Abstract

Virtual reality (VR) games and educational systems commonly employ interaction cues to provide information on how to take appropriate actions at particular moments. Interaction cues can be employed for different purposes, such as informing the user to look, go, pick, and operate. Additionally, different types of interaction cues can directly affect usability and user experiences. In our early research, we conducted two ecologically valid empirical studies with a preexisting VR training application and evaluated the effects of delayed interaction cues, in addition to comparing the purposes of interaction cues for learning and retention. Our results indicated that immediate interaction cues afford significantly faster training times than delayed interaction cues. Additionally, with further analyses, we found that immediate interaction cues afford significantly better retention of task performance than delayed interaction cues, and the purpose of an interaction cue also has a significant effect on task learning and retention. We then developed the Online Methodology for Interaction Cue Evaluation (OnMICE) to overcome the limitations imposed by the COVID-19 pandemic on participant availability. We used OnMICE to evaluate the effects of interaction cues and replicated evaluation results of a prior VR-based study. To better understand interaction cues, we have also presented a new methodology for analyzing interaction cues based on the concept of examining their systemic functional grammar (SFG), which we call the Methodology for Analyzing the Grammar of Interaction Cues (MAGIC).Our MAGIC methodology inherently yields five grammatical properties of interaction cues, which can be used to predict whether one cue will outperform another. We have used our OnMICE methodology to demonstrate the efficacy of our MAGIC methodology. In conclusion, we have presented several methods for analyzing interaction cues for VR applications.

Notes

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Graduation Date

2023

Semester

Summer

Advisor

McMahan, Ryan

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Identifier

CFE0009883

Language

English

Release Date

February 2024

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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