Online teaching interaction method and device, storage medium and computer equipment

Abstract

The invention discloses an online teaching interaction method and device, a storage medium and computer equipment. Comprising the steps that learning behavior information generated by a student based on an online teaching course is acquired, the learning behavior information comprises class monitoring information and a homework file, and the homework file comprises at least one homework question and homework content corresponding to the homework question; comparing the homework content with the standard answers of the homework questions, and determining homework completion condition information; processing the homework completion condition information through a scoring model associated with the type to which the homework question belongs, and determining the homework score of the homework question; and based on the homework question, the homework score and the class monitoring information, learning suggestion information is fed back to the student terminal. According to the method, the accuracy of student homework error analysis is improved, the teaching quality can be improved, personalized tutoring and learning suggestions can be made, and the students can know own learning conditions in time and dynamically adjust the tutoring content and difficulty so as to adapt to the requirements of each student.

Description

Technical Field

[0001] The present application relates to the field of educational technology, and in particular to an interactive method, device, storage medium and computer equipment for online teaching. Background Art

[0002] The existing education system often adopts unified teaching content and methods, which are difficult to adjust according to each student's learning progress and understanding ability. This causes some students to be unable to keep up with the teaching progress or to deeply understand the learning content, which is not conducive to promoting the matching of students' self-learning progress on learning software with their learning progress in school, and thus easily affects students' learning efficiency. Summary of the invention

[0003] In view of this, the present application provides an interactive method, apparatus, storage medium and computer equipment for online teaching to improve students' learning effects and teaching quality.

[0004] According to a first aspect of the present application, an interactive method for online teaching is provided, which is applied to a server, and includes:

[0005] Acquire learning behavior information generated by students based on online teaching courses, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content;

[0006] Comparing the homework content with the standard answer to the homework question to determine the homework completion status information;

[0007] Processing the homework completion information through a scoring model associated with the type of the homework title to determine the homework score of the homework title;

[0008] Based on the homework title, the homework score and the class monitoring information, learning suggestion information is fed back to the student terminal so that the student terminal displays the learning suggestion information.

[0009] According to a second aspect of the present application, an interactive method for online teaching is provided, which is applied to a student end and includes:

[0010] In response to the target operation, obtaining learning behavior information generated by the student based on the online teaching course, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content;

[0011] Sending the learning behavior information to a server, so that the server processes the homework completion information through a scoring model associated with the type of the homework title, determines that the homework title is a homework grade, and determines learning suggestion information based on the homework title, the homework grade and the class monitoring information, wherein the homework completion information is determined by comparing the homework content with a standard answer to the homework title;

[0012] In response to receiving the learning suggestion information sent by the server, displaying the learning suggestion information.

[0013] According to a third aspect of the present application, an interactive device for online teaching is provided, which is applied to a server, and includes:

[0014] An acquisition module, used to acquire learning behavior information generated by students based on online teaching courses, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content;

[0015] An evaluation module is used to compare the homework content with the standard answer to the homework question to determine the homework completion information; and to process the homework completion information through a scoring model associated with the type of the homework question to determine the homework score of the homework question;

[0016] The interactive module is used to feed back learning suggestion information to the student terminal based on the homework title, the homework score and the class monitoring information, so that the student terminal displays the learning suggestion information.

[0017] According to a fourth aspect of the present application, an interactive device for online teaching is provided, which is applied to a student end and includes:

[0018] An acquisition module, configured to acquire, in response to a target operation, learning behavior information generated by students based on online teaching courses, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content;

[0019] A communication module, configured to send the learning behavior information to a server, so that the server processes the homework completion information through a scoring model associated with the type of the homework title, determines that the homework title is a homework grade, and determines learning suggestion information based on the homework title, the homework grade and the class monitoring information, wherein the homework completion information is determined by comparing the homework content with a standard answer to the homework title;

[0020] A display module is used to display the learning suggestion information in response to receiving the learning suggestion information sent by the server.

[0021] According to a fifth aspect of the present application, a readable storage medium is provided, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the above-mentioned interactive method of online teaching are implemented.

[0022] According to the sixth aspect of the present application, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the steps of the above-mentioned online teaching interaction method are implemented.

[0023] Through the above technical solution, on the one hand, the differences in the way homework is graded for different subjects and different question types are fully considered, and the students' homework completion is automatically evaluated through the scoring model associated with the type of homework questions. This can not only reduce the subjective bias caused by manual grading, but also improve the accuracy of students' homework error analysis, which helps to improve the quality of teaching. On the other hand, by analyzing students' mastery of different knowledge points through their homework scores and learning behaviors, and providing personalized tutoring and learning suggestions, students can understand their learning situation in a timely manner, and can dynamically adjust the tutoring content and difficulty to meet the needs of each student, helping students to master knowledge more effectively and improve learning effects.

[0024] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0026] Figure 1 One of the flow diagrams of the interactive method for online teaching provided in an embodiment of the present application is shown;

[0027] Figure 2 The second flowchart of the interactive method for online teaching provided in the embodiment of the present application is shown;

[0028] Figure 3 One of the structural block diagrams of the interactive device for online teaching provided in an embodiment of the present application is shown;

[0029] Figure 4 The second structural block diagram of the interactive device for online teaching provided in the embodiment of the present application is shown;

[0030] Figure 5A schematic diagram showing an application environment of an interactive method for online teaching provided in an embodiment of the present application is shown;

[0031] Figure 6 A schematic diagram of the electronic structure of a computer device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0032] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0033] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0034] Those skilled in the art will appreciate that, unless expressly stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, or there may be intermediate elements. In addition, the "connection" or "connection" used herein may include wireless connection or wireless fusion. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0035] Now, exemplary embodiments according to the present application will be described in more detail with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in a variety of different forms and should not be interpreted as being limited to the embodiments set forth herein. It should be understood that these embodiments are provided to make the disclosure of the present application thorough and complete, and to fully convey the concepts of these exemplary embodiments to those of ordinary skill in the art.

[0036] The interactive method for online teaching provided by the embodiment of the present invention can be applied in Figure 5In the application environment, the student end and the teaching end communicate with the server end through the network. The server end can receive the online teaching courses recorded by the teaching end, and send them to the student end for playback in a video-on-demand or live broadcast mode. During the student's study, the student end obtains the learning behavior information generated by the student based on the online teaching course. The server end determines the homework completion information through the learning behavior information, and processes the homework completion information through the scoring model associated with the type of the homework title to determine the homework grade of the homework title. Based on the homework title, homework grade and class monitoring information, the server end feeds back learning suggestion information to the student end, so that the student end can display the learning suggestion information.

