[please re-circulate to relevant lists]
Andrew Howe, High School Student, American School in Japan
July 20, 1pm
Talk title: An Approach to Improving SAT Reading Scores Using Data Science
Van Pelt-Dietrich Library Center, Room 134
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.
Bio:
Andrew C. Howe was born in Tokyo, Japan and is currently a junior in
High School at the American School in Japan. He has been interested in
Data Science and Machine Learning since 9th grade and has been
studying the field since then. Andrew has been doing research on how
to use eye-gaze data to analyze the reading behavior of SAT test
takers objectively for almost 2 years and has had his results
published at I-ETC (Harvard University), VJSE (Shibaura Institute of
Technology), and most recently at ITS (University of Quebec at
Montreal).
