A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Using support vector machines to identify literacy skills: Evidence from eye movements
Tekijät: Ya Lou, Yanping Liu, Johanna K. Kaakinen, Xingshan Li
Kustantaja: Springer New York LLC
Julkaisuvuosi: 2017
Journal: Behavior Research Methods
Vuosikerta: 49
Numero: 3
Aloitussivu: 887
Lopetussivu: 895
Sivujen määrä: 9
ISSN: 1554-351X
eISSN: 1554-3528
DOI: https://doi.org/10.3758/s13428-016-0748-7
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.