ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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Description
Arizona's English Language Development Model (ELD Model) is intended to increase and accelerate the learning of English by English Language Learners (ELLs), so that the students can then be ready, when they know the English language, to learn the other academic subjects together with their English speaking peers. This model is part of a response to comply with the Flores Consent Order to improve services for ELLs in Arizona public schools. Whether or not it actually has improved instruction for ELLs has been the subject of much debate and, in 2012, after four years of the requirement to use Arizona's ELD Model, the ELL students who were identified as reclassified for the six districts in the study did not pass the Arizona's Instrument to Measure Standards (AIMS) test. The model's requirement to separate students who are not proficient from students who are proficient, the assessment used for identification of ELLs, and the Structured English Immersion four hours of English only instruction are at the nexus of the controversy, as the courts accepted the separate four hour SEI portion of the model for instruction as sufficient to meet the needs of ELLs in Arizona (Garcia, 2011, Martinez, 2012, Lawton, 2012, Lillie, 2012). This study examines student achievement in Reading and Math as measured by AIMS standards-based tests in six urban K-8 public school districts between 2007-2012. This period was selected to cover two years before and four years after the ELD model was required. Although the numbers of ELLs have decreased for the State and for the six urban elementary districts since the advent of the Arizona ELD Model, the reclassified ELL subgroup in the studied districts did not pass the AIMS for all the years in the study. Based on those results, this study concludes with the following recommendations. First, to study the coming changes in the language assessments and their impact on ELLs' student achievement in broad and comprehensive ways; second, to implement a model change allowing school districts to support their ELLs in their first language; and, finally, to establish programs that will allow ELLs full access to study with their English speaking peers.
ContributorsRoa, Myriam (Author) / Fischman, Gustavo E (Thesis advisor) / Lawton, Stephen B. (Committee member) / Diaz, René X (Committee member) / Arizona State University (Publisher)
Created2012
Description
This research by studies the computational performance of four different mixed integer programming (MIP) formulations for single machine scheduling problems with varying complexity. These formulations are based on (1) start and completion time variables, (2) time index variables, (3) linear ordering variables and (4) assignment and positional date variables. The objective functions that are studied in this paper are total weighted completion time, maximum lateness, number of tardy jobs and total weighted tardiness. Based on the computational results, discussion and recommendations are made on which MIP formulation might work best for these problems. The performances of these formulations very much depend on the objective function, number of jobs and the sum of the processing times of all the jobs. Two sets of inequalities are presented that can be used to improve the performance of the formulation with assignment and positional date variables. Further, this research is extend to single machine bicriteria scheduling problems in which jobs belong to either of two different disjoint sets, each set having its own performance measure. These problems have been referred to as interfering job sets in the scheduling literature and also been called multi-agent scheduling where each agent's objective function is to be minimized. In the first single machine interfering problem (P1), the criteria of minimizing total completion time and number of tardy jobs for the two sets of jobs is studied. A Forward SPT-EDD heuristic is presented that attempts to generate set of non-dominated solutions. The complexity of this specific problem is NP-hard. The computational efficiency of the heuristic is compared against the pseudo-polynomial algorithm proposed by Ng et al. [2006]. In the second single machine interfering job sets problem (P2), the criteria of minimizing total weighted completion time and maximum lateness is studied. This is an established NP-hard problem for which a Forward WSPT-EDD heuristic is presented that attempts to generate set of supported points and the solution quality is compared with MIP formulations. For both of these problems, all jobs are available at time zero and the jobs are not allowed to be preempted.
ContributorsKhowala, Ketan (Author) / Fowler, John (Thesis advisor) / Keha, Ahmet (Thesis advisor) / Balasubramanian, Hari J (Committee member) / Wu, Teresa (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2012
Description
The pandemic has not only increased economic inequities within variouscommunities, but it has also exacerbated the social, emotional, and math achievement
inequities of middle school students, creating an environment that increases the potential
for heightened anxiety and peer conflict. Now, more than ever, it is imperative that
educators not only understand the existence and impact of these social and emotional
inequities but have the knowledge and skills to effectively address them. Within this
study, I facilitated a 10-week online community of practice with three middle school
math teachers, entitled The More than Math Collective (MTMC), with the purpose of
improving participant self-efficacy with SEL, developing their professional capital,
discussing various strategies to address the social and emotional skill needs of students in
their classrooms, and providing time for implementation of the discussed strategies. At
the conclusion of the study, most participants reported an increase in self-efficacy, human
capital, and decisional capital while only one out of three participants reported an
increase in social capital. All participants described a positive impact on their students
and their professional growth due to their participation in the MTMC and the various
strategies that were learned and implemented in their classrooms. Given the small sample
size, more research can be done to determine if the results of this study may be
transferable to other educational settings.
