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Browsing by Author "Koslovsky, Matthew, committee member"

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    Advancing equity in middle school science: the role of classroom cultures and curricular structures
    (Colorado State University. Libraries, 2023) Singleton, Corinne, author; Birmingham, Daniel, advisor; Jennings, Louise, committee member; Koslovsky, Matthew, committee member; Most, David, committee member; Penuel, William R., committee member
    This dissertation explores the role of classroom culture in shaping equitable student experiences and outcomes in science education, and examines how curricular structures might further reinforce equity. Here, equity in science education means supporting student identification, belonging, and learning in science, with particular attention to disrupting historical patterns of inequity that have created barriers to participation for students from historically marginalized populations. Classroom culture is a critical component of equity because it shapes student experiences and opportunities within science and determines whose voices, experiences, epistemologies, and cultural connections have credence within science learning. For their part, curricula shape how students interact with science content and serve to expand or constrain the breadth, depth, and rigor of the content that students experience. The outcomes of study are student interest and belonging, both critical for broadening participation in science because they are associated not only with improved learning, but also with meaningful participation in classroom science communities, course-taking patterns, and career decisions. The first two papers in this dissertation draw on large-scale survey data from 847 middle-school students in more than 30 OpenSciEd classrooms across the country. We use hierarchical linear modeling with students nested within classrooms to examine the extent of classroom-level variation in classroom culture, and how key features of equitable science classroom cultures relate to student interest and belonging in science. In both cases, we find significant classroom-level variation in culture, suggesting that classroom culture can be an important lever for equitable transformation. The first paper explores the relationship between classrooms reflecting collective enterprise and care with student interest in science. We find a strong and consistent relationship between collective enterprise and care, respectively, with student interest. We propose that these attributes of classroom culture may bolster student interest in science by supporting relationships and by connecting with the cognitive, emotional, and values-related components of interest. The second paper examines how classroom epistemologies of science relate to students' sense of belonging in science. Again we find a strong and consistent relationship between classrooms reflecting broader and more flexible epistemologies of science, with belonging in science. We consider the tensions between the science-as-practice vision of science education and the pervasive cultures of school science to contextualize the observed variation in classroom epistemologies of science. We argue that a concerted "epistemic boost" in science education may be necessary to fully realize the science-as-practice vision of science education. Finally, the third paper uses data from 38 teacher interviews to understand aspects of the science curriculum that teachers found supported their efforts to build equitable science classrooms. While many curricula address equity through increased representation of minority scientists or through guidance for teachers around equitable instruction, I argue that the design of curricular structures has been underappreciated as a potential venue for bolstering equitable science participation opportunities for students. I propose that curricular structures designed to support deep and authentic content learning can serve double duty by structuring student learning tasks and participation in ways that reinforce equitable classroom cultures. Collectively, these three papers contribute to the goal of expanding opportunities for students to connect with and succeed in science. They focus on valuable potential levers for equity, namely classroom culture and curricular structures. They help us to understand how relational and epistemic aspects of the classroom culture, and intentionally designed curricular structures, have the potential to expand how students understand science as a discipline, its value and relevance for their lives, and their own place within the world of science.
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    Bayesian data assimilation for CFD modeling of turbulent combustion
    (Colorado State University. Libraries, 2022) Wang, Yijun, author; Gao, Xinfeng, advisor; Zupanski, Milija, committee member; Guzik, Stephen, committee member; Windom, Bret, committee member; Koslovsky, Matthew, committee member
    Achieving accurate CFD prediction of turbulent combustion is challenging due to the multiscale nature of the dynamical system and the need to understand the effect of the small-scale physical features. Since direct numerical simulation (DNS) is still not feasible even for today's computing power, Reynolds-averaged Navier-Stokes (RANS) or large-eddy simulation (LES) is commonly used as the practical approach for turbulent combustion modeling. Nevertheless, physical models employed by RANS or LES for describing the interactions between the turbulence, chemical kinetics, and thermodynamic properties of the fluid are often inadequate because of the uncertainties in the dynamical system, including those in the model parameters, initial and boundary conditions, and numerical methods. Understanding and reducing these uncertainties are critical to the CFD prediction of turbulence and chemical reactions. To achieve this, this dissertation is focused on the development of a Bayesian computational framework for the uncertainty estimation of the dynamical system. In the framework, a data assimilation (DA) algorithm is integrated to obtain a more accurate solution by combining the CFD model and available data. This research details the development, verification, and validation of a multi-algorithm system (referred to as DA+CFD system) that aims to increase the predictability of CFD modeling of turbulent and combusting flows. Specifically, in this research, we develop and apply a Bayesian computational framework by integrating our high-order CFD algorithm, Chord, with the maximum likelihood ensemble filter to improve the CFD prediction of turbulent combustion in complex geometry. The verified and validated system is applied to a time-evolving, reacting shear-layer mixing problem and turbulent flows in a bluff-body combustor with and without C3H8-air combustion. Results demonstrate the powerful capability of the DA+CFD system in improving our understanding of the uncertainties in model and data and the impact of data on the model. This research makes novel contributions, including (i) the development of a new alternative approach to improve the predictability of CFD modeling of turbulent combustion by applying data assimilation, (ii) the derivation of new insights on factors, such as where, what, and when data should be assimilated and thus providing potential guidance to experimental design, and (iii) the demonstration of data assimilation as a potentially powerful approach to improve CFD modeling of turbulent combustion in engineering applications and reduce the uncertainties with data. Future work will focus on a performance study of the present DA+CFD system for turbulent combustion of high Reynolds numbers and understanding the uncertainty in model parameters for developing and assessing physical models based on available information.
