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The Rise and Fall of Public Confidence in Higher Education
No full textDuring the 20th century, college came to be viewed as an essential credential on the path to middle-class prosperity. But in the 21st century, a growing percentage of Americans have come to doubt the value of higher education. Consequently, the future for colleges and universities has never been more uncertain.
This Article explores the rise and decline of public confidence in higher education. Part One explores how higher education gained the confidence of the American people. In the 1800s, the idea that higher education would uplift the country became an article of national faith. The 20th century saw further gains as both federal financial support for higher education and college enrollment reached unprecedented levels. As the 21st century began, higher education seemed to have unstoppable momentum. American universities dominated global rankings and attracted hundreds of thousands of foreign students.
Part Two explains why the public lost confidence in higher education in the 21st century. The public’s growing disenchantment stems from the perception that colleges and universities push political agendas, fail to teach relevant skills, and leave students with heavy debt loads. Amid growing public criticism, colleges and universities have experienced major enrollment declines in the 2010s and 2020s.
Part Three concludes by proposing reforms to revitalize higher education’s standing in public opinion. The first critical reform is to diversify the ideological make-up of colleges and universities. The second is for colleges to teach students to accept, respect, and even celebrate differences of opinion. The third is for higher education to make attracting and retaining lower-income students a priority
Toward a Welcoming Classroom: Assessment of Student Perceptions of Writing Education in Introductory Honors College Courses
No full textThis study examines student perceptions of an introductory-level writing program in honors at a large, public R1 university. Author considers the efficacy of pedagogical interventions aimed at identifying gaps in the experience of writing for students from BIPOC and first-generation backgrounds and at developing more inclusive teaching practices. Collective revisions to the program’s writing philosophy, objectives, and rubrics helped instructors gain clarity and consistency across sections. A two-part survey was conducted at the beginning and end of the semester, and metrics were calculated for all respondents (N = 176) and targeted subgroups (n = 37; n = 30). Results indicate that while BIPOC students performed above their peers, first-generation students performed below their peers. Author identifies pedagogical practices that students report to be beneficial and offer suggestions on how honors educators can sustain strong and inclusive writing programs within an array of curricular and co-curricular offerings. Benchmarks and selected survey questions are appended
Kennesaw State University: Program Profile
No full textFounded in 1963, Kennesaw State University (KSU) has grown rapidly from a junior college to a doctoral-granting comprehensive university of nearly 45,000 students. Situated in the northwest suburbs of Atlanta, Georgia, KSU is now one of the 50 largest public institutions in the U.S. and has been classified as a Carnegie R2, a doctoral research institution with high research activity. Although KSU originally served a commuter student population, its average undergraduate student age has steadily decreased and, according to the most recently published KSU Factbook (2024–2025), is currently 21.4 years. KSU’s student body is evenly balanced between male and female students. It has a racial/ethnic composition of 39.7% White, 27.9% Black (non-Hispanic), 15.7% Hispanic, 6% Asian, 5% multi-racial, 2.6% undeclared, 0.1% American Indian or Alaskan Native, 0.2% Native Hawaiian or Other Pacific Islander, and 3.1% international. KSU’s international students represent 103 countries
Further Evaluation of Speed Tables on Roadway Curves: Simulation and Expeditionary Deployments
No full textThe road network leading up to a United States military base is known as an Entry Control Facility, or ECF. Entry control facilities are used to shield military installations by monitoring vehicles and refusing access to unauthorized vehicles. Passive control devices, including speed tables, are used to disrupt or delay threat vehicles attempting to navigate ECFs at high speeds. The purpose of this research was to recommend optimized configurations of speed tables in combination with roadway curves which allow safe, efficient traversal for authorized vehicles and disruption or delay for threat vehicles.
First, an analytical model of a Tesla Model Y was developed and calibrated with accurate weight, inertia, suspension properties, and engine power models and comparing to level-terrain performance and handling tests. Next, a previously developed Ford Crown Victoria model and the newly developed Model Y model were used to investigate speed table spacing on various curves and recommend an optimized spacing and layout condition. To compare results and determine optimized layouts, a Critical Speed Table Calculator (CSTC) was developed to identify the conditions in which a threat vehicle could traverse three consecutive speed tables on a curve in the least time and with the lowest lateral path disruption. Additionally, research evaluated a “standard” speed table spacing configuration that balanced recommendations across multiple speed and curve combinations. Research also investigated the design, manufacturing, and testing of expeditionary speed tables. In this secondary research effort, simulation and design work were conducted to create a lowweight, surface mounted speed tables with segmented partitions.