ContributorsNorcini, Erica (Author) / Dorn, Sherman (Thesis advisor) / Firetto, Carla (Committee member) / Grant, Cara (Committee member) / Arizona State University (Publisher)
Created2023
Description
In image classification tasks, images are often corrupted by spatial transformationslike translations and rotations. In this work, I utilize an existing method that uses
the Fourier series expansion to generate a rotation and translation invariant representation of closed contours found in sketches, aiming to attenuate the effects of distribution shift caused by the aforementioned transformations. I use this technique to
transform input images into one of two different invariant representations, a Fourier
series representation and a corrected raster image representation, prior to passing
them to a neural network for classification. The architectures used include convolutional neutral networks (CNNs), multi-layer perceptrons (MLPs), and graph neural
networks (GNNs). I compare the performance of this method to using data augmentation during training, the standard approach for addressing distribution shift, to see
which strategy yields the best performance when evaluated against a test set with
rotations and translations applied. I include experiments where the augmentations
applied during training both do and do not accurately reflect the transformations encountered at test time. Additionally, I investigate the robustness of both approaches
to high-frequency noise. In each experiment, I also compare training efficiency across
models. I conduct experiments on three data sets, the MNIST handwritten digit
dataset, a custom dataset (QD-3) consisting of three classes of geometric figures from
the Quick, Draw! hand-drawn sketch dataset, and another custom dataset (QD-345)
featuring sketches from all 345 classes found in Quick, Draw!. On the smaller problem space of MNIST and QD-3, the networks utilizing the Fourier-based technique to
attenuate distribution shift perform competitively with the standard data augmentation strategy. On the more complex problem space of QD-345, the networks using the
Fourier technique do not achieve the same test performance as correctly-applied data
augmentation. However, they still outperform instances where train-time augmentations mis-predict test-time transformations, and outperform a naive baseline model
where no strategy is used to attenuate distribution shift. Overall, this work provides
evidence that strategies which attempt to directly mitigate distribution shift, rather
than simply increasing the diversity of the training data, can be successful when
certain conditions hold.
ContributorsWatson, Matthew (Author) / Yang, Yezhou YY (Thesis advisor) / Kerner, Hannah HK (Committee member) / Yang, Yingzhen YY (Committee member) / Arizona State University (Publisher)
Created2023
Description
Climate change is one of the most pressing issues affecting the world today. One of the impacts of climate change is on the transmission of mosquito-borne diseases (MBDs), such as West Nile Virus (WNV). Climate is known to influence vector and host demography as well as MBD transmission. This dissertation addresses the questions of how vector and host demography impact WNV dynamics, and how expected and likely climate change scenarios will affect demographic and epidemiological processes of WNV transmission. First, a data fusion method is developed that connects non-autonomous logistic model parameters to mosquito time series data. This method captures the inter-annual and intra-seasonal variation of mosquito populations within a geographical location. Next, a three-population WNV model between mosquito vectors, bird hosts, and human hosts with infection-age structure for the vector and bird host populations is introduced. A sensitivity analysis uncovers which parameters have the most influence on WNV outbreaks. Finally, the WNV model is extended to include the non-autonomous population model and temperature-dependent processes. Model parameterization using historical temperature and human WNV case data from the Greater Toronto Area (GTA) is conducted. Parameter fitting results are then used to analyze possible future WNV dynamics under two climate change scenarios. These results suggest that WNV risk for the GTA will substantially increase as temperature increases from climate change, even under the most conservative assumptions. This demonstrates the importance of ensuring that the warming of the planet is limited as much as possible.
