Digital Commons Network^™

Using Analysis Of Teaching And Assessment Practices To Revise Implementation Of A Force Unit In An 8th Grade Science Course, Caleb Christine

Graduate Research Papers

One thing I have noticed from my fellow teachers is a desire to improve how we teach and to engage our students in active learning. As a new teacher I have that same desire. Accomplishing my goal of being an effective teacher while also improving how the material is presented requires an approach that includes evaluating not only how the students are performing, but also how they are responding to my instruction.

I used self reflection journals to keep track of how I was presenting material in my classroom. I also collected information from completion of ungraded assignments and investigations. …

The Impacts Of A Problem-Based Learning Unit On Computational Thinking Skills In A 7th Grade Lego Robotics Classroom, Madison Beeler

Graduate Research Papers

The primary focus of this study was to determine what impact problem-based learning (PBL) has on developing computational thinking skills, self-efficacy, and earth science understanding in a middle school robotics classroom. Twenty-eight students took part in a 36-day instructional unit based on the 5E Learning Cycle. Students explored Martian geology, analyzing rock data collected by the Perseverance rover mission to make connections between landforms, integrating Earth science with computational thinking tasks. Pre and post-test data assessed growth of computational thinking and self-efficacy. Statistical analysis showed significant growth in student self-efficacy, while gains in computational thinking were not statistically significant. Students …

Transforming Lives For Eternity: 2024-2025 Year End Summary, Southern Adventist University

Strategic Plans

Southern Adventist University's Strategic Plan Year End Summary for the year 2024-2025.

Improving Chatbot Interactions Through Ai-Driven Hate Speech Detection: Evolving To A Safer Digital Environment-Poster, Rata Gheorghita, Wellington Mariano

ICT

This project aims to explore how Machine Learning can contribute to a better digital interaction, mainly focusing on environments such as online chats, social media, and customer support as they are now an imperative part of daily communication. With this, concerns around hate speech in digital conversations is critical (Council of Europe, 2024). This study focus on the development of a Hate Speech Language Detection Chatbot using machine learning techniques. The key purpose of the chatbot is to monitor and detect harmful content in real time, reducing the need for manual intervention. The creation and implementation of such a tool …

Exogenous Variables In Time Series Forecasting During Economic Volatility: A Cross-Sector And Cross-Crisis Evaluation., Orla Kavanagh

ICT

Economic disruptions pose challenges for time series forecasting, but also offer opportunities to evaluate the value of exogenous information. This study investigates the role of external indicators during the COVID-19 pandemic and the 2025 U.S. tariff shock, using data from Dell (technology) and Boeing (aerospace/manufacturing). ARIMA, SARIMA, and LSTM models were tested with candidate exogenous variables. Results indicate that the benefit of external signals is highly context-dependent: sector, disruption type, and forecast horizon all influence effectiveness. LSTM models generally excel at longer horizons with exogenous inputs, while ARIMA-based models perform better short-term. Findings emphasize that careful variable selection, ongoing evaluation, …

Simulación Clínica En La Formación De Psicólogos Organizaciones: Burnout Y Acoso Laboral, Patricia Vizuete Escobar, Sara Uceda Gutiérrez, Esther Martínez Miguel, Irene Morueco Moreno

Capítulos de libros

No abstract provided.

Credit Card Fraud Detection., Sonia Ndonga

ICT

This capstone project applies machine learning to detect credit card fraud, addressing a critical financial threat to banks and payment providers. Using an anonymised dataset of 284,807 transactions, which is highly imbalanced with only 0.172% fraudulent cases, three models—Logistic Regression, Random Forest, and Gradient Boosting—were developed and evaluated. The pipeline incorporates data preprocessing, feature engineering, hyperparameter tuning, cross-validation, and interpretability analysis using SHAP values, SHAPASH, and permutation importance. Random Forest achieved the highest performance with an ROC AUC of 0.97 and Average Precision of 0.66. The study also considers fairness, threshold optimisation, and practical deployment strategies, providing a robust automated …

