Intelligent modification tape
By using an intelligent modification tape image acquisition and analysis system, weak knowledge points in students' practice process are identified, and a practice sequence is generated. This solves the problem of insufficient intelligent analysis of incorrect question training in existing technologies, and improves learning effectiveness and practicality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUIZHOU JINGSHI CHENGTOU INTELLIGENT EDUCATION IND CO LTD
- Filing Date
- 2022-09-29
- Publication Date
- 2026-07-21
Smart Images
Figure CN115512372B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent device technology, and in particular to intelligent modification tape. Background Technology
[0002] Students' mistakes during the learning process can objectively reflect the gaps in their current knowledge. If these mistakes can be identified in a timely manner and targeted measures are taken to fill these gaps, students can not only complete their current learning tasks efficiently, but also lay a solid foundation for their next stage of learning.
[0003] Currently, student error correction training generally falls into two categories: one is self-directed practice, such as students copying their mistakes into their error notebooks and practicing them. However, this method requires strong student autonomy and lacks the ability to intelligently tailor exercises to specific student errors. Even if students compile their errors, they cannot target the corresponding knowledge points, resulting in poor utilization of error correction. The second category involves organizing student practice through online testing platforms. These platforms can collect student errors and intelligently compile error sets for practice. However, most student learning still relies on paper-based learning, making online practice less practical and more costly. Furthermore, error analysis only considers the final submitted result and cannot comprehensively analyze the student's practice process, leading to lower accuracy in identifying weak knowledge points. Summary of the Invention
[0004] This invention provides an intelligent correction tape that can intelligently collect incorrect answers, analyze students' mistakes during the practice process, improve the comprehensiveness of the analysis of weak knowledge points, reduce the amount of data analysis, improve data analysis efficiency, reduce costs, and enhance practicality.
[0005] To achieve the above objectives, the basic solution of the present invention is as follows:
[0006] The intelligent correction tape includes a correction tape body, on which an image acquisition device is provided for capturing images of practice exercises.
[0007] It also includes a wrong question information analysis system, which includes a modification operation analysis module, a modification amount analysis module, a question analysis module, a weight analysis module, and a wrong question practice module;
[0008] The modification operation analysis module is used to acquire the exercise images captured by the image acquisition device and analyze the modification operations based on the exercise images.
[0009] The modification amount analysis module is used to analyze the modification amount of erroneous content based on the exercise practice images and modification operations;
[0010] The question analysis module is used to analyze the wrong questions corresponding to the incorrect content based on the exercise practice images and modification operations.
[0011] The weight analysis module is used to analyze the weight of incorrect questions based on the amount of modification.
[0012] The incorrect question practice module is used to generate the incorrect question practice order and the corresponding practice questions for each incorrect question based on the weight of each incorrect question.
[0013] The principles and advantages of this invention are as follows:
[0014] By installing an image capture device on the correction tape, students' errors can be captured while they are making corrections. Compared to analyzing the submitted answer sheets after completing exercises, this method can identify errors during the practice process, helping to analyze whether students hesitated or made changes during the problem-solving process. Even if the final answer is correct, the corrections made during the process can reveal the student's weaknesses in the current question. In addition, only the areas modified by the student using the correction tape need to be captured, reducing the amount of data analysis and improving efficiency. Finally, simply installing an image capture device on the correction tape allows for precise monitoring of students' learning progress, reducing costs and increasing practicality. Furthermore, the close proximity of the correction tape to the student's practice questions allows for clear capture of the student's errors and the corresponding incorrect questions, which is beneficial for subsequent data analysis.
[0015] By analyzing video footage of students' practice exercises, specifically by overwriting their handwriting with correction tape, we can identify logical errors and areas where students lack sufficient understanding of the relevant knowledge points. Then, based on the video footage and correction actions, we analyze the amount of corrections made to the errors and the corresponding incorrect questions. We further determine the weight of each incorrect question based on its correction amount, and finally generate a practice order and corresponding exercises for each incorrect question based on these weights. The principle is that a larger amount of correction indicates a later discovery of logical errors and a weaker grasp of the relevant knowledge points. Therefore, prioritizing the practice of incorrect questions based on the amount of correction follows the student's learning pattern: initial focus is higher, and motivation is greater. Practicing questions with higher weights at this stage allows us to prioritize the weakest areas and improve learning effectiveness.
