Digital Commons Network^™

Fourth And Fifth-Grade Teacher Experiences With Els In The Science Classroom, Kiana Cheverez

Graduate Thesis and Dissertation post-2024

Science classrooms are places where students build knowledge through interacting with others and building new experiences by experimenting (Lee et al., 2009). English Learners (ELs) are students learning English, and the population of ELs has steadily risen in America (Tran, 2015). Although ELs are learning English, they are still expected to participate in conversations, exams, and learning in the Science classroom, regardless of their English abilities. This project focused on understanding teachers’ perspectives and experiences teaching ELs in the Science classroom. To do this, the researcher completed a phenomenological study and interviewed teachers to gain insight into their experiences. Afterward, …

External Boundary Conditions For Solidification Process Modeling Of Equiaxed Investment Castings, Weston Olson

Graduate Thesis and Dissertation post-2024

The equiaxed investment casting process is a multi-physics problem which requires knowledge from engineers who have expertise in materials, metallurgy, fluid dynamics, thermodynamics, and heat transfer. Process modeling is a tool used by foundries to help predict casting defects such as shrinkage porosity, hot tears, dimensional distortion, and poor grain structure. ProCAST is the numerical simulation suite used in this dissertation and is considered an industry standard due to its extensive proven track record. The reliability of these predictions is strongly dependent on the accuracy of the thermal boundary conditions set in the model. This dissertation consists of two studies. …

Numerical And Analytical Evaluations Of Impact Of Atmospheric Particles On Aircraft, Brendon A. Cavainolo

Graduate Thesis and Dissertation post-2024

The Volume-of-Fluid method, an Eulerian multiphase flow model that adds a volume fraction transport equation to the CFD governing equations, is widely used for any fluid-fluid interface tracking problem. There are important aspects of multiphase flow that impact aircraft flight, especially flight in extreme environments. These extreme environments can range from wet, icy conditions to sandstorms, and volcanic debris. The problems posed by these harsh environments are only exacerbated by aircraft that tend to travel at higher Mach-numbers. The specific aims of the proposed research include application of the Volume-of-fluid method to the following aspects of aircraft flight: shock-droplet interactions, …

A Framework For Improving Employee Experience In The Hospitality Industry, Scott Hall

Graduate Thesis and Dissertation post-2024

This dissertation explores the unique aspects of employee experience within the hospitality industry. By conducting in-depth interviews with 40 employees, both within and outside of the hospitality sector, this research aims to identify the factors that distinguish the employee experience in hospitality and to lay the groundwork for future improvements in this field.

The study employs a thematic analysis to uncover key themes that define the hospitality employee experience. The findings reveal that job satisfaction in hospitality is significantly influenced by factors such as workplace culture, career development opportunities, and the intrinsic satisfaction derived from serving guests. Moreover, the research …

Shock Tube Measurements Of Sustainable Aviation Fuel Sooting Tendencies For Supersonic Commercial Transport, Koby D. Rouviere

Graduate Thesis and Dissertation post-2024

Sustainable Aviation fuels (SAFs) have been launched to the forefront of the aviation industry as the desire to push the boundaries of flight continues to grow. The Concorde, a symbol of cutting-edge aerospace technology, was decommissioned due to its extreme inefficiency, as it formed high levels of soot, carbon monoxide (CO), and ethylene (C₂H₄), among other emissions. The UCF shock tube experiments aim to investigate the variation between sustainable aviation fuel and traditional commercial jet fuel under supersonic conditions similar to those seen on the Concorde. The test mixtures were formulated using Jet-A, Gevo’s Alcohol-to-Jet sustainable fuel, a 50/50 blend …

Heat Transfer And Pseudo-Boiling Of Supercritical Carbon Dioxide In A Parallel-Flow Microchannel And A Micro-Jets Impingement Device, Pranzal Ahmed

Graduate Thesis and Dissertation post-2024

Carbon dioxide (CO₂) is increasingly being used as a highly efficient, environmentally friendly and sustainable refrigerant/heat transfer fluid. Because of its high specific heat and cooling capacity, supercritical CO₂ has the potential to be widely used in a range of applications, such as power generation and thermal management of data centers/electronics. Near the critical condition, the thermo-physical properties undergo drastic changes, which includes a boiling-like phenomenon in the supercritical region (pseudo-boiling), which can affect the heat transfer performance. Thus, knowledge about the heat transfer process in the supercritical phase of CO₂ is necessary. In this experimental …

