Term of Award

Spring 2019

Degree Name

Master of Science, Computer Science (M.S.C.S.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Computer Science

Committee Chair

Pradipta De

Committee Member 1

Andrew Allen

Committee Member 2

Charles Hodges

Abstract

The process of learning is not merely determined by what the instructor teaches, but also by how the student receives that information. An attentive student will naturally be more open to obtaining knowledge than a bored or frustrated student. In recent years, tools such as skin temperature measurements and body posture calculations have been developed for the purpose of determining a student's affect, or emotional state of mind. However, measuring eye-gaze data is particularly noteworthy in that it can collect measurements non-intrusively, while also being relatively simple to set up and use. This paper details how data obtained from such an eye-tracker can be used to predict a student's attention as a measure of affect over the course of a class. From this research, an accuracy of 77% was achieved using the Extreme Gradient Boosting technique of machine learning. The outcome indicates that eye-gaze can be indeed used as a basis for constructing a predictive model.

OCLC Number

1112110080

Research Data and Supplementary Material

No

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