Online and offline combined digital interactive teaching method and system

Abstract

The invention relates to the technical field of mixed learning, in particular to an online and offline combined digital interactive teaching method and system, and the method comprises the following steps: collecting online and offline learning data of students, carrying out the statistics of the number and frequency of various interaction types, including the number of discussion times, the number of question answering times and the group cooperation frequency, and generating an initialized interactive data set. According to the method, redundancy and deficiency of various interactions are calculated through the information entropy technology, information vacancy in learning is helped to be recognized, teaching content is optimized, student requirements are matched, the optimized design defines a learning target, an expected interaction frequency is set, the interaction adjustment effect is verified through a simulation test, it is ensured that teaching activities are more targeted, and the teaching efficiency is improved. The interaction frequency is dynamically adjusted in real-time teaching, the sense of participation of students is improved, the learning effect can be monitored in real time, the learning motivation and achievement of the students are improved, and implementation of personalized learning is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid learning, and in particular to a digital interactive teaching method and system combining online and offline. Background Art

[0002] Blended learning is a field of educational technology that combines traditional face-to-face teaching with modern online learning methods. In this model, students can access teaching content through digital platforms while also interacting with teachers and classmates in class to enhance their learning experience. The core advantages of blended learning are flexibility and accessibility, which can provide students with personalized learning paths and rhythms while using technology to improve learning efficiency and engagement. This approach makes educational resources richer and more adaptable to the needs and learning styles of different students.

[0003] Among them, the digital interactive teaching method that combines online and offline learning refers to creating an interactive teaching environment by combining online learning content with offline classroom activities. The purpose of this method is to improve students' learning motivation and sense of participation, and enhance understanding and memory through real-time feedback and group interaction. It also supports teachers to better monitor students' progress and understanding, and adjust teaching strategies and content to meet students' specific needs. This teaching method is a practice of the concept of hybrid learning, which aims to maximize the advantages of educational technology and improve the quality and effectiveness of education.

[0004] Existing hybrid learning technologies have shortcomings in actual operation. The traditional hybrid learning model relies on fixed teaching content and lacks real-time monitoring of students' learning process, which makes it difficult to cope with individual differences and changes in learning rhythm. This model is relatively simple in terms of interaction frequency and content, and it is difficult to effectively stimulate students' interest in learning, resulting in some students lacking a sense of participation in the learning process. In addition, the updating and adjustment of teaching content lags behind, and fails to reflect students' actual needs in a timely manner, affecting learning outcomes. For example, in the face of students with different learning abilities, the unified teaching method causes some students to be unable to keep up with the progress, while the remaining students feel bored and boring, resulting in poor overall learning outcomes. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a digital interactive teaching method and system that combines online and offline teaching.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a digital interactive teaching method combining online and offline, comprising the following steps:

[0007] S1: Collect students’ online and offline learning data, count the number and frequency of various types of interactions, including the number of discussions, the number of problem-solving, and the frequency of group collaboration, and generate an initial interaction dataset;

[0008] S2: According to the initialized interaction data set, using information entropy technology, by calculating the information entropy of each interaction type, identifying information redundancy and missing patterns, and obtaining information entropy analysis results;

[0009] S3: Based on the information entropy analysis results, the content of digital teaching is adjusted, and the teaching path is optimized by avoiding redundant information and supplementing missing information, so as to match the online and offline learning needs of students and obtain an optimized teaching design;

[0010] S4: using the optimized teaching design, setting learning goals and expected interaction frequency, designing a feedback control strategy to adjust the interaction frequency, using simulation tests to verify the effect of the interaction adjustment, and generating an interaction frequency adjustment model;

[0011] S5: deploying the interaction frequency adjustment model in real-time teaching, adjusting the students' interaction frequency in real time, monitoring the effect of students' interaction, comparing the preset learning effect, and obtaining a teaching activity adjustment plan;

[0012] S6: Implement the teaching activity adjustment plan, analyze the correlation between teaching activities and learning outcomes, compare learning outcomes before and after the adjustment, organize teaching feedback, and generate teaching effectiveness evaluation results.

[0013] As a further solution of the present invention, the initialized interactive data set includes statistical data on the number of students' online discussions, the number of problem answers, and the frequency of offline group collaboration; the information entropy analysis results include information redundancy patterns, information missing patterns, and quantity records of interaction types for each interaction type identified by information entropy calculation; the optimized teaching design includes adjusted digital teaching content, supplemented missing information parts, avoided redundant information parts, and updated teaching paths; the interaction frequency adjustment model includes a designed feedback control strategy, a preset interaction frequency target, and verification results of simulation tests; the teaching activity adjustment plan includes a real-time deployed interaction frequency adjustment model, monitoring data on student interaction effects, and comparative analysis results with preset learning effects; the teaching effect evaluation results include correlation analysis between teaching activities and learning effects, comparison of learning outcomes before and after adjustment, and collated teaching feedback information.

[0014] As a further solution of the present invention, the online and offline learning data of students are collected, and the number and frequency of various types of interactions are counted, including the number of discussions, the number of problem solving, and the frequency of group cooperation. The steps of generating the initialization interaction data set are specifically as follows:

[0015] S101: Collect students' online and offline learning data, set up data collection nodes, collect interactive data of multiple types of classrooms and online platforms, including the number of discussions, the number of questions answered, and the frequency of group cooperation, use tables to record instances of each type of data, and synchronize node data to obtain interactive type records;

[0016] S102: Based on the interaction type records, perform record deduplication, exclude invalid and incomplete records, organize the records in a unified format, check the consistency of the records, and obtain processed data records;

[0017] S103: Using the processed data records, calculate the number of occurrences and frequencies of differentiated interaction types, analyze the distribution and trend of the interaction data, organize the data results into a table format, identify the activity levels of multiple types of interactions, and obtain an initialized interaction data set.