Based on simulation results, a robust suite of speed table installation recommendations was assembled for curve radii spanning from 150 ft to 3000 ft. This included recommendations for radius-controlled speed table spacings, speed-controlled speed table spacing, and a uniform speed table spacing recommendation. Recommended speed table spacing ranged from 67 ft to 199 ft based on curve radius or desired limiting speed. Uniform speed table spacings ranged from 77 ft for curves under 400 ft, 83 ft spacing for curves 450 ft to 750 ft, and 90 ft spacing for curve larger than 750 ft. These configurations create a threat vehicle delay from 2 s to 5 s at their most critical threat case, and cause vehicle disruption or rollover at speeds greater than their most critical case. It has also been concluded that expeditionary/portable speed tables can take the place of reinforced concrete speed tables based on simulation and testing. Multiple speed tables on a curve were determined to be an effective measure to mitigate threat vehicles when installed in ECFs. Future simulation work is recommended to explore additional unique combinations of passive threat deterrence assemblies, including combinations of speed tables, chicanes and roundabouts. Manufacturing and live testing is recommended to further confirm the capabilities of the expeditionary speed table design options.
Advisor: Cody S. Stoll
Traffic Prediction for Research and Education Networks: Anomaly-aware Deep Learning and Benchmarking
No full textResearch and Education Networks (RENs) and High-Performance Computing (HPC) environments are critical infrastructures for modern scientific discovery, demanding sustained high-throughput and low-latency data transfers. Unlike commercial networks, RENs exhibit unique traffic characteristics, including predominant “elephant flows,” inherent burstiness, and complex temporal-spatial dynamics often decoupled from human-driven cycles. Traditional traffic forecasting methods, tailored for commercial Wide Area Networks (WANs), consistently fail to capture these distinct REN dynamics, leading to inefficient resource management and potential impediments to scientific progress.
This thesis addresses this critical gap by developing and validating a robust, scalable, and anomaly-aware traffic forecasting framework specifically tailored for REN/HPC networks. Our contributions are threefold: (1) We developed and analyzed a two-month, multi-billionpacket traffic corpus from ten Internet2 core routers, overcoming data scarcity challenges and providing a unique foundation for large-scale empirical analysis. (2) We designed and empirically validated a novel hybrid GRU-LSTM model that effectively captures both long-term dependencies and short-term fluctuations in REN traffic, demonstrating the critical impact of prediction lead time on operational utility. (3) We integrated Isolation Forest anomaly detection into forecasting models, significantly enhancing robustness against unexpected traffic surges, and conducted comprehensive benchmarking of various state-of-the-art deep learning models (N-BEATS, TiDE, PatchTST) to assess their performance with anomaly awareness.
Our findings demonstrate that tailored hybrid deep learning models, augmented with anomaly detection and optimized for lead time, achieve superior forecasting accuracy and robustness in REN environments. This work provides valuable tools for proactive resource management, congestion prevention, and optimized network operations, thereby accelerating scientific discovery and ensuring the efficient utilization of critical digital infrastructures.
Advisor: Byrav Ramamurth
A Better Great Plains: Bridging the Implementation Gap for a Better Future
No full textThe Northern Great Plains is experiencing extensive native grassland loss and landscape degradation, jeopardizing both ecological integrity and the socio-economic systems that rely upon these ecosystems. In an early effort to lessen degradation, the United States Department of Agriculture (USDA) introduced the Conservation Reserve Program (CRP) which generated ecosystem services benefits such as reduced soil erosion, improved water quality, and increased species diversity through the retirement of cropland and rangeland for 10- or 15-year contracts. However, CRP has not fully modernized alongside today’s agriculturalists and adoption remains shaped by an historically fraught socio-political context characterized by deep-seated mistrust between ranchers, federal agencies, and scientific authorities. Regenerative agriculture, specifically regenerative ranching, has emerged as a promising paradigm that addresses the limitations of an aging CRP by advancing soil health, enhancing forage productivity, fostering socio-ecological resilience, and promoting more adaptive management.
