the interaction conversion coefficient of the second progress group during the second interaction. It represents the interaction conversion coefficient of the third interaction of the second progress group. Indicates the second progress group The interaction conversion coefficient at the interaction time, It is the ratio of the difference between the maximum value of the interaction conversion coefficient of the second progress group in the target education stage and the mean value of the interaction conversion coefficient to the mean value of the interaction conversion coefficient, which represents the overall interaction effect of the target teaching stage. The smaller the value, the better the overall effect. The corresponding target interaction positive index represents the actual participation index. For its ( The corresponding target interaction positive index) is inversely proportional to the result. Use The function normalizes the result so that The larger the value, the better the participation effect in the target teaching stage.

[0199] S1043. Obtain the interaction rhythm adjustment coefficient according to the same-level participation index.

[0200] In one embodiment, obtaining the interaction rhythm adjustment coefficient according to the same-order participation index in step S1043 includes the following sub-steps S10431-S10432:

[0201] S10431. Obtain the maximum value among the same-order participation indexes.

[0202] Get the maximum value of the peer participation index of the second progress group and the peer participation index of the first progress group.

[0203] S10432. Obtain the interaction rhythm adjustment coefficient according to the maximum value of all interaction durations corresponding to each progress group and the same-level participation index.

[0204] According to the progress of teaching time during classroom teaching, the overall duration of different interactions of each progress group is obtained.

[0205] Peer participation index of the target teaching stage in the teaching process based on the first progress group , the peer participation index of the target teaching stage during the teaching process of the second progress group , the overall duration of the first progress group in the target teaching phase , and the overall duration of the second progress group in the target teaching phase , get the interaction rhythm adjustment coefficient :

[0206] ;

[0207] Where, In order to adjust the interaction rhythm coefficient in the teaching process of the target teaching stage, The peer participation index for the first progress group in the target teaching phase, The peer participation index for the second progress group in the target teaching phase, The total duration of the first progress group's target teaching phase, that is, the sum of all interaction durations of the first progress group during the target teaching phase. The total duration of the target teaching phase for the second progress group is the sum of all interaction durations of the second progress group during the target teaching phase. The function selects classes with better participation status, and then uses the time difference coefficient of the class as an adjustment parameter.

[0208] Step S105: adjusting the interaction time of other progress groups that have not started the target teaching stage according to the interaction rhythm adjustment coefficient.

[0209] In one embodiment, in step S105, the interaction time of other progress groups that have not started the target teaching stage is adjusted according to the interaction rhythm adjustment coefficient, including executing the following steps S1051-S1052 for each other progress group:

[0210] S1051. Obtain the time of the teaching phase that has been completed by other progress groups.

[0211] S1052. Obtain the interaction moment at which the other current progress groups start the target teaching stage according to the time and the interaction rhythm adjustment coefficient.

[0212] When adjusting the interactive time of other progress groups that have not yet completed the target teaching stage, you can first obtain the teaching time of the teaching stage that the progress group to be adjusted has completed. , then by Obtain the interaction time of other progress groups to be adjusted during the target teaching phase. The teacher uses the obtained interaction time The target teaching stage is carried out for the progress group to be adjusted. The interactive time of the target teaching stage for other progress groups to be adjusted, To adjust the interaction rhythm coefficient during the teaching process of the target teaching stage, The teaching time of the completed teaching stage of the progress group to be adjusted.

[0213] For example, assuming that the target teaching stage is the third teaching stage, the progress groups that have already started the third teaching stage are the first progress group and the second progress group. If there are multiple progress groups that have already started the third teaching stage, then the two progress groups that are about to start the third teaching stage are obtained for analysis. The progress group that has not yet started the third teaching stage is the third progress group, and the third progress group has completed the first and second teaching stages. Then, first, according to steps S101-S104, the interactive rhythm adjustment coefficient of the third teaching stage is obtained. When the third teaching phase is carried out for the third progress group, the teaching time of the first and second teaching phases completed by the third progress group is first obtained. , and then by the formula Obtain the interactive moments of the third progress group in the third teaching stage , and finally, in the interactive moment Upon arrival, the teacher will carry out the third teaching stage teaching for the third progress group. For example, the third teaching stage teaching can enable the teacher to mobilize random questions from the database question bank corresponding to the target teaching stage to conduct interactive teaching and explanation with the students.

[0214] Determining the interaction moment at which the other progress group begins the target teaching phase based on the time and the interaction rhythm adjustment coefficient can also be achieved through a neural network. A recurrent neural network, such as an RNN, is trained to predict the interaction moment at which the other progress group begins the target teaching phase. Specific neural network training is well known, and the specific process is not detailed here.