[0018] As a further solution of the present invention, according to the initialized interactive data set, information entropy technology is used to calculate the information entropy of each interaction type, identify information redundancy and missing patterns, and obtain the information entropy analysis results in the following steps:

[0019] S201: extracting the occurrence frequency of the interaction type in the initialization interaction data set, calculating the information entropy of multiple interaction types using the entropy weight method, and obtaining the information entropy measurement value;

[0020] S202: Analyze the information entropy of multiple types of online and offline interactions of students according to the information entropy measurement value, identify the interaction types with abnormal information entropy values, and mark the abnormal points of the data by comparing the information entropy values ​​of the interaction types to obtain the information redundancy and missing analysis results;

[0021] S203: Through the information redundancy and missing analysis results, identify key information missing and redundant patterns in the data set, propose adjustment and supplement strategies, optimize the quality and integrity of the data, and obtain information entropy analysis results.

[0022] As a further solution of the present invention, the formula of the entropy weight method is as follows:

[0023]

[0024] Among them, E is the information entropy value, p i represents the frequency of the i-th type of interaction in the total interactions, k represents the normalization constant, and w i represents the weight coefficient of the i-th type of interaction, log(p i ) is the information content of the probability calculated using the natural logarithm, and n represents the total number of interaction types.

[0025] As a further solution of the present invention, based on the information entropy analysis results, the content of digital teaching is adjusted, and the teaching path is optimized by avoiding redundant information and supplementing missing information to match students' online and offline learning needs. The steps of obtaining the optimized teaching design are as follows:

[0026] S301: Based on the information entropy analysis result, the digital teaching content is adjusted in real time, redundant information is identified and deleted, the learning effect is optimized by combining interactive elements, the rationality of the deleted content is verified through feedback and data comparison, and the redundant information adjustment result is obtained;

[0027] S302: According to the redundant information adjustment result, determine the missing information content, supplement the required teaching materials through online and offline surveys, increase the interactive methods that meet the needs of students, adjust the content structure to optimize the learning experience, and obtain the information supplement adjustment result;

[0028] S303: Supplement the adjustment results through the information, integrate the adjusted teaching content, design the teaching path, match the digital courses according to the students' online and offline learning needs, verify that the new design meets the learning objectives, and generate an optimized teaching design.

[0029] As a further solution of the present invention, the optimized teaching design is used to set learning goals and expected interaction frequency, a feedback control strategy is designed to adjust the interaction frequency, and a simulation test is used to verify the effect of the interaction adjustment. The steps of generating an interaction frequency adjustment model are specifically as follows:

[0030] S401: Based on the optimized teaching design, online and offline learning goals and expected interaction frequencies are set, interaction indicators are defined, including discussion times, question answering frequency, and group collaboration frequency, and related quantitative goals are formulated to obtain interaction goal setting results;

[0031] S402: designing a feedback control strategy based on the interaction goal setting result, and adjusting the teaching content and interaction form by monitoring the deviation between the students' real-time interaction frequency and the set goal to obtain the feedback control strategy;

[0032] S403: Using the feedback control strategy, conduct simulation tests to evaluate the impact of the adjusted interaction frequency on student learning outcomes, collect feedback data and analyze the interaction effects to obtain an interaction frequency adjustment model.

[0033] As a further solution of the present invention, the steps of deploying the interaction frequency adjustment model in real-time teaching, adjusting the students' interaction frequency in real time, monitoring the effect of students' interaction, and comparing the preset learning effect to obtain the teaching activity adjustment plan are as follows:

[0034] S501: adopting the interaction frequency adjustment model, deploying the model in a real-time teaching environment, collecting student interaction data in real time, checking the timeliness of data collection, recording the real-time interaction frequency of students online and offline, and obtaining real-time monitoring results of interaction frequency;

[0035] S502: Based on the real-time monitoring result of the interaction frequency, compare the preset learning effect with the real-time interaction effect, analyze the data difference, identify the teaching link that needs to be adjusted, display the interaction effect through a data visualization tool, and obtain the learning effect comparison analysis result;

[0036] S503: According to the learning effect comparison and analysis results, adjustment measures are formulated for the links where the interaction frequency exceeds the normal threshold, and a teaching activity adjustment plan is obtained by increasing the interactive links and optimizing the teaching content.

[0037] As a further solution of the present invention, the steps of implementing the teaching activity adjustment plan, analyzing the correlation between teaching activities and learning effects, comparing learning outcomes before and after the adjustment, collating teaching feedback, and generating teaching effect evaluation results are specifically as follows:

[0038] S601: Implement the teaching activity adjustment plan, conduct courses according to the adjusted plan, record students' participation and interaction frequency, check the consistency of the implementation process, and obtain the teaching activity implementation results;

[0039] S602: Based on the teaching activity implementation results, analyzing the correlation between the teaching activities and the learning effects, collecting students' learning achievement data, and analyzing the influencing factors by comparing the learning results before and after the adjustment to obtain the analysis results of the correlation between the teaching activities and the learning effects;

[0040] S603: Based on the analysis results of the correlation between the teaching activities and the learning effects, organize the teaching feedback, record the students' opinions, combine the differentiated data sources, extract the key improvement points, and generate the teaching effect evaluation results.

