Intelligent student homework correcting electronic base plate

Abstract

The invention discloses an intelligent student homework correction electronic base plate, belongs to the technical field of intelligent base plates, and can realize automation of a whole homework correction process. Comprising a base plate, a supporting column, a camera and a controller, the base plate is provided with a first bearing area, and the first bearing area is used for carrying work; one end of the supporting column is fixedly connected with the base plate; the camera is mounted at the other end of the supporting column; and the controller is electrically connected with the camera and is used for receiving the test question image shot by the camera, correcting the test question image and sending a correction result to preset terminal equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent pads, and in particular to an electronic pad for intelligently correcting student homework. Background Art

[0002] Homework grading is a core process for teachers to assess students' knowledge, identify problems, and provide targeted guidance. With the rapid development of information technology, the education sector is constantly exploring how to improve teaching quality and efficiency through modern methods. Traditionally, grading student homework relies primarily on manual marking by teachers, a time-consuming and labor-intensive process.

[0003] While some technical solutions exist, such as using scanners to digitize students' handwritten answer sheets and then using computers to grade them, this approach still has numerous limitations: 1. Manual electronic grading relies on the teacher's subjective judgment, failing to achieve comprehensive, objective evaluation, and teachers have limited time and energy for grading. 2. Existing grading systems are mostly single-function tools that lack comprehensive intelligent grading capabilities and are unable to provide effective feedback and guidance.

[0004] The disclosure of the above background technology content is only used to assist in understanding the concept and technical solution of the present invention. It does not necessarily belong to the prior art of this patent application. In the absence of clear evidence that the above content has been disclosed on the filing date of this patent application, the above background technology should not be used to evaluate the novelty and creativity of this application. Summary of the Invention

[0005] This application provides an electronic pad for intelligently grading student homework, which can automate the entire process of homework grading.

[0006] To achieve the above objectives, the present application discloses the following technical solutions:

[0007] An electronic pad for intelligently correcting student homework comprises a base plate, a support column, a camera and a controller. The base plate is provided with a first carrying area for placing homework; one end of the support column is fixedly connected to the base plate; the camera is mounted on the other end of the support column; the controller is electrically connected to the camera, and is used to receive and correct test questions images taken by the camera and send the correction results to a preset terminal device.

[0008] In an embodiment of the present application, the base plate provides a flat and stable bearing platform for the homework, and the support column uses a fixed mechanical structure to lift the camera to a preset height, forming a bird's-eye view shooting angle, ensuring that the homework content is completely within the camera's field of view. After the camera automatically captures the homework image, it sends the image data to the controller via wired or wireless transmission for homework correction. In this way, the entire process of homework correction can be automated, without the need for manual intervention in image acquisition and answer verification. The correction results are directly transmitted to the teacher's terminal device, reducing the time spent on homework correction from several hours of traditional manual processing to minutes, while eliminating the evaluation bias caused by subjective factors of teachers during the correction process.

[0009] In some possible implementations, the base plate is further provided with a second support area, on which an LCD screen tablet is mounted. This facilitates students in performing calculations or drafting. Specifically, the LCD screen tablet allows students to write and erase at any time without having to turn to a new page or waste paper, thus increasing the flexibility and convenience of homework.

[0010] In some possible implementations, the LCD screen handwriting board is electrically connected to the controller; the student homework intelligent correction electronic pad further includes a test question positioning block, which is slidably disposed on the bearing surface of the first bearing area;

[0011] The controller is provided with a job completion button, and the controller includes:

[0012] A clear button response module, configured to control the camera to capture the calculated image of the LCD handwriting board and the image of the job located on the first carrying area in response to the triggering of the clear button of the LCD handwriting board;

[0013] An image binding module is used to bind the captured calculation image to the test question that is closest to the test question positioning block and store it in a preset database;

[0014] a correction execution module, configured to correct the homework to obtain a set of wrong questions based on the multiple homework images captured in response to the triggering of the homework completion button;

[0015] The error analysis module is used to retrieve the calculation image corresponding to each wrong question in the wrong question set, identify and mark the error points in the calculation image;

[0016] The data sending module is used to send the wrong questions and their corresponding calculation images marked with error points and reference answers to a preset terminal device.