This study, drawing upon qualitative thematic analysis of self-identified regenerative ranchers, develops a synthesized definition of regenerative ranching integrating rotational grazing, strategic land rest, cover cropping, forage diversification, and the minimization of tillage and synthetic inputs. Guided by the Theory of Planned Behavior (TPB), this research identifies three principal barriers to participation in government programs such as CRP: negative attitudes toward government intervention, identity-based tensions with conventional management paradigms, and low perceived behavioral control over management practices.
This study also represents a novel application of the Reserves-as-Catalysts (RAC) framework in the Northern Great Plains, revealing that proximity to natural areas complements more favorable orientations toward wildlife and conservation initiatives. These findings advance the understanding that social-psychological factors influence conservation program participation and underscore the need for federal agencies to align policy instruments with regenerative principles, expand financial and regulatory flexibility, and engage ranchers as co-producers of conservation outcomes.
Finally, a new cohesive theoretical framework is proposed, synthesizing key elements of TPB and RAC models to integrate findings across chapters. Collectively, this work demonstrates that trust-building and context-sensitive engagement are critical to scaling regenerative practices and achieving meaningful grassland conservation.
Advisor: Gwendŵr Meredit
Recovery and Characterization of \u3cem\u3eMoraxella\u3c/em\u3e Species from Bovine Specimens
No full textThe family Moraxellaceae is made up of a diverse group of 24 named species that fall into three phylogenetic groups. While most Moraxella spp. act as commensal respiratory and/or ocular flora of mammals, some are associated with disease. One of these diseases, infectious bovine keratoconjunctivitis (IBK), has significant welfare and economic impacts to both cattle and cattle producers. Moraxella bovis is the only experimentally determined cause of IBK, however, an association of Moraxella bovoculi with disease has been proposed. Currently, available vaccines do not have high levels of efficacy in the field. Culture based methods are the gold standard for diagnosis of bacterial infections, however, the lack of sensitivity in samples from non-sterile sites is problematic. One objective of this work was to develop a selective culture medium to inhibit the growth of contaminants to increase the frequency of isolation of Moraxella spp. The developed medium, Moraxella Selective Vancomycin Agar (MSVA), decreased the amount of bacterial contamination present while increasing the frequency of isolations of Moraxella spp., particularly that of M. bovoculi. Another objective of this work was to characterize previously unidentifiable Moraxella spp. that had been recovered from bovine specimens. The characterization of these organisms identified the different strains as Moraxella oculi, Moraxella haemolytica, and a likely identification of Moraxella nasibovis. Two of these strains had not been reported in the United States or in bovine specimens previously. The increased recovery and characterization of different Moraxella strains within ruminant populations can help us better understand the role of different Moraxella spp. involved with IBK and lead to the possibility of additional preventative and/or treatment options for this disease.
Advisor: John Dustin Lo
Transitive Pedagogy in Creative Teaching: The Lived Experiences of K–6 Teachers Who Intentionally Adapt for Creativity
No full textCreative pedagogy in the K–6 classroom is being increasingly recognized as an essential tool for increasing student engagement, practicing critical thinking, and fostering higher-order learning. However, research gaps in creative pedagogy are contextualized accounts of teacher thinking, planning, and enactment of strategies for student creativity. This study explored the lived experiences of teachers as they constructed and enacted creative lesson adaptations and how they identified and supported creative behaviors in students. An interpretative phenomenological analysis was employed, involving semi-structured think-aloud and stimulated recall interviews with five K–6 teachers who consistently adapt instruction to promote student creativity. Findings revealed that; (a) teachers cultivated opportunities for student growth in ownership, b) teachers navigated classroom tensions by designing tangible boundaries and tangible outcomes, c) teachers balanced tangible boundaries and outcomes with designed activities that promoted deeper thinking Based on the discussion of findings and extant literature, I coined the concept of transitive pedagogy, a phenomenon that adds insights into how teachers intentionally navigate between structure and flexibility to foster creativity, ownership, and deeper thinking in K–6 classrooms. Implication for the study include insights contribute to a deeper understanding of teacher decision-making in creative pedagogy. These implications can offer guidance for designing instructional practices for teacher training, practice, and policy that cultivate creativity in elementary education.
