UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
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Item Learning Together: The Lived Experience of Bridging in Scholars Studio(2023) Nardi, Lisa; Hultgren, Francine H; Education Policy, and Leadership; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This hermeneutic phenomenological investigation tends to the connections made in Scholars Studio—an interdisciplinary learning community for first-year students at a public Historically Black College and University (HBCU). In this study, I ask, What is the lived experience of bridging in Scholars Studio? I conceptualize bridging as a pedagogical orientation characterized by making connections across disciplines, between theory and praxis, across time and distance, and with one another. Bridging creates dynamic spaces that resist binary relationships, thus creating the potential for transformation. This study is grounded in the philosophy of Martin Heidegger, Mariana Ortega, Hans-Georg Gadamer, Edward Casey, and David Michael Levin, and follows the methodological structure set forth by Max van Manen. This research captures conversations that bridge the experience of twelve participants—including faculty, students, and staff—who partook in a learning community focused on Black men in education. Through these conversations, the participants affirm the importance of curricula grounded in African American and African history and culture. As participants cross the metaphorical bridge, they consider the “edges” they encounter that are both full of risk and possibility. These edges push them outside of their comfort zones in search of wholeness and create potential sites for improvisation. I end by opening new possibilities for Scholars Studio, including grounding the work in African principles and considering future directions.Item LEARNING LATENT REPRESENTATIONS AND INTRINSIC LAWS OF COMPLEX SYSTEMS(2021) Mavridis, Christos; Baras, John; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The phenomenal increase of interest towards intelligent autonomous systems observed in recent years is leading the way towards automation augmented with actual machine intelligence. How can a robot reliably react to and operate within its environment? How is this connected to how humans dynamically process sensory information? The answers to these famous problems are not only linked to deeper understanding, but also to the decision-making potential of autonomous systems in general. Towards this direction, it is important to understand and formally ana- lyze the fundamental properties of learning, as a continuous, dynamic, and adaptive process of acquiring new understanding, knowledge, or skills. The contribution of this dissertation is towards two broad and open learning and control problems with applications in image and sound classification, graph partitioning, reinforcement learning, identification and control of multi-agent systems, intelligent transporta- tion, and human-robot interaction. The first problem is connected to the existence of a universal learning archi- tecture in human cognition, which is a widely accepted conjecture supported by established experimental findings from neuroscience. Towards this goal, we study the properties of learning with progressively growing models, and propose the Online Deterministic Annealing (ODA) algorithm that serves as a hierarchical, progressive, interpretable, and knowledge-based learning framework, that can be viewed as an open-box deep learning architecture that requires minimal hyper-parameter tuning. We make use of the mathematical theory and properties of the ODA algorithm to develop efficient and adaptive learning algorithms not only for unsupervised and supervised learning, but also for reinforcement learning, graph partitioning, and detection of leaders in networked systems. Leader detection in networked systems is connected to the second problem we consider: learning the interaction laws of complex collectives, ranging from animal flocks to social networks. This is a time-dependent learning problem with dynamical constraints, where data are often noisy and sparse, and lies beyond the traditional boundaries of machine learning algorithms. We use the ODA algorithm to infer the leadership structure of the networked system. We then adopt an energy-based port-Hamiltonian modeling framework and large-scale optimization techniques to learn the intrinsic structure and interaction laws of the system, which can be used to design defense mechanisms against adversarial UAV swarm attacks. Finally, to study real-life animal flocks and their coordination, we study the interaction laws of networked systems in the macroscopic scale. Due to the fact that real-life observations of the agents of an animal flock are rarely available, we propose a novel learning algorithm based on mathematical principles from mean-field game-theory, to infer the coordination laws of large swarms by observing the evolution of their density over time. This is the first time such a progressive learning approach has been developed and studied in the context of decision-making in such a diverse research area. The insights provided by this work can lead to new developments in machine intelligence based on autonomous, continuously adaptive algorithms that can be used reliably in real-life applications to improve quality of life.Item Runtime Adaptation in Embedded Computing Systems using Markov Decision Processes(2019) Sapio, Adrian; Bhattacharyya, Shuvra S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)During the design and implementation of embedded computing systems (ECSs), engineers must make assumptions on how the system will be used after