[0016] In summary, this solution enables intelligent collection of incorrect answers, analysis of students' mistakes during practice, and comprehensiveness of the analysis of weak knowledge points. It also reduces the amount of data analysis, improves efficiency, lowers costs, enhances practicality, and helps analyze the mastery of each weak knowledge point. Furthermore, it generates and sorts practice questions for students to practice, thereby improving learning outcomes.
[0017] Furthermore, the modification amount includes continuous modification time and continuous modification distance;
[0018] The modification amount analysis module includes a modification time analysis module and a modification distance analysis module;
[0019] The modification time analysis module is used to analyze the continuous modification time based on the exercise practice images and modification operations;
[0020] The modified distance analysis module is used to analyze the continuous modification distance based on the exercise practice image and modification operation.
[0021] Beneficial effect: The magnitude of the modification is comprehensively analyzed by combining the continuous modification time and continuous modification distance (the length of the modification band used).
[0022] Furthermore, the modification time analysis module includes a single modification time analysis module, an interval time analysis module, and a continuous modification time generation module;
[0023] The single modification time analysis module is used to analyze the time of a single continuous modification based on the exercise practice image and modification operation;
[0024] The interval time analysis module is used to analyze the interval time between adjacent modification operations and generate a merged result of modification operations based on the interval time.
[0025] The continuous modification time generation module is used to generate continuous modification time based on the single continuous modification time and the combined result of modification operations.
[0026] Beneficial effect: When the interval between adjacent modification operations is short, it indicates that the two modifications may have been made during a continuous thinking process. The modification operations were not continuous only because there was a brief pause in the thinking process. Therefore, in this solution, the operation merging result is generated based on the interval between adjacent modification operations to determine whether adjacent modification operations should be merged, so as to improve the accuracy of the analysis results of continuous modification time.
[0027] Furthermore, the incorrect question practice module includes a sequence generation module, a knowledge point analysis module, and a question generation module;
[0028] The sequence generation module is used to generate the order of incorrect questions practice based on the weight of each incorrect question;
[0029] The knowledge point analysis module is used to analyze the knowledge points corresponding to the incorrect questions and generate knowledge point analysis results.
[0030] The exercise generation module is used to generate practice questions corresponding to each incorrect question based on the results of the knowledge point analysis.
[0031] Beneficial effects: Based on the knowledge points corresponding to each wrong question, corresponding practice questions are generated, allowing for practice from the root of the knowledge points, which helps to deeply grasp the various question types involved in that knowledge point.
[0032] Furthermore, the knowledge point analysis module is used to analyze the knowledge points corresponding to the incorrect questions based on the pre-stored knowledge network;
[0033] The pre-stored knowledge network includes several knowledge points, the relationships between each knowledge point, and the corresponding video explanations for each knowledge point.
[0034] Beneficial effect: By storing various knowledge points through a pre-stored knowledge network, the knowledge points corresponding to the incorrect questions can be analyzed based on the pre-stored knowledge network.
[0035] Furthermore, the incorrect question practice module also includes a practice result analysis module and a question push module;
[0036] The exercise result analysis module is used to obtain the exercise results of the exercise questions;
[0037] The exercise push module is used to determine whether to start practicing the next wrong question based on the practice results; if so, it pushes the next wrong question's corresponding exercise questions according to the order of wrong question practice; if not, it pushes the knowledge point explanation video.
[0038] Beneficial effects: Based on the practice results, determine whether to start practicing the next wrong question. If the practice results are poor, it means that the knowledge point of the current wrong question has not been fully mastered. Therefore, a video explaining the knowledge point is pushed for further learning. If the practice results are good, it means that the knowledge point corresponding to the current wrong question has been initially mastered. Therefore, you can proceed to practice the next wrong question.
[0039] Furthermore, the modified belt body is also equipped with a control button for controlling the opening and closing of the image acquisition device.
[0040] Beneficial effects: By adding control buttons to the main body of the modified belt, the image acquisition device can be turned on and off through the control buttons. When not in use, the image acquisition device can be turned off to reduce power consumption and protect user privacy.
[0041] Furthermore, the image acquisition device is a miniature camera.
[0042] Beneficial effects: The miniature camera captures images, is small in size and light in weight, and does not affect the normal use of the modification tape.
