Big data accurate teaching read-over error question arrangement data management system

Abstract

The invention provides a big data accurate teaching reading error arrangement data management system, belongs to the field of teaching reading, and solves the problems that an existing teaching reading system is simple in function and cannot realize multi-dimensional error arrangement and auxiliary teaching of students, teachers and schools. Comprising a mobile component, a student recording layer subsystem, a teacher auxiliary layer subsystem, a grade management layer subsystem, a campus management layer subsystem, a system management subsystem, a data acquisition subsystem, a data analysis subsystem, a visual display subsystem, a data transmission subsystem, a system cloud platform and a marking software subsystem arranged on terminal equipment, and the data acquisition subsystem and the visual display subsystem are arranged on the mobile assembly. According to the invention, the data acquisition subsystem and the visual display subsystem can be driven to move, a user can conveniently carry out real-time teaching batch, data acquisition, reading and visual observation can be carried out, and intelligent wrong question arrangement and intelligent auxiliary teaching can be carried out from multiple dimensions of students, teachers, grades, schools and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of teaching and marking, and relates to a wrong question sorting data management system, in particular to a big data precise teaching and marking wrong question sorting data management system. Background Art

[0002] In order to review and effectively master knowledge and improve academic performance in study or actual work, students usually use error books. Teachers in schools also often recommend students to use error books, with the purpose of copying the questions that they encounter and are prone to make mistakes in the error books, and focusing on reviewing the wrong questions and related knowledge points in the future to reduce the occurrence of mistakes in the future.

[0003] As for different wrong questions, for students, analyzing the causes of wrong questions according to their different difficulty levels can solve the problems more efficiently. For teachers, for questions that most students get wrong, they also need to judge the difficulty of the questions themselves to decide how to explain them. Therefore, the difficulty analysis of wrong questions is particularly important both in student learning and teacher teaching.

[0004] There is a lack of personalized teaching review and organization suitable for students, teachers, grades, and campuses, and it is impossible to conduct intelligent review and organization of wrong questions and intelligent assisted teaching from multiple dimensions such as students, teachers, grades, and campuses.

[0005] Therefore, we propose a data management system for accurate teaching and reviewing wrong questions, which can achieve stable mobility and facilitate users to conduct real-time teaching review, data collection, review, and visual observation, and conduct intelligent review and collation of wrong questions and intelligent assisted teaching from multiple dimensions such as students, teachers, grades, and campuses. Summary of the invention

[0006] The purpose of the present invention is to address the above-mentioned problems in the existing technology and propose a big data precise teaching review and wrong question sorting data management system. The technical problem to be solved by the invention is: how to achieve stable movement, facilitate users to conduct real-time teaching review, data collection, review, and visual observation, and conduct intelligent wrong question review and intelligent assisted teaching from multiple dimensions such as students, teachers, grades, and campuses.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A big data precision teaching review and wrong question sorting data management system comprises a mobile component, a student record layer subsystem, a teacher auxiliary layer subsystem, a grade management layer subsystem, a campus management layer subsystem, a system management subsystem, a data acquisition subsystem, a data analysis subsystem, a visual display subsystem, a data transmission subsystem and a system cloud platform and a review software subsystem arranged on a terminal device, wherein the data transmission subsystem is electrically connected to the student record layer subsystem, the teacher auxiliary layer subsystem, the grade management layer subsystem, the campus management layer subsystem, the system management subsystem, the data acquisition subsystem, the data analysis subsystem, the visual display subsystem, the system cloud platform and the review software subsystem respectively, and the data acquisition subsystem and the visual display subsystem are arranged on the mobile component.

[0009] The working principle of the present invention is as follows: the mobile component drives the data acquisition subsystem and the visual display subsystem to move, and users such as teachers or school administrators use them to correct homework and test papers. The data acquisition subsystem collects various data, including test papers, homework and answer sheets. The marking software subsystem can perform software operations and can also collect information such as test papers and homework, and perform marking and sorting of wrong questions. The student record layer subsystem is used to count and process students' personal wrong question data, the teacher auxiliary layer subsystem is used to count and process students' and class wrong question data, the grade management layer subsystem is used to count and process grade wrong question data, the campus management layer subsystem is used to count and process campus data, the system management subsystem is used for system management and system settings, the data analysis subsystem is used for data analysis and processing, the visual display subsystem is used to observe various chart data, the data transmission subsystem is used to transmit data stably and efficiently, the system cloud platform is the back-end cloud platform, and various types of data are gathered on the cloud platform to form a comprehensive student database and store it.

[0010] The mobile assembly includes a mobile frame, and universal wheels are arranged at the four corners of the lower end of the mobile frame. A storage box, an operation panel and a plurality of placement tubes are arranged on the mobile frame. The storage box is located at the lower side of the operation panel, and the plurality of placement tubes are located at the side of the operation panel. A mouse and keyboard tray assembly is arranged at the lower end of the operation panel, a mounting seat is fixed at the middle part of the lower end of the operation panel, and a scale is arranged at the upper end of the operation panel.

[0011] With the above structure, the mobile frame is pulled, and the universal wheel facilitates the movement of the device. The placement tube is used to place pens and other supplies. The storage box is used to place paper, which is placed inside the storage box when the folding scanner is not in use. The user places the homework or test paper on the operation panel, the ruler is used to limit the position where the homework or test paper is placed, and the mouse and keyboard tray assembly is used to place the mouse and keyboard to control the visualization display subsystem and the data acquisition subsystem.

[0012] The mouse and keyboard tray assembly comprises a pull-out frame, which is fixed at the lower end of the operation panel. A mouse and keyboard tray is slidably arranged on the inner side of the pull-out frame, and a mouse and a keyboard are placed on the mouse and keyboard tray.

[0013] With the above structure, when using the mouse and keyboard, the mouse and keyboard tray is pulled out from the drawer frame and located at the side of the operation panel, making it easy to use the mouse and keyboard.

[0014] The data acquisition subsystem includes a printer, a paper grading machine and a folding scanner. The printer and the paper grading machine are arranged inside the mobile frame. The printer and the paper grading machine are located between the storage box and the mouse and keyboard tray assembly. The printer is located on the lower side of the paper grading machine. The folding scanner is placed inside the storage box when not in use. The folding scanner includes a flip frame. When the flip frame is in use, it is snap-connected to the upper end of the mounting seat. A rotating seat is rotatably provided at the end of the flip frame. Two fill light lamps and a scanning camera are fixed on the rotating seat. When the folding scanner is in use, the fill light lamp and the two scanning cameras are located directly above the operation panel.

[0015] With the above structure, when in use, the folding scanner is removed from the inside of the storage box, the flip frame is snap-connected to the upper end of the mounting seat, and then the flip frame of the folding scanner is opened, and the rotating seat is rotated so that the fill light and the two scanning cameras are located directly above the operating panel. The user places the homework and test paper on the operating panel, and the two scanning cameras scan the homework and test paper to collect homework information in real time. The fill light is used for fill light, the marking machine is used to scan and read the answer sheet, and mark the answer sheet. The printer is used to print data, and print new homework, test paper and various learning charts generated based on the wrong question data.

[0016] The visualization display subsystem is a software tablet computer, which is hinged on the upper front side of the operation panel through a damping shaft, and the mouse, keyboard, printer, paper marking machine and folding scanner are all electrically connected to the software tablet computer.

[0017] With the above structure, when in use, the software tablet is unfolded through the damping hinge, and the software tablet is convenient for the upper front side of the operation panel. The software tablet displays in real time various data information such as homework and test papers scanned by the scanning camera, wrong question display, learning charts, etc., and the printer, paper marking machine, folding scanner and software tablet are controlled through the cooperation of mouse and keyboard.

[0018] The system management subsystem includes a user management module, a data security management module and a system setting module. The user management module creates accounts, allocates permissions and manages information for users with different roles, including but not limited to teachers, students, grade administrators and campus administrators; the data security management module uses encryption technology to store and transmit wrong question data, and backs up data regularly to prevent data loss or leakage; the system setting module configures and manages but not limited to the basic parameters of the system, data acquisition sources and analysis algorithms to ensure the normal operation and performance optimization of the system.

[0019] The marking software subsystem is a marking computer software, a marking APP or a marking applet, and the marking computer software, the marking APP or the marking applet is provided with a homework correction module, a camera shooting and collection module, a search and answering module, a wrong question book module, a subject selection module, a grade selection module, a textbook version selection module and a connection and printing module.

