Innovative Strategies: Power of Props
Description
Recruitment of students in engineering programs is a critical endeavor for universities striving to thrive in an increasingly competitive landscape. This master’s thesis investigates the effectiveness of utilizing an ASU-inspired Mandalorian armor set as a recruitment prop at engineering recruitment events. The research questions posed in this study delve into the behavioral response of event attendees and evaluate the prop's effectiveness in generating interest and initiating interactions with ASU recruiting staff. Drawing on a combination of observational data, thematic analysis, and insights from the literature review, this study evaluates the prop's impact on booth traffic, attendee engagement, and overall recruitment efforts. The observational data collected from two recruitment events on March 22, 2024, and March 24, 2024, revealed fluctuations in attendee engagement with the prop, with substantial visitor traffic observed on March 22, 2024, compared to March 24, 2024. The thematic analysis provided deeper insights into the prop's role as a conversation starter and attraction for both adults and children, highlighting its ability to spark curiosity and inquiries about its significance and association with ASU and engineering programs. The literature review supported these findings, offering insights into the dynamics of college choice processes and the strategic deployment of marketing practices in higher education recruitment. Concepts from studies by Han (2014), Rogers et al. (2010), Muller et al. (2010), and Brignull & Rogers (2003) informed the design attribution, booth interaction strategies, and logistical considerations associated with the prop's deployment.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2024
Agent
- Author (aut): Reynolds, Zane
- Thesis advisor (ths): Jordan, Shawn
- Committee member: McDaniel, Troy
- Committee member: Nichols, Kevin
- Publisher (pbl): Arizona State University
Virality in the Digital Age: Contextualization, Messaging Strategies, and Framing Detection
Description
Social networking platforms have redefined communication, serving as conduits forswift global information dissemination on contemporary topics and trends. This research
probes information cascade (IC) dynamics, focusing on viral IC, where user-shared information
gains rapid, widespread attention. Implications of IC span advertising, persuasion,
opinion-shaping, and crisis response.
First, this dissertation aims to unravel the context behind viral content, particularly in
the realm of the digital world, introducing a semi-supervised taxonomy induction framework
(STIF). STIF employs state-of-the-art term representation, topical phrase detection,
and clustering to organize terms into a two-level topic taxonomy. Social scientists then
assess the topic clusters for coherence and completeness. STIF proves effective, significantly
reducing human coding efforts (up to 74%) while accurately inducing taxonomies
and term-to-topic mappings due to the high purity of its topics. Second, to profile the
drivers of virality, this study investigates messaging strategies influencing message virality.
Three content-based hypotheses are formulated and tested, demonstrating that incorporation
of “negativity bias,” “causal arguments,” and “threats to personal or societal core
values” - singularly and jointly - significantly enhances message virality on social media,
quantified by retweet counts. Furthermore, the study highlights framing narratives’ pivotal
role in shaping discourse, particularly in adversarial campaigns. An innovative pipeline
for automatic framing detection is introduced, and tested on a collection of texts on the
Russia-Ukraine conflict. Integrating representation learning, overlapping graph-clustering,
and a unique Topic Actor Graph (TAG) synthesis method, the study achieves remarkable
framing detection accuracy. The developed scoring mechanism maps sentences to automatically
detect framing signatures. This pipeline attains an impressive F1 score of 92%
and a 95% weighted accuracy for framing detection on a real-world dataset.
In essence, this dissertation focuses on the multidimensional exploration of information cascade, uncovering the context and drivers of content virality, and automating framing detection.
Through innovative methodologies like STIF, messaging strategy analysis, and
TAG Frames, the research contributes valuable insights into the mechanics of viral content
spread and framing nuances within the digital landscape, enriching fields such as advertisement,
communication, public discourse, and crisis response strategies.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2023
Agent
- Author (aut): Mousavi, Maryam
- Thesis advisor (ths): Davulcu, Hasan HD
- Committee member: Li, Baoxin
- Committee member: Corman, Steven
- Committee member: McDaniel, Troy
- Publisher (pbl): Arizona State University
Mild Traumatic Brain Injury Executive Function Rehabilitation Through Serious Gamification
Description
The purpose of the present study is to explore a potential rehabilitation alternative/additive, when time, insurance, finances, or lack of knowledge are limitations for mild traumatic brain injury (mTBI) executive function (EF) rehabilitation. The experimental intervention involved two sets of participants an experimental group and a control group. Participants within the experimental and control groups partook in initial (week 1) and final (week 6) EF and TBI assessments. The experimental group additionally participated in four weeks (weeks 2 - 5) of an experimental intervention in beta stage of a web-based application. The aim of the intervention was to train EF skills planning, organization, and cognitive flexibility through serious gamification. At the conclusion of the study, it was observed that participants within the experimental group achieved higher scores on the experimental executive function assessment when compared to the control group. The difference in scores can be attributed to the weekly participation in executive function training.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2023
Agent
- Author (aut): Ezenyilimba, Akuadasuo
- Thesis advisor (ths): Cooke, Nancy
- Committee member: McDaniel, Troy
- Committee member: Gray, Rob
- Publisher (pbl): Arizona State University
Oasis – A Mobile Solution for Improving Gambling Self-Regulation
Description
The Oasis app is a self-appraisal tool for potential or current problem gamblers to take control of their habits by providing periodic check-in notifications during a gambling session and allowing users to see their progress over time. Oasis is backed by substantial background research surrounding addiction intervention methods, especially in the field of self-appraisal messaging, and applies this messaging in a familiar mobile notification form that can effectively change user’s behavior. User feedback was collected and used to improve the app, and the results show a promising tool that could help those who need it in the future.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2023-05
Agent
- Author (aut): Blunt, Thomas
- Thesis director: Meuth, Ryan
- Committee member: McDaniel, Troy
- Contributor (ctb): Barrett, The Honors College
