Digital Commons Network^™

Utilizing Recommendation Systems To Achieve More Personalized Themed Experiences, Bryan P. Mcgowan

Graduate Thesis and Dissertation post-2024

Digital platforms such as Amazon, Netflix, and YouTube demonstrate the effectiveness of recommendation systems to personalize content and drive revenue, yet themed attractions have historically offered limited personalization. This thesis explores how recommendation systems can enhance adaptive themed experiences, focusing on branching narratives and dynamic environments that respond to guest behavior. Research from literature relevant to recommendation systems, along with analyses of successful attractions and digital experiences, informed the creative solutions developed for The Nexus. In this immersive walkthrough attraction, guests pass through a magical portal into a multiverse that adapts to their preferences and actions. This research shows how …

Reframing Recovery: Comparing Music Interventions And Traditional Rehabilitation Methods For Cognitive Function In Traumatic Brain Injury Patients, Laura Velez

Honors Undergraduate Theses

Traumatic brain injury (TBI) affects millions annually, often resulting in long-term cognitive deficits that hinder independence and quality of life. While traditional rehabilitation methods address many physical and functional limitations, cognitive impairments—such as deficits in attention, memory, and executive function—often persist. Emerging evidence highlights music-based interventions as a promising complement to standard therapies, yet comparative research remains limited. This integrative literature review examined the effects of music interventions versus traditional rehabilitation methods on cognitive function in adults with TBI. Using a systematic search of databases including MEDLINE, CINAHL Ultimate, APA PsycInfo, and Music Index, eight peer-reviewed studies were selected for …

Consequences Of Artificial Intelligence In Health Insurance: Lawsuits, Policy, And Ethics, Alyssa N. Roberts

Honors Undergraduate Theses

In recent years, the healthcare system has been burdened by a multitude of obstacles that hinder the ability to provide effective, affordable, and timely care. Among these, one of the most significant challenges is the role that health insurance plays in shaping the quality of care. Health insurance companies are designed to decrease financial strain on patients, but they have introduced inefficiencies through delayed coverage approvals, increased denials, and administrative costs. Artificial intelligence (AI) has started to play an integral role in resolving these issues for the health insurance industry. Through its quick automated claim processing, fraud screening, and reduced …

Comparing Inter-Rater Reliability For Two Coding Methodologies In Meta-Analysis Data Extraction, Christine D. Destefano

Graduate Thesis and Dissertation post-2024

Meta-analyses are critical tools for evidence-based policymaking, but their validity depends heavily on accurate data extraction from primary studies. While previous research has focused on procedural aspects of coding and reliability of effect size extraction, there remains a significant gap in understanding how to improve inter-rater reliability (IRR) for moderator variables that provide crucial context for meta-analytic findings. This study compared two methodological approaches for coding moderator variables: traditional low-inference coding and a novel drill-down questioning technique adapted from database development principles. Using a comparative analysis design, two independent coders extracted data from 18 primary studies included in a published …

The Representation Of Racially And Ethnically Diverse Students Identified With Giftedness In Third Grade In Florida, Ioanna Katsavria

Graduate Thesis and Dissertation post-2024

This study examines the proportional representation of Black, Hispanic, White, and Asian third-grade gifted students across urban and rural school districts in the U.S. state of Florida. It incorporates secondary quantitative datasets for the Fall 2023 and Spring 2024 school terms from the Florida Department of Education (FLDOE, n.d.), collected in the Fall 2024, along with qualitative data gathered in Spring 2025 from the websites of sixty-seven county-based school districts. Significant disparities among all groups of racially and ethnically diverse students were exposed through the quantitative data analysis. Notably, Black, Hispanic, and Asian students, especially in rural areas, were shown …

Developing, Designing, And Evaluating Knightly Codes: A System Of Resources Supporting Motivation Using Automated Feedback, Kyle S. Dencker

Graduate Thesis and Dissertation post-2024

Providing meaningful practice and feedback is a critical component of supporting students' learning, but it is also labor-intensive and time-consuming for instructors. This study evaluated the effectiveness of Knightly Codes, a supplemental system designed to provide automated, tiered feedback to students in an introductory, college-level Computer Science course designed to help them develop strategies to tackle real-world coding problems and maintain or improve their interest and motivation. Key design features include increasingly specific guidance as students submit multiple incorrect solutions, gamification elements including progress checks, badges, and personal and global statistics, and reflection surveys to promote self-regulated learning. A total …

