Digital Commons Network^™

A Conceptual Framework For Cognitive Engagement And Human Agency In Ai-Mediated Learning, Sam Rhodes, Zuhal Yilmaz, Terrie Galanti, Younggon Bae, Victoria Delaney, Zareen Gul Aga, Stephen Hutt

School of Mathematical & Statistical Sciences Faculty Publications

The purpose of this chapter is to propose a conceptual framework that unpacks AI use in instructional practice across three interrelated dimensions: human agency, cognitive demand, and AI orchestration. We ground this work in historical perspectives on technology's mediating role in education. We then propose the conceptual framework and three characterizations of the use of AI in school instruction: cognitive load reducer, cognitive capacity builder, and cognitive companion. We then illustrate a worked example of each, including a curricular example and sample student prompts, and conclude with questions that educators might ask themselves as they consider if and how to …

Multi-Timescale Monitoring And Modeling For Resilient Smart Grids: Pmu Anomaly Detection And Battery Digital Twins, Muhammad Imran Hossain

Graduate Theses, Dissertations, and Problem Reports (ETD)

Modern power systems face failures at very different timescales. Cyber-physical disturbances may emerge within seconds, while battery degradation develops over hundreds of operating cycles. Both monitoring problems share the same underlying difficulty: power systems produce measurement data in abundance, but reliably labeled examples of abnormal or degraded operation are scarce. Rare grid events are difficult to label, and battery degradation data are heterogeneous across cells, cycling protocols, and chemistries. This thesis addresses these challenges through two complementary domain-informed learning frameworks that constrain representation learning using information specific to each physical problem.

First, at the system level, T-BiGAN, a Transformer-augmented bidirectional …

Dataless Neural Networks For Boolean Satisfiability And Network Optimization, Andrew Evan Gautier

Graduate Theses, Dissertations, and Problem Reports (ETD)

Combinatorial optimization problems (COPs) require searching over a finite solution space subject to constraints, with the goal of satisfying an objective function. They arise in operations research, scheduling, resource allocation, circuit design, and many other fields. Many problems in combinatorial optimization (including satisfiability and network design) are NP-hard. Traditionally, researchers have built approximate solvers that return near- optimal solutions efficiently by developing increasingly sophisticated heuristics and meta- heuristics. Deep learning has provided new opportunities for improving combinatorial solvers by leveraging neural guidance to prune the search space. Traditional neural networks have distinct drawbacks in this context: separate training and …

Comparing Unet And Hrnet For Geomorphic Feature Extraction From Digital Terrain Model-Derived Land Surface Parameters, Behnam Solouki

Graduate Theses, Dissertations, and Problem Reports (ETD)

Automated extraction of geomorphic features from high spatial resolution terrain data is important for environmental monitoring, geohazard assessment, and landscape change analysis. This study compares two convolutional neural network architectures, UNet and High-Resolution Network (HRNet), for semantic segmentation of mine benches and sinkholes from lidar-derived digital terrain models and land surface parameters. Mine benches represent anthropogenic, elongated, stepped landforms associated with historic surface mining, whereas sinkholes are natural, compact depressions common in karst terrain. Three terrain-derived input layers were used: square-root-transformed slope, broad-scale topographic position index, and fine-scale annulus topographic position index. Models were trained using five training sample sizes, …

Multimodal Representation Learning For Face Understanding: From Caption Supervision To Foundation Model Adaptation, Md Mahedi Hasan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The rapid advancement of intelligent surveillance systems and the increasing demand for reliable biometric identification in border security, public safety, and digital forensics require robust face understanding under unconstrained conditions, including low resolution, pose variation, and occlusion. While Vision Transformer (ViT)-based foundation models have greatly improved visual representation learning, their patch-based tokenization and lack of spatial inductive bias limit their ability to capture fine-grained details in low-resolution inputs. This dissertation investigates multimodal representation learning for face understanding through natural language supervision, large-scale face-caption pre-training, and parameter-efficient foundation model adaptation. It hypothesizes that textual supervision provides complementary semantic cues that improve …