[0215] The present invention analyzes the interactive data of live interactive teaching of students participating in the target teaching stage during the live teaching process to obtain the interactive positive index of each interaction. Based on this index, the participation willingness of the participating students can be accurately determined, and the interactive rhythm adjustment coefficient of the target teaching stage can be obtained according to the interactive positive index of each interaction. With this interactive rhythm adjustment coefficient, the interactive time of the target teaching stage of other progress groups that have not yet entered the target teaching stage is adaptively adjusted. Since the interactive data of the previous participating students are taken into account when adjusting the interactive time, the obtained interactive rhythm adjustment coefficient is more accurate, which can improve the student adaptability in the teaching process and improve the teaching quality. That is, since the coefficient is calculated based on the rich and real interactive data of the previous participating students, it fully covers the behavioral characteristics and feedback of the students in different interactive scenarios, and thus can comprehensively and accurately reflect the ideal rhythm and pattern of interaction in the teaching stage. With this accurate interactive rhythm adjustment coefficient, the present invention is able to adaptively adjust the interactive time of the target teaching stage of other progress groups that have not yet entered the target teaching stage. During the adjustment process, by consistently referencing the interaction data of previously participating students, the interaction moments are closely aligned with the students' actual participation patterns and learning needs, further optimizing the accuracy of the interaction rhythm adjustment coefficient. A more accurate interaction rhythm adjustment coefficient allows the teaching process to more precisely adapt to students' learning pace and willingness to participate. Teachers can flexibly adjust teaching methods and interaction sessions based on students' actual status, ensuring a close fit between teaching activities and student needs. This significantly improves student adaptability during the teaching process and ultimately leads to a significant improvement in teaching quality.

[0216] In addition, the present invention can also effectively address the quality of interaction in online education situations with high staff turnover. This is because when analyzing the interactive data of students participating in the target teaching phase of live teaching, the interactive positivity index and interactive rhythm adjustment coefficient obtained are completely derived from the actual performance of the students who actually participated in the interaction. These students' interactive behaviors fully reflect their status and enthusiasm in the learning process. Because of this, based on these data indicators based on real performance, it is possible to accurately understand the students' participation patterns and needs. In the case of high staff turnover in freshman education, the interactive performance data of actual participating students in the past provides strong support for optimizing the interactive links. On the one hand, based on the interactive positivity index and interactive rhythm adjustment coefficient, the interaction time can be flexibly and reasonably arranged to ensure that the interactive links are consistent with the students' participation habits and learning status, avoiding the disconnection between interaction and students due to staff turnover. On the other hand, this data helps teachers more accurately grasp the teaching rhythm and adjust teaching strategies in a timely manner based on the actual feedback of students, thereby effectively improving the quality of interaction and enabling online teaching to remain efficient in a complex and changing staff turnover environment.

[0217] The present invention also includes the following implementation method, first obtaining the interaction data of the two progress groups during the live teaching process, obtaining the interaction positivity index of each interaction of each progress group based on the interaction data (the calculation method of the interaction positivity index is similar to step S103 in the above embodiment, and will not be repeated here), and then dividing the activities of each progress group into different teaching stages through the teaching stage division method of step S1041, and then obtaining the interaction rhythm adjustment coefficient for the two progress groups in each teaching stage (the calculation method of the interaction rhythm adjustment coefficient is similar to step S104 in the above embodiment, and will not be repeated here), and then adjusting the interaction time of other progress groups that have not performed the teaching stage based on the obtained interaction rhythm adjustment coefficient of the corresponding teaching stage.

[0218] In the present invention, it is assumed that the order of live broadcast is that progress group 1 broadcasts the first teaching stage, then progress group 2 broadcasts the first teaching stage, progress group 2 broadcasts the first teaching stage, then progress group 3 broadcasts the first teaching stage, progress group 3 broadcasts the first teaching stage, then progress group 4 broadcasts the first teaching stage, and so on. Then, when determining the interactive rhythm adjustment coefficient, after both progress group 1 and progress group 2 have completed the first teaching stage, the first interactive rhythm adjustment coefficient can be obtained through the interactive data of progress group 1 and the interactive data of progress group 2. When progress group 3 broadcasts the first teaching stage, the interactive moment of progress group 3 broadcasting the first teaching stage is adjusted according to the first interactive rhythm adjustment coefficient. After progress group 3 completes the first teaching stage, the second interactive rhythm adjustment coefficient is obtained through the interactive data of progress group 2 and the interactive data of progress group 3. When progress group 4 broadcasts the first teaching stage, the interactive moment of progress group 4 broadcasting the first teaching stage is adjusted according to the second interactive rhythm adjustment coefficient, and so on.