Using Electroencephalography To Predict Metabolic Cost, Jordan Grubb

Graduate Thesis and Dissertation post-2024

Electroencephalography (EEG) is a technique used to non-invasively record electrical signals from the brain that can be used to understand how the brain functions and controls movements. EEG, however, is susceptible to recording electrical artifact signals that do not originate from brain activity. Common artifacts that come up in EEG data are generated by eye movements, heart contractions, muscle contractions, motion artifacts, electromagnetic interference signals, and powerline noise. These artifacts are often filtered out of EEG data or reduced to clean the signals and preserve the information from the brain. However, some of these signals may provide useful information about …

Typography And English As A Second Language (Esl) Reading: Impact Of Variable Font Width, Letter Spacing, And Line Spacing On Reading Performance And Experience, Md Mamunur Rashid

Graduate Thesis and Dissertation post-2024

The mixed-method experimental study explores whether basic alterations in digital text formatting can enhance reading performance and experience of reading for the adult English as a Second Language (ESL) populations. Native English speakers are included as a comparison group. The study specifically examines the effects of varying font width, letter spacing, and line spacing on reading speed, comprehension, and self-reported levels of enjoyment and perceived difficulty.

The participants comprise 459 proficient adult readers, split between native English, native Spanish, and native Mandarin speakers. Each participant silently reads a series of 150-word English passages at an 8th-grade difficulty level, with manipulations …

Architectural And Training Regime Modifications To Transformer Models For Low Data Applications, Azwad Tamir

Graduate Thesis and Dissertation post-2024

A significant catalyst of the recent Artificial Intelligence and Machine Learning revolution is attributed to large language models (LLMs), which include many prominent AI systems such as ChatGPT, Bard, and Gemini. The majority of high-performance LLM models are based on the transformer architecture and the primary factor behind the recent enhancement in the performance of these models is attributed to the increase of layer quantity and dimensionality, resulting in an exponential rise in the total number of learnable parameters, necessitating substantial data for effective training. This may pose a significant barrier in certain application domains, such as bioinformatics, pharmaceutical research, …

Bridging Data And Theory: A Gray-Box Approach To Traffic Flow Dynamics, Shakib Mustavee

Graduate Thesis and Dissertation post-2024

The dissertation demonstrates the applicability of the data-driven dynamical systems (DDS) approach at both macroscopic and microscopic scales to extract mathematical structures from observed traffic data. Firstly, a novel DDS model is developed to capture the evolution of queue lengths in a signalized arterial corridor, dividing the dynamics into quasiperiodic and chaotic components. The proposed model effectively captures nonlinear dynamics from time series data. The Reproducing Kernel Hilbert Space (RKHS) method is employed to estimate mathematically guaranteed Koopman eigenvalues and functions of the system, providing valuable insights into the periodic and chaotic behaviors of the system. Secondly, to further characterize …

Advanced Analysis Of Mode Veering Variations In Academic Blisks, Nicolas Olore

Graduate Thesis and Dissertation post-2024

This thesis presents an in-depth investigation into the vibrational dynamics of bladed disks (blisks), with a focus on understanding the conditions that lead to mode veerings and shifts in natural frequencies under varying geometric configurations. By utilizing maps of frequencies across nodal diameters, this study explores the effects of altering inner and outer disk radii, blade Aspect Ratios, and height ratios on the modal behavior of blisks. The study analyzes these geometric modifications to determine their influence on both mode interactions and frequency shifts, offering insights into how the number of veerings can be controlled. Mode veerings are of particular …

Robust Machine Learning Based Frameworks For Localization And Mmwave Beam Prediction In Massive Mimo Systems, Katarina Vuckovic

Graduate Thesis and Dissertation post-2024

The primary focus of this doctoral dissertation is the investigation of robust massive Multiple Input Multiple Output (MIMO) communication systems. The study centers on two closely related issues: i) single-site user localization, and ii) millimeter Wave (mmWave) beam prediction. The motivation is to develop a system that simultaneously offers users reliable communication and precise localization using a single infrastructure. This dissertation specifically addresses three key challenges in data-driven solution: i) the limited availability of position-labeled training datasets, ii) robustness in time-varying dynamic environments, and iii) the generalization of models to unseen scenarios. The study introduces three localization and two beam …

