Intelligent application method and device of electronic blackboard, teacher terminal and medium

Abstract

The invention relates to the field of electronic blackboards, in particular to an intelligent application method and device of an electronic blackboard, a teacher terminal and a medium. The method comprises the steps that blackboard writing content on the electronic blackboard is captured in real time, a teacher can visually see and manage own teaching progress and content, meanwhile, a clear learning path is provided for students, and the students are promoted to understand and memorize classroom content. By analyzing the classroom image of each student, the learning state of the student can be judged, so that personalized learning evaluation is realized. Based on learning state evaluation, a customized knowledge network diagram and test questions are generated for each student, the knowledge network diagram and the test questions corresponding to each student are sent to an electronic blackboard of a corresponding student end after class, and the students can perform targeted review and consolidation by checking the knowledge network diagram and the customized test questions of themselves, so that the learning efficiency of the students is improved. Therefore, the learning efficiency of students is improved.

Description

Technical Field

[0001] The present application relates to the field of electronic blackboards, and in particular to an intelligent application method, device, teacher terminal and medium of an electronic blackboard. Background Art

[0002] With the rapid development of science and technology, electronic blackboards have emerged. Electronic blackboards are a teaching tool that integrates a variety of high-tech means. They are mainly used to replace traditional blackboards. Entering the era of information-based multimedia teaching, the emergence of electronic blackboards in online teaching scenarios is of great significance.

[0003] During online teaching, teachers can write, draw, mark key points, etc. on the electronic blackboard on the teacher's end. The electronic blackboard can save content for students to review and can be shared with students in real time over the Internet. Students can take notes, solve problems, ask questions, etc. on the corresponding electronic blackboard on the student end. They can also mark their questions or understanding of the teacher's explanation on the electronic blackboard, thereby promoting communication between them and teachers and classmates.

[0004] However, in an online teaching environment, the knowledge transmitted by the electronic blackboard is only what the teacher records on the electronic blackboard, and the knowledge received by each student is exactly the same. Because each student's learning ability and knowledge reserves may vary to a certain extent, the method of uniformly transmitting knowledge only through the electronic blackboard may not effectively improve students' learning efficiency. Summary of the invention

[0005] In order to improve students' learning efficiency, the present application provides an intelligent application method, device, teacher terminal and medium of an electronic blackboard.

[0006] In the first aspect, the present application provides an intelligent application method of an electronic blackboard, which adopts the following technical solution: An intelligent application method of an electronic blackboard, comprising: Obtaining the blackboard content information corresponding to the current class, wherein the blackboard content information includes the knowledge points recorded by the teacher on the electronic blackboard, the recording location corresponding to each knowledge point, and the retention time corresponding to each knowledge point; Obtaining a classroom image of each student corresponding to the current classroom, wherein the classroom image is a frame image of a received video of a student in the classroom, and the classroom image includes complete facial image information of the student; Determine the learning status of each student based on the classroom image and the blackboard content information; Based on each student's learning status and the blackboard content information, generate a knowledge network diagram and customized test questions corresponding to each student; At the end of the current class, after-class exercises, the knowledge network diagram corresponding to each student, and customized test questions are displayed on the electronic blackboard of the corresponding student terminal.

[0007] By adopting the above technical solution, the content of the blackboard on the electronic blackboard is captured in real time. Teachers can intuitively see and manage their teaching progress and content. At the same time, it also provides students with a clear learning path. Students can instantly receive the teacher's blackboard information through the electronic blackboard, including knowledge points, locations and retention time, which promotes students' understanding and memory of classroom content. By analyzing each student's classroom image (including facial expressions, concentration, etc.), it can intelligently judge the student's learning status, such as concentration, confusion, fatigue, etc., so as to achieve personalized learning assessment. Based on the learning status assessment, a customized knowledge network diagram and test questions are generated for each student, so as to design according to the student's specific learning needs and mastery, which helps to make up for knowledge gaps and strengthen weak links. After class, the corresponding knowledge network diagram and test questions of each student are sent to the electronic blackboard of the corresponding student end. Students can review and consolidate their own knowledge network diagrams and customized test questions, thereby improving students' learning efficiency.

[0008] In a possible implementation, based on the classroom image and the blackboard content information, the learning status corresponding to each student is determined, and the process of determining the learning status corresponding to each student includes: Recognize facial features of students in the classroom image, and segment the classroom image based on the facial features to obtain separate student facial images of the students; generating eye movement data of the student based on a student facial image corresponding to a plurality of continuous classroom images of the student, the eye movement data including a gaze area and a gaze duration; The learning status of the student is determined based on the blackboard content information and the student's eye movement data.

[0009] By adopting the above technical solution and analyzing each student's eye movement data, including gaze area and gaze duration, we can gain an in-depth understanding of students' focus, interest and confusion points in the learning process, which will help teachers grasp students' learning status more accurately and provide more targeted teaching guidance.

[0010] In a possible implementation, determining the learning status of the student based on the blackboard content information and the student's eye movement data includes: Determining the student's learning activity based on the recording position corresponding to each knowledge point in the blackboard content and the student's gaze area; Determining the student's learning concentration based on the retention time of each knowledge point in the blackboard content and the student's gaze time; The learning status of the student is determined based on the learning activity level and the learning concentration level.

[0011] By adopting the above technical solution, the recording position corresponding to each knowledge point in the blackboard content is compared with the student's gaze area, which can accurately capture the student's learning activity in class. This activity not only reflects the student's immediate interest in the knowledge point, but also reflects their participation and understanding of the classroom content. At the same time, the retention time of each knowledge point in the blackboard content (that is, the display time of the knowledge point on the blackboard) and the student's gaze time are combined to evaluate the student's learning concentration.

