A learning progress real-time tracking and feedback optimization method and system for online teaching

Abstract

The application relates to the technical field of online teaching supervision, and discloses a learning progress real-time tracking and feedback optimization method and system for online teaching, which realizes online teaching progress supervision based on knowledge point fragmentation by intelligently cutting online teaching videos according to target online teaching time sequence knowledge point information and target online teaching videos and in combination with video processing software; realizes online learning effect immersive testing by efficiently and timely judging the online learning completion state of online learning students based on student online learning achievement oral text information and target online teaching time sequence knowledge point information; and realizes point-to-point efficient pushing of knowledge point videos that students do not understand in online teaching knowledge point fragmentation learning by intelligently constructing online teaching videos that students do not understand according to student online learning time sequence text information and target online teaching fragmentation videos and in combination with video processing software.

Description

Technical Field

[0001] This invention relates to the technical field of online teaching supervision, specifically to a method and system for real-time tracking, feedback, and optimization of learning progress in online teaching. Background Technology

[0002] Online learning progress tracking and feedback monitoring systems utilize internet technology to dynamically monitor students' learning progress and effectiveness, aiming to ensure learning quality. Current systems typically use dynamic identity verification and post-class quizzes to record students' actual learning progress and outcomes. However, in practice, these methods fail to effectively enhance students' learning interest and effectiveness. Furthermore, the addition of various identity verification, attention verification, and post-class quizzes creates a coercive atmosphere, reducing student interest and increasing study time. Existing systems also fail to enable fragmented learning of online knowledge points or immersive interactive feedback, further diminishing student interest and learning outcomes.

[0003] Chinese invention patent application CN115115482A, published on September 27, 2022, discloses a monitoring method, device, and storage medium for online video teaching. This method responds to student clicks that trigger online learning, which includes two stages: online video and online exams. The online video begins playing; it randomly triggers student identity and liveness verification, pausing the video playback and saving its current progress; after successful verification, playback resumes based on the current progress; once the student has completed the online video learning, it marks the video as watched and initiates an online exam; after passing the exam, the video duration is recorded as valid learning time. However, this technical solution fails to achieve fragmented learning of knowledge points or immersive interactive feedback, thus reducing student interest and learning effectiveness. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing online teaching progress tracking and feedback systems, which fail to enable fragmented learning of online teaching knowledge points or immersive interactive feedback, thus reducing student interest and learning effectiveness, this new system aims to achieve the following: accurate collection of online teaching videos, dynamic generation of online teaching sequence text information, intelligent extraction of online teaching sequence knowledge points, fragmented video editing and playback, step-by-step collection of student online learning outcome oral text information, scientific assessment of student online learning completion status, autonomous identification of student-ununderstood online teaching sequence knowledge points, accurate identification of student-ununderstood online teaching sequence text information, autonomous construction of student-ununderstood online teaching videos, immersive interactive monitoring of online teaching progress and effectiveness, and ultimately, improved student interest and learning effectiveness in online teaching.

[0005] (II) Technical Solution This invention is achieved through the following technical solution: a method for real-time tracking and feedback optimization of learning progress in online teaching, the method comprising the following steps: S1. Collect target online teaching videos, perform text information conversion processing based on the online teaching videos to obtain target online teaching time-series text information; extract text information from online teaching knowledge points to obtain target online teaching time-series knowledge point information; trim online teaching videos to obtain target online teaching fragmented videos and execute online teaching video playback tasks. S2. Collect students' spoken text information about their online learning outcomes, judge and process the students' online learning completion status, and obtain the students' online learning completion status judgment information; when completed, execute the next online teaching fragmented video playback assignment until all online teaching fragmented video playback assignments are completed; S3. When not completed, identify and process the online teaching knowledge points that the online learners do not understand to obtain the information on the online teaching sequence knowledge points that the learners do not understand; identify and process the online teaching text information that the online learners do not understand to obtain the online teaching sequence text information that the learners do not understand; construct and process the online teaching videos that the online learners do not understand to obtain the online teaching videos that the learners do not understand and execute the online teaching video playback task.

