Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
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 give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
74 results
Search Results
Item TRANSFORMING ENVIRONMENTAL EDUCATION: EXPLORING THE IMPACT OF DATA PHYSICALIZATION ON CHILDREN'S LEARNING(2024) Lin, Yi-Hsieh; Aston, Jason; Information Studies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This paper explores the integration of data physicalization in Education for SustainableDevelopment (ESD), focusing on its potential to enhance the learning experience for young audiences, particularly those aged 7-12. By examining current approaches in ESD and analyzing the impact of tangible data interactions on children's understanding and engagement with sustainability issues, the study underscores the importance of innovative educational methods. Preliminary findings indicate that data physicalization help enhance comprehension, engagement, and active learning among young learners. The research contributes to the discourse on effective ESD practices, advocating for the inclusion of data physicalization techniques in educational curriculums to better prepare youth for addressing global environmental challenges.Item THREE ESSAYS ON QUANTUM TECHNOLOGY APPLICATIONS(2024) Stein, Amanda; Wang, Ping; Information Studies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examines quantum technology applications in three essays. Essay 1 portrays how companies are beginning to innovate with quantum computing in four case studies. The cases employ and enrich the Diffusion of Innovations theory as a conceptual framework for quantum computing innovation adoption and management. Essay 2 follows the evolution of quantum sensing with two cases of how organizations currently use the technology and plan to use it in the future. These cases illustrate how people and organizations use their discourse to develop an organizing vision for adopting and applying quantum sensing. Essay 3 focuses on the relationships between quantum technology and artificial intelligence through a literature review using grounded theory. The essay provides examples on how the two technologies interact and recommendations to stakeholders for future advancement. In summary, while the science and engineering side of quantum technologies is still developing, understanding how quantum technologies are and will be applied can help inform business and public policies.Item DEVELOPING MULTIMODAL LEARNING METHODS FOR VIDEO UNDERSTANDING(2024) Sun, Mingwei; Zhang, Kunpeng; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In recent years, the field of deep learning, with a particular emphasis on multimodal representation learning, has experienced significant advancements. These advancements are largely attributable to groundbreaking progress in areas such as computer vision, voice recognition, natural language processing, and graph network learning. This progress has paved the way for a multitude of new applications. The domain of video, in particular, holds immense potential. Video is often considered the most potent form of digital content for communication and the dissemination of information. The ability to effectively and efficiently comprehend video content could prove instrumental in a variety of downstream applications. However, the task of understanding video content presents numerous challenges. These challenges stem from the inherently unstructured and complex nature of video, as well as its interactions with other forms of unstructured data, such as text and network data. These factors contribute to the difficulty of video analysis. The objective of this dissertation is to develop deep learning methodologies capable of understanding video across multiple dimensions. Furthermore, these methodologies aim to offer a degree of interpretability, which could yield valuable insights for researchers and content creators. These insights could have significant managerial implications.In the first study, I introduce an innovative network based on Long Short-Term Memory (LSTM), enhanced with a Transformer co-attention mechanism, designed for the prediction of apparent emotion in videos. Each video is segmented into clips of one-second duration, and pre-trained ResNet networks are employed to extract audio and visual features at the second level. I construct a co-attention Transformer to effectively capture the interactions between the audio and visual features that have been extracted. An LSTM network is then utilized to learn the spatiotemporal information inherent in the video. The proposed model, termed the Sec2Sec Co-attention Transformer, outperforms several state-of-the-art methods in predicting apparent emotion on a widely recognized dataset: LIRIS-ACCEDE. In addition, I conduct an extensive data analysis to explore the relationships between various dimensions of visual and audio components and their influence on video predictions. A notable feature of the proposed model is its interpretability, which enables us to study the contributions of different time points to the overall prediction. This interpretability provides valuable insights into the functioning of the model and its predictions. In the second study, I introduce a novel neural network, the Multimodal Co-attention Transformer, designed for the prediction of personality based on video data. The proposed methodology concurrently models audio, visual, and text representations, along with their intra-relationships, to achieve precise and efficient predictions. The effectiveness of the proposed approach is demonstrated through comprehensive experiments conducted on a real-world dataset, namely, First Impressions. The results indicate that the proposed model surpasses state-of-the-art methods in performance while preserving high computational efficiency. In addition to evaluating the performance of the proposed model, I also undertake a thorough interpretability analysis to examine the contribution across different levels. The insights gained from the findings offer a valuable understanding of personality predictions. Furthermore, I illustrate the practicality of video-based personality detection in predicting outcomes of MBA admissions, serving as a decision support system. This highlights the potential importance of the proposed approach for both researchers and practitioners in the field. In the third study, I present a novel generalized multimodal learning model, termed VAN, which excels in learning a unified representation of \textbf{v}isual, \textbf{a}coustic, and \textbf{n}etwork cues. Initially, I utilize state-of-the-art encoders to model each modality. To augment the efficiency of the training process, I adopt a pre-training strategy specifically designed to extract information from the music network. Subsequently, I propose a generalized Co-attention Transformer network. This network is engineered to amalgamate the three distinct types of information and to learn the intra-relationships that exist among the three modalities, a critical facet of multimodal learning. To assess the effectiveness of the proposed model, I collect a real-world dataset from TikTok, comprising over 88,000 videos. Extensive experiments demonstrate that the proposed model surpasses existing state-of-the-art models in predicting video popularity. Moreover, I have conducted a series of ablation studies to attain a deeper comprehension of the behavior of the proposed model. I also perform an interpretability analysis to study the contributions of each modality to the model performance, leveraging the unique property of the proposed co-attention structure. This research contributes to the field by proffering a more comprehensive approach to predicting video popularity on short-form video platforms.Item Value sets for the analysis of