Current Awardee

Using Machine Learning to Predict Eye Movements in Skilled and Unskilled Readers

Jessica Montag
Jessica Montag

College: Liberal Arts and Sciences
Award year: 2021-2022
NCSA collaborators: Volodymyr Kindratenko, Dawei Mu, Eliu Huerta

Many children and adults struggle to attain reading proficiency. Contrary to our subjective experience we only see clearly 7 to 10 letters at a time when we read. Because we do not see the text on the page all at once, we move our eyes through the text during reading. Therefore, skilled reading is defined by eye movements that efficiently gather visual information. Eye tracking is the ideal method for gathering information about eye movements during reading. We can watch and measure the process of meaning extraction in real time. The field has large datasets of eye movements but poor models of those eye movements. We do not understand the knowledge and processes that underlie skilled reading. The goal of this project is to use deep learning to develop a better model capable of predicting eye movements during reading by integrating visual and linguistic information. Better model of skilled reading will help inform reading interventions and education by giving more detailed reader profiles.