[0041] A digital interactive teaching system combining online and offline, the digital interactive teaching system combining online and offline is used to execute the above-mentioned digital interactive teaching method combining online and offline, the system comprises:

[0042] The data collection module collects students’ online and offline learning data, including the number of discussions, the number of questions answered, and the frequency of group collaboration, to generate an initial interactive data set;

[0043] The information entropy calculation module performs information entropy calculation on the multiple interaction types in the initialization interaction data set, identifies information redundancy and information missing patterns, and generates information entropy analysis results;

[0044] The teaching path optimization module adjusts the digital teaching content based on the information entropy analysis results, optimizes the teaching path to match the students' learning needs by avoiding information redundancy and supplementing information missing, and generates an optimized teaching design;

[0045] The model building module designs learning objectives and expected interaction frequency according to the optimized teaching design, conducts multiple rounds of simulation tests, and generates an interaction frequency adjustment model;

[0046] The teaching activity implementation module adjusts the students' interaction frequency according to the interaction frequency adjustment model, monitors the students' interaction effect, compares the preset learning effect, and compares the learning results before and after the adjustment to generate a teaching effect evaluation result.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by collecting students' online and offline learning data, the number and frequency of interaction types can be deeply analyzed to generate a detailed interaction data set. The redundancy and lack of various types of interactions are calculated through information entropy technology to help identify information gaps in learning, optimize teaching content and match student needs. The optimized design clarifies the learning goals and sets the expected interaction frequency. The effect of interaction adjustment is verified through simulation tests to ensure that teaching activities are more targeted. Dynamically adjusting the interaction frequency in real-time teaching not only enhances students' sense of participation, but also monitors learning effects in real time, forming an effective feedback and adjustment mechanism for teaching activities. The implementation of this processing logic makes teaching more flexible and efficient, improves students' learning motivation and results, and promotes the realization of personalized learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a digital interactive teaching method combining online and offline, comprising the following steps:

[0060] S1: Collect online and offline learning data of students majoring in logistics management, count the number and frequency of various types of interactions, including the number of discussions, the number of problem-solving, and the frequency of group collaboration, and generate an initial interaction dataset;

[0061] S2: Based on the initialized interaction data set, information entropy technology is used to calculate the information entropy of each interaction type, identify information redundancy and missing patterns, and obtain information entropy analysis results;

[0062] S3: Based on the information entropy analysis results, adjust the content of digital teaching, avoid redundant information and supplement missing information, adjust the frequency of student interaction, optimize the teaching path, match students' online and offline learning needs, and obtain the optimized teaching design;

[0063] S4: Using the optimized instructional design, apply nonlinear control theory, set learning goals and expected interaction frequency, design feedback control strategies to adjust the interaction frequency, use simulation tests to verify the effect of interaction adjustment, and generate an interaction frequency adjustment model;

[0064] S5: Deploy the interaction frequency adjustment model in real-time teaching, adjust the students' interaction frequency in real time, monitor the real-time effect of students' interaction, compare the preset learning effect, and obtain the teaching activity adjustment plan;

[0065] S6: Implement the teaching activity adjustment plan, analyze the correlation between teaching activities and learning outcomes, generate teaching effectiveness evaluation results by comparing learning outcomes before and after the adjustment, and organize teaching feedback.

[0066] The initialization interaction data set includes statistical data on the number of students' online discussions, the number of problem answers, and the frequency of offline group collaboration. The information entropy analysis results include the information redundancy pattern, information missing pattern, and quantity record of interaction types for each interaction type identified by information entropy calculation. The optimized teaching design includes the adjusted digital teaching content, the supplemented missing information parts, the avoided redundant information parts, and the updated teaching path. The interaction frequency adjustment model includes the designed feedback control strategy, the preset interaction frequency target, and the verification results of the simulation test. The teaching activity adjustment plan includes the real-time deployed interaction frequency adjustment model, the monitoring data of the students' interaction effect, and the comparative analysis results with the preset learning effect. The teaching effect evaluation results include the correlation analysis between teaching activities and learning effects, the comparison of learning outcomes before and after adjustment, and the collated teaching feedback information.

[0067] See also Figure 2 , collect students' online and offline learning data, count the number and frequency of various types of interactions, including the number of discussions, the number of problem solving, and the frequency of group cooperation. The steps to generate the initial interaction data set are as follows:

[0068] S101: Collect students' online and offline learning data, set data collection nodes, collect interactive data of multiple types of classrooms and online platforms, including the number of discussions, the number of questions answered, and the frequency of group cooperation, use tables to record instances of each type of data, and synchronize node data to obtain the execution process of interactive type records as follows;

[0069] In the logistics management course, students' online and offline interaction data are collected, and data collection nodes are set up. The collected data include the number of discussions, the number of questions answered, and the frequency of group cooperation. Data is collected through various classroom activities and different types of interactions on the network platform. For example, on the network platform, the number of forum posts and replies can be used to quantify the discussion frequency, and the number of questions answered can be recorded through online quizzes and Q&A. Group cooperation can be counted by the number of times project shared files are accessed and modified. Tables are used to record instances of each type of data, and node data is synchronized. This comprehensive data collection and recording method can help teachers gain an in-depth understanding of students' learning interactions, provide scientific data support for course teaching, and obtain records of interaction types.

[0070] S102: Based on the interaction type records, perform record deduplication, exclude invalid and incomplete records, organize the records in a unified format, check the consistency of the records, and obtain the processed data records. The execution process is as follows;

[0071] Based on the interaction type records, deduplication operations need to be performed, including excluding invalid and incomplete records. This process can be achieved by comparing data formats and integrity. For example, by checking whether each field of the record meets the preset format requirements, confirm that each data is complete. For duplicate data, a unique identifier can be used to mark and remove it. After the operation, the records need to be organized in a unified format to ensure data consistency, lay a solid foundation for subsequent data analysis, and obtain processed data records.