Health Behavior In A Culture-Driven Society: Using Mixed Methods Research To Examine Medical Pluralism In Zimbabwe, Stephen Mhere

Graduate Thesis and Dissertation post-2024

This dissertation examined the role of cultural influences on health-seeking behaviors and healthcare choices among Zimbabweans, focusing on medical pluralism – where individuals simultaneously access traditional, faith-based, and allopathic medical systems. It applied the PEN-3 Cultural Model to examine Cultural Identity, Relationships and Expectations, and Cultural Empowerment. Using a convergent parallel mixed-methods design, data were collected via a Shona-language survey on WhatsApp. Multiple choice, closed-ended, and open-ended survey questions were texted to participants, and they responded by text or audio messages. Response data were collected from 137 study participants. Multinomial logistic regression was used for quantitative data analysis, while deductive …

Teacher Retention And The Disabilities-To-Prison Pipeline, Angela P. Mccloud-Bond

Graduate Thesis and Dissertation post-2024

There is an overrepresentation of individuals with disabilities in the criminal and juvenile justice systems. Similar to the commonly known School-to-Prison Pipeline, Christoper Mallet has coined the term the Disabilities-to-Prison Pipeline to refer to this growing problem. Research has not yet determined how individual variables at the school or district levels lead to the expansion of the Disabilities-to-Prison Pipeline. Mallot (2021) presented three possible hypotheses to explain the expansion of this phenomenon: the differential treatment hypothesis, the school failure hypothesis, and the susceptibility hypothesis. There is a significant gap in the literature that focuses specifically on the differential treatment of …

Examining The Impact African American Male Teachers Have On The Math Scores Of Underserved Students, Demetrice Thomas

Graduate Thesis and Dissertation post-2024

This study contributed to a growing body of evidence emphasizing that teacher identity profoundly influences not only how students learn but also how they perceive themselves and their potential. As Gershenson (2022) noted, “Black teachers provide benefits to all students, fostering higher achievement and stronger aspirations,” highlighting the essential role of diverse educators in shaping student success. Consistent academic gains observed among students taught by African American male teachers reinforce the urgent need for educational systems to prioritize equity not only in student outcomes but also in diversifying the teaching workforce. Despite African American male teachers comprising less than 2% …

Methods And Applications Of Coherent Analog Photonic Processing, Andrew B. Klein

Graduate Thesis and Dissertation post-2024

Photonic processing is a compelling approach to address the increasing computational requirements of neural networks, promising increased processing speed and better energy efficiency for operations such as matrix-vector multiplication. However, in practice the limitations of photonic hardware necessitate that these systems adopt fixed-point encoding rather than the floating-point standard used by digital electronics. The reduction in accuracy incurred by this requirement must be managed and addressed for real world applications. In this work, we present a reconfigurable, scalable, and parallelizable photonic multiplier cell using coherent balanced photodetection of analog signals to produce signed multiplications. This style of system outperforms other …

Exploring The Role Of Geofencing, Geolocation, And Audio Descriptions In Enhancing Mobility And Inclusion For Individuals With Blindness And Visual Impairments In Museums And Indoor Spaces, Javier Enrique Molinares

Graduate Thesis and Dissertation post-2024

This dissertation explores the challenges individuals with blindness and visual impairments face in navigating cultural spaces, explicitly focusing on museums, and emphasizes the importance of inclusion. The study explores how conventional accessibility solutions can accidentally establish obstacles which restrict autonomy and diminish chances for meaningful participation. Accessibility challenges were identified through qualitative observations at the Gateway Museum, located within the Kennedy Space Center Visitor Complex in Merritt Island, Florida. Additionally, interviews and a participatory co-design methodology involving five participants with blindness and visual impairments were conducted. These methods led to the development of a mobility application that combines geolocation technology, …

Transformative Pedagogies Through Iterative Instructional Design, Nikki F. Barnes

Graduate Thesis and Dissertation post-2024

Pedagogical reforms have fallen short, limited to strategies and symptomatic approaches instead of systemic restructuring. Addressing learning environments as reciprocal communities, this dissertation is a critical making project where I developed a model integrating relationship-based practices. I applied Indigenous pedagogies guided by my research question: How does combining Indigenous methods, intersectional feminisms, and digital humanities transform course development and deployment as a communal process of collective learning? Using design justice principles and digital learning affordances, this work seeks to increase learning engagement and outcomes and participant experiences by explicitly embedding community-building along with content. Building with the liberation pedagogies of …