[0012] In a possible implementation, the learning status includes the degree of concentration on each knowledge point, wherein a knowledge network diagram corresponding to each student is generated based on the learning status of each student and the blackboard content information, and the generation process of the knowledge network diagram corresponding to each student includes: Get the textbook content corresponding to the current class; Based on the teaching material content, determining the correlation between every two knowledge points in the blackboard content information; Determine a target knowledge point corresponding to the student from the knowledge points corresponding to the plurality of blackboard content information, wherein the concentration corresponding to the target knowledge point is less than a concentration threshold; Based on the target knowledge points and the degree of association between every two knowledge points, a knowledge network diagram corresponding to the student is generated.

[0013] By adopting the above technical solution, by obtaining the textbook content corresponding to the current class, the correlation between every two knowledge points recorded by the teacher on the electronic blackboard is determined based on the textbook content, and combined with the student's concentration on each knowledge point, the target knowledge points with concentration below the threshold and the knowledge points with high correlation corresponding to these target knowledge points are used as the knowledge points that the student needs to learn, and based on the knowledge points that the student needs to learn, the corresponding knowledge network diagram of the student is generated, which realizes a personalized learning path tailored for each student, focusing on strengthening training and guidance on these weak links, thereby helping students to improve their learning effects more effectively.

[0014] In a possible implementation, based on the learning status of each student and the blackboard content information, a customized test question corresponding to each student is generated. The generation process of the customized test question corresponding to each student includes: Based on the target knowledge points and the correlation between every two knowledge points, determine the knowledge points to be strengthened corresponding to the students; Obtaining a textbook question bank corresponding to the textbook content, and selecting strengthening test questions related to the knowledge points to be strengthened from the textbook question bank; Based on the correlation between every two of the knowledge points to be strengthened, the corresponding ranking of the strengthened test questions is determined to obtain the customized test questions for the students.

[0015] By adopting the above technical solution, by identifying the target knowledge points corresponding to the students (i.e. the knowledge points whose concentration is below the threshold) and using the correlation between the knowledge points to determine the knowledge points to be strengthened, the weak links in the students' learning process can be accurately located. Subsequently, the strengthening test questions related to these knowledge points to be strengthened are selected from the textbook question bank to ensure the pertinence and effectiveness of the test questions. This testing method can not only help students consolidate and deepen their understanding of the knowledge points, but also encourage them to actively discover and solve their own learning problems, thereby improving learning efficiency and learning results.

[0016] In a possible implementation, after acquiring the classroom image of each student corresponding to the current classroom, the method further includes: Identify each student's classroom image and check if there are any students with sleepy expressions; When there are students with sleepy expressions, the students with sleepy expressions are identified as target students, and sleepiness test questions are generated based on the knowledge points currently recorded in the blackboard content information; The sleepiness test questions and the student information of the target student are displayed at a specific location of the electronic blackboard at the teacher's end, and the content recorded at the specific location is not synchronized to the electronic blackboard at the student's end.

[0017] By adopting the above technical solution, by identifying students' expressions in classroom images, especially sleepy expressions, students who may be affected in learning due to fatigue or lack of concentration can be found immediately. These students are identified as target students, and sleepiness test questions are generated based on the knowledge points in the current blackboard content. This can not only help teachers quickly evaluate the target students' mastery of the current knowledge points, but also awaken students' attention by asking questions, prompting them to refocus on participating in the class. This immediate intervention helps prevent students from missing important knowledge points due to long-term sleepiness, thereby improving the overall teaching effect.

[0018] In a possible implementation, after displaying the after-class exercises, the knowledge network diagram corresponding to each student, and the customized test questions on the electronic blackboard of the corresponding student terminal, the method further includes: Receiving each student's first answer result for the after-class exercise questions and the second answer result for the corresponding customized test questions, and determining the correctness of each student's answer based on the first answer result and the second answer result; Determine the knowledge mastery of each student based on the correctness of each student's answer, wherein the knowledge mastery includes the mastery of each knowledge point in the blackboard content information; According to each student's knowledge mastery, annotations are made on each student's knowledge network diagram, and the annotated knowledge network diagram is displayed on the electronic blackboard at the corresponding student end.

[0019] By adopting the above technical solution, by receiving the answer results of each student for after-class exercises and customized test questions, and determining the correctness of the answers in real time, students can be provided with immediate learning feedback, so that students can timely understand their mastery of knowledge points, find weak links in learning, and make self-adjustments and strengthen learning accordingly. At the same time, teachers can also comprehensively understand and analyze students' learning situations based on these feedback results, and provide a basis for subsequent personalized teaching guidance. By analyzing the knowledge mastery of each student in detail, marking it on its knowledge network diagram, and displaying the marked knowledge network diagram on the electronic blackboard of the corresponding student end, students can not only intuitively see their learning results and progress, but also stimulate their autonomous learning and self-reflection ability. Students can clearly understand which knowledge points they have mastered well and which knowledge points are still insufficient by observing their own knowledge network diagrams. This self-reflection process helps students to participate in learning more actively, formulate more reasonable learning plans and strategies to make up for their knowledge shortcomings and improve the overall learning effect.

[0020] In a second aspect, the present application provides an intelligent application device of an electronic blackboard, which adopts the following technical solution: An intelligent application device for an electronic blackboard, comprising: The first acquisition module is used to acquire the blackboard content information corresponding to the current class, wherein the blackboard content information includes the knowledge points recorded by the teacher on the electronic blackboard, the recording position corresponding to each knowledge point, and the retention time corresponding to each knowledge point; A second acquisition module is used to acquire a classroom image of each student corresponding to the current classroom, wherein the classroom image is a frame image received from a video of the student in the classroom, and the classroom image contains complete facial image information of the student; A determination module, used to determine the learning status corresponding to each student based on the classroom image and the blackboard content information; A generation module, used to generate a knowledge network diagram and customized test questions corresponding to each student based on the learning status of each student and the blackboard content information; The display module is used to display after-class exercises, the knowledge network diagram corresponding to each student, and customized test questions on the electronic blackboard of the corresponding student terminal at the end of the current class.

