Educational training management system and educational training method

Abstract

The invention relates to the technical field of education management, in particular to an education training management system and an education training method, and the system comprises a learning condition arrangement module, a content collection module, a material revision module, a teaching arrangement module and a task monitoring module. According to the method, a knowledge point positioning mode of time anchor points is constructed through homework annotations and time consumption data, student feedback information is mapped to section numbers to form a traceable question section, the section numbers are sequentially aggregated to form a continuous content structure, logic reconstruction of teaching content in time sequence is promoted, and a docking relation is established between classroom interaction records and teaching plan texts. The method comprises the following steps: complementing missing instances or task entries in a lecture, performing semantic matching on content tags and vacant sections of a class schedule, inserting supplementary tasks into reasonable positions according to content logic, binding class hour numbers with homework states and class messages, and generating an entries list with task pointing and feedback behaviors for supervising the execution condition of teaching tasks in the whole process. And the difference section number and the corresponding class information are positioned.

Description

Technical Field

[0001] This invention relates to the field of education management technology, and in particular to an education and training management system and an education and training method. Background Technology

[0002] The field of educational management technology encompasses management systems for all aspects of organizing, planning, implementing, and evaluating educational and training activities. Its core components include the allocation of educational resources, tracking of the learning process, performance evaluation, student management, and curriculum scheduling. The aim of educational management technology is to improve the efficiency of education and training management, simplify management processes, and enhance the utilization rate of educational resources through information technology. With the continuous development of information technology, educational management technology has gradually evolved from traditional manual management towards intelligent and automated approaches, gradually forming various innovative models including online education, digital learning, and intelligent assessment.

[0003] One type of education and training management system refers to an information management solution for education and training institutions. This system primarily involves the digital management of multiple aspects of the education and training process, such as student personal information management, course scheduling management, grade management, and teacher resource allocation. Through a unified database management platform, the system automates the processing of student information, course scheduling, and teacher management, thereby improving the operational efficiency of education and training institutions. The system also supports education administrators in real-time monitoring of student learning progress and grade changes through data analysis functions, enabling timely adjustments to teaching strategies. Furthermore, the system adopts a modular design, ensuring that each functional module can operate independently and collaborate efficiently, thus guaranteeing management flexibility and scalability.

[0004] In existing technologies, task feedback typically remains at the level of result presentation, lacking a mechanism for integration with process recording. This leads to a reliance on concentrated feedback at the end of the semester to pinpoint weaknesses in teaching, failing to capture the dynamic fluctuations throughout the teaching process. Teaching arrangements often rely on pre-set templates, with insufficient traceable data sources to support the scheduling of lessons. Course adjustments fail to reflect changes in teaching needs, resulting in teaching progress lagging behind student performance. Lesson plan revisions have long depended on periodic manual compilation, leading to a disconnect between classroom data and document content, resulting in inconsistencies between actual implementation and materials, thus affecting the accuracy of teaching research. Task supervision focuses on result verification while neglecting the construction of behavioral chains. Assignment status and classroom feedback are not presented in the same structure, causing a break in the supervision data structure. This results in discrepancies where students appear to have completed tasks but lack actual process participation, impacting the effectiveness of teaching assessment. Course execution results are often aggregated by class, lacking a mechanism for precise identification of task nodes, making it difficult to support detailed difference analysis based on content objectives, thus limiting precise intervention in teaching problems. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing an education and training management system and an education and training method.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an education and training management system comprising:

[0007] The learning progress module retrieves the annotations and submission times from the homework feedback, calculates the time spent based on the start and submission timestamps of the homework, divides the work into segments according to time intervals, marks the problem segments based on the time spent corresponding to the tags, and obtains the teaching problem items to be processed.

[0008] The content collection module locates the chapter number and section number corresponding to the teaching problem item to be processed, concatenates the numbers with adjacent time sequence, embeds them into the existing teaching schedule, identifies the content aggregation situation, and obtains a centralized teaching content package.

[0009] The material revision module, based on the centralized teaching content package, reviews the lesson plans and classroom interaction records, locates the parts of the handouts that are missing examples and task links, fills in the relevant content and updates the lesson plan registration information to obtain supplementary teaching task records.

[0010] Based on the supplementary teaching task records, the teaching arrangement module inserts the new tasks into the teaching plan, updates the class time slots, and pushes the task change content according to the content to be supplemented and the blank positions in the teaching arrangement table, so as to obtain the revised teaching task arrangement.

[0011] The task monitoring module adjusts the arrangement based on the teaching tasks, checks the completion status of the class tasks and classroom feedback, summarizes the class hours and classes with misaligned records, and obtains the implementation status of the course tasks.

[0012] As a further aspect of the present invention, the teaching problem item to be processed includes knowledge point tag category, associated annotation period, and corresponding time interval; the centralized teaching content package includes session number segment, teaching chapter mapping group, and timetable cross-density value; the supplementary teaching task record includes missing paragraph number, task description item, and registration path identifier; the teaching task correction arrangement includes updating the session list, inserting content tags, and changing record nodes; and the course task implementation status includes class difference sessions, task matching level, and feedback data index.

[0013] As a further aspect of the present invention, the learning information processing module includes:

[0014] The annotation extraction submodule obtains the annotation content and submission time of the assignment feedback in the assignment task platform, extracts the knowledge point tags associated with each annotation, classifies all annotations according to the tag identifiers, and maps the classified tags and corresponding submission times to the tag set and time set respectively to obtain the tag classification time correspondence set;

[0015] The tag merging submodule, based on the time data in the set corresponding to the tag classification time, compares the start and end times of the work within the preset time interval with the division criteria, and categorizes each tag into the corresponding time period. It then combines the work duration within the time period to form a matching comparison, thereby obtaining the tag segment time consumption distribution data.

[0016] The content recognition submodule, based on the tag frequency and corresponding segment duration information in the tag segment time distribution data, screens tags that have both high frequency of occurrence and long duration within the same time period, extracts the corresponding segments and tag items as key content, and obtains the teaching problem items to be processed.

[0017] As a further aspect of the present invention, the content aggregation module includes:

[0018] The section extraction submodule, based on the teaching question item to be processed, retrieves the chapter number and section number corresponding to the question item in the course calendar, arranges the section numbers in the order of teaching arrangement, selects the repeated number positions as the sections of interest, and obtains the section number location set;

[0019] The segment generation submodule, based on the section number location set, sequentially connects the question items in adjacent sections, splices together the teaching content composed of consecutive numbers, sets the connected segments as the teaching processing range, and obtains a group of teaching segment combinations.

[0020] The merging submodule, based on the teaching segment combination group, fills in the corresponding teaching task content covered by the combination group, and merges the covered teaching content into the original arrangement structure of the teaching task table to obtain a centralized teaching content package.

[0021] As a further aspect of the present invention, the material revision module includes:

[0022] The content verification submodule, based on the centralized teaching content package, calls the text structure and classroom interaction records in the lesson plan archive by comparing the chapter number in the teaching task with the corresponding content and teaching task node in the handout, reads the text paragraphs and interactive descriptions, and obtains the chapter content comparison results.

[0023] The element screening submodule identifies the missing instance descriptions or task activities in each record based on the chapter content comparison results, calculates the proportion of missing items in the teaching items, and lists the missing content in the registration column in the form of a form to obtain a list of missing teaching elements.