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    Causality and clustering in complex settings
    (Colorado State University. Libraries, 2023) Gibbs, Connor P., author; Keller, Kayleigh, advisor; Fosdick, Bailey, advisor; Koslovsky, Matthew, committee member; Kaplan, Andee, committee member; Anderson, Brooke, committee member
    Causality and clustering are at the forefront of many problems in statistics. In this dissertation, we present new methods and approaches for drawing causal inference with temporally dependent units and clustering nodes in heterogeneous networks. To begin, we investigate the causal effect of a timeout at stopping an opposing team's run in the National Basketball Association (NBA). After formalizing the notion of a run in the NBA and in light of the temporal dependence among runs, we define the units under study with careful consideration of the stable unit-treatment-value assumption pertinent to the Rubin causal model. After introducing a novel, interpretable outcome based on the score difference, we conclude that while comebacks frequently occur after a run, it is slightly disadvantageous to call a timeout during a run by the opposing team. Further, we demonstrate that the magnitude of this effect varies by franchise, lending clarity to an oft-debated topic among sports' fans. Following, we represent the known relationships among and between genetic variants and phenotypic abnormalities as a heterogeneous network and introduce a novel analytic pipeline to identify clusters containing undiscovered gene to phenotype relations (ICCUR) from the network. ICCUR identifies, scores, and ranks small heterogeneous clusters according to their potential for future discovery in a large temporal biological network. We train an ensemble model of boosted regression trees to predict clusters' potential for future discovery using observable cluster features, and show the resulting clusters contain significantly more undiscovered gene to phenotype relations than expected by chance. To demonstrate its use as a diagnostic aid, we apply the results of the ICCUR pipeline to real, undiagnosed patients with rare diseases, identifying clusters containing patients' co-occurring yet otherwise unconnected genotypic and phenotypic information, some connections which have since been validated by human curation. Motivated by ICCUR and its application, we introduce a novel method called ECoHeN (pronounced "eco-hen") to extract communities from heterogeneous networks in a statistically meaningful way. Using a heterogeneous configuration model as a reference distribution, ECoHeN identifies communities that are significantly more densely connected than expected given the node types and connectivity of its membership without imposing constraints on the type composition of the extracted communities. The ECoHeN algorithm identifies communities one at a time through a dynamic set of iterative updating rules and is guaranteed to converge. To our knowledge this is the first discovery method that distinguishes and identifies both homogeneous and heterogeneous, possibly overlapping, community structure in a network. We demonstrate the performance of ECoHeN through simulation and in application to a political blogs network to identify collections of blogs which reference one another more than expected considering the ideology of its' members. Along with small partisan communities, we demonstrate ECoHeN's ability to identify a large, bipartisan community undetectable by canonical community detection methods and denser than modern, competing methods.
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    Evaluating statistical methods to predict indoor black carbon in an urban birth cohort
    (Colorado State University. Libraries, 2022) WeMott, Sherry D., author; Magzamen, Sheryl, advisor; Koslovsky, Matthew, committee member; Rojas-Rueda, David, committee member
    Though individuals in the United States spend a majority of their time indoors, epidemiologic studies often use ambient air pollution data for exposure assessment. We used several modeling approaches to predict indoor black carbon (BC) from outdoor BC and housing characteristics to support future efforts to estimate personal air pollution exposure given time spent indoors. Households from the Healthy Start cohort in Denver, CO were recruited to host two paired indoor/outdoor low-cost air samplers for one-week sampling periods during spring 2018, summer 2018, and winter 2019. Participants also completed questionnaires about housing characteristics like building type, flooring, and use of heating and cooling systems. Sampled filters were analyzed for BC using transmissometry. Ridge, Lasso and multiple regression techniques were used to build the best predictive model of indoor BC given the available set of covariates. Leave-one-out cross-validation was used to assess the predictive accuracy of each model. A total of 27 households participated in the study, and BC data were available for 39 filters. We had limited comparable data on seasonality as winter data were excluded from the analysis due to high variability and low confidence in outdoor measurements. Shortened runtimes and other performance issues suggest insufficient weatherproofing of our monitors for low temperatures. Of the three modeling approaches, Ridge LSE showed the best predictive performance (MPSE 0.50). The final inference model included the following covariates: outdoor PM2.5, outdoor BC, hard floors, and pets in the home (adj. R2=0.27). These factors accounted for approximately 27% of the variability in indoor BC concentrations measured in Denver, CO homes. In the absence of personal monitoring, household characteristics and time-activity patterns may be used to calibrate ambient air pollution concentrations to the indoor environment for improved estimation of personal exposure.