Does Diverse Seeding Adoption Reduce Production Costs? The Case Of Diverse Forage Seeding Strategies In West Virginia., Ivan Andres Wong

Graduate Theses, Dissertations, and Problem Reports (ETD)

Feed and harvested forage represent a major share of production costs in Appalachian cow-calf systems, where a pronounced summer forage gap forces producers to rely on purchased hay. This thesis evaluates whether adopting diverse forage seeding strategies reduces production costs relative to the existing pasture management practice, accounting for the spatial heterogeneity of West Virginia’s production environments. The analysis proceeds in two steps. First, an unsupervised K-means clustering of 14 farms enrolled in the NRCS Conservation Innovation Grant field trial groups the farms into two agroclimatic clusters, Highland-Cool and LowlandWarm, using elevation, growing-season Growing Degree Days, growing-season precipitation, and soil …

Dielectrophoretic Biomarkers For Cancer Detection And Cell Analysis: Integrating Microfluidics And Ai, Raphael Olumide Oladokun

Graduate Theses, Dissertations, and Problem Reports (ETD)

Pancreatic ductal adenocarcinoma (PDAC) carries a five-year survival rate of 13%, driven largely by late-stage diagnosis and the absence of sensitive noninvasive screening tools. Current diagnostic methods lack the sensitivity to detect disease-associated systemic changes at early and precancerous stages, representing a critical clinical gap. This dissertation addresses the central hypothesis that PDAC and its pathological subtypes induce measurable, disease-associated electrophysiological changes in circulating PBMCs that can serve as correlative liquid-biopsy biomarkers. To test this hypothesis, this work asks: can dielectrophoresis (DEP), impedance and zeta potential analysis with AI-assisted classification distinguish benign pancreatic conditions (non-malignant control), precancerous lesions (primarily IPMNs), …

Timing, Spectral, And Single-Pulse Properties Of Radio Pulsars, Evan Francis Lewis

Graduate Theses, Dissertations, and Problem Reports (ETD)

Radio pulsars are highly magnetized, rapidly rotating neutron stars which emit beams of radio waves from their magnetic poles. Their incredible rotational stability allows for their use in a variety of high-energy astrophysical studies, though the details of their internal structure and emission mechanisms are still unknown. We use observations of a cross-section of the pulsar population in order to investigate the nature of their evolution, their emission properties, and their environments.

First we derive timing solutions for three pulsars discovered through their sporadic single pulses. It was possible to obtain timing solutions through a set of single-pulse times of …

Sedimentological And Geochemical Investigation Of The Origin Of Upper Pennsylvanian Black Shales, East-Central Appalachian Basin, Tyler Hickey

Graduate Theses, Dissertations, and Problem Reports (ETD)

In the east-central Allegheny foreland basin, laminated black shales overlying Upper Pennsylvanian Glenshaw Fm. coal seams record paleoenvironmental conditions associated with the termination of peat swamp deposition before, during, and after the Carboniferous Coal Forest Collapse (CCFC), a major paleobotanical evolutionary event during the Late Paleozoic Ice Age (LPIA) that contributed to extinction of lepidodendrons near the Desmoinesian-Missourian boundary (~306 Ma). Previous investigations of the CCFC commonly implicate an episode of aridification during intensification of LPIA glaciation that stressed the lepidodendrons beyond their ecological tipping point; however, the cause of this aridification is unclear. An investigation of Glenshaw Fm. black …

Adventures In Black Hole Binary Waveform Modeling, Siddharth Mahesh

Graduate Theses, Dissertations, and Problem Reports (ETD)

The modeling of black hole binaries requires accurate descriptions of both their relativistic vacuum dynamics and their interaction with surrounding astrophysical environments. Although these problems are often treated separately, this dissertation develops a unified view: key observables of black hole binary systems can be understood by identifying the dynamical instabilities that control their evolution. In the vacuum problem, the relevant unstable structure is associated with the remnant spacetime and its role in shaping the merger-ringdown gravitational waveform. In the environmental problem, the relevant instability is the epicyclic instability of orbits in the binary potential, which controls the location and evolution …

