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Sensors 2016, 16(9), 1457; doi:10.3390/s16091457

Online Learners’ Reading Ability Detection Based on Eye-Tracking Sensors

1
Center of Educational Information Technology, South China Normal University, Guangzhou 510631, China
2
College of Communication Engineering, Chongqing University, Chongqing 400044, China
3
School of Economics & Management, South China Normal University, Guangzhou 510006, China
4
Department of Building & Real Estate, The Hong Kong Polytechnic University, Hong Kong 999077, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 30 July 2016 / Revised: 27 August 2016 / Accepted: 30 August 2016 / Published: 10 September 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2944 KB, uploaded 10 September 2016]   |  

Abstract

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. View Full-Text
Keywords: eye-tracking sensors; online learner; reading ability detection; computational model eye-tracking sensors; online learner; reading ability detection; computational model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhan, Z.; Zhang, L.; Mei, H.; Fong, P.S.W. Online Learners’ Reading Ability Detection Based on Eye-Tracking Sensors. Sensors 2016, 16, 1457.

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