Predicting Affect in an Intelligent Tutoring System

Abstract

In this thesis we investigate the usefulness of various data sources for predicting emotions relevant to learning, specifically boredom and curiosity. The data was collected during a study with MetaTutor, an intelligent tutoring system (ITS) designed to promote the use of self-regulated learning strategies. We used a variety of machine learning and feature selection techniques to predict studentsā€˜ self-reported emotions from eye tracking data, distance from the screen, electrodermal activity, and an ensemble of all three sources. We also examine the optimal amount of interaction time needed to make predictions using each source, as well as which gaze features are most predictive of each emotion. The findings provide insight into how to detect when students disengage from MetaTutor.

Publication
In University of British Columbia
Natasha Jaques
Natasha Jaques

My research is focused on Social Reinforcement Learning–developing algorithms that use insights from social learning to improve AI agents' learning, generalization, coordination, and human-AI interaction.

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