High-Resolution Laser Diagnostics For Ion Measurements In Magnetized Plasmas, Katey Stevenson

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this work, we investigate ion heating in two plasma systems (helicon plasmas and flux rope plasmas) using the PHAse Space MApping (PHASMA) experiment. Our studies rely on high-resolution laser diagnostics, specifically laser-induced fluorescence (LIF), to measure ion velocity distribution functions (IVDFs). The temperature and bulk flow of each species are inferred from their respective VDFs. This dissertation is organized into three main parts: the study of ion heating in helicon plasmas, pulsed LIF modeling and diagnostic development, and the study of ion heating in flux rope plasmas. Helicon plasma sources efficiently generate high-density plasmas and are widely used to …

A Digital Calibration Source For 21 Cm Cosmology Telescopes, Kalyani Balkrishna Bhopi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Precise calibration of radio telescope beams and gains is a central requirement for 21 cm intensity mapping experiments, which aim to measure large scale cosmological structure through the redshifted emission line of neutral hydrogen. Bright astrophysical foregrounds dominate the sky at these frequencies, and separating them from the cosmological signal demands precise control over instrumental systematics, particularly the telescope beam and its frequency-dependent response. Existing aerial calibration sources are incoherent broadband emitters, detectable only as total power. They provide no direct phase information and suffer from poor sensitivity in low signal-to-noise regimes.

We present the Precision Emitter for 21cm Array …

Evaluation Of Potential Factors Limiting The Expansion Of Invasive Carp (Hypophthalmichthys Spp.) In The Ohio River Basin, Erin Shepta

Graduate Theses, Dissertations, and Problem Reports (ETD)

The introduction of aquatic invasive species is one of the greatest threats to freshwater biodiversity around the world. Two species of invasive carp, bighead carp (Hypophthalmichthys nobilis) and silver carp (H. molitrix) have spread throughout much of the Mississippi River Basin, resulting in serious ecological impacts to native communities. Their spread in the Ohio River, a major tributary of the Mississippi River, appears to have slowed, with limited evidence of upstream advancements. Here, we investigated abiotic and biotic factors that may be contributing to the reduced spread of invasive carp in the Ohio River Basin. Generally, …

Development Of Novel Methods For Investigative Leads In Firearm And Explosive Related Incidents, Addio Joseph Fiordigigli Jr.

Graduate Theses, Dissertations, and Problem Reports (ETD)

Criminal outbreaks and gun violence persist as a multifaceted global crisis, marked by escalating rates of homicide, suicide, and terrorism that destabilize communities and strain public resources. The proliferation of illicit firearms and homemade explosive devices, along with the complexity of modern criminal networks, necessitates innovative scientific tools to strengthen preventive intelligence operations and ensure that the legal process is grounded in physical evidence. More efficient detection of traces left behind at a crime scene can help prevent further harm or injury from follow-up attacks. This study aims to develop and evaluate new triage methods for characterizing gunshot residues (GSR) …

Geospatial Analysis Of Sinkhole Density And Characteristics In West Virginia, Marissa Shannon Loftus

Graduate Theses, Dissertations, and Problem Reports (ETD)

Creation of a sinkhole map of West Virginia provides useful data to help identify areas susceptible to sinkhole related hazards, as well as allowing for detailed analysis of factors controlling karst development. Newly available, statewide LiDAR-derived digital elevation data have made mapping sinkholes over larger spatial extents feasible. For this study, sinkholes were mapped for all areas of West Virginia that are underlain by carbonate rocks; then the distribution and characteristics were evaluated overall and in four focus areas (Berkeley and Jefferson counties, Pendleton County, Greenbrier County, and Monroe County). These areas correspond to broader physiographic regions, with Berkeley, Jefferson, …

Spectral Properties And The Behavior Of The Current-Current Correlation Measure For The Luttinger-Sy Model, Jonathan Benoit