Enhancing Fake News And Rumor Detection Using Metadata, Context, And User Interaction With A Hybrid Gcn-Transformer., Lucas Romulo Zuin Gigli

ICT

The increasing influence of social media has significantly impacted how news spreads among users, making the detection of misinformation, including fake news and rumours, a critical task. Previous research has explored content-based and metadata-driven approaches, while recent advancements have leveraged Graph Neural Networks (GNNs) such as GCNs, GATs, and SAGE to analyse user interactions and propagation patterns. This thesis investigates the effectiveness of a Hybrid Graph-Transformer Convolutional Neural Network (GCN-Transformer) for fake news detection, combining graph-based learning with Transformer attention mechanisms. Through in-depth data analysis and advanced detection techniques, this model aims to enhance predictive performance and mitigate the spread …

Discovering Latent Themes: Mixed-Methods Comparative Analysis Of Topic Extraction And Clustering., Laura Byrne

ICT

This study investigates whether clustering and topic modeling can uncover themes within the 167 verses of the King James Version of the Book of Esther. A standardized preprocessing pipeline was applied, and TF-IDF and sentence-embedding feature spaces were used to evaluate topic extraction (NMF, GSDMM, BERTopic, Top2Vec) and clustering (HDBSCAN, DBSCAN, Gaussian Mixtures, Agglomerative) using coherence, cluster validity, lexical distinctiveness, and cross-model similarity metrics. NMF provided the most interpretable topics, GSDMM yielded compact low-overlap topics, BERTopic offered entity-centered groupings, and Top2Vec highlighted broad thematic regions. Cross-model analysis revealed overlapping motifs, notably decree and banquet themes, but clusters and topics did …

Implementation Of Time Series And Neural Networks For Forecasting Agricultural Prices In The Irish Market: A Comparative Analysis Of Milk, Beef, And Potatoes., César Augusto Núñez

ICT

Agricultural price volatility represents a central challenge for the Irish agri-food sector, affecting the stability of producers, cooperatives, and policymakers. This study aimed to compare three predictive approaches applied to strategic commodities such as milk, beef, and potatoes: a traditional statistical time series model (SARIMA) and two deep learning architectures (RNN and LSTM). Using historical price series collected over a decade, the models were developed and evaluated following a rigorous methodological process that included data preparation, algorithm training, and validation of results using performance metrics widely used in time series research. The findings show that the SARIMA model was most …

Enhancing Uk Electricity Price Forecasting Using Deep Learning., Stephen Cooke

ICT

Accurate short-term electricity price forecasting (EPF) is crucial for efficient operation of the UK’s multi-layered power market, impacting generators, traders, the ESO, and policymakers. Prices are highly volatile and non-linear due to renewables, demand fluctuations, and market coupling across Day-Ahead, Intraday, and Balancing Mechanism venues. Traditional statistical models often fail under such dynamics, while machine learning and deep learning approaches—particularly LSTM, GRU, and hybrid architectures—effectively capture temporal dependencies and exogenous drivers. Empirical evidence shows that these models outperform classical baselines, enabling more accurate scheduling, risk management, and financial savings.

Evaluating The Effectiveness Of Variational Mode Decomposition(Vmd)-Enhanced Hybrid Models For Forecasting Volatile Price And Indicator Time Series., Danny Allen

ICT

Forecasting volatile time series, such as commodity prices and market volatility indices, is critical for financial and operational decision-making. This study evaluates the impact of Variational Mode Decomposition (VMD) on hybrid forecasting models combining ARIMA, GARCH, and LSTM. Using gold, platinum, and VIX datasets, models were assessed via MAE, RMSE, and computational time. Results show that VMD improves ARIMA forecasts, and achieves optimal performance when combined with ARIMA-LSTM using performance weighting reducing MAE by 0.62-3.81 and reducing RMSE by 1.48-6.20. Conversely, VMD integration with ARIMA-GARCH and LSTM decreased accuracy. While VMD enhances predictive performance, it increases computational time substantially, increasing …