Comparison Of Bearing Capacity Estimates To The Barcelona Basic Model Implemented Through Finite Element Modeling In Plaxis2d, Carlee M. Shaw

Graduate Thesis and Dissertation post-2024

The design of foundations is greatly influenced by its bearing capacity and settlement behavior, especially for that of shallow foundations which lie within the unsaturated zone of soil. Similarly, bearing capacity of beach sands can be utilized to determine beach trafficability, as vehicular loads may be assumed within the unsaturated zone. Therefore, similar scenarios should be analyzed as partially saturated conditions, unlike conventional bearing capacity estimations. To analyze such conditions, the concept of soil suction has more recently been of consideration. In this paper, the load-settlement behavior of a sand in partially saturated conditions is explored through finite element modelling …

High-Power Picosecond Source At 2.05-Μm For Pumping Mid-Infrared Optical Parametric Chirped Pulse Amplifiers, Chase E. Geiger

Graduate Thesis and Dissertation post-2024

Broadband few-cycle high-power sources based on optical parametric amplifiers (OPA) in the mid-infrared (mid-IR) region are of great interest in a variety of applications such as spectroscopy, remote chemical sensing, medical procedures, and attosecond science. Among all the mid-IR nonlinear crystals for parametric amplification, zinc germanium phosphide (ZGP) has received significant attention due to its high nonlinear coefficient, high thermal conductivity, and high damage threshold, making it suitable for generating high average powers in the 3 – 12 µm range. A ZGP OPA requires a pump source above 2 µm due to a sharp increase in absorption immediately below 2 …

Mindful Approaches, Transforming Hearts: Cultivating Elementary Students' Positive Mathematics Identity Development Through An Equity-Based Morning Mathematics Club, Kayla N. Blankenship

Graduate Thesis and Dissertation 2023-2024

This dissertation examines how third through fifth-grade students' mathematics identities developed through a strengths-based mathematics club focused on equitable teaching practices. In this study, theories of Communities of Practice (Wenger, 1998) and Figured Worlds (Holland et al., 1998) were combined with recommendations from Catalyzing Change (NCTM, 2020) and equity-based practices (Aguirre et al., 2013) to promote students’ positive mathematics identity development. Students with positive mathematics identities have increased mathematics engagement and achievement, and are more likely to seek careers in STEM. Mathematics identities are developed through and influenced by various sociocultural, personal, and educational factors. Incorporating evidence from student surveys, …

A Chat Analysis Of The Use Of Educational Video Games To Teach Physical Science To Middle School Girls, Stephen G. Petit

Graduate Thesis and Dissertation 2023-2024

As students transition into middle school science courses, their interest and engagement in the subject declines. This decline is most significant among girls, particularly for topics in physical science. While several factors have been identified that contribute to the decline, one area that is emerging as a new way to help improve girls' view of science is the use of educational video games to improve engagement and interest in science content, particularly as video games have been steadily rising in popularity with girls. This study used Cultural Historical Activity Theory to understand how middle school girls engage with an educational …

An Examination Of The Effect Of Instructional Facility Size On Student Achievement In The State Of Florida, Paul B. Tweed

Graduate Thesis and Dissertation post-2024

This correlational study used publicly available Florida Department of Education (FLDOE) data on attributes of educational facilities and student performance to determine the degree of correlation between the physical size of instructional space and student outcomes. The sample used in this study comprised sixty-seven school districts in Florida. A quantitative correlational analysis was utilized to compare the amount of instructional space available to each student in a district and student outcomes in terms of district graduation rates and district scores on statewide assessments. A statistically significant, negative correlation was found between per capita instructional space, district graduation rates, and district …

Exploring Fraction Comprehension And Interest In Elementary Education Through Ai-Powered Personalized Learning, Kenneth Holman

Graduate Thesis and Dissertation post-2024

Fraction proficiency is crucial for foundational mathematics and broader STEM fields. This dissertation investigates the integration of Artificial Intelligence (AI) in mathematics classrooms, with a primary focus on students with mathematical learning challenges. AI technologies offer unique advantages for personalizing instruction and providing tailored feedback. The empirical study in the dissertation addresses a gap in the research related to AI's application during primary school fraction instruction.