[0006] Preferably, the steps are as follows: First, collect the target online teaching video; second, perform text information conversion processing on the online teaching video to obtain the target online teaching time-series text information; third, perform text information extraction processing on the online teaching knowledge points to obtain the target online teaching time-series knowledge point information; fourth, perform video trimming processing to obtain the target online teaching fragmented video, and then execute the online teaching video playback assignment. S11. Obtain the teaching videos that students watch online through an online education platform and obtain the target online teaching video. The online education platform includes any one of NetEase Cloud Classroom, Tencent Classroom, and Xueersi Online School. S12. Using video processing software, the target online teaching video is translated into teaching text information based on timestamps, and target online teaching time-series text information is generated; the target online teaching time-series text information represents a combination of online teaching text and time features generated based on the time features of the online teaching video; the video processing software includes either FunClip or SAM3; S13. Using a large text AI model, the target online teaching time-series text information is analyzed and refined to extract online teaching knowledge points, and a target online teaching time-series knowledge point information set is generated. ,in , and They represent the first and second parts of the extraction process. The first, the second and the third The target online teaching time sequence knowledge point information; the target online teaching time sequence knowledge point information represents the combination information of online teaching text and time features constructed based on the time characteristics of online teaching videos and online teaching knowledge points; the text AI big model includes any one of Baidu Wenxin Yiyan, Alibaba Tongyi Qianwen, and Tencent Hunyuan. S14. Based on the target online teaching time sequence knowledge point information set and the target online teaching video, perform online teaching video trimming processing to obtain the target online teaching fragmented video set and execute the online teaching video playback task.

[0007] Preferably, the steps for processing online teaching videos by trimming them according to the target online teaching time-series knowledge point information set and the target online teaching videos to obtain a target online teaching fragmented video set and then performing the online teaching video playback task are as follows: S141. Using video processing software, based on the target online teaching time-series knowledge point information set... The target online teaching time sequence knowledge point information described in the article to The target online teaching video is segmented according to the corresponding time period, and a fragmented video set of the target online teaching is generated. ,in This represents the target online teaching time sequence knowledge point information. The corresponding target online teaching fragmented videos; This represents the target online teaching time sequence knowledge point information. The corresponding target online teaching fragmented videos; This represents the target online teaching time sequence knowledge point information. The corresponding target online teaching fragmented videos; S142. Using an online education platform to provide the target online teaching fragmented video set The target online teaching fragmented video described in the article Assignments include playing online teaching videos.

[0008] Preferably, the following steps are taken: Collect students' spoken text information about their online learning outcomes; process and judge the students' online learning completion status to obtain completion status information; when completion is achieved, proceed to the next online teaching fragmented video playback assignment until all online teaching fragmented video playback assignments are completed: S21. Collect online learning text information of students' oral statements about the teaching knowledge after learning online teaching videos through microphone, and generate oral text information of students' online learning results; S22. Based on the student's online learning outcome oral transcript information and the target online teaching sequence knowledge point information. The system processes the online learning completion status of learners to obtain their online learning completion status information. When completion is achieved, the system executes the next online teaching fragmented video playback assignment until all online teaching fragmented video playback assignments are completed.

[0009] Preferably, the learning outcomes are based on the student's spoken text information and the target online teaching sequence knowledge point information. The process involves determining the online learning completion status of learners and obtaining their completion status information. Upon completion, the next online teaching video playback assignment is executed until all online teaching video playback assignments are completed. The steps are as follows: S221. Obtain the student's online learning outcome spoken text information and the target online teaching sequence knowledge point information. ; S222. Based on a text-based AI model, the student's spoken text information about online learning outcomes is compared with the target online teaching time-series knowledge point information. Analyze the online teaching text information of online learners, and generate online learning completion status judgment information based on the analysis results; When the student's online learning outcome verbal text information and The analysis of online learning text information from online learners is consistent, indicating that online learners have fully mastered the material. If the corresponding teaching knowledge point is identified, the online learning completion status of the student will be output as "completed," and execution will continue. The corresponding online teaching fragmented video playback assignments will continue until the target online teaching fragmented video set is completed. The target online teaching fragmented video described in the article to Corresponding online teaching assignments using fragmented video playback; When the student's online learning outcome verbal text information and The inconsistent analysis of online learning text information among online learners indicates that online learners have not fully grasped the material. For the corresponding teaching knowledge points, the output information indicating the student's online learning completion status is "incomplete".