Exploring The Determinants Of Life Expectancy At Birth: Predicting And Forecasting Global Health Trends Using Statistical, Machine Learning, And Deep Learning Models., Emma Rath

ICT

Accurate life expectancy forecasting is essential for health policy planning, yet research comparing statistical, machine learning, and deep learning approaches under real-world constraints remains limited. This study evaluates ARIMA/ARIMAX, tree-based, and neural network models using Irish and global datasets, considering small samples, missing data, and COVID-19 shocks. ARIMAX with lagged socioeconomic variables outperformed LSTM and other ML/DL methods. Income-based stratification improved predictive accuracy and interpretability, with SHAP analysis highlighting GDP per capita for developed countries and school enrolment and trade indicators for developing contexts. Results provide practical guidance for policymakers and establish limits for model complexity under constrained health data.

Bound Preserving Discontinuous Galerkin Methods For Euler Equations And Nonequilibrium Flows, Fangyao Zhu

Dissertations, Master's Theses and Master's Reports

This dissertation is composed of four chapters in which we will closely examine the high order bound preserving discontinuous Galerkin methods for solving partial differential equations, specifically non-equilibrium chemical reacting flows and Euler equations under gravitational fields. A shared requirement between the two is the necessity for positive values of both density and pressure. Due to this physical nature of the two systems, constructing a positivity preserving scheme become very essential in our research.

For non-equilibrium flows where multi-reactions and multi-species are involved, we are also required to keep the bounds of the mass fraction of each species in between …

Leveraging Product And Process Characteristics Across The Concrete Pavement Life Cycle To Integrate Global Warming Potential Into Project Procurement Processes, Michelle Annette Cooper

Dissertations, Master's Theses and Master's Reports

The objective of this research is to support sustainable procurement of concrete pavements by linking materials-level global warming potential (GWP) to the project-level. Infrastructure owners require reliable environmental product declarations (EPDs) and methodologies for integrating GWP into the procurement process to ensure equitable decision-making. This work provides insights into current EPD reliability by assessing the sensitivity of concrete GWP to materials-level contributors to recommend a level of supply chain specificity needed to effectively communicate GWP. A benchmarking methodology was developed and implemented to establish reference values for procuring sustainable products. Having provided evidence towards EPD reliability, this work presents a …

Test Stand Development: An Analysis Of Excavation Forces Within A Simulated Lunar Environment, Faith D. Gaertner

Dissertations, Master's Theses and Master's Reports

Space exploration has rapidly developed in recent years as scientists look to the lunar surface for water ice in permanently shadowed regions. The excavation and decomposition of water outside of Earth’s atmosphere could provide in-situ resources for propellant manufacturing and sustaining life. However, without informed design parameters for lunar exploration and infrastructure development, time will be wasted on systems unfit for the harsh environment. Testing facilities that simulate the environmental conditions are required to better understand the forces these complex systems may face while extraterrestrial.

The force test stand is a unique mechanism that collects tensile and compressive force data …

Initial Fungal Decomposer Community Determines Wood-Derived Carbon In Discrete Soil Carbon Pools In Aspen Clearcuts, Theresa K. Meyer

Dissertations, Master's Theses and Master's Reports

Soil carbon storage can be affected by forest management activities that alter the amount of biomass at a site, including slash retention and salvage harvesting. Initial fungal colonization of retained biomass may affect soil carbon storage due to differences in fungal decomposition strategies. To examine the effect of wood addition on wood-derived soil carbon storage and the interaction between initial fungal decomposer communities and wood application depth, δ¹³C modified wood inoculated with white- or brown-rot fungi was applied to soils across Michigan’s Upper Peninsula. Wood-derived carbon was assessed after 3.5 years in bulk soil samples and in three …

Life Cycle Assessment And Life Cycle Cost Assessment Of A Floating Treatment Wetland In A Wastewater Lagoon Located In Copper Harbor, Michigan, Erin Browne