[0037] The student end or the teaching end may be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, and portable wearable devices. The server end may be implemented by an independent server or a server cluster consisting of multiple servers. The present invention is described in detail below through specific embodiments.

[0038] In this embodiment, an interactive method for online teaching is provided, which is described by taking a student end and a server end as examples. Figure 1 As shown, the method includes:

[0039] Step 101: The student terminal obtains the learning behavior information generated by the student based on the online teaching course in response to the target operation.

[0040] Among them, the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content. It is understandable that the homework title can be an exercise question formulated by the teacher based on the content of the online teaching course, or it can be an exercise question related to the online teaching course in the system exercise bank. The homework file can be in text format or image format, for example, an image of a paper test paper, a video homework, or an answer completed by the student on the system. Class monitoring information is used to monitor students' behavior during learning, such as interactive behavior (questioning, discussing, commenting, etc.), listening behavior, homework behavior, leaving behavior, etc.

[0041] Specifically, the target operation is used to trigger the learning end to collect data. For example, the student end responds to the control operation of playing an online teaching course and starts collecting monitoring images during class, or the student end responds to the control operation of submitting homework and obtains the homework files submitted by the students.

[0042] Step 102: The learning end sends the learning behavior information to the server end.

[0043] It is understandable that in order to ensure the timeliness of monitoring, the learning end can send the learning behavior information to the server at the same time as it is obtained, or it can send it to the server after obtaining the complete learning behavior information when stopping data collection. For example, as the online teaching course is played, the learning end can continuously send the newly generated class monitoring information to the server. Or after the online teaching course is finished and the students complete the homework, the homework file will be sent to the server in full.

[0044] Step 103: The server obtains the learning behavior information generated by the students based on the online teaching course.

[0045] It is understandable that after obtaining the learning behavior information, the server can pre-process the learning behavior information to unify the data standardization and consistency from different learning ends. Exemplarily, students upload homework and test data through the online learning platform, so that the server can use the API interface to obtain homework and test data from different data sources (learning end), or the server periodically obtains data from the data source in batches, reducing the number of API requests and improving efficiency. After obtaining the homework and test data, the server calls a data conversion tool (such as Apache Nifi) to standardize data in different formats to ensure the consistency of subsequent processing. In the process of data conversion, the server verifies the acquired data to ensure the integrity and accuracy of the data itself. Clean the data during the verification process to filter out low-quality data such as fuzzy and garbled characters to improve data quality. Finally, the processed data is stored in a relational database (such as MySQL, PostgreSQL) or a NoSQL database (such as MongoDB). Specifically, a data synchronization tool (such as Debezium) can be used to synchronize data from different data sources to a unified database. During the data synchronization process, the server compares the acquired data with the data already stored in the database, and only collects the data that has changed since the last collection, avoiding repeated collection of the same data, thereby reducing the amount of data that needs to be processed and improving processing efficiency.

[0046] In actual application scenarios, the learning end can perform a slice upload operation on the learning behavior information based on the get out of class status and the data volume of the learning behavior information. Specifically, the learning end determines the upper limit of the data transmission volume based on the get out of class status; the learning end matches the data volume of the learning behavior information with the data volume interval, and determines the slice unit data volume that matches the data volume of the learning behavior information; the learning end slices the learning behavior information based on the slice unit data volume to obtain at least one slice data; the learning end sends at least one slice data to the server based on the upper limit and the slice unit data volume.

[0047] Therefore, the upper limit of data transmission volume is used as a constraint to upload one or more shard data in batches. This achieves the effect of dynamically adjusting the data transmission strategy. While ensuring the normal loading of the course, uploading in batches increases the probability of successful data transmission, which helps to reduce the amount of data to be retransmitted.

[0048] In step 104, the server compares the homework content with the standard answer to the homework question to determine the homework completion status information.

[0049] The homework completion information includes whether the homework title is correct, the content of the correct answer and the content of the incorrect answer, etc.

[0050] For example, for homework content in text format, a natural language processing (NLP) model can be used to analyze the difference between the homework content and the standard answer to evaluate the quality of homework completion. For homework content in image format, a convolutional neural network (CNN) model can be used to identify and evaluate the content and correctness of image homework.

[0051] In one embodiment, before step 104, the interactive method of online teaching also includes: identifying text content and / or graphic content in a printed format in the homework file as a homework title, and identifying text content and / or graphic content in a handwritten format in the answer area of ​​the homework title as homework content corresponding to the homework title.

[0052] In this embodiment, for homework that is not answered by the system, students are often required to upload written answers or test paper files. The questions and answers in the homework file are automatically identified by distinguishing the handwritten part and the printed part in the homework file. This allows the system to quickly identify the printed questions and the student's handwritten problem-solving steps, reducing grading errors caused by manual misjudgment of the question range or unclear definition of the answer area, so as to facilitate subsequent analysis of students' completion of different types of homework questions.

[0053] In actual application scenarios, identifying the text content in the printed format of a job file as the job title, and identifying the text content in the handwritten format in the answer area of ​​the job title as the job content corresponding to the job title, specifically includes: identifying the contour information of the text content and / or graphic content in the job file, and the file title information; inputting the contour information into a classification model to determine the classification result of the text content; using the text content and / or graphic content in the printed format as the job title; matching the preset question in the preset answer file associated with the job title and the file title information, and using the empty area of ​​the preset question matching the job title as the answer area of ​​the job title; using the text content and / or graphic content in the handwritten format located in the answer area as the job content corresponding to the job title.

[0054] The classification results include printed format and handwritten format. The classification model is trained based on handwritten format samples and printed format samples. The classification model can learn the different patterns of the two types of text in terms of contour features, and can accurately and quickly determine whether the text is handwritten or printed based on the learned rules. The preset answer file is used to record homework questions that have not been filled in. The preset answer file has the same format as the materials used by students to answer questions, so that the answer area can be quickly located through the preset answer file, for example, a blank test paper.

[0055] In this embodiment, by extracting the outline information of the text content and / or the graphic content, the morphological characteristics of the text and the graphic can be obtained from the visual feature level. The outline of handwriting is often irregular, and the thickness of the strokes changes, and the shape of the starting and ending points of the strokes is relatively natural and casual. The printed outline is relatively regular and the lines are smooth. By extracting the outline information, these differences can be captured to assist in the accurate identification of the title and the answer content. And the preset questions with the same homework title are found from the preset answer file, and the answer area of ​​the homework title is located according to the empty area of ​​the preset title in the preset answer file, so as to quickly locate the student's homework content. Thereby, it can effectively avoid the misjudgment of the title and the answer area caused by factors such as format differences and typesetting changes, and is compatible with the title settings and format requirements of different homework files, which improves the versatility and adaptability of the homework processing system, so that the server can automatically detect the relationship between each homework title and its corresponding homework content, and provide a good foundation for subsequent data analysis.

