Interactive teaching method, system and equipment for online education and medium

Abstract

The invention relates to the technical field of online education, in particular to an interactive teaching method, system and device for online education and a medium. According to the invention, the timeliness of information transmission is enhanced through real-time labeling of a teacher and synchronous display of a student terminal; the understanding degree of the students is evaluated through the interactive answering task, so that the teacher can adjust the teaching strategy in time; secondly, comprehensively collecting student learning behavior data including annotation information viewing frequency, answering time, accuracy and the like, and generating personalized learning reports and teaching suggestions based on the multi-dimensional learning behavior data; the problem of real-time interaction is solved, comprehensive monitoring and accurate guidance of the learning process are realized through data analysis, and the effect of online education is remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of online education, and in particular to an interactive teaching method, system, device and medium for online education. Background Art

[0002] With the rapid development of Internet technology, online education has become an important part of the education field. Online education breaks the limitations of time and space and provides learners with a more flexible and convenient way of learning. However, how to achieve efficient interaction and personalized teaching in a virtual environment has become an important challenge facing online education.

[0003] At present, common online education platforms usually use live video or recorded video for teaching, supplemented by online homework and examination systems. Some platforms have also introduced interactive functions such as real-time bullet screen and online Q&A to enhance communication between teachers and students.

[0004] However, in the existing online education methods, it is difficult for teachers to grasp students' learning status and degree of understanding in real time, and they are unable to adjust teaching strategies in a timely manner; secondly, the students' learning process lacks effective supervision and guidance, which can easily lead to problems of distraction and low learning efficiency. This situation needs to be further improved. Summary of the invention

[0005] In order to solve the problems of distraction and low learning efficiency in existing online education methods, the present application provides an interactive teaching method, system, device and medium for online education, which adopts the following technical solutions: In a first aspect, the present application provides an interactive teaching method for online education, comprising the following steps: Send teaching resources to the teacher's device and receive real-time annotation information from the teacher's device; Transmitting the real-time annotation information to the student terminal device, and sending the interactive question answering task to the student terminal device; Receive the answer results from the student terminal device, evaluate the correctness of the answer results and send the evaluation results to the teacher terminal device; Collect and analyze students' learning behavior data to generate personal learning reports and customized teaching suggestions for the students. The learning behavior data includes the viewing frequency of real-time annotation information, the time taken to answer questions, and the accuracy rate of answering questions.

[0006] By adopting the above technical solutions, in traditional online classes, teachers are often unable to understand students' understanding of key content in a timely manner, and students are easily distracted due to lack of interaction, resulting in poor learning effects. This application first enhances the timeliness of information transmission by synchronizing the teacher's real-time annotation with the student end; then evaluates students' understanding through interactive answering tasks, so that teachers can adjust teaching strategies in a timely manner; then comprehensively collects students' learning behavior data, including the frequency of viewing annotated information, the time taken to answer questions and the accuracy rate, etc. Based on these multi-dimensional learning behavior data, personalized learning reports and teaching suggestions are generated; it not only solves the problem of real-time interaction, but also realizes comprehensive monitoring and precise guidance of the learning process through data analysis, significantly improving the effect of online education.

[0007] Optionally, the method further comprises the following steps: Record the time taken by the student to answer each type of question; Classify each type of question into a difficulty level according to the time taken by the student to answer the question; Adjust the order of sending the same type of interactive question-answering tasks to the student's student terminal device according to the difficulty level from low to high.

[0008] By adopting the above technical solution, in traditional online learning systems, all students often face the same sequence of questions, which causes some students to feel frustrated because the questions are too difficult, while other students feel a lack of challenge because the questions are too easy, which ultimately affects the learning effect; this application records the time each student takes to answer different types of questions, divides each type of question into personalized difficulty levels, and automatically adjusts the order in which questions are sent to students based on the difficulty levels, ensuring that the difficulty of the questions gradually increases. This not only provides each student with a tailored learning path, but also avoids students' frustration while maintaining a moderate challenge.