The first manuscript presents a comprehensive literature review of AI in mathematics education, synthesizing research from 2020 - 2024. The second manuscript describes a quasi-experimental study evaluating Mathbot, an AI-based personalized learning platform. Repeated …

Exploring The Influence Of Inclusive Middle School Classrooms On Resilience, Character Education, And Life Skills Development In Students With Disabilities, Katherine Bonilla

Graduate Thesis and Dissertation post-2024

This study explored the effects of inclusive middle school classrooms on the academic performance, attendance, and social-emotional development of students with disabilities. Using a quantitative, comparative, and correlational design, the research investigated how inclusion influences these outcomes. The study examined data from 1,198 students, including both exceptional student education (ESE) and non-ESE participants, and gathered teacher perceptions to assess the impact of inclusion on social-emotional learning (SEL). Key metrics analyzed included Florida Assessment of Student Thinking (FAST) scores for mathematics and English Language Arts (ELA), attendance rates, and teacher survey responses regarding students' SEL competencies, such as resilience, teamwork, and …

Supporting Online Student Success Via Online Instructor Best Practices, Tori Jayne Taylor

Graduate Thesis and Dissertation post-2024

Fully online learning provides flexibility students appreciate but can be impersonal and socially isolating. Students can experience lower achievement and more frequent course withdrawal because they struggle to connect to the content, instructor, and other students. Twelve experienced general education instructors from a large university were interviewed to understand how they notice and support students who are at risk of underachieving or failing fully online courses. Interview data were triangulated using course content analyses from two of the instructors’ online courses. Thematic data analysis was guided by the Community of Inquiry (CoI) framework.

Instructors in smaller courses use a variety …

Teacher Agency In Dual Language Programs: Examining Constraining And Facilitating Factors Through An Ecological Lens, Marcio R. Soares Gomes

Graduate Thesis and Dissertation post-2024

In the state of Florida (USA), several public schools offer dual-language education programs that include the equal use of Spanish and English to teach both English to speakers of other languages, the vast majority of whom are immigrants, and Spanish to school-age speakers of the majority language. Using the ecological approach (Priestley et al., 2015), as defined by Emirbayer and Mische’s (1998), this study seeks to understand how teachers in dual language programs achieve agency to protect the interests of their English learners. Through the lens of the ecological perspective, the study attempts to answer the following research questions: i) …

Elementary General Education Teachers' Beliefs About Inclusive Practices In General Education Classrooms In The Cayman Islands, Tracey Ann Trail

Graduate Thesis and Dissertation post-2024

Research indicates that teachers' beliefs are pivotal in shaping their inclusive practices. However, there is a dearth of local research on general education teachers’ beliefs about inclusive practices in the Cayman Islands. This qualitative study aimed to fill this gap by understanding the beliefs of general education elementary teachers about inclusive practices in general education classrooms in the Cayman Islands. This encompassed their beliefs about inclusive education, supporting student learning and participation, and their self-efficacy beliefs about implementing inclusive practices. Data about the teacher’s beliefs were collected using two semi-structured interviews with nine general education elementary public elementary school teachers. …

The Role Of The Principal In The Recognition Of High Ability Students Who Are Black, English Language Learners, And Low Socio-Economic Status, Nadia N. Winston

Graduate Thesis and Dissertation post-2024

Researchers consider the underrepresentation of Black, English Language Learner, and Low Economic status students who are high ability in gifted and advanced programs, an issue largely due to multiple issues (i.e. lack of standard definition or single conception, identification testing flaws, bias). Though many district and school related mitigation factors have been studied and suggested, there is very little research on the school principal and their role in mitigating these effects. This sequential, exploratory mixed methods study examined the challenges leaders face when implementing policies and processes for supporting and recognizing Black, English language learner, and low socio-economic students who …

Empathy In Digital Healthcare, Michael R. Powell

Graduate Thesis and Dissertation post-2024

This dissertation investigates how the rapid expansion of telehealth, particularly in response to the COVID-19 pandemic, has reshaped clinician-patient interactions, with a focus on the role of empathy. Telehealth has broadened access to care and introduced new efficiencies, but it also presents distinct challenges in fostering the deep emotional connections traditionally associated with in-person consultations. By exploring how empathy is mediated through digital platforms, this research offers valuable insights into how empathetic connections can be supported, though not fully replicated, in virtual healthcare settings. Through a mixed-methods approach, including qualitative interviews with healthcare providers, the study examines both the barriers …

The Role Of School Culture And Autonomous Motivation In Supporting Academic Achievement And Teacher Retention In A Public Charter School: An Ethnographic Case Study, Amanda W. Reschke

