Predicting Affect from Gaze Data During Interaction with an Intelligent Tutoring System
Using eye-tracking data collected while students interact with an Intelligent Tutoring System, we train machine learning models to predict when students are experiencing boredom and curiosity. Which analyze which features are most relevant to detecting when students are engaged and curious vs. disengaged and bored.
J. M. Harley
Intelligent Tutoring Systems
Predicting Affect in an Intelligent Tutoring System
My Master’s Thesis investigated the usefulness of different data sources for automatically predicting when students using an Intelligent Tutoring System were engaged and curious, or disengaged and bored. Detailed comparisons of machine learning algorithms trained with eye-tracking data, Electrodermal Activity (EDA) and distance from the screen revealed that distance (which can be obtained with cheap infra-red sensors) provided one of the simplest and most reliable signals of student engagement.
University of British Columbia