[0056] Step 105 , the server processes the homework completion information through the scoring model associated with the type of the homework title to determine the homework score of the homework title.

[0057] The type of homework title can be subject type (such as Chinese, mathematics, physics, etc.), question type (such as true or false, multiple choice, subjective question, etc.), or format type (such as text, graphics). The higher the homework score of the homework title, the higher the accuracy and quality of the homework content, which means that the student has a higher degree of mastery of the relevant knowledge points of the topic.

[0058] It should be noted that the scoring model is trained based on different types of homework questions, sample answers and scoring labels, so that different scoring models can learn the scoring rules of different types of homework questions. Scoring models include but are not limited to regression models and classification models. For example, when the homework question is a multiple-choice question, the correctness of the answer and the score of the multiple-choice question are used as samples to train the regression model, so that the trained regression model can accurately evaluate the score of the multiple-choice question; when the homework question is a Chinese reading comprehension question, the semantics, keywords and corresponding scores of the sample answers are used as samples to train the regression model, so that the trained regression model can accurately evaluate the score of the Chinese reading comprehension question; taking art questions as an example, if the color characteristics, color distribution and corresponding scores of the sample images are used as samples to train the classification model, the content of the student's work can be quantitatively scored from multiple dimensions such as color, composition, and creativity.

[0059] In this embodiment, a suitable scoring algorithm is selected according to different types of assignments. The scoring can be analyzed and scored from multiple dimensions such as text semantics, graphic structure, content accuracy, content completeness, etc., and the quality of the student's answer is comprehensively considered to make the scoring more in line with the student's actual performance, solve the problem of data diversity and complexity in the evaluation process, ensure the reliability and consistency of the evaluation results, avoid the negligence and subjective bias that may occur in manual scoring, and ensure that the scoring of each question is accurate.

[0060] Step 106: The server provides learning suggestion information to the student based on the homework title, homework score and class monitoring information.

[0061] In one embodiment, step 106, that is, the server feeds back learning suggestion information to the student based on the homework title, homework score and class monitoring information, specifically includes the following steps:

[0062] Step 106 - 1 , the server determines the test knowledge points corresponding to the homework questions and the knowledge points to be mastered corresponding to the target questions based on the knowledge point question bank.

[0063] Among them, the target questions are homework questions that belong to the same subject type and whose homework scores are less than the preset scores. The knowledge point question bank includes exercises and standard answers related to different knowledge points. The exercise questions in the knowledge point question bank are classified and stored according to knowledge points, question types, difficulty, etc. Exercises and standard answers can be extracted by collecting authoritative textbooks, teaching reference books, academic papers, test papers and other materials, or by searching high-quality educational resource websites and academic databases on the Internet to filter out content related to knowledge points. They can also be created by teachers on the teaching side. It is understandable that the server can regularly update and supplement the knowledge point question bank to ensure the timeliness and completeness of the question bank content. Test knowledge points include mastery knowledge points.

[0064] In this embodiment, the server uses the pre-configured knowledge point question bank to retrieve the test knowledge points related to the homework questions completed by the students, as well as the knowledge points to be mastered related to the target questions that did not meet the standard. This specifically analyzes which knowledge points and problem-solving methods the students have problems with, and assists students in understanding the content they need to master, so as to help students learn and improve more targetedly, make learning more targeted, avoid blind learning, and improve learning efficiency.

[0065] It is worth mentioning that subjective questions often cannot involve multiple knowledge points and multiple problem-solving methods. If the mastery of knowledge points is distinguished by the right or wrong completion of the homework, there will be inaccurate problems. To this end, for homework questions involving multiple knowledge points, when the homework completion information includes the similarity between the homework content and the standard answer to the homework question, if the target question involves multiple knowledge points to be mastered, the multiple knowledge points to be mastered are filtered based on the homework completion information of the target question to update the knowledge points to be mastered with a similarity greater than the preset similarity as the mastered knowledge points. In this way, even if the question score is low, the mastered knowledge points that show a high similarity in the homework can be found from the multiple knowledge points involved in the question. In this way, we can gain an in-depth understanding of each student's mastery of different knowledge points, improve the accuracy of the analysis of the mastery of knowledge points, and provide reliable data support for the subsequent adjustment of learning plans and the allocation of teaching resources.

[0066] Step 106-2: The server determines the learning status of the test knowledge point based on the class monitoring information.

[0067] Among them, the learning status includes the learned status and the micro-learning status.

[0068] In actual application scenarios, step 106-2 specifically includes: obtaining the duration and triggering time of students' screen gaze behavior in class monitoring information; if the duration is greater than the preset duration, determining the learning period based on the triggering time and duration; marking the test knowledge points corresponding to the learning period in the online teaching course as learned.

[0069] Among them, the preset duration can be set more reasonably according to the difficulty of the knowledge points and the duration of the course, for example, 5 minutes, 10 minutes, etc.

[0070] In this embodiment, when the online teaching course is in the class state, when the student has the behavior of looking at the screen, it can be determined that the student is listening carefully at this time. Traverse each screen-looking behavior in the class monitoring information. Start recording the duration and triggering time of the student's screen-looking behavior. When the duration of the screen-looking behavior is greater than the preset duration, the student is in a state of long-term looking and good learning effect. The triggering moment of this screen-looking behavior is taken as the starting moment, and the end moment of learning is determined after the duration. The time period in which the starting moment and the end moment are located is the student's learning period. On the contrary, when the duration of the student's screen-looking behavior is short, it means that the student is not focused, and the corresponding knowledge points in the course cannot be guaranteed to be mastered by the student. After the server determines the learning period, it checks whether the time range of each test knowledge point involved in the online teaching course has an intersection with the learning period. If there is an intersection, the test knowledge point is added to the list of knowledge points that have been learned. The knowledge points that the student has been able to master are screened out by the duration of the student's screen-looking behavior. On the one hand, the classification accuracy of the knowledge points is improved, helping students better understand their own learning habits and learning progress. On the other hand, it can facilitate reasonable adjustments to personal learning plans when providing subsequent feedback on learning suggestions, briefly review the knowledge points that students have mastered, and spend more time explaining the content that students have not yet mastered, thereby improving teaching efficiency.

[0071] Step 106-3: The server determines the priority of the test knowledge points based on the learning status and the knowledge points to be mastered.

