Research And Application Of Learning Status Evaluation Based On Eye Movements

Lacking of feedback has always been a problem for most of the online learning application.While the online learning community is growing fast,both of the web based and novel mobile based online learning application are urgent for collecting better student feedback.In this paper,we design and implement a evaluation system that can track the movements of human eyes and analyses students' learning status according to their eye movement records.We want to apply this system in the area of online education and see if it can enhance the feedback of online learning.The system works like a pipeline: We captures students' facial images periodically and locate and eye corners and iris centers with the help of the technique of computer vision and digital image processing.Then we track the relative location of eye corners and iris to detect students' eye movements.We model the eye movements and use them as the input of a HMM classifier,which will output our final result: the estimation of students' learning status.The system we developed can be deployed with minimum financial cost and run in real time on users' personal computers and mobile devices.The information we collected can be used to evaluate the teaching and learning quality,help the teacher to optimize their lessons.This work tries to understand students' cognitive process by analyses their eye movements during the lecture especially when the students are watching lecture videos on the website or with an App.By design and implement this evaluation system,we devised and practiced all the techniques needed to evaluate learning status using eye movements records,(including learning status modeling,eye movements detection,classifier training and testing,systemimplement and deployment)and the result indicates our method is quite promising.