SAT reading analysis using eye-gaze tracking technology and machine learning

Abstract

We propose a method using eye-gaze tracking technology and machine learning for the analysis of the reading section of the Scholastic Aptitude Test (SAT). An eye-gaze tracking device tracks where the reader is looking on the screen and provides the coordinates of the gaze. This collected data allows us to analyze the reading patterns of test takers and discover what features enable test takers to score higher. Using a machine learning approach, we found that the time spent on the passage at the beginning of the test (in minutes), number of times switching between the passage and the questions, and the total time spent doing the reading test (in minutes) have the greatest impact in distinguishing higher scores from lower scores.