[0072] Among them, priority is used to distinguish the degree of demand for learning knowledge points, and different priorities correspond to different learning plans and learning materials.

[0073] In the actual application scenario, step 106-3 specifically includes: determining the mastered knowledge points in the test knowledge points other than the knowledge points to be mastered; if the mastered knowledge points are in a learned state, determining the mastered knowledge points as the first priority; if the mastered knowledge points are in an unlearned state, determining the mastered knowledge points as the second priority; if the knowledge points to be mastered are in a learned state, determining the knowledge points to be mastered as the third priority; if the knowledge points to be mastered are in an unlearned state, determining the knowledge points to be mastered as the fourth priority.

[0074] In this embodiment, the test knowledge points are divided into four levels. If the knowledge points that have been mastered are in the state of learning, it means that the student has learned the knowledge points in the course, and the knowledge points have been mastered through the homework test. At this time, the server can set the knowledge points that have been mastered as the first priority, and the learning time can be appropriately reduced, and regular review can be done. If the knowledge points that have been mastered are in the state of not learning, it means that the student has not paid too much attention to the knowledge points in the course. Although the quality of the homework content completed by the student is high, there is a possibility of misjudgment. At this time, the server can set the knowledge points that have been mastered as the second priority, and the student needs to pay attention to whether the knowledge points have been understood, and conduct contact practice tests again. If the knowledge points to be mastered are in the state of learning, it means that the student has not understood the knowledge points in the learning process. At this time, the server can set the knowledge points to be mastered as the third priority, and it is necessary to relearn, understand and practice. If the knowledge points to be mastered are in the state of not learning, it means that the student has not yet learned the knowledge points. At this time, the server can set the knowledge points to be mastered as the fourth priority, and it is necessary to invest more time and energy to learn and understand. This will help accurately analyze students' knowledge mastery and learning focus, further deepen knowledge, fill knowledge gaps, avoid blind and disorderly learning, and improve the pertinence and efficiency of learning.

[0075] Step 106-4: The server feeds back learning suggestion information to the student based on the test knowledge points and priorities.

[0076] In this embodiment, the learning status of students for different knowledge points is analyzed through class monitoring information. The degree of mastery of each knowledge point by the students is determined by testing the learning status of the knowledge points and the knowledge points to be mastered, and the appropriate learning priority is matched. In this way, the strengths and weaknesses of students in learning can be accurately identified, and review and intensive training can be flexibly carried out through priority, so as to avoid spending too much time on the content that has been mastered, make learning more targeted, and realize the reasonable allocation of educational resources, so that students can reduce knowledge blind spots as soon as possible and improve learning effects.

[0077] In actual application scenarios, step 106-4 specifically includes: determining a learning plan for the test knowledge point based on priority; based on the learning plan, retrieving exercises for the test knowledge point belonging to the target priority from the exercise question bank; and sending the learning plan and exercises to the student end.

[0078] Among them, the study plan includes the study / review time for different knowledge points, learning methods (such as consulting materials, scientific experiments, participating in research discussions) and materials needed for learning.

[0079] In this embodiment, a personalized study plan and exercises can be formulated for each student based on the student's learning ability, learning progress, and learning preferences. This avoids blind learning and meaningless repetitive exercises, allows students to focus on mastering specific priority knowledge points, makes learning more targeted, and makes more efficient use of time, so that students with different learning levels and progress can receive appropriate guidance and training in their studies, meeting the learning habits of different students.

[0080] Step 107 : The learning end displays the learning suggestion information in response to receiving the learning suggestion information sent by the server.

[0081] The interactive method of online teaching provided by the embodiment of the present application, on the one hand, fully considers the differences in the way of grading homework of different subjects and different question types, and automatically evaluates the completion of students' homework through the scoring model associated with the type of homework title, which can not only reduce the subjective bias caused by manual grading, but also improve the accuracy of students' homework error analysis, which helps to improve the quality of teaching. On the other hand, by analyzing the students' mastery of different knowledge points through their homework scores and learning behaviors, and providing personalized tutoring and learning improvement directions, students can understand their learning situation in a timely manner, and can dynamically adjust the tutoring content and difficulty to meet the needs of each student, helping students to master knowledge more effectively and improve learning effects.

[0082] In one embodiment, if Figure 2 As shown, in the scenario of live courses, the interactive methods of online teaching also include:

[0083] Step 201, if there is student interaction behavior in the class monitoring information, the server establishes a long connection between the student end and the teaching end.

[0084] Among them, the teaching end is used to record online teaching courses.

[0085] Specifically, student interactive behaviors include but are not limited to questioning, commenting, and discussing. The rules for determining student interactive behaviors can be reasonably configured according to system settings, and the embodiments of the present application do not make specific limitations. For example, a student types a question text in the course question window, and the server confirms that there is a questioning behavior. Voice input is detected in the surveillance video, and the voice semantics are questions, and the server confirms that there is a questioning behavior. The student clicks the interactive button, enters the interactive page and selects the interactive method, and the server confirms that there is an interactive behavior.

[0086] Step 202: If the online teaching course is in class, the student terminal responds to the interactive operation and obtains student interaction information.

[0087] Step 203: The student terminal sends the student interaction information to the server terminal based on the long connection between the student terminal and the teaching terminal.

[0088] Step 204, the server synchronously updates the student interaction information generated by the student side and the teacher interaction information generated by the teaching side based on the long connection.

[0089] Step 205: The student terminal receives the teacher interaction information generated by the teaching terminal based on the long connection between the student terminal and the teaching terminal.

[0090] Step 206: The student terminal displays the teacher interaction information.

[0091] In this embodiment, when student interaction is detected, a long connection is created between the student end and the teaching end to monitor the interaction content between each student end and the teaching end in real time, and synchronize the interaction content to the student end and the teaching end in time. In this way, the teacher can see the students' questions, doubts, and understanding of knowledge points in real time, and give targeted answers and guidance immediately. Students can also know the solutions to the problems in real time, which helps students better master knowledge and improve learning effects.

[0092] Furthermore, in one embodiment, in the process of synchronously updating the student interaction information generated by the student end and the teacher interaction information generated by the teaching end based on the long connection, the online teaching interaction method also includes: if the online teaching course is in a class state, and the student interaction information includes audio and video information, the server recognizes the student's voice features and / or student facial features based on the audio and video information; the server performs emotion recognition processing on the student's voice features and / or student facial features to determine the student's emotional state; if the emotional state is an abnormal emotional state, the server adjusts the interactive content of the online teaching course based on the emotional state.

