Abstract
Researchers detail new data in Machine Learning. According to news originating from Singapore, Singapore, by NewsRx correspondents, research stated, “Aiming at the low English reading proficiency of ESL (English as second language) students in online learning environments, this study proposed an eye-movement-based machine learning monitoring model to detect English reading proficiency in real time. Eye-movement data from 43 students while completing online English reading tasks were recorded and 31 eye-movement features were extracted from the taxonomy of fixation, saccade, movement direction and gaze velocity.” Financial support for this research came from Singapore Maritime Institute Research Project. Our news journalists obtained a quote from the research from Nanyang Technological University, “During the model training phase, LightGBM achieved an accuracy of 96.51 % in detection. An interpretable model, SHAP (SHapley Additive exPlanation), was used to explain the main effects of eye-movement features in detection, where high gaze velocity, absolute saccade direction, and average saccade duration were found to be strong indicators of English reading proficiency. Furthermore, SHAP analysis allows the identification of individual factors contributing to differences in English reading proficiency.”
NETL
NSTL
Elsevier