Theses and Dissertations--Mathematics

The Luttinger-Sy Model, sometimes referred to as the Pieces Model, is a Random Schrodinger Operator on L²(R) which is characterized in part by "pieces" whose endpoints are chosen by a Poisson Point Process. The Hamiltonian in this setting is then given as a direct sum of Laplacians with Dirchlet boundary conditions on each piece. In this work, we show several spectral properties of the Luttinger-Sy Model, including proving the deterministic spectrum is [0,infinity) and that a Wegner-type and Minami-type estimate both hold. Additionally, we show that the finite-volume Current-Current Correlation Measure is singular continuous with respect …

Student Conceptions Of Summation And Limits In The Definite Integral Following Quantitatively Focused Instruction, Caleb Daniel Holloway

2026 Scholarly Teaching Conference: Concurrent Session Papers

Recent studies have examined how students form productive conceptions of the definite integral and discussed techniques for promoting such conceptions. In this paper I present findings from interviews held with six students enrolled in second-semester calculus, four of whom had received quantitatively focused instruction on the definite integral. All six were chosen for their observed use of summation conceptions on definite integral problems, and here their conceptions are explored further. Additionally, we gain insight on their thinking regarding limits as related to the definite integral. The findings presented here add to our understanding of student thinking regarding the integral and …

Test Retakes In Introductory Math Courses, John Hird, Chris Mcclain

2026 Scholarly Teaching Conference: Poster Session Papers

In this poster, we describe the implementation of an exam retake model using specifications grading for math courses taken by non-STEM majors. This system was implemented by two faculty members over three years in two sequential courses. During that time, we tried several versions of allowed retakes, with varying restrictions on partial credit. Some of the challenges that we faced were scaling the system for use by different faculty members and with different courses, managing faculty workload on writing and grading multiple exams, and managing student expectations.

Techniques For Inclusive College Mathematics Classrooms, Christopher Anderson Davis, Josh Hiller, Anil Venkatesh

Journal of Mathematics and Science: Collaborative Explorations

Creating a welcoming and inclusive mathematics classroom is an important responsibility for all college mathematics faculty. But where do we begin? In this paper we present several techniques based on White’s (2024) building inclusive classrooms strategies framework, which the authors have found to be successful in making their own classrooms more inclusive. The techniques include the inclusion of contemporary diverse voices, exploring the history of applications of mathematical concepts, elevating the diverse intellectual history of mathematics, bringing diverse guest speakers into the classroom, and mentoring students as they engage in doing research. For each strategy we provide narrative comments based …

Connections Between Mathematics And Chemistry: An Interdisciplinary Undergraduate Curricular Initiative, George Ashline, Bret Findley

Journal of Mathematics and Science: Collaborative Explorations

In this article, we describe an undergraduate curricular initiative involving mathematics and chemistry faculty and students. Supported by a grant from the National Science Foundation Scholarships in Science, Technology, Engineering, and Mathematics Program, a program designed to encourage the study of and connections between quantitative fields and life sciences, this interdisciplinary collaboration features undergraduate student research and involves the development of curricular connections between mathematics and chemistry. Through this initiative, we have created activities for calculus courses featuring chemistry applications as well as activities for chemistry courses showcasing mathematical concepts. After the implementation of these curricular enhancements, we gathered student …

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Journal of Mathematics and Science: Collaborative Explorations

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A Major Update And Improved Validation Functionality In The Mwtab Python Library And The Metabolomics Workbench File Status Website, P. Travis Thompson, Hunter N. B. Moseley

Markey Cancer Center Faculty Publications

Background: The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. Although not as rapidly as in the past, MW has steadily evolved, updating its mwTab and JSON deposition text file formats and its web-based infrastructure. However, the growth of MW has been exponential since its inception in 2013 and continues to be exponential, with the number of datasets hosted on the repository increasing by 50% since April 2024. As part of regular maintenance to keep up with changes to …

Challenges And Applications Of Fine-Grained Temporal Action Understanding: Modeling Temporal Granularity And Data Efficiency, Halil I. Helvaci