[0017] Once the clear button is triggered, the controller synchronizes the camera with the image of the calculation on the LCD tablet and the work image on the first loading area. Using a pre-set image processing algorithm, the calculation process is then linked to the corresponding test question in a temporal and spatial manner. The physical location information of the test question's locating block is combined with the coordinate data output by the image processing algorithm to accurately determine the correspondence between the calculation steps and the specific test question, forming a traceable data chain covering the entire problem-solving process.

[0018] When the homework completion button is triggered, the system generates a collection of incorrect answers based on multiple batches of homework images. It also automatically retrieves the calculation images associated with the incorrect answers and uses a deep learning-based error pattern recognition model to mark the key nodes of the calculation steps. Finally, the marked calculation images, incorrect answer content, and reference answers are integrated into a structured data package and sent to the terminal device, allowing teachers and parents to simultaneously obtain a holographic view of the student's answer results and thought process. This breaks the limitation of traditional homework grading that separates answers from the problem-solving process, and achieves precise identification of the causes of errors.

[0019] In some possible implementations, the test question positioning block can be slidably magnetically attached to the bearing surface of the first bearing area. In this way, the magnetic attachment mechanism provides a reliable fixing force, allowing the positioning block to be quickly adjusted and repositioned near the test question.

[0020] In some possible implementations, the image binding module includes:

[0021] A coordinate recognition unit is used to identify the center point coordinates of the test question positioning block using a grayscale centroid method. The center point coordinates are generated based on the edge symmetry detection of the positioning block;

[0022] A boundary extraction unit, used to extract boundary features of test questions in the homework image based on the Sobel operator;

[0023] A region analysis unit, used to determine the region coordinate range of each test question in the image through connected domain analysis;

[0024] A target matching unit is used to calculate the Euclidean distance between the center point coordinates and the center coordinates of the title of each question area, and mark the question area corresponding to the minimum distance as the target binding question;

[0025] The data association unit is used to establish an association relationship between the calculation image and the regional coordinate range of the target binding test question, and store it in the distributed storage node of the preset database.

[0026] In some possible implementations, the review execution module includes:

[0027] An image clustering unit, used to divide the job image into several similar image groups based on an image similarity algorithm;

[0028] An image screening unit is used to compare the number of answer areas on multiple homework images in a similar image group and screen the target correction image with the largest number of answer areas;

[0029] The image segmentation unit is used to segment the single-topic image set from the region boundary using an image segmentation algorithm based on connected domain analysis.

[0030] A text recognition unit is used to perform OCR character recognition on a single-question image set to generate student answer text data;

[0031] The grading unit is used to match the student's answer text data with the preset standard answer database for semantic similarity;

[0032] The wrong question generation unit generates a set of correction results containing error type codes through a difference marking algorithm to form a wrong question set.

[0033] Using an image similarity algorithm, homework images are divided into similar groups, enabling rapid classification of similar question types or answer patterns, providing a data foundation for subsequent screening of optimal grading samples. Similar groups are then compared for the number of answer areas, counting the number of answer areas in each image and selecting the target images with the largest number of answer areas, ensuring that the grading process is based on the most complete student response data.

[0034] An image segmentation algorithm using connected domain analysis is used to segment a collection of single-question images from region boundaries, breaking down complex assignment images into independent question units and providing structural support for refined processing. Optical character recognition (OCR) is performed on the segmented single-question images to generate student response text data. Optical character recognition technology is used to convert handwritten answers into structured data, bridging the gap between manual writing and machine processing. This allows semantic similarity matching of student response text data with a database of standard answers. A difference labeling algorithm is then used to generate a set of correction results containing error type codes based on semantic differences, forming a collection of incorrect questions.

[0035] In some possible implementations, the image screening unit includes:

[0036] A grayscale processing subunit is used to perform grayscale preprocessing on each job image in the similar image group to generate a corresponding grayscale image;

[0037] An edge detection subunit is used to extract edge features from a grayscale image using a Canny edge detection algorithm to generate an edge detection binary image.