[0093] The interactive content includes the teacher interaction information generated by the teaching end and / or the content display scene of the online teaching course. Specifically, the content display scene may include teaching tools such as electronic whiteboards and PPT, display windows for playing teaching videos and displaying document materials, question discussion areas, portrait video areas, etc.

[0094] It is understandable that a deep neural network (DNN) model can be used to extract speech features from speech data, such as acoustic features (e.g., fundamental frequency, volume, speech rate, rhythm), language features (semantics, vocabulary), etc. A face detection algorithm (MTCNN) model using deep learning is used to detect the position and size of the face in the video frame, and the position of facial feature points is determined through an active shape model.

[0095] After extracting facial features and / or voice features, you can use classification algorithms such as support vector machines (SVM), decision trees, random forests, or convolutional neural networks (CNN) or recurrent neural networks (RNN) to judge students' emotional states, such as happiness, sadness, anger, surprise, confusion, etc., based on facial features and / or voice features. Take convolutional neural networks (CNN) as an example to illustrate, use sentiment dictionaries to match sentiment words in texts, and make preliminary judgments on sentiment. Collect and annotate training data, and select appropriate analysis models, such as VADER, BERT, etc. Use preprocessed training data to train the analysis model, and continuously optimize the analysis model parameters to obtain a sentiment analysis model to ensure that the sentiment analysis model can accurately identify students' emotional states.

[0096] In this embodiment, it is considered that each student has different acceptance and emotional response to knowledge. The server can identify the emotional state of the student through the student's voice features and / or facial features. Therefore, the interactive content is adjusted when the emotional state is abnormal, and the interactive content that is more in line with the student's current state is provided to the student, so as to alleviate the negative emotions generated by the student in the learning process, and let the student learn in a more comfortable and pleasant environment, thereby enhancing the student's enthusiasm and involvement in learning, helping the student to better understand and master the knowledge, and making the learning process smoother and more efficient.

[0097] For example, more detailed explanations of current knowledge points can be pushed to students who are confused, and more interesting cases or interactive sessions can be provided to students who are bored, making learning more personalized and improving students' learning experience.

[0098] It is worth mentioning that after obtaining the students' emotional state, the final emotional state can be obtained by combining the emotion recognition results of speech and face through weighted averaging or multimodal deep learning models, thereby enhancing the accuracy of emotion recognition.

[0099] In actual application scenarios, when the interactive content includes teacher interaction information, the server adjusts the interactive content of online teaching courses based on the emotional state, specifically including the following implementation methods:

[0100] Method 1: If the teacher interaction information includes interaction text, the server adjusts the text based on the emotional state matching and updates the interaction text based on the adjusted text.

[0101] In this embodiment, the server uses the correspondence between the emotional state and the preset text to match the adjustment text with abnormal emotional state. The interactive text output by the teaching end is corrected by adjusting the text. The server then feeds back the corrected interactive text to the student in real time through the WebSocket long connection. The corrected interactive text can be consistent with the student's current emotional changes, avoiding aggravating the student's negative emotions due to improper expression. Students can feel understood and cared for, thereby enhancing their positive feelings about the learning process and improving their learning experience.

[0102] For example, when students feel anxious during learning and ask questions about knowledge points, the teacher inputs the answers to the questions through the teaching end. The server will match the encouraging words "It's okay, take it one step at a time" with the answers provided by the teacher, which can make students feel warm and supported and help them stabilize their emotions.

[0103] Method 2: If the teacher's interactive information includes interactive voice, the server adjusts the text based on the emotional state matching, updates the voice text of the interactive voice based on the adjusted text, and converts the updated voice text into the target interactive voice.

[0104] In this embodiment, based on the same principle as the interactive text, the voice text of the interactive speech is first updated by the adjustment text matching the abnormal emotional state, and then the updated voice text is converted into the target interactive speech and fed back to the student end. This allows the corrected target interactive speech to conform to the current emotional changes of the students, avoiding aggravating the negative emotions of the students due to improper expressions. Students can feel understood and cared for, thereby enhancing their positive feelings about the learning process and improving their learning experience.

[0105] Method three: the server sends the emotional state to the teaching end so that the teaching end displays the emotional state.

[0106] In this embodiment, the server directly sends the abnormal emotional state to the teaching end, so that the teacher can appropriately adjust the content of the interaction according to the student's current emotional state, thereby achieving the purpose of soothing the student's negative emotions.

[0107] In actual application scenarios, the interactive content includes the content display scenario of online teaching courses. The interactive content of online teaching courses is adjusted based on the emotional state. The specific methods are as follows:

[0108] Method 1: The server sends the rendering parameters of the emotion intervention module corresponding to the emotion state to the student end; the student end receives the rendering parameters of the emotion intervention module corresponding to the abnormal emotion state of the student, and renders and displays the emotion intervention module based on the rendering parameters.

[0109] Among them, the emotional intervention module is used to display intervention content designed according to the emotional characteristics of each student. The intervention content can be text, animation, emoticons, light combinations and other pictures that can attract attention.

[0110] In this embodiment, when a student is in an abnormal emotional state, the server can quickly send the rendering parameters of the module for intervening emotions to the student, and the student can quickly render and display the emotion intervention module according to the rendering parameters while displaying the course. In this way, after seeing the emotion intervention module, students can better understand and manage their emotions and avoid further deterioration of negative emotions.

[0111] Exemplarily, the system generates appropriate interactive text responses, voice responses, or contextually appropriate videos based on the student's emotional state.

[0112] Method 2: The server matches the display parameters of the online teaching course based on the emotional state and sends the display parameters to the student end; the student end receives the display parameters of the online teaching course that matches the student's emotional state and displays the online teaching course based on the display parameters.

[0113] The display parameters include but are not limited to at least one of the following: screen brightness, screen contrast, screen filter, screen display size, spatial environment display mode, etc. The spatial environment display mode is used to change the spatial elements of the online teaching course display window, for example, the personalized skin of the display window. The display parameters can be used to adjust the look and feel of the display screen or page spatial elements of the online teaching course.

[0114] In this embodiment, when the emotional state of the student is an abnormal emotional state, the student's perception of the online teaching course can be changed by adjusting the screen display parameters of the student end, thereby achieving the purpose of interfering with negative emotions and reducing students' fatigue and bad emotions during online learning.

[0115] It should be noted 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.

[0116] Furthermore, if Figure 3 As shown, as a specific implementation of the above-mentioned interactive method of online teaching, an embodiment of the present application provides an interactive device 300 for online teaching, and the interactive device 300 for online teaching includes: an acquisition module 301, an evaluation module 302 and an interactive module 303.