[0024] The task collection submodule, based on the missing teaching element list, uniformly collects the contents recorded in the form into the supplementary task list, and sets the folder path in the teaching material upload directory to archive the task list, thus obtaining supplementary teaching task records.

[0025] As a further aspect of the present invention, the formula for calculating the proportion of missing items in all teaching items is as follows:

[0026] ;

[0027] in, Representing the The percentage of missing items in a group of teaching segments out of all teaching items. Representing the The first in the group The number of instance description fields that a teaching record should include. Representing the The first in the group The number of task activity fields that a teaching record should include. Representing the The first in the group The weight coefficient of the teaching objective corresponding to each teaching record in relation to the overall content.

[0028] As a further aspect of the present invention, the teaching arrangement module includes:

[0029] Based on the supplementary teaching task record, the lesson matching submodule looks up the lesson number and content number in the teaching schedule table, compares the coverage relationship between the content tag and the empty number in the supplementary task, and extracts the unarranged position segment in sequence to obtain the supplementary content location result.

[0030] Based on the supplementary content location results, the content insertion submodule inserts the content tags into the corresponding lesson number in the original order of the lesson, calculates the content coverage span, rearranges the lesson number in the insertion order, writes the updated information into the teaching schedule, and obtains the lesson structure update information.

[0031] The task registration submodule updates the lesson structure information, compares the updated content with the original entries in the teacher's task list, adds the newly added lesson number and corresponding task tag to the record row, and lists the added record items in the annotation area to obtain the revised teaching task arrangement.

[0032] As a further aspect of the present invention, the formula for calculating the content coverage span is specifically as follows: ;

[0033] in, The representative content covers a wide range, Representing the The content weight value associated with each content tag in the corresponding lesson. Representing the The content tag is numbered at the end of the corresponding lesson. Representing the The starting position number of each content tag in the corresponding lesson. Representing the The number of times each content tag is embedded in a lesson. Representing the The absolute value of the difference between the original lesson number and its rearranged number. This represents the total number of content tags in the current lesson. This represents the total number of all rearranged class hours.

[0034] As a further aspect of the present invention, the task monitoring module includes:

[0035] The lesson check submodule corrects the arrangement based on the teaching task, reads the homework submission records and classroom feedback message data in the lesson arrangement, maps the student submission status and message content in each lesson record to the actual lesson identifier, and classifies the incomplete and completed records by lesson to obtain the lesson task execution identifier table.

[0036] The node comparison submodule, based on the lesson task execution identifier table, calls the lesson number and corresponding target item of the task node in the teaching plan, the corresponding student submission status and task node information position, filters out the lesson information of mismatched cases, and adds the corresponding class mark to obtain the corresponding differences of the task node.

[0037] The results collection submodule, based on the differences corresponding to the task nodes, organizes all the difference in terms of class combination information, categorizes and enters it into the verification form structure, and organizes it into a traceable feedback data list by class dimension to obtain the course task implementation status.

[0038] An educational training method includes the following steps:

[0039] S1: Obtain the homework annotations and submission times from the homework task platform, classify the knowledge point tags associated with the annotations, combine the chapter arrangements corresponding to the homework submission time period, and match the high-frequency tags with the teaching content corresponding to the continuous time period to obtain the teaching issues to be processed.

[0040] S2: Based on the teaching problem items to be processed, retrieve the corresponding chapter number and session number from the course calendar, arrange the problem items within adjacent numbers in chronological order, locate the corresponding content range in the teaching schedule, and obtain a centralized teaching content package;

[0041] S3: Based on the centralized teaching content package, call the text structure and classroom interaction records in the lesson plan archive, browse the corresponding lecture text, mark the positions where there are missing example descriptions and task content, import them into the task list for unified registration, and obtain supplementary teaching task records.

[0042] S4: Based on the supplementary teaching task record, compare the class hour structure and teaching content number in the teaching schedule table, number and sort the supplementary tasks and the empty positions in the original class hour structure and fill them into the plan table, and record the changed content at the same time to obtain the revised teaching task arrangement;

[0043] S5: Based on the teaching task adjustment arrangement, check the homework completion status and classroom feedback messages under the adjusted class time, group the submission and feedback information that did not appear in the timetable registration node by class time, and register them in the process record list to obtain the course task implementation status.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0045] In this invention, a knowledge point positioning method is constructed by using homework annotations and time consumption data to build time anchors. Student feedback information is mapped to the session number to form a traceable problem segment. The session number sequence is aggregated to form a continuous content structure, promoting the logical reconstruction of teaching content in time sequence. Classroom interaction records are linked with lesson plan texts to supplement missing examples or task items in the handouts. Content tags are semantically matched with empty sessions in the timetable. Supplementary tasks are inserted into reasonable positions according to content logic. Homework status is bound to class messages with session number, generating an itemized list with task orientation and feedback behavior. This is used to monitor the execution of teaching tasks throughout the process and to locate different sessions and corresponding class information. Attached Figure Description

[0046] Figure 1 This is a system flowchart of the present invention;

[0047] Figure 2 This is a system block diagram of the present invention;

[0048] Figure 3 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0051] See also Figure 1 An education and training management system includes:

[0052] The learning progress module obtains the annotation content and submission time from the homework feedback in the homework task platform, extracts the knowledge point tags associated with the annotations for classification, calculates the homework time based on the homework start and submission timestamps, divides the homework into segments according to preset time intervals, and marks the key problem segments based on the time consumption corresponding to the tags involved in the annotations, thus obtaining the teaching problem items to be processed.

[0053] The content collection module extracts the corresponding teaching chapter number and session number in the course calendar based on the teaching problem items to be processed, analyzes the connectivity between the time arrangements of the numbers, integrates adjacent problem items into a teaching segment, collects them in the teaching schedule and records the cross density, and obtains a centralized teaching content package.

[0054] The material revision module is based on the centralized teaching content package. It calls the text structure and classroom interaction records in the lesson plan archive, screens the items in the handout structure that are missing example descriptions or task activities, extracts the missing content to form a supplementary task list, imports it into the teaching material supplementation channel for registration and filing, and obtains supplementary teaching task records.

[0055] The teaching arrangement module retrieves supplementary teaching task records, calls the adjustable class hour positions and content numbers in the teaching arrangement table, matches the content tags of the supplementary tasks with the uncovered positions in the existing arrangement, inserts new content, updates the teaching progress record, and synchronizes it to the teacher task list interface to obtain the revised teaching task arrangement.

[0056] The task monitoring module adjusts the teaching task schedule, collects the homework submission status and classroom feedback messages for the corresponding class period, compares the correspondence between the student completion records and the task nodes in the teaching plan, collects inconsistent class number and class data, integrates them into a checklist for process tracking, and obtains the course task implementation status.

[0057] The pending teaching issues include knowledge point tag categories, associated annotation time periods, and corresponding time intervals. The centralized teaching content package includes session number segments, teaching chapter mapping groups, and timetable cross-density values. Supplementary teaching task records include missing paragraph numbers, task description entries, and registration path identifiers. Teaching task correction arrangements include updating the session list, inserting content tags, and changing record nodes. The course task implementation status includes class-specific session differences, task matching levels, and feedback data indexes.