Graduate Thesis and Dissertation post-2024

U.S. public schools are in crisis amid declining student achievement and widespread teacher shortages, particularly in the wake of the Covid-19 pandemic, putting students at risk for poor academic outcomes. Data from the National Center for Education Statistics (NCES) shows 4^th and 8^th grade students’ average reading and math scores have decreased by 5 points and 7 points respectively since 2019. Furthermore, NCES data showed that 53% of public schools reported being understaffed for the upcoming school year in August 2022, and by October 2022, 45% of public schools reported at least one vacant teaching position. While many …

Machine Learning Algorithms To Study Multi-Modal Data For Computational Biology, Khandakar Tanvir Ahmed

Graduate Thesis and Dissertation 2023-2024

Advancements in high-throughput technologies have led to an exponential increase in the generation of multi-modal data in computational biology. These datasets, comprising diverse biological measurements such as genomics, transcriptomics, proteomics, metabolomics, and imaging data, offer a comprehensive view of biological systems at various levels of complexity. However, integrating and analyzing such heterogeneous data present significant challenges due to differences in data modalities, scales, and noise levels. Another challenge for multi-modal analysis is the complex interaction network that the modalities share. Understanding the intricate interplay between different biological modalities is essential for unraveling the underlying mechanisms of complex biological processes, including …

Eavesdropping-Driven Profiling Attacks On Encrypted Wifi Networks: Unveiling Vulnerabilities In Iot Device Security, Ibrahim A. Alwhbi

Graduate Thesis and Dissertation 2023-2024

Abstract—This dissertation investigates the privacy implications of WiFi communication in Internet-of-Things (IoT) environments, focusing on the threat posed by out-of-network observers. Recent research has shown that in-network observers can glean information about IoT devices, user identities, and activities. However, the potential for information inference by out-of-network observers, who do not have WiFi network access, has not been thoroughly examined. The first study provides a detailed summary dataset, utilizing Random Forest for data summary classification. This study highlights the significant privacy threat to WiFi networks and IoT applications from out-of-network observers. Building on this investigation, the second study extends the research …

Optimizing Ai With Advanced Data Structuring: A Comparative Analysis Of K-Means And Gmm Clustering Techniques, Amir Alipour Yengejeh

Data Science and Data Mining

This study presents a detailed comparison of Kmeans and Gaussian Mixture Model (GMM) clustering algorithms, illustrating their unique capabilities and limitations across various synthetic datasets. By utilizing metrics such as the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI), the research provides nuanced insights into how these algorithms handle datasets with varying structures and complexities. For instance, while both K-means and GMM show robust performance on well-separated clusters, GMM demonstrates a distinct advantage in scenarios with overlapping clusters or unbalanced data distributions. Conversely, K-means excels in identifying clear, distinct groupings, highlighting its utility in simpler clustering contexts. This study …

Next-Generation High-Performance Virtual Reality And Augmented Reality Light Engines, Zhiyong Yang

Graduate Thesis and Dissertation 2023-2024

The immersive virtual reality (VR) and the optical see-through augmented reality (AR) are expected to revolutionize human lives in work, education, entertainment, healthcare, spatial computing, and digital twins, just to name a few. Next-generation VR/AR devices should exhibit a wide field-of-view (FoV), crisp image without screen-door effect, high dynamic range, compact form factor and lightweight, and low power consumption. Such demanding requirements pose a significant challenge to traditional direct-view display panels. To address these technical challenges, novel approaches need to be proposed. This dissertation is devoted to developing next-generation high-performance display light engines toward high resolution density, high optical efficiency, …

Advancing Cancer Classifcation Through Machine Learning Analysis Of Rna-Seq Gene Expression Data, Emil Agbemade, Amina Issoufou Anaroua, Dimitri Bamba

Data Science and Data Mining

This study delves into the classifcation of various cancer types using the RNA-Seq (HiSeq) PANCAN dataset from the UCI Machine Learning Repository, which encompasses a rich collection of gene expression data across multiple tumor samples. To improve cancer diagnosis and treatment, our methodology confronts the challenges inherent in high-dimensional datasets, such as the Hughes Effect and the Curse of Dimensionality, through innovative feature selection methods and machine learning approaches. A key component of our strategy includes the use of tree-based algorithms, particularly Random Forest, to refine the dataset to seventy genes of utmost relevance for tumor classifcation, and the application …