being built and deployed. Traditionally, these important decisions were made at design time for a fleet of ECSs prior to deployment. In contrast to this approach, this research explores and develops techniques to enable adaptation of ECSs at runtime to the environments and applications in which they operate. Adaptation is enabled such that the usage assumptions and performance optimization decisions can be made autonomously at runtime in the deployed system. This thesis utilizes Markov Decision Processes (MDPs), a powerful and well established mathematical framework used for decision making under uncertainty, to control computing systems at runtime. The resulting control is performed in ways that are more dynamic, robust and adaptable than alternatives in many scenarios. The techniques developed in this thesis are first applied to a reconfigurable embedded digital signal processing system. In this effort, several challenges are encountered and resolved using novel approaches. Through extensive simulations and a prototype implementation, the robustness of the adaptation is demonstrated in comparison with the prior state-of-the-art. The thesis continues by developing an efficient algorithm for conversion of MDP models to actionable control policies - a required step known as solving the MDP. The solver algorithm is developed in the context of ECSs that contain general purpose embedded GPUs (graphics processing units). The novel solver algorithm, Sparse Parallel Value Iteration (SPVI), makes use of the parallel processing capabilities provided by such GPUs, and also exploits the sparsity that typically exists in MDPs when used to model and control ECSs. To extend the applicability of the runtime adaptation techniques to smaller and more strictly resource constrained ECSs, another solver - Sparse Value Iteration (SVI) is developed for use on microcontrollers. The method is explored in a detailed case study involving a cellular (LTE-M) connected sensor that adapts to varying communications profiles. The case study reveals that the proposed adaptation framework outperforms a competing approach based on Reinforcement Learning (RL) in terms of robustness and adaptation, while consuming comparable resource requirements. Finally, the thesis concludes by analyzing the various logistical challenges that exist when deploying MDPs on ECSs. In response to these challenges, the thesis contributes an open source software package to the engineering community. The package contains libraries of MDP solvers, parsers, datasets and reference solutions, which provide a comprehensive infrastructure for exploring the trade-offs among existing embedded MDP techniques, and experimenting with novel approaches.Item Consciousness of Design: Transforming the Academic Environment(2018) Gilloran, Sarah; Abrams, Michael; Architecture; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Employing didactic design, this thesis seeks to explore advances in traditional design teaching methods to provide architecture students with hands-on interactive learning environments. These methods are emphasized through the human body's connection to architecture. Traditionally, higher education puts a focus on cognitive knowledge with a disregard to the bodily experience. The proposed academic design curriculum allows students to learn how to design using multi-sensory interactions with the built environment.Item THE ROLE OF PROACTIVITY IN OVERCOMING THREAT: A MODEL OF TEAM LEARNING(2014) Firth, Brady; Tangirala, Subrahmaniam; Business and Management: Management & Organization; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Team learning is critical for teams to be successful in dynamic environments. However, teams often experience threats that can lead to rigid approaches to their work. Threats can cause teams to rely on well-known responses to their tasks and prevent them from exploring new ideas and opportunities. Consequently, threats can be associated with diminished learning in teams. I focus on this issue by examining the following question: What enables teams to reduce the negative effects of threat on team learning? I argue that when confronting threat, teams composed of members with higher proactive personality are likely to more positively frame the threat and engage in behaviors that enable them to explore alternative approaches to their work. Therefore, I propose that proactivity can help teams buffer against the negative effects of threat on team learning processes, which include behaviors such as seeking feedback, engaging in experimentation, and discussing errors. I test my hypotheses in an experimental study in which 94 5-person teams work on a command and control simulation. I manipulate a) team composition with respect to proactivity and b) threat, which was conceptualized as a potential loss to personal reputation and public discrediting for poor performance. Results indicate that irrespective of their proactivity levels, teams demonstrated high levels of team learning processes in the absence of threat. By contrast, in the presence of threat, only teams in the high proactivity condition maintained high levels of learning processes whereas teams in the low proactivity condition displayed significantly diminished learning processes and (subsequent) performance.Item How Electrical Engineering Students Design Computer Programs(2014) Danielak, Brian Adam; Elby, Andrew; Gupta, Ayush; Curriculum and Instruction; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)When professional programmers begin designing programs, we know they often spend time away from a computer, using tools such as pens, paper, and whiteboards as they discuss and plan their designs (Petre, van der Hoek, & Baker, 