[0021] In a third aspect, the present application provides a teacher terminal, which adopts the following technical solution: A teacher terminal, the teacher terminal comprising: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the intelligent application method of the electronic blackboard described in the first aspect above.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium, comprising: storing a computer program that can be loaded by a processor and execute the intelligent application method of the electronic blackboard described in the first aspect above.

[0023] In summary, this application at least includes the following beneficial technical effects: 1. By capturing the blackboard content on the electronic blackboard in real time, teachers can intuitively see and manage their teaching progress and content. At the same time, it also provides students with a clear learning path. Students can instantly receive the teacher's blackboard information through the electronic blackboard, including knowledge points, locations and retention time, which promotes students' understanding and memory of classroom content. By analyzing each student's classroom image (including facial expressions, concentration, etc.), it can intelligently judge the student's learning status, such as concentration, confusion, fatigue, etc., to achieve personalized learning assessment. Based on the learning status assessment, a customized knowledge network diagram and test questions are generated for each student, which are designed according to the student's specific learning needs and mastery, which helps to make up for knowledge gaps and strengthen weak links. After class, the corresponding knowledge network diagram and test questions of each student are sent to the electronic blackboard on the corresponding student side. Students can review and consolidate their own knowledge network diagrams and customized test questions, thereby improving students' learning efficiency; 2. By receiving each student's answer results for after-class exercises and customized test questions, and determining the correctness of the answers in real time, it can provide students with instant learning feedback, so that students can timely understand their mastery of knowledge points, find weak links in learning, and make self-adjustments and strengthen learning accordingly. At the same time, teachers can also comprehensively understand and analyze students' learning situations based on these feedback results, and provide a basis for subsequent personalized teaching guidance. By conducting a detailed analysis of each student's knowledge mastery, marking their knowledge network diagram, and displaying the marked knowledge network diagram on the electronic blackboard of the corresponding student end, students can not only intuitively see their learning results and progress, but also stimulate their autonomous learning and self-reflection abilities. Students can observe their own knowledge network diagrams and clearly understand which knowledge points they have mastered well and which knowledge points are still insufficient. This self-reflection process helps students to participate in learning more actively, formulate more reasonable learning plans and strategies to make up for their knowledge shortcomings and improve the overall learning effect. . BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a schematic diagram of the connection and interaction between a teacher terminal and a student terminal provided in an embodiment of the present application; Figure 2 It is a flow chart of an intelligent application method of an electronic blackboard provided in an embodiment of the present application; Figure 3 It is a block diagram of an intelligent application device of an electronic blackboard provided in an embodiment of the present application; Figure 4 This is a schematic diagram of a teacher terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following is combined with Figure 1 -Attached Figure 4 This application is described in further detail.

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] During online teaching, teachers can write, draw, and mark key points on the electronic blackboard on the teacher's end. At the same time, the electronic blackboard is different from the traditional blackboard. After the current page of the electronic blackboard is recorded, it can switch to the next blank page for recording. The electronic blackboard can save the content of each page for students to review, and can be shared with students in real time through the Internet. Students can take notes, solve problems, ask questions, etc. on the corresponding electronic blackboard on the student end. They can also mark their questions or understanding of the teacher's explanation on the electronic blackboard, thereby promoting their communication with teachers and classmates. However, in the online teaching environment, the knowledge transmitted by the electronic blackboard is only the content recorded by the teacher on the electronic blackboard, and the knowledge received by each student is exactly the same. Because each student's learning ability and knowledge reserve may be different, the method of uniform knowledge transmission only through the electronic blackboard may not effectively improve students' learning efficiency.

[0028] In view of this, the present application embodiment provides an intelligent application method of an electronic blackboard, see Figure 1 The teacher terminal establishes a connection with each student terminal. Each student terminal is equipped with a camera. The teacher terminal can obtain the video and image of the students taken by the student terminal. At the same time, the teacher terminal can synchronize the content on the electronic blackboard of the teacher terminal to the electronic blackboard of the student terminal. The teacher terminal can also send some specific information to the electronic blackboard corresponding to one or some student terminals for display.

[0029] The present application embodiment provides an intelligent application method of an electronic blackboard, such as Figure 2 As shown, the method provided in the embodiment of the present application is executed by a teacher terminal, which is a terminal device, wherein the terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and the embodiment of the present application is not limited here. The method includes steps S201-step S205, wherein: S201. Obtain the blackboard content information corresponding to the current class.

[0030] The blackboard content information includes the knowledge points recorded by the teacher on the electronic blackboard, the recording location corresponding to each knowledge point, and the retention time corresponding to each knowledge point. The knowledge points are the knowledge points recorded by the teacher when preparing for the current class, or the knowledge points in the textbook corresponding to the current class.

[0031] During the teaching process, the teacher will write, talk and record on the electronic blackboard of the teacher's terminal to display the knowledge points. However, the display capacity of a page of the electronic blackboard is limited. Therefore, during the teaching process, the teacher will generally switch to the next blank page to continue recording knowledge points after the current page of the electronic blackboard is full. Therefore, the recording position corresponding to the knowledge point is the position of the knowledge point in the current page, such as the upper half of the current page, or the lower left corner of the current page, etc. The retention time corresponding to the knowledge point is the time between the recording time of the knowledge point and the current page switching time.

[0032] S202, obtaining the classroom image of each student corresponding to the current classroom.

[0033] The classroom image is a frame image received from a video of a student in the classroom, and the classroom image contains complete facial image information of the student.

[0034] In the classroom, in order to facilitate the supervision of students' learning status, the camera of the student terminal is generally kept on, so the teacher terminal can receive the student video or student image captured by the camera of the student terminal. Specifically, if the received video is a student video, the frame image containing the student's complete facial image information is filtered out from the student video to serve as the classroom image of the student; if the received video is a student image, the frame image containing the student's complete facial image information is filtered out from the received multiple student images to serve as the classroom image of the student.

[0035] S203: Determine the learning status of each student based on the classroom image and blackboard content information.

