Using support vector machines to identify literacy skills: Evidence from eye movements.
Is inferring readers' literacy skills possible by analyzing their eye movements during text reading? This study used Support Vector Machines (SVM) to analyze eye movement data from 61 undergraduate students who read a multiple-paragraph, multiple-topic expository text. Forward fixation time, first-pass rereading time, second-pass fixation time, and regression path reading time on different regions of the text were provided as features. The SVM classification algorithm assisted in distinguishing high-literacy-skilled readers from low-literacy-skilled readers with 80.3 % accuracy. Results demonstrate the effectiveness of combining eye tracking and machine learning techniques to detect readers with low literacy skills, and suggest that such approaches can be potentially used in predicting other cognitive abilities.
Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, China.|University of Chinese Academy of Sciences, Beijing, China.
Ya
Y
Lou
Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, China.
Yanping
Y
Liu
Department of Psychology, University of Turku, Turku, Finland.
Johanna K
JK
Kaakinen
Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing, China. lixs@psych.ac.cn.
Xingshan
X
Li
D000328:Adult; D000465:Algorithms; D005133:Eye Movements; D005260:Female; D006801:Humans; D000067010:Literacy; D008297:Male; D011932:Reading; D060388:Support Vector Machine; D055815:Young Adult
Eye movements; Literacy skills; Support vector machines