[0010] Preferably, when incomplete, the following steps are taken: identifying and processing the online teaching knowledge points that online learners do not understand to obtain information on the sequence of online teaching knowledge points that students do not understand; identifying and processing the online teaching text information that online learners do not understand to obtain information on the sequence of online teaching text that students do not understand; and constructing and processing the online teaching videos that online learners do not understand to obtain the online teaching videos that students do not understand and then executing the online teaching video playback assignment: S31. When the online learning completion status of the student is determined to be incomplete, the text AI model is used to determine the online learning outcome based on the student's spoken text information and the target online teaching sequence knowledge point information. Text analysis of online teaching knowledge points was performed to identify the target online teaching time-series knowledge point information. The system identifies online teaching content that students did not understand, and constructs a list of these content. This information represents a combination of online teaching text and time features, built upon the temporal characteristics of the online teaching video and the online teaching content that students did not understand. S32. Based on the information of online teaching sequence knowledge points that the students do not understand and the target online teaching sequence text information, perform online teaching text information recognition and processing that the students do not understand to obtain online teaching sequence text information that the students do not understand. S33. Based on the online teaching sequence text information that the student did not understand and the target online teaching fragmented video... The system processes online teaching videos that students did not understand, generates these videos, and then performs the online teaching video playback task.

[0011] Preferably, the steps for identifying and processing the online teaching text information that the student did not understand, based on the student's uncomprehended online teaching sequence knowledge point information and the target online teaching sequence text information, to obtain the student's uncomprehended online teaching sequence text information are as follows: S321. Using the KMP search algorithm, the time feature matching is performed between the online teaching time sequence knowledge point information that the student does not understand and the target online teaching time sequence text information to search for the online teaching time sequence text information corresponding to the online teaching time sequence knowledge point information that the student does not understand, and the online teaching time sequence text information that the student does not understand is constructed; the online teaching time sequence text information that the student does not understand represents the combination information of online teaching text and time features constructed based on the time features of the online teaching video and the online teaching text information that the student does not understand.

[0012] Preferably, based on the online teaching sequence text information that the student did not understand and the target online teaching fragmented video... The steps for creating and processing online teaching videos that students did not understand, and then executing online teaching video playback assignments, are as follows: S331. The Boyer-Moore search algorithm is used to compare the online teaching time-series text information that the students did not understand with the target online teaching fragmented video. Time feature matching is performed to search for teaching video information corresponding to the online teaching time sequence text information that the student did not understand. Then, video processing software is used to extract the teaching videos corresponding to the online teaching time sequence text information that the student did not understand from the target online teaching fragmented videos. The video is then cropped and output, and after data identification, a "student-uncomprehended online teaching video" is generated. This "student-uncomprehended online teaching video" refers to the fragmented online teaching video of the target program. Online learners did not understand the teaching videos corresponding to the knowledge points they were learning. S332. Perform the online teaching video playback task according to step S142 for the online teaching video that the student did not understand.

[0013] A real-time learning progress tracking and feedback optimization system for online teaching is provided to implement the aforementioned real-time learning progress tracking and feedback optimization method for online teaching. The system includes an online teaching progress control module, an online teaching verification module, and an online teaching optimization module. The online teaching progress control module includes an online teaching video acquisition unit, an online teaching text information construction unit, an online teaching knowledge point acquisition unit, an online teaching video trimming unit, and an online teaching video playback unit; The online teaching video acquisition unit acquires target online teaching videos through an online education platform; the online teaching text information construction unit converts and processes the online teaching video text information based on the target online teaching videos and combines it with video processing software to obtain target online teaching time-series text information; the online teaching knowledge point acquisition unit extracts and processes the online teaching knowledge point text information based on the target online teaching time-series text information and combines it with a text AI model to obtain target online teaching time-series knowledge point information; the online teaching video trimming unit trims the online teaching video based on the target online teaching time-series knowledge point information and the target online teaching videos and combines it with video processing software to obtain target online teaching fragmented videos; the online teaching video playback unit performs online teaching video playback tasks based on the target online teaching fragmented videos and in conjunction with the online education platform. The online teaching verification module includes a student online learning outcome collection unit and a student online learning completion status judgment unit. The online learning outcome collection unit collects the spoken text information of the online learning outcome of the students through a microphone; the online learning completion status judgment unit performs online learning completion status judgment processing based on the spoken text information of the online learning outcome of the students and the target online teaching time sequence knowledge point information to obtain the online learning completion status judgment information of the students. The online teaching optimization module includes a unit for identifying online teaching knowledge points that students do not understand, a unit for identifying online teaching text information that students do not understand, and a unit for constructing online teaching videos that students do not understand. The "Unrecognized Online Teaching Knowledge Points" unit identifies unrecognized online teaching knowledge points based on the student's spoken text information of online learning outcomes and the target online teaching time-series knowledge point information, combined with a text AI model, to obtain information on unrecognized online teaching time-series knowledge points. The "Unrecognized Online Teaching Text Information" unit identifies unrecognized online teaching text information based on the unrecognized online teaching time-series knowledge point information and the target online teaching time-series text information, to obtain information on unrecognized online teaching time-series text information. The "Unrecognized Online Teaching Video Construction Unit" constructs unrecognized online teaching videos based on the unrecognized online teaching time-series text information and the target online teaching fragmented video, combined with video processing software, to obtain unrecognized online teaching videos.