[0036] Among them, the learning status can include the students' attention to each knowledge point, and can also include the students' activity in class. Here, activity is an indicator used to measure the students' participation and concentration in the classroom learning process, specifically referring to the real-time degree when students follow the teacher to record or explain knowledge points, that is, whether students can closely and instantly follow the teacher's ideas and rhythm, and focus on the content presented or taught by the teacher.

[0037] Specifically, after obtaining the classroom image, the face detection algorithm (such as Haar feature + AdaBoost, HOG + SVM, deep learning models such as MTCNN, FaceBoxes, etc.) can be used to detect the face area of ​​the classroom image to extract the face of the classroom image to obtain the student's facial image. At the same time, on the detected facial image, the intersection of the line connecting the centers of the two eyes and the bottom of the nose is used as the origin to establish a three-dimensional head coordinate system. According to the results of the facial key point detection, the coordinates of the left and right eyeball centers in the image are found, and the inclination angle of the eyeball is calculated by calculating the angle between the line connecting the center of the eyeball and the center of the face and the horizontal line, and the inclination angle of the eyeball is mapped to the line of sight based on the inclination angle of the eyeball.

[0038] Furthermore, based on the recorded position corresponding to the current knowledge point and the direction of the student's line of sight, determine whether the student is looking at the current knowledge point. If the student is looking at the current knowledge point, the student's attention to the knowledge point is determined to be 1. If the student is not looking at the current knowledge point, the student's gaze time within the retention time of the knowledge point is determined, and the ratio of the gaze time to the retention time is used as the student's attention to the knowledge point. More specifically, the classroom images of the students taken within the retention time of the knowledge point are screened out from multiple images, and the classroom images of the students within the retention time are compared to determine the gaze time of the knowledge point within the retention time of the knowledge point. Among them, if the student's gaze time at the current knowledge point is greater than the retention time of the knowledge point, the student is very likely to be in a daze at this time, and the student's attention to the knowledge point is determined to be 0.

[0039] According to the recording position and recording time corresponding to each knowledge point, the student's line of sight direction at the time of recording each knowledge point is identified, the moment when the line of sight direction is consistent with the recording position is taken as the target moment, and the ratio of the number of target moments to the number of recording moments is calculated to obtain the student's activity level.

[0040] Furthermore, based on the above method, each student's attention to each knowledge point and activity in class can be obtained, thereby obtaining each student's learning status.

[0041] S204. Generate a knowledge network diagram and customized test questions corresponding to each student based on the learning status and blackboard content information of each student.

[0042] After obtaining the learning status of each student, that is, after obtaining each student's attention to each knowledge point and the activity in the classroom, obtain at least two preset knowledge network diagrams and preset test question sets corresponding to the current classroom, wherein one preset knowledge network diagram is a simplified knowledge network diagram and the other is a detailed knowledge network diagram. Specifically, the process of generating each student's knowledge network diagram can be: taking the knowledge point whose attention of the student is lower than the first attention threshold as the first knowledge point, and judging whether the activity of the student is greater than the activity threshold. If the activity of the student is not greater than the activity threshold, the detailed knowledge network diagram is taken as the initial knowledge network diagram of the student, and the first knowledge point is marked in the initial knowledge network diagram to generate the knowledge network diagram of the student; if the activity of the student is greater than the activity threshold, the simplified knowledge network diagram is taken as the initial knowledge network diagram of the student, and the first knowledge point is marked in the initial knowledge network diagram to generate the knowledge network diagram of the student.

[0043] Furthermore, the process of generating customized test questions for each student may be: taking the knowledge point whose attention level of the student is lower than the second attention level threshold as the second knowledge point, and selecting test questions related to the second knowledge point from the preset test question set as the customized test questions for the student. The first attention level threshold and the second attention level threshold may be the same or different, which is not limited in the embodiments of the present application.

[0044] S205. At the end of the current class, after-class exercises, the knowledge network diagram corresponding to each student, and customized test questions are displayed on the electronic blackboard of the corresponding student terminal.

[0045] After the current class is over, after-class exercises, each student's knowledge network diagram and customized test questions can be sent to the electronic blackboard on the student side, so that students can view their own knowledge network diagrams through the electronic blackboard on their terminals and take customized test questions.

[0046] The embodiment of the present application provides an intelligent application method of an electronic blackboard. By capturing the blackboard content on the electronic blackboard in real time, the teacher can intuitively see and manage his or her own teaching progress and content. At the same time, it also provides a clear learning path for students. Students can instantly receive the teacher's blackboard information through the electronic blackboard, including knowledge points, positions and retention time, which promotes students' understanding and memory of classroom content. By analyzing each student's classroom image (including facial expressions, concentration, etc.), it is possible to intelligently judge the student's learning state, such as concentration, confusion, fatigue, etc., so as to achieve personalized learning evaluation. Based on the learning state evaluation, a customized knowledge network diagram and test questions are generated for each student, so as to design according to the student's specific learning needs and mastery, which helps to make up for knowledge gaps and strengthen weak links, and after class, the knowledge network diagram and test questions corresponding to each student are sent to the electronic blackboard of the corresponding student end. Students can review and consolidate their own knowledge network diagrams and customized test questions, thereby improving students' learning efficiency.

[0047] In a possible implementation of the embodiment of the present application, in the above step S203, the learning status corresponding to each student is determined based on the classroom image and the blackboard content information, wherein the process of determining the learning status corresponding to each student may include: Recognize the facial features of students in the classroom image, and segment the classroom image based on the facial features to obtain separate student facial images of the students; Generate eye movement data of the student based on the student's facial image corresponding to a plurality of continuous classroom images of the student, the eye movement data including gaze area and gaze duration; Determine students' learning status based on the blackboard content information and students' eye movement data.

[0048] Specifically, the received classroom video is decoded to extract continuous frame images, and each frame image is preprocessed, including denoising, grayscale, binarization and other steps to improve the accuracy of subsequent processing. Facial feature recognition algorithms, such as Haar features, HOG features combined with SVM classifiers, are used to perform facial feature recognition on the preprocessed images to accurately locate the student's facial area in the image. Based on the results of facial feature recognition, image segmentation techniques (such as graph cut-based methods, level set methods, etc.) are used to segment the student's facial image from the entire classroom image to obtain a separate facial image of the student.

