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12th Grade Academic Outcomes Associated with Middle- and High-School Music Enrollment Among Low-Income, Ethnically Minoritized Students
Full text linkResearchers have long studied the relationship between musical training and cognitive and academic outcomes using a variety of theories about why musical experiences might influence non-music outcomes. However, inconsistent findings leave some researchers questioning the existence of strong transfer effects. Because pre-existing socio-economic advantages support both academic performance and selection into music electives, it is challenging to disentangle the possible impact of music from a child’s natural and/or privileged trajectory. Researchers interested in the “effects” of in-school music participation who cannot employ random assignment must understand and control for pre-existing differences between music and non-music groups using longitudinal, quasi-experimental methods to examine outcomes. This was the goal of this dissertation. The current large-scale, 5-cohort, 14-year longitudinal study followed a large (n = 20,161) sample of mostly low-income (80% qualify for free/reduced-price lunch) and ethnically minoritized (59% Hispanic, 34% Black, 7% White/Other) students from preschool through middle and high school (6th-12th grade) in a large metropolitan, public school system. Information on student demographics, music course enrollment, and academic performance were collected from school records/transcripts, and cognitive school readiness was directly assessed at age four. I examined the relationship between in-school ensemble music elective enrollment (i.e., band, chorus, guitar, orchestra, keyboard, general music, and music theory) and student academic outcomes in 12th grade (non-arts GPA, days absent, suspension, diploma, and high school completion). Importantly, I controlled for selection effects of music enrollment (i.e., race/ethnicity, gender, poverty, special education status, English language learner status, English proficiency, and 8th grade GPA) and enrollment in other arts electives (i.e., dance, drama, & visual arts) to better compare music to non-music students. I used linear (overall GPA, academic GPA, days absent) and logistic (suspension, graduation) regression. The following research questions were be explored: 1) After controlling for selection factors (i.e., gender, race/ethnicity, SES, disability status, early-ELL status, and 8th grade GPA) and enrollment in other arts electives in high school (9th-12th grade), are there differences in high school academic outcomes between music and non-music students? 2) Does earlier 8th grade GPA moderate the relationship between years of music enrollment (0-4 years for high school and 0-7 years for middle and high school combined) and 12th grade academic outcomes? 3) Does breadth/dosage of music enrollment (i.e., enrollment in multiple types of music vs. staying with the same type of music) matter in predicting high school outcomes related to music participation? Results show that controlling for selection factors, music enrollment, and more years of music throughout middle and high school, were related to significantly lower odds of school suspension in 12th grade, and higher odds of earning a diploma and completing high school. Music enrollment in some models was related to slightly lower 12th grade non-arts GPA. Results were the same regardless of the student’s initial 8th grade GPA. Taking multiple types of music courses in secondary school (breadth/dosage) was also related to positive 12th grade outcomes (better attendance, better school completion) both by itself and above and beyond being enrolled in music. It appears that the social experience of being in high school music electives supports positive engagement, persistence, and graduation outcomes in high school and taking multiple types of music further helps
Topics in Extremal and Computational Graph Theory
Full text linkGraph theory studies the mathematical structure between objects, and can be used to model relationships and processes in networks. Examples of real-world networks include social networks, powerline networks, and transportation networks. In addition, graph theory is more broadly related to computer science and optimization. The research discussed in this dissertation has connections with all of these areas. First, we address problems in extremal graph theory to prove best-possible edge density conditions sufficient to guarantee traceability, Hamiltonicity, chorded pancyclicity, Hamiltonian-connected-ness, -path Hamiltonicity, -Hamiltonicity, -Hamiltonian-connectedness, and -connectedness in -free graphs. We characterize the extremal graphs. Then we extend these edge density conditions to clique density conditions. Equivalently, we