[0219] Based on the same inventive concept, the embodiments of the present application also provide an AI-based personalized education system for implementing the aforementioned AI-based personalized education method. The implementation solution provided by this system is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of one or more AI-based personalized education system embodiments provided below can be found in the limitations of the AI-based personalized education method above and will not be repeated here.

[0220] In an exemplary embodiment, Figure 8 As shown, a personalized education system based on artificial intelligence is provided, including:

[0221] The progress group acquisition module 11 is used to acquire two progress groups that have completed the target teaching stage in the live teaching;

[0222] An interactive data acquisition module 12 is used to acquire interactive data of each interaction in the target teaching stage corresponding to each progress group;

[0223] An interaction positivity index acquisition module 13 is configured to acquire an interaction positivity index of each interaction in each progress group according to the interaction data;

[0224] An interaction rhythm adjustment coefficient acquisition module 14 is used to acquire the interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positivity index of each interaction in each progress group;

[0225] The adjustment module 15 is configured to adjust the interaction time of other progress groups that have not yet started the target teaching stage according to the interaction rhythm adjustment coefficient.

[0226] In one embodiment, the interaction data includes: the total number of students, interaction time, and the actual answering time of each student who actually participated in the interaction; the interaction positivity index acquisition module is specifically used to:

[0227] The following steps are performed on the interaction data of each interaction in each progress group:

[0228] Obtaining answer status parameters based on the actual answering time of the current interaction and the total number of students;

[0229] Obtaining an interference coefficient according to the interaction duration of the current interaction;

[0230] An interaction positivity index of the current interaction is obtained according to the answering state parameter and the interference coefficient.

[0231] In one embodiment, the interaction data further includes: the actual number of students who actually participated in the interaction; the interaction positivity index acquisition module is specifically used to:

[0232] Obtaining the number of first people whose actual answering time is less than or equal to the first preset time;

[0233] Obtain the number of second people whose actual answering time is greater than the first preset time;

[0234] Obtaining a first ratio based on the first number of personnel and the total number of students;

[0235] Obtaining a second ratio based on the second number of personnel and the total number of students;

[0236] Obtaining a participant ratio coefficient based on the actual number of participants and the total number of participants;

[0237] Obtain the total answering time according to the actual answering time;

[0238] The answer status parameter is obtained according to the participant number ratio coefficient, the total answering time, the actual number of people, the first ratio and the second ratio.

[0239] In one embodiment, the interaction positive index acquisition module is specifically used to:

[0240] Get the reference time required to complete each question in the current interaction;

[0241] The interference coefficient is obtained according to the interaction duration and all the reference times.

[0242] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically used to:

[0243] Obtaining an interaction conversion coefficient corresponding to each of two adjacent interactions of each progress group according to the interaction positivity index of each of two adjacent interactions of each progress group;

[0244] Obtaining a peer participation index for each progress group according to the interaction conversion coefficient;

[0245] The interaction rhythm adjustment coefficient is obtained according to the same-order participation index.

[0246] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically used to:

[0247] The following steps are performed for the interaction positivity index corresponding to each two adjacent interactions:

[0248] Obtain the minimum value of the interaction positivity index of two adjacent interactions;

[0249] Obtaining the absolute value of the difference between the interaction positivity indexes of the two current adjacent interactions;

[0250] The interaction conversion coefficient of the two current adjacent interactions is obtained according to the absolute value of the difference and the minimum value.

[0251] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically used to:

[0252] The following steps are performed for the interaction conversion coefficient of each progress group:

[0253] Obtain the mean of all interaction conversion coefficients of the current progress group;

[0254] Obtaining a maximum interactive conversion coefficient value among the interactive conversion coefficients;

[0255] Obtaining a target interaction positive index corresponding to the maximum interaction conversion coefficient value;

[0256] The peer participation index of the current progress group is obtained according to the mean, the maximum interaction conversion coefficient value and the target interaction positivity index.

[0257] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically used to:

[0258] Obtaining the maximum value among the same-order participation indexes;

[0259] The interaction rhythm adjustment coefficient is obtained according to the maximum value of all interaction durations corresponding to each of the progress groups and the same-level participation index.

[0260] In one embodiment, the adjustment module is specifically configured to:

[0261] For each additional progress group, perform the following steps:

[0262] Get the time of the teaching stage that other progress groups have completed;

[0263] The interaction moment at which the other current progress groups start the target teaching stage is obtained according to the time and the interaction rhythm adjustment coefficient.