[0020] With the above structure, the homework correction module can perform intelligent correction on different homework contents, the camera shooting and acquisition module is used to take photos and scan, shoot and upload the homework or test paper contents, the search and answering module can input contents, and perform intelligent search for questions and books, etc. The wrong question book module can display various wrong questions, the subject selection module is used to select subjects, the grade selection module is used to select subject grades, the textbook version selection module is used to select textbooks, and the connection and printing module is wirelessly connected to the printer for printing processing.

[0021] The student record layer subsystem includes a student individual wrong question sorting module, a student personalized auxiliary learning module and a communication and interaction module.

[0022] Using the above structure, the student individual wrong question sorting module classifies and organizes the wrong questions based on the students' wrong question data according to multiple dimensions such as subject, grade, knowledge point, etc., to form a structured wrong question database; automatically removes duplicate wrong question records to ensure the accuracy and uniqueness of the data; and marks each wrong question in detail to facilitate subsequent query and analysis; the student personalized auxiliary learning module intelligently recommends personalized learning paths, including the order of reviewing knowledge points, and recommends related learning materials and exercises; students mark key wrong questions in the system, collect wrong questions and record them in notes, so as to focus on them during review; the communication and interaction module sets up a wrong question discussion area, where students can communicate and interact with classmates or teachers on wrong questions, and share problem-solving ideas and learning experiences.

[0023] The teacher assistance layer subsystem includes a learning situation analysis and insight module, a teaching strategy customization module, a resource integration and recommendation module, a teaching effectiveness evaluation module and a communication, collaboration and management module. The learning situation analysis and insight module includes a class overall learning situation report unit and a student individual learning situation tracking unit; the teaching strategy customization module includes a teaching focus determination unit and a personalized teaching plan generation unit; the resource integration and recommendation module includes a teaching material matching unit and an expanded learning resource push unit; the teaching effectiveness evaluation module includes a wrong question correction effect tracking unit and a teaching strategy effectiveness analysis unit; the communication, collaboration and management module includes a home-school communication bridge building unit and a teacher team collaboration support unit.

[0024] Using the above structure, the class overall learning situation report unit automatically generates a comprehensive analysis report on the wrong questions of the class students in various subjects and knowledge points, providing data basis for the adjustment of the overall teaching plan of the class; the student individual learning situation tracking unit deeply explores the learning trajectory of each student's wrong questions; teachers can easily view the details of the wrong questions of specific students, providing accurate entry points for personalized tutoring; the teaching focus determination unit intelligently recommends teaching key points and difficult content, and formulates corresponding teaching strategy suggestion libraries for different error types; the personalized teaching plan generation unit tailors personalized teaching plans for each student; teachers can flexibly edit and improve the plans according to actual teaching situations; the teaching material matching unit automatically selects and matches relevant teaching materials in the massive teaching resource library; supports teachers to preview, evaluate and collect recommended teaching materials, and teachers upload their own high-quality teaching resources to Resource library, to realize the co-construction and sharing of resources; the extended learning resource push unit recommends extended learning resources for students who have spare time or need to further consolidate their knowledge. Teachers can flexibly set the push scope and timing of extended learning resources according to students' different learning levels and needs; the wrong question correction effect tracking unit monitors students' performance in the wrong question review and subsequent learning process in real time, and automatically generates an evaluation report on the effect of wrong question correction; the teaching strategy effectiveness analysis unit conducts data-driven effectiveness analysis on various teaching strategies adopted by teachers, and generates a teaching suggestion report; the home-school communication bridge building unit generates concise and clear learning feedback reports, which are regularly pushed to parents, and provides an online communication platform where teachers and parents can communicate and interact on students' learning problems at any time; the teacher team collaboration support unit supports the sharing of teaching resources and collaborative lesson preparation among teachers of the same subject to improve teaching quality.

[0025] The grade management layer subsystem includes a data aggregation and integration module, a learning situation monitoring and early warning module, a teaching resource allocation module, a teaching quality assessment module and an examination and competition management module. The data aggregation and integration module includes a multi-class data collection unit and a data standardization processing unit; the situation monitoring and early warning module includes a grade-wide learning situation analysis unit and a learning anomaly early warning unit; the teaching resource allocation module includes a resource demand analysis unit and a resource allocation and optimization unit; the teaching quality assessment module includes a teacher teaching effectiveness evaluation unit and a teaching strategy effectiveness evaluation unit; the examination and competition management module includes an examination arrangement and organization unit and a competition activity planning and implementation unit.

[0026] With the above structure, the multi-class data collection unit is seamlessly connected with the student record layer subsystem of each class, automatically collecting the wrong question data, homework completion status, test scores and other information of students in each class of the whole grade; the data standardization processing unit formulates unified data standards and specifications, and establishes data mapping relationships; the grade overall learning situation analysis unit conducts a comprehensive analysis of the learning situation of students in all subjects and knowledge points of the whole grade; the learning abnormality warning unit sets the warning threshold through real-time monitoring and analysis of students' wrong question data; the resource demand analysis unit accurately identifies the learning needs and resource gaps of students in the whole grade; the resource allocation and optimization unit uniformly manages and allocates teaching resources within the grade; the teacher teaching effectiveness evaluation unit conducts an objective and comprehensive evaluation of the teaching effectiveness of teachers in the whole grade; the teaching strategy effectiveness evaluation unit evaluates the effectiveness of various teaching strategies implemented within the whole grade; the examination arrangement and organization unit assists the grade management team in arranging and organizing various examinations; the competition activity planning and implementation unit plans and organizes students of the whole grade to participate in various subject competitions.

[0027] The campus management subsystem includes a school data overview and analysis module, a faculty management and optimization module, a teaching resource coordination and allocation module, a teaching strategy and curriculum system optimization module, and a home-school cooperation and education ecology construction module; the school data overview and analysis module includes a teaching big data dashboard unit and a teaching quality comprehensive evaluation unit; the faculty management and optimization module includes a teacher teaching behavior analysis unit and a teacher professional development planning and support unit; the teaching resource coordination and allocation module includes a teaching resource inventory management unit and a teaching resource demand analysis and allocation unit; the teaching strategy and curriculum system optimization module includes a teaching strategy effectiveness evaluation and promotion unit and a curriculum system rationality analysis and adjustment unit; the home-school cooperation and education ecology construction module includes a home-school communication data analysis and guidance unit and an education ecology partner management unit.

[0028] Using the above structure, the teaching big data dashboard unit builds a visual school-wide teaching big data dashboard to centrally display the school's key data indicators in teaching; the teaching quality comprehensive evaluation unit conducts a comprehensive and objective comprehensive evaluation of the school's overall teaching quality; the teacher teaching behavior analysis unit deeply explores the behavioral data of teachers in the daily teaching process to help teachers continuously optimize their teaching behaviors and improve their teaching level; the teacher professional development planning and support unit formulates personalized professional development plans for teachers based on the evaluation results of teachers' teaching ability, personal career development intentions and the overall teaching needs of the school; the teaching resource inventory management unit comprehensively sorts out and informationizes all kinds of teaching resources in the school; the teaching resource demand analysis and allocation unit accurately analyzes and predicts the demand for teaching resources; the teaching strategy effect evaluation and promotion unit conducts a comprehensive effect evaluation of various teaching strategies implemented within the school; the curriculum system rationality analysis and adjustment unit conducts an in-depth analysis of the school's existing curriculum system; the home-school communication data analysis and guidance unit integrates the communication data between the school and parents, and formulates targeted home-school communication strategies and plans based on the data analysis results; the education ecological partner management unit manages and maintains the cooperation relationship between the school and education ecological partners such as off-campus education institutions, enterprises, and communities, and enhances the school's social influence and educational competitiveness.

[0029] The data analysis subsystem includes a data preprocessing module, a learning behavior analysis module, a knowledge mastery analysis module, a teaching effect analysis module and a prediction analysis module; the data preprocessing module includes a data cleaning unit, a data conversion unit and a data integration unit; the learning behavior analysis module includes a learning trajectory tracking unit and a learning habit insight unit; the knowledge mastery analysis module includes a knowledge point mastery degree assessment unit and a knowledge weakness location and attribution unit; the teaching effect analysis module includes a teacher teaching effectiveness assessment unit and a teaching strategy effect analysis unit; the prediction analysis module includes a learning performance prediction unit and a learning risk warning unit.

[0030] Using the above structure, the data cleaning unit removes noise data from the original data; the data conversion unit uniformly converts data of different formats and types into a format suitable for analysis; the data integration unit integrates data from multiple data sources and establishes correlations between data; the learning trajectory tracking unit records students' detailed behavioral data throughout the learning process; the learning habit insight unit analyzes students' learning habit characteristics and provides personalized learning habit improvement suggestions and learning plan adjustment plans; the knowledge point mastery assessment unit accurately assesses students' mastery of each knowledge point; the knowledge weakness location and attribution unit accurately locates students' knowledge weaknesses; the teacher teaching effectiveness assessment unit evaluates teachers' teaching effectiveness from multiple dimensions; the teaching strategy effectiveness analysis unit analyzes the impact of different teaching strategies adopted by schools or teachers on students' learning effectiveness; the learning performance prediction unit predicts students' performance in future exams or learning tasks; and the learning risk warning unit warns students of other risks they may face in the learning process.