[0038] The region analysis subunit is used to perform connected domain analysis based on the edge detection binary graph, detect the answer area in each homework image, and generate a detection result set including the boundary coordinates of the answer area;

[0039] A quantity counting subunit is used to count the total number of answer areas corresponding to each job image based on the number of connected domains of each answer area in the detection result set, and generate a statistical list containing the correspondence between image identifiers and the number of answers;

[0040] The image marking subunit is used to sort the number of answers of each homework image in the statistical list in descending order, and mark the homework image that ranks first as the target correction image.

[0041] In some possible implementations, the error analysis module includes: a data collating unit for collating the retrieved calculation images and question information corresponding to the wrong questions into a data set in a preset format;

[0042] A model calling unit is used to call a preset math problem correction model and send the data set as an input parameter to the model;

[0043] A result receiving unit, configured to receive the recognition result returned by the correction model, which includes the calculated image information marked with error points and the corresponding error analysis;

[0044] The data sending module includes:

[0045] An answer acquisition unit is used to obtain reference answers corresponding to the wrong question set from a preset standard answer database;

[0046] A data encapsulation unit is used to integrate wrong questions, calculation images marked with wrong points, and reference answers into a data package according to a predetermined data structure;

[0047] Communication transmission unit, used to send to the preset terminal device.

[0048] In some possible implementations, the camera is a rotatable zoom camera, so that the camera can be adjusted accordingly as needed, thereby facilitating the acquisition of high-quality images.

[0049] In some possible implementations, the support column is foldable, so that the electronic pad for intelligently grading student homework can be stored more conveniently. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for describing the embodiments or the prior art. It should be understood that the same reference numerals represent the same elements in all drawings. In the drawings, the dimensions of certain features may be distorted for clarity and ease of understanding.

[0051] Figure 1 A schematic diagram of the structure of an electronic pad for intelligently grading student assignments provided in some embodiments of the present application;

[0052] Figure 2 for Figure 1 The data processing logic block diagram of the controller in the electronic pad for intelligent correction of student homework shown;

[0053] Figure 3 for Figure 2 The data processing logic block diagram of the fixed binding module shown;

[0054] Figure 4 for Figure 2 The data processing logic block diagram of the correction execution module shown;

[0055] Figure 5 for Figure 2 The data processing logic block diagram of the image screening unit shown;

[0056] Figure 6 for Figure 2 The data processing logic block diagram of the error analysis module shown;

[0057] Figure 7 for Figure 2 The data processing logic block diagram of the data generation module shown. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] It should be noted that, in the description of the present invention, terms such as "center", "upper", "lower", "horizontal", and "inner" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0060] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections; they may refer to direct connections, indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0061] See also Figure 1The embodiment of the present application provides an electronic pad for intelligently correcting student homework, including a base plate 1, a support column 2, a camera 3 and a controller 5. The base plate 1 is provided with a first carrying area 11, and the first carrying area 11 is used to place homework; one end of the support column 2 is fixedly connected to the base plate 1; the camera 3 is installed on the other end of the support column 2; the controller 5 is electrically connected to the camera 3, and is used to receive and correct the test image taken by the camera 3 and send the correction result to a preset terminal device.

[0062] The base plate 1 provides a flat and stable platform for homework, while the support column 2, with its fixed mechanical structure, elevates the camera 3 to a preset height, creating a bird's-eye view and ensuring that the entire homework content is within the camera's field of view. After the camera 3 automatically captures the homework image, it transmits the image data to the controller 5 via wired or wireless transmission for homework grading. This eliminates the need for human intervention in image acquisition and answer verification, and the grading results are directly transmitted to the teacher's terminal device, reducing the time required for homework grading from hours to minutes, while also eliminating assessment bias caused by subjective factors during the grading process.

[0063] In some embodiments, the camera 3 is a rotatable zoom camera 3. In this way, the camera 3 can be adjusted accordingly as needed, thereby facilitating the acquisition of high-quality images.

[0064] In some embodiments, the support column 2 is foldable, so that the electronic pad for intelligently correcting students' homework can be stored more conveniently.