[0009] Optionally, the method further comprises the following steps: Calculate the correct answer rate of the student on each type of question; Identify the target question type for which the student has the lowest correct rate; For the target question type, corresponding exercises are selected from a question bank and sent to the student's student terminal device.

[0010] By adopting the above technical solution, in traditional online learning systems, students are often required to complete fixed sets of exercises, which may cause some students to waste time on content they have already mastered, while lacking sufficient exercises for weak links that really need to be strengthened; this application first calculates the students' correct answer rate on each type of question, identifies the target question type with the lowest student correct rate, and then intelligently selects corresponding exercises from the question bank for the identified target question type and pushes them to the student's learning device; it can provide each student with a highly personalized exercise plan.

[0011] Optionally, the method further comprises the following steps: Monitor the changes in the student's facial expressions in front of the camera in real time and identify the student's current facial expressions; According to the preset emotion model, the recognized expressions are converted into concentration scores and confusion indexes; Displaying the student's concentration score and confusion index in real time on the teacher's device; When the concentration score of the student is lower than a preset threshold or the confusion index is higher than a preset threshold, the information of the student is highlighted on the teacher's device; Based on the student's concentration score and confusion index, combined with the course difficulty setting and the student's historical learning behavior, the difficulty of the teaching content and the frequency of interaction are dynamically adjusted.

[0012] By adopting the above technical solutions, in traditional online classes, teachers are often unable to intuitively perceive students' concentration and understanding, which greatly reduces the teaching effect. This application introduces real-time expression monitoring technology to capture students' facial expression changes. Through a preset emotional model, expression information is converted into quantifiable concentration scores and confusion indexes. These indicators are then displayed in real time on the teacher's side, and abnormal situations are highlighted. Based on real-time data, combined with the difficulty of the course and students' historical learning behavior, the difficulty of the teaching content and the frequency of interaction are automatically adjusted. This not only provides teachers with intuitive feedback on students' emotional states, but also enables intelligent adjustments to teaching content and methods, greatly improving the interactivity and pertinence of online education.

[0013] Optionally, based on the student's concentration score and confusion index, combined with the course difficulty setting and the student's historical learning behavior, the difficulty of the teaching content and the interaction frequency are dynamically adjusted, specifically including the following steps: Obtaining course difficulty setting information, and obtaining overall teaching parameter information according to the course difficulty setting information, wherein the overall teaching parameter information includes initial content complexity and initial interaction frequency; Acquire the historical learning behavior data of the student, and acquire personalized teaching parameters according to the historical learning behavior data and the overall teaching parameter information, wherein the learning content is divided into a plurality of knowledge modules, and the personalized teaching parameters include the sub-module content complexity and sub-module interaction frequency of each knowledge module; According to the personalized teaching parameters, a corresponding interactive question answering task push instruction is generated and triggered for each knowledge module; Acquire the concentration score and confusion index of the student in the target knowledge module in real time, and calculate the average concentration score and confusion index of the student in the target knowledge module; Compare the average concentration score and the average confusion index with the corresponding preset ideal ranges to obtain the learning state deviation value of the student in the target knowledge module; The learning state deviation value is compared with a preset adjustment threshold value. If the learning state deviation value exceeds the preset adjustment threshold value, an adjustment instruction for the complexity of the teaching content and the interaction frequency is triggered.

[0014] By adopting the above technical solution, this application integrates multi-dimensional data such as course difficulty setting, student historical learning behavior, real-time concentration and confusion index to form personalized teaching parameters; subdivides the learning content into multiple knowledge modules, and sets independent content complexity and interaction frequency for each module; calculates the learning status deviation value by real-time monitoring of students' learning status in each knowledge module; finally, based on the deviation value, triggers dynamic adjustment of teaching content complexity and interaction frequency; realizes refined adjustment of teaching content and interaction strategy, and can also perform instant optimization based on students' real-time feedback, greatly improving the personalization level of online education.

