Zhan Zehui, Zhang Lei, Mei Hu, Fong Patrick S W
Center of Educational Information Technology, South China Normal University, Guangzhou 510631, China.
College of Communication Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2016 Sep 10;16(9):1457. doi: 10.3390/s16091457.
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.
大学在线学习者阅读能力的检测通常存在问题且耗时。因此，本研究采用了眼动追踪传感器，以记录人眼的时空运动。学习者的瞳孔、眨眼、注视、扫视和回视被视为检测阅读能力的主要指标。根据经验性眼动追踪数据建立了一个计算模型，并应用基于低秩约束的多特征正则化机器学习机制。该模型具有良好的泛化能力，随机运行100次时误差仅为4.9%。它在节省时间和提高精度方面具有明显优势，预测单个学习者的阅读能力仅需20分钟的测试时间。