[0065] In some embodiments, the base plate 1 is further provided with a second supporting area 12, on which a liquid crystal display tablet 4 is mounted. This facilitates students' calculations and drafting. Specifically, the liquid crystal display tablet 4 allows students to write and erase at any time without having to turn to a new page or waste paper, thus improving the flexibility and convenience of homework.

[0066] See also Figure 2 In some embodiments, the LCD screen handwriting board 4 is electrically connected to the controller 5; the student homework intelligent correction electronic pad further includes a test question positioning block 6, which is slidably disposed on the bearing surface of the first bearing area 11;

[0067] The controller 5 is provided with a job completion button 51, and the controller 5 includes:

[0068] The clear button response module 52 is used to control the camera 3 to capture the calculated image of the LCD handwriting board 4 and the image of the job located on the first carrying area 11 in response to the triggering of the clear button 41 of the LCD handwriting board 4;

[0069] An image binding module 53 is used to bind the captured calculation image to the test question that is closest to the test question positioning block 6 and store it in a preset database;

[0070] The correction execution module 54 is used to respond to the triggering of the homework completion button 51 and correct the homework according to the multiple homework images captured to obtain a set of wrong questions;

[0071] The error analysis module 55 is used to retrieve the calculation image corresponding to each wrong question in the wrong question set, identify and mark the error points in the calculation image;

[0072] The data sending module 56 is used to send the wrong questions and their corresponding calculation images marked with the wrong points and the reference answers to a preset terminal device.

[0073] Thus, when the clear button 41 is triggered, the controller 5 synchronously controls the camera 3 to capture the real-time calculation image on the LCD screen tablet 4 and the work image of the first loading area 11. Using a pre-set image processing algorithm, a temporal and spatial correlation is established between the calculation process and the corresponding test question. The physical location information of the test question locator 6 is combined with the coordinate data output by the image processing algorithm to accurately determine the correspondence between the calculation steps and the specific test question, forming a traceable data chain covering the entire problem-solving process.

[0074] After the homework completion button 51 is triggered, the system generates a collection of incorrect answers based on multiple batches of homework images. It also automatically retrieves the calculation images associated with the incorrect answers and uses a deep learning-based error pattern recognition model to mark the key nodes of the calculation steps. Finally, the marked calculation images, incorrect answer content, and reference answers are integrated into a structured data package and sent to the terminal device, allowing teachers and students' parents to simultaneously obtain a holographic view of the student's answer results and thought process. This breaks the limitation of traditional homework grading, which separates answers from the problem-solving process, and achieves the precise location of the cause of the error.

[0075] In some embodiments, the test question positioning block 6 can be slidably magnetically adsorbed on the supporting surface of the first supporting area 11. In this way, the magnetic adsorption mechanism provides a reliable fixing force, which enables the positioning block to be quickly adjusted and repositioned near the test question.

[0076] See also Figure 3 In some embodiments, the image binding module 53 includes:

[0077] A coordinate recognition unit 521 is used to recognize the center point coordinates of the test question positioning block 6 using a grayscale centroid method. The center point coordinates are generated based on the edge symmetry detection of the positioning block.

[0078] A boundary extraction unit 522 is used to extract boundary features of test questions in the homework image based on the Sobel operator;

[0079] A region analysis unit 523 is used to determine the region coordinate range of each test question in the image through connected component analysis;

[0080] The target matching unit 524 is used to calculate the Euclidean distance between the center point coordinates and the center coordinates of the title of each question area, and mark the question area corresponding to the minimum distance as the target bound question;

[0081] The data association unit 525 is used to establish an association relationship between the calculation image and the regional coordinate range of the target binding test question, and store it in the distributed storage node of the preset database.

[0082] See also Figure 4 In some embodiments, the correction execution module 54 includes:

[0083] An image clustering unit 541 is used to divide the job image into several similar image groups based on an image similarity algorithm;

[0084] An image screening unit 542 is configured to compare the number of answer areas on multiple homework images in a similar image group and screen the target correction image with the largest number of answer areas;

[0085] The image segmentation unit 543 is used to segment the single-topic image set from the region boundary using an image segmentation algorithm based on connected domain analysis.