ContributorsMancuso, Marina (Author) / Milner, Fabio A (Thesis advisor) / Kuang, Yang (Committee member) / Kostelich, Eric (Committee member) / Eikenberry, Steffen (Committee member) / Manore, Carrie (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation centers on the development of Bayesian methods for learning differ- ent types of variation in switching nonlinear gene regulatory networks (GRNs). A new nonlinear and dynamic multivariate GRN model is introduced to account for different sources of variability in GRNs. The new model is aimed at more precisely capturing the complexity of GRN interactions through the introduction of time-varying kinetic order parameters, while allowing for variability in multiple model parameters. This model is used as the drift function in the development of several stochastic GRN mod- els based on Langevin dynamics. Six models are introduced which capture intrinsic and extrinsic noise in GRNs, thereby providing a full characterization of a stochastic regulatory system. A Bayesian hierarchical approach is developed for learning the Langevin model which best describes the noise dynamics at each time step. The trajectory of the state, which are the gene expression values, as well as the indicator corresponding to the correct noise model are estimated via sequential Monte Carlo (SMC) with a high degree of accuracy. To address the problem of time-varying regulatory interactions, a Bayesian hierarchical model is introduced for learning variation in switching GRN architectures with unknown measurement noise covariance. The trajectory of the state and the indicator corresponding to the network configuration at each time point are estimated using SMC. This work is extended to a fully Bayesian hierarchical model to account for uncertainty in the process noise covariance associated with each network architecture. An SMC algorithm with local Gibbs sampling is developed to estimate the trajectory of the state and the indicator correspond- ing to the network configuration at each time point with a high degree of accuracy. The results demonstrate the efficacy of Bayesian methods for learning information in switching nonlinear GRNs.
ContributorsVélez-Cruz, Nayely (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Moraffah, Bahman (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2023
Description
Computable properties of quantum states are given a dual gravitational interpretation via the AdS/CFT correspondence. For holographic states, boundary entanglement entropy is dual to the area of bulk geodesics, known as Ryu-Takayanagi surfaces. Furthermore, the viability of states to admit a holographic dual at all is constrained by their entanglement structure. Entanglement therefore defines a coarse classification of states in the Hilbert space. Similarly, how a state transforms under a group of operators also provides a classification on the Hilbert space. Certain states, e.g. stabilizer states, are invariant under large sets of operations, and consequently can be simulated on a classical computer. Cayley graphs offer a useful representation for a group of operators, where vertices represent group elements and edges represent group generators. In this representation, the orbit of a state under action of the group can also be represented as a ``reachability graph'', defined as a quotient of the group Cayley graph. Reachability graphs can be dressed to encode entanglement information, making them a useful tool for studying entanglement dynamics under quantum operations. Further quotienting a reachability graph by group elements that fix a chosen state property, e.g. entanglement entropy, builds a ``contracted graph''. Contracted graphs provide explicit bounds on state parameter evolution under quantum circuits. In this work, an upper bound on entropy vector evolution under Clifford group action is presented. Another important property of quantum systems is magic, which quantifies the difficulty of classically simulating a quantum state. Magic and entanglement are intimately related, but the two are not equivalent measures of complexity. Nonetheless, entanglement and magic play complementary roles when describing emergent gravitational phenomena in AdS/CFT. This manuscript describes the interplay between entanglement and magic, and offers a holographic interpretation for magic as cosmic brane back-reaction.
ContributorsMunizzi, William Richard (Author) / Keeler, Cynthia (Thesis advisor) / Parikh, Maulik (Committee member) / Baumgart, Matthew (Committee member) / Schmidt, Kevin (Committee member) / Arizona State University (Publisher)
Created2024
Description
This thesis examines the critical relationship between data, complex models, and other methods to measure and analyze them. As models grow larger and more intricate, they require more data, making it vital to use that data effectively. The document starts with a deep dive into nonconvex functions, a fundamental element of modern complex systems, identifying key conditions that ensure these systems can be analyzed efficiently—a crucial consideration in an era of vast amounts of variables. Loss functions, traditionally seen as mere optimization tools, are analyzed and recast as measures of how accurately a model reflects reality. This redefined perspective permits the refinement of data-sourcing strategies for a better data economy. The aim of the investigation is the model itself, which is used to understand and harness the underlying patterns of complex systems. By incorporating structure both implicitly (through periodic patterns) and explicitly (using graphs), the model's ability to make sense of the data is enhanced. Moreover, online learning principles are applied to a crucial practical scenario: robotic resource monitoring. The results established in this thesis, backed by simulations and theoretical proofs, highlight the advantages of online learning methods over traditional ones commonly used in robotics. In sum, this thesis presents an integrated approach to measuring complex systems, providing new insights and methods that push forward the capabilities of machine learning.
ContributorsThaker, Parth Kashyap (Author) / Dasarathy, Gautam (Thesis advisor) / Sankar, Lalitha (Committee member) / Nedich, Angelia (Committee member) / Michelusi, Nicolò (Committee member) / Arizona State University (Publisher)
Created2024