[0072] S103: Using the processed data records, calculating the number and frequency of occurrence of differentiated interaction types, analyzing the distribution and trend of the interaction data, organizing the data results into a table form, identifying the activity levels of multiple types of interactions, and obtaining the execution flow of the initialization interaction data set is as follows;

[0073] Calculate the occurrence and frequency of differentiated interaction types according to the formula Calculate the average number of occurrences of the interaction type. Where N represents the average number of occurrences of the interaction type, x i represents the number of times the i-th type of interaction occurs, and n represents the total number of interaction types. Formula explanation and formula calculation derivation process: In the logistics management course, there are three types of interaction: forum discussion (x1 = 150 times), question answering (x2 = 100 times), and group cooperation (x3 = 50 times). The average number of occurrences of each type of interaction is calculated. The number of occurrences of each type of interaction is summed up to get Divide by the total number of interaction types, that is, n = 3, The results show that the average number of times each interaction type occurs is 100 times, which helps to identify the most active interaction types in the course and provide a basis for further optimizing teaching activities.

[0074] See also Figure 3 ,According to the initial interaction data set, the information entropy technology is used to calculate the information entropy of each interaction type, identify information redundancy and missing patterns, and obtain the information entropy analysis results in the following steps:

[0075] S201: extract the occurrence frequency of the interaction type in the initialization interaction data set, use the entropy weight method to calculate the information entropy of multiple interaction types, and obtain the execution process of the information entropy measurement value as follows;

[0076] The formula of the entropy weight method is as follows:

[0077]

[0078] Among them, E is the information entropy value, p i represents the frequency of the i-th type of interaction in the total interactions, k represents the normalization constant, and w i represents the weight coefficient of the i-th type of interaction, log(pi ) is the information content of the probability calculated using the natural logarithm, and n represents the total number of interaction types.

[0079] 1. Data acquisition and parameter calculation:

[0080] The number of times each type of interaction in the monitoring data set occurs is set, and interaction type A occurs 100 times, interaction type B occurs 50 times, and interaction type C occurs 150 times.

[0081] Calculate the total number of interactions, which in this case is 100+50+150=300.

[0082] Calculate the relative frequency p of each interaction type i ,Right now

[0083] 2. Setting of weight coefficient:

[0084] w i The value is set based on the business importance of the interaction type. If interaction type A is the most important, it has the highest weight. The weight is set to w A =0.5, w B =0.2, w C = 0.3. The weight values ​​are set based on data analysis to reflect the business impact of each interaction.

[0085] Setting of normalization constant k: To ensure that the entropy value is between 0 and 1, the constant k is set to 1.

[0086] 3. Calculate information entropy:

[0087] Using the natural logarithm to calculate log(p i ) values: log(0.333)≈-1.099, log(0.167)≈-1.791, log(0.5)=-0.693.

[0088] Multiply the frequency, logarithm and weight of each item and sum them up: E = -1×[0.333×(-1.099)×0.5+0.167×(-1.791)×0.2+0.5×(-0.693)×0.3]

[0089] Calculation result: E = -1 × [0.333 × (-1.099) × 0.5 + 0.167 × (-1.791) × 0.2 + 0.5 × (-0.693) × 0.3] ≈ -1 × [-0.183 - 0.06 - 0.104] = 0.347

[0090] The results show that the information entropy value obtained by weighted information entropy calculation is 0.347, indicating that the distribution of interaction types in the data set has a certain degree of unevenness, and a higher information entropy value indicates the existence of diversity and uncertainty. The results can be further used to evaluate the diversity of interaction types and analyze the contribution of different interaction types to the overall impact, which is of great value for formulating strategies and optimizing resource allocation.

[0091] S202: According to the information entropy measurement value, the information entropy of multiple types of online and offline interactions of students is analyzed, and the interaction types with abnormal information entropy values ​​are identified. By comparing the information entropy values ​​of the interaction types, the abnormal points of the data are marked, and the execution process of obtaining the information redundancy and missing analysis results is as follows;

[0092] Analyze the information entropy of students' online and offline interactions, according to the formula Calculate the information entropy of each interaction type. In the formula, H represents information entropy, a i represents the probability of the occurrence of the i-th type of interaction, and n represents the total number of interaction types. Formula explanation and formula calculation derivation process: Assume that in a semester, there are three types of interactions: online question and answer (x1 = 300 times), group discussion (x2 = 200 times), project cooperation (x3 = 100 times), and the total number of interactions T = 600 times. Calculate the probability of each interaction Substitute into the information entropy formula to calculate: H = -(0.5log(0.5)+

[0093] 0.333log(0.333)+0.167log(0.167))≈1.011bits. This result shows that the information entropy of interaction types is 1.011bits, reflecting the diversity and randomness of students' interaction types. A high information entropy value indicates that the distribution of interaction types is more uniform without obvious bias.

[0094] S203: Through the information redundancy and missing analysis results, identify the key information missing and redundant patterns in the data set, propose adjustment and supplement strategies, optimize the quality and integrity of the data, and obtain the execution process of the information entropy analysis results as follows;

[0095] By studying the above-mentioned information entropy analysis results, we can identify key information missing and redundant patterns in the data set, which involves the optimization and adjustment of the data. For example, if we find that the information entropy of a certain type of interaction is abnormally high, it is because of data entry errors or too much redundant data, while abnormally low information entropy indicates serious information missing. We can supplement and adjust the problems, such as increasing the frequency of data collection, optimizing the data entry process, and improving data quality and completeness. This will help optimize the entire data processing process, ensure the accuracy and efficiency of teaching analysis, and obtain information entropy analysis results.