[0043] Furthermore, the modification operation analysis module includes an operation analysis module and a typo analysis module;
[0044] The operation analysis module is used to modify operations based on the analysis of exercise images;
[0045] The error analysis module is used to analyze the errors in the exercise images and adjust the modification operations accordingly.
[0046] Beneficial effects: By analyzing user errors in the practice exercise videos, corrections caused by user errors can be eliminated, improving the effectiveness and relevance of subsequent analysis. Attached Figure Description
[0047] Figure 1 This is a logic block diagram of the intelligent modification error analysis system according to an embodiment of the present invention.
[0048] Figure 2 This is a cross-sectional view of the modification tape body in the usage state of the intelligent modification tape in an embodiment of the present invention.
[0049] Figure 3 This is a cross-sectional view of the modification tape body in a flat position in an embodiment of the present invention. Detailed Implementation
[0050] The following detailed description illustrates the specific implementation method:
[0051] The markings in the accompanying drawings include: 1. Modification tape body, 2. Spherical hole, 3. Press switch, 4. Gravity ball.
[0052] Example 1:
[0053] The intelligent correction tape includes the correction tape itself (1) and the error analysis system.
[0054] The modified body 1 is equipped with an image acquisition device for capturing images of practice exercises and a control button for controlling the opening and closing of the image acquisition device. In this embodiment, the image acquisition device is a miniature camera. Users can open and close the image acquisition device as needed by controlling the button, thereby reducing the power consumption of the image acquisition device and protecting user privacy.
[0055] like Figure 1 As shown, the error information analysis system includes a modification operation analysis module, a modification volume analysis module, a question analysis module, a weight analysis module, and an error practice module.
[0056] The modification operation analysis module is used to acquire the exercise images captured by the image acquisition device and analyze the modification operations based on the exercise images. The modification operation analysis module includes an operation analysis module and a typographical error analysis module.
[0057] The operation analysis module is used to analyze and modify the operation based on the exercise practice images; specifically, it uses image recognition technology to analyze whether the user has covered the exercise content with the modification tape. If so, it indicates that a modification operation has occurred.
[0058] The error analysis module is used to analyze errors in practice exercise videos and adjust correction operations accordingly. In this embodiment, since students may make careless errors (errors unrelated to knowledge points) during practice exercises, the module first analyzes errors based on frequency values and template technology, and then deletes the corresponding correction operations to improve the effectiveness and relevance of subsequent analysis. Specifically, based on the practice exercise videos, the module performs text recognition on the user's written content and uses the following three models to determine errors:
[0059] I. Word Frequency Norm Model. An electronic document library is established by collecting exercise resources from various subjects. Answers are extracted separately and their word frequencies are analyzed to establish a word frequency norm model C. The word frequency norm model is in dictionary form and can serve as a basic reference for error detection. For any given word w, the word frequency of w is defined as:
[0060]
[0061] That is, the ratio of the number of times word w appears in the dictionary to the total number of times all words are recorded in the dictionary; where f n The number of times the word 'w' appears. This represents the number of times each word in the dictionary is recorded in the word frequency norm model.
[0062] II. Word Frequency User Model. Learners' modified content is collected and transcribed into text data. Word frequency analysis is performed in real time, and the data is incrementally stored in dictionary form as the word frequency user model U. Subsequently, term matching analysis is performed on C and U. For any given word w, the word frequency of w is defined as:
[0063]
[0064] That is, the ratio of the number of times word w appears in the dictionary to the total number of times all words are recorded in the dictionary; where f n The number of times the word 'w' appears. This represents the number of times each word in the dictionary is recorded in the word frequency user model.
[0065] Third, compare and statistically analyze the word frequency distributions of Cw and Uw. Using Cw as the basic template, calculate the word frequency variation degree V of all words in both and sort them in reverse order. For the current target word w, its variation degree Vw is defined as:
[0066]
[0067] Vw represents a user's personalized word usage that deviates from the norm. Different subjects require different subject-specific error models to be built using the same method.
[0068] The word frequency user model and word frequency norm model are sorted by the degree of variation through the third model to form the final typo model. The current correction content is then compared with the pre-stored entries in the model. If they exist, the current writing content is determined to be a typo; if they do not exist, the current writing content is determined not to be a typo.