[0086] The text recognition unit 544 is used to perform OCR character recognition on the single-question image set to generate student answer text data;

[0087] Correction unit 545, used for matching the student's answer text data with the preset standard answer database for semantic similarity;

[0088] The wrong question generating unit 546 generates a correction result set including error type codes through a difference marking algorithm to form a wrong question set.

[0089] Using an image similarity algorithm, homework images are divided into similar groups, enabling rapid classification of similar question types or answer patterns, providing a data foundation for subsequent screening of optimal grading samples. Similar groups are then compared for the number of answer areas, counting the number of answer areas in each image and selecting the target images with the largest number of answer areas, ensuring that the grading process is based on the most complete student response data.

[0090] An image segmentation algorithm using connected domain analysis is used to segment a collection of single-question images from region boundaries, breaking down complex assignment images into independent question units and providing structural support for refined processing. Optical character recognition (OCR) is performed on the segmented single-question images to generate student response text data. Optical character recognition technology is used to convert handwritten answers into structured data, bridging the gap between manual writing and machine processing. This allows semantic similarity matching of student response text data with a database of standard answers. A difference labeling algorithm is then used to generate a set of correction results containing error type codes based on semantic differences, forming a collection of incorrect questions.

[0091] See also Figure 5 In some embodiments, the image screening unit 542 includes:

[0092] Grayscale processing subunit 5421, used to perform grayscale preprocessing on each job image in the similar image group to generate a corresponding grayscale image;

[0093] The edge detection subunit 5422 is used to extract edge features from the grayscale image using the Canny edge detection algorithm to generate an edge detection binary image;

[0094] The region analysis subunit 5423 is used to perform connected domain analysis based on the edge detection binary graph, detect the answer area in each homework image, and generate a detection result set including the boundary coordinates of the answer area;

[0095] The number counting subunit 5425 is used to count the total number of answer areas corresponding to each homework image based on the number of connected domains of each answer area in the detection result set, and generate a statistical list containing the correspondence between image identifiers and the number of answers;

[0096] The image marking subunit 5426 is used to sort the number of answers of each homework image in the statistical list in descending order, and mark the homework image that ranks first as the target correction image.

[0097] See also Figure 6 and Figure 7 In some embodiments, the error analysis module 55 includes:

[0098] The data collating unit 551 is used to collate the retrieved calculation images and the question information corresponding to the wrong questions into a data set in a preset format;

[0099] The model calling unit 552 is used to call the preset math problem correction model and send the data set as input parameters to the model;

[0100] The result receiving unit 553 is used to receive the recognition result returned by the correction model, which includes the calculation image information marked with error points and the corresponding error analysis;

[0101] The data sending module 56 includes:

[0102] The answer acquisition unit 561 is used to obtain reference answers corresponding to the wrong question set from a preset standard answer database;

[0103] The data packaging unit 562 is used to integrate the wrong questions, the calculation images marked with the wrong points, and the reference answers into a data package according to a predetermined data structure;

[0104] The communication transmission unit 563 is used to send the data to the preset terminal device.

[0105] The present invention and its embodiments are described above. This description is not restrictive. What is shown in the full text is only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs a structure and embodiment similar to this technical solution without creatively designing, they shall all fall within the scope of protection of the present invention.

Claims

1. An electronic pad for intelligently correcting student homework, characterized in that: include: A base plate, wherein a first bearing area is provided on the base plate, and the first bearing area is used for placing workpieces; A support column, one end of which is fixedly connected to the base plate; a camera mounted on the other end of the support column; The controller is electrically connected to the camera and is used to receive the test image captured by the camera, correct the test image, and send the correction result to a preset terminal device.

2. The electronic pad for intelligently correcting student homework according to claim 1, characterized in that: The base plate is further provided with a second carrying area, and a liquid crystal screen handwriting board is provided on the second carrying area. The liquid crystal screen handwriting board is electrically connected to the controller.