Dissertations, Master's Theses and Master's Reports

A full-scale floating treatment wetland was implemented in a wastewater treatment lagoon in Copper Harbor, Michigan. Copper Harbor is a small, rural community located in a cold climate. Many studies have focused on the performance of floating treatment wetlands in mesocosm studies in warmer climates, but there is not a lot of research conducted on the feasibility and performance of full-scale floating treatment wetlands in wastewater lagoons, specifically in cold climates. The 2024 treatment season was the first season with the floating treatment wetland implemented in the wastewater lagoon. A preliminary assessment of the cost and environmental impacts of a …

Patient Handling Interventions And Their Role In Reducing Musculoskeletal Disorders Among Healthcare Workers, Ashley L. Hawke

Dissertations, Master's Theses and Master's Reports

Musculoskeletal disorders (MSDs) are prominent among healthcare professionals, especially those who perform manual patient handling tasks. Assistive devices (e.g., slider sheet, gait belt, mechanical lift) have been used for patient handling. However, the effectiveness of these devices in reducing MSD risk factors is varied. Additionally, the relationship between healthcare workers’ acceptability of patient handling devices and organizational policies that facilitate or hinder widespread implementation is not well understood. The goal of my dissertation was to conduct a comprehensive analysis of existing patient handling devices and their role in mitigating MSD risk among healthcare workers. For Study 1, I conducted a …

Stepper Motor Load Estimation Using A Neural Network, Soham S. Kulkarni

Dissertations, Master's Theses and Master's Reports

Bipolar stepper motors are common in scenarios that require low-cost precision including 3d printers. The ability to estimate stepper motor load provides an opportunity for health monitoring. For example, excessive load estimates may indicate a future problem in the driven system such as bearing wear. Stepper motor drivers often have built-in stall detection strategies that exploit the driver's precise knowledge of the high-frequency voltage pulses being sent to the motor. The goal of this work was to develop a retrofit approach to stepper motor load estimation using externally measured voltage, current, and speed. A stepper motor dynamometer was created to …

Florentine Impressions: Artists Abroad, Jaylin Vander Wiel

Honors Program Theses

Florence, Italy has long been a destination for traveling artists and those looking for international inspiration. I wanted to be part of this tradition and experience my own contemporary version of the Grand Tour, a rite of passage trip to Europe for men in the 17th to early 19th century. Several notable female artists also participated in the Grand Tour or their own European travel and served as inspiration for this project. As someone who values travel and planned to study abroad, I was drawn to this history and the idea of developing as a person and artist through immersion …

Comparing Custom And Transfer-Learning Cnn Models For Chest X-Ray Classification: Evaluating Performance And Scalability With Kubernetes Orchestration., Christopher Anich

ICT

Chest radiography (CXR) is the primary imaging tool for respiratory diseases, but interpretation can be time-consuming and requires expertise. This study compares a small custom CNN (Victorio) with three transfer-learning models (MobileNetV2, EfficientNet-B0, ResNet-50) for three-class CXR classification (normal, pneumonia, tuberculosis) and evaluates deployment on a Kubernetes cluster. Using a balanced dataset of ~13,500 CXRs, models were trained with ImageNet pre-training and from scratch. Transfer-learning models achieved near-perfect accuracy (99–100%) and AUROC, while the custom CNN reached 87–91%. Explainability analyses confirmed models focused on relevant pulmonary regions. Kubernetes deployment showed low latency, with most delays due to service overhead rather …

Investigating The Effectiveness Of Traditional Vs Hybrid Time Series Models In Operational Planning., Cristina Priolo

ICT

This study conducts a systematic comparison of traditional and hybrid time series forecasting models for operational planning in a SaaS company, using large multichannel, multilanguage datasets aggregated at six-hour intervals. Classical models (ARIMA, SARIMA, ETS) are evaluated against hybrid models (ARIMA-ANN, SARIMA-ANN, ETS-ANN) to assess their ability to capture both linear and nonlinear patterns. Forecasts over a three-month horizon were evaluated using RMSE, MAE, and MAPE, with hyperparameter optimization applied to all models. Results show that hybrid models, particularly ARIMA-ANN and SARIMA-ANN, outperform traditional models in predicting volatile and high-volume data, while traditional models remain competitive for stable datasets. These …

Acoustic Analysis Of Vowels In Foreign-Accented English, Hannah Nedder

Honors Program Theses

No abstract provided.