[0117] The acquisition module 301 is used to acquire the learning behavior information generated by the students based on the online teaching course, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content;

[0118] Evaluation module 302, used to compare the homework content with the standard answer of the homework question to determine the homework completion information; and to process the homework completion information through the scoring model associated with the type of the homework question to determine the homework score of the homework question;

[0119] The interactive module 303 is used to feed back learning suggestion information to the student terminal based on the homework title, homework score and class monitoring information, so that the student terminal can display the learning suggestion information.

[0120] In one embodiment, the interaction module 303 includes:

[0121] A determination module (not shown in the figure) is used to determine the test knowledge points corresponding to the homework questions and the knowledge points to be mastered corresponding to the target questions based on the knowledge point question bank, wherein the target questions are homework questions belonging to the same subject type and the homework scores are less than the preset scores; and, based on the class monitoring information, determine the learning status of the test knowledge points; and, based on the learning status and the knowledge points to be mastered, determine the priority of the test knowledge points;

[0122] The communication module (not shown in the figure) is used to provide learning suggestion information to the student side based on the test knowledge points and priorities.

[0123] In one embodiment, a rename card is determined, which is specifically used to determine the mastered knowledge points in the test knowledge points except the knowledge points to be mastered; if the mastered knowledge points are in a learned state, the mastered knowledge points are determined to be the first priority; if the mastered knowledge points are in an unlearned state, the mastered knowledge points are determined to be the second priority; if the knowledge points to be mastered are in a learned state, the knowledge points to be mastered are determined to be the third priority; if the knowledge points to be mastered are in an unlearned state, the knowledge points to be mastered are determined to be the fourth priority.

[0124] In one embodiment, the interaction module 303 further includes:

[0125] A recommendation module (not shown in the figure) is used to determine a learning plan for the test knowledge point based on the priority; and, based on the learning plan, retrieve exercises for the test knowledge point belonging to the target priority from the exercise question bank;

[0126] The communication module is specifically used to send the study plan and exercises to the student end.

[0127] In one embodiment, the determination module is further used to filter the multiple knowledge points to be mastered based on the homework completion information of the target question if the target question corresponds to multiple knowledge points to be mastered, so as to update the knowledge points to be mastered whose similarity is greater than a preset similarity as mastered knowledge points.

[0128] In one embodiment, the determination module is also used to obtain the duration and trigger time of students' screen gaze behavior in class monitoring information; if the duration is greater than the preset duration, the learning period is determined based on the trigger time and the duration; and the test knowledge points corresponding to the learning period in the online teaching course are marked as learned.

[0129] In one embodiment, the online teaching interactive device 300 further includes:

[0130] The content recognition module (not shown in the figure) is used to recognize the text content and / or graphic content in the printed format in the homework file as the homework title, and to recognize the text content and / or graphic content in the handwritten format in the answer area of ​​the homework title as the homework content corresponding to the homework title.

[0131] In one embodiment, a content recognition module is specifically used to recognize outline information of text content and / or graphic content in a job file, as well as file title information; input the outline information into a classification model to determine a classification result of the text content, the classification result includes a print format and a handwritten format, and the classification model is trained based on handwritten format samples and print format samples; use the text content and / or graphic content in the print format as a job title; match the job title with a preset question in a preset answer file associated with the file title information, and use the empty area of ​​the preset question that matches the job title as the answer area of ​​the job title; use the text content and / or graphic content in the handwritten format located in the answer area as the job content corresponding to the job title.

[0132] In one embodiment, the interaction module 303 is also used to establish a long connection between the student end and the teaching end if there is student interaction behavior in the class monitoring information, wherein the teaching end is used to record online teaching courses; based on the long connection, the student interaction information generated by the student end and the teacher interaction information generated by the teaching end are synchronously updated.

[0133] In one embodiment, the online teaching interactive device 300 further includes:

[0134] A feature recognition module (not shown in the figure) is used to recognize student voice features and / or student facial features based on the audio and video information if the online teaching course is in class and the student interaction information includes audio and video information;

[0135] An emotion recognition module (not shown in the figure) is used to perform emotion recognition processing on the student's voice features and / or the student's facial features to determine the student's emotional state;

[0136] The interaction module 303 is also used to adjust the interactive content of the online teaching course based on the emotional state if the emotional state is an abnormal emotional state, wherein the interactive content includes teacher interaction information and / or content display scenes of the online teaching course.

[0137] In one embodiment, the interactive content includes teacher interactive information, and the interactive module 303 is specifically used to adjust the text based on emotional state matching if the teacher interactive information includes interactive text, and update the interactive text based on the adjusted text.

[0138] In one embodiment, the interactive content includes teacher interactive information, and the interactive module 303 is specifically used to adjust the text based on emotional state matching if the teacher interactive information includes interactive voice, update the voice text of the interactive voice based on the adjusted text, and convert the updated voice text into the target interactive voice.

[0139] In one embodiment, the interactive content includes teacher interactive information, and the interactive module 303 is specifically used to send the emotional state to the teaching end so that the teaching end displays the emotional state.

[0140] In one embodiment, the interactive content includes a content display scene of an online teaching course, and the interactive module 303 is specifically used to send the rendering parameters of the emotion intervention module corresponding to the emotion state to the student end, so that the student end renders and displays the emotion intervention module based on the rendering parameters.

[0141] In one embodiment, the interactive content includes a content display scene of an online teaching course, and the interactive module 303 is specifically used to match the display parameters of the online teaching course based on the emotional state; and send the display parameters to the student end so that the student end displays the online teaching course based on the display parameters.

[0142] Furthermore, if Figure 4 As shown, as a specific implementation of the above-mentioned interactive method of online teaching, an embodiment of the present application provides an interactive device 400 for online teaching, and the interactive device 400 for online teaching includes: an acquisition module 401, a communication module 402 and a display module 403.

[0143] The acquisition module 401 is used to obtain the learning behavior information generated by the students based on the online teaching course in response to the target operation, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content;

[0144] The communication module 402 is used to send the learning behavior information to the server, so that the server processes the homework completion information through the scoring model associated with the type of the homework title, determines that the homework title is the homework score, and determines the learning suggestion information based on the homework title, the homework score and the class monitoring information. The homework completion information is determined by comparing the homework content with the standard answer to the homework title;

[0145] The display module 403 is used to display the learning suggestion information in response to receiving the learning suggestion information sent by the server.