[0058] See also Figure 2 The learning progress summary module includes:

[0059] The annotation extraction submodule obtains the annotation content and submission time of the assignment feedback in the assignment task platform, extracts the knowledge point tags associated with each annotation, classifies all annotations according to the tag identifiers, and maps the classified tags and corresponding submission times to the tag set and time set respectively to obtain the tag classification time correspondence set;

[0060] First, based on the fixed structure of the feedback item, the start and end positions of the annotation are identified, and the complete annotation text contained in the field is extracted. Simultaneously, the task process is divided into time periods based on the task time record to indicate the specific time distribution of the tags. After identification, the knowledge point tags contained in the annotation text are extracted. Character filtering is performed according to tag identification rules; for example, "electric field" and "field strength" in "electric field strength remains consistent between the positive and negative plates" are identified as two independent tags. The extracted tag data is then merged into a tag set and divided according to tag content. Tags with consistent expressions or similar semantics are grouped together. If there are three expressions such as "speed," "rate," and "v," they are grouped into the unified tag "speed." After classification, each tag category needs to be structurally associated with the corresponding annotation location for subsequent tracking of the feedback batch to which the tag belongs, thus forming a chain of attribution between annotations and tags. During this process, the statistics on homework time need to be verified in conjunction with classroom interaction records and lesson plan texts. Due to issues such as missing time records and incomplete manual entry, it is necessary to review the actual classroom interaction audio and video records, students' online behavior trajectories, and teachers' grading progress. By comprehensively judging the authenticity and reference value of the time data from multiple dimensions of information, a more accurate basis for dividing time periods can be formed. After completing the tag classification, the feedback data also includes a sequence number field used to identify the order in which annotations are generated. The value of this field can be matched with each tag in the aforementioned tag set to indicate the positional distribution of the tag in the homework process. For example, if the tag "friction" is associated with the sequence number "17", it can be identified that the tag appears in the 17th step of the homework feedback process. This process needs to be executed cyclically among all annotations and tags to form a dual association table of annotation order and tag classification. Lesson plans and classroom records serve as supplementary verification materials, providing valuable information for determining the classroom context and related teaching activities in which tags appear. However, the current materials do not clearly describe how the classroom scene interacts with the annotations. Further analysis of various document resources is needed to clarify the access paths and measurement dimensions, ensuring the effective operation of the entire chain of tag identification, classification, and location. Finally, after completing all classification, matching, and indexing operations, a one-to-one mapping between the tag set and the location set should be established to obtain the tag classification time correspondence set.

[0061] The tag merging submodule is based on the time data in the set corresponding to the tag classification time. It compares the start and end times of the work with the division criteria within the preset time interval, and classifies each tag into a time period according to the corresponding time. It also combines the work duration within the time period to form a matching comparison and obtain the tag segment time distribution data.

[0062] First, the time field of each tag's corresponding data item is extracted into a continuous set of data points. Then, the task process is divided into time periods based on the specific task's time consumption record. A closed interval division scheme is constructed using the start and end positions in the task record. The task process is divided into several segments according to fixed-length time period boundaries. For example, the entire task is divided into five segments, numbered T1 to T5. The time field value of a tag item is assigned based on the time period boundary it falls into. That is, if the time value of a tag A is between the upper and lower boundaries of the second segment T2, then the tag is assigned to segment T2. After all tags have been assigned to segments, the task items within each time period are extracted, and the duration range value within the segment is calculated based on the difference between the start and end positions of the task, thus reflecting the task's time consumption performance within that segment. Subsequently, the number of tags within the segment is compared with the task duration range value to establish a mapping relationship between tag density and task consumption. For example, if there are 4 tags in segment T3 and the task duration range is 17, then the average tag duration unit is 4.25. Based on this logic, the labels and task durations for each time period are compared and matched to obtain the time distribution data for each label segment.

[0063] The content recognition submodule uses the tag frequency and corresponding segment duration information in the tag segment time distribution data to screen tags that have both high frequency of occurrence and long duration within the same time period, extracts the corresponding segment and tag item as key content, and obtains the teaching problem items to be processed.

[0064] First, the tags are initially categorized by section number, forming an index table corresponding to sections and tags. Then, based on the time consumption records of specific assignments, the execution status of the assignments is divided into time periods. On this basis, the frequency of each tag under each section is extracted and statistically summarized by tag name, forming a tag frequency data group. Next, based on the duration information of all assignments under each section, the difference in the execution range of the assignments is used as the basis for time consumption statistics, forming the corresponding section time consumption data group. Since the time consumption is not explicitly recorded in class, there are practical difficulties in implementation. Therefore, it is necessary to consult the accompanying lesson plans and classroom interaction records to estimate the length of assignment execution. Specifically, this can be done by comparing and evaluating the teacher's teaching schedule, student submission times, and interactive Q&A time points to obtain relatively reasonable time consumption estimates. Subsequently, each tag is screened for its frequency and duration within its corresponding segment. The screening criteria establish judgment intervals based on preset frequency and duration benchmarks: the frequency benchmark is set as the weighted deviation threshold of the average number of times the tag appears in the segment; for example, if the average is 5 and the deviation weight is 1.2, the frequency benchmark is 6. The duration benchmark is based on the median duration of the task in the segment, with an upward and downward fluctuation percentage; for example, if the median is 20 minutes and the fluctuation percentage is ±25%, the benchmark interval is 15 to 25 minutes. Tags with a frequency higher than 6 and a duration exceeding 15 to 25 minutes are considered to meet the screening criteria. For example, "Newton's Second Law" has a frequency of 7 and a duration of 27 minutes in segment T4, and should be included in the labeling range. After screening all segments, all tags that simultaneously meet both frequency and duration criteria are extracted and combined according to their segment number to form an output item containing the tag and its corresponding segment, ultimately yielding the teaching problem item to be processed.

[0065] See also Figure 2 The content aggregation module includes:

[0066] The session extraction submodule retrieves the chapter and session numbers corresponding to the teaching issues to be processed from the course calendar, arranges the session numbers in the order of teaching arrangement, selects the positions of the repeated numbers as the sessions of interest, and obtains the session number location set.

