Digital Commons Network^™

Cohabitation And Child Educational Outcomes: An Examination Of Family Stability And Transition In Australia, Shana Pribesh, Emily E. Pulsipher, Mikaela J. Dufur, Jonathan A. Jarvis, Ashley Weisman, Yuanyuan Yue

STEMPS Faculty Publications

Cohabitation has become an increasingly common context for childrearing, yet children living with cohabiting parents often exhibit poorer academic outcomes than peers with married parents. This study examines whether these disparities stem from cohabitation itself, subsequent family transitions, or underlying mechanisms related to resources, stress, or selectivity. Using data from the Growing Up in Australia: Longitudinal Study of Australian Children (LSAC), we follow 920 children born to cohabiting parents and track family structure changes alongside teacher-rated literacy and mathematics performance from ages 6 to 11 years. Generalized estimating equation models show that, although children whose parents transitioned to single-parent or …

Redefining Educational Technology: A Critical Collaborative Inquiry, Aras Bozkurt, Helen Crompton, Robert Farrow, Agnes Kukulska-Hulme, Jon Dron, Richard West, Agnieszka (Aga) Palalas, Matth Bower, Junhong Xiao, Ahmed Tlili, Danah Henriksen, Angelica Pazurek, Henk Huijser, Thomas K. F. Chiu, Petar Jandrić, Katy Jordan, John Curry, Royce Kimmons, Mutlu Cukurova, Thomas Reeves, Gwo-Jen Hwang, Peter Shea, Jason Lodge, Martin Weller, Davy Ng, Tutaleni Iita Asino

STEMPS Faculty Publications

Educational technologists have not settled on a fixed definition of the field and likely never will. However, attempting to define the field helps to understand the epistemological meanings that shape what the field sees, values, and considers worth pursuing. Through a critical historical review spanning over a century, alongside theoretical engagement with the concepts of entanglement and distributed agency, this paper identifies three key insufficiencies in current educational technology frameworks. These are the persistence of an instrumental-facilitative paradigm that treats technology as a resource deployed by human agents; the theoretical dissolution of the pedagogy-technology dichotomy that existing definitions have not …

Reduced Threat-Related Neural Efficiency: A Possible Biomarker For Pediatric Anxiety Disorders, Julia O. Linke, Reut Naim, Simona P. Haller, Parmis Khosravi, Beck Scheinberg, Meghan E. Byrne, Anita Harrewijn, Ellen Leibenluft, Melissa A. Brotman, Anderson M. Winkler, Daniel Samuel Pine

School of Medicine Publications

Objective: Pediatric anxiety disorders are common and predict adult psychopathology, yet current treatments, such as cognitive-behavioral therapy (CBT), produce lasting remission in less than 50% of affected youths. To support the search for improved, mechanistically grounded interventions, this study evaluated neural efficiency, defined as similarity in functional connectivity between a threat task and rest, as a potential biomarker. The study evaluated neural efficiency in relation to anxiety diagnosis and treatment response.

Methods: The authors compared 103 youths with an anxiety disorder diagnosis (mean age, 12.5 years [SD=2.91], 62% female) to 103 youths with no psychiatric diagnosis (mean age, 13.4 years …

State Climate Superfunds, Rachel Rothschild

Articles

The harmful effects of climate change have already arrived in cities and states across America, with disasters increasing markedly in recent years along with more gradual environmental changes like sea-level rise and drought. To protect populations and natural resources, significant funding will be necessary for preventative measures as well as disaster response.

At present, it is states and ordinary taxpayers who must shoulder the enormous costs and planning for climate adaptation. A number of state legislators, however, have recently proposed enacting new laws that would require the companies who have most profited from fossil fuel usage to assist in funding …

Ai-Powered Lawyering: Ai Reasoning Models, Retrieval Augmented Generation, And The Future Of Legal Practice, Daniel Schwarcz, Sam Manning, J. J. Prescott, Patrick Barry, David R. Cleveland, Beverly Rich

Articles

Generative AI is set to transform the legal profession, though its most promising uses and ultimate effects are still unclear. While AI models like GPT-4 improve efficiency, they can also “hallucinate” and may undermine legal judgment, particularly in complex tasks typically handled by skilled lawyers. This article examines two emerging AI innovations that may mitigate these concerns: Retrieval Augmented Generation (RAG), which grounds AI-powered analysis in legal sources, and AI reasoning models, which structure complex reasoning before generating output. We conduct the first randomized controlled trial assessing these technologies, assigning upper-level law students to complete legal tasks using a RAG-powered …