[0014] (III) Beneficial Effects This invention provides a method and system for real-time tracking, feedback, and optimization of learning progress in online teaching. It offers the following advantages: I. Accurately acquire target online teaching videos through online education platforms, providing real data support for fragmented learning of online teaching knowledge points; efficiently translate online teaching video text information based on target online teaching videos and combined with video processing software; simultaneously, intelligently and accurately extract online teaching knowledge point text information using a large text AI model, realizing the construction of a multimodal data format of teaching videos, teaching text, and teaching knowledge points, thereby improving the scientific nature of online teaching; intelligently trim online teaching videos based on the target online teaching time-series knowledge point information and target online teaching videos, and scientifically fragment online teaching video playback assignments using the online education platform, realizing the monitoring of online teaching progress based on fragmented knowledge points, thereby improving the scientific and intelligent nature of online teaching progress monitoring.

[0015] Second, by using microphones to promptly acquire students' spoken text information about their online learning outcomes, fragmented and dynamic monitoring of students' learning effectiveness can be achieved. Based on students' spoken text information about their online learning outcomes and the knowledge points of the target online teaching sequence, the completion status of students' online learning can be efficiently and promptly judged, enabling immersive verification of online teaching effectiveness and increasing the online teaching learning atmosphere and students' learning interest.

[0016] Third, by combining students' spoken text information about their online learning outcomes with the target online teaching sequence knowledge points and a large-scale text AI model, intelligent identification of online teaching knowledge points that students did not understand is achieved, enabling precise capture of knowledge points that students did not understand during fragmented online teaching learning; efficient identification of online teaching text information that students did not understand is based on the target online teaching sequence knowledge point information and the target online teaching sequence text information, enabling precise collection of teaching text information that students did not understand during fragmented online teaching learning; and intelligent construction of online teaching videos that students did not understand is based on the target online teaching fragmented video information and video processing software, enabling efficient point-to-point collection of online teaching knowledge point videos that students did not understand during fragmented online teaching learning, enabling precise push of knowledge points that students did not understand, improving the intelligence and applicability of online teaching feedback, and enhancing the learning quality of online learning students. Attached Figure Description

[0017] Figure 1 A schematic diagram of a module for a real-time tracking and feedback optimization system for online teaching provided by the present invention; Figure 2 The flowchart illustrates a method for real-time tracking and feedback optimization of learning progress in online teaching, as provided by this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] An embodiment of a method and system for real-time tracking, feedback and optimization of learning progress in online teaching is as follows: Example 1: Please see Figures 1-2 A method for real-time tracking and feedback optimization of learning progress in online teaching, comprising the following steps: S1. Collect target online teaching videos, perform text information conversion processing based on the online teaching videos to obtain target online teaching time-series text information; extract text information from online teaching knowledge points to obtain target online teaching time-series knowledge point information; trim online teaching videos to obtain target online teaching fragmented videos and execute online teaching video playback tasks. S2. Collect students' spoken text information about their online learning outcomes, judge and process the students' online learning completion status, and obtain the students' online learning completion status judgment information; when completed, execute the next online teaching fragmented video playback assignment until all online teaching fragmented video playback assignments are completed; S3. When not completed, identify and process the online teaching knowledge points that the online learners do not understand to obtain the information on the online teaching sequence knowledge points that the learners do not understand; identify and process the online teaching text information that the online learners do not understand to obtain the online teaching sequence text information that the learners do not understand; construct and process the online teaching videos that the online learners do not understand to obtain the online teaching videos that the learners do not understand and execute the online teaching video playback task.