[0031] The data transmission subsystem transmits the collected data to the data processing center or server in real time through a wired or wireless network to ensure timely and accurate transmission of the data. Wireless network communication technology includes but is not limited to Wi-Fi and Bluetooth, and wired network communication technology includes but is not limited to Ethernet. The data is sent to the system cloud platform for storage and analysis.

[0032] With the above structure, the wired network has the advantages of fast transmission speed and high stability; the wireless network also makes it convenient for customers to use the review APP or review applet; the two methods work stably to ensure timely and accurate transmission of data.

[0033] Compared with the existing technology, this big data precision teaching marking wrong question sorting data management system has the following advantages: the mobile component drives the data collection subsystem and the visual display subsystem to move, and users such as teachers or school administrators use it for real-time marking of homework and test papers. The data collection subsystem collects various data, including test papers, homework and answer sheets, etc. The marking software subsystem can perform software operations, and can also collect information such as test papers and homework, and perform marking wrong questions and sorting. The student record layer subsystem is used to count and process students' personal wrong question data, the teacher auxiliary layer subsystem is used to count and process students' and class wrong question data, the grade management layer subsystem is used to count and process grade wrong question data, the campus management layer subsystem is used to count and process campus data, the system management subsystem is used for system management and system settings, the data analysis subsystem is used for data analysis and processing, the visual display subsystem is used to observe various chart data, the data transmission subsystem is used to transmit data stably and efficiently, the system cloud platform is the back-end cloud platform, and various types of data are gathered on the cloud platform to form a comprehensive student database and store it. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is the overall system block diagram of the present invention.

[0035] Figure 2 It is a schematic diagram of the upper three-dimensional structure of the mobile component of the present invention when it is not working.

[0036] Figure 3 It is a schematic diagram of the bottom three-dimensional structure of the mobile component of the present invention when it is not working.

[0037] Figure 4 It is a schematic diagram of the bottom three-dimensional structure of the mobile component of the present invention when it is working.

[0038] Figure 5 It is a system block diagram of the system management subsystem in the present invention.

[0039] Figure 6 It is a system block diagram of the teaching software subsystem in the present invention.

[0040] Figure 7 It is a system block diagram of the student recording layer subsystem of the present invention.

[0041] Figure 8 It is a system block diagram of the teacher-assisting layer subsystem in the present invention.

[0042] Fig. 9 It is a system block diagram of the grade management layer subsystem in the present invention.

[0043] Fig.10 It is a system block diagram of the campus management subsystem in the present invention.

[0044] Fig.11 It is a system block diagram of the data analysis subsystem in the present invention.

[0045] In the figure, 1. mobile frame; 2. universal wheel; 3. storage box; 4. printer; 5. marking machine; 6. mouse and keyboard tray assembly; 7. operation panel; 8. software tablet; 9. folding scanner; 10. ruler; 11. placement tube; 12. mouse and keyboard tray; 13. pull-out frame; 14. mounting seat; 15. fill light; 16. scanning camera; 17. rotating seat; 18. flip frame. DETAILED DESCRIPTION

[0046] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solution of the present invention, but the present invention is not limited to these embodiments.

[0047] like Figure 1-Figure 11As shown, this big data precision teaching marking and wrong question sorting data management system includes a mobile component, a student record layer subsystem, a teacher auxiliary layer subsystem, a grade management layer subsystem, a campus management layer subsystem, a system management subsystem, a data acquisition subsystem, a data analysis subsystem, a visual display subsystem, a data transmission subsystem and a system cloud platform and a marking software subsystem arranged on a terminal device. The data transmission subsystem is electrically connected to the student record layer subsystem, the teacher auxiliary layer subsystem, the grade management layer subsystem, the campus management layer subsystem, the system management subsystem, the data acquisition subsystem, the data analysis subsystem, the visual display subsystem, the system cloud platform and the marking software subsystem respectively, and the data acquisition subsystem and the visual display subsystem are arranged on the mobile component.

[0048] The mobile component drives the movement of the data collection subsystem and the visualization subsystem. Users such as teachers or school administrators use it to mark homework and test papers. The data collection subsystem collects various data, including test papers, homework and answer sheets. The marking software subsystem can perform software operations and can also collect information such as test papers and homework, mark wrong questions and organize them; the student record layer subsystem is used to count and process students' personal wrong question data, the teacher auxiliary layer subsystem is used to count and process students' and class wrong question data, the grade management layer subsystem is used to count and process grade wrong question data, the campus management layer subsystem is used to count and process campus data, the system management subsystem is used for system management and system settings, the data analysis subsystem is used for data analysis and processing, the visualization subsystem is used to observe various chart data, the data transmission subsystem is used to transmit data stably and efficiently, the system cloud platform is the back-end cloud platform, and various types of data are gathered on the cloud platform to form a comprehensive student database and store it.

[0049] The mobile assembly includes a mobile frame 1, and universal wheels 2 are provided at the four corners of the lower end of the mobile frame 1. A storage box 3, an operation panel 7, and a plurality of placement tubes 11 are provided on the mobile frame 1. The storage box 3 is located at the lower side of the operation panel 7, and the plurality of placement tubes 11 are located at the side of the operation panel 7. A mouse and keyboard tray assembly 6 is provided at the lower end of the operation panel 7, and a mounting seat 14 is fixed at the middle of the lower end of the operation panel 7. A scale 10 is provided at the upper end of the operation panel 7. When the mobile frame 1 is pulled, the universal wheels 2 facilitate the movement of the device. The placement tube 11 is used to place supplies such as pens. The storage box 3 is used to place paper, which is placed inside the storage box 3 when the folding scanner 9 is not in use. The user places the homework or test paper on the operation panel 7. The scale 10 is used to limit the position where the homework or test paper is placed. The mouse and keyboard tray assembly 6 is used to place the mouse and keyboard, and is used to control the visualization display subsystem and the data acquisition subsystem.

[0050] The mouse and keyboard tray assembly 6 includes a pull-out frame 13, which is fixed to the lower end of the operation panel 7. A mouse and keyboard tray 12 is slidably provided inside the pull-out frame 13, and a mouse and a keyboard are placed on the mouse and keyboard tray 12. When using the mouse and the keyboard, the mouse and the keyboard tray 12 is pulled out from the pull-out frame 13 and is located at the side of the operation panel 7, so that the mouse and the keyboard can be used conveniently.

[0051] The data acquisition subsystem includes a printer 4, a paper grading machine 5 and a folding scanner 9. The printer 4 and the paper grading machine 5 are arranged inside the mobile frame 1. The printer 4 and the paper grading machine 5 are located between the storage box 3 and the mouse and keyboard tray assembly 6. The printer 4 is located on the lower side of the paper grading machine 5. The folding scanner 9 is placed inside the storage box 3 when not in use. The folding scanner 9 includes a flip frame 18. When in use, the flip frame 18 is snap-connected to the upper end of the mounting seat 14. The end of the flip frame 18 is rotatably provided with a rotating seat 17. Two fill light lamps 15 and a scanning camera 16 are fixed on the rotating seat 17. When the folding scanner 9 is in use, the fill light 15 and the two scanning cameras 16 are located directly above the operating panel 7. When in use, the folding scanner 9 is removed from the inside of the storage box 3, the flip frame 18 is snap-connected to the upper end of the mounting seat 14, and then the flip frame 18 of the folding scanner 9 is opened, and the rotating seat 17 is rotated to make the fill light 15 and the two scanning cameras 16 located directly above the operating panel 7. The user places the homework and test paper on the operating panel 7, and the two scanning cameras 16 scan the homework and test paper to collect the homework information in real time. The fill light 15 is used for fill light, and the marking machine 5 is used to scan and read the answer sheet and mark the answer sheet. The printer 4 is used for data printing, and prints new homework, test paper and various learning charts generated based on the wrong question data.

[0052] The visualization display subsystem is a software tablet computer 8, which is hinged to the upper front side of the operation panel 7 through a damping shaft. The mouse, keyboard, printer 4, paper grading machine 5 and folding scanner 9 are all electrically connected to the software tablet computer 8, and the marking software subsystem is set on the software tablet computer 8. When in use, the software tablet computer 8 is unfolded through the damping shaft hinge, and the software tablet computer 8 is convenient for the upper front side of the operation panel 7. The software tablet computer 8 displays various data information such as homework and test papers scanned by the scanning camera 16 in real time, wrong question display, learning charts, etc., and controls the printer 4, paper grading machine 5, folding scanner 9, and software tablet computer 8 to work through the cooperation of the mouse and keyboard.