The Value Of Balance: Exploring Timelessness Of Narrative Styles And Character Dynamics Through Animation, Jessica Gray

Graduate Thesis and Dissertation post-2024

Dazzling: A Timekeepers Tale is a 2D/3D animated short film that explores timeless storytelling, perception of time, anthropomorphism, and friendship. The film illustrates the necessity for balance between curiosity and caution when striving to learn. Through the dual protagonists’ contrasting personalities, they reach a deeper understanding of each other, thus learning the value of balance. This theme was constructed around a fantasy space that will function as a means of understanding varying perceptions of time. Thesis research explores ideas of balance between traditional Western narrative structures such as the Hero’s Journey, Aesopic fables, and Buddy Comedy within a combination of …

Screen Time On Stage: The Effect Of Internet And The Smartphone On The Actor, Nathan A. Olmeda

Graduate Thesis and Dissertation post-2024

This thesis examines the potential link between an actor’s cell phone use and their work in the rehearsal process. Using Paula Thomson and S. Victoria Jaque’s Creativity and the Performing Artist and Nichola Carr’s The Shallows: What the Internet Is Doing To Our Brains, I analyze the neural networks of the brain that are engaged in acting and their functions to develop a greater understanding of their association to behaviors and emotional responses that may impact the actor in the rehearsal/performance space. Furthermore, I investigate the emergence of smartphone use within the cultural zeitgeist and its effect on the …

On The Pedagogy Of Modes, Joshua M. Sanfilippo

Graduate Thesis and Dissertation post-2024

Undergraduate music students in their junior and senior years have a solid foundation in music fundamentals based on the four-semester music theory sequence taken during their sophomore and junior years, but most have a weakness in the area of diatonic modes. This is not to say that they are unaware of the modes or that they do not understand how to identify or construct them, but rather that students are unable to conceptualize modality in a pre-tonal context. To the modern undergraduate music student, the modes blossomed out of the major scale—a notion counter to historical truth. This backwards way …

Occupancy Prediction In Airbnb Listings Using Deep And Classical Machine Learning Models, Liam Kilkenny

Honors Undergraduate Theses

Airbnb hosts and renters lack reliable tools to anticipate future occupancy, which is critical for setting competitive prices, planning availability, and making informed booking decisions. This is especially true as artificial intelligence capabilities continue to advance, so focusing on using the most efficient and effective models is vital. This is especially important when considering an average user, who might want a specialized model but has limited resources. By using the correct models, accuracy and computing time can be optimized for the end user. This thesis addresses that practical gap by evaluating several state-of-the-art machine learning models for predicting Airbnb listing …

Criminal Defenses In The Age Of Ai: An Examination Of How Technological Advancements Are Challenging Traditional Duress And Mistake Of Fact Defenses, Lauren Lepage

Honors Undergraduate Theses

AI has rapidly evolved into a defining force of modern life, transforming industries by automating tasks, streamlining workflows, and enhancing everyday experiences. However, the same advancements, especially in deepfakes, now enable highly convincing manipulation of audio, images, and video, blurring the line between truth and fabrication. As these technologies grow more accessible, they not only reshape digital interactions but also challenge core principles of criminal law and culpability. This research examines the rapid advancement of Artificial Intelligence (AI) and deepfake technology and analyzes the challenges they pose to traditional criminal defenses. Employing a doctrinal legal research approach, it evaluates statutory …

Ultra-Plex Immunofluorescence Stainings To Classify Precancerous Lesions For The Stratification Of Their Risk For Progression Toward Esophageal Squamous Cell Carcinoma, Arthi Jaishankar

Honors Undergraduate Theses

Esophageal squamous cell carcinoma (ESCC) is the most common and fatal type of esophageal cancer since it is usually only diagnosed at a later stage. Similar to other cancers, ESCC arises through precursor lesions which are known as dysplasias and occurs when cells have uncontrolled growth and changes to both the tissue structure and gene expression. Clinical intervention at the precancerous stage would greatly improve survival rates, however predicting which lesions will actually become malignant continues to be a challenge. The current traditional method for ESCC diagnosis is hematoxylin and eosin (H&E) histopathology, but it is not sufficient for predicting …

