Personalized education management method for students based on artificial intelligence

Abstract

The present invention belongs to the technical field of big data processing, and provides a student personalized education management method based on artificial intelligence. The method analyzes historical learning records through a recommendation algorithm to generate a recommended course video list for the current student; performs knowledge point target detection on the recommended course video list for the current student to obtain personalized screening courses; updates the recommended course video list for the current student according to the personalized screening courses; and can avoid the data sparsity problem of collaborative filtering of common recommendation algorithms based on implicit feedback through intelligent screening of big data of all students, reduce the problems of false detection and missed detection of target detection, and eliminate the cold start problem of the recommendation algorithm; and improves the accuracy of recommendation by focusing on the positioning of video segments.

Claims

1. A personalized student education management method based on artificial intelligence, characterized in that: The method comprises the following steps: The server receives the personalized course screening request sent by the client; In response to a personalized course screening request, historical learning records are imported on the server side; the historical learning records are analyzed by a recommendation algorithm to generate a recommended course video list for the current student; Perform knowledge point target detection on the recommended course video list of the current student to obtain personalized screening courses; update the recommended course video list of the current student according to the personalized screening courses; Send the updated recommended course video list for the current student to the client of the current student; Among them, the method of performing knowledge point target detection on the recommended course video list of the current student to obtain personalized screening courses includes: setting a similarity threshold and a duration threshold, in each course video, if the pixel difference between two adjacent image frames within a period of time is less than the similarity threshold and the time length is greater than the duration threshold, then marking the video within this period of time as a knowledge meta-segment; Mark the knowledge meta-segment with the most pauses in each knowledge meta-segment of the course video as the knowledge point segment, and the image frame with the most pauses in the knowledge point segment as the knowledge point target, or mark the region of interest on the image frame with the most pauses in the knowledge point segment as the knowledge point target; The basic video is the course video with the highest cumulative pause times or cumulative viewing times in the recommended course video list of the current student; The set of course videos except the basic videos in all course videos is taken as the candidate video set; target detection is performed on each course video in the candidate video set, and when the knowledge point target is detected, target tracking and data annotation are performed; the course video in which the knowledge point target appears is taken as the target course video; the video segment in which the knowledge point target appears in the target course video is marked as the knowledge point associated video segment; Wherein, VideoS is the set of video segments associated with knowledge points in the target course video; VideoST(i) is the number of knowledge meta-segments in the i-th video segment associated with knowledge point in VideoS, and i is the sequence number; Traverse VideoST(i) in the range of i in sequence, and record the sequence number i when VideoST(i) first meets the conditions: VideoST(i)>VideoST(i+1) and VideoST(i)>VideoST(i-1) as the divergence sequence number Divi; record the sequence number i when VideoST(i) first meets the conditions: VideoST(i)<VideoST(i+1) and VideoST(i)<VideoST(i-1) as the convergence sequence number Coni; The start time of the video segment associated with the knowledge point corresponding to VideoST(Divi) in VideoS is defined as StartT; the end time of the video segment associated with the knowledge point corresponding to VideoST(Coni) in VideoS is defined as ENDT; the video segment between StartT and ENDT in each target course video is marked as a focused video segment; The target course videos with the longest focused video segments are used as personalized filtering courses.

2. The method for personalized education management of students based on artificial intelligence according to claim 1 is characterized in that: The method for importing historical learning records on the server side is: importing historical learning records on the server side, the historical learning records include the cumulative number of times students have watched the course video, the cumulative viewing time, and the cumulative number of times students have paused and dragged the course video.

3. The method for personalized education management of students based on artificial intelligence according to claim 1 is characterized in that: The method for updating the recommended course video list for the current student according to the personalized screening courses includes: adding the personalized screening courses to the recommended course video list for the current student.

4. The method for personalized education management of students based on artificial intelligence according to claim 1, characterized in that: The method for updating the recommended course video list for the current student according to the personalized screening courses includes: deleting the course videos in which the knowledge point objectives do not appear from the recommended course video list for the current student.

5. The method for personalized education management of students based on artificial intelligence according to claim 1 is characterized in that: The method for updating the recommended course video list for the current student based on personalized screening courses is replaced by: if the current student's learning progress value is higher than the average learning progress of all students, the duration of the focused video segment of the personalized screening course is recorded as GT2, and all course videos with a total knowledge point segment duration less than GT2 in the recommended course video list of all current students are deleted, and the personalized screening course is added to the recommended course video list of the current student.

6. The method for personalized education management of students based on artificial intelligence according to claim 1 is characterized in that: The method for updating the recommended course video list for the current student based on personalized screening courses is replaced by: if the current student's learning progress value is lower than the average learning progress of all students, the personalized screening course is replaced by: the target course video with the minimum duration of the focused video segment as the personalized screening course; the duration of the focused video segment of the personalized screening course is recorded as GT1, and all course videos with a total duration of knowledge point segments greater than GT1 in the recommended course video list of all current students are deleted, and the personalized screening course is added to the recommended course video list of the current student.

7. The method for personalized education management of students based on artificial intelligence according to claim 1, characterized in that: The method for updating the recommended course video list for the current student based on the personalized screening course is replaced by: the number of knowledge meta-segments in the knowledge point associated video segment in the target course video is recorded as the knowledge point increment; KL(j) represents the jth knowledge point increment in the target course video, where j is the sequence number of the knowledge point increment; Judge each KL(j) in the range of j: if KL(j) is less than the mean value of the knowledge point increments in all target course videos and greater than the minimum value of the knowledge point increments in all target course videos, then add the target course video corresponding to KL(j) to the recommended course video list of the current student; If KL(j) is greater than the mean of the knowledge point increments in all target course videos, and the target course video corresponding to KL(j) already exists in the recommended course video list of the current student, then the target course video corresponding to KL(j) is deleted from the recommended course video list of the current student to obtain an updated recommended course video list for the current student.

8. The student personalized education information management system based on artificial intelligence is characterized by: The student personalized education information management system based on artificial intelligence includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the student personalized education management method based on artificial intelligence described in any one of claims 1-7 are implemented.

Images