Leveraging Epigenomics And Transcriptomics To Develop High-Dimensional Biomarkers Of Neuroinflammation, Steven C. Pike

Dartmouth College Ph.D Dissertations

Neuroinflammation is a critical physiological phenomenon implicated in most neurological disorders, either as a cause or a consequence of pathology. Our understanding of neuroinflammation is limited by the inability to directly profile the cells of interest. Thus, developing biomarkers of neuroinflammation is clinically urgent as we know neuroinflammation correlates with patient outcomes and treatment success. The recent advances in “omics” technologies, particularly epigenomics and transcriptomics, provides the opportunity for high-dimensional profiling of biospecimens for potential biomarkers. Within this dissertation, I demonstrate that biomarkers obtained from “omics” platforms can detect neuroimmunological phenomena in clinical and preclinical settings. In chapter one, DNA …

Transforming Lives For Eternity: 2025-2030 Strategic Plan, Southern Adventist University

Strategic Plans

Southern Adventist University's 2025-2030 Strategic Plan.

The Effects Of Of Both Parent And Child Technology Use On Developing Literacy Abilities, Ava Mcmenomy

Honors Program Theses

This study explored the effects of both parent and child technology use on developing literacy abilities. Research is limited in this area, but is becoming increasingly important as technology continues to evolve and become more commonplace in the lives of both adults and children. This study aimed to determine if there was a correlation between technology use and decreased literacy abilities of children. This study also explored if increased technology use in conjunction with decreased quality of parent-child interactions resulted in decreased literacy skills. Research was conducted through a survey and 15 responses were analyzed. No decreased literacy abilities of …

Investigating The Ideal Candidate For The Fda’S Over-The-Counter Hearing Aids, Jamie N. Mayer

Capstone Showcase

Hearing loss is an underdiagnosed and undertreated medical condition affecting millions of Americans across a variety of ages and stages of life. Over-the-counter hearing aids are a new category of hearing aids created by the FDA. By targeting affordability and accessibility, over-the-counter hearing aids reduce barriers to care that individuals with hearing loss may face. There is a major impact of hearing loss on adults, specifically, for our elderly population who struggle with communication, staying engaged socially, and the additional stress of comorbid conditions. Elderly adults have the potential to derive immense benefit from over-the-counter hearing aids, specifically for those …

Bait, Code, And Censor- Queer Representation In Animation Through Queercoding, Queerbaiting, And Effects Of Censorship, Link Bertrand

Capstone Showcase

This paper talks about how queerness has been portrayed in media, with a specific focus on animation. This paper includes discussions on queer theory, and the ideas of censorship and representation. Creative themes include film history, animation, and show production. The shows mentioned in detail as examples are The Owl House, Voltron: Legendary Defenders, and Gravity Falls, with other mentions of DeadEnd: Paranormal Park and Heartstopper.

Race Without Racism: Religious School Curricula And The Race-Neutral Legacy Of Brown, Vania Blaiklock

Scholarly Articles

Current conversations about race and equity curricula in primary and secondary education exclude examining religious curricula because of their private classification. Yet, this omission prevents us from exploring how religious curricula might mirror the legal transformation of Brown’s racial equality legacy to constitutional race neutrality. This Article brings religious curricula into these conversations by specifically linking the Court’s race-neutral transformation of Brown to the way religious curricula frame discussions about race without racism. Throughout the Article, I argue that the Court’s transformation of Brown is not just a top-down legal framework but also a bottom-up educational ideology. By making …