[0020] For further details, please refer to Figures 1-2 The steps for collecting target online teaching videos, converting and processing the online teaching videos into textual information to obtain the target online teaching timeline text information, extracting and refining the textual information of the online teaching knowledge points to obtain the target online teaching timeline knowledge point information, and cropping the online teaching videos to obtain fragmented online teaching videos and then executing the online teaching video playback assignment are as follows: S11. Obtain the teaching videos that students watch online through online education platforms and obtain the target online teaching videos. The online education platforms include any one of NetEase Cloud Classroom, Tencent Classroom, and Xueersi Online School. S12. Using video processing software, the target online teaching video is translated into teaching text information based on timestamps, and target online teaching time-series text information is generated; the target online teaching time-series text information represents the combination of online teaching text and time features generated based on the time features of the online teaching video; the video processing software includes either FunClip or SAM3; S13. Analyze and extract online teaching knowledge points from the target online teaching time-series text information using a large text AI model, and generate a target online teaching time-series knowledge point information set. ,in , and They represent the first and second parts of the extraction process. The first, the second and the third The target online teaching time sequence knowledge point information; the target online teaching time sequence knowledge point information represents the combination information of online teaching text and time features constructed based on the time characteristics of online teaching videos and online teaching knowledge points; the text AI big model includes any one of Baidu Wenxin Yiyan, Alibaba Tongyi Qianwen, and Tencent Hunyuan. S14. Based on the target online teaching time sequence knowledge point information set and the target online teaching video, perform online teaching video trimming to obtain the target online teaching fragmented video set and execute the online teaching video playback task.

[0021] The steps for trimming online teaching videos based on the target online teaching timeline knowledge point information set and the target online teaching videos to obtain a fragmented set of target online teaching videos and then performing the online teaching video playback assignment are as follows: S141. Using video processing software, based on the target online teaching time sequence knowledge point information set Information on the sequence of knowledge points in online teaching for middle school students to The corresponding time period is used to segment the target online teaching videos and generate a fragmented video set of the target online teaching videos. ,in This indicates the sequence of knowledge points in the online teaching. The corresponding target online teaching fragmented videos; This indicates the sequence of knowledge points in the online teaching. The corresponding target online teaching fragmented videos; This indicates the sequence of knowledge points in the online teaching. The corresponding target online teaching fragmented videos; S142. Using online education platforms to provide fragmented online teaching video sets for the target audience. Fragmented online teaching videos for medium-sized targets Assignments include playing online teaching videos.

[0022] The online teaching video acquisition unit accurately acquires target online teaching videos using an online education platform, providing real data support for fragmented learning of online teaching knowledge points. The online teaching text information construction unit and the online teaching knowledge point acquisition unit work together to efficiently translate the online teaching video text information based on the target online teaching video and using video processing software. Simultaneously, they utilize a large-scale text AI model to intelligently and accurately extract the online teaching knowledge point text information, achieving multi-modal data construction of teaching videos, teaching texts, and teaching knowledge points, thus improving the scientific nature of online teaching. The online teaching video trimming unit and the online teaching video playback unit work together to intelligently trim the online teaching video based on the target online teaching sequence knowledge point information and the target online teaching video, using video processing software. They then combine this with the online education platform to scientifically and fragmentedly execute online teaching video playback assignments, enabling the monitoring of online teaching progress based on fragmented knowledge points, thus improving the scientific and intelligent nature of online teaching progress monitoring.

[0023] For further details, please refer to Figures 1-2 The system collects students' spoken text information about their online learning outcomes, processes and judges their online learning completion status, and obtains completion status information. When completion is achieved, the next online teaching fragmented video playback assignment is executed until all online teaching fragmented video playback assignments are completed. The operation steps are as follows: S21. Collect online learning text information of students' oral statements about the teaching knowledge after learning online teaching videos through microphone, and generate oral text information of students' online learning results; S22. Based on the students' online learning outcomes, oral transcripts, and the target online teaching sequence of knowledge points. The system processes the online learning completion status of learners to obtain their online learning completion status information. When completion is achieved, the system executes the next online teaching fragmented video playback assignment until all online teaching fragmented video playback assignments are completed.