3. The electronic pad for intelligently correcting student homework according to claim 1, characterized in that: The LCD screen handwriting board is electrically connected to the controller; the electronic pad for intelligently correcting student homework also includes a test question positioning block, which is slidably arranged on the bearing surface of the first bearing area; The controller is provided with a job completion button, and the controller includes: a clear button response module, configured to control the camera to capture the calculated image of the LCD handwriting board and the image of the job located on the first carrying area in response to the triggering of the clear button of the LCD handwriting board; An image binding module is used to bind the captured calculation image to the test question that is closest to the test question positioning block and store it in a preset database; a correction execution module, configured to respond to the triggering of the homework completion button and correct the homework according to the multiple homework images captured to obtain a set of wrong questions; The error analysis module is used to retrieve the calculation image corresponding to each wrong question in the wrong question set, identify and mark the error points in the calculation image; The data sending module is used to send the wrong questions and their corresponding calculation images marked with error points and reference answers to a preset terminal device.

4. The electronic pad for intelligently correcting student homework according to claim 3, characterized in that: The test question positioning block can be slidably and magnetically adsorbed on the bearing surface of the first bearing area.

5. The electronic pad for intelligently correcting student homework according to claim 3, characterized in that: The image binding module includes: A coordinate recognition unit, configured to recognize the center coordinates of the test question positioning block using a grayscale centroid method, wherein the center coordinates are generated based on edge symmetry detection of the positioning block; A boundary extraction unit, used to extract boundary features of test questions in the homework image based on the Sobel operator; A region analysis unit, used to determine the region coordinate range of each test question in the image through connected domain analysis; A target matching unit is used to calculate the Euclidean distance between the center point coordinates and the center coordinates of the title of each question area, and mark the question area corresponding to the minimum distance as the target binding question; The data association unit is used to establish an association relationship between the calculation image and the regional coordinate range of the target binding test question, and store the association relationship in a distributed storage node of a preset database.

6. The electronic pad for intelligently correcting student homework according to claim 3, characterized in that: The correction execution module includes: An image clustering unit, used to divide the job image into several similar image groups based on an image similarity algorithm; An image screening unit, configured to compare the number of answer areas on a plurality of homework images in the similar image group, and screen the target correction image with the largest number of answer areas; An image segmentation unit, configured to segment a single-topic image set from a region boundary using an image segmentation algorithm based on connected domain analysis; A text recognition unit, configured to perform OCR character recognition on the single-question image set to generate student answer text data; A correction unit, configured to perform semantic similarity matching between the student's answer text data and a preset standard answer database; The wrong question generating unit generates a correction result set including error type codes through a difference marking algorithm to form the wrong question set.

7. The electronic pad for intelligently correcting student homework according to claim 6, characterized in that: The image screening unit includes: A grayscale processing subunit, configured to perform grayscale preprocessing on each job image in the similar image group to generate a corresponding grayscale image; An edge detection subunit, configured to extract edge features from the grayscale image using a Canny edge detection algorithm to generate an edge detection binary image; A region analysis subunit, configured to perform connected domain analysis based on the edge detection binary graph, detect the answer region in each homework image, and generate a detection result set including the boundary coordinates of the answer region; a number counting subunit, configured to count the total number of answer areas corresponding to each homework image based on the number of connected domains of each answer area in the detection result set, and generate a statistical list containing the correspondence between image identifiers and the number of answers; The image marking subunit is used to sort the number of answers of each homework image in the statistical list in descending order, and mark the homework image that ranks first as the target correction image.

8. The electronic pad for intelligently correcting student homework according to claim 3, characterized in that: It is characterized in that The error analysis module includes: A data collating unit, for collating the retrieved calculation images and question information corresponding to the wrong questions into a data group in a preset format; A model calling unit, configured to call a preset math problem grading model and send the data set as an input parameter to the model; A result receiving unit, configured to receive the recognition result returned by the correction model, which includes the calculated image information marked with error points and the corresponding error analysis; The data sending module includes: An answer acquisition unit is used to obtain reference answers corresponding to the wrong question set from a preset standard answer database; A data encapsulation unit is used to integrate wrong questions, calculation images marked with wrong points, and reference answers into a data package according to a predetermined data structure; Communication transmission unit, used to send to the preset terminal device.

9. The electronic board for intelligently correcting student homework according to any one of claims 1 to 8, characterized in that: The camera is a rotatable zoom camera.

10. The electronic board for intelligently correcting student homework according to any one of claims 1 to 8, characterized in that: The support column is foldable.

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