[0015] Optionally, the method further comprises the following steps: Receiving teaching resources uploaded by teachers, and identifying the file format and size of the teaching resources; According to a preset compression algorithm, the teaching resources are optimized and compressed, and tags are added to the teaching resources, wherein the tags include subjects, difficulty levels, and keywords; Receiving a resource search request from the student, automatically screening matching teaching resources according to the resource search request, and sending the matching teaching resources to the student's student terminal device; Receive and store the supplementary materials uploaded by the student, and associate the supplementary materials with the teaching resources.

[0016] By adopting the above technical solution, this application identifies the format and optimizes the compression of the uploaded teaching resources to ensure the efficient transmission of resources; through the intelligent labeling system, multi-dimensional label information is added to each teaching resource to improve the searchability of the resources; then based on the students' search requests, the teaching resources are accurately matched and pushed; finally, a student contribution mechanism is introduced to allow students to upload supplementary materials and associate them with the original teaching resources to form a dynamically updated knowledge base.

[0017] In a second aspect, the present application provides an interactive teaching system for online education, comprising: The annotation information receiving module is used to send teaching resources to the teacher's terminal device and receive real-time annotation information from the teacher's terminal device; The answering task sending module is used to transmit the real-time annotation information to the student terminal device and send the interactive answering task to the student terminal device; The evaluation result sending module is used to receive the answer results from the student terminal device, evaluate the correctness of the answer results and send the evaluation results to the teacher terminal device; The behavior data collection module is used to collect and analyze students' learning behavior data, generate personal learning reports and customized teaching suggestions for the students, and the learning behavior data includes the viewing frequency of real-time annotation information, the time taken to answer questions, and the accuracy rate of answering questions.

[0018] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned interactive teaching method for online education when executing the computer program.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned interactive teaching method for online education.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: 1. In traditional online classes, teachers often cannot understand students' understanding of key content in a timely manner, and students are easily distracted due to lack of interaction, resulting in poor learning results. This application firstly enhances the timeliness of information transmission by synchronizing the teacher's real-time annotation with the student's terminal; then evaluates students' understanding through interactive answering tasks, so that teachers can adjust teaching strategies in a timely manner; then comprehensively collects students' learning behavior data, including the frequency of viewing the annotated information, the time taken to answer questions and the accuracy rate, etc. Based on these multi-dimensional learning behavior data, personalized learning reports and teaching suggestions are generated; this not only solves the problem of real-time interaction, but also realizes comprehensive monitoring and precise guidance of the learning process through data analysis, significantly improving the effect of online education; 2. In traditional online learning systems, all students often face the same sequence of questions, which causes some students to feel frustrated because the questions are too difficult, while other students feel that the questions are too easy and lack challenges, which ultimately affects the learning effect; this application records the time each student takes to answer different types of questions, and divides each type of question into personalized difficulty levels. According to the divided difficulty levels, the order of sending questions to students is automatically adjusted to ensure that the difficulty of the questions gradually increases. This can not only provide each student with a tailored learning path, but also avoid students from feeling frustrated while maintaining a moderate challenge; 3. This application introduces real-time expression monitoring technology to capture changes in students' facial expressions; through a preset emotion model, the expression information is converted into a quantifiable concentration score and confusion index; these indicators are then displayed in real time on the teacher's side, and abnormal situations are highlighted; based on real-time data, combined with the course difficulty and students' historical learning behavior, the difficulty of the teaching content and the frequency of interaction are automatically adjusted; it not only provides teachers with intuitive feedback on students' emotional state, but also enables intelligent adjustment of teaching content and methods, greatly improving the interactivity and pertinence of online education. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of an interactive teaching method for online education according to an embodiment of the present application; Figure 2 This is a flow chart of dividing difficulty in an interactive teaching method for online education according to an embodiment of the present application; Figure 3 It is a flowchart of identifying target question types in an interactive teaching method for online education according to an embodiment of the present application; Figure 4 It is a flowchart of identifying a concentration score and a confusion index in an interactive teaching method for online education according to an embodiment of the present application; Figure 5 It is a flowchart of adjusting difficulty and interaction frequency in an interactive teaching method for online education according to an embodiment of the present application; Figure 6 It is a flow chart of integrating teaching resources in an interactive teaching method for online education according to an embodiment of the present application; Figure 7 This is a module diagram of an interactive teaching system for online education according to an embodiment of the present application; Figure 8 It is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0023] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0024] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0025] In the first aspect, the present application provides an interactive teaching method for online education, referring to Figure 1 , including the following steps: S110, sending teaching resources to the teacher's device, and receiving real-time annotation information from the teacher's device.