[0049] Furthermore, for the student facial images corresponding to multiple consecutive classroom images of the student, eye tracking technology is used to perform a detailed analysis of the eye area (including pupil position, white of the eye area, etc.) to extract eye movement features. At the same time, image processing technology (such as edge detection, morphological operations, etc.) is used to track the movement trajectory of the pupil, thereby obtaining the student's eye movement sub-data, and the eye movement sub-data extracted from multiple consecutive frames are integrated to form the student's eye movement data.

[0050] The content on the electronic blackboard is divided into different areas. The area where the current knowledge point is located is determined based on the recording position corresponding to the knowledge point. Combined with the student's gaze area and the area where the current knowledge point is located, it is determined whether the student is looking at the current knowledge point. If the student is looking at the current knowledge point, the student's attention to the current knowledge point is determined to be 1; if the student is not looking at the current knowledge point, the student's attention to the current knowledge point is determined to be 0. According to the recording position and recording time corresponding to each knowledge point, the student's gaze area at the recording time of each knowledge point is identified, and the moment when the gaze area is consistent with the recording position is taken as the target moment. The ratio of the number of target moments to the number of recorded moments is calculated to obtain the student's activity.

[0051] Furthermore, if a student stares at a certain knowledge point area for a long time and the staring time exceeds a preset threshold, it can be considered that the student is in a daze state; if the student frequently switches the staring area, or the staring time is short, it may indicate that the student is not focused enough or has difficulty understanding this part of the content.

[0052] According to the above analysis results, the students’ learning status is divided into different categories (such as active, focused, distracted, and dazed), thus obtaining the students’ learning status.

[0053] A possible implementation of the embodiment of the present application, in the above embodiment, based on the blackboard content information and the student's eye movement data, determining the student's learning status includes: Determine the students' learning activity based on the recording position of each knowledge point in the blackboard content and the students' gaze area; Determine the students' learning concentration based on the retention time of each knowledge point in the blackboard content and the students' gaze time; Determine students' learning status based on their level of learning activity and learning concentration.

[0054] Among them, the level of learning activity is used to characterize students' participation and concentration in the classroom learning process.

[0055] After obtaining the students' eye movement data, the students' gaze area can be compared with the location of the knowledge points in the blackboard content to determine whether the students' gaze falls on the recorded location of a certain knowledge point. Specifically, the movement range and frequency of the students' gaze points within a certain time window (such as the retention time of the knowledge point) are counted. If the gaze points switch frequently and over a wide range, it indicates that the students may be more active in the learning process and try to absorb multiple information points. Alternatively, the frequency and duration of students' gazes at each knowledge point are counted. Frequent attention to multiple knowledge points also indicates active learning.

[0056] Compare the duration of the students' gaze on each knowledge point with the retention time of the knowledge point. If the student's gaze time is close to or equal to the retention time of the knowledge point, it means that the student is very focused on the knowledge point. Alternatively, analyze the stability of the student's gaze when looking at a certain knowledge point, that is, whether the gaze point stays stably in the area of ​​the knowledge point without obvious jumps, which also reflects the student's concentration.

[0057] Furthermore, the evaluation results of learning activity and learning concentration are combined to make a comprehensive judgment. For example, if both exceed the threshold, the student's learning status can be judged as "highly active and focused"; if the activity level is high but the concentration level is low, it may indicate that the student is easily distracted; if both are low, it may indicate that the student lacks motivation or encounters difficulties in learning.

[0058] In a possible implementation of the embodiment of the present application, in the above step S204, when the learning status includes the concentration on each knowledge point, a knowledge network diagram corresponding to each student is generated based on the learning status of each student and the blackboard content information, wherein the generation process of the knowledge network diagram corresponding to each student may include: Get the textbook content corresponding to the current class; Based on the textbook content, determine the correlation between every two knowledge points in the blackboard content information; Determine a target knowledge point corresponding to the student from the knowledge points corresponding to the multiple blackboard content information, and the concentration corresponding to the target knowledge point is less than the concentration threshold; Based on the target knowledge points and the correlation between every two knowledge points, a knowledge network diagram corresponding to the students is generated.

[0059] The knowledge points taught in each class generally correspond to textbook content. Therefore, the textbook content corresponding to the current class can be obtained by pre-entering the electronic textbook library, parsing the textbook content, and extracting the key knowledge points, concepts, theoretical frameworks, etc. Based on the textbook content, analyze the logical relationship between every two knowledge points in the blackboard content information. Specifically, the relationship extraction technique can be used to extract the relationship between multiple knowledge points in the blackboard content information from the multiple knowledge points contained in the textbook content, including causal relationships, parallel relationships, progressive relationships, etc. According to factors such as the closeness and importance of the relationship, a quantitative value is assigned to the correlation between every two knowledge points. For example, a direct causal relationship may have a higher correlation value, while a more distant parallel relationship has a lower correlation value.

[0060] Furthermore, knowledge points with a concentration less than a preset threshold are regarded as target knowledge points for students. The selected target knowledge points are the parts that students may not fully understand or master during the learning process. With the target knowledge points as the core, a preliminary knowledge network framework is constructed based on the correlation between knowledge points to show the logical connection between the target knowledge points and other knowledge points.

[0061] The network structure is optimized according to students' learning habits and cognitive levels. Specifically, the knowledge points with the highest correlation with the target knowledge points are highlighted so that students can learn them first; and the node size and color attributes in the network are adjusted according to the difficulty of the knowledge points. At the same time, learning suggestions are added to the knowledge network diagram. Among them, learning suggestions can guide students on how to learn these target knowledge points more effectively, including learning methods, learning resource recommendations, etc.

[0062] Furthermore, the generated knowledge network diagram is presented in a visual way on the electronic blackboard of the student end. Students can intuitively understand their learning status by viewing these diagrams and adjust their learning strategies accordingly.

