Doctoral Dissertations
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ItemExamining the Relationship between Food Environment, Food Choice, and Diet Quality in a Southeastern Metro County in the United States(Middle Tennessee State University, 2024)There has been a vast array of research examining food environment and its related influential factors. This body of research encompasses studies examining how individuals interact and how diet and food choice are influenced by the food environment, various methods and procedures in measuring the food environment, and its relationship with other health-related factors, among other topics. This current study contains an integrative literature review to explore the relationship between food environment, food choice, and diet. The Torraco checklist for integrative literature reviews was followed. The search for relevant literature was conducted using the search terms food environment, food choice, diet, and other relevant terms. Articles examining food environment and food choice, food environment and diet quality, and food choice and diet quality were reviewed. The review synthesizes findings from various studies and discusses challenges and variations in methodological practices. Prior literature has failed to explore the interconnection between food environment, food choice, and diet quality; warranting further inquiry. The third part of this study aims to further explain the relationship between food environment, food choice, and diet quality. An adapted NEMS-P and NEMS-S/CS was utilized to assess and compare individuals perceived and observed food environment, along with Steptoe’s Food Choice questionnaire, and diet quality measures. A logistic regression and crosstabulation were utilized to examine the relationship between perceived food environment, food choice, and diet quality. Maps were created via ArcGIS for the observed food environment. It was found that there is a relationship between some food environment and food choice measures and diet quality. The relationship between workplace and home food environment is also explored in the second part of this work. A questionnaire was used to measure the perceived food environment around the workplace and home and perceived diet quality. A logistic regression and crosstabulation were used to assess the relationship of workplace and home food environment on diet quality. Results revealed mixed findings, where fast-food outlets around the home but not the workplace were related to diet quality. These topics warrant further investigation by future research to better understand the relationship between food environment and related factors.
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ItemAn Exploration of Community Within the Context of a University’s Tennis Program(Middle Tennessee State University, 2024)Within sport management studies, scholars have overlooked the importance of the involvement and engagement of the surrounding community in relation to sport organizations. Using an ethnographic methodology to guide the research, this study analyzed the conceptualization of a tennis community within the context of a university’s tennis program. The study also explored the power relation and sources of power between a city and university partnership. In particular, this study was guided by the following questions: 1) What has shaped the participants’ subjective understanding of the concept of community around the university’s tennis programs? and 2) How have power dynamics (social, political, economic, cultural) in the context of the community and the university’s tennis program played a role in shaping the meaning-making of community around the university’s tennis program? The methods used to answer the research questions included six months of ethnographic participant observations, 23 semi-structured interviews, and records kept through a research journal. Analysis of the data, generated results that have been developed into two separate manuscripts. The first manuscript showed the factors identified by the community members in the development of place identity with the Tennis Complex. The second manuscript presented the way in which sources of power were created within the community and the ways in which power has been exercised in the research context. Overall findings serve to provide leaders of sport organizations with additional evidence-base literature emphasizing the several benefits of community, the importance of place and identity, the strategies to adapt for community engagement, and the importance of partnerships and forms of collaborations.
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ItemEXPLORING A TRACKED EDUCATIONAL SYSTEM: TEACHER PERCEPTIONS OF THE LEARNING ENVIRONMENT, STUDENT POTENTIAL, AND STUDENT SUCCESS(Middle Tennessee State University, 2024)Abstract Tracking is the practice of grouping students into homogeneous classes based on their perceived ability or intelligence. It is a pervasive system used by school districts across the United States and, like most widely used practices, it has its proponents and opponents. Through a qualitative case study approach, this study aims to explore teachers’ understanding of tracking, their perceptions of tracking, and their role in tracking when planning and executing instruction for students in their tracked classes. The findings from this study reveal disparities in the amount of academic exposure provided to students in different tracks. This dissertation also includes implications and recommendations for school and district leaders, teachers, and students as the goal of education is to assist and support all students as they reach their full potential.