[0067] First, the chapter number and course number fields corresponding to the knowledge point need to be extracted from the original record table. The course number is used to associate specific course information in the course calendar, while the chapter number is used to locate its specific teaching session position in the course timeline. For example, if the chapter number of knowledge point K013 is CH04 and the corresponding course number is C02, then the session number corresponding to CH04 needs to be found in the calendar of course C02. If it corresponds to session 08, then this number is marked as the mapped session for this question item in the course timeline. To combine the actual teaching load and learning pace adjustment, it is necessary to further compare the session division with the homework time records, set the start and end time intervals for each homework assignment, match the homework submission time with the student's actual time consumption, aggregate and statistically analyze the average time consumption of the homework corresponding to each session by course number, and link it with the session to which the knowledge point belongs. The intersection point of the corresponding homework time period and its corresponding session number is recorded synchronously in the original record. After extracting the chapter and session numbers for all problem items and matching the assignment time, the sessions are arranged in ascending order of session number. Sessions with duplicate numbers in the sequence are checked and marked. Duplicate occurrence is determined by the same session number appearing more than once in the extracted list. For example, if session number 06 appears three times in the complete sequence, corresponding to three different problem items, then session number 06 should be considered a session requiring attention and added to the session attention set. The identified sessions of attention are then screened a second time to determine if they appear consecutively in the original schedule. If sessions 08, 09, and 10 are consecutive and all in the attention set, a column marking these sessions as continuous feedback segments is added to the record table, and a reminder label is set for them in the course calendar for subsequent processing. This continuity determination is based on whether adjacent positions in the session sequence form an increasing natural number sequence, without using a session difference algorithm to ensure accuracy. Due to potential issues such as data acquisition delays, incomplete student assignment time records, or inconsistencies in subjective reporting during implementation, it is necessary to review course lesson plans and classroom interaction records for correction. Lesson plan review can use course number indexes to match teaching objectives and design content, helping to determine whether the teaching focus of each session overlaps with student feedback. Classroom interaction records can be cross-analyzed based on timestamps and session codes, including dimensions such as quiz time, question frequency, and immediate feedback content, to evaluate the classroom-level representation of knowledge point mastery. The measurement criteria can include the density of interactive events, question focus, and student response time in each session. Finally, all session numbers marked as duplicates or consecutive are summarized and output, generating a pairing table of numbers with course names and chapter names, ultimately resulting in a session number location set.

[0068] The segment generation submodule, based on the section number location set, sequentially connects the question items in adjacent sections, splices together the teaching content composed of consecutive numbers, sets the connected segments as the teaching processing scope, and obtains a group of teaching segment combinations.

[0069] First, when processing all lesson numbers in numerical order, the lesson order should be marked according to the teaching calendar. After sorting the lesson numbers, the time periods should be divided based on the time spent on specific assignments. Based on the sorting, a connection should be established between every two consecutive lessons to determine if the connection constitutes a continuous teaching content chain. Time consumption statistics should be based on the system record of the time students spent completing assignments. If necessary, lesson plans and classroom interaction records should be consulted to confirm the time span and difficulty correspondence between assignment assignment and completion, ensuring the rationality of the time period division. For example, if numbers 07, 08, and 09 appear consecutively and correspond to three different question items, and if question item P1 is associated with 07, P2 with 08, and P3 with 09, then 07 to 09 can be merged into a continuous teaching segment. During processing, the connection determination method should be set as follows: the difference between two lesson numbers is 1, and their corresponding question item category fields are consistent, such as both being "practice feedback type" or "unclear operation type," then they can be classified into the same type of segment. If a segment has consecutive numbers but inconsistent types, it will not be included in the merge chain. For example, segments 10 and 11 belong to "practice feedback type" and "concept omission type" respectively, and need to be separated and labeled independently. After confirming the connection relationship, consecutive numbers are named as segment numbers according to the starting and ending segment numbers. For example, segment A is segment 07-09, and segment B is segment 12-13. Each segment contains a corresponding content field in the identifier table, recording four data items: segment number, content description, student number, and feedback type. After all segments are set, it is also necessary to check whether any segments appear repeatedly in multiple segments. If duplicate segments exist, the segment with the earlier number is retained first, and the segment number is removed from subsequent segments to ensure that there is no overlap or conflict between segments. Finally, the entire segment set is output as a teaching processing list, and each segment is used as an input field for subsequent scheduling optimization and adjustment for association and registration, ultimately resulting in a teaching segment combination group.

[0070] The merging submodule is based on the teaching segment combination group, and fills in the teaching task content covered by the combination group. The covered teaching content is then merged into the original arrangement structure of the teaching task table to obtain a centralized teaching content package.

[0071] First, locate the start and end periods of each segment in the group. Divide the time into time segments based on the homework time records within that time period to improve the accuracy of teaching task scheduling. Search the teaching task content list item by item for the teaching content corresponding to each period number. Each item must include three basic fields: teaching objective, teaching method, and class period tag. Refer to lesson plans and classroom interaction records to verify whether the actual teaching matches the original plan and to help judge the quality and time rationality of task execution. Confirm whether the period number is already registered in the teaching task table. If the registration status is "empty," mark it as a class period to be covered. Then, extract the complete period segments covered by the segment content in the group. Batch import the teaching content corresponding to these segments into the current course plan, inserting them in the original period number order. For example, if segment B covers periods 12 to 14, the corresponding teaching content should be inserted into the scheduling record table in the order of periods 12, 13, and 14. When performing an insertion operation, it is necessary to check for overlapping tasks in each period. If overlapping tasks exist, a replacement operation is performed, and the content before replacement is recorded in the redundant task temporary table for retention. If the period is empty, the task is inserted directly. After insertion, the "Source Segment Number" field of the newly added teaching content needs to be updated to identify the combined segment from which the task originates. Combined with the homework time statistics, the collection dimensions should be clearly defined, such as student completion time, submission frequency, and number of interactive comments, and these should be used to measure the workload of each teaching task. Periods that deviate from the expected time range should be marked in the task structure table for subsequent adjustments. Next, the course number and task tag fields of all filled periods are summarized and integrated with the original teaching task table structure. Blank segments in the structure are uniformly filled to ensure that there are no missing task segments in consecutive class periods. For example, if only periods 10 and 13 were originally planned, the combined group covering segments need to be filled into the positions of periods 11 and 12 to complete the segment closure. After completing the population, all updated lesson content is imported into a unified teaching task structure table, and the consistency of the three parameters—course number, teaching topic, and segment tag—is verified. Finally, a centralized teaching content package is obtained.

[0072] See also Figure 2 The materials revision module includes:

[0073] The content verification submodule is based on the centralized teaching content package. It calls up the text structure and classroom interaction records in the lesson plan file by comparing the chapter number in the teaching task with the corresponding content and teaching task node in the handout. It reads the text paragraphs and interaction descriptions to obtain the chapter content comparison results.

[0074] First, locate the teaching task corresponding to the assigned number. Extract the teaching task nodes from the teaching task list according to course number, teaching stage, and lesson number. Mark the teaching objectives, classroom activities, and practice tasks recorded in the task nodes. Divide the learning time into specific time periods based on the homework time records. Using the time required to complete each assignment as a basis, calculate the average time taken by student submission records and timestamps to help determine the time spent on teaching content both inside and outside of class, thus more accurately corresponding the position of the task node on the actual teaching timeline. Then, retrieve the corresponding chapter text from the lesson plan archive. Set the paragraph extraction method to be distinguished by paragraph number. Divide the paragraph corresponding to each teaching objective into units such as P1, P2, P3, etc. During the statistical operation, pay attention to the consistency between the lesson plan format and paragraph numbering. If there are any issues with formatting or numbering, manual standardization should be performed in advance. Next, keyword matching will be performed between the teaching objectives in each teaching task node and the paragraph titles in the lesson plan. This will be done by directly comparing the keyword coverage. For example, if the teaching objective is "mastering positional changes," the lesson plan paragraph titles will be checked for terms such as "position," "movement," or "path." If a match is found, the paragraph will be added to the matching list. If a match fails, manual review is required to check for discrepancies in wording or unclear semantic coverage. Then, the interaction records for each matching item will be retrieved. These records are from the classroom interaction record sheet. The implementation should adhere to standardized recording formats. Interactions such as questions, answers, discussions, or demonstrations will be located by the teaching period number and time field. Initial screening can be performed by searching for keywords or tags in the interaction records, followed by further semantic comparison with the lesson plan paragraph content. If the teaching objective paragraph mentions "movement route calculation," and the corresponding interaction record contains "student asking about the movement formula" or "teacher demonstrating broken line calculation," then the task node is considered to have triggered valid interaction. If no match is found, the paragraph will be marked as a non-interactive segment. It is important to note that in the implementation phase at the "classroom" level, the current recording method does not clearly reflect the time rhythm and task completion of teaching activities. It is recommended to introduce mechanisms such as time stamping, speaker role labeling, and behavior category standardization to enhance the accessibility and measurability of interaction records, thereby improving the accuracy and operational efficiency of the comparison process. After all comparisons are completed, the results should be output in a form by lesson number. Each record should contain five fields: chapter number, matching segment number, presence of teaching objectives, presence of matching interactive behavior, and a summary of related content. After forming the form, a screening process should be conducted to mark entries with missing teaching objectives or missing interactions as comparison gap records. Finally, the comparison status of all task nodes should be summarized to generate a structured text, yielding the chapter content comparison results.

