Date of Award

Spring 2021

Project Type

Thesis

Program or Major

Electrical and Computer Engineering

Degree Name

Master of Science

First Advisor

Andrew Kun

Second Advisor

Caitlin Mills

Third Advisor

Shaad MD Mahmud

Abstract

Visual behavior provides a dynamic trail of where attention is directed. It is considered the behavioral interface between engagement and gaining information, and researchers have used it for several decades to study user's behavior. This thesis focuses on employing visual attention to understand user's behavior in two contexts: 3D learning and gauging URL safety. Such understanding is valuable for improving interactive tools and interface designs. In the first chapter, we present results from studying learners' visual behavior while engaging with tangible and virtual 3D representations of objects. This is a replication of a recent study, and we extended it using eye tracking. By analyzing the visual behavior, we confirmed the original study results and added more quantitative explanations for the corresponding learning outcomes. Among other things, our results indicated that the users allocate similar visual attention while analyzing virtual and tangible learning material. In the next chapter, we present a user study's outcomes wherein participants are instructed to classify a set of URLs wearing an eye tracker. Much effort is spent on teaching users how to detect malicious URLs. There has been significantly less focus on understanding exactly how and why users routinely fail to vet URLs properly. This user study aims to fill the void by shedding light on the underlying processes that users employ to gauge the UR L's trustworthiness at the time of scanning. Our findings suggest that users have a cap on the amount of cognitive resources they are willing to expend on vetting a URL. Also, they tend to believe that the presence of "www" in the domain name indicates that the URL is safe.

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