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ItemDEEP LEARNING ALGORITHMS FOR TIME-DEPENDENT PARTIAL DIFFERENTIAL EQUATIONS(Middle Tennessee State University, 2024)Deep learning algorithms have demonstrated encouraging outcomes in resolving Partial Differential Equations. The advent of physics-informed Neural Networks has greatly enhanced the precision and effectiveness of Deep Learning-based approaches for solving partial differential equations. The basic idea of such Deep Learning algorithms is constraining the output of neural networks to satisfy the physics laws and certain conditions by incorporating the physical laws and boundary conditions directly into the loss function for training the neural networks. Using this technology, we propose a variant of the Physics- Informed Neural Network to identify time-varying parameters of the Susceptible-Infectious-Recovered- Deceased model for COVID-19 by fitting daily reported cases. The learned parameters are verified with an ordinary differential equation solver, and the effective reproduction number is calculated. Additionally, a Long Short-Term Memory network predicts future weekly time-varying parameters, demonstrating the accuracy and effectiveness of combining these two models. Then, we explore the method that can solve the partial differential equations using the sparse data. We combine a neural network with a numerical approach to address time-dependent partial differential equations using initial conditions and limited observed data. The Gated Recurrent Units network estimates time iteration schemes, integrating prior knowledge of governing equations. A numerical implicit approach is applied to calculate new time iteration schemes, with the loss function incorporating the difference between these schemes. After that, we propose a novel physics-informed encoder-decoder gated recurrent neural network to solve time-dependent partial differential equations without using observed data. The encoder approximates the underlying patterns and structures of solutions over the entire spatiotemporal domain. The approximated solution is processed by the decoder, a Gated Recurrent Units layer, utilizing the initial condition as the initial state to retain critical information in the hidden states. Boundary conditions are enforced in the final prediction to enhance model performance. The effectiveness of these two methods has been validated through their application to several problems. Additionally, we observe the traditional physics-informed neural network often fails to converge due to imbalances in the multi-component loss function within the back-propagated gradients during training. The standard approach to mitigate this issue involves adding appropriate weights to each component of the loss function, but determining the correct weights is challenging. Therefore, we introduce the Self- Learning Physics-Informed Neural Network to solve some non-linear partial differential equations. In this method, weights are learned by separate neural networks, eliminating the need for hyper-parameter fine-tuning. The effectiveness of our method is demonstrated by the Burgers’ and Burgers-Fisher equations.
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ItemBuilding Tomorrow's Workforce Through the Self-Actualization of Post-Secondary Students in Tennessee(Middle Tennessee State University, 2024)Tennessee’s future will be determined by the number of post-secondary credentials of its residents to support economic development. Additionally, Tennesseans must earn living wages to support their livelihoods. Finally, the psychological development of Tennesseans must be supported, which allows individuals to reach their fullest potential. While critical state initiatives such as Drive to 55, TN Promise, and tnAchieves have been in place for nearly a decade, Tennesseans are not graduating from post-secondary institutions at the rate that is critical for individual and state success. In reviewing initiatives and success in higher education, the study was informed by important place-based promise programs and the various definitions of student success. An understanding of self-actualization, humanistic education, and measurement of self-actualization informed how the study identifies the essence of the experience. Utilizing a phenomenological research approach and an interpretive framework of social constructivism, this study has informed the essence and lived experiences of self-actualization among tnAchieves students at Motlow State Community College. Overall findings indicate the essence of self-actualization among students persisting toward a post-secondary credential is represented by a sliding scale-factor duality of shared support and independent motivation. The implications of these findings to policy and practice are addressed. Specifically, implications for policy outlines considerations for policy makers related to revisioning a state master plan in the future, as well as various degrees of tnAchieves program success. Implications for practice support the humanistic education model, as well as effective student support services, peer-to-peer support models, and family engagement.