Advisor: Lydiah Kiramb
Enhancing Printability of Pure Copper through Tailored Scan Strategies in Blue-laser Powder Bed Fusion
No full textAdditive Manufacturing (AM) is emerging as an alternative to classical manufacturing techniques; it involves a layer wise building strategy to create 3D structures with either polymers or metals. Laser powder bed fusion falls within one of the seven main classifications of different AM techniques; it is the leading technique used worldwide in relation to the creation of AM metal structures. Copper is commonly used across several sectors in thermal management and electrical components such as cooling systems, heat exchangers, electrical wiring to name few due to its thermal and electrical properties such as thermal and electrical conductivities of 401 W/m·K and 5.96 × 107 S/m respectively. Generally, LPBF is conducted with the use of a Near Infrared (NIR) Laser of with wavelength range (1060–1080) nm, however copper reflects approximately 95% of light when irradiated with NIR lasers. Due to copper’s high thermal conductivity which results in rapid heat loss and high reflectance of NIR lasers has created multiple challenges in creating AM parts with high relative density. However, recent research has been focused on visible wavelength (400–700) nm lasers that can be absorbed at a higher percentage when copper is irradiated. Hence this research utilizes a blue wavelength (443 nm) laser to explore potential parameters that can be used to create copper structures with high relative density as well as intentionally porous copper structures as foundational proof of concept. A key component of the LPBF process is the scan strategy used to create 3D structures therefore, various forms of scan strategies including alternate layer 90° and 67° raster scan rotations were explored in this research as a method to analyze their effects on AM part quality. In addition, thermal management strategies such as beam wobbling and preheating were conducted to combat the reduction in melt pool dimension as the build height increased. This research was able to achieve up to 99% relative density for a copper AM part with the implementation of beam wobbling scan strategy. Furthermore, the findings and processing parameters explored in this research will add to the limited amount of literature related to AM copper printing with visible lasers which could provide more in-depth knowledge about processing highly thermally conductive materials.
Advisor: Qilin Gu
Geometric Modeling, Reconstruction and Evaluation of Maize Leaf Morphology in 3D
No full textMaize is a vital crop for global food security, yet quantitative characterization of its three-dimensional (3D) morphology remains a major challenge due to the geometric complexity of curved leaf structures and occlusions during data collection. This work presents an integrated framework for the digital reconstruction, parametric modeling, and functional analysis of maize leaf morphology and canopy architecture, advancing the precision and interpretability of phenotyping and modeling in smart agriculture.
First, we propose a descriptive and parametric model that represents maize leaves through three fundamental components: midrib, cross-section, and blade contour. Each is described by geometric curves and controlled by biologically meaningful parameters. This parametric representation enables scalable, configurable, and high-fidelity reconstruction of leaf morphology, facilitating downstream applications such as geometric analysis, light interception simulation, and synthetic dataset generation for phenotyping pipelines.
Second, we introduce a divide-and-conquer strategy for point cloud completion and surface reconstruction, addressing common challenges of occlusion and incompleteness in 3D plant data. The method reconstructs leaf skeletons, cross-sections, and width profiles individually before assembling them into complete 3D surfaces via gliding operations. This approach, informed by intrinsic morphological priors, achieves superior flexibility, accuracy, and interpretability compared to conventional deep learning methods, while maintaining robustness across synthetic and real-world datasets.
Finally, we explore how leaf morphology influences canopy architecture and light interception using correlation analysis and Bayesian optimization. Results demonstrate that midrib curvature, leaf width distribution, and undulation jointly regulate leaf area and canopy light distribution. The optimization framework identifies an ideal canopy structure characterized by upright upper leaves, moderately erect middle leaves, and relatively flat lower leaves, which can balance light capture and self-shading for enhanced photosynthetic efficiency.
These studies establish a unified pipeline from geometric modeling to functional optimization of maize morphology, bridging the gap between structural realism and physiological relevance. The proposed framework provides both theoretical and computational foundations for digital-twin modeling, precision phenotyping, and morphology-informed breeding strategies in maize and other crops.
Advisor: Yufeng G