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Item type:Item, Access status: Open Access , REACHING THE FINISH LINE: ADULT STUDENTS PERSPECTIVES ON CHALLENGES IN PERSISTING IN AN AVIATION CAREER TECHNOLOGY PROGRAM(University of Oklahoma – Graduate College, 2026) Stephens, Kristi; Guthery, Sarah; Heddy, Benjamin; Maiden, Jeffrey; Sperling, JennyCareer technology education plays a significant role in training students for skilled trade jobs that support the local, state, and national economic workforce. Adult students have non-traditional challenges that can impact their ability to complete a program in which they enroll. Specifically, adult students enrolled in aviation programs have unique barriers and challenges that can affect their ability to persist. There are not enough students completing the aviation curriculum and becoming certified airframe and powerplant mechanics compared to the job openings for that career pathway. This qualitative, bounded case study examined the challenges experienced by adult students enrolled in an aviation career technology program so that educational institutions and career technology centers can understand what barriers adult students encounter while enrolled. The main research question for this study looked at the perspectives of adult students enrolled in an aviation career and technology education, as well as the institutional and personal challenges the students face in completion of the program. The methodology for this study utilized one-on-one interviews of adult students enrolled in an aviation program at an Oklahoma City Metropolitan area career technology center. Tinto's model of student retention was the framework used as the lens for data collection and analysis, along with understanding how student integration, institutional commitment, and support services impact student completion rates. Thematic analysis and coding were used to analyze the data obtained from the semi-structured interviews. The information obtained from this study can help career technology organizations change practices and guide strategic planning processes to increase adult student completion and retention rates. Keywords: Tinto’s model of student retention, career technology, aviation, persistenceItem type:Item, Access status: Open Access , Assessing the Skill and Reliability of NCAR-CESM SMYLE Predictions of the North Atlantic Subtropical High in Multi-Year Timescales(University of Oklahoma – Graduate College, 2026) Glanton, Gwyneth Lillian; Pegion, Kathleen; Furtado, Jason; Becker, EmilyThe North Atlantic Subtropical High (NASH), also known as the Bermuda High, is a semi-permanent high-pressure system located over the North Atlantic Ocean that influences summertime climate across the southeastern United States (SEUS). Variations in the strength and position of the NASH western ridge affect regional circulation and are associated with changes in precipitation, heat, drought, and coastal inundation. Since low-frequency sea surface temperature variability may contribute to variations in NASH position, initialized climate predictions could provide a source of multi-year (1-2 years) predictability for the NASH and its regional impacts. This study evaluates the ability of the NCAR Community Earth System Model Seasonal-to-Multiyear Large Ensemble (SMYLE) hindcasts to predict the NASH western ridge and associated SEUS precipitation at lead times extending to two years. SMYLE western-ridge positions are evaluated against ERA5 reanalysis, and precipitation forecasts are verified against CHIRPS observations. Systematic biases in the simulated NASH are addressed before evaluating western-ridge position and quadrant prediction. Precipitation prediction is assessed using probabilistic verification metrics, including the Brier Score, Brier Skill Score, and receiver operating characteristic area under the curve (ROC AUC). Precipitation skill is additionally conditioned on whether individual ensemble members correctly predict the observed NASH quadrant to determine whether improved representation of the NASH is associated with improved precipitation prediction. SMYLE exhibits a systematic bias in the NASH western ridge position, particularly in longitude, and removing the mean bias improves agreement with ERA5. Considerable case-to-case variability in western-ridge position remains after bias correction, and quadrant prediction varies with initialization, lead time, season, and observed NASH quadrant. SEUS precipitation forecasts exhibit limited skill relative to climatology, although SMYLE retains some ability to discriminate between above- and below-average precipitation conditions. Conditioning the forecasts on correct NASH quadrant prediction generally improves precipitation prediction relative to members that incorrectly predict the NASH, but these improvements do not consistently result in greater skill than climatology or the full ensemble. Circulation