[0075] The element screening submodule identifies the missing instance descriptions or task activities in each record based on the chapter content comparison results, calculates the proportion of missing items in the teaching items, and lists the missing content in the registration column in the form of a form to obtain a list of missing teaching elements.

[0076] The formula for calculating the percentage of missing items in all teaching items is as follows:

[0077] ;

[0078] in, Representing the The percentage of missing items in a group of teaching segments out of all teaching items. Representing the The first in the group The number of instance description fields that a teaching record should include. Representing the The first in the group The number of task activity fields that a teaching record should include. Representing the The first in the group The weight coefficient of the teaching objective corresponding to each teaching record in relation to the overall content;

[0079] Assumptions: Suppose that the t groups of teaching segments contain a total of m=3 teaching records. Through field label analysis and field content identification, the following monitoring data is obtained:

[0080] The first record should contain the following number of instance description fields: The number of task activity fields should be [number]. The corresponding teaching objective is at the analytical level, and the standard weights for the analytical level are... ;

[0081] The second record should contain the following number of instance description fields: The number of task activity fields should be [number]. The corresponding teaching objective is at the application level, and the weight is set as follows: ;

[0082] The third record should contain the following number of instance description fields: The number of task activity fields should be [number]. The corresponding learning objective is the memory level, and the weight is set as follows: ;

[0083] Substituting into the numerator of the formula, the calculation is as follows:

[0084] Article 1: ;

[0085] Article 2:

[0086] ;

[0087] Article 3: ;

[0088] The total of the numerators is: ;

[0089] The denominator is calculated as follows:

[0090] Article 1: ;

[0091] Article 2: ;

[0092] Article 3: ;

[0093] The sum of the denominators is:

[0094] ;

[0095] Overall formula calculation:

[0096] ;

[0097] The results indicate that the proportion of missing items in the current teaching segment combination is 0.7657% of all teaching items, meaning that a certain proportion of teaching records are missing in instance descriptions or task activity configurations. This ratio serves as an important indicator for screening and sorting the missing content registration column and is used in the analysis of the structural integrity of teaching content and quality assessment and control.

[0098] The task collection submodule is based on the missing teaching element list. It collects the contents recorded in the form into the supplementary task list and sets the folder path in the teaching material upload directory to archive the task list, thus obtaining supplementary teaching task records.

[0099] First, the four fields of chapter number, session position, missing type, and task description in each record of the form are read sequentially. For each record, the corresponding course number field is extracted based on the chapter number. All missing records under the same course number are categorized and arranged in session order to form independent content blocks. Then, each categorized content block is uniformly included in the supplementary task list. To enhance the rationality of task scheduling, supplementary tasks are categorized and segmented in conjunction with homework time records. The time division is based on 15-minute units, and the estimated completion time of each supplementary task is evaluated based on the historical homework time statistics for each session. Time statistics are based on student homework submission records, system check-in data, and student feedback on the interactive platform. The calculation of task completion start and end time, average submission time, and interaction frequency is used to make a comprehensive judgment. If there are omissions or data anomalies, the teacher's lesson plan, class schedule, course objective achievement indicators, and classroom interaction records, including classroom Q&A, blackboard photos, voice discussions, and other supplementary materials, can be consulted to restore and analyze the proportion of missing content in the class, teaching pace, and student participation. If the lesson plan does not clearly indicate whether a certain content has been implemented, the comparison between classroom interaction records and student notes shall prevail. If necessary, video recordings can be used for verification. The supplementary task numbering mechanism is set as course number + session number + abbreviation of the missing item. For example, if course number is C05, session number is 08, and the missing item is task explanation, then the supplementary number would be C05-08-TJ. This task list includes the task number, missing type, session, and task description. After completion, all supplementary tasks are imported into the teaching materials upload directory as documents. The upload directory structure is set as follows: course number as the parent folder name, session number as the subdirectory name. For example, if C05 is the main directory and 08 is the subdirectory, the missing item document should be saved in the C05 / 08 path, with the filename consistent with the supplementary task number, named C05-08-TJ.docx. Subsequently, an index file is created in each session directory, recording all task numbers, descriptions, and creation times in the current subdirectory for easy retrieval and scheduling tracking. After all supplementary tasks are compiled, a unified task list is created. This list includes a number, period, missing type, task name, archive path, and operation status. The operation status is marked with three states: "Not Executed," "Executed," and "Pending Review," indicating whether the task has been inserted into the timetable, entered the teaching scheduling process, or is still under review, respectively. Finally, after all information is summarized, a text record is output, resulting in the supplementary teaching task record.

[0100] See also Figure 2 The teaching arrangement module includes:

[0101] The lesson matching submodule, based on the supplementary teaching task record, looks up the lesson number and content number in the teaching schedule table, compares the coverage relationship between the content tag and the empty number in the supplementary task, and extracts the unarranged position segment in sequence to obtain the supplementary content location result.

[0102] First, based on reading the tag field and course number field of each supplementary content in the task list, and combining the assignment time records, the time is divided into different time periods. Through the time consumption data of specific assignments, the rationality of the task's time commitment and its matching with the class period in the actual teaching schedule is further analyzed. First, the records are grouped and categorized according to course number. Within each group, tasks are sorted by class period order, and a matching key tag field is set for each supplementary task. For example, "experiment explanation," "example supplement," and "case demonstration" are categorized as demonstration tags. Then, all class period numbers and content number fields are retrieved from the teaching schedule table, and all class period entries with a status of "empty" or "unoccupied" are extracted to form a set of vacant class periods, with an accompanying content description field for matching. To improve the accuracy of task insertion, in practice, the actual teaching content and interaction of each class are obtained by reviewing lesson plans and classroom interaction records, thereby providing more accurate semantic supplementation to the class period description. The review process includes accessing teacher feedback systems, course playback data, and student learning feedback questionnaires. Evaluation criteria are based on a comprehensive analysis of data such as interaction frequency, the number of assignments, and the time taken to complete assignments. During the matching phase, the tag field of the supplementary task is compared with the description field of the vacant session using a two-field keyword matching method. For example, if the task tag is "experiment explanation" and the vacant session description includes phrases like "no assigned experiment" or "no demonstration content," a coverage relationship is established, and the task is considered suitable for that session. After all coverage relationships are established, the sessions are sorted in ascending order of their session numbers in the teaching schedule table. For example, if the insertable sessions are 11, 13, and 16, the sorting is 11 < 13 < 16, prioritizing the sessions with the earlier numbers as the positions for supplementary tasks. If a task has multiple fillable options, the first two session positions are prioritized, and the "candidate session" field is marked in the timetable. After completing the session coverage operation for all tasks, the task number, candidate session number, tag matching content, and conflict status are all imported into the results table. The conflict determination criteria are whether there is already a task in the original schedule for that period that is not marked as "replaceable". If there is no conflict, it is marked as "fillable"; if there is a conflict, it is marked as "requires review". The result of supplementing the content location is obtained.