[0053] The system management subsystem includes a user management module, a data security management module and a system settings module. The user management module creates accounts, assigns permissions and manages information for users with different roles, including but not limited to teachers, students, grade administrators and campus administrators. The data security management module uses encryption technology to store and transmit wrong question data and backs up data regularly to prevent data loss or leakage. The system settings module configures and manages but not limited to the system's basic parameters, data acquisition sources and analysis algorithms to ensure the normal operation of the system and performance optimization.

[0054] The marking software subsystem is a marking computer software, a marking APP or a marking applet. The marking computer software, the marking APP or the marking applet is provided with a homework marking module, a camera shooting and acquisition module, a search and question answering module, a wrong question book module, a subject selection module, a grade selection module and a textbook version selection module as well as a connection and printing module. The homework marking module performs intelligent marking on different homework contents. The camera shooting and acquisition module is used to take photos and scan, take photos and upload homework or test paper contents. The search and question answering module can input contents and perform intelligent search for questions and books. The wrong question book module can display various wrong questions. The subject selection module is used to select subjects. The grade selection module is used to select subject grades. The textbook version selection module is used to select textbooks. The connection and printing module wirelessly connects to the printer for printing.

[0055] The student record layer subsystem includes the student individual wrong question sorting module, the student personalized auxiliary learning module and the communication and interaction module. The student individual wrong question sorting module classifies and sorts the wrong questions based on the student's wrong question data according to multiple dimensions such as subject, grade, knowledge point, etc., to form a structured wrong question database; automatically removes duplicate wrong question records to ensure the accuracy and uniqueness of the data; and annotates each wrong question in detail, such as the chapter to which it belongs, the test point, and the type of error, to facilitate subsequent query and analysis; the student personalized auxiliary learning module intelligently recommends personalized learning paths, including the order of reviewing knowledge points, and recommends related learning materials and exercises; students mark key wrong questions in the system, collect wrong questions and record notes, so as to facilitate focus during review; the communication and interaction module sets up a wrong question discussion area, where students can interact with classmates or teachers on wrong questions and share problem-solving ideas and learning experiences.

[0056] The teacher support layer subsystem includes the learning situation analysis and insight module, the teaching strategy customization module, the resource integration and recommendation module, the teaching effect evaluation module and the communication, collaboration and management module. The learning situation analysis and insight module includes the class overall learning situation report unit and the student individual learning situation tracking unit; the teaching strategy customization module includes the teaching focus determination unit and the personalized teaching plan generation unit; the resource integration and recommendation module includes the teaching material matching unit and the extended learning resource push unit; the teaching effect evaluation module includes the wrong question correction effect tracking unit and the teaching strategy effectiveness analysis unit; the communication, collaboration and management module includes the home-school communication bridge building unit and the teacher team collaboration support unit. The class overall learning situation report unit automatically generates a comprehensive analysis report on the wrong questions of the class students in various subjects and knowledge points. The report covers multi-dimensional data visualization such as the distribution of wrong questions, the proportion of error types, and the proportion of wrong questions of different difficulty levels. For example, intuitive bar charts and line charts are used to present the fluctuations in the overall mastery of the class in different knowledge sections, and provide horizontal comparison data with other classes in the same grade, so that teachers can evaluate the position of the class's teaching effectiveness in the grade, discover potential teaching gaps and improvement directions, and provide data basis for the adjustment of the class's overall teaching plan;

[0057] The student individual learning situation tracking unit deeply explores the learning trajectory of each student's wrong questions, records in detail the historical accumulation of students' wrong questions, recent wrong question change trends, and repeated mistakes on different knowledge points, and clearly presents the individual student's learning growth trend or learning difficulties through visual methods such as learning curves; teachers can easily view the details of specific students' wrong questions, including the answer status of each wrong question, wrong answers, corresponding knowledge point explanation links, and the system's intelligent analysis of the root cause of the error speculation, to help teachers deeply understand students' thinking errors and knowledge gaps, and provide accurate entry points for personalized tutoring;

[0058] The teaching focus determination unit intelligently recommends teaching focus and difficult content based on the analysis results of the class as a whole and individual students' wrong questions. For example, the knowledge points corresponding to the frequently wrong questions and the concepts that students generally have difficulty understanding are automatically marked as key teaching objects. Teachers can review and adjust these recommended focus points based on their teaching experience to ensure that the teaching plan is closely centered on the actual needs of students. A corresponding teaching strategy suggestion library is formulated for different types of errors, from which teachers can choose appropriate methods for teaching intervention, such as designing special exercises, conducting group discussions, and conducting case demonstrations, so as to effectively solve various types of student errors.

[0059] The personalized teaching plan generation unit customizes personalized teaching plans for each student based on the learning situation analysis data. The plan includes review plans for students' weak knowledge points, recommended learning resources, and expected learning goal setting. Teachers can flexibly edit and improve the plan according to the actual teaching situation to ensure the feasibility and effectiveness of the plan. It provides a teaching progress scheduling function. Teachers can integrate personalized teaching plans into daily teaching plans according to the curriculum standards and the actual learning progress of the class, reasonably allocate teaching time, and promote teaching activities in an orderly manner to achieve the implementation of precise teaching.

[0060] The teaching material matching unit automatically selects and matches relevant teaching materials from the massive teaching resource library according to the teaching focus and students' wrong questions. The materials are accurately recommended by the system, which can directly assist teachers in conducting targeted classroom teaching for students' wrong questions and improve the utilization efficiency of teaching resources. It supports teachers to preview, evaluate and collect recommended teaching materials. Teachers upload their own high-quality teaching resources to the resource library to realize the co-construction and sharing of resources, enrich the content diversity of the resource library, and provide more diverse material choices for subsequent teaching activities.

[0061] The extended learning resource push unit recommends extended learning resources to students who have spare time or need to further consolidate their knowledge. Learning resources help stimulate students' interest in learning and spirit of exploration, broaden their knowledge and horizons, and cultivate their comprehensive quality and innovation ability. Teachers can flexibly set the push scope and timing of extended learning resources according to students' different learning levels and needs. For example, relevant resources can be pushed to specific student groups after completing the teaching of a certain knowledge point or during the review stage at the end of the semester, so as to achieve stratified teaching and personalized learning support.

[0062] The error correction effect tracking unit monitors students' performance in the review of wrong questions and subsequent learning in real time, records students' re-answers to wrong questions and changes in scores in related knowledge point tests; by comparing and analyzing the data before and after students' wrong questions are corrected, the system automatically generates an error correction effect evaluation report, which intuitively shows the students' progress or remaining problems; teachers can adjust teaching strategies and tutoring methods in a timely manner based on the effect evaluation report, and provide secondary tutoring or intensive training for students who have not yet mastered the knowledge, to ensure that students truly understand and master the knowledge content involved in the wrong questions and improve the effectiveness of teaching;

[0063] The teaching strategy effectiveness analysis unit conducts data-driven effectiveness analysis on various teaching strategies adopted by teachers. It evaluates the pros and cons of teaching strategies by comparing the changes in students' wrong question data, the extent of improvement in grades, and changes in learning attitudes before and after the implementation of different teaching strategies. For example, it analyzes the difference in the effectiveness of group cooperative learning strategies and traditional lecture-based teaching strategies in solving certain types of wrong questions, providing empirical evidence for teachers to optimize their teaching methods. Based on the results of the teaching strategy effectiveness analysis, the system automatically generates a teaching suggestion report, recommending to teachers a combination of teaching strategies that is more suitable for specific teaching scenarios and student groups, helping teachers to continuously improve their teaching level and quality, and achieve continuous improvement and innovation in teaching methods.

[0064] The home-school communication bridge building unit integrates students' learning data on wrong questions, generates concise and clear learning feedback reports, and regularly sends them to parents. The report includes students' recent learning performance, analysis of wrong questions, teachers' teaching suggestions, and matters that parents need to cooperate with, so that parents can understand their children's learning situation in a timely manner and promote home-school co-education. It provides an online communication platform where teachers and parents can communicate and interact with students on their learning problems at any time. Teachers can provide parents with personalized family education guidance based on students' wrong questions, and parents can also provide teachers with feedback on students' learning situation and problems at home, forming a joint force for home-school education and jointly promoting students' growth.