[0024] Based on the students' online learning outcomes, oral transcripts, and the target online teaching sequence of knowledge points. The process involves determining the online learning completion status of learners and obtaining their completion status information. Upon completion, the next online teaching video playback assignment is executed until all online teaching video playback assignments are completed. The steps are as follows: S221. Obtain the students' online learning outcome oral transcripts and the target online teaching sequence of knowledge points. ; S222. Based on a text-based AI model, integrate students' online learning outcomes with their spoken text information and the target online teaching time-series knowledge points. Analyze the online teaching text information of online learners, and generate online learning completion status judgment information based on the analysis results; When students verbally present their online learning outcomes in text format and... The analysis of online learning text information from online learners is consistent, indicating that online learners have fully mastered the material. If the corresponding teaching knowledge point is selected, the online learning completion status of the student will be output as "completed," and execution will continue. The corresponding online teaching fragmented video playback assignments will continue until the target is achieved. Fragmented online teaching videos for medium-sized targets to Corresponding online teaching assignments using fragmented video playback; When students verbally present their online learning outcomes in text format and... The inconsistent analysis of online learning text information among online learners indicates that online learners have not fully grasped the material. For the corresponding teaching knowledge points, the output message indicating that the student's online learning completion status is "incomplete".

[0025] The online learning outcome collection unit uses microphones to promptly acquire students' spoken text information about their online learning outcomes, enabling fragmented and dynamic monitoring of students' learning effectiveness. The online learning completion status judgment unit efficiently and promptly judges students' online learning completion status based on their spoken text information about their online learning outcomes and the target online teaching sequence knowledge points, achieving immersive verification of online teaching learning effectiveness and enhancing the online teaching learning atmosphere and students' learning interest.

[0026] For further details, please refer to Figures 1-2 When incomplete, the following steps are taken: First, identify and process the online teaching knowledge points that students do not understand to obtain information on the sequence of knowledge points they do not understand. Second, identify and process the online teaching text information that students do not understand to obtain information on the sequence of text information they do not understand. Third, construct and process the online teaching videos that students do not understand to obtain the online teaching videos they do not understand and execute the online teaching video playback assignment. S31. When the online learning completion status of a student is judged as incomplete, the text AI model is used to combine the student's spoken text information about their online learning outcomes with the target online teaching sequence knowledge points. Conduct text analysis of online teaching knowledge points to identify the target online teaching time sequence knowledge point information. The information on online teaching time sequence knowledge points that students did not understand is identified, and this information is constructed. The information on online teaching time sequence knowledge points that students did not understand represents a combination of online teaching text and time features constructed based on the time characteristics of online teaching videos and the online teaching knowledge points that students did not understand. S32. Based on the online teaching sequence knowledge points that students do not understand and the target online teaching sequence text information, identify and process the online teaching text information that students do not understand to obtain the online teaching sequence text information that students do not understand. S33. Based on the online teaching sequence text information that students did not understand, and the target online teaching fragmented videos. The system processes online teaching videos that students did not understand, generates these videos, and then performs the online teaching video playback task.

[0027] The steps for identifying and processing the online teaching text information that students do not understand, based on the knowledge points and target text information of the online teaching sequence, are as follows: S321. The KMP search algorithm is used to match the time features of the online teaching sequence knowledge points that students do not understand with the target online teaching sequence text information, search for the online teaching sequence text information corresponding to the online teaching sequence knowledge points that students do not understand, and construct the online teaching sequence text information that students do not understand; the online teaching sequence text information that students do not understand represents the combination information of online teaching text and time features constructed based on the time features of online teaching videos and the online teaching text information that students do not understand.

[0028] Based on students' lack of understanding of online teaching sequence text information and fragmented online teaching videos. The steps for creating and processing online teaching videos that students did not understand, and then executing online teaching video playback assignments, are as follows: S331. The Boyer-Moore search algorithm is used to combine the online teaching time-series text information that students do not understand with the target online teaching fragmented video. Time feature matching was performed to search for teaching video information corresponding to the online teaching time sequence text information that students did not understand. Then, video processing software was used to extract the teaching videos corresponding to the online teaching time sequence text information that students did not understand from the target online teaching fragmented videos. The video is then cropped and output, and after data labeling, a "student-uncomprehended online teaching video" is generated. This "student-uncomprehended online teaching video" represents fragmented online teaching videos. Online learners did not understand the teaching videos corresponding to the knowledge points they were learning. S332. Perform the online teaching video playback task according to step S142 for online teaching videos that students do not understand.