[0103] Based on the supplementary content location results, the content insertion submodule inserts the content tags into the corresponding lesson number in the original order of the lesson, calculates the content coverage span, rearranges the lesson number in the insertion order, writes the updated information into the teaching schedule, and obtains the lesson structure update information.

[0104] The specific formula for calculating the content coverage span is as follows: ;

[0105] in, The representative content covers a wide range, Representing the The content weight value associated with each content tag in the corresponding lesson. Representing the The content tag is numbered at the end of the corresponding lesson. Representing the The starting position number of each content tag in the corresponding lesson. Representing the The number of times each content tag is embedded in a lesson. Representing the The absolute value of the difference between the original lesson number and its rearranged number. This represents the total number of content tags in the current lesson. The total number of all rearranged class hours;

[0106] Assumption: Let the total number of content tags in the current lesson be counted through the course content management system. ;

[0107] For each content tag :

[0108] (Core Concepts);

[0109] (Supplementary knowledge points);

[0110] (Key skills);

[0111] , , , , , ;

[0112] , , ;

[0113] Calculate the coverage value for each content tag using the components in the formula:

[0114] for :

[0115] ;

[0116] for :

[0117] ;

[0118] for :

[0119] ;

[0120] Add the coverage values ​​of the three content tags above:

[0121] ;

[0122] Suppose that during the adjustment of the lesson structure, there are The lesson numbering has changed, and the absolute value of the difference between the original number and the rearranged number is:

[0123] ;

[0124] ;

[0125] ;

[0126] Calculate the average value ;

[0127] Substitute the total coverage value and the average difference into the formula:

[0128] ;

[0129] The results indicate that the coverage span of content tags in the current lesson is 17.2921, suggesting a discrepancy between the distribution range of content tags within the lesson and the adjusted lesson numbering. This value can be used to assess the rationality of the content structure and guide further course optimization.

[0130] The task registration submodule updates information based on the lesson structure, compares the updated content with the original items in the teacher's task list, adds the newly added lesson number and corresponding task tag to the record line, and lists the added record items in the annotation area to obtain the revised arrangement of teaching tasks.

[0131] First, each updated record is compared with the original entries in the teacher task list. Specifically, the course number, session number, and tag content in the updated record are used as the matching criteria. Entries with the same course number are then searched in the teacher task list. If the session number already exists in the task list, that record is extracted. It is then checked whether the task tag field is already filled in the original record. If it is empty, the tag content from the updated record is directly written into that field. If it is not empty but the content is inconsistent, the original tag is added to the comparison result annotation area, and the original record is retained. A "Tag Source" field is added to the newly inserted record to indicate that the task originated from a supplementary arrangement. A "Change Type" field is also added to mark the operation as "supplementary entry" or "replacement" for subsequent filtering. If the session number is not present in the original list, a new record must be added, complete with the five basic fields: course number, session number, task tag, teacher name, and time node. Simultaneously, the time period should be divided based on the homework time record, clearly defining the specific homework content and corresponding tasks. This new record should be marked with its line number and insertion position, and a "Registration Status" label should be added at the end of the record (common labels include "Pending Confirmation," "Synchronized," and "Requires Proofreading"). There are some issues with the homework time statistics during implementation. This needs to be supplemented and verified by reviewing lesson plans and classroom interaction records. The lesson plan review should consider the homework arrangements and objectives of each lesson in the course design, and refer to the actual teaching progress in class to reasonably divide the task completion time. Classroom interaction records can be used to assess the students' actual engagement and the intensity of feedback on task difficulty, thereby measuring the reasonableness of the task completion time. A time period tagging system centered on homework items needs to be established to compare the time distribution of supplemented tasks with the original tasks, and to further optimize teaching arrangements and task allocation. After completing the above operations, merge all supplementary records and updated content to generate a unified update list. This list should include all newly added or modified session numbers and tag content, and record the change operations and record sources. After sorting, group by course number, arrange in ascending order by session number to form a structured list, and finally obtain the revised teaching task arrangement.

[0132] Please see Figure 2 The task monitoring module includes:

[0133] The lesson check submodule adjusts the schedule based on the teaching tasks, reads the homework submission records and classroom feedback messages in the lesson schedule, matches the student submission status and message content in each lesson record to the actual lesson identifier, and sorts the incomplete and completed records by lesson to obtain the lesson task execution identifier table.

[0134] Extract all registered assignment submission records and classroom feedback messages from the class schedule table. The data reading order for each class number is as follows: first, match the course number and session number; then, extract the submission status field from the assignment record and the message text field from the message table. Simultaneously, combine the assignment completion time records to divide the assignment completion status into multiple intervals (e.g., 0–10 minutes, 10–30 minutes, over 30 minutes, etc.) to further refine the assessment of student engagement. When processing the assignment submission status field, each record must be checked to determine whether the field value is "submitted" or "not submitted." For example, if student ID 202305's submission status is "not submitted" in the record for session number 12, this record is included in the incomplete set. Simultaneously, extract the message records for that student in the same session. If the message field is an empty string or the field content length is less than 10 characters, it is considered as not participating in classroom feedback, and this status is marked accordingly. Regarding the statistics of homework time, if there are errors in the system's operational recording or data gaps, verification and supplementation should be carried out by referring to the teaching plan arrangements (including homework instructions) and classroom interaction records (such as answer sheet click records, discussion forum activity, video playback duration, etc.) for that session. The time arrangement nodes in the teaching plan and classroom interaction data can be used to verify the reasonable time range and abnormal fluctuations in homework completion, thereby achieving calibration and reasonable division of time data. Subsequently, the homework status, message status, and time distribution of all students under session number 12 are merged and summarized into a task completion status group for that session. After completing the processing of this group, the next session number is processed in sequence, such as number 13, 14, etc. Throughout the entire process, status recording rules need to be set, that is, the status record under each session number should at least include the following fields: course number, session number, student number, homework status, message status, whether two items are missing, recording time node, homework time range. Each field must have a clear value, and if a field has no data, it should be marked as "missing". After all the lesson numbering is completed, the task completion status of all lessons is summarized into a master table and sorted in ascending order by lesson number. Finally, the task performance record at the lesson level is completed, and the lesson task execution identifier table is obtained.

[0135] The node comparison submodule is based on the lesson task execution identifier table. It calls the lesson number and corresponding target item of the task node in the teaching plan, the corresponding student submission status and task node information position, filters out the lesson information of mismatched cases, and adds the corresponding class mark to obtain the corresponding differences of the task node.