composites further show that correct quadrant classification does not necessarily correspond to an exact representation of the NASH western ridge or regional precipitation. In particular, the direction of longitudinal western-ridge errors is associated with systematic precipitation differences, with eastward ridge errors generally corresponding to wetter SMYLE conditions and westward errors to drier conditions. These results demonstrate that accurate prediction of the NASH provides some useful information for SEUS precipitation prediction, but NASH quadrant prediction alone is insufficient to determine precipitation skill in this region. The results highlight both the potential value of the NASH as a source of multi-year regional climate predictability and the importance of representing the position of the western ridge when translating circulation predictability into regional precipitation forecasts.Item type:Item, Access status: Open Access , Comparing Model-Based, Feature-Based, and Deep Learning Approaches to Time-Series Clustering for Intensive Longitudinal Data: Monte Carlo Studies and an Empirical Demonstration(University of Oklahoma – Graduate College, 2026) Meyer, Theo; Song, Hairong; Heo, Ihnwhi; Kim, JeongjinIntensive longitudinal data allow researchers to ask whether individuals form subgroupswith different within-person dynamics. This thesis compared five parameter-based, feature-based, and convolutional neural network representations for clustering in two Monte Carlostudies, robustness analyses and extensions, and an empirical demonstration. In order-homogeneous autoregressive populations, direct coefficient estimates produced the strongestsubgroup recovery. In mixed-order populations, deep learning representations were morecompetitive for shorter series, whereas direct estimation gained ground as series lengthened. InStudy 1, series length accounted for 50.7% of the variation in Model-Based recovery and samplesize for 0.1%. In extensions where scale or volatility defined membership, representationssensitive to those properties performed best. Cluster-number selection was a separate problem:re-expressing the same fitted autoregressive information in redundant, strongly dependentcoordinates markedly changed the selected count. In an empirical demonstration adapting theprocedure of Li et al. (2025) to affect data, the Bayesian information criterion with a Gaussianmixture selected two clusters for three representations and one for three others, whereas the gapstatistic with k-means selected one for every representation. Overall, recovery depended onalignment between the representation and the subgroup-defining signal, within-seriesinformation, and the geometry presented to the clustering algorithm. Membership recovery andsubgroup enumeration should therefore be evaluated separately, as properties of the wholerepresentation-and-algorithm pipeline.Item type:Item, Access status: Open Access , Cultivating Emotional Intelligence for Orchestra Auditions(University of Oklahoma – Graduate College, 2026) Moore, Larry Wayne; Stoops, Anthony; Baughman, Melissa; Cavazos, Jenel; Ruck, JonathanThis mixed-methods action research study investigated the impact of an emotional intelligence (El) training intervention on the performance anxiety and audition readiness of 102 middle and high school orchestra students. Participants were categorized into three groups: the primary intervention groups received training in breathing techniques, positive affirmations, and reflective practice, while a subset (Group 2) participated in an enhanced mock-audition protocol. Analysis of survey data, practice journals, and video reflections indicated that students actively engaged with the curriculum, specifically identifying breathing and affirmations as the most effective techniques for reducing performance anxiety and improving practice quality. Findings demonstrated that while the intervention benefited all participants, the Group 2 cohort achieved significantly higher mean scores in audition-specific confidence and readiness, highlighting the value of applying regulation skills in realistic, low-stakes practice contexts. Longitudinally, the study revealed a marked developmental shift toward "reflective sophistication," as evidenced by improved rubric scores in emotional awareness - increasing from a mean of 1.8 to 2.7 - and a consistent movement from reactive, "Yellow" or "Red" emotional states toward sustained performance resilience. These results suggest that integrating El training into the music curriculum empowers students to separate their self-worth from musical outcomes, fostering a healthier and more focused approach to high-stakes performance.Item type:Item, Access status: Open Access , ELUNA 101(2018-05-02) Tabatabai, Habib; Jerabek, AlexanderAn overview of Ex Libris Users of North America organization operations, benefits, responsibilities, membership, and structure.