TAMU-CC Repository

The Texas A&M University‐Corpus Christi (TAMU‐CC) repository is an open online site for storing and sharing digital content created or owned by the TAMU‐CC community. Content includes published and unpublished research and scholarship as well as archival materials. The service is managed by the Mary and Jeff Bell Library in cooperation with the Texas Digital Library. Learn more


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Computational thinking in 5th grade social studies
(2023-12) Cook, Margaret
The literature review and associated unit of curriculum explores the integration of computational thinking (CT) into 5th-grade social studies education, addressing the decline in social studies education and test scores. It analyzes diverse perspectives on CT, its components, challenges in implementation, and its potential impact on critical thinking. Implementing the CT strategies into curriculum unveils insights into CT's ability to offer a structured approach through the PRADA framework and problem-based learning as ways to bridge CT concepts with social studies topics. Overall, the curriculum advocates for a balanced approach to effectively integrate CT in elementary education, highlighting how CT can enhance student engagement and critical thinking skills in the social studies classroom.
Joy beyond the binary: Experiences and constructions of joy in nonbinary lives
(2023-12) Otter, Bailey
In recent years, sociologists have begun exploring the experiences of gender-expansive populations. However, these studies are oversaturated with negative experiences of prejudice and discrimination and do not accurately portray the joy which accompanies identifying outside of the gender binary. To address what shuster and Westbrook (2022) coined as the joy deficit in sociology and trans studies, I conducted in-depth interviews with 28 nonbinary participants, in which they discussed their experiences of joy. These interviews unveiled three primary themes relating to nonbinary joy. First, participants experienced joy through exploring and constructing their gender identities. Second, participants described how expressing their gender in their desired ways and having their gender affirmed creates joy. Third, participants experienced joy through being in safe spaces and being connected with queer and nonbinary communities. Through examining these dimensions of joy within nonbinary lives, this research contributes to a deeper understanding of the positive aspects of gender diversity and informs discussions surrounding identity, community, resilience, and well-being for gender-expansive populations.
Breast health and preventative screening
(2023-09-06) Walker-Smith, Tammy
Breast health and its importance in every stage of a woman’s life is discussed from adolescence, across the life span, and addressing the aging process. The importance of self-breast exams, clinical breast exam, and screening mammograms play an integral part of ensuring early detection of breast cancer. The risk factors, genetic mutations, and how breast cancer is triggered is discussed. The advancements in screening recommendations, genetic testing, and treatments is explored and what innovative approaches are being taken to prevent, treat, and cure breast cancer.
Commuters: A waterbird provides a new view of how species may utilize cities and wildlands
(2023-07-06) Shlepr, Katherine R; Evans, Betsy A; Gawlik, Dale E
Traditional classifications of vertebrates’ responses to urbanization fail to capture the behaviour of those that rely on both urban and wildland resources for population persistence. Here, we use the wood stork (Mycteria americana), a species that makes daily foraging trips up to 74 km away from its nest, as an example of a previously unrecognized response to urbanization. We monitored nests and sampled diets at stork colonies in south Florida (USA) during 2014–2020 to investigate how storks use urban habitats. We found that urban development now comprises up to 51.6% of the land cover within the 30-km core foraging area surrounding colonies and that storks access alternative prey types within these urban areas. Our results also showed that urban-nesting storks outperformed wildland-nesting storks when the hydrological condition of the wetlands was suboptimal for foraging. Though storks still require healthy wetlands for population persistence, urban habitat benefitted storks when hydrological patterns were not ideal for prey production in wildlands. This ‘commuter’ response to urbanization, whereby individuals opt to utilize both urban and wildland resources within short time periods, may apply to other vertebrates with large home ranges.
Aggregation strategies to improve XAI for geoscience models that use correlated, high-dimensional rasters
(2023-10-30) Krell, Evan; Kamangir, Hamid; Collins, Waylon; King, Scott A.; Tissot, Philippe
Complex machine learning architectures and high-dimensional gridded input data are increasingly used to develop highperformance geoscience models, but model complexity obfuscates their decision-making strategies. Understanding the learned patterns is useful for model improvement or scientific investigation, motivating research in eXplainable artificial intelligence (XAI) methods. XAI methods often struggle to produce meaningful explanations of correlated features. Gridded geospatial data tends to have extensive autocorrelation so it is difficult to obtain meaningful explanations of geoscience models. A recommendation is to group correlated features and explain those groups. This is becoming common when using XAI to explain tabular data. Here, we demonstrate that XAI algorithms are highly sensitive to the choice of how we group raster elements. We demonstrate that reliance on a single partition scheme yields misleading explanations. We propose comparing explanations from multiple grouping schemes to extract more accurate insights from XAI. We argue that each grouping scheme probes the model in a different way so that each asks a different question of the model. By analyzing where the explanations agree and disagree, we can learn information about the scale of the learned features. FogNet, a complex three-dimensional convolutional neural network for coastal fog prediction, is used as a case study for investigating the influence of feature grouping schemes on XAI. Our results demonstrate that careful consideration of how each grouping scheme probes the model is key to extracting insights and avoiding misleading interpretations.