[0029] The system employs several key technologies: First, a unit identifies online teaching knowledge points that students don't understand. This unit uses students' spoken text about their online learning outcomes and the target online teaching sequence to intelligently identify these points using a large-scale AI text model. This allows for precise capture of knowledge points not understood during fragmented learning. Second, a unit identifies online teaching text information that students don't understand. This unit efficiently identifies text information that students don't understand during fragmented learning, based on both the student's spoken text and the target online teaching sequence. This allows for precise collection of text information that students don't understand during fragmented learning. Third, a unit constructs online teaching videos that students don't understand. This unit uses the student's spoken text and the target online teaching fragments, combined with video processing software, to intelligently construct videos that students don't understand during fragmented learning. This allows for efficient point-to-point collection of videos containing knowledge points that students don't understand, enabling precise delivery of these knowledge points and improving the intelligence and applicability of online teaching feedback, ultimately enhancing the learning quality of online learners.

[0030] Example 2: Please see Figures 1-2 A real-time learning progress tracking and feedback optimization system for online teaching is provided to implement a method for real-time tracking and feedback optimization of learning progress in online teaching. The system includes an online teaching progress control module, an online teaching verification module, and an online teaching optimization module. The online teaching progress control module includes an online teaching video acquisition unit, an online teaching text information construction unit, an online teaching knowledge point acquisition unit, an online teaching video trimming unit, and an online teaching video playback unit; The system comprises the following components: an online teaching video acquisition unit, which acquires target online teaching videos through an online education platform; an online teaching text information construction unit, which transforms and processes the online teaching video text information using video processing software to obtain target online teaching time-series text information; an online teaching knowledge point acquisition unit, which extracts and processes online teaching knowledge point text information using the target online teaching time-series text information and a large text AI model to obtain target online teaching time-series knowledge point information; an online teaching video trimming unit, which trims the online teaching video based on the target online teaching time-series knowledge point information and the target online teaching video using video processing software to obtain target online teaching fragmented videos; and an online teaching video playback unit, which performs online teaching video playback tasks based on the target online teaching fragmented videos and in conjunction with the online education platform. The online teaching assessment module includes a unit for collecting students' online learning outcomes and a unit for judging students' online learning completion status. The online learning outcome collection unit collects the spoken text information of students' online learning outcomes through a microphone; the online learning completion status judgment unit judges the online learning completion status of students based on the spoken text information of students' online learning outcomes and the target online teaching sequence knowledge point information, and obtains the online learning completion status judgment information of students. The online teaching optimization module includes a unit for identifying online teaching knowledge points that students do not understand, a unit for identifying online teaching text information that students do not understand, and a unit for constructing online teaching videos that students do not understand. The system comprises three parts: a student-ununderstood online teaching knowledge point identification unit, a student-narrated online learning outcome knowledge point identification unit, a target online teaching timeline knowledge point identification unit, and a text AI model to identify and process online teaching knowledge points that students did not understand; a student-ununderstood online teaching text information identification unit, a student-ununderstood online teaching text information identification unit, and a student-ununderstood online teaching video construction unit, which constructs online teaching videos that students did not understand based on the student-ununderstood online teaching text information and target online teaching fragmented videos, using video processing software.

[0031] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for real-time tracking and feedback optimization of learning progress in online teaching, characterized in that, The method includes the following steps: S1. Collect target online teaching videos, perform text information conversion processing based on the online teaching videos to obtain target online teaching time sequence text information; perform text information extraction processing on the online teaching knowledge points to obtain target online teaching time sequence knowledge point information; The online teaching videos are trimmed to obtain fragmented online teaching videos, and the online teaching video playback task is executed. S2. Collect students' spoken text information about their online learning outcomes, judge and process the students' online learning completion status, and obtain the students' online learning completion status judgment information; when completed, execute the next online teaching fragmented video playback assignment until all online teaching fragmented video playback assignments are completed; S3. When not completed, identify and process the online teaching knowledge points that the online learners do not understand, and obtain information on the online teaching sequence knowledge points that the learners do not understand. The system identifies and processes online teaching text information that learners do not understand, resulting in the time-series text information of online teaching that learners do not understand. The system processes online teaching videos that learners did not understand, generates these videos, and then executes the online teaching video playback assignment.

2. The method for real-time tracking and feedback optimization of learning progress in online teaching according to claim 1, characterized in that: The operation steps of S1 are as follows: S11. Obtain the teaching videos that students watch online through online education platforms, and obtain the target online teaching videos; S12. Using video processing software, the target online teaching video is translated into teaching text information based on timestamps, and target online teaching time sequence text information is generated. S13. Using a large text AI model, the target online teaching time-series text information is analyzed and refined to extract online teaching knowledge points, and a target online teaching time-series knowledge point information set is generated. The include , and ;in , and They represent the first and second parts of the extraction process. The first, the second and the third Information on the sequential knowledge points of online teaching for each objective; S14. Based on the target online teaching time sequence knowledge point information set and the target online teaching video, perform online teaching video trimming processing to obtain the target online teaching fragmented video set and execute the online teaching video playback task.