introduce variants of the extremal number \ex(n,F) and the generalized extremal number \ex(n,H,F)---that is, the maximum numbers of edges and copies of , respectively, in -vertex, -free graphs---in which we require that these graphs not be traceable, Hamiltonian, chorded pancyclic, Hamiltonian-connected, -path Hamiltonian, -Hamiltonian, -Hamiltonian-connected, or -connected, and we determine their exact values for and . Second, we address problems in computational graph theory, first revisiting and generalizing a known result about a doubly exponentialsequence that describes the number of -ary ordered rooted trees of height where is a fixed integer. We show that such a sequence has the form where for each given and . We provide the first detailed analysis of the real number sequence and show, in particular, that this sequence is strictly decreasing and has a limit when tends to infinity. We then turn our attention to a more general setting for sequences that describe the number of ordered rooted trees of a given height. This has applications in algorithm analyses where searches in rooted trees are performed. We consider infinite ordered rooted trees in which each ordered rooted subtree induced by all the vertices on levels h\in\nats or less is a finite ordered rooted tree of height of a certain type. In particular, we study those infinite trees in which each vertex has infinitely many descendants. We first give a complete characterization of those infinite trees for which the number of ordered rooted subtrees of , where is the subtree of induced by all the vertices of on level or less, is bounded by a polynomial in . We then present natural lower and upper bounds for and use those to obtain a tight threshold function for which we have for infinitely many h\in\nats. This threshold function can be presented in terms of the Lambert W function; the upper branch of the two inverses of the function . Finally, we investigate some theoretical properties of for those infinite trees that have a finite width when viewed as partially ordered sets (posets) with the root as the sole maximum element. In particular, we show that for large enough the function is given by a polynomial in for sufficiently large and we determine the degree and the leading coefficient of this polynomial
Co-Developing a Game-Centered Approach to Teaching Martial Arts: A Dialogical Process With Expert Instructors
Full text linkThe current dissertation is presented in three manuscripts. The first manuscript, titled “Martial Arts and Combat Sports for Youth: A 10-year Scoping Review”, aims to identify and categorize literature with a pedagogical focus on martial arts and combat sports (MA&CS) for youth. With key terms related to MA&CS and young people and following PRISMA guidelines for scoping reviews, it includes the peer-reviewed, empirical articles from six databases in English and Portuguese from 2013-2022. The paper analyzes the psychological, social, affective, physical, cognitive, and academic benefits of MA&CS interventions for youth. The second and third manuscripts are a result of nine months of engagement with martial art (MA) instructors to co-develop a Tactical Games curriculum for teaching children in Brazil. The organization (Social Service of Commerce; SESC Rio de Janeiro) where this study took place offered MAs free of charge for 7-10-year-old children. The participants of this study were five expert instructors from four different MAs and two collaborators. The objective of the second manuscript titled “A game-centered approach to teaching combative activities for children: Expanding the game classification” is to present combative activities as games in pedagogical contexts. We propose combative activities as an expanded classification of games based on a document co-developed with the instructors. The Tactical Games approach was used as a conceptual framework. Observations of lesson plan implementation were conducted to check for the fidelity of the Tactical Games approach. We explain and present “combative activities” as a new game classification, focusing on tactical problems of different combative activities, as well as providing examples of tactical questions. Manuscript three, titled “Creating value with expert martial art instructors in a social learning space: a professional development initiative”, uses the tenants of the value creation framework. This manuscript aims to highlight the perspective of MA instructors regarding the key factors of co-developing a Tactical Games approach for children within a social learning space (SLS). Data sources included written and audio researcher journal entries, recordings of dialogic engagement with a critical friend, instructors, and collaborators, recordings of meetings with instructors, written observation of two