[0069] After determining that the user's writing content is a practice exercise and contains typos, the corresponding modification operations are first removed. Secondly, the system will periodically (daily / weekly) organize and send the content to the user as a reminder, eliminating the need for repeated learning and practice, and pointing out these non-knowledge-related deficiencies to learners.
[0070] The modification amount analysis module is used to analyze the modification amount of erroneous content based on the exercise practice images and modification operations; in this embodiment, the modification amount includes continuous modification time and continuous modification distance; the modification amount analysis module includes a modification time analysis module and a modification distance analysis module.
[0071] The modification time analysis module is used to analyze the continuous modification time based on the exercise practice images and modification operations, which indirectly reflects the amount of content modified by the user. In this embodiment, the modification time analysis module includes a single modification time analysis module, an interval time analysis module, and a continuous modification time generation module.
[0072] The Single Modification Time Analysis Module is used to analyze the time of a single continuous modification based on the exercise video and modification operation. Specifically, when a user performs a modification operation, the module analyzes the time when the user starts and ends the single modification operation based on the exercise video, and calculates the user's single continuous modification time accordingly.
[0073] The interval time analysis module is used to analyze the interval time between adjacent modification operations and generate a merged modification operation result based on the interval time. Specifically, when the interval time between adjacent modification operations is less than the interval time threshold, a merged modification operation result is generated to merge the two modification operations; otherwise, they are not merged. In this embodiment, the interval time threshold is two seconds.
[0074] The continuous modification time generation module is used to generate continuous modification times based on the single continuous modification time and the result of merging modification operations. It merges the modification operations that need to be merged to generate continuous modification times.
[0075] The distance analysis module is used to analyze the continuous modification distance based on the exercise video and modification operations. In this embodiment, when a user performs a modification operation, the module analyzes the user's single modification distance (i.e., the length of a single modification band) based on the exercise video, and adjusts the single modification distance based on the modification operation merging results from the interval time analysis module to generate the continuous modification distance.
[0076] The question analysis module is used to analyze the wrong questions corresponding to the erroneous content based on the exercise practice images and modification operations. Specifically, based on the exercise practice images, it obtains the question images within a preset range from the modification operation execution area, uses text recognition technology to identify the question images, and determines the question stem closest to the modification operation execution area as the wrong question corresponding to the erroneous content.
[0077] In this embodiment, "error content" refers to the content that has been modified and covered. The error content, the time of a single modification, and the distance between single modifications are interconnected. That is, after merging adjacent single modification times, adjacent single modification distances and error content are also merged. If, during the error content merging process, the distance between error contents exceeds a distance threshold, all merging operations are canceled.
[0078] The weight analysis module is used to analyze the weight of incorrect questions based on the amount of modification; specifically, it analyzes the weight of each incorrect question based on the continuous modification time and continuous modification distance. In this embodiment, incorrect questions at the word level (continuous modification time ≤ 1 second and continuous modification distance ≤ 2 cm) are assigned a weight of 0.2; incorrect questions at the sentence level (1 second < continuous modification time ≤ 3 seconds or 2 sentences < continuous modification distance ≤ 5 cm) are assigned a weight of 0.5; and incorrect questions at the paragraph level (continuous modification time > 3 seconds or continuous modification distance > 5 cm) are assigned a weight of 0.8. When the continuous modification time or continuous modification distance of the same incorrect content meets the higher weight level, the higher weight level is used to assign its weight. For example, if the continuous modification time for an incorrect content is 2 seconds and the continuous modification distance is 6 cm, then a weight of 0.8 is assigned.
[0079] The error correction practice module is used to generate the order of error correction practice and the corresponding practice questions for each error correction based on the weight of each error correction question. The error correction practice module includes an order generation module, a knowledge point analysis module, a question generation module, a practice result analysis module, and a question push module.
[0080] The sequence generation module is used to generate the order of incorrect questions based on the weight of each incorrect question. Specifically, the incorrect questions are sorted from high to low according to their corresponding weights, and incorrect questions with the same weight are randomly sorted within each other.