[0026] Among them, teaching resources can include but are not limited to electronic courseware, video tutorials, picture materials, audio explanations and other multimedia content. These resources are transmitted to the teacher's device through the network and can be flexibly called during the teaching process. Real-time annotation information is the teacher's instant supplement and emphasis on teaching resources during the teaching process, which can include text annotations, voice notes, graphic marks, etc.

[0027] Specifically, teachers can draw lines, circle notes, add text descriptions, and other operations on electronic courseware, and these operations will be captured by the system in real time and converted into annotation information. For example, when explaining a math problem, the teacher can add red underlines at the key steps and add text descriptions such as "Here is the key to solving the problem." The system will associate these annotation information with the courseware content and prepare to transmit them to the student's terminal device.

[0028] S120, transmitting the real-time annotation information to the student-end device, and sending the interactive question-answering task to the student-end device.

[0029] The transmission of real-time annotation information ensures that students can see the teacher's emphasis and supplementary explanations in a timely manner, which helps students better understand and grasp the teaching content. The interactive answering task is designed to test students' mastery of the knowledge they have learned, and can include multiple-choice questions, fill-in-the-blank questions, short-answer questions and other forms.

[0030] Specifically, when the teacher adds annotations to the courseware, this information will be immediately synchronized to each student's device, and the student can see the courseware content and annotations that are exactly the same as the teacher's. For example, after the teacher finishes explaining a knowledge point, the system can automatically push a set of related interactive questions to the student end. These questions may be preset or temporarily added by the teacher according to the teaching progress. Students need to complete these questions within the specified time to test their understanding.

[0031] S130, receiving the answer results from the student-side device, evaluating the correctness of the answer results and sending the evaluation results to the teacher-side device.

[0032] The system will collect the answers of each student, including the selected answers, the filled-in content, or the uploaded solution process. The evaluation process can be automatic or combined with manual scoring by teachers. The evaluation results include not only whether the answer is correct or not, but also multiple dimensions such as completion time and rationality of the solution ideas.

[0033] Specifically, for objective questions (such as multiple-choice questions and true-or-false questions), the system can automatically compare answers and give scores. For subjective questions (such as short-answer questions and calculation questions), the system can perform preliminary evaluations through keyword matching, formula recognition and other technologies, and then present the results to the teacher for final confirmation. The evaluation results will be displayed on the teacher's device in an intuitive form such as charts and data, allowing the teacher to quickly understand the answering situation of the entire class. For example, the system may generate a pie chart showing the distribution of the correct rate of the entire class on a certain question, helping teachers quickly identify the knowledge points that need to be focused on.

[0034] S140. Collect and analyze students' learning behavior data to generate students' personal learning reports and customized teaching suggestions. The learning behavior data includes the viewing frequency of real-time annotation information, the time taken to answer questions, and the accuracy rate of answering questions.

[0035] Among them, the collection of learning behavior data is ongoing, covering all activities of students throughout the learning process.

[0036] Specifically, the system records the number of times and duration each student views the teacher's annotated information, and analyzes the students' attention to different knowledge points. The time data for answering questions can reflect the students' proficiency in solving problems, while the accuracy of answering questions directly reflects the degree of knowledge mastery. For example, the system may find that a student spends a long time on geometry problems but has a high accuracy rate, while spending a short time on algebra problems but has a low accuracy rate. Based on this, the system can suggest in the learning report that the student should pay more attention to time management when solving geometry problems, and needs to strengthen the understanding and practice of basic concepts for algebra problems.