professional development sessions, WhatsApp conversations, documents provided by the institution, and final interviews. Data were analyzed using thematic analysis. The three themes presented discuss: (a) ways that resistance was negotiated to achieve changes in our SLS, (b) dialogue as the key ingredient to creating value, and (c) ideas from the instructors on ways to keep the SLS ongoing. The main takeaways of the current dissertation are that (a) MAs can provide multiple positive benefits for youth in and out of school, (b) a game-centered approach (GCA) provides a student-centered pedagogy to teaching MAs, (c) SLSs are a promising way to bridge research with practice through dialogue which led to the co-creation of a novel GCA to teaching MAs that was found valuable for the local context, and (d) SLSs as professional development needs to be intentionally planned but also flexible
Translocating Swift Foxes (Vulpes velox): Insights on Personality, Stress, and Success
Full text linkIn principle, conservation translocations can bolster species and ecosystem recovery, but
in practice, they are costly and prone to failure due to low post-release survival. Until
recently, studies have prioritized how extrinsic population- or community-level factors
affect translocation outcomes. However, intrinsic attributes like personality and stress
physiology could impact how individuals cope with translocation and acclimatize to the
release site. This thesis investigated relationships among bold (risk-taking) personality,
biomarkers of stress, post-release movement, and survival using an ongoing swift fox
(Vulpes velox) reintroduction program as a case study
Gustav Klemp World War I Narrative: English Translation
Full text linkEnglish language text translation from a manuscript originally handwritten in German. This manuscript is a narrative that documents the military service experience of Gustav Klemp, a Prussian medic with the German army who served on the Eastern Front during World War I
Improving Mobile Positioning Within an Image-Based Hybrid Geocrowdsourcing System
Full text linkThe field of Geo-crowdsourced data (GcD), also known as volunteered geographic information (VGI), has grown exponentially within the past fifteen years, from a mostly theoretical idea proposed by academics to a giant industry providing an indispensable service to commercial and government interests alike. Many applications rely on crowdsourced data to provide up-to-date traffic notifications and operation statuses of businesses. GcD is utilized extensively by humanitarian efforts, non-government organizations (NGOs), and the intelligence and defense communities to provide agencies with human geography data, topographic data, and human intelligence data that would have been much harder to collect using traditional methods. Despite the monumental growth and advances of GcD over the past decade, problems stemming from positional accuracy of data sources affect the overall accuracy of GIS. Low positional accuracy is a persistent problem with this data that prevents wider acceptance within the wider GIS industry. The lack of positional accuracy is more severe in some applications over others, especially ones where the user is moving and is dependent on accurate GPS positioning as opposed to reporting applications where general location is sufficient when accompanied by additional information. The aim of the research within this document is to examine if advancements in mobile positioning hardware, the Global Positioning System (GPS), and front-end dashboard interfaces for geo-crowdsourcing applications/programs over the past five years will address the lack of positional accuracy. Improving positional accuracy within GcD will not only encourage wider usage among the GIS field, but it could prove essential for emergency response, military mobility, and mitigating the threat of terrorism
Ground-based Light Curve Follow-up Validation observations of TESS object of interest TOI 5907
Full text link“We present the findings of ground-based light curve follow-up validation observations of TESS object of interest TOI-5907. The TESS mission only captured a transit in one sector, making TOI-5907 a prime target for additional observations. Following time-series observations during a predicted transit using GMU’s 0.8 m Ritchey-Chretien on 20 June 2024 and 28 June 2024, the images were reduced, plate solved, and aligned using AstroImageJ to generate a light curve. This light curve was supplemented with TESS data and fitted using EXOFASTv2 to generate updated values for the system. While the NEB check was unable to rule out the possibility of an eclipsing binary in this instance, a transit was detected on the 20th. This allowed for an updated period and transit duration, which will be valuable for future observations.