2010). But, we're only beginning to analyze and understand the complexity of what happens during such early-stage design work. And, our accounts are almost exclusively about what professionals do. For all we've begun to understand about what happens in early-stage software design, we rarely apply the same research questions and methods to students' early-stage design work. This dissertation tries to redress that imbalance. I present two case studies — derived from my 10 study participants — of electrical engineering (EE) students designing computer programs in a second-semester computer programming course. In study 1, I show how analyzing a student's code snapshot history and conducting clinical interviews tells us far more about her design trajectory than either method could alone. From that combined data I argue students' overall software designs can be consequentially shaped by factors — such as students' stances toward trusting their code or believing a current problem is a new instance of an old one — that existing code snapshot research is poorly equipped to explain. Rather, explanations that add non-conceptual constructs including affective state and epistemological stance can offer a more complete and satisfactory account of students' design activities. In study 2, I argue computer science and engineering education should move beyond conceptual-knowledge and concept deficit explanations of students' difficulties (and capabilities) in programming. I show that in doing design students do, say, write, and gesture things that: – Are outside the phenomenological scope of most (mis)conceptions accounts of programming – Would be explained differently under frameworks that emphasize manifold epistemological resources. Some student difficulties can be recast as epistemological blocks in activity rather than conceptual knowledge deficits. Similarly, some students' productive capacities can be understood as epistemologically-related stances toward an activity, rather than evidencing particular knowledge of specific computational concepts. – Would suggest different instructional interventions if teachers attended to the stabilizing aspects — such as epistemological dynamics — that help these episodes of activity cohere for students.Item Scalable learning for geostatistics and speaker recognition(2011) Srinivasan, Balaji Vasan; Duraiswami, Ramani; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With improved data acquisition methods, the amount of data that is being collected has increased severalfold. One of the objectives in data collection is to learn useful underlying patterns. In order to work with data at this scale, the methods not only need to be effective with the underlying data, but also have to be scalable to handle larger data collections. This thesis focuses on developing scalable and effective methods targeted towards different domains, geostatistics and speaker recognition in particular. Initially we focus on kernel based learning methods and develop a GPU based parallel framework for this class of problems. An improved numerical algorithm that utilizes the GPU parallelization to further enhance the computational performance of kernel regression is proposed. These methods are then demonstrated on problems arising in geostatistics and speaker recognition. In geostatistics, data is often collected at scattered locations and factors like instrument malfunctioning lead to missing observations. Applications often require the ability interpolate this scattered spatiotemporal data on to a regular grid continuously over time. This problem can be formulated as a regression problem, and one of the most popular geostatistical interpolation techniques, kriging is analogous to a standard kernel method: Gaussian process regression. Kriging is computationally expensive and needs major modifications and accelerations in order to be used practically. The GPU framework developed for kernel methods is extended to kriging and further the GPU's texture memory is better utilized for enhanced computational performance. Speaker recognition deals with the task of verifying a person's identity based on samples of his/her speech - "utterances". This thesis focuses on text-independent framework and three new recognition frameworks were developed for this problem. We proposed a kernelized Renyi distance based similarity scoring for speaker recognition. While its performance is promising, it does not generalize well for limited training data and therefore does not compare well to state-of-the-art recognition systems. These systems compensate for the variability in the speech data due to the message, channel variability, noise and reverberation. State-of-the-art systems model each speaker as a mixture of Gaussians (GMM) and compensate for the variability (termed "nuisance"). We propose a novel discriminative framework using a latent variable technique, partial least squares (PLS), for improved recognition. The kernelized version of this algorithm is used to achieve a state of the art speaker ID system, that shows results competitive with the best systems reported on in NIST's 2010 Speaker Recognition Evaluation.Item NURTURING THE CHILD: AN ARCHITECTURE OF COMMUNITY, LANDSCAPE AND LEARNING.