[0063] A possible implementation of the embodiment of the present application is that in the above embodiment, the learning status and blackboard content information of each student are used to generate a customized test question corresponding to each student. The generation process of the customized test question corresponding to each student includes: Based on the target knowledge points and the correlation between every two knowledge points, determine the corresponding knowledge points to be strengthened for students; Obtain the textbook question bank corresponding to the textbook content, and select the strengthening test questions related to the knowledge points to be strengthened from the textbook question bank; Based on the correlation between every two knowledge points to be strengthened, the corresponding order of the strengthened test questions is determined to obtain customized test questions for students.

[0064] Specifically, according to the student's concentration on each knowledge point in class, the student's mastery of different knowledge points is assigned to four levels: "proficient mastery", "basic mastery", "need to be strengthened" and "not mastered", and the number of test questions corresponding to each level is obtained. Among them, a concentration range corresponds to a level, and a level corresponds to a number of test questions. According to the concentration range to which the concentration of the knowledge point belongs, the level corresponding to the knowledge point is determined, and the number of test questions corresponding to the knowledge point is determined according to the level.

[0065] After obtaining the level corresponding to each knowledge point and the correlation between every two knowledge points, the knowledge points whose correlation with the target knowledge point is greater than the correlation threshold are taken as associated knowledge points, and the associated knowledge points and the target knowledge points are determined as the students' knowledge points to be strengthened.

[0066] Furthermore, a textbook question bank containing various exercises related to the textbook content is accessed and included, and based on each student's knowledge points to be strengthened and the number of test questions corresponding to each target knowledge point, questions that are directly related to or can indirectly strengthen these knowledge points are screened from the question bank.

[0067] Based on the correlation between the knowledge points to be strengthened, these knowledge points are sorted, and the knowledge point combinations with high correlation are tested first, so that students can build a more comprehensive knowledge network in the test. According to the sorting results, the selected strengthening test questions are combined in the order of knowledge points to form a customized test question for the student. In a possible implementation of the embodiment of the present application, after the above step S202, that is, after obtaining the classroom image of each student corresponding to the current classroom, the method may further include: Identify each student's classroom image and check if there are any students with sleepy expressions; When there are students with sleepy expressions, the students with sleepy expressions are identified as target students, and sleepiness test questions are generated based on the knowledge points currently recorded in the blackboard content information; The sleepiness test questions and the student information of the target students are displayed at a specific location of the electronic blackboard at the teacher's end, and the content recorded at the specific location is not synchronized to the electronic blackboard at the student's end.

[0068] Among them, a specific area is set on the electronic blackboard interface of the teacher's terminal, and the content of the specific area will not be synchronized to the electronic blackboard of the student's terminal. The specific area is used to display the sleepiness test questions and the student information of the target students, so as to remind the teacher to notice the students who are in a sleepy state. Ensure this. Specifically, by adding filtering rules during the data transmission process, the content of the specific area will not be synchronized to the electronic blackboard of the student's terminal, and only non-sensitive and general teaching content will be synchronized to the student's terminal.

[0069] When there are students who are sleepy in class, the efficiency of the students' listening to the class will be greatly reduced. At this time, the teacher can remind or perform other operations to make the students awake and concentrate on the class. Therefore, a trained machine learning or deep learning model can be used to recognize the expressions of students in the image to recognize multiple expressions including sleepiness, so as to identify students who are sleepy. Specifically, the preprocessed classroom image is input into the expression recognition model, and the expression of each student is analyzed in real time to determine whether there are students with sleepy expressions. When the model identifies students with sleepy expressions, these students are identified as target students and their student information (such as student number, name, etc.) is recorded.

[0070] Furthermore, with the real-time capture of classroom images and expression recognition, the electronic blackboard at the teacher's end should be able to update the sleepiness test questions and the information of the target students in real time, so that the teacher can adjust the teaching strategy and focus objects in time.

[0071] In a possible implementation of the embodiment of the present application, after the above step S205, that is, after displaying the after-class exercises, the knowledge network diagram corresponding to each student, and the customized test questions on the electronic blackboard of the corresponding student terminal, the method further includes: Receive each student's first answer result for the after-class exercise questions and the second answer result for the corresponding customized test questions, and determine the correctness of each student's answer based on the first answer result and the second answer result; Based on the correctness of each student's answer, determine each student's knowledge mastery, including the mastery of each knowledge point in the blackboard content information; According to each student's knowledge mastery, annotations are made on each student's knowledge network diagram, and the annotated knowledge network diagram is displayed on the electronic blackboard at the corresponding student end.

[0072] Among them, the correctness of the answer is used to represent the correct answer rate and the wrong answer rate.

[0073] After receiving the after-class exercises and customized tests, students can complete the after-class exercises and customized tests on the electronic blackboard of the student terminal. After the students complete the exercises and submit the results, the results are automatically uploaded to the teacher terminal through the student terminal.

[0074] The teacher's terminal uses a preset answer library and intelligent grading algorithm to automatically grade the answer results of the after-class exercises and customized test questions submitted by each student, determine the correctness of the answer to each question, and obtain the first answer result of the after-class exercises and the second answer result of the customized test questions. The first answer result and the second answer result both include detailed information such as the score of each question, the correct answer, and the student's answer options.

[0075] Furthermore, the after-class exercises and customized test questions are mapped to the knowledge points in the blackboard content information to determine the specific knowledge points tested by each question. Based on the correctness of the students' answers, the ratio of the number of questions answered correctly by the students on each knowledge point to the total number of questions and the difficulty of the questions are weighted and calculated to determine the students' mastery of each knowledge point.

[0076] According to each student's knowledge mastery, the corresponding knowledge network diagram is marked. The marking can include color coding (such as green for good mastery, yellow for basic mastery, and red for insufficient mastery), icon marking (such as a check mark for correctness and a cross for errors) or text description (such as "need to strengthen practice"), etc. The marking process is dynamic, that is, as the student's answer results are updated, the marking on the knowledge network diagram is adjusted accordingly.