Theses and Dissertations--Electrical and Computer Engineering

Fine-grained Temporal Action Segmentation (TAS) has become a cornerstone of video understanding, offering dense frame-level predictions essential for clinical assessment, surgical skill evaluation, and human-computer interaction. While TAS methods have delivered strong results on coarse-grained benchmarks, two fundamental challenges persist: (1) global attention mechanisms dilute boundary information critical for subsecond precision, a phenomenon we term the temporal granularity bottleneck, and (2) dense frame-level annotation remains prohibitively expensive, with most datasets requiring exhaustive labeling of lengthy untrimmed videos. These challenges are particularly pronounced in medical domains, where sub-second primitives define clinical outcomes while expert annotation remains scarce. In this dissertation, we …

Generative Ai Peer Tutoring To Support Peer-Reviewed Source Identification And Evaluation, Rae Mair, Michelle Kelley, Taylar Wenzel, Andrea C. Borowczak, Yuqing Li, Mike Borowczak

Faculty Scholarship and Creative Works

This chapter explores the design and evaluation of a generative artificial intelligence peer tutor prompt to support college students in identifying and evaluating peer-reviewed sources for academic research. Grounded in literature on peer tutoring, Socratic dialogue, and AI-supported learning, the authors describe an iterative prompt engineering process designed to transform large language models (LLMs) into Socratic-style peer tutors capable of scaffolding student reasoning without completing tasks for them. Five guiding criteria for an effective peer tutor shaped development and evaluation: cognitive congruence, step-by-step guidance, avoiding giving answers, adaptability to student level, metacognitive transparency, and following assignment directions. Across multiple human-centered …

Visual Analytics For Interpretable Quantum Computing, Shaolun Ruan

Dissertations and Theses Collection (Open Access)

Quantum computing has entered a stage of increasing practicality. Many quantum hardware vendors such as IBM, Rigetti, Honeywell, and IonQ now enable experiments on real devices in the Noisy Intermediate-Scale Quantum (NISQ) era. These platforms show computational advantages in domains such as optimization, machine learning, and materials science. However, they remain limited by hardware noise and the absence of human-interpretable information. Existing visual metaphors, such as the Bloch Sphere for single-qubit states or circuit schematics for algorithm design, struggle to convey multi-qubit entanglement or measurement probabilities in ways accessible to human reasoning. Likewise, the rise of variational quantum circuits and …

Constrained Reinforcement Learning: From Single-Agent Safety To Multi-Agent Coordination, Hao Jiang

Dissertations and Theses Collection (Open Access)

Real-world decision-making systems such as autonomous driving and largescale ride-pooling must operate under strict safety and resource constraints. Traditional Reinforcement Learning (RL) methods, while powerful in simulation, often fail to guarantee such constraints, limiting their real-world deployment. The fundamental challenge lies in integrating constraint satisfaction with long-term reward optimization, especially when outcomes are stochastic and interdependent across multiple agents.

This dissertation advances the field of Constrained Reinforcement Learning (CRL) from both single-agent safety and multi-agent coordination perspectives. In the single-agent setting, we introduce a Reward Penalty framework that augments the state space with cumulative cost and penalizes only trajectories that …

Improving Pid Control With Bayesian Optimization For Adversarially Robust Federated Learning, Adrian Pena, Sergei Chuprov, Raman Zatsarenko, Leon Reznik

Computer Science Faculty Publications

Federated Learning (FL) often suffers from unstable convergence and reduced robustness under non-IID data and malicious attacks. In this paper, we present FedPIDAvg_tuned, a control-theoretic aggregation framework for improving stability and adversarial robustness in FL. In particular, our approach combines a server-side Proportional–Integral–Derivative (PID) controller with Bayesian optimization to tune controller gains for different data and attack conditions. The PID controller regulates global model updates through feedback on loss dynamics, providing adaptive scaling that improves stability and convergence. The tuned gains are applied within a trust-weighted trimmed-mean mechanism to remove adversarial or outlier updates. Using the Flower framework, we evaluate …

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 …

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 …