[0096] See also Figure 4Based on the information entropy analysis results, the content of digital teaching is adjusted. By avoiding redundant information and supplementing missing information, the teaching path is optimized to match students' online and offline learning needs. The specific steps of the optimized teaching design are as follows:

[0097] S301: Based on the information entropy analysis results, the digital teaching content is adjusted in real time, the redundant information parts are identified and deleted, the learning effect is optimized by combining interactive elements, and the rationality of the deleted content is verified through feedback and data comparison. The execution process of obtaining the redundant information adjustment result is as follows;

[0098] Based on the results of information entropy analysis, the digital teaching content is adjusted immediately. The process includes identifying and deleting redundant information. For example, if the video explanation and text content of a teaching unit are highly repetitive, consider deleting the video or text to reduce redundancy. Combined with students' interactive data, course elements are adjusted to optimize learning effects, such as adding more interactive quizzes or discussion sessions to increase student participation. By collecting student feedback and conducting data comparison and analysis, the rationality of deleting redundant information is verified to ensure that the adjusted teaching content can more effectively support learning objectives and obtain the results of redundant information adjustment.

[0099] S302: According to the redundant information adjustment result, determine the missing information content, supplement the required teaching materials through online and offline surveys, increase the interactive methods that meet the needs of students, adjust the content structure to optimize the learning experience, and obtain the execution process of the information supplement adjustment result as follows;

[0100] Based on the aforementioned redundant information adjustment results, we further identified the missing information, which involved a comprehensive review of the textbooks, investigating students' needs through online questionnaires and offline interviews, understanding which knowledge points were not fully covered, and supplementing relevant teaching materials accordingly. For example, if students reported that they did not have enough understanding of a certain professional concept, we could add case analysis or practical videos to strengthen this part of the content. At the same time, we could adjust the content structure to optimize the overall learning experience, and add more interactive methods that met students' needs, such as group discussions and role-playing. This information supplementation and content adjustment could significantly improve learning efficiency and student satisfaction, and we could obtain information supplementation and adjustment results.

[0101] S303: Supplement the adjustment results with information, integrate the adjusted teaching content, design the teaching path, match the digital courses according to the students' online and offline learning needs, verify that the new design meets the learning objectives, and generate the execution process of the optimized teaching design as follows;

[0102] Integrate the adjusted teaching content, design the teaching path, and follow the formula Calculate the effectiveness of teaching content. In the formula, A represents the overall effectiveness of teaching content, w brepresents the importance weight of the b-th teaching element, v b Represents the contribution of this element to the learning effect, n b Represents the total number of teaching elements. Detailed explanation of the formula and the process of formula calculation and derivation: The adjusted teaching content includes three types of elements: video explanation (weight w1 = 0.5, contribution value v1 = 0.8), interactive test (weight w2 = 0.3, contribution value v2 = 0.7), and case study (weight w3 = 0.2, contribution value v3 = 0.9). Calculate the effectiveness of the teaching content: A = 0.5 0.8 + 0.3 0.7 + 0.2 0.9 = 0.4 + 0.21 + 0.18 = 0.79. The results show that the effectiveness score of the adjusted teaching design is 0.79, which reflects that the teaching content is well matched with students' needs and meets the learning objectives, which helps to improve students' learning outcomes and satisfaction.

[0103] See also Figure 5 ,Use the optimized teaching design, set the learning objectives and expected interaction frequency, design the feedback control strategy to adjust the interaction frequency, use simulation test to verify the effect of interaction adjustment, and generate the interaction frequency adjustment model in the following steps:

[0104] S401: Based on the optimized teaching design, set online and offline learning goals and expected interaction frequency, define interaction indicators, including discussion times, question answering frequency, and group collaboration frequency, formulate related quantitative goals, and obtain the execution process of interaction goal setting results as follows;

[0105] Based on the optimized teaching design, specific learning goals and interaction frequencies are set for online and offline learning environments, including the definition of detailed interaction indicators, such as the number of discussions, problem-solving frequency and group collaboration frequency. Specific quantitative goals are set for each type of interaction, with at least three online discussions per week, the problem-solving frequency being at least five questions answered in each class, and group collaboration completing a project at least once every two weeks. The goals are determined through correlation analysis with learning outcomes, aiming to promote students' active participation through clear interaction frequencies, improve learning efficiency and effectiveness, and achieve the results of setting interaction goals.

[0106] S402: Based on the interactive goal setting results, a feedback control strategy is designed. By monitoring the deviation between the students' real-time interactive frequency and the set goal, the teaching content and interactive form are adjusted. The execution process of the feedback control strategy is as follows;

[0107] Design a feedback control strategy by monitoring the deviation between the students’ real-time interaction frequency and the set target, and then calculate the feedback control strategy according to the formula Δf = f real -f target , calculate the deviation between the actual interaction frequency and the target frequency. In the formula, Δf represents the deviation of the interaction frequency, freal represents the interaction frequency monitored in real time, f target represents the target interaction frequency. Formula explanation and formula calculation process: In one semester, the target discussion frequency is set to 3 times per week (f target =3), the monitoring results show that the actual discussion frequency is 4 times per week (f real =4). Calculate the deviation: Δf = 4-3 = 1. The results show that the actual interaction frequency is 1 time / week higher than the target frequency, indicating that the teaching content and interaction form need to be further adjusted to better control the teaching rhythm and deepen the learning content, thereby improving the teaching effect.

[0108] S403: Using the feedback control strategy, conduct simulation tests to evaluate the impact of the adjusted interaction frequency on students’ learning outcomes, collect feedback data and analyze the interaction effects, and obtain the execution flow of the interaction frequency adjustment model as follows;

[0109] Using the designed feedback control strategy, simulation tests are conducted to evaluate the impact of the adjusted interaction frequency on student learning outcomes. The process involves collecting and analyzing student feedback data, collecting data through an online learning platform, and monitoring the actual impact of different forms of interaction on student engagement. Analysis of the data can show whether an increase in interaction frequency leads to higher academic performance or better student satisfaction. This method allows teachers to adjust teaching strategies based on real-time data, which helps to achieve more precise dynamic adjustment of teaching content and form, ensure the optimization of teaching activities, and build an interaction frequency adjustment model.