[0077] The annotated knowledge network diagram is pushed to the electronic blackboard of the corresponding student terminal so that students can intuitively see their knowledge mastery. Students can interact with the annotated knowledge network diagram through the electronic blackboard of the student terminal, such as clicking on a certain annotation to view detailed analysis, wrong question analysis or related learning resource links. Teachers can view the knowledge mastery of all students through the teacher terminal to adjust and optimize the subsequent teaching plan.

[0078] The above-mentioned embodiment introduces an intelligent application method of an electronic blackboard from the perspective of method flow, and the following embodiment introduces an intelligent application device of an electronic blackboard from the perspective of a virtual module or a virtual unit. Please refer to the following embodiment for details.

[0079] See also Figure 3 The intelligent application device 30 of the electronic blackboard may specifically include: a first acquisition module 301, a second acquisition module 302, a determination module 303, a generation 304 and a display module 305, specifically: An intelligent application device 30 for an electronic blackboard, comprising: The first acquisition module 301 is used to obtain the blackboard content information corresponding to the current class, and the blackboard content information includes the knowledge points recorded by the teacher on the electronic blackboard, the recording position corresponding to each knowledge point, and the retention time corresponding to each knowledge point; The second acquisition module 302 is used to acquire a classroom image of each student corresponding to the current classroom, where the classroom image is a frame image of a received video of a student in the classroom, and the classroom image contains complete facial image information of the student; A determination module 303 is used to determine the learning status of each student based on the classroom image and the blackboard content information; A generation module 304 is used to generate a knowledge network diagram and customized test questions corresponding to each student based on the learning status and blackboard content information of each student; The display module 305 is used to display after-class exercises, the knowledge network diagram corresponding to each student, and customized test questions on the electronic blackboard of the corresponding student terminal at the end of the current class.

[0080] In a possible implementation of the embodiment of the present application, the determination module 303 determines the learning status corresponding to each student based on the classroom image and the blackboard content information. When determining the learning status corresponding to each student, it can be specifically used to: Recognize the facial features of students in the classroom image, and segment the classroom image based on the facial features to obtain separate student facial images of the students; Generate eye movement data of the student based on the student's facial image corresponding to a plurality of continuous classroom images of the student, the eye movement data including the fixation area and the fixation duration; Determine students' learning status based on the blackboard content information and students' eye movement data.

[0081] In a possible implementation of the embodiment of the present application, when the determination module 303 determines the learning status of the student based on the blackboard content information and the student's eye movement data, it can be specifically used to: Determine the students' learning activity based on the recording position of each knowledge point in the blackboard content and the students' gaze area; Determine the students' learning concentration based on the retention time of each knowledge point in the blackboard content and the students' gaze time; Determine students' learning status based on their level of learning activity and learning concentration.

[0082] In a possible implementation of the embodiment of the present application, the learning status includes the degree of concentration on each knowledge point, wherein the generation module 304 generates a knowledge network diagram corresponding to each student based on the learning status of each student and the blackboard content information. When generating the knowledge network diagram corresponding to each student, it can be specifically used for: Get the textbook content corresponding to the current class; Based on the textbook content, determine the correlation between every two knowledge points in the blackboard content information; Determine a target knowledge point corresponding to the student from the knowledge points corresponding to the multiple blackboard content information, and the concentration corresponding to the target knowledge point is less than the concentration threshold; Based on the target knowledge points and the correlation between every two knowledge points, a knowledge network diagram corresponding to the students is generated.

[0083] In a possible implementation of the embodiment of the present application, the generation module 304 generates a customized test question corresponding to each student based on the learning status and blackboard content information of each student. When generating a customized test question corresponding to each student, it can be specifically used to: Based on the target knowledge points and the correlation between every two knowledge points, determine the corresponding knowledge points to be strengthened for students; Obtain the textbook question bank corresponding to the textbook content, and select the strengthening test questions related to the knowledge points to be strengthened from the textbook question bank; Based on the correlation between every two knowledge points to be strengthened, the corresponding order of the strengthened test questions is determined to obtain customized test questions for students.

[0084] In a possible implementation of the embodiment of the present application, the intelligent application device 30 of the electronic blackboard further includes: The recognition module is used to recognize each student's classroom image and determine whether there are students with sleepy expressions; The first determination module is used to determine the students with sleepy expressions as target students when there are students with sleepy expressions, and generate sleepiness test questions based on the knowledge points currently recorded in the blackboard content information; The first display module is used to display the sleepiness test questions and the student information of the target student at a specific location of the electronic blackboard at the teacher's end. The content recorded at the specific location is not synchronized to the electronic blackboard at the student's end.

[0085] In a possible implementation of the embodiment of the present application, the intelligent application device 30 of the electronic blackboard further includes: A receiving module, used to receive each student's first answer result for the after-class exercise questions and the second answer result for the corresponding customized test questions, and determine the correctness of each student's answer based on the first answer result and the second answer result; The second determination module is used to determine the knowledge mastery of each student based on the correctness of each student's answer, and the knowledge mastery includes the mastery of each knowledge point in the blackboard content information; The second display module is used to mark each student's knowledge network diagram according to the knowledge mastery of each student, and display the marked knowledge network diagram on the electronic blackboard of the corresponding student terminal.

[0086] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0087] See also Figure 4 , the embodiment of the present application also introduces a teacher terminal from the perspective of a physical device, such as Figure 4 As shown, Figure 4 The teacher terminal 40 shown includes: a processor 401 and a memory 403. The processor 401 and the memory 403 are connected, such as through a bus 402. Optionally, the teacher terminal 40 may also include a transceiver 404. It should be noted that in actual applications, the transceiver 404 is not limited to one, and the structure of the teacher terminal 40 does not constitute a limitation on the embodiments of the present application.