Zinc Or Swim: Investigating Zinc Pollution From Restoration Materials, Megan R. Jensik

Honors Undergraduate Theses

Zinc is an essential nutrient, but can be toxic at high concentrations, negatively affecting various biological functions. Gabions are galvanized metals coated in zinc that are used for many human purposes, including coastal restoration efforts attempting to avoid using plastic materials. However, gabions can degrade in marine environments, potentially leaching metal into the water and soil. There is a knowledge gap regarding the occurrence and impacts of zinc pollution in the areas where these gabions are deployed, including the Indian River Lagoon (IRL). To address this, sediment samples were collected from the IRL and analyzed for their physical and chemical …

Comparative Analysis Of Estimation Techniques On Human Performance In Seek-And-Place Tasking, Adam J. Wehr

Graduate Thesis and Dissertation post-2024

Accurate parameter estimation is fundamental to statistical modeling, shaping the validity of inference, prediction, and decision-making. Although Maximum Likelihood Estimation (MLE) and Bayesian methods are well established theoretically, their performance relative to alternative estimators in small-sample, skewed-data contexts remains less clear. This thesis addresses this gap through a systematic evaluation of estimation techniques applied to human response time data from a controlled seek-and-place jigsaw puzzle task, where distributions are characteristically right-skewed.

A performance-modified lognormal model was developed to capture the relationship between task complexity and response times. Four estimation approaches were investigated: MLE, Method of Moments (MoM), Bayesian estimation under …

Least Squares Support Vector Data Description With Adaptive Optimizers, Daniel Markwei

Graduate Thesis and Dissertation post-2024

One-class classification has emerged as a powerful technique for data description, enabling a model to learn exclusively from data belonging to a single (target) class. By focusing solely on patterns within this class, this classification procedure is implemented in order to successfully assess whether incoming input deviates or is part of the target group. This approach develops a sophisticated understanding of the defining characteristics of the target data in relation to other groups. Consequently, any data point that deviates from the target group is flagged as an anomaly because it diverges from the learned distribution. One way to explore the …

Fast Differentiable Projection Layers Onto High-Dimensional Polytopes For Large-Scale Predictive Modeling, Camilo Gomez

Graduate Thesis and Dissertation post-2024

Recent trends in statistical machine learning have revealed the power of integrating optimization procedures as first-class components within end-to-end large-scale predictive systems. These structured computations—often formulated as projections onto geometric objects like polytopes—enable models to inject domain-specific inductive biases and enforce desirable properties in the learned representations useful in downstream classification or regression tasks. This dissertation explores the role of such projections in enhancing learning performance and generalization, particularly in the context of predictive modeling with data intensive systems.

Motivated by the limitations of traditional surrogate loss functions such as logistic loss for classification or cross-entropy for multiclass problems, this …

Domain-Constrained Clustering: A Two-Stage Framework For Muscle Fiber Type Identification, Fanchao Yi

Graduate Thesis and Dissertation post-2024

This dissertation introduces a novel Two-Stage Clustering Framework for the automated identification of human skeletal muscle fiber types using fluorescence microscopy image intensity data. By integrating two complementary clustering methods, this framework overcomes the subjectivity and labor-intensive nature of traditional manual methods, offering an objective and efficient solution for large-scale muscle physiology research. The initial framework combined Density Peaks Clustering (DPC) with a Gaussian Mixture Model employing a t-distribution (GMM-t). This version was validated by comparing its clustering results with manual counts at the individual subject level. We successfully applied this framework in two published studies \cite{brennan2020,hinkley2023}, demonstrating its practical …

A Unified Thermodynamic Framework For Regime Discovery, Entropy-Aware Optimization, And Emergent Leadership In Flocking Systems, Victor Nkwocha

Graduate Thesis and Dissertation post-2024

This thesis presents a unified framework for modeling, optimizing, and analyzing collective behavior in flocking systems through a thermodynamic and statistical lens. In the first study, we employ entropy-regularized Gaussian Mixture Models (GMM) to identify distinct thermodynamic regimes—ordered, transitional, and disordered—based on the energy and entropy profiles of simulated agent-based flocks. The second study introduces the Organic Adaptive Flocking Algorithm (OAFA), a biologically inspired, entropy-aware extension of the Boids model. OAFA dynamically adjusts rule weights based on local entropy signals and is optimized via a multi-objective NSGAII framework to balance cohesion, separation, energy efficiency, and disorder. Results show that entropy …