Monitoring, Oversight, And Learning In Medical Ai, W. Nicholson Price Ii

Articles

When medical AI errs, it often goes unnoticed. If there’s a specific patient injury, and the link to AI is obvious, that problem might be reported to the Food and Drug Administration (FDA), but not always. And many other types of problems, like worse performance on specific groups or ineffective integration into health system workflows, simply don’t fall within the contours of regularized reporting. Even if they are noticed by the health system—far from a given—there’s no obvious way to share that information more broadly. Against this backdrop, there are justified calls for better oversight and reporting. But there’s the …

The Classical Limit In Geometric Quantization By Group Extension, Paul Bracken

School of Mathematical & Statistical Sciences Faculty Publications

Physical systems as a rule are associated with a symmetry group. The group approach to geometric quantization makes use of this to introduce a quantization by means of group extension. This procedure is discussed and applied to a physical system whose group law has its origin with the Galilean group. The main intention is to investigate the classical limit and its relationship under this approach to geometric quantization. The classical quantization conditions are obtained based on a deeper foundation.

Envisioning The Role Of Community-Owned Stores & Aligning Federal Food Policies: A Participatory Approach To Food Security And Food Sovereignty In Sambaa K’E, Denendeh (Northwest Territories, Canada), Narayan Subramoniam

Theses and Dissertations (Comprehensive)

Northern Indigenous communities have relied on traditional food systems for meaningful, nutritious diets since time immemorial. But many Indigenous communities in remote northern regions of Canada still face disproportionately elevated levels of food insecurity due to climate change radically altering the availability for traditional foods, colonial food policies imposing Western diets, and the high costs of transporting store-bought foods. Extreme weather events such as wildfires further isolate fly-in remote communities increasing both the costs of transporting store-bought foods and the difficulties of harvesting traditional foods. There are many examples of community-based initiatives for supporting traditional and locally grown foods, however, …

Science Fiction Cinema And The Technological Sublime: Spectacle And The Infinite From Early Cinema To Digital, David Jackson Dr.

Theses and Dissertations (Comprehensive)

This dissertation examines how science fiction cinema constructs and reimagines the technological sublime in the twentieth century: the experience of awe, terror, and admiration provoked by human-made power and scale. Drawing on the philosophical foundations of the sublime in Edmund Burke and Immanuel Kant and extending through Leo Marx’s and David E. Nye’s theorization of the technological sublime, I propose an aesthetic mode in which representation inflected with affect translates technological spectacle into examinations of humanity’s role and agency in the face of overwhelming technologized worlds and in some cases, even the universe itself.

Through three distinct historical periods …

Implementation Of A 3-Dimensional Hydrodynamic Numerical Model On A Large Northern Lake, Kirin Lorente

Theses and Dissertations (Comprehensive)

Climate change is impacting high latitude regions disproportionately more than those at a lower latitude. With polar lakes being highly sensitive to changes in the climate, shifts in lake ice dynamics and hydrodynamics are indicators of such changes in regional and global climates. These changes impact future seasons in a positive feedback loop, where changes in winter ice cover impact open water nutrients levels and lake thermal structure. This highlights the importance for long-term monitoring. Despite the abundance of lakes distributed across Northern Canada, there has been limited research conducted on these sentinels of climate change.

Specifically, Great Bear Lake …

Cognitive Load Classification Using Functional Near-Infrared Spectroscopy, Pratham Shah

Theses and Dissertations (Comprehensive)

This thesis investigates the classification of cognitive load using functional near-infrared spectroscopy (fNIRS) signals recorded during an N-back working memory task. The study introduces a novel short-channel correction layer designed to suppress superficial physiological noise adaptively, addressing limitations of traditional General Linear Model (GLM) based regression. A single participant dataset comprising 69 validated sessions was analyzed using both conventional machine learning and deep learning approaches. Traditional classifiers: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Random Forests, and Gradient Boosting were first evaluated using statistical features (mean, variance, peak, and slope). Among these, Gradient Boosting achieved the highest accuracy (55.6%), …

A Novel Federated Llm Framework For Distributed Traffic Modelling In Intelligent Transportation Systems, Seerat Kaur

Theses and Dissertations (Comprehensive)

Intelligent transportation systems (ITS) depend on accurate traffic prediction to support congestion management, infrastructure planning, and real-time operational decisions. Despite substantial progress in data-driven forecasting, several challenges continue to limit practical deployment: traffic data is distributed across independent regional authorities, making centralized aggregation infeasible, standard federated aggregation strategies ignore traffic-specific characteristics that meaningfully affect model quality, and existing models produce only numerical outputs without interpretable reasoning that urban planners can act upon. This thesis addresses these challenges through four contributions that collectively advance privacy-preserving, explainable, and scalable traffic forecasting.