[0037] In one embodiment, referring to Figure 2 , the method further comprises the following steps: S210. Record the time taken by students to answer each type of question.

[0038] Among them, this embodiment records the time students take to answer different types of questions, providing basic data for subsequent difficulty assessment and question sequence adjustment.

[0039] Specifically, the system will accurately record the time it takes for students to answer questions from the time the question is displayed to the time the answer is submitted. For example, for a multiple-choice question, the system may record that the student took 35 seconds; and for a proof question, the system may record that the student took 5 minutes and 23 seconds. These time data will be stored in association with information such as the question type and difficulty to form a database of students' answering time. The system can also record detailed information such as the number of interruptions and modifications made by students during the answering process to more comprehensively reflect students' answering behavior.

[0040] S220. Classify each type of question into different difficulty levels based on the time it takes students to answer the questions.

[0041] In this embodiment, the system uses the collected answering time data combined with preset evaluation criteria to classify the difficulty level for each type of question.

[0042] Specifically, the system may set multiple difficulty levels, such as "easy", "medium", "difficult" and "challenging". For each type of question, the system will calculate the student's average answering time and compare it with the preset time standard. At the same time, factors such as the correct answer rate will be considered to comprehensively assess the difficulty level.

[0043] S230. Adjust the order of sending the same type of interactive question-answering tasks to the student's student terminal device in order of difficulty level from low to high.

[0044] In this embodiment, the system rearranges the order of pushing the interactive question-answering tasks according to the difficulty levels determined in the aforementioned steps.

[0045] In one embodiment, referring to Figure 3 , the method further comprises the following steps: S310. Calculate the correct rate of students' answers to each type of question.

[0046] Among them, this embodiment provides data support for subsequent weak link identification and targeted exercises by calculating the correct answer rates of students on different types of questions.

[0047] S320. Identify the target question types with the lowest student accuracy rate.

[0048] Specifically, the system sorts the accuracy rates of various types of questions calculated in the previous step, and finds one or more question types with the lowest accuracy rates.

[0049] S330. For the target question type, select corresponding exercises from the question bank and send them to the student's student terminal device.

[0050] In this embodiment, the system selects corresponding exercises from a preset question bank based on the identified target question types, and pushes these questions to the students.

[0051] Specifically, the system will access a pre-established question bank, in which questions are marked and categorized according to type, difficulty, etc. For the identified target question types, the system will select a certain number of relevant exercises and push them to the student's learning device.

[0052] In one embodiment, referring to Figure 4 , the method further comprises the following steps: S410, monitor the changes in the student's facial expressions in front of the camera in real time, and identify the student's current facial expressions.

[0053] Specifically, the system uses the camera of the student's device to capture the student's facial image at regular intervals (such as every second). These images will be transmitted to the image processing module in the background in real time, and the student's current expression will be identified through facial feature point positioning, expression feature extraction and other technologies. For example, the system may identify expression states such as "focused", "confused", "tired" and "excited". These expression data will be recorded in the form of time series to provide a basis for subsequent emotion analysis.

[0054] S420: Convert the recognized facial expressions into concentration scores and confusion indexes according to a preset emotion model.

[0055] In this embodiment, the system uses a pre-trained emotion model to convert the recognized expression data into a quantifiable learning status indicator.

[0056] Specifically, the emotion model may associate different expressions with concentration and confusion. For example, a "concentrated" expression may correspond to a higher concentration score (such as 90 points) and a lower confusion index (such as 10 points); while a "confused" expression may correspond to a lower concentration score (such as 60 points) and a higher confusion index (such as 80 points).

[0057] S430. Display the student's concentration score and confusion index in real time on the teacher's device.

[0058] Specifically, a real-time updated data panel may be displayed on the teacher's device, which contains the concentration score and confusion index of each student in the class. These data can be presented in a variety of forms such as numbers, charts or colors. For example, the system may use a gradient from green to red to indicate the level of concentration, and use a bar chart to show the changing trend of the confusion index. The teacher can use this panel to quickly scan the status of the entire class and identify students who need special attention.

