AI assessment method based on learner behaviors

A learner and behavior technology, applied in the field of AI assessment based on learner behavior, can solve the problems of wasting teaching resources, losing online learning, etc., and achieve the effect of solving network delay, increasing accuracy, and preventing learning content omission

Pending Publication Date: 2021-07-09
上海网梯数码科技有限公司
View PDF4 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, when learning online courses, learners only need to log in to the system through the account and password, and then they can view the online teaching videos in the system. This method has large loopholes. Now various marketing websites on the Internet, On the marketing APP, there are "online class brushing" services, and the price is relatively considerable. This method of using account password as identity verification and only watching the market as the assessment content will make a large number of learners buy "online class brushing" services, thus losing the function of online learning and wasting a lot of teaching resources
[0004] Based on this, the present invention aims to solve the problem of how to reliably assess network teaching, thereby improving learning efficiency and reducing the waste of teaching resources, and proposes an AI assessment method based on learner behavior

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • AI assessment method based on learner behaviors

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035] An AI assessment method based on learner behavior, comprising the following steps:

[0036] S1: Preparation stage, collect image information, configure system rules;

[0037] S2: In the preparation stage of video playback, before the video is played, the viewer's image needs to be collected, and the living body and identity verification are performed;

[0038] S3: video playback stage, if identity verification is successful, then allow to play video file, if identity verification fails, then repeat S2 step;

[0039] S4: If the time for repeating the S2 step exceeds the system rule setting threshold in S1, it is judged that the recognition fails, and the video playback is terminated;

[0040] In the S1, the collected image information is the photo information of the learner taken by the camera. In the S1, the configured system rules include the capture rules for video playback and the face recognition rules, where the capture rules include continuous Unidentified toler...

Embodiment 2

[0048] An AI assessment method based on learner behavior, comprising the following steps:

[0049] S1: Preparation stage, collect image information, configure system rules;

[0050] S2: In the preparation stage of video playback, before the video is played, the viewer's image needs to be collected, and the living body and identity verification are performed;

[0051] S3: video playback stage, if identity verification is successful, then allow to play video file, if identity verification fails, then repeat S2 step;

[0052] S4: If the time for repeating the S2 step exceeds the system rule setting threshold in S1, it is judged that the recognition fails, and the video playback is terminated;

[0053] In the S1, the collected image information is the photo information of the learner taken by the camera. In the S1, the configured system rules include the capture rules for video playback and the face recognition rules, where the capture rules include continuous Unidentified toleran...

Embodiment 3

[0066] An AI assessment method based on learner behavior, comprising the following steps:

[0067] S1: Preparation stage, collect image information, configure system rules;

[0068] S2: In the preparation stage of video playback, before the video is played, the viewer's image needs to be collected, and the living body and identity verification are performed;

[0069] S3: video playback stage, if identity verification is successful, then allow to play video file, if identity verification fails, then repeat S2 step;

[0070] S4: If the time for repeating the S2 step exceeds the system rule setting threshold in S1, it is judged that the recognition fails, and the video playback is terminated;

[0071] In the S1, the collected image information is the photo information of the learner taken by the camera. In the S1, the configured system rules include the capture rules for video playback and the face recognition rules, where the capture rules include continuous Unidentified toler...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an AI assessment method based on learner behaviors, and relates to the technical field of network learning assessment. The method includes the following steps: a preparation stage, a video playing preparation stage, a video playing stage, collection of image information as learner photo information shot by a camera, and configured system rules comprising a video playing snapshot rule and a face recognition rule. The snapshot rule comprises a continuously unidentified tolerance time threshold t1 under the condition that a human face is unidentified, a snapshot frequency n within the tolerance time threshold t1, a repeatedly verified threshold t2 under the condition that verification succeeds and a video is played, and a matching degree threshold t3 of a system comparison image and storage information. According to the method, the situation that online courses are swiped on behalf can be effectively prevented, on the other hand, AI living body recognition is adopted, the recognition accuracy is improved, multiple times of recognition can be continued within a threshold value if recognition fails, and the short interval problem that matching cannot be achieved due to network delay and light rays is solved.

Description

technical field [0001] The invention relates to the technical field of network learning assessment, in particular to an AI assessment method based on learner behavior. Background technique [0002] In order to better support the largest online education in the world, in order to further improve the quality of teaching, and to solve the problem that education supervisors are not clear about the learning situation, it is necessary to strictly check and verify the authenticity and effectiveness of learners. Behavior analysis. [0003] In the existing technology, when learning online courses, learners only need to log in to the system through the account and password, and then they can view the online teaching videos in the system. This method has large loopholes. Now various marketing websites on the Internet, On the marketing APP, there are "online class brushing" services, and the price is relatively considerable. This method of using account password as identity verificatio...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06F21/32G06Q50/20G09B5/08
CPCG06F21/32G06Q50/205G09B5/08G06V40/161G06V40/45
Inventor 张杰闫蕾
Owner 上海网梯数码科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products