[0146] In one embodiment, the acquisition module 401 is further used to obtain student interaction information in response to an interactive operation if the online teaching course is in a class state;

[0147] The communication module 402 is also used to send student interaction information and receive teacher interaction information generated by the teaching end based on the long connection between the student end and the teaching end, wherein the long connection is established when there is student interaction behavior in the class monitoring information, and the teaching end is used to record online teaching courses;

[0148] The display module 403 is also used to display teacher interaction information.

[0149] In one embodiment, the student interaction information includes audio and video information, and the communication module 402 is further used to receive rendering parameters of the emotion intervention module corresponding to the student's emotion state belonging to an abnormal emotion state, wherein the rendering parameters are sent by the server when the emotion state belongs to an abnormal emotion state, and the emotion state is obtained by performing emotion recognition processing on the student's voice features and / or the student's facial features in the audio and video information;

[0150] The display module 403 is further used to render and display the emotion intervention module based on the rendering parameters.

[0151] In one embodiment, the communication module 402 is further used to receive display parameters of the online teaching course that match the student's emotional state, wherein the display parameters are sent by the server when the emotional state belongs to an abnormal emotional state, and the emotional state is obtained by performing emotion recognition processing on the student's voice features and / or the student's facial features in the audio and video information;

[0152] The display module 403 is also used to display online teaching courses based on display parameters.

[0153] For the specific definition of the interactive device for online teaching, please refer to the definition of the interactive method for online teaching above, which will not be repeated here. Each module in the above-mentioned interactive device for online teaching can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0154] Based on the above Figure 1 and Figure 2 The method shown in the embodiment of the present application accordingly provides a readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned Figure 1 Figure 2 Test method shown.

[0155] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0156] Based on the above Figure 1 , Figure 2 The method shown, and Figure 3 , Figure 4 In order to achieve the above-mentioned purpose, the virtual device embodiment shown in FIG. Figure 6 As shown, the embodiment of the present application further provides a computer device, the computer device 600 includes a processor 601 and a memory 602, the memory 602 stores a program or instruction that can be run on the processor 601, and the program or instruction is executed by the processor 601 to implement the above-mentioned Figure 1 and Figure 2 Test method shown.

[0157] The memory 602 can be used to store software programs and various data. The memory 602 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 602 may include a volatile memory or a non-volatile memory, or the memory 602 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 602 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0158] The processor 601 may include one or more processing units; optionally, the processor 601 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 601.

[0159] The computer device may specifically be a personal computer, a server, a network device, etc.

[0160] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0161] Those skilled in the art will appreciate that the computer device structure provided in this embodiment does not limit the computer device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0162] Through the description of the above implementation methods, technical personnel in this field can clearly understand that the present application can be implemented by means of software plus necessary general hardware platforms, and can also be implemented by hardware to obtain the learning behavior information generated by students based on online teaching courses, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content; compare the homework content and the standard answer to the homework title to determine the homework completion information; process the homework completion information through the scoring model associated with the type of the homework title to determine the homework grade of the homework title; based on the homework title, homework grade and class monitoring information, feedback learning suggestion information to the student end, so that the student end can display the learning suggestion information. Alternatively, in response to the target operation, the learning behavior information generated by the students based on the online teaching course is obtained, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content; the learning behavior information is sent to the server, so that the server processes the homework completion information through the scoring model associated with the type of homework title, determines that the homework title is the homework grade, and determines the learning suggestion information based on the homework title, the homework grade and the class monitoring information, and the homework completion information is determined by comparing the homework content with the standard answer to the homework title; in response to receiving the learning suggestion information sent by the server, the learning suggestion information is displayed. In the embodiment of the present application, on the one hand, the differences in the grading methods of different subjects and different question types are fully considered, and the student's homework completion status is automatically evaluated through the scoring model associated with the type of homework title, which can not only reduce the subjective bias caused by manual grading, but also improve the accuracy of the student's homework error analysis, which is helpful to improve the teaching quality. On the other hand, by analyzing students' homework scores and learning behaviors to understand their mastery of different knowledge points and providing personalized tutoring and learning suggestions, students can understand their learning situation in a timely manner, and can dynamically adjust the tutoring content and difficulty to meet the needs of each student, helping students to master knowledge more effectively and improve learning outcomes.

[0163] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0164] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. An interactive method for online teaching, characterized in that: Applied to the server, the method includes: Acquire learning behavior information generated by students based on online teaching courses, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content; Comparing the homework content with the standard answer to the homework question to determine the homework completion status information; Processing the homework completion information through a scoring model associated with the type of the homework title to determine the homework score of the homework title; Based on the homework title, the homework score and the class monitoring information, learning suggestion information is fed back to the student terminal so that the student terminal displays the learning suggestion information.

2. The interactive method for online teaching according to claim 1, characterized in that: The feeding back learning suggestion information to the student terminal based on the homework title, the homework score and the class monitoring information includes: Determine the test knowledge points corresponding to the homework questions and the knowledge points to be mastered corresponding to the target questions based on the knowledge point question bank, wherein the target questions are the homework questions that belong to the same subject type and whose homework scores are less than a preset score; Determine the learning status of the test knowledge point based on the class monitoring information; Determining the priority of the test knowledge points based on the learning status and the knowledge points to be mastered; Based on the test knowledge points and the priorities, learning suggestion information is fed back to the student terminal.

3. The interactive method for online teaching according to claim 2, characterized in that: The determining the priority of the test knowledge point based on the learning status and the knowledge point to be mastered includes: Determine the mastered knowledge points among the test knowledge points except the knowledge points to be mastered; If the mastered knowledge point is in a learned state, determining the mastered knowledge point as the first priority; If the mastered knowledge point is in an unlearned state, determining the mastered knowledge point as the second priority; If the knowledge point to be mastered is in a learned state, determining the knowledge point to be mastered as the third priority; If the knowledge point to be mastered is in an unlearned state, it is determined that the knowledge point to be mastered is the fourth priority.

4. The interactive method for online teaching according to claim 2, characterized in that: The feeding back learning suggestion information to the student terminal based on the test knowledge point and the priority includes: Determine a learning plan for the test knowledge point based on the priority; Based on the learning plan, retrieve exercises related to the test knowledge points belonging to the target priority from the exercise question bank; The study plan and the exercises are sent to the student terminal.

5. The interactive method for online teaching according to claim 2, characterized in that: The homework completion information includes the similarity between the homework content and the standard answer to the homework question, and the method further includes: If the target topic corresponds to a plurality of the to-be-mastered knowledge points, the plurality of the to-be-mastered knowledge points are filtered based on the homework completion information of the target topic, so as to update the to-be-mastered knowledge points having a similarity greater than a preset similarity as the mastered knowledge points.