[0088] Processor 401 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 401 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0089] The bus 402 may include a path to transmit information between the above components. The bus 402 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 402 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0090] The memory 403 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0091] The memory 403 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 401. The processor 401 is used to execute the application code stored in the memory 403 to implement the contents shown in the above method embodiment.

[0092] The teacher terminal includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The teacher terminal shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0093] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0094] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0095] The above are only some implementation methods of the present application. It should be pointed out that ordinary technicians in this technical field can make several improvements and modifications without departing from the principles of the present application, and these improvements and modifications should also be regarded as the protection scope of the present application.

Claims

1. An intelligent application method of an electronic blackboard, characterized in that: The method is executed by a teacher terminal, and includes: Obtaining the blackboard content information corresponding to the current class, wherein the blackboard content information includes the knowledge points recorded by the teacher on the electronic blackboard, the recording location corresponding to each knowledge point, and the retention time corresponding to each knowledge point; Obtaining a classroom image of each student corresponding to the current classroom, wherein the classroom image is a frame image of a received video of a student in the classroom, and the classroom image includes complete facial image information of the student; Determine the learning status of each student based on the classroom image and the blackboard content information; Based on each student's learning status and the blackboard content information, generate a knowledge network diagram and customized test questions corresponding to each student; At the end of the current class, after-class exercises, the knowledge network diagram corresponding to each student, and customized test questions are displayed on the electronic blackboard of the corresponding student terminal.

2. The intelligent application method of the electronic blackboard according to claim 1 is characterized in that: The learning status corresponding to each student is determined based on the classroom image and the blackboard content information. The process of determining the learning status corresponding to each student includes: Recognize facial features of students in the classroom image, and segment the classroom image based on the facial features to obtain separate student facial images of the students; generating eye movement data of the student based on a student facial image corresponding to a plurality of continuous classroom images of the student, the eye movement data including a gaze area and a gaze duration; The learning status of the student is determined based on the blackboard content information and the student's eye movement data.

3. The intelligent application method of the electronic blackboard according to claim 2 is characterized in that: The determining the learning status of the student based on the blackboard content information and the student's eye movement data includes: Determining the student's learning activity based on the recording position corresponding to each knowledge point in the blackboard content and the student's gaze area; Determining the student's learning concentration based on the retention time of each knowledge point in the blackboard content and the student's gaze time; The learning status of the student is determined based on the learning activity level and the learning concentration level.

4. The intelligent application method of electronic blackboard according to claim 1 is characterized in that: The learning status includes the degree of concentration on each knowledge point, wherein a knowledge network diagram corresponding to each student is generated based on the learning status of each student and the blackboard content information, and the generation process of the knowledge network diagram corresponding to each student includes: Get the textbook content corresponding to the current class; Based on the teaching material content, determining the correlation between every two knowledge points in the blackboard content information; Determine a target knowledge point corresponding to the student from the knowledge points corresponding to the plurality of blackboard content information, wherein the concentration corresponding to the target knowledge point is less than a concentration threshold; Based on the target knowledge points and the degree of association between every two knowledge points, a knowledge network diagram corresponding to the student is generated.

5. The intelligent application method of the electronic blackboard according to claim 4 is characterized in that: Based on the learning status of each student and the blackboard content information, a customized test question corresponding to each student is generated. The generation process of the customized test question corresponding to each student includes: Based on the target knowledge points and the correlation between every two knowledge points, determine the knowledge points to be strengthened corresponding to the students; Obtaining a textbook question bank corresponding to the textbook content, and selecting strengthening test questions related to the knowledge points to be strengthened from the textbook question bank; Based on the correlation between every two of the knowledge points to be strengthened, the corresponding ranking of the strengthened test questions is determined to obtain the customized test questions for the students.

6. The intelligent application method of electronic blackboard according to claim 1, characterized in that: After acquiring the classroom image of each student corresponding to the current classroom, the method further includes: Identify each student's classroom image and check if there are any students with sleepy expressions; When there are students with sleepy expressions, the students with sleepy expressions are identified as target students, and sleepiness test questions are generated based on the knowledge points currently recorded in the blackboard content information; The sleepiness test questions and the student information of the target student are displayed at a specific location of the electronic blackboard at the teacher's end, and the content recorded at the specific location is not synchronized to the electronic blackboard at the student's end.

7. The intelligent application method of electronic blackboard according to claim 1, characterized in that: After displaying the after-class exercises, the knowledge network diagram corresponding to each student, and the customized test questions on the electronic blackboard of the corresponding student terminal, the method further includes: Receiving each student's first answer result for the after-class exercise questions and the second answer result for the corresponding customized test questions, and determining the correctness of each student's answer based on the first answer result and the second answer result; Determine the knowledge mastery of each student based on the correctness of each student's answer, wherein the knowledge mastery includes the mastery of each knowledge point in the blackboard content information; According to each student's knowledge mastery, annotations are made on each student's knowledge network diagram, and the annotated knowledge network diagram is displayed on the electronic blackboard at the corresponding student end.

8. An intelligent application device for an electronic blackboard, characterized in that: include: The first acquisition module is used to acquire the blackboard content information corresponding to the current class, wherein the blackboard content information includes the knowledge points recorded by the teacher on the electronic blackboard, the recording position corresponding to each knowledge point, and the retention time corresponding to each knowledge point; A second acquisition module is used to acquire a classroom image of each student corresponding to the current classroom, wherein the classroom image is a frame image received from a video of the student in the classroom, and the classroom image contains complete facial image information of the student; A determination module, used to determine the learning status corresponding to each student based on the classroom image and the blackboard content information; A generation module, used to generate a knowledge network diagram and customized test questions corresponding to each student based on the learning status of each student and the blackboard content information; The display module is used to display after-class exercises, the knowledge network diagram corresponding to each student, and customized test questions on the electronic blackboard of the corresponding student terminal at the end of the current class.

9. A teacher terminal, characterized in that: The teacher terminal includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the intelligent application method of the electronic blackboard as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the intelligent application method of the electronic blackboard as described in any one of claims 1 to 7.

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