3. The method for real-time tracking and feedback optimization of learning progress in online teaching according to claim 2, characterized in that: The operation steps of S14 are as follows: S141. Using video processing software, based on the target online teaching time-series knowledge point information set... The above to The target online teaching video is segmented according to the corresponding time period, and a fragmented video set of the target online teaching is generated. The include , and ;in Indicates the The corresponding target online teaching fragmented videos; Indicates the The corresponding target online teaching fragmented videos; Indicates the The corresponding target online teaching fragmented videos; S142. The above-mentioned education is conducted through an online education platform. The above Assignments include playing online teaching videos.

4. The method for real-time tracking and feedback optimization of learning progress in online teaching according to claim 3, characterized in that: The operation steps of S2 are as follows: S21. Collect online learning text information of students' oral statements about the teaching knowledge after learning online teaching videos through microphone, and generate oral text information of students' online learning results; S22, Based on the student's online learning outcome oral text information, the... The system processes the online learning completion status of learners to obtain their online learning completion status information. When completion is achieved, the system executes the next online teaching fragmented video playback assignment until all online teaching fragmented video playback assignments are completed.

5. The method for real-time tracking and feedback optimization of learning progress in online teaching according to claim 4, characterized in that: The operation steps of S22 are as follows: S221. Obtain the student's online learning outcome spoken text information and the... ; S222. Based on a text AI big data model, the student's online learning outcome spoken text information is compared with the... Analyze the online teaching text information of online learners, and generate online learning completion status judgment information based on the analysis results; When the student's online learning outcome verbal text information and If the online teaching text information of the online learners is identical, then the online learning completion status judgment information of the learners is output as "completed," and execution continues. The corresponding online teaching fragmented video playback assignments will continue until the completion of the above. The above to Corresponding online teaching assignments using fragmented video playback; When the student's online learning outcome verbal text information and If the online teaching text information of the learners is different, the output of the learner's online learning completion status information will be "incomplete".

6. The method for real-time tracking and feedback optimization of learning progress in online teaching according to claim 5, characterized in that: The operation steps of S3 are as follows: S31. When the student's online learning completion status is determined to be incomplete, the text AI model is used to determine the student's online learning outcome based on the student's spoken text information and the... Perform text analysis of online teaching knowledge points to identify the... The online teaching sequence knowledge points that students did not understand were identified, and a list of these points was constructed. S32. Based on the information of online teaching sequence knowledge points that the students do not understand and the target online teaching sequence text information, perform online teaching text information recognition and processing that the students do not understand to obtain online teaching sequence text information that the students do not understand. S33, based on the student's misunderstanding of the online teaching sequence text information, the... The system processes online teaching videos that students did not understand, generates these videos, and then executes the online teaching video playback assignment.

7. The method for real-time tracking and feedback optimization of learning progress in online teaching according to claim 6, characterized in that: The operation steps of S32 are as follows: S321. Using the KMP search algorithm, the online teaching time sequence knowledge point information that the student did not understand is matched with the target online teaching time sequence text information by time feature matching, and the online teaching time sequence text information corresponding to the online teaching time sequence knowledge point information that the student did not understand is searched out, and the online teaching time sequence text information that the student did not understand is constructed.

8. The method for real-time tracking and feedback optimization of learning progress in online teaching according to claim 7, characterized in that: The operation steps of S33 are as follows: S331. The Boyer-Moore search algorithm is used to compare the online teaching sequence text information that the student did not understand with the... Time feature matching is performed to search for the teaching video information corresponding to the online teaching time sequence text information that the student did not understand. Then, using video processing software, the teaching videos corresponding to the online teaching time sequence text information that the student did not understand are extracted from the... The video is then cropped and output, and after data identification, online teaching videos that students did not understand are generated; S332. Perform the online teaching video playback task according to step S142 for the online teaching video that the student did not understand.

9. A real-time learning progress tracking and feedback optimization system for online teaching, used to implement the real-time learning progress tracking and feedback optimization method for online teaching as described in any one of claims 1-8, characterized in that: The system includes an online teaching progress control module, an online teaching verification module, and an online teaching optimization module.

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