[0110] See also Figure 6 ,The steps of deploying the interaction frequency adjustment model in real-time teaching,adjusting the students’ interaction frequency in real time,monitoring the effect of students’ interaction, comparing the preset learning effect,and obtaining the teaching activity adjustment plan are as follows:

[0111] S501: Adopt the interaction frequency adjustment model, deploy the model in the real-time teaching environment, collect student interaction data in real time, check the timeliness of data collection, record the real-time interaction frequency of students online and offline, and obtain the execution process of the real-time monitoring result of the interaction frequency as follows;

[0112] An interaction frequency adjustment model is adopted and deployed in a real-time teaching environment. The process includes real-time collection of students' interaction data to ensure the timeliness of data collection. For example, the number and type of online and offline interactions of each student are automatically recorded through the intelligent teaching platform. The data includes the number of times students speak in discussion forums, the number of times they participate in interactive Q&A, and the frequency of collaboration in group projects. By analyzing real-time data, teachers can instantly understand the students' interactive activity and better adjust teaching strategies and content to ensure that students can actively participate in the learning process and obtain real-time monitoring results of interaction frequency.

[0113] S502: Based on the real-time monitoring results of the interaction frequency, compare the preset learning effect with the real-time interaction effect, analyze the data differences, identify the teaching links that need to be adjusted, and display the interaction effect through data visualization tools to obtain the execution process of the learning effect comparison analysis results as follows;

[0114] Based on the results of real-time monitoring of interaction frequency, further comparison of preset learning effects and real-time interaction effects involves analyzing the data differences between the two. By using statistical software, the expected interaction frequency and the actual observed value are compared to identify teaching links where the interaction frequency has not reached the expected target or the link has exceeded expectations. Data visualization tools, such as charts and graphs, are used to intuitively display the differences so that both teachers and students can clearly see what needs to be improved. Such analysis not only helps teachers adjust teaching plans, but also promotes students' understanding of their own learning progress and obtains comparative analysis results of learning effects.

[0115] S503: According to the comparative analysis results of the learning effects, adjustment measures are formulated for the links where the interaction frequency exceeds the normal threshold. By increasing the interactive links and optimizing the teaching content, the execution process of the teaching activity adjustment plan is as follows;

[0116] For links where the interaction frequency exceeds the normal threshold, formulate adjustment measures according to the formula C = α·

[0117] (f actual -f threshold ), calculate the intensity of the measures that need to be adjusted. In the formula, C represents the intensity of the adjustment measures, α represents the adjustment coefficient, and f actual represents the actual interaction frequency, f threshold Represents the normal interaction frequency threshold. Formula explanation and formula calculation derivation process: In a certain teaching session, the normal interaction frequency threshold is 5 times per class (f threshold =5), the actual monitored interaction frequency is 8 times per class (f actual =8), and the adjustment coefficient is set to 0.5. The intensity of the adjustment measure is calculated as: C = 0.5·(8-5) = 1.5. The results show that it is necessary to moderately increase the interactive links in the teaching process or optimize the teaching content to reduce the actual interaction frequency, make it closer to the set normal threshold, and improve the overall quality and effect of teaching activities.

[0118] See also Figure 7 , implement the teaching activity adjustment plan, analyze the correlation between teaching activities and learning effects, compare the learning outcomes before and after the adjustment, organize the teaching feedback, and generate the teaching effect evaluation results. The specific steps are:

[0119] S601: Implement the teaching activity adjustment plan, conduct courses according to the adjusted plan, record students' participation and interaction frequency, check the consistency of the implementation process, and obtain the implementation results of the teaching activities. The execution process is as follows;

[0120] Implement the teaching activity adjustment plan, including conducting courses according to the adjusted plan. During the process, teachers need to closely record students' participation and interaction frequency to ensure the consistency of teaching activities. The teaching management system records student attendance, number of interactions, and student-submitted assignments and feedback for each course. The data will be used to verify the consistency and effectiveness of the implementation of the teaching content and ensure that all teaching links are strictly implemented in accordance with the new plan. This method can monitor and adjust the teaching process in real time, ensure teaching quality, and obtain the results of the implementation of teaching activities.

[0121] S602: Based on the results of the teaching activities, the correlation between the teaching activities and the learning effects is analyzed, the learning results data of the students are collected, and the influencing factors are analyzed by comparing the learning results before and after the adjustment. The execution process of obtaining the results of the correlation analysis between the teaching activities and the learning effects is as follows;

[0122] Based on the results of the implementation of teaching activities, further analyze the correlation between teaching activities and learning outcomes, including collecting and comparing students' learning outcome data, such as test scores, classroom performance and homework scores. By comparing the data before and after the adjustment, identify the specific impact of changes in teaching activities on learning performance. The analysis helps reveal whether improvements in teaching methods or content have significant positive or negative effects on students' learning outcomes, adjust teaching strategies to improve the quality of education, and obtain the results of the correlation analysis between teaching activities and learning outcomes.

