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.
Highlights
IntroductionReading ability is one of the most important predictors of students’ success in online courses [1]
Reading ability is one of the most important predictors of students’ success in online courses [1].its detection is always complicated and problematic
Reading abilities is to provide different kinds of reading materials and ask students to answer sets of questions according to what they have read. Their reading ability appraisal can be assessed based on the scores obtained by students
Summary
Reading ability is one of the most important predictors of students’ success in online courses [1]. Bias might exist if students’ reading ability is judged purely based on their success rate on a reading test, and the results might not be valid and reliable To solve these problems, eye-tracking sensors are gradually attracting some educational experts’. Of a trial in in usability research, perception research, and different formssuch in reading seconds, with a general scale of fixations per second It is reported by Steichen et al. Fixation rate is the number of fixations divided by a period such as the duration of a trialwith in time on task and was found to be negatively correlated to task by difficulty byetNakayama et al.with [18]. Duration is often associated with deeper and more effortful cognitive processing
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