[0065] The teacher team collaboration support unit supports the sharing of teaching resources and collaborative lesson preparation among teachers of the same subject. Teachers can share their teaching experiences, excellent teaching cases and teaching strategies for wrong questions in the system, and can also view the sharing content of other teachers to achieve experience exchange and complementarity. Through team collaborative lesson preparation, they can jointly discuss how to better use wrong question data to improve teaching quality, formulate unified teaching standards and teaching plans, and ensure the continuity and consistency of teaching. It provides teaching data analysis and discussion functions for grade groups or subject teaching and research groups. For example, organize teachers to conduct collective analysis of wrong question data after a certain exam or stage of teaching, find out common problems in grade or subject teaching, and jointly discuss solutions, carry out teaching research activities, and promote the improvement of the overall teaching level of the teacher team.

[0066] The grade management subsystem includes a data aggregation and integration module, a learning situation monitoring and early warning module, a teaching resource allocation module, a teaching quality assessment module and an examination and competition management module. The data aggregation and integration module includes a multi-class data collection unit and a data standardization processing unit; the situation monitoring and early warning module includes a grade-wide learning situation analysis unit and a learning anomaly early warning unit; the teaching resource allocation module includes a resource demand analysis unit and a resource allocation and optimization unit; the teaching quality assessment module includes a teacher teaching effectiveness evaluation unit and a teaching strategy effectiveness evaluation unit; the examination and competition management module includes an examination arrangement and organization unit and a competition activity planning and implementation unit.

[0067] The multi-class data collection unit seamlessly connects with the student record layer subsystem of each class, automatically collecting the wrong question data, homework completion status, test scores and other information of students in each class of the whole grade; supports the reception and conversion of multiple data formats to ensure the integrity and accuracy of the data; performs preliminary cleaning and sorting of the collected data, removes duplicate, erroneous or incomplete data records, and lays a solid foundation for subsequent in-depth analysis;

[0068] The data standardization processing unit formulates unified data standards and specifications, and standardizes the learning data from different classes and subjects to make the data comparable and consistent; for example, uniformly encodes knowledge points, and converts grades into standardized scores; establishes data mapping relationships, associates and integrates student personal information, class information, subject information, etc. with learning data, and forms a panoramic view of the learning data of all students in the grade, facilitating multi-angle data analysis and query;

[0069] The overall grade learning situation analysis unit conducts a comprehensive analysis of the learning situation of all students in each subject and knowledge point based on the aggregated data; generates a visual learning information table to display key indicators such as the average score, excellent rate, pass rate, and distribution of wrong questions of the grade students, and presents the dynamic change trend of the grade learning situation in the form of intuitive charts; deeply explores the rules and characteristics behind the data, analyzes the learning differences between different classes, the balance between subjects, and the fluctuations of students' learning status in different time periods, and provides comprehensive learning situation insights for the grade teaching management team;

[0070] The learning anomaly warning unit sets an early warning threshold and timely detects individual or group students with learning anomalies through real-time monitoring and analysis of students' wrong answer data. For example, when a student's wrong answer number continues to increase over a period of time and his / her grades decline significantly, or when a class's wrong answer rate in a certain subject far exceeds the grade average, the system automatically issues an early warning message. The early warning message is pushed to grade administrators, class teachers, and teachers of related subjects through multiple channels so that they can take timely intervention measures, such as organizing individual tutoring, adjusting teaching progress or teaching methods, etc., to prevent students' learning problems from further deteriorating.

[0071] The resource demand analysis unit accurately identifies the learning needs and resource gaps of all students in different subjects and knowledge points based on the results of the learning situation analysis. For example, when it is found that the error rate of a certain knowledge point is high, it analyzes whether it is due to insufficient teaching resources or low resource quality. Combined with teacher teaching feedback and student learning needs survey data, it comprehensively evaluates the current status and needs of grade teaching resources to provide a basis for the rational allocation of teaching resources.

[0072] The resource allocation and optimization unit conducts unified management and deployment of teaching resources within the grade; reasonably allocates teaching resources according to the actual needs of each class and subject to ensure balanced allocation and efficient use of resources; establishes a teaching resource sharing mechanism to promote resource exchange and cooperation between teachers of different classes and subjects; for example, shares excellent teachers' teaching courseware, teaching design and other resources with teachers of all grades, and encourages teachers to upload their own high-quality teaching resources to the grade resource library to achieve co-construction, sharing and continuous optimization of resources;

[0073] The teacher teaching effectiveness evaluation unit conducts an objective and comprehensive evaluation of the teaching effectiveness of teachers in the entire grade based on students' learning data, especially the changes in wrong question data; the evaluation indicators include multiple dimensions such as the extent of student performance improvement, the rate of wrong question correction, and the results of student satisfaction surveys on teacher teaching; it generates a teacher teaching effectiveness evaluation report, analyzes in detail the strengths and weaknesses of each teacher in the teaching process, and provides targeted improvement suggestions; the evaluation report can be used as an important reference for grade management in teacher performance appraisal, professional title evaluation, teaching training arrangements, etc.

[0074] The teaching strategy effectiveness evaluation unit evaluates the effectiveness of various teaching strategies implemented throughout the grade; by comparing the changes in students' learning data before and after the implementation of different teaching strategies, analyze the impact of teaching strategies on students' academic performance, learning interest, learning ability, etc.; based on the evaluation results, screen out effective teaching strategies that are suitable for the characteristics of students in this grade and the actual teaching situation, and promote and optimize them throughout the grade; at the same time, timely adjust or eliminate teaching strategies with poor results to continuously improve the overall teaching level of the grade;

[0075] The examination arrangement and organization unit assists the grade management team in arranging and organizing various examinations (such as midterm and final examinations, monthly examinations, mock examinations, etc.); including functions such as determining the examination time, assigning examination venues, arranging examination staff, and automatically generating examination schedules and related notification documents; connecting with the school examination system to realize the electronic management of examination papers, including operations such as uploading, downloading, distributing, and recycling examination papers, and improving the efficiency and accuracy of examination organization work;

[0076] The competition planning and implementation unit plans and organizes students from all grades to participate in various subject competitions, such as mathematics competitions, Chinese composition competitions, English speech competitions, etc.; formulates competition activity plans, including competition rule formulation, registration management, competition time arrangement, judges organization and other functional modules; collects and analyzes data during the competition, such as student competition results, awards, etc., evaluates students' subject expertise and comprehensive qualities through competition results, provides data support for selecting outstanding students to participate in higher-level competitions, and also provides reference and reference for subject teaching.

[0077] The campus management subsystem includes the school data overview and analysis module, the faculty management and optimization module, the teaching resource coordination and allocation module, the teaching strategy and curriculum system optimization module, and the home-school cooperation and education ecology construction module; the school data overview and analysis module includes the teaching big data dashboard unit and the teaching quality comprehensive evaluation unit; the faculty management and optimization module includes the teacher teaching behavior analysis unit and the teacher professional development planning and support unit; the teaching resource coordination and allocation module includes the teaching resource inventory management unit and the teaching resource demand analysis and allocation unit; the teaching strategy and curriculum system optimization module includes the teaching strategy effect evaluation and promotion unit and the curriculum system rationality analysis and adjustment unit; the home-school cooperation and education ecology construction module includes the home-school communication data analysis and guidance unit and the education ecology partner management unit.

[0078] The teaching big data dashboard unit builds a visual school-wide teaching big data dashboard, which centrally displays the school's key data indicators in teaching, such as the overall academic performance distribution of each grade, the average performance and trend changes of each subject, the comparison of students' error rate by grade and subject, and the comprehensive score of teachers' teaching quality evaluation. These data are presented in the form of intuitive charts (bar charts, line charts, pie charts, heat maps, etc.), so that the school management can grasp the basic situation and dynamic changes of the school's teaching at a glance; it provides data screening and drilling functions, allowing the management to flexibly screen and deeply mine the data according to different needs (such as time range, grade, subject, class, etc.), so as to understand the situation of specific teaching fields or groups in more detail and discover potential teaching problems or advantages.