(2009) Stratton Treadway, Catherine Marie; Kelly, Brian; Architecture; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis explores the making of place for all preschool children, those with special needs and those considered to be "typically developing." Scale, materiality, light and shadow, color, the four fundamental elements of nature, and the nature of ritual and routine all play a role in the children's experience of place and are explored here as part of the design process. This thesis asks, "What are the contributions that architecture and landscape can make towards nurturing the whole child including children with diverse needs?" The result is a supportive learning and healing environment for children who are defined as having special needs and their "typically developing" peers. A landscape of learning and play will be a significant focus. To support the children and their families, a range of community involvement will be incorporated, and the large recreation center site will be redeveloped as community space.Item A Tale of Two Courses; Teaching and Learning Undergraduate Abstract Algebra(2007-11-21) Fukawa-Connelly, Timothy P; Campbell, Patricia F; Fey, James T; Curriculum and Instruction; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The abstract algebra course is an important point in the education of undergraduate mathematics majors and secondary mathematics teachers. Abstract algebra teachers have multiple goals for student learning, and the literature suggests that students have difficulty meeting these goals. Advisory reports have called for a move away from lecture toward investigation-based class sessions as a means of improving student understanding. Thus, it is appropriate to understand what is happening in the current teaching and associated learning of abstract algebra. The present study examined teaching and learning in two abstract algebra classrooms, one consciously using a lecture-based (i.e., deduction-theory-proof, or DTP) mode of instruction and the other an investigative approach. Instructional data was collected in classroom observations, and multiple written instruments and a set of interviews were used to evaluate student learning. Each instructor hoped students would develop a deep and connected knowledge base and attempted to create classroom environments where students were constantly engaged as a means of doing so. In the lecture class, writing proofs was the central activity of class meetings; nearly every class period included at least one proof. In the investigative class, the processes of computing and searching for patterns in various structures were emphasized. At the end of the semester, students demonstrated mixed levels of proficiency. Generally, students did well on items that were relatively familiar, and poorly when the content or context was unfamiliar. In the DTP course, two students demonstrated significant proficiency with analytical argument; the remainder demonstrated mixed proficiency with proof and very little proficiency with other content. The students in the investigative class all seemed to develop similar levels of proficiency with the content, and demonstrated more willingness to explore unknown structures. This study may prompt discussions about the relative importance of developing proof-proficiency, students' ability to formulate and investigate hypotheses, developing students' content knowledge, and students' ability to operate in and analyze novel structures.Item Middle School Students' Learning and Motivation: A Self-determination Perspective(2007-08-28) sun, haichun; Chen, Ang; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Self-determination theory (SDT) explains human motivation by focusing on the importance of motivational regulation based on three basic needs: the needs for competence, autonomy, and relatedness. SDT, when applied in education, emphasizes helping learners internalize extrinsic motivation so as to regulate their learning behavior from an amotivation state to intrinsic motivation. Guided by self-determination theory, the dissertation study was designed for two major purposes: (a) examining the inter-relationships of the components in the self-regulation model to verify its tenability in motivating middle school learners in physical education, and (b) identifying the contribution of the self-regulated motivations to knowledge and skill learning in physical education. Two separate studies were conducted to answer the research questions. In Study 1, 297 sixth grade students from 15 randomly selected middle schools provided need satisfaction and self-regulated motivation data for a two-step structural equation modeling analysis. The results indicated that students' satisfaction of autonomy and competence accounted for a large portion of variability in intrinsic motivation and in identified regulation. Satisfaction of autonomy also contributed to introjected regulation. Satisfaction of any of the needs did not contribute to the external regulation. It was also found that individuals who exhibited satisfaction in competence need lessened amotivation. Unexpectedly, it was found that satisfying the need for relatedness is likely to lead students to becoming amotivated in physical education. In Study 2, 242 participants provided data on SDT components and their learning on health related fitness knowledge and two motor skills determined using a pre- and post-assessment research design. Descriptive statistics showed that students were motivated but learned little. Subsequent structural equation modeling analyses revealed that extrinsic motivation and intrinsic motivation did not contribute to knowledge and skill achievement and amotivation impeded knowledge learning. The findings imply that when competence-based learning achievement is absent, learners can be motivated but do not achieve what they are expected to achieve. The findings provide theoretical insights to developing a constructivist learning environment to direct students' motivation toward learning in physical education and strongly suggest that a curriculum reform in physical education is needed to strengthen competence-based learning (knowledge and skill growth).