The first contribution provides a systematic review of 129 peer-reviewed publications, …

Taylorkan-Vit: Parameter-Efficient Vision Transformers For Medical Image Classification, Kaniz Fatema, Dr. Emad Mohammed, Dr. Sukhjit Singh Sehra

Theses and Dissertations (Comprehensive)

Effective and interpretable classification of medical images remains a critical challenge in computer-aided diagnosis, particularly in data-scarce and resource-constrained clinical settings where traditional deep learning models prove impractical. This study addresses the fundamental barrier to Vision Transformer adoption in medical imaging—massive parameter counts and data requirements—through a systematic two-phase methodology. Phase 1 evaluates three spline-based Kolmogorov–Arnold Network (KAN) variants to identify the optimal nonlinear approximation function for parameter-efficient medical image classification: SBTAYLOR-KAN (B-splines with Taylor series), SBRBF-KAN (B-splines with Radial Basis Functions), and SBWAVELET-KAN (B-splines with Morlet wavelets). Comprehensive experiments across brain MRI, chest X-rays, and tuberculosis datasets—without any image …

Permafrost Microtopography And Active Layer Hydrology In The Western Canadian Arctic, Tobias L. Leonhard

Theses and Dissertations (Comprehensive)

The growing interest in Arctic regions will likely increase the use of nuclear energy in the Arctic. While this energy source has a high potential to improve economic opportunities in the Arctic, it also increases the release of radioactive material (radionuclides). Radionuclides are already found in the Arctic and are traced back to natural as well as anthropogenic sources. Water is one of the dominant transport vectors for solutes such as radionuclides or other contaminants. Therefore, improving our understanding of Arctic hydrology is important for assessing future environmental risks such as the release of radionuclides. Topographic and microtopographic features are …

The Christ Of The Asbury Road, E. Stanley Jones

The Asbury Journal

This is an unpublished sermon, given by E. Stanley Jones on May 8, 1942, at 11:00 am at a special assembly at Asbury College. The day was named E. Stanley Jones day by the administration, and the 1942 yearbook was dedicated to Dr. Jones, who was also a member of the Board of Trustees. Jones had been asked to adapt the idea of his most well-known book, The Christ of the Indian Road, to his experience and knowledge of Asbury in Wilmore, Kentucky. He provided several interesting points to help ground Asbury’s theological position of holiness and build on that …

Memory Effects In Many-Body Systems, Jeffrey Beckstrand

Master's Projects

This thesis investigates memory effects in many-body systems through the MoriZwanzig Formalism for projected dynamics of a Hamiltonian System which yields the Generalized Langevin Equation (GLE). The GLE is a stochastic differential equation (SDE) that studies the dynamics of observables under the effects of many other observables in the system. Although satisfying, the GLE has a term called the Memory Kernel that encodes the past of the system and introduces a computational challenge by introducing a non-Markovian property to the equation. The kernel is often approximated by introducing a delta function, which simplifies the computation, but at the loss of …

Javatutor: An Integrated Llm Framework For Structured Programming Tutoring And Problem-Solving In Java, Amey Makarand Dhongade

Master's Projects

Large Language Models (LLMs) are being adopted more actively to assist in pro- gramming tasks, but much of the available AI code assistants devices are encouraging the rapid generation of solutions, as opposed to assisting students in developing an understanding. As a result, they can give the students correct code without teaching on how to judge a problem and how to build solutions or how to debug their programs themselves. This study describes JavaTutor, an LLM-based tutoring system that is used to assist learners in the learning process in a three-step process of understand- ing, generating a solution, and refining …

Safe-R2r: A Safety-Aware And Budget-Optimized Retrieval Controller For Rag Systems, Mandar Sunil Gondane