[0079] The teaching quality comprehensive evaluation unit conducts a comprehensive and objective comprehensive evaluation of the school's overall teaching quality based on multi-dimensional teaching data; the evaluation dimensions cover students' learning outcomes (exam scores, homework completion quality, corrected wrong questions, etc.), teachers' teaching performance (teaching plan implementation, classroom teaching evaluation, contribution to student performance improvement, etc.), teaching resource utilization efficiency (the use of textbooks and teaching aids, the utilization rate of laboratories and multimedia equipment, the activity of online learning platforms, etc.) and teaching environment and atmosphere (student learning attitude survey, teacher-student interaction frequency, campus culture support for teaching, etc.); generates a detailed teaching quality evaluation report, which includes not only quantitative data and chart analysis of various evaluation indicators, but also qualitative evaluation conclusions and improvement suggestions; the report provides an important basis for the school management to formulate long-term teaching development strategies and annual teaching work plans, and serves as the core material for reporting the school's teaching work to the education authorities;

[0080] The teacher teaching behavior analysis unit deeply mines the behavioral data of teachers in the daily teaching process, including lesson preparation records, classroom teaching activities (such as teaching time, number of questions, participation in interactive sessions, etc.), homework assignment and correction, extracurricular tutoring records, and communication records with students and parents. Through the analysis of these data, we can gain insights into the teaching style, work attitude and application characteristics of teaching methods of teachers. We establish a teacher teaching behavior model, compare and analyze it with the standards of excellent teaching behavior, and generate a personalized teaching behavior analysis report for each teacher. The report points out the advantages and disadvantages of teachers in the teaching process, and provides targeted improvement suggestions and training course recommendations to help teachers continuously optimize their teaching behavior and improve their teaching level.

[0081] The Teacher Professional Development Planning and Support Unit formulates personalized professional development plans for teachers based on the results of their teaching ability assessment, their personal career development intentions, and the overall teaching needs of the school. The planning content includes setting teaching skills improvement goals, arranging training courses and academic seminars, recommending and guiding teaching research topics, and rewarding plans for teaching achievements. It integrates teacher training resources inside and outside the school to build a teacher training resource library, covering rich resources such as online and offline courses, expert lecture videos, teaching case libraries, and educational research literature. Teachers can independently choose learning content according to their own professional development plans, and the system tracks teachers' learning progress and learning effects, and provides teachers with learning achievement certification and feedback, encouraging teachers to actively participate in professional development activities and promote the overall quality of the teaching staff.

[0082] The teaching resource inventory management unit conducts a comprehensive sorting and information management of various teaching resources of the school, including teaching materials, experimental equipment, multimedia teaching facilities, books and materials, online learning platform accounts and course resources, etc.; establishes a detailed teaching resource inventory database, records the name, model, quantity, purchase time, usage status, storage location and other information of the resources, and updates the borrowing, return and maintenance of resources in real time; provides teaching resource inventory query and early warning functions, and school management and teachers can query the inventory information of required teaching resources at any time. When the inventory quantity of a certain resource is lower than the preset early warning threshold, the system automatically issues an early warning notification to remind relevant departments to purchase or allocate resources in a timely manner to ensure the smooth progress of teaching activities without being affected by resource shortages;

[0083] The teaching resource demand analysis and allocation unit conducts accurate analysis and prediction of the demand for teaching resources based on the school's teaching plan, curriculum arrangement, and the actual teaching needs of each grade and subject. It formulates a scientific and reasonable teaching resource allocation plan by analyzing the impact of factors such as changes in student numbers, curriculum adjustments, and innovations in teaching methods on the demand for teaching resources. It implements the on-campus sharing and dynamic allocation mechanism of teaching resources, and timely allocates idle or low-utilization teaching resources to teaching departments or classes in urgent need according to the needs of different teaching stages and teaching tasks. For example, during the examination period, some multimedia classroom equipment is allocated to the examination room for examination monitoring. When conducting experimental teaching activities in a certain subject, laboratory equipment and experimental materials are reasonably allocated to improve the overall utilization efficiency of teaching resources and reduce teaching costs.

[0084] The teaching strategy effectiveness evaluation and promotion unit conducts a comprehensive effectiveness evaluation of various teaching strategies implemented within the school (such as differentiated teaching, mobile class teaching, project-based learning, group cooperative learning, etc.); by collecting and analyzing students' learning data under different teaching strategies (data changes in academic performance, learning interests, learning ability improvement, etc.), teachers' teaching feedback, and parents' opinions and suggestions, objectively evaluate the advantages and disadvantages and scope of application of each teaching strategy; based on the results of the teaching strategy effectiveness evaluation, select excellent teaching strategies suitable for the school's situation and student characteristics, and formulate detailed promotion plans; the promotion plan includes organizing teacher training and experience exchange activities, compiling teaching strategy application guidelines, setting up teaching strategy pilot classes or grades and gradually expanding the promotion scope, etc., to ensure that excellent teaching strategies can be effectively promoted and applied throughout the school, and continuously improve the overall teaching level of the school;

[0085] The unit for analyzing and adjusting the rationality of the curriculum system conducts an in-depth analysis of the school's existing curriculum system, and evaluates it from multiple dimensions, such as the degree of achievement of curriculum objectives, the integrity and cutting-edge nature of curriculum content, the logic and coherence of curriculum settings, the connection and complementarity between courses, and the degree of support for the comprehensive quality training of students. By analyzing the distribution of students' wrong questions in the process of learning various subject courses, the trend of changes in academic performance, and the further study and employment of graduates, the problems and deficiencies in the curriculum system are discovered. The curriculum system is optimized and adjusted based on national education policies and regulations, the trend of social demand for talents, and the school's educational positioning and characteristics. The adjustments include the optimization of the curriculum structure (such as increasing the proportion of elective courses, setting up interdisciplinary comprehensive courses, etc.), the updating and expansion of curriculum content (introducing cutting-edge knowledge of disciplines, integrating practical teaching content, etc.), the improvement of curriculum implementation methods (adopting a hybrid teaching model, strengthening practical teaching links, etc.), and the improvement of the curriculum evaluation system (establishing diversified curriculum evaluation indicators, focusing on the combination of process evaluation and final evaluation, etc.), to ensure that the school's curriculum system can adapt to the needs of the development of the times and cultivate high-quality talents with innovative spirit and practical ability.

[0086] The home-school communication data analysis and guidance unit integrates the communication data between the school and parents, including parent meeting records, parent WeChat group chat records, parent feedback on school work, parent questionnaire survey results, etc.; through the analysis of these data, understand the parents' attention and satisfaction with the school's teaching work, as well as their expectations and needs for students' education, and find out the problems and obstacles in home-school communication; provide home-school communication skills training and guidance for school management and teachers, and formulate targeted home-school communication strategies and plans based on data analysis results; for example, for students' academic performance and wrong questions that parents pay more attention to, the school can regularly send detailed academic situation analysis reports to parents, and organize parent training activities to guide parents on how to help students organize and review wrong questions at home, so as to improve the synergy and effectiveness of family education and school education;

[0087] The Education Ecology Partner Management Unit manages and maintains the cooperative relationship between the school and other education ecosystem partners such as off-campus education institutions, enterprises, and communities; establishes an education ecosystem partner database to record the basic information of partners, content of cooperation projects, evaluation of cooperation results and other data; through analyzing cooperation data, evaluates the support and contribution value of partners to the school's education and teaching work, screens out high-quality partners and expands the scope and depth of cooperation; organizes and carries out on-campus and off-campus education resource integration and sharing activities, such as inviting corporate experts to campus to conduct career planning lectures, organizing students to carry out social practice activities in the community, and cooperating with off-campus education institutions to carry out extracurricular extension courses, etc., to build an open, diverse, and collaboratively developed education ecological environment, provide students with rich learning experiences and growth opportunities, and enhance the school's social influence and educational competitiveness.

[0088] The data analysis subsystem includes a data preprocessing module, a learning behavior analysis module, a knowledge mastery analysis module, a teaching effect analysis module and a predictive analysis module; the data preprocessing module includes a data cleaning unit, a data conversion unit and a data integration unit; the learning behavior analysis module includes a learning trajectory tracking unit and a learning habit insight unit; the knowledge mastery analysis module includes a knowledge point mastery level assessment unit and a knowledge weakness location and attribution unit; the teaching effect analysis module includes a teacher teaching effectiveness assessment unit and a teaching strategy effect analysis unit; the predictive analysis module includes a learning performance prediction unit and a learning risk warning unit.