[0136] After extracting each lesson number record, the corresponding task node data listed in the teaching plan needs to be retrieved. In the task node data, three fields are read sequentially: lesson number, course objective, and preset task name. These are then used as a reference standard for comparison with the student submission status in the lesson execution record. In the comparison process, the lesson number is used as an index to locate records with the same number one-to-one. The objective in the task node is cross-analyzed with the student's assignment submission status. If the task objective is "complete chart drawing," but the student's submission status for that lesson is "not submitted" or "empty record," it is directly marked as a mismatch. Further, the classroom feedback field is read. If this field has no relevant content for the same lesson, or is in the form of "no feedback" or "empty section," then the lesson execution is considered unsatisfactory. To enhance the accuracy of judgment, it is necessary to introduce assignment time records to divide time periods. Based on the start and end time fields recorded on the student assignment submission platform, the time consumed by each task is calculated and divided into three intervals: short (≤10 minutes), medium (11–30 minutes), and long (>30 minutes). By mapping task complexity to expected time consumption, if the task objective is "to complete chart drawing" but the time consumed is <10 minutes, and the result is "not submitted" or "empty record," then this anomaly is further strengthened into a "rapid abandonment mismatch." Such records need to be judged in conjunction with classroom interaction records. Classroom interaction records should review the interactive nodes set in the lesson plan and the teacher-student interaction logs generated by the system, including fields such as the number of answers, question reach rate, and roll call response status. By comparing them with the corresponding task session number, if a student does not appear in any interactive node feedback in that session, or the interaction is in a state of "silence," "absence," or "no response," combined with the teaching objectives and interaction frequency requirements of the task in the lesson plan, it can be comprehensively identified as a lack of execution. The aforementioned non-compliant session numbers are centrally compiled to form a preliminary discrepancy list. This list includes fields such as course number, session number, student number, task objective, submission status, feedback content, assignment time range, and interaction status. To ensure traceability of the list information at the class level, the corresponding class name field for each session in the teaching schedule data is extracted and appended to each record in the discrepancy list. Duplicate session and class combinations are then filtered out from the generated discrepancy list. Duplicate items are summarized by class and their discrepancy intensity is marked. If two or more consecutive sessions with mismatch records appear in a single class, that class will be marked as a "concentrated discrepancy area." This marking field is used as the basis for constructing discrepancy ranges and determining centralized intervention in subsequent summarization. The final list is sorted in ascending order by session number and output by class, ultimately yielding the discrepancies corresponding to each task node.

[0137] The results collection submodule, based on the difference values ​​corresponding to task nodes, organizes all difference period and class combination information, categorizes and enters it into the verification form structure, and organizes it into a traceable feedback data list by class dimension to obtain the course task implementation status.

[0138] First, all period records are grouped by the class name field to generate an initial index table for the class dimension. This table lists all period numbers with discrepancies, using the class name as the primary key. Simultaneously, each period is divided into time periods based on the assignment time records. Tasks are categorized into "short-time tasks," "medium-time tasks," and "long-time tasks" based on completion time and submission timeliness. Time period information is appended to the task identifier for subsequent analysis. Next, the target item field and feedback status field attached to each discrepancy record are read, and the target items are categorized. For example, "experimental operation," "graphical analysis," and "report writing" are categorized as E, G, and R task types, respectively, for later task classification in the feedback data list. Finally, the combination of period, task type, and time period information is written as key fields into the verification form structure. The form design includes seven fields: period number, class name, task type, time period, submission status, classroom feedback, and discrepancy level. The difference level field is determined based on whether there are two missing items (i.e., assignment not submitted and no feedback). If both items are missing, it is recorded as Level 3; if only one item is missing, it is recorded as Level 2; if both items are present but do not meet the task objectives, it is recorded as Level 1. The field value uses a numeric level identifier. Assignment time statistics are based on the start and end times of student submission records, file modification timestamps, and online platform operation records. If there are any abnormalities in the implementation, the corresponding lesson plan and classroom interaction records for that period need to be reviewed, including the teaching task arrangement, the frequency of teacher-student interaction, and the content of classroom Q&A, etc. The cause of the abnormality is determined by comparing the planned and actual task execution. Classroom interaction records can be obtained from the teaching management platform, including student online behavior logs, operation tracks of classroom interaction tools (such as answering devices, discussion boards), etc. The lesson plan content is matched with the actual task performance in conjunction with the teaching objectives. The evaluation criteria are whether the task is fully carried out, whether student participation meets expectations, and whether feedback covers key aspects. After filling in the fields, a sub-list is generated for each class, sorted by class name. Each record in the sub-list corresponds to a difference node and includes the source period and record time to ensure traceability integrity. Then, all sub-lists of classes are integrated into a class task feedback master table according to course number. The master table uses course number + class name as a composite primary key and includes a record statistics field at the end of the table, which includes the number of difference periods under the class, the highest difference level, and a list of task types with problems. For example, if a class has 6 difference periods, the highest level is 3, and the task types involved are E and R, then these are filled in the statistics field. Finally, all verification forms and feedback master tables are archived together and output as a text-based structured list, thus obtaining the course task implementation status.

[0139] Please see Figure 3 An educational training method, comprising the following steps:

[0140] S1: Obtain the homework annotations and submission times from the homework task platform, classify the knowledge point tags associated with the annotations, combine the chapter arrangements corresponding to the homework submission time period, and match the high-frequency tags with the teaching content corresponding to the continuous time period to obtain the teaching issues to be processed.

[0141] S2: Based on the teaching issues to be processed, retrieve the corresponding chapter number and session number from the course calendar, arrange the issues within adjacent numbers in chronological order, locate the corresponding content range in the teaching schedule, and obtain the concentrated teaching content package;

[0142] S3: Based on the centralized teaching content package, call the text structure and classroom interaction records in the lesson plan archive, browse the corresponding lecture text, mark the positions where there are missing example descriptions and task content, import them into the task list for unified registration, and obtain supplementary teaching task records.

[0143] S4: Based on the supplementary teaching task records, compare the class hour structure and teaching content number in the teaching schedule table, number and sort the supplementary tasks and the empty positions in the original class hour structure and fill them into the plan table, and record the changed content at the same time to obtain the revised teaching task arrangement;

[0144] S5: Based on the revised arrangement of teaching tasks, check and adjust the completion status of assignments and classroom feedback messages under the class period, group the submissions and feedback information that did not appear in the timetable registration node by class period, and register them in the process record list to obtain the implementation status of course tasks.

[0145] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An education and training management system, characterized in that, The system includes: The learning progress module retrieves the annotations and submission times from the homework feedback, calculates the time spent based on the start and submission timestamps of the homework, divides the work into segments according to time intervals, marks the problem segments based on the time spent corresponding to the tags, and obtains the teaching problem items to be processed. The content collection module locates the chapter number and section number corresponding to the teaching problem item to be processed, concatenates the numbers with adjacent time sequence, embeds them into the existing teaching schedule, identifies the content aggregation situation, and obtains a centralized teaching content package. The material revision module, based on the centralized teaching content package, reviews the lesson plans and classroom interaction records, locates the parts of the handouts that are missing examples and task links, fills in the relevant content and updates the lesson plan registration information to obtain supplementary teaching task records. Based on the supplementary teaching task records, the teaching arrangement module inserts the new tasks into the teaching plan, updates the class time slots, and pushes the task change content according to the content to be supplemented and the blank positions in the teaching arrangement table, so as to obtain the revised teaching task arrangement. The task monitoring module adjusts the arrangement based on the teaching tasks, checks the completion status of the class tasks and classroom feedback, summarizes the class hours and classes with misaligned records, and obtains the implementation status of the course tasks.