[0059] S440. When the student's concentration score is lower than a preset threshold or the confusion index is higher than a preset threshold, the student's information is highlighted on the teacher's device.

[0060] S450. Based on students’ concentration scores and confusion index, combined with course difficulty settings and students’ historical learning behaviors, dynamically adjust the difficulty of teaching content and interaction frequency.

[0061] In one embodiment, referring to Figure 5 In step S450, based on the student's concentration score and confusion index, combined with the course difficulty setting and the student's historical learning behavior, the difficulty of the teaching content and the interaction frequency are dynamically adjusted, which specifically includes the following steps: S451. Obtain course difficulty setting information, and obtain overall teaching parameter information based on the course difficulty setting information, wherein the overall teaching parameter information includes initial content complexity and initial interaction frequency.

[0062] S452. Obtain the student's historical learning behavior data, and obtain personalized teaching parameters based on the historical learning behavior data and overall teaching parameter information.

[0063] Among them, the learning content is divided into several knowledge modules, and the personalized teaching parameters include the complexity of the sub-module content and the sub-module interaction frequency of each knowledge module.

[0064] S453. Generate and trigger corresponding interactive question-answering task push instructions for each knowledge module based on personalized teaching parameters.

[0065] S454. Obtain the concentration scores and confusion indexes of students in the target knowledge module in real time, and calculate the average concentration scores and confusion indexes of students in the target knowledge module.

[0066] S455. Compare the average concentration score and the average confusion index with the corresponding preset ideal ranges to obtain the student's learning status deviation value in the target knowledge module.

[0067] S456: Compare the learning state deviation value with the preset adjustment threshold. If the learning state deviation value exceeds the preset adjustment threshold, an adjustment instruction for the sub-module content complexity and the sub-module interaction frequency is triggered.

[0068] In one embodiment, referring to Figure 6 , the method further comprises the following steps: S610: Receive teaching resources uploaded by teachers, and identify the file format and size of the teaching resources.

[0069] S620. Optimize and compress the teaching resources according to a preset compression algorithm, and add tags to the teaching resources. The tags include subjects, difficulty levels, and keywords.

[0070] S630: Receive a resource search request from a student, automatically screen matching teaching resources based on the resource search request, and send the resources to the student's student-end device.

[0071] S640, receiving and storing the supplementary materials uploaded by the students, and associating the supplementary materials with the teaching resources.

[0072] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0073] On the second aspect, the present application provides an interactive teaching system for online education. The interactive teaching system for online education of the present application is described below in combination with the above-mentioned interactive teaching method for online education.

[0074] Reference Figure 7 , an interactive teaching system for online education, comprising: The annotation information receiving module is used to send teaching resources to the teacher's terminal device and receive real-time annotation information from the teacher's terminal device; The answering task sending module is used to transmit the real-time annotation information to the student terminal device and send the interactive answering task to the student terminal device; The evaluation result sending module is used to receive the answer results from the student terminal device, evaluate the correctness of the answer results and send the evaluation results to the teacher terminal device; The behavior data collection module is used to collect and analyze students' learning behavior data, generate students' personal learning reports and customized teaching suggestions. The learning behavior data includes the frequency of viewing real-time annotation information, the time taken to answer questions, and the accuracy of answering questions.

[0075] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The electronic device includes a processor, a memory and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an interactive teaching method for online education is implemented.

[0076] Those skilled in the art will understand that Figure 8 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0077] In one embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0078] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0079] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An interactive teaching method for online education, characterized in that: The steps include: Send teaching resources to the teacher's device and receive real-time annotation information from the teacher's device; Transmitting the real-time annotation information to the student terminal device, and sending the interactive question answering task to the student terminal device; Receive the answer results from the student terminal device, evaluate the correctness of the answer results and send the evaluation results to the teacher terminal device; Collect and analyze students' learning behavior data to generate personal learning reports and customized teaching suggestions for the students. The learning behavior data includes the viewing frequency of real-time annotation information, the time taken to answer questions, and the accuracy rate of answering questions.

