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. 2016 Sep 10;16(9):1457.
doi: 10.3390/s16091457.

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

Zehui Zhan  1 , Lei Zhang  2 , Hu Mei  3 , Patrick S W Fong  4
Affiliations

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

Zehui Zhan et al. Sensors (Basel). .

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.

Keywords: computational model; eye-tracking sensors; online learner; reading ability detection.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The composition of the eye. Note: The pupil is the area located in the center of the iris. The limbus is the boundary of iris, which is surrounded by the sclera.
Figure 2
Figure 2
Diagram indicating fixations, saccades and regressions.
Figure 3
Figure 3
Experiment Setup. Note: The participant’s computer is located in front of the experimenter’s computer, thus the experimenter can observe the participant’s behavior when doing the experiments. The eye-tracking sensor is an Eyelink II helmet that is worn on the participant’s head. A handle controller is placed in front of the participant, for inputting answers to the test.
Figure 4
Figure 4
Experimental process: (a) Participants doing the eye-tracking experiment in the lab; (b) The calibration process of the eye-tracking sensors; (c) The eye-tracker helmet (EyeLink II) used in this experiment.
Figure 5
Figure 5
Eye-tracking indicator contribution in the computation model. Notes: As can be seen, fixation rate, saccade duration, and Chinese exam score were the factors that contributed most to the computational model, while blink count, fixation position Y, and reading duration contributed the least to the model. Most of the indicator contribution is consistent with the empirical data, except the blink count and blink rate, which make very little contribution to the model.
Figure 6
Figure 6
(a) Indicator contribution of all 42 trials and (b) contribution coefficient of trials.
Figure 7
Figure 7
Convergence curve of objective function.
See this image and copyright information in PMC

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