Master's Projects

Retrieval-Augmented Generation (RAG) improves factual grounding in large language models by incorporating external evidence during inference. However, most RAG systems rely on fixed retrieval strategies that ignore query difficulty, computational cost, and prediction uncertainty. This project introduces SAFE-R2R (Safety-Aware and Budget-Optimized Reason-to-Retrieve), a retrieval controller that treats retrieval as a query-dependent decision problem. SAFE-R2R organizes retrieval into a multi-rung ladder ranging from no retrieval to deeper retrieval with reranking. At each rung, the system generates an answer, computes reliability signals, and combines them into a risk score used within a conformal calibration framework to decide whether to accept the answer …

Multimodal Emotion Detection System, Shubhankar Sameer Munshi

Master's Projects

Trying to understand emotion from speech is a problem that is present in human computer interaction. Nevertheless, there are still some shortcomings in current SER methods. Text-based systems may miss vital vocal cues, such as sarcasm, tone changes, and delivery. On the other hand, purely audio-based systems are prone to noise and unstable acoustic features. The combination of linguistic and acoustic features in multimodal approaches partially solves this problem, but many existing approaches use inflexible multimodal fusion techniques that cannot adjust their behaviors according to the quality of input signals. In this work, we propose a multimodal approach based on …

Modeling Sealed Deck Construction In Collectible Card Games Using Learning-To-Rank Approach, Michael Dinh Nguyen

Master's Projects

Artificial intelligence has demonstrated strong performance in complex decision-making domains such as chess and Go, motivating research into its application for games with even richer rules and combinatorial complexity. In collectible card games like Magic: The Gathering, deck construction from a constrained card pool is a critical and challenging task that requires evaluating card strength, synergy, and resource balance. This project explores whether machine learning, specifically learning-to-rank (LTR), can effectively model these human decision processes to construct competitive decks in a sealed format. The results found here can also be applicable to other areas of note, such as sports drafting …

Stepwise Sudoku Reasoning Training Using Transformers, Lok Man Chu

Master's Projects

Sudoku is a constraint satisfaction problem that serves as a testbed for studying reasoning and stepwise deduction. While many solvers can produce correct solutions, they often fail to generate human interpretable sequences of logically consistent steps. This study investigates whether a transformer trained on stepwise deduction traces can learn to solve Sudoku puzzles through sequential, logically deducible moves. Results show that solve accuracy improves significantly with more training data, reaching approximately 78% at 1500k samples. Stepwise analysis indicates that the model effectively learns simple strategies, achieving near-perfect performance. However, performance on more complex strategies remains limited. Overall, while the model …

Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudresh

Master's Projects

Robot navigation in a multi-agent setting requires a balance between safety and efficiency, especially in dense environments. In these two-dimensional spaces, the scope for geometric errors is much less and could lead to collisions or immobility. This project proposes to address the navigation task using a two-phase path planning pipeline that combines reinforcement learning and convex optimization in a scalable and robust manner. The first phase consists of generating diverse collision-free paths using a Q-learning agent that is trained on a visibility graph representation of the environment. The discretization of the environment using waypoint-based graphs allows the agent to train …

The Coral Microbiome Under Climate Change: Shifting Symbiotic Interactions, Katherine E. Parker, Kate M. Quigley

Biological Sciences Faculty Publications

Host–microbe symbioses are central to organismal health, yet these complex partnerships are often strongly shaped by the environment. Climate change is increasingly altering environmental conditions, disrupting the balance of host–microbe interactions and pushing them along a continuum from mutualism (both partners benefit) toward parasitism (one partner gains at the other’s expense). Reef-building corals provide a clear example of this vulnerability, as the coral holobiont, an ecological unit comprising diverse photosynthetic algal symbionts (Symbiodiniaceae) and other microbial partners, depends largely on these mutualistic interactions for nutrition, immunity, and stress tolerance. Increasingly, evidence suggests that under heat stress, the algal symbionts retain …

Evaluating Species At Risk In Data-Limited Fisheries: A Productivity-Susceptibility Analysis For Marine Aquarium Fish, Gabrielle A. Baillargeon, Alice A. Wynn, Jemelyn Grace P. Baldisimo, Michael F. Tlusty, Andrew L. Rhyne

Biological Sciences Faculty Publications

The marine aquarium trade (MAT) is a significant global industry harvesting millions of wild-caught, live coral reef fishes for public and private aquaria markets in the United States and Europe annually, while supporting fisher livelihoods in the Indo-Pacific. This diverse and species-rich trade is considered data-limited, creating barriers to quantifying the current and future socio-ecological sustainability of the fishery. We present a revised and expanded productivity–susceptibility analysis (PSA) that serves as a holistic risk assessment to estimate the vulnerability of marine aquarium fish to overfishing. Our global analysis includes 306 species that are actively in trade. Improvements to the PSA …