[0123] S603: Based on the analysis results of the correlation between teaching activities and learning effects, organize teaching feedback, record students’ opinions, combine differentiated data sources, extract key improvement points, and generate the execution process of teaching effect evaluation results as follows;

[0124] Organize teaching feedback, record students' opinions, and follow the formula Calculate the comprehensive evaluation score of teaching effect. In the formula, R o Represents the comprehensive evaluation score of teaching effectiveness, a o represents the importance weight of the oth feedback, b o Represents the satisfaction score of the feedback, n o Represents the total number of feedback items. Formula explanation and formula calculation derivation process: Set to collect three student feedback items: course content quality (weight a1 = 0.5, satisfaction b1 = 4), interaction frequency (weight a2 = 0.3, satisfaction b2 = 3), homework feedback timeliness (weight a3 = 0.2, satisfaction b3 = 5). Calculate the comprehensive evaluation score of teaching effectiveness: R o=0.5·4+0.3·3+0.2·5=2+0.9+1=3.9. The results show that the teaching effectiveness evaluation score is 3.9. The higher score shows that students are more satisfied with the teaching content and interactive methods, reflecting the effectiveness of the teaching adjustment plan and providing a specific basis for further improving teaching strategies.

[0125] See also Figure 8 , a digital interactive teaching system combining online and offline, the digital interactive teaching system combining online and offline is used to execute the above-mentioned digital interactive teaching method combining online and offline, the system comprises:

[0126] The data collection module collects students’ online and offline learning data, including the number of discussions, the number of questions answered, and the frequency of group collaboration, to generate an initial interactive data set;

[0127] The information entropy calculation module calculates the information entropy of various interaction types in the initial interaction data set, identifies information redundancy and information missing patterns, and generates information entropy analysis results;

[0128] The teaching path optimization module adjusts the digital teaching content based on the information entropy analysis results, optimizes the teaching path to match students' learning needs by avoiding information redundancy and supplementing information missing, and generates an optimized teaching design;

[0129] The model building module designs learning objectives and expected interaction frequency based on the optimized teaching design, conducts multiple rounds of simulation tests, and generates an interaction frequency adjustment model;

[0130] The teaching activity implementation module adjusts the model according to the interaction frequency, adjusts the students' interaction frequency, monitors the students' interaction effect, compares the preset learning effect, and compares the learning outcomes before and after the adjustment to generate the teaching effect evaluation results.

[0131] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A digital interactive teaching method combining online and offline, characterized in that: The following steps are involved: Collect students' online and offline learning data, count the number and frequency of various types of interactions, including the number of discussions, the number of problem-solving, and the frequency of group collaboration, and generate an initial interaction data set; According to the initialized interactive data set, information entropy technology is used to calculate the information entropy of each interaction type, identify information redundancy and missing patterns, and obtain information entropy analysis results; Based on the information entropy analysis results, the content of digital teaching is adjusted, and the teaching path is optimized by avoiding redundant information and supplementing missing information, so as to match the online and offline learning needs of students and obtain an optimized teaching design; Using the optimized teaching design, setting learning goals and expected interaction frequency, designing a feedback control strategy to adjust the interaction frequency, using simulation tests to verify the effect of the interaction adjustment, and generating an interaction frequency adjustment model; Deploy the interaction frequency adjustment model in real-time teaching, adjust the students' interaction frequency in real time, monitor the effect of students' interaction, compare the preset learning effect, and obtain the teaching activity adjustment plan; Implement the teaching activity adjustment plan, analyze the correlation between teaching activities and learning outcomes, compare learning outcomes before and after the adjustment, organize teaching feedback, and generate teaching effectiveness evaluation results.

2. The digital interactive teaching method combining online and offline teaching according to claim 1 is characterized in that: The initialized interactive data set includes statistical data on the number of students' online discussions, the number of problem answers, and the frequency of offline group collaborations; the information entropy analysis results include information redundancy patterns, information missing patterns, and quantity records of interaction types for each type of interaction identified by information entropy calculation; the optimized teaching design includes adjusted digital teaching content, supplemented missing information parts, avoided redundant information parts, and updated teaching paths; the interaction frequency adjustment model includes a designed feedback control strategy, a preset interaction frequency target, and verification results of simulation tests; the teaching activity adjustment plan includes a real-time deployed interaction frequency adjustment model, monitoring data on student interaction effects, and comparative analysis results with preset learning effects; the teaching effect evaluation results include correlation analysis between teaching activities and learning effects, comparison of learning outcomes before and after adjustment, and collated teaching feedback information.

3. The digital interactive teaching method combining online and offline teaching according to claim 1 is characterized in that: Collect students' online and offline learning data, count the number and frequency of various types of interactions, including the number of discussions, the number of problem-solving, and the frequency of group collaboration. The steps to generate the initial interaction data set are as follows: Collect students' online and offline learning data, set up data collection nodes, collect interactive data from multiple types of classrooms and online platforms, including the number of discussions, the number of questions answered, and the frequency of group cooperation, use tables to record instances of each type of data, and synchronize node data to obtain interactive type records; Based on the interaction type records, perform record deduplication, exclude invalid and incomplete records, organize the records in a unified format, check the consistency of the records, and obtain processed data records; The processed data records are used to calculate the number of occurrences and frequencies of differentiated interaction types, analyze the distribution and trend of the interaction data, organize the data results into a table form, identify the activity levels of multiple types of interactions, and obtain an initialized interaction data set.

4. The digital interactive teaching method combining online and offline teaching according to claim 1 is characterized in that: According to the initialized interactive data set, the information entropy technology is used to calculate the information entropy of each interaction type, identify information redundancy and missing patterns, and obtain the information entropy analysis results in the following steps: Extracting the occurrence frequency of the interaction type in the initialization interaction data set, using the entropy weight method to calculate the information entropy of multiple interaction types, and obtaining the information entropy measurement value; According to the information entropy measurement value, the information entropy of multiple types of online and offline interactions of students is analyzed, and the interaction types with abnormal information entropy values ​​are identified. By comparing the information entropy values ​​of the interaction types, the abnormal points of the data are marked to obtain the information redundancy and missing analysis results; Through the information redundancy and missing analysis results, the key information missing and redundant patterns in the data set are identified, adjustment and supplementation strategies are proposed, the quality and integrity of the data are optimized, and the information entropy analysis results are obtained.