Learn-to-Optimize Frameworks for Large Networks: Models and Application
Full text linkResearch on combinatorial optimization problems is vital due to their widespread applications in various real-world areas such as transportation, logistics, production, resource allocation, timetabling, digital services, finance, and many other domains. Large-scale networks can adequately capture the structure of complex systems containing thousands of nodes (e.g., physical components) and edges (e.g., mutual interactions). Therefore, network optimization methods have become a popular approach for addressing combinatorial optimization problems in various application fields such as infrastructure systems. The majority of problems are formulated as Mixed Integer Programming (MIP) models, which become NP-hard resulting from the exponential increase in the number of decision variables and the unique structure of the feasible region for large-scale problems. To overcome the computational challenges, leveraging the valuable information during the search process to embed well-informed machine learning methods in optimization algorithms has emerged as a promising research area. This dissertation aims to develop scalable Learn-to-Optimize frameworks for the restoration of large-scale infrastructure networks. First, we incorporated supervised learning mechanisms and decision variable decomposition to improve the performance of Branch-and-Price for single-objective deterministic models. Next, we included a non-parametric learning component to guide the search direction of objective function decomposition-based evolutionary algorithms for deterministic multi-objective MIP models. Finally, a supervised learning module is embedded in the stage-wise Nested Benders Decomposition algorithm to improve the convergence rate and scalability for multi-stage stochastic MIP models. The contributions of this work can be summarized as follows: (1) Develop Learn-to-Optimize frameworks for large-scale networks, (2) Identify the helpful information and associated feature vectors for training the machine learning algorithms, (3) Design modular frameworks (in terms of optimization, machine learning, and decomposition components) to make them applicable to a wide range of problems with minimum changes, and (4) Incorporate network features in developing more efficient algorithms for large-scale networks. Different sets of benchmark problems and multiple city-scale infrastructure networks are used to show the added value of the proposed Learn-to-Optimize framework compared to baseline optimization algorithms regarding solution quality, convergence rate, and computational time
Virtual Effects: A Qualitative Research Study on the Influence of Event Context on Genre Use and Genre Knowledge
No full textThis work is embargoed by the author and will not be publicly available until May 2029.In the early days of the pandemic, public health protocols cautioned against in-person gatherings to minimize the viral transmission of COVID-19. As a result, the circumstances of business events changed, with many organizers adopting virtual platforms to facilitate executive communication. This dissertation acknowledges that shift and its evolution since, seeking to better understand the practices and perspectives of practitioners routinely engaged with business events today. To do that, it draws from scholarship on assemblage, genre, multimodal rhetorics, and professional writing, conducting a qualitative study focused on six practitioners during the preparation process for two different events, one completely virtual and another largely in person. As the findings of this study suggest, the contexts for an event – specifically the differences between in-person and virtual environments – serve as more than mere components of the rhetorical situations that practitioners will encounter. Instead, they prompt important rhetorical negotiations that can extend our understanding of genre knowledge and the rhetorical strategies this knowledge informs. This dissertation uses the insights of practitioners to propose a framing of genre knowledge based on the negotiations stemming from event contexts rather than event texts, arguing for a wider lens to encompass the rhetorical moves practitioners make. It also highlights the medley of genres utilized to prepare for events and analyzes several rhetorical moves specific to events, reinforcing these environments as rich spaces for investigation.2029-05-1
Enhancing Extended Reality Experiences by Understanding The Environments
No full textIn the rapidly evolving domain of Extended Reality (XR), the advancement of scene understanding stands as an increasingly significant frontier, enhancing the creation of more realistic and immersive experiences. Whether it is about the depiction of intricate virtual landscapes in Virtual Reality (VR) or the adaptation of virtual content within real environments in Augmented Reality (AR), the potential of scene understanding to shape the future trajectory of XR is significant and clear. This dissertation addresses the multifaceted challenges that arise when seeking to facilitate XR experiences through advanced scene understanding techniques. Three core dimensions are addressed: a method to automatically evaluate the perceptual desirability of viewpoints within VR environments, aiming to enhance the user's teleportation experience; an approach to weave storylines involving virtual characters and objects, taking into account complex spatial-temporal relationships within interactive AR contexts; and the generation of multi-character and multi-object interactions in virtual 3D scenarios, exploring their integration into AR landscapes. This research aims to provide insights for enhancing XR experiences to new levels of immersion and perceptual fidelity