[0089] The data cleaning unit removes noise data, such as duplicate records, wrong marks, missing values, etc., from the raw data collected from different teaching links (homework, tests, classroom interactions, etc.); for example, in the wrong questions data of students' homework, delete the repeated wrong questions information caused by system failure or human operation errors, correct the obviously wrong knowledge point annotations, supplement the missing student answer details, etc., to ensure the accuracy and completeness of the data; use data verification rules and algorithms to identify and correct inconsistencies in the data; for example, check the rationality of the score data of the same student in different exams, ensure that the student identity information matches the score record correctly, and ensure the logical consistency and reliability of the data;

[0090] The data conversion unit uniformly converts data of different formats and types into a format suitable for analysis; for example, converts student answers in text format into structured data for subsequent semantic analysis and error type judgment; standardizes date formats, grade data formats, etc. to meet the requirements of data analysis tools and algorithms; normalizes or standardizes data to make data of different dimensions comparable; for example, normalizes student test scores to a value range between 0 and 1, or standardizes scores to a specific mean and standard deviation, so that data characteristics between different students and different subjects can be more accurately compared when conducting multi-dimensional data analysis;

[0091] The data integration unit integrates data from multiple data sources (such as teaching systems of different disciplines, school academic management systems, etc.) and establishes associations between data; for example, it associates students' basic information, course information, academic performance, details of wrong questions, and other data to build a comprehensive data view centered on students, so as to comprehensively analyze students' learning situations from multiple perspectives; it solves conflicts in the data integration process, such as different definitions of the same concept by different data sources or inconsistent data updates; it ensures that the integrated data can accurately reflect the actual teaching situation by formulating data fusion rules and priority strategies, and avoids data conflicts from misleading the analysis results;

[0092] The learning trajectory tracking unit records the detailed behavioral data of students throughout the learning process, including learning time, learning order, learning time for different knowledge points, submission time and order of homework and tests, etc.; by analyzing these data, draw a learning trajectory diagram for individual students to intuitively display the students' learning path and learning rhythm; compare the learning trajectories of different students to find the differences and commonalities in learning patterns; for example, analyze the time allocation characteristics of excellent students in the stages of preview, review, and homework completion, as well as the differences between them and students with learning difficulties, to provide a reference for formulating personalized learning suggestions;

[0093] The learning habit insight unit analyzes students' learning habit characteristics, such as whether they complete homework on time, whether they actively preview and review, and their concentration during the learning process (by analyzing data such as pauses and switching frequencies during the learning process). Based on these analysis results, the unit conducts a quantitative assessment of students' learning habits and divides them into different habit types, such as self-discipline, passive, and procrastination. It provides personalized learning habit improvement suggestions and learning plan adjustment plans for students with different learning habit types. For example, for procrastination students, it designs reminder mechanisms and task decomposition strategies to help them gradually improve their learning habits and improve their learning efficiency.

[0094] The knowledge point mastery assessment unit uses knowledge graph technology and machine learning algorithms based on students' wrong question data to accurately assess students' mastery of each knowledge point; divides knowledge points into different mastery levels, such as not mastered, preliminary understanding, proficient mastery, etc., and displays students' mastery of the entire knowledge system in a visual way (such as knowledge point mastery heat map); analyzes the changing trend of students' mastery of knowledge points at different learning stages (such as unit learning, semester learning, and academic year learning), predicts the difficulties and knowledge bottlenecks that students may encounter in subsequent learning, and provides a basis for teachers to adjust teaching strategies in advance and provide targeted tutoring;

[0095] The knowledge weakness location and attribution unit accurately locates students' knowledge weaknesses through in-depth analysis of students' wrong questions and the correlation between knowledge points. For example, it uses natural language processing technology to analyze the incorrect statements in students' answers to wrong questions and, combined with the logical structure of knowledge points, determines whether the core concept of a certain knowledge point is not understood clearly or there is a problem with the transfer and application of knowledge points. It also conducts attribution analysis on knowledge weaknesses to determine whether they are caused by improper teaching methods, insufficient learning resources, or differences in students' own learning attitudes or learning abilities. Through multi-dimensional attribution analysis, it provides accurate direction for formulating targeted teaching intervention measures and learning improvement plans.

[0096] The teacher teaching effectiveness evaluation unit evaluates the teaching effectiveness of teachers from multiple dimensions, including the improvement of students' academic performance, the improvement of knowledge points, and the results of students' satisfaction surveys on teachers' teaching. By establishing a teaching effectiveness evaluation model, these factors are comprehensively considered to generate quantitative evaluation indicators for each teacher's teaching effectiveness. The unit compares the differences in teaching effectiveness of different teachers in the same teaching content or in different classes, and analyzes the key factors affecting teaching effectiveness, such as the choice of teaching methods, the grasp of teaching rhythm, and the effect of classroom interaction. It provides teachers with suggestions for teaching improvement and sharing of excellent teaching experience to promote the overall improvement of teachers' teaching level.

[0097] The teaching strategy effectiveness analysis unit analyzes the impact of different teaching strategies (such as traditional lecture-based teaching, group cooperative learning, project-based learning, etc.) adopted by schools or teachers on students' learning outcomes; evaluates the effectiveness of teaching strategies by comparing changes in students' learning data before and after the implementation of different teaching strategies, such as the reduction in the error rate and the speed of improvement in knowledge mastery; provides schools and teachers with teaching strategy optimization suggestions based on the results of the teaching strategy effectiveness analysis, recommends teaching strategy combinations suitable for different teaching contents and student groups, realizes accurate selection and dynamic adjustment of teaching strategies, and improves teaching quality and efficiency;

[0098] The learning performance prediction unit uses machine learning prediction models (such as linear regression, decision trees, neural networks, etc.) based on students' historical learning data (including learning behavior data, knowledge acquisition data, past test scores, etc.) to predict students' performance in future tests or learning tasks. The prediction results are presented in the form of probability distribution or specific score ranges to provide forward-looking learning guidance for teachers and students. Based on the learning performance prediction results, students who may have learning difficulties or declining grades are identified in advance, providing a basis for teachers to develop early warning mechanisms and intervention measures. For example, for students with low predicted grades, teachers can arrange tutoring plans in advance, adjust teaching progress, or provide additional learning resources to help students avoid learning crises.

[0099] The learning risk warning unit warns students of other risks they may face during the learning process, such as the risk of forgetting knowledge, the risk of insufficient learning motivation, the risk of dropping out of school, etc.; by analyzing students' learning behavior data and psychological state data (such as the reduction in the frequency of logging into the learning platform, the delay in submitting homework, the decrease in attention to learning resources, etc.), combined with machine learning models and educational psychology theories, potential learning risk signals can be discovered in a timely manner; when the system detects learning risks, it automatically sends warning information to teachers, parents and students, and provides corresponding risk response strategies and suggestions; for example, for students with a higher risk of forgetting knowledge, targeted review plans and memory enhancement training methods are recommended; for students with insufficient learning motivation, incentive mechanisms and interest cultivation programs are provided to help students regain their enthusiasm for learning and reduce learning risks.

[0100] The data transmission subsystem transmits the collected data to the data processing center or server in real time through a wired or wireless network to ensure timely and accurate data transmission. Wireless network communication technologies include but are not limited to Wi-Fi and Bluetooth, and wired network communication technologies include but are not limited to Ethernet. The data is sent to the system cloud platform for storage and analysis. Wired networks have the advantages of fast transmission speed and high stability; wireless networks also make it convenient for customers to use the review APP or review applet; the two methods work stably together to ensure timely and accurate data transmission.

[0101] The working principle of the present invention is as follows: the mobile frame 1 is pulled, and the universal wheel 2 is convenient for driving the device to move. The placement tube 11 is used to place supplies such as pens, and the storage box 3 is used to place paper, which is placed inside the storage box 3 when the folding scanner 9 is not in use; when the mobile component moves, it drives the data acquisition subsystem and the visual display subsystem to move, and users such as teachers or school administrators use it to correct homework and test papers. First, several system management subsystems perform user registration management, system management and system settings; when in use, the folding scanner 9 is removed from the storage box 3, the flip frame 18 is snap-connected to the upper end of the mounting seat 14, and then the flip frame 18 of the folding scanner 9 is opened, and the rotating seat 17 is rotated to make the fill light 15 and the two scanning cameras 16 located directly above the operating panel 7; the mouse and keyboard tray 12 is pulled out from the pull-out frame 13 and located on the side of the operating panel 7, which is convenient for using the mouse and keyboard; the software tablet computer 8 is unfolded through the damping shaft hinge; the user places the homework and test paper on the operating panel 7 On the top, two scanning cameras 16 scan homework and test papers, collect homework information in real time, and the fill light 15 is used for fill light; the scanning camera 16 scans data such as homework and test papers; the scanned information is transmitted to the software tablet computer 8, and the data analysis subsystem processes the information of homework and test papers, obtains wrong questions, and displays the wrong questions in real time, so that users can quickly correct homework and test papers; the data information is transmitted to the system cloud platform through the data transmission system; the student record layer subsystem is used to count and process the student's personal wrong question data, the teacher auxiliary layer subsystem is used to count and process the student and class wrong question data, the grade management layer subsystem is used to count and process the grade wrong question data, and the campus management layer subsystem is used to count and process the campus data; the data analysis subsystem is used for data analysis and processing, the visualization display subsystem is used to observe various chart data, the data transmission subsystem is used to transmit data stably and efficiently, the system cloud platform is the back-end cloud platform, and various types of data are gathered on the cloud platform to form a comprehensive student database and store it.