- Contributor (ctb): Computer Science and Engineering Program
An Analysis of Accessible Design in the Game Industry
Description
Games have traditionally had a high barrier to entry because they necessitate unique input devices, fast reaction times, high motor skills, and more. There has recently been a push to change the design process of these games to include people with disabilities so they can interact with the medium of games as well. This thesis examines the current guiding principles of accessible design, who they are being developed by, and how they might help guide future accessible design and development. Additionally, it will look at modern games with accessibility features and classify them in terms of the Game Accessibility Guidelines. Then, using an interview with a lead developer at a game studio as aid, there will be an examination into modern game industry practices and what might be holding developers or studios back when it comes to accessible design. Finally, further suggestions for these developers and studios will be made in order to help them and others improve in making their games more accessible to people with disabilities.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2023-05
Agent
- Author (aut): Davis, Robert
- Thesis director: McDaniel, Troy
- Committee member: Selgrad, Justin
- Contributor (ctb): Barrett, The Honors College
- Contributor (ctb): Computer Science and Engineering Program
- Contributor (ctb): School for the Future of Innovation in Society
Sensing Door States using Consumer Grade Barometric Pressure Sensor
Description
The aging population has become a pressing social issue, as the younger generation is busy with work and financial stability, leaving little time to care for the elderly. Technological advances in smart homes provide an opportunity for the elderly to live more comfortably and conveniently in their own homes. In this study, we conducted research on the definition of a smart home, the existing usage of the pressure sensor, the classification of the pressure sensor, and its working theory. We are curious about if a consumer-grade barometric sensor is sensitive enough in the home environment. Then, we set up the testing equipment with a consumer-grade barometric pressure sensor, an Adalogger FeatherWing, and an Arduino board. After programming the Arduino, we collected the data from the BME680 sensor in different states (open or closed) of the door, and then analyzed and visualized it using MATLAB. Furthermore, we also explored some potential scenarios and applications for the BME680 sensor. With the help of the BME680 sensor, smart home technology has the potential to improve the lives of older adults and ease the burden on younger generations.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2023-05
Agent
- Author (aut): Liu, Xianyao
- Thesis director: McDaniel, Troy
- Committee member: Chowdhury, Abhik
- Contributor (ctb): Barrett, The Honors College
- Contributor (ctb): Engineering Programs
"Can I Consider You My Friend?" Moving Beyond One-Sided Conversation in Social Robotics
Description
As people begin to live longer and the population shifts to having more olderadults on Earth than young children, radical solutions will be needed to ease the
burden on society. It will be essential to develop technology that can age with the
individual. One solution is to keep older adults in their homes longer through smart
home and smart living technology, allowing them to age in place. People have many
choices when choosing where to age in place, including their own homes, assisted
living facilities, nursing homes, or family members. No matter where people choose to
age, they may face isolation and financial hardships. It is crucial to keep finances in
mind when developing Smart Home technology.
Smart home technologies seek to allow individuals to stay inside their homes for
as long as possible, yet little work looks at how we can use technology in different
life stages. Robots are poised to impact society and ease burns at home and in the
workforce. Special attention has been given to social robots to ease isolation. As
social robots become accepted into society, researchers need to understand how these
robots should mimic natural conversation. My work attempts to answer this question
within social robotics by investigating how to make conversational robots natural and
reciprocal.
I investigated this through a 2x2 Wizard of Oz between-subjects user study. The
study lasted four months, testing four different levels of interactivity with the robot.
None of the levels were significantly different from the others, an unexpected result. I
then investigated the robot’s personality, the participant’s trust, and the participant’s
acceptance of the robot and how that influenced the study.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2022
Agent
- Author (aut): Miller, Jordan
- Thesis advisor (ths): McDaniel, Troy
- Committee member: Michael, Katina
- Committee member: Cooke, Nancy
- Committee member: Bryan, Chris
- Committee member: Li, Baoxin
- Publisher (pbl): Arizona State University
Monocular Visual Odometry: Deep Learning vs Classical Approaches
Description
Visual Odometry is one of the key aspects of robotic localization and mapping. Visual Odometry consists of many geometric-based approaches that convert visual data (images) into pose estimates of where the robot is in space. The classical geometric methods have shown promising results; they are carefully crafted and built explicitly for these tasks. However, such geometric methods require extreme fine-tuning and extensive prior knowledge to set up these systems for different scenarios. Classical Geometric approaches also require significant post-processing and optimization to minimize the error between the estimated pose and the global truth. In this body of work, the deep learning model was formed by combining SuperPoint and SuperGlue. The resulting model does not require any prior fine-tuning. It has been trained to enable both outdoor and indoor settings. The proposed deep learning model is applied to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset along with other classical geometric visual odometry models. The proposed deep learning model has not been trained on the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. It is only during experimentation that the deep learning model is first introduced to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. Using the monocular grayscale images from the visual odometer files of the Karlsruhe Institute of Technology and Toyota Technological Institute dataset, through the experiment to test the viability of the models for different sequences. The experiment has been performed on eight different sequences and has obtained the Absolute Trajectory Error and the time taken for each sequence to finish the computation. From the obtained results, there are inferences drawn from the classical and deep learning approaches.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2022
Agent
- Author (aut): Vaidyanathan, Venkatesh
- Thesis advisor (ths): Venkateswara, Hemanth
- Thesis advisor (ths): McDaniel, Troy
- Committee member: Michael, Katina
- Publisher (pbl): Arizona State University