5. The digital interactive teaching method combining online and offline teaching according to claim 4 is characterized in that: The formula of the entropy weight method is as follows: Among them, E is the information entropy value, p i represents the frequency of the i-th type of interaction in the total interactions, k represents the normalization constant, and w i represents the weight coefficient of the i-th type of interaction, log(p i ) is the information content of the probability calculated using the natural logarithm, and n represents the total number of interaction types.

6. The digital interactive teaching method combining online and offline teaching according to claim 1 is characterized in that: Based on the information entropy analysis results, the content of digital teaching is adjusted, and the teaching path is optimized by avoiding redundant information and supplementing missing information to match students' online and offline learning needs. The specific steps of obtaining the optimized teaching design are as follows: Based on the information entropy analysis results, the digital teaching content is adjusted in real time, redundant information parts are identified and deleted, the learning effect is optimized by combining interactive elements, the rationality of the deleted content is verified through feedback and data comparison, and the redundant information adjustment results are obtained; According to the redundant information adjustment results, determine the missing information content, supplement the required teaching materials through online and offline surveys, increase the interactive methods that meet the needs of students, adjust the content structure to optimize the learning experience, and obtain the information supplement adjustment results; Use the information to supplement the adjustment results, integrate the adjusted teaching content, design the teaching path, match the digital courses according to the students' online and offline learning needs, verify that the new design meets the learning objectives, and generate an optimized teaching design.

7. The digital interactive teaching method combining online and offline teaching according to claim 1 is characterized in that: Using the optimized teaching design, setting learning goals and expected interaction frequency, designing feedback control strategies to adjust the interaction frequency, using simulation tests to verify the effect of interaction adjustment, and generating the interaction frequency adjustment model in the following specific steps: Based on the optimized teaching design, online and offline learning goals and expected interaction frequencies are set, interaction indicators are defined, including the number of discussions, question answering frequency, and group collaboration frequency, and related quantitative goals are formulated to obtain interaction goal setting results; Based on the interactive goal setting results, a feedback control strategy is designed, and by monitoring the deviation between the students' real-time interactive frequency and the set goal, the teaching content and interactive form are adjusted to obtain the feedback control strategy; The feedback control strategy is used to conduct simulation tests to evaluate the impact of the adjusted interaction frequency on students' learning outcomes, collect feedback data and analyze the interaction effects to obtain an interaction frequency adjustment model.

8. The digital interactive teaching method combining online and offline teaching according to claim 1 is characterized in that: The steps of deploying the interaction frequency adjustment model in real-time teaching, adjusting the students' interaction frequency in real time, monitoring the effect of students' interaction, comparing the preset learning effect, and obtaining the teaching activity adjustment plan are as follows: Adopt the interaction frequency adjustment model, deploy the model in a real-time teaching environment, collect student interaction data in real time, check the timeliness of data collection, record the real-time interaction frequency of students online and offline, and obtain real-time monitoring results of interaction frequency; Based on the real-time monitoring results of the interaction frequency, the preset learning effect and the real-time interaction effect are compared, the data differences are analyzed, the teaching links that need to be adjusted are identified, the interaction effect is displayed through data visualization tools, and the learning effect comparison analysis results are obtained; According to the comparative analysis results of the learning effects, adjustment measures are formulated for the links where the interaction frequency exceeds the normal threshold, and a teaching activity adjustment plan is obtained by increasing the interactive links and optimizing the teaching content.

9. The digital interactive teaching method combining online and offline teaching according to claim 1, characterized in that: The specific steps of implementing the teaching activity adjustment plan, analyzing the correlation between teaching activities and learning effects, comparing learning outcomes before and after the adjustment, collating teaching feedback, and generating teaching effect evaluation results are as follows: Implement the teaching activity adjustment plan, conduct courses according to the adjusted plan, record students' participation and interaction frequency, check the consistency of the implementation process, and obtain the results of the teaching activity implementation; Based on the results of the teaching activities, the correlation between the teaching activities and the learning effects is analyzed, the learning results of the students are collected, and the influencing factors are analyzed by comparing the learning results before and after the adjustment to obtain the correlation analysis results between the teaching activities and the learning effects; Based on the analysis results of the correlation between the teaching activities and learning effects, organize the teaching feedback, record the students' opinions, combine differentiated data sources, extract key improvement points, and generate teaching effect evaluation results.

10. A digital interactive teaching system combining online and offline, characterized in that: According to any one of claims 1 to 9, the digital interactive teaching method combining online and offline teaching comprises: The data collection module collects students’ online and offline learning data, including the number of discussions, the number of questions answered, and the frequency of group collaboration, to generate an initial interactive data set; The information entropy calculation module performs information entropy calculation on the multiple interaction types in the initialization interaction data set, identifies information redundancy and information missing patterns, and generates information entropy analysis results; The teaching path optimization module adjusts the digital teaching content based on the information entropy analysis results, optimizes the teaching path to match the students' learning needs by avoiding information redundancy and supplementing information missing, and generates an optimized teaching design; The model building module designs learning objectives and expected interaction frequency according to the optimized teaching design, conducts multiple rounds of simulation tests, and generates an interaction frequency adjustment model; The teaching activity implementation module adjusts the students' interaction frequency according to the interaction frequency adjustment model, monitors the students' interaction effect, compares the preset learning effect, and compares the learning results before and after the adjustment to generate a teaching effect evaluation result.

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