[0102] In summary, the mobile component drives the movement of the data collection subsystem and the visualization subsystem, and users such as teachers or school administrators use it to correct homework and test papers in real time. The data collection subsystem collects various data, including test papers, homework and answer sheets. The marking software subsystem can perform software operations and can also collect information such as test papers and homework, mark wrong questions and organize them; the student record layer subsystem is used to count and process students' personal wrong question data, the teacher auxiliary layer subsystem is used to count and process students' and class wrong question data, the grade management layer subsystem is used to count and process grade wrong question data, the campus management layer subsystem is used to count and process campus data, the system management subsystem is used for system management and system settings, the data analysis subsystem is used for data analysis and processing, the visualization subsystem is used to observe various chart data, the data transmission subsystem is used to transmit data stably and efficiently, and the system cloud platform is the back-end cloud platform. Various types of data are gathered on the cloud platform to form a comprehensive student database and store it.

[0103] The specific embodiments described herein are merely examples of the spirit of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in similar ways, but they will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. A big data precise teaching and reviewing wrong questions data management system, characterized by: It includes a mobile component, a student record layer subsystem, a teacher auxiliary layer subsystem, a grade management layer subsystem, a campus management layer subsystem, a system management subsystem, a data acquisition subsystem, a data analysis subsystem, a visual display subsystem, a data transmission subsystem and a system cloud platform, as well as a review software subsystem arranged on a terminal device. The data transmission subsystem is electrically connected to the student record layer subsystem, the teacher auxiliary layer subsystem, the grade management layer subsystem, the campus management layer subsystem, the system management subsystem, the data acquisition subsystem, the data analysis subsystem, the visual display subsystem, the system cloud platform and the review software subsystem, respectively, and the data acquisition subsystem and the visual display subsystem are arranged on the mobile component.

2. According to claim 1, a big data precision teaching and reviewing wrong questions and sorting data management system is characterized in that: The mobile assembly comprises a mobile frame (1), the four corners of the lower end of the mobile frame (1) are each provided with a universal wheel (2), the mobile frame (1) is provided with a storage box (3), an operating panel (7) and a plurality of placement tubes (11), the storage box (3) is located at the lower side of the operating panel (7), the plurality of placement tubes (11) are located at the side of the operating panel (7), a mouse and keyboard tray assembly (6) is provided at the lower end of the operating panel (7), a mounting seat (14) is fixed at the middle of the lower end of the operating panel (7), and a scale (10) is provided at the upper end of the operating panel (7).

3. According to claim 2, a big data precision teaching and reviewing wrong questions and sorting data management system is characterized in that: The mouse and keyboard tray assembly (6) comprises a pull-out frame (13), the pull-out frame (13) is fixed to the lower end of the operation panel (7), a mouse and keyboard tray (12) is slidably provided inside the pull-out frame (13), and a mouse and a keyboard are placed on the mouse and keyboard tray (12).

4. According to claim 3, a big data precision teaching and reviewing wrong questions and sorting data management system is characterized in that: The data acquisition subsystem comprises a printer (4), a paper grading machine (5) and a folding scanner (9). The printer (4) and the paper grading machine (5) are arranged inside the mobile frame (1). The printer (4) and the paper grading machine (5) are located between the storage box (3) and the mouse and keyboard tray assembly (6). The printer (4) is located at the lower side of the paper grading machine (5). The folding scanner (9) is placed inside the storage box (3) when not in use. The folding scanner (9) comprises a flip frame (18). When in use, the flip frame (18) is snap-connected to the upper end of the mounting seat (14). A rotating seat (17) is rotatably provided at the end of the flip frame (18). A fill light (15) and two scanning cameras (16) are fixed on the rotating seat (17). When the folding scanner (9) is in use, the fill light (15) and the two scanning cameras (16) are located directly above the operation panel (7).

5. According to claim 4, a big data precision teaching and reviewing wrong questions and sorting data management system is characterized in that: The visualization display subsystem is a software tablet computer (8), which is hinged to the upper front side of the operation panel (7) via a damping shaft, and the mouse, keyboard, printer (4), paper marking machine (5) and folding scanner (9) are all electrically connected to the software tablet computer (8), and the marking software subsystem is arranged on the software tablet computer (8).

6. A big data precise teaching and reviewing wrong questions and sorting data management system according to claim 5, characterized in that: The system management subsystem includes a user management module, a data security management module and a system setting module. The user management module creates accounts, allocates permissions and manages information for users with different roles, including but not limited to teachers, students, grade administrators and campus administrators; the data security management module uses encryption technology to store and transmit wrong question data, and backs up data regularly to prevent data loss or leakage; the system setting module configures and manages but not limited to the basic parameters of the system, data acquisition sources and analysis algorithms to ensure the normal operation and performance optimization of the system.

7. A big data precise teaching and reviewing wrong questions and sorting data management system according to claim 6, characterized in that: The marking software subsystem is a marking computer software, a marking APP or a marking applet, which is equipped with a homework correction module, a camera shooting and collection module, a search and answering module, a wrong question book module, a subject selection module, a grade selection module, a textbook version selection module and a connection and printing module; the student record layer subsystem includes a student individual wrong question sorting module, a student personalized auxiliary learning module and a communication and interaction module.

8. A big data precise teaching and reviewing wrong questions and sorting data management system according to claim 7, characterized in that: The teacher-assisting layer subsystem includes a learning situation analysis and insight module, a teaching strategy customization module, a resource integration and recommendation module, a teaching effect evaluation module, and a communication, collaboration, and management module. The learning situation analysis and insight module includes a class overall learning situation report unit and a student individual learning situation tracking unit; the teaching strategy customization module includes a teaching focus determination unit and a personalized teaching plan generation unit; the resource integration and recommendation module includes a teaching material matching unit and an expanded learning resource push unit; the teaching effect evaluation module includes a wrong question correction effect tracking unit and a teaching strategy effectiveness analysis unit; the communication, collaboration, and management module includes a home-school communication bridge building unit and a teacher team collaboration support unit; The grade management layer subsystem includes a data aggregation and integration module, a learning situation monitoring and early warning module, a teaching resource allocation module, a teaching quality assessment module and an examination and competition management module. The data aggregation and integration module includes a multi-class data collection unit and a data standardization processing unit; the situation monitoring and early warning module includes a grade-wide learning situation analysis unit and a learning anomaly early warning unit; the teaching resource allocation module includes a resource demand analysis unit and a resource allocation and optimization unit; the teaching quality assessment module includes a teacher teaching effectiveness evaluation unit and a teaching strategy effectiveness evaluation unit; the examination and competition management module includes an examination arrangement and organization unit and a competition activity planning and implementation unit.

9. A big data precise teaching and reviewing wrong questions and sorting data management system according to claim 8, characterized in that: The campus management subsystem includes a school data overview and analysis module, a teacher team management and optimization module, a teaching resource coordination and deployment module, a teaching strategy and curriculum system optimization module, and a home-school cooperation and education ecology construction module; the school data overview and analysis module includes a teaching big data dashboard unit and a teaching quality comprehensive evaluation unit; the teacher team management and optimization module includes a teacher teaching behavior analysis unit and a teacher professional development planning and support unit; the teaching resource coordination and deployment module includes a teaching resource inventory management unit and a teaching resource demand analysis and deployment unit; the teaching strategy and curriculum system optimization module includes a teaching strategy effect evaluation and promotion unit and a curriculum system rationality analysis and adjustment unit; the home-school cooperation and education ecology construction module includes a home-school communication data analysis and guidance unit and an education ecology partner management unit; The data analysis subsystem includes a data preprocessing module, a learning behavior analysis module, a knowledge mastery analysis module, a teaching effect analysis module and a prediction analysis module; the data preprocessing module includes a data cleaning unit, a data conversion unit and a data integration unit; the learning behavior analysis module includes a learning trajectory tracking unit and a learning habit insight unit; the knowledge mastery analysis module includes a knowledge point mastery degree assessment unit and a knowledge weakness location and attribution unit; the teaching effect analysis module includes a teacher teaching effectiveness assessment unit and a teaching strategy effect analysis unit; the prediction analysis module includes a learning performance prediction unit and a learning risk warning unit.

10. A big data precise teaching and reviewing wrong questions and sorting data management system according to claim 9, characterized in that: The data transmission subsystem transmits the collected data to the data processing center or server in real time through a wired or wireless network to ensure timely and accurate transmission of the data. Wireless network communication technology includes but is not limited to Wi-Fi and Bluetooth, and wired network communication technology includes but is not limited to Ethernet. The data is sent to the system cloud platform for storage and analysis.

Images