6. The interactive method for online teaching according to claim 2, characterized in that: The determining the learning status of the test knowledge point based on the class monitoring information includes: Obtaining the duration and triggering time of the student's screen gaze behavior in the class monitoring information; If the duration is longer than a preset duration, determining a learning period based on the triggering moment and the duration; The test knowledge point corresponding to the learning period in the online teaching course is marked as a learned state.

7. The interactive method for online teaching according to claim 1, characterized in that: After obtaining the learning behavior information generated by the students based on the online teaching course, the method further includes: The text content and / or graphic content in the printed format of the homework file is identified as the homework title, and the text content and / or graphic content in the handwritten format in the answer area of ​​the homework title is identified as the homework content corresponding to the homework title.

8. The interactive method for online teaching according to claim 7, characterized in that: The step of identifying the text content in the print format in the homework file as the homework title, and identifying the text content in the handwritten format in the answer area of ​​the homework title as the homework content corresponding to the homework title, includes: Identify outline information of text content and / or graphic content in the job file, as well as file title information; Inputting the outline information into a classification model to determine a classification result of the text content, wherein the classification result includes a printed format and a handwritten format, and the classification model is trained based on handwritten format samples and printed format samples; Using the text content and / or graphic content in the print format as the homework title; Matching the homework title with a preset title in the preset answer file associated with the file title information, and using the empty area of ​​the preset title matching the homework title as the answer area of ​​the homework title; The text content and / or graphic content in the handwritten format located in the answer area is used as the homework content corresponding to the homework title.

9. The interactive method for online teaching according to any one of claims 1 to 8, characterized in that: The method further comprises: If there is student interaction behavior in the class monitoring information, establish a long connection between the student terminal and the teaching terminal, wherein the teaching terminal is used to record the online teaching course; The student interaction information generated by the student end and the teacher interaction information generated by the teaching end are synchronously updated based on the long connection.

10. The interactive method for online teaching according to claim 9, characterized in that: The method further comprises: If the online teaching course is in class, and the student interaction information includes audio and video information, identifying the student's voice features and / or student's facial features based on the audio and video information; Performing emotion recognition processing on the student's voice features and / or student's facial features to determine the student's emotional state; If the emotional state is an abnormal emotional state, the interactive content of the online teaching course is adjusted based on the emotional state, wherein the interactive content includes the teacher interaction information and / or the content display scene of the online teaching course.

11. The interactive method for online teaching according to claim 10, characterized in that: The interactive content includes the teacher interactive information, and the adjusting the interactive content of the online teaching course based on the emotional state includes: If the teacher interaction information includes an interaction text, the text is adjusted based on the emotional state matching, and the interaction text is updated based on the adjustment text; and / or, If the teacher interaction information includes interactive speech, matching and adjusting text based on the emotional state, updating the speech text of the interactive speech based on the adjustment text, and converting the updated speech text into target interactive speech; and / or, The emotional state is sent to the teaching end so that the teaching end displays the emotional state.

12. The interactive method for online teaching according to claim 10, characterized in that: The interactive content includes a content display scene of an online teaching course, and adjusting the interactive content of the online teaching course based on the emotional state includes: Sending rendering parameters of the emotion intervention module corresponding to the emotion state to the student terminal, so that the student terminal renders and displays the emotion intervention module based on the rendering parameters; and / or, The display parameters of the online teaching course are matched based on the emotional state, and the display parameters are sent to the student terminal, so that the student terminal displays the online teaching course based on the display parameters.

13. An interactive method for online teaching, applied to a student terminal, characterized in that: The method comprises: In response to the target operation, obtaining learning behavior information generated by the student based on the online teaching course, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content; Sending the learning behavior information to a server, so that the server processes the homework completion information through a scoring model associated with the type of the homework title, determines that the homework title is a homework grade, and determines learning suggestion information based on the homework title, the homework grade and the class monitoring information, wherein the homework completion information is determined by comparing the homework content with a standard answer to the homework title; In response to receiving the learning suggestion information sent by the server, displaying the learning suggestion information.

14. The interactive method for online teaching according to claim 13, characterized in that: The method further comprises: If the online teaching course is in a class state, in response to the interactive operation, obtaining student interaction information; Based on a long connection between the student terminal and the teaching terminal, the student interaction information is sent and the teacher interaction information generated by the teaching terminal is received, wherein the long connection is established when there is a student interaction behavior in the class monitoring information, and the teaching terminal is used to record the online teaching course; The teacher interaction information is displayed.

15. The interactive method for online teaching according to claim 13, characterized in that: The student interaction information includes audio and video information, and the method further includes: Receiving rendering parameters of an emotion intervention module corresponding to when the student's emotion state belongs to an abnormal emotion state, and rendering and displaying the emotion intervention module based on the rendering parameters, wherein the rendering parameters are sent by the server when the emotion state belongs to an abnormal emotion state, and the emotion state is obtained by performing emotion recognition processing on the student's voice features and / or the student's facial features in the audio and video information; and / or, Receive display parameters of the online teaching course that match the student's emotional state, and display the online teaching course based on the display parameters, wherein the display parameters are sent by the server when the emotional state belongs to an abnormal emotional state, and the emotional state is obtained by performing emotion recognition processing on the student's voice features and / or student facial features in the audio and video information.

16. An interactive device for online teaching, characterized in that: The device comprises: An acquisition module, used to acquire learning behavior information generated by students based on online teaching courses, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content; An evaluation module, used to compare the homework content with the standard answer to the homework question to determine the homework completion status information; and Processing the homework completion information through a scoring model associated with the type of the homework title to determine the homework score of the homework title; The interactive module is used to feed back learning suggestion information to the student terminal based on the homework title, the homework score and the class monitoring information, so that the student terminal displays the learning suggestion information.

17. An interactive device for online teaching, characterized in that: The device comprises: An acquisition module, configured to acquire, in response to a target operation, learning behavior information generated by students based on online teaching courses, wherein the learning behavior information includes class monitoring information and homework files, and the homework files include at least one homework title and its corresponding homework content; A communication module, configured to send the learning behavior information to a server, so that the server processes the homework completion information through a scoring model associated with the type of the homework title, determines that the homework title is a homework grade, and determines learning suggestion information based on the homework title, the homework grade and the class monitoring information, wherein the homework completion information is determined by comparing the homework content with a standard answer to the homework title; A display module is used to display the learning suggestion information in response to receiving the learning suggestion information sent by the server.

18. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or instruction is executed by the processor, the steps of the interactive method of online teaching as described in any one of claims 1 to 15 are implemented.

19. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the interactive method for online teaching as described in any one of claims 1 to 15 is implemented.

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