2. The education and training management system according to claim 1, characterized in that, The pending teaching issues include knowledge point tag categories, associated annotation time periods, and corresponding time intervals. The centralized teaching content package includes session number segments, teaching chapter mapping groups, and timetable cross-density values. The supplementary teaching task records include missing paragraph numbers, task description entries, and registration path identifiers. The teaching task correction arrangements include updating the session list, inserting content tags, and changing record nodes. The course task implementation status includes class-specific session differences, task matching levels, and feedback data indexes.

3. The education and training management system according to claim 1, characterized in that, The learning progress processing module includes: The annotation extraction submodule obtains the annotation content and submission time of the assignment feedback in the assignment task platform, extracts the knowledge point tags associated with each annotation, classifies all annotations according to the tag identifiers, and maps the classified tags and corresponding submission times to the tag set and time set respectively to obtain the tag classification time correspondence set; The tag merging submodule, based on the time data in the set corresponding to the tag classification time, compares the start and end times of the work within the preset time interval with the division criteria, and categorizes each tag into the corresponding time period. It then combines the work duration within the time period to form a matching comparison, thereby obtaining the tag segment time consumption distribution data. The content recognition submodule, based on the tag frequency and corresponding segment duration information in the tag segment time distribution data, screens tags that have both high frequency of occurrence and long duration within the same time period, extracts the corresponding segments and tag items as key content, and obtains the teaching problem items to be processed.

4. The education and training management system according to claim 1, characterized in that, The content aggregation module includes: The section extraction submodule, based on the teaching question item to be processed, retrieves the chapter number and section number corresponding to the question item in the course calendar, arranges the section numbers in the order of teaching arrangement, selects the repeated number positions as the sections of interest, and obtains the section number location set; The segment generation submodule, based on the section number location set, sequentially connects the question items in adjacent sections, splices together the teaching content composed of consecutive numbers, sets the connected segments as the teaching processing range, and obtains a group of teaching segment combinations. The merging submodule, based on the teaching segment combination group, fills in the corresponding teaching task content covered by the combination group, and merges the covered teaching content into the original arrangement structure of the teaching task table to obtain a centralized teaching content package.

5. The education and training management system according to claim 1, characterized in that, The material revision module includes: The content verification submodule, based on the centralized teaching content package, calls the text structure and classroom interaction records in the lesson plan archive by comparing the chapter number in the teaching task with the corresponding content and teaching task node in the handout, reads the text paragraphs and interactive descriptions, and obtains the chapter content comparison results. Based on the comparison results of the chapter content, the element screening submodule identifies the parts of each record that are missing instance descriptions or task activities, calculates the proportion of missing items in the teaching items, and lists the missing content in the registration column in the form of a form to obtain a list of missing teaching elements. The task collection submodule, based on the missing teaching element list, uniformly collects the contents recorded in the form into the supplementary task list, and sets the folder path in the teaching material upload directory to archive the task list, thus obtaining supplementary teaching task records.

6. The education and training management system according to claim 5, characterized in that, The formula for calculating the percentage of missing items in all teaching items is as follows: ； in, Representing the The percentage of missing items in a group of teaching segments out of all teaching items. Representing the The first in the group The number of instance description fields that a teaching record should include. Representing the The first in the group The number of task activity fields that a teaching record should include. Representing the The first in the group The weight coefficient of the teaching objective corresponding to each teaching record in relation to the overall content.

7. The education and training management system according to claim 1, characterized in that, The teaching arrangement module includes: Based on the supplementary teaching task record, the lesson matching submodule looks up the lesson number and content number in the teaching schedule table, compares the coverage relationship between the content tag and the empty number in the supplementary task, and extracts the unarranged position segment in sequence to obtain the supplementary content location result. Based on the supplementary content location results, the content insertion submodule inserts the content tags into the corresponding lesson number in the original order of the lesson, calculates the content coverage span, rearranges the lesson number in the insertion order, writes the updated information into the teaching schedule, and obtains the lesson structure update information. The task registration submodule updates the lesson structure information, compares the updated content with the original entries in the teacher's task list, adds the newly added lesson number and corresponding task tag to the record row, and lists the added record items in the annotation area to obtain the revised teaching task arrangement.

8. The education and training management system according to claim 7, characterized in that, The specific formula for calculating the content coverage span is as follows: ； in, The representative content covers a wide range, Representing the The content weight value associated with each content tag in the corresponding lesson. Representing the The content tag is numbered at the end of the corresponding lesson. Representing the The starting position number of each content tag in the corresponding lesson. Representing the The number of times each content tag is embedded in a lesson. Representing the The absolute value of the difference between the original lesson number and its rearranged number. This represents the total number of content tags in the current lesson. This represents the total number of all rearranged class hours.

9. The education and training management system according to claim 1, characterized in that, The task monitoring module includes: The lesson check submodule corrects the arrangement based on the teaching task, reads the homework submission records and classroom feedback message data in the lesson arrangement, maps the student submission status and message content in each lesson record to the actual lesson identifier, and classifies the incomplete and completed records by lesson to obtain the lesson task execution identifier table. The node comparison submodule, based on the lesson task execution identifier table, calls the lesson number and corresponding target item of the task node in the teaching plan, the corresponding student submission status and task node information position, filters out the lesson information of mismatched cases, and adds the corresponding class mark to obtain the corresponding differences of the task node. The results collection submodule, based on the differences corresponding to the task nodes, organizes all the difference in terms of class combination information, categorizes and enters it into the verification form structure, and organizes it into a traceable feedback data list by class dimension to obtain the course task implementation status.

10. An educational training method, characterized in that, The implementation of an education and training management system according to any one of claims 1-9 includes the following steps: S1: Obtain the homework annotations and submission times from the homework task platform, classify the knowledge point tags associated with the annotations, combine the chapter arrangements corresponding to the homework submission time period, and match the high-frequency tags with the teaching content corresponding to the continuous time period to obtain the teaching issues to be processed. S2: Based on the teaching problem items to be processed, retrieve the corresponding chapter number and session number from the course calendar, arrange the problem items within adjacent numbers in chronological order, locate the corresponding content range in the teaching schedule, and obtain a centralized teaching content package; S3: Based on the centralized teaching content package, call the text structure and classroom interaction records in the lesson plan archive, browse the corresponding lecture text, mark the positions where there are missing example descriptions and task content, import them into the task list for unified registration, and obtain supplementary teaching task records. S4: Based on the supplementary teaching task record, compare the class hour structure and teaching content number in the teaching schedule table, number and sort the supplementary tasks and the empty positions in the original class hour structure and fill them into the plan table, and record the changed content at the same time to obtain the revised teaching task arrangement; S5: Based on the teaching task adjustment arrangement, check the homework completion status and classroom feedback messages under the adjusted class time, group the submission and feedback information that did not appear in the timetable registration node by class time, and register them in the process record list to obtain the course task implementation status.

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