Clustering And Classification Methods For Forensic Science Applications, Slun Booppasiri

Graduate Thesis and Dissertation post-2024

Finite mixture models have been widely used to cluster data consisting of homogeneous subpopulations. In forensic palynology, pollen is used as a proxy to link individuals or items to a crime scene. Mixtures of pollen data-including willow and mustard and blank samples-were analyzed using flow cytometry. Willow and mustard clusters tend to have multivariate normal distributions, while a background cluster has multivariate non-normal distribution. We propose a finite mixture model capable of handling the mixtures of pollen in terms of univariate and multivariate distribution. The proposed methods are applied in simulated and mixture of pollen datasets.

Finite mixture models typically …

Robust Multiple Change Point Detection For Streaming Data, Randyll Pandohie

Graduate Thesis and Dissertation post-2024

The growing prevalence of high-frequency, high-dimensional data streams in domains such as finance, cybersecurity, and industrial monitoring has intensified the demand for real-time multiple change point detection methods. These methods are expected to identify distributional shifts as they occur while also determining the number and locations of change points—even in the presence of noise, outliers, and limited labeled data. Traditional batch-based approaches and many existing machine learning models fall short in streaming contexts due to their reliance on static datasets and sensitivity to contamination.

This thesis proposes a unified framework for robust multiple change point detection in streaming environments. The …

The Relationship Between The U.S Stock Market And Energy Commodities, Griffin Seel, Mari Arroyave, Jack Griffin, James Murphy

High Impact Practices Student Showcase Spring 2025

This project began with the goal of finding the predictive power of oil and natural gas on the U.S. stock market. We gathered and used government-issued data on foreign oil imports volume and grade, domestic crude oil prices, domestic natural gas prices and volume, and S&P 500 prices from the 2023 fiscal year. We wrote a program in R that used several different criteria to give us the relationship between the above variables in a simple, optimal, mathematical model. We also used methods from our course such as the Box Cox transformation test to refine our variables. Ultimately, we found …

Integrated Compliant Structure For A Hand Exoskeleton, Tristan R. Koopman

Honors Undergraduate Theses

This thesis presents the design and prototyping of a wearable hand exoskeleton that integrates a flexible structural framework to assist with hand movement while maintaining comfort and anatomical conformity. The goal was to create a device that supports tendon-driven actuation through a compliant structure, combining elements of rigidity and flexibility to match the natural geometry and motion of the human hand. Traditional hand exoskeletons often trade off motion for structure or vice versa. This project aims to bridge that gap with a hybrid compliant design that balances flexibility and support. The design process followed an iterative approach involving rapid prototyping …

High-Performance Computational Kernels For Algorithm-System Co-Design In Machine Learning: Enabling Efficient Gcn Training And Llm Inference Serving., Shakya D. Jayakody Arachchige

Graduate Thesis and Dissertation post-2024

The rapid advancement of machine learning, from Graph Convolutional Networks (GCNs) to transformer-based Large Language Models (LLMs), continues to expose fundamental limitations in memory efficiency, scalability, and system reliability. GCNs are critical for domains such as biomedical modeling, social networks, and recommendation systems, yet their reliance on sparse general matrix–matrix multiplication (SpGEMM) makes them highly sensitive to GPU memory constraints and irregular access patterns. As graph data scales, out-of-core computation becomes inevitable, but existing systems are hindered by I/O bottlenecks and underutilized GPU resources. Our contribution with AIRES demonstrates how algorithm–system co-design can alleviate these challenges: by introducing block-wise data …

Development Of Generalizable Data-Driven Traffic State Prediction Solutions: A Comprehensive Approach, Md Mobasshir Rashid

Graduate Thesis and Dissertation post-2024

Increased urbanization and population growth lead to more traffic in the transportation system, causing severe congestion. Particularly during disruptive events such as hurricanes or traffic incidents, the traffic flow in the network worsens significantly. As a result, there is a need for data-driven traffic prediction models to reduce congestion in real-time, and the prediction model should be generalizable to be applicable across different dynamic scenarios. Additionally, a robust and adaptable traffic prediction model can help traffic managers to take proactive actions to optimize traffic flow. However, the majority of data-driven traffic prediction models suffer from limited applicability as they are …