[0264] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0265] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. Personalized education method based on artificial intelligence, characterized by: The method comprises: Get the two progress groups that have completed the target teaching phase in the live teaching; Obtaining interaction data for each interaction during the target teaching phase corresponding to each progress group; the interaction data includes: the total number of students, interaction duration, the actual response time of each student who actually participated in the interaction, and the actual number of students who actually participated in the interaction; Obtain the interaction positivity index of each interaction in each progress group based on the interaction data, including: obtaining the number of first people whose actual answering time is less than or equal to the first preset time; obtaining the number of second people whose actual answering time is greater than the first preset time; obtaining the first ratio based on the number of first people and the total number of students; obtaining the second ratio based on the number of second people and the total number of students; obtaining the proportion coefficient of the number of participants based on the actual number of people and the total number of students; obtaining the total answering time based on the actual answering time; obtaining the answering status parameters based on the proportion coefficient of the number of participants, the total answering time, the actual number of people, the first ratio and the second ratio; obtaining the reference time required to complete each question in the current interaction; obtaining the interference coefficient based on the interaction time and all reference times; obtaining the interaction positivity index of the current interaction based on the answering status parameters and the interference coefficient; Obtaining the interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positivity index of each interaction in each progress group, including: obtaining the interaction conversion coefficient corresponding to each two adjacent interactions of each progress group according to the interaction positivity index of each two adjacent interactions of each progress group; obtaining the same-order participation index of each progress group according to the interaction conversion coefficient; and obtaining the interaction rhythm adjustment coefficient according to the same-order participation index; Adjust the interaction time of other progress groups that have not started the target teaching stage according to the interaction rhythm adjustment coefficient; The step of obtaining the interaction conversion coefficient corresponding to each two adjacent interactions of each progress group according to the interaction positivity index of each two adjacent interactions includes: performing the following steps on the interaction positivity index corresponding to each two adjacent interactions: obtaining the minimum value of the interaction positivity indexes of the two current adjacent interactions; obtaining the absolute value of the difference between the interaction positivity indexes of the two current adjacent interactions; and obtaining the interaction conversion coefficient of the two current adjacent interactions according to the absolute value of the difference and the minimum value. Obtaining the peer participation index of each progress group according to the interaction conversion coefficient, including: performing the following steps on the interaction conversion coefficient of each progress group: obtaining the average value of all interaction conversion coefficients of the current progress group; obtaining the maximum interaction conversion coefficient value among the interaction conversion coefficients; obtaining the target interaction positivity index corresponding to the maximum interaction conversion coefficient value; obtaining the peer participation index of the current progress group according to the average value, the maximum interaction conversion coefficient value and the target interaction positivity index; Obtaining an interaction rhythm adjustment coefficient according to the same-order participation index, including: obtaining a maximum value among the same-order participation indexes; obtaining the interaction rhythm adjustment coefficient according to all interaction durations corresponding to each progress group and the maximum value among the same-order participation indexes.

2. The personalized education method based on artificial intelligence according to claim 1, characterized in that: The step of adjusting the interaction time of other progress groups that have not started the target teaching stage according to the interaction rhythm adjustment coefficient includes: For each additional progress group, perform the following steps: Get the time of the teaching stage that other progress groups have completed; The interaction moment at which the other current progress groups start the target teaching stage is obtained according to the time and the interaction rhythm adjustment coefficient.

3. Personalized education system based on artificial intelligence, characterized by: The method for personalized education based on artificial intelligence as described in any one of claims 1-2, wherein the system comprises: The progress group acquisition module is used to obtain the two progress groups that have completed the target teaching stage in the live teaching; An interactive data acquisition module, configured to acquire interactive data of each interaction in the target teaching stage corresponding to each progress group; An interaction positivity index acquisition module, configured to acquire the interaction positivity index of each interaction in each progress group according to the interaction data; An interaction rhythm adjustment coefficient acquisition module, configured to acquire the interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positivity index of each interaction in each progress group; An adjustment module, configured to adjust the interaction time of other progress groups that have not yet started the target teaching stage according to the interaction rhythm adjustment coefficient; The interaction data includes: the total number of students, interaction time, and the actual answer time of each student who actually participated in the interaction; the interaction positive index acquisition module is specifically used to: The following steps are performed on the interaction data of each interaction in each progress group: Obtaining answer status parameters based on the actual answering time of the current interaction and the total number of students; Obtaining an interference coefficient according to the interaction duration of the current interaction; An interaction positivity index of the current interaction is obtained according to the answering state parameter and the interference coefficient.

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