[0081] The knowledge point analysis module is used to analyze the knowledge points corresponding to the incorrect questions and generate knowledge point analysis results. This module analyzes the knowledge points corresponding to the incorrect questions based on a pre-stored knowledge network. The pre-stored knowledge network includes several knowledge points, the relationships between these knowledge points, explanation videos for each knowledge point, and classic questions corresponding to each knowledge point. Relationships include correlation, subordination, and intersection. Specifically, the module performs language analysis on the identified incorrect question stems and matches the analysis results with the knowledge points in the knowledge network to analyze the knowledge points corresponding to the incorrect questions. If a current incorrect question corresponds to multiple knowledge points, all knowledge points are identified.
[0082] The exercise generation module is used to generate practice questions corresponding to each incorrect question based on the knowledge point analysis results. In this embodiment, classic questions corresponding to the knowledge points of the incorrect questions are extracted from the knowledge network as practice questions.
[0083] The practice result analysis module is used to obtain the practice results of the practice questions.
[0084] The exercise recommendation module determines whether to start practicing the next incorrect question based on the user's practice results. If yes, it recommends the next exercise corresponding to the incorrect question according to the order of the incorrect questions practiced; otherwise, it recommends a video explaining the knowledge point. In this embodiment, the original incorrect question is first provided for testing. If the user's practice result shows a correct answer, two questions are randomly selected from the classic questions corresponding to the knowledge point of the current incorrect question for repeated testing. If both practice questions show a correct answer, it is determined that the user should start practicing the next incorrect question; otherwise, it is determined that the user should not start practicing the next incorrect question.
[0085] In this embodiment, a prompt for correcting a question is sent via a mobile device on the same day the question is submitted. The mastery of the knowledge points corresponding to the incorrect question is then checked at intervals of three days, one week, two weeks, and five weeks, achieving the effect of reviewing and learning new knowledge.
[0086] In summary, this solution enables intelligent collection of incorrect answers, analysis of students' mistakes during practice, and comprehensiveness of the analysis of weak knowledge points. It also reduces the amount of data analysis, improves efficiency, lowers costs, enhances practicality, and helps analyze the mastery of each weak knowledge point. Furthermore, it generates and sorts practice questions for students to practice, thereby improving learning outcomes.
[0087] Example 2:
[0088] The basic principle of Example 2 is the same as that of Example 1, except that in Example 2, the modified tape body 1 has a spherical hole 2, and a push-button switch 3 and a gravity ball 4 are provided inside the spherical hole 2. The gravity ball 4 can roll inside the spherical hole 2, and the push-button switch 3 is used to control the opening and closing of the image acquisition device. Figure 2 As shown, when the user uses the editing tape, the gravity ball 4 rolls to the push switch 3, and the push switch 3 is pressed down by the pressure of the gravity ball 4, thus turning on the image acquisition device. Figure 3 As shown, when the user is not in use and is modifying the tape, the gravity ball 4 rolls to other positions, the push switch 3 is not pressed, and the image acquisition device is turned off.
[0089] The modification tape body 1 is also equipped with a vibration sensor, which is used to detect the vibration data of the modification tape body 1 when the press switch 3 is turned on. If the time without vibration data exceeds the time threshold, the control prompt device will issue a flat placement prompt to remind the user to place the modification tape properly.
[0090] The principle is that when using the correction tape, the user needs to hold the correction tape and tilt it, then press and slide it to make the correction paper inside the correction tape adhere to the paper. Therefore, this solution is designed with a spherical hole 2, and a press switch 3 and a gravity ball 4 are set in the spherical hole 2. When the correction tape is in use, the gravity ball 4 rolls to the press switch 3. When the correction tape is laid flat, the gravity ball 4 rolls to other positions, realizing the intelligent opening and closing of the image acquisition device.
[0091] Example 3:
[0092] The basic principle of Example 3 is the same as that of Example 1. The difference is that in Example 3, the error analysis module is also used to analyze the end time of the user's writing operation and the execution time of the modification operation based on the exercise practice video, and to calculate the interval between the writing operation and the modification operation, and to analyze the error situation based on the interval. Specifically, when the interval between the writing operation and the modification operation is less than a preset time threshold, the writing operation is determined to be a practice error, and the corresponding modification operation is deleted. The principle is that when the interval between the user finishing writing and modifying is short, it indicates that the user has less thinking content and less logical processing during the interval, and the probability of correcting the answer is low. It is more likely that the user made a mistake during the writing process, so the writing operation is determined to be a practice error, and the corresponding modification operation is deleted.
[0093] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.