2. The interactive teaching method for online education according to claim 1, characterized in that: The method further comprises the following steps: Record the time taken by the student to answer each type of question; Classify each type of question into a difficulty level according to the time taken by the student to answer the question; Adjust the order of sending the same type of interactive question-answering tasks to the student's student terminal device according to the difficulty level from low to high.

3. The interactive teaching method for online education according to claim 1, characterized in that: The method further comprises the following steps: Calculate the correct answer rate of the student on each type of question; Identify the target question type for which the student has the lowest correct rate; For the target question type, corresponding exercises are selected from a question bank and sent to the student's student terminal device.

4. The interactive teaching method for online education according to claim 1, characterized in that: The method further comprises the following steps: Monitor the changes in the student's facial expressions in front of the camera in real time and identify the student's current facial expressions; According to the preset emotion model, the recognized expressions are converted into concentration scores and confusion indexes; Displaying the student's concentration score and confusion index in real time on the teacher's device; When the concentration score of the student is lower than a preset threshold or the confusion index is higher than a preset threshold, the information of the student is highlighted on the teacher's device; Based on the student's concentration score and confusion index, combined with the course difficulty setting and the student's historical learning behavior, the difficulty of the teaching content and the frequency of interaction are dynamically adjusted.

5. The interactive teaching method for online education according to claim 4, characterized in that: Based on the student's concentration score and confusion index, combined with the course difficulty setting and the student's historical learning behavior, the difficulty of the teaching content and the interaction frequency are dynamically adjusted, specifically including the following steps: Obtaining course difficulty setting information, and obtaining overall teaching parameter information according to the course difficulty setting information, wherein the overall teaching parameter information includes initial content complexity and initial interaction frequency; Acquire the historical learning behavior data of the student, and acquire personalized teaching parameters according to the historical learning behavior data and the overall teaching parameter information, wherein the learning content is divided into a plurality of knowledge modules, and the personalized teaching parameters include the sub-module content complexity and sub-module interaction frequency of each knowledge module; According to the personalized teaching parameters, a corresponding interactive question answering task push instruction is generated and triggered for each knowledge module; Acquire the concentration score and confusion index of the student in the target knowledge module in real time, and calculate the average concentration score and confusion index of the student in the target knowledge module; Compare the average concentration score and the average confusion index with the corresponding preset ideal ranges to obtain the learning state deviation value of the student in the target knowledge module; The learning state deviation value is compared with a preset adjustment threshold value. If the learning state deviation value exceeds the preset adjustment threshold value, an adjustment instruction for the sub-module content complexity and the sub-module interaction frequency is triggered.

6. The interactive teaching method for online education according to claim 1, characterized in that: The method further comprises the following steps: Receiving teaching resources uploaded by teachers, and identifying the file format and size of the teaching resources; According to a preset compression algorithm, the teaching resources are optimized and compressed, and tags are added to the teaching resources, wherein the tags include subjects, difficulty levels, and keywords; Receiving a resource search request from the student, automatically screening matching teaching resources according to the resource search request, and sending the matching teaching resources to the student's student terminal device; Receive and store the supplementary materials uploaded by the student, and associate the supplementary materials with the teaching resources.

7. An interactive teaching system for online education, characterized in that: include: The annotation information receiving module is used to send teaching resources to the teacher's terminal device and receive real-time annotation information from the teacher's terminal device; The answering task sending module is used to transmit the real-time annotation information to the student terminal device and send the interactive answering task to the student terminal device; The evaluation result sending module is used to receive the answer results from the student terminal device, evaluate the correctness of the answer results and send the evaluation results to the teacher terminal device; The behavior data collection module is used to collect and analyze students' learning behavior data, generate personal learning reports and customized teaching suggestions for the students, and the learning behavior data includes the viewing frequency of real-time annotation information, the time taken to answer questions, and the accuracy rate of answering questions.

8. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the interactive teaching method for online education described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the interactive teaching method for online education described in any one of claims 1-6 are implemented.

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