Online Learners' Reading Ability Detection Based on Eye-Tracking Sensors.
The detection of university online learners' reading ability is generally problematic and time-consuming. Thus the eye-tracking sensors have been employed in this study, to record temporal and spatial human eye movements. Learners' pupils, blinks, fixation, saccade, and regression are recognized as primary indicators for detecting reading abilities. A computational model is established according to the empirical eye-tracking data, and applying the multi-feature regularization machine learning mechanism based on a Low-rank Constraint. The model presents good generalization ability with an error of only 4.9% when randomly running 100 times. It has obvious advantages in saving time and improving precision, with only 20 min of testing required for prediction of an individual learner's reading ability.
Center of Educational Information Technology, South China Normal University, Guangzhou 510631, China. zhanzehui@m.scnu.edu.cn.
Zehui
Z
Zhan
College of Communication Engineering, Chongqing University, Chongqing 400044, China. leizhang@cqu.edu.cn.
Lei
L
Zhang
School of Economics & Management, South China Normal University, Guangzhou 510006, China. meihu@m.scnu.edu.cn.
Hu
H
Mei
Department of Building & Real Estate, The Hong Kong Polytechnic University, Hong Kong 999077, China. patrick.fong.bre@polyu.edu.hk.
Patrick S W
PS
Fong
D000293:Adolescent; D000465:Algorithms; D005133:Eye Movements / Q000502:physiology*; D005403:Fixation, Ocular / Q000502:physiology; D006801:Humans; D020407:Internet; D007858:Learning; D008962:Models, Theoretical; D011932:Reading; D012438:Saccades / Q000502:physiology; D055815:Young Adult
computational model; eye-tracking sensors; online learner; reading ability detection