Claims
1. A smart modification tape, comprising a modification tape body, characterized in that: The modified tape body is equipped with an image acquisition device for capturing images of practice exercises; It also includes a wrong question information analysis system, which includes a modification operation analysis module, a modification amount analysis module, a question analysis module, a weight analysis module, and a wrong question practice module; The modification operation analysis module is used to acquire the exercise images captured by the image acquisition device and analyze the modification operations based on the exercise images. The modification amount analysis module is used to analyze the modification amount of erroneous content based on the exercise practice images and modification operations; The question analysis module is used to analyze the wrong questions corresponding to the incorrect content based on the exercise practice images and modification operations. The weight analysis module is used to analyze the weight of incorrect questions based on the amount of modification. The error correction practice module is used to generate the error correction practice order and the corresponding practice questions for each error correction based on the weight of each error correction. The modification operation analysis module includes an operation analysis module and a typo analysis module; The operation analysis module is used to modify operations based on the analysis of exercise images; The error analysis module is used to analyze the errors in the exercise practice images and adjust the modification operations accordingly. The correction tape body has a spherical hole, inside which is a push-button switch and a gravity ball. The gravity ball can roll within the spherical hole. The push-button switch is used to control the opening and closing of the image acquisition device. When the user uses the correction tape, the gravity ball rolls to the push-button switch, and the push-button switch is pressed by the pressure of the gravity ball, thus turning on the image acquisition device. When the user is not using the correction tape, the gravity ball rolls to other positions, the push-button switch is not pressed, and the image acquisition device is turned off. The modification tape body is also equipped with a vibration sensor, which is used to detect the vibration data of the modification tape body when the switch is pressed to turn it on. If the time without vibration data exceeds the time threshold, the control prompt device will issue a flat placement prompt to remind the user to place the modification tape properly.
2. The intelligent modification tape according to claim 1, characterized in that: The error analysis module is also used to analyze the end time of the user's writing operation and the execution time of the modification operation based on the exercise practice image, and to calculate the interval between the writing operation and the modification operation, and to analyze the error situation based on the interval time; The amount of modification includes continuous modification time and continuous modification distance; The modification amount analysis module includes a modification time analysis module and a modification distance analysis module; The modification time analysis module is used to analyze the continuous modification time based on the exercise practice images and modification operations; The modified distance analysis module is used to analyze the continuous modification distance based on the exercise practice image and modification operation.
3. The intelligent modification tape according to claim 2, characterized in that: The modification time analysis module includes a single modification time analysis module, an interval time analysis module, and a continuous modification time generation module. The single modification time analysis module is used to analyze the time of a single continuous modification based on the exercise practice image and modification operation; The interval time analysis module is used to analyze the interval time between adjacent modification operations and generate a merged result of modification operations based on the interval time. The continuous modification time generation module is used to generate continuous modification time based on the single continuous modification time and the combined result of modification operations.
4. The intelligent modification tape according to claim 3, characterized in that: The incorrect question practice module includes a sequence generation module, a knowledge point analysis module, and a question generation module; The sequence generation module is used to generate the order of incorrect questions practice based on the weight of each incorrect question; The knowledge point analysis module is used to analyze the knowledge points corresponding to the incorrect questions and generate knowledge point analysis results. The exercise generation module is used to generate practice questions corresponding to each incorrect question based on the results of the knowledge point analysis.
5. The intelligent modification tape according to claim 4, characterized in that: The knowledge point analysis module is used to analyze the knowledge points corresponding to the wrong questions based on the pre-stored knowledge network. The pre-stored knowledge network includes several knowledge points, the relationships between each knowledge point, and the corresponding video explanations for each knowledge point.
6. The intelligent modification tape according to claim 5, characterized in that: The incorrect question practice module also includes a practice result analysis module and a question push module; The exercise result analysis module is used to obtain the exercise results of the exercise questions; The exercise push module is used to determine whether to start practicing the next wrong question based on the exercise results; If yes, then push the next practice question corresponding to the wrong question according to the order of the wrong questions practiced; otherwise, push the video explaining the knowledge point.
7. The intelligent modification tape according to claim 1, characterized in that: The modified tape body is also equipped with a control button for controlling the opening and closing of the image acquisition device.
8. The intelligent modification tape according to claim 1, characterized in that: The image acquisition device is a miniature camera.