Brilliance In The Margins: An Exploration Of The Lived Experience Of Black Women Faculty In Stem, Mykella C. Mitchell

West Chester University Graduate Theses, Dissertations, and Final Projects

This qualitative research study explores the lives of Black women STEM faculty at Ivy and Ivy+ institutions. The existing literature reveals a distinct gap in research as it pertains to this demographic, due to the limited number of Black women who hold faculty positions and the limited research that centers their voices. This study intentionally centered the voices of Black women scholars using portraiture, which helped to avoid the creation of deficit‑based stories. Furthermore, this research was guided by the conceptual frameworks of whiteness as smartness and meritocracy. Portraits were created from the lives of six tenured and tenure‑track Black …

Arts Enriched Head Start Preschool And School Readiness: Are Benefits Accentuated For Children Facing Greater Economic Hardship?, Isabella Capera

West Chester University Graduate Theses, Dissertations, and Final Projects

Children growing up in impoverished and low-income households face opportunity gaps that limit their school readiness (Blair & Raver, 2015). Head Start preschool, which serves families facing economic hardship, has been demonstrated to reduce these gaps and enhance school readiness. Further, multiple studies have suggested that arts enriched Head Start may offer particular benefits (e.g., Brown et al., 2010, 2018; Lobo & Winsler, 2008). Yet research to date is limited and has not examined whether the benefits of arts enriched preschool may be uniquely helpful for children facing the greatest economic risk. The present study examines growth in school readiness …

Connecting Classroom Physics To Real-World Research: A Citizen Science Approach In Ap Physics 1, Heather M. Landgarten

West Chester University Graduate Theses, Dissertations, and Final Projects

Advanced Placement (AP) Physics 1 emphasizes evidence-based reasoning, quantitative modeling, and scientific inquiry. However, secondary science instruction often remains focused on procedural problem solving and standardized assessment rather than authentic scientific practice. This study examined the integration of citizen science projects into an AP Physics 1 curriculum to provide students with authentic research experiences while reinforcing physics concepts during the post-AP examination instructional period.

Using a classroom-based mixed-methods pre–post design, students participated in citizen science activities through publicly available research platforms, including Zooniverse. Students collected and classified authentic scientific data, analyzed real-world datasets, developed research questions, compared observations with theoretical …

The Role Of Technology In Education: Friend Or Fiend?, Michaela A. Morgan

West Chester University Graduate Theses, Dissertations, and Final Projects

In this paper we will be examining the role of educational technology in the United States through a critical lens with the purpose of arguing that the growing dependence on EdTech, standardized testing, and data-driven instruction has contributed to the privatization, dehumanization, and inequity of public education. Drawing from critical pedagogy, sociocultural theory, and educational research both recent and decades prior. In my paper I explore how technology in schools often functions less as a liberatory tool and more as a mechanism of surveillance, commodification, and control. We consider the historical roots of educational inequality, including eugenics, meritocracy, and colonial …

Tree-Based Graph Neura Networks For Natural Language Inference: From Structure-Only To Hybrid Architectures, Jason P. Lunder

EWU Masters Thesis Collection

Large transformer models achieve strong performance on natural language understanding tasks but require hundreds of millions of parameters and extensive pretraining. This thesis investigates whether graph neural networks operating on dependency parse trees can provide more parameter-efficient sentence representations for natural language inference, evaluated on two NLI tasks: entailment classification and semantic textual similarity.

Tree Matching Networks (TMN) adapt Graph Matching Networks to linguistic dependency trees with rich node and edge features, evaluated against a BERT baseline at matched parameter counts on identical training data. Tree Transformer Networks (TTN) extend TMN with transformer-based aggregation and tree-aware positional encodings, with component …

Assessing Forest Health And Small Mammals After A Limited Suppression Wildfire In Colville National Forest, Samantha J. Kennel

EWU Masters Thesis Collection

In the face of a wildfire crisis, there is a growing need to expand the pace and scale of forest treatments, including the strategic use of wildfire as a management tool. However, ecological outcomes remain uncertain in forests shaped by more than a century of fire suppression. The 2022 Thor Fire, a managed wildfire in the Colville National Forest, Washington, provides an opportunity to evaluate forest health in a relatively understudied system. Using a control–impact design, I established 100 monitoring plots in burned and adjacent unburned forests to assess how wildfire and fire severity influence forest structure, fuels, and tree …