real-world patient data: Problems, theory, and solutions(2024) Gold, Sigfried; Lutters, Wayne; Information Studies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Observational, retrospective, in silico studies based on real-world data—that is, data for research collected from sources other than randomized clinical trials—cost a minute fraction of randomized clinical trials and are essential for clinical research, pharmacoepidemiology, clinical quality measurement, health system administration, value-based care, clinical guideline compliance, and public health surveillance. They offer an alternative when randomized trials cannot provide large enough patient cohorts or patients representative of real populations in terms of comorbidities, age range, disease severity, rare conditions.Improvements in the speed, frequency, and quality of research investigations using real-world data have accelerated with the emergence of distributed research networks based on common data models over the past ten years. Analyses of repositories of coded patient data involve data models, controlled medical vocabularies and ontologies, analytic protocols, implementations of query logic, value sets of vocabulary terms, and software platforms for developing and using these. These studies generally rely on clinical data represented using controlled medical vocabularies and ontologies—like ICD10, SNOMED, RxNorm, CPT, and LOINC—which catalogue and organize clinical phenomena such as conditions, treatments, and observations. Clinicians, researchers, and other medical staff collect patient data into electronic health records, registries, and claims databases with each phenomenon represented by a code, a concept identifier, from a medical vocabulary. Value sets are groupings of these identifiers that facilitate data collection, representation, harmonization, and analysis. Although medical vocabularies use hierarchical classification and other data structures to represent phenomena at different levels of granularity, value sets are needed for concepts that cover a number of codes. These lists of codes representing medical terms are a common feature of the cohort, phenotype, or other variable definitions that are used to specify patients with particular clinical conditions in analytic algorithms. Developing and validating original value sets is difficult to do well; it is a relatively small but ubiquitous part of real-world data analysis, it is time-consuming, and it requires a range of clinical, terminological, and informatics expertise. When a value set fails to match all the appropriate records or matches records that do not indicate the phenomenon of interest, study results are compromised. An inaccurate value set can lead to completely wrong study results. When value set inaccuracy causes more subtle errors in study results, conclusions may be incorrect without catching researchers’ attention. One hopes in this case that the researchers will notice a problem and track it down to a value set issue. Verifying or measuring value set accuracy is difficult and costly, often impractical, sometimes impossible. Literature recognizing the deleterious effects of value set quality on the reliability of observational research results frequently recommends public repositories where high-quality value sets for reuse can be stored, maintained, and refined by successive users. Though such repositories have been available for years and populated with hundreds or thousands of value sets, regular reuse has not been demonstrated. Value set quality has continued to be questioned in the literature, but the value of reuse has continued to be recommended and generally accepted at face value. The hope for value set repositories has been not only for researchers to have access to expertly designed value sets but for incremental refinement, that, over time, researchers will take advantage of others’ work, building on it where possible instead of repeating it, evaluating the accuracy of the value sets, and contributing their changes back to the repository. Rather than incremental improvement or indications of value sets being vetted and validated, what we see in repositories is proliferation and clutter: new value sets that may or may not have been vetted in any way and junk concept sets, created for some reason but never finished. We have found general agreement in our data that the presence of many alternative value sets for a given condition often leads value set developers to ignore all of them and start from scratch, as there is generally no easy way to tell which will be more appropriate for the researcher’s needs. And if they share their value set back to the repository, they further compound the problem, especially if they neglect to document the new value set's intention and provenance. The research offered here casts doubt on the value of reuse with currently available software and infrastructure for value set management. It is about understanding the challenges value sets present; understanding how they are made, used, and reused; and offering practice and software design recommendations to advance the ability of researchers to efficiently make or find accurate value sets for their studies, leveraging and adding to prior value set development efforts. This required field work, and, with my advisors, I conducted a qualitative study of professionals in the field: an observational user study with the aim of understanding and characterizing normative and real-world practices in value set construction and validation, with a particular focus on how researchers use the knowledge embedded in medical terminologies and ontologies to inform that work. I collected data through an online survey of RWD analysts and researchers interviews with a subset of survey participants, and observation of certain participants performing actual work to create value sets. We performed open coding and thematic analysis on interview and observation transcripts, interview notes, and open-ended question text from the surveys. The requirements, recommendations, and theoretical contributions in prior literature have not been sufficient to guide the design of software that could make effective leveraging of shared value sets a reality. This dissertation presents a conceptual framework, real-world experience, and deep, detailed account of the challenges to reuse, and makes up that deficit with a high-level requirements roadmap for improved value set creation tools. I argue, based on the evidence marshalled throughout, that there is one way to get researchers to reuse appropriate value sets or to follow best practices in determining whether a new one is absolutely needed creating their own and dedicate sufficient and appropriate effort to create them well and prepare them for reuse by others. That is, giving them software that pushes them to do these things, mostly by making it easy and obviously beneficial to do them. I offer a start in building such software with Value Set Hub, a platform for browsing, comparing, analyzing, and authoring value sets—a tool in which the presence of multiple, sometimes redundant, value sets for the same condition strengthens rather than stymies efforts to build on the work of prior value set developers. Particular innovations include the presentation of multiple value sets on the same screen for easy comparison, the display of compared value sets in the context of vocabulary hierarchies, the integration of these analytic features and value set authoring, and value set browsing features that encourage users to review existing value sets that may be relevant to their needs. Fitness-for-use is identified as the central challenge for value set developers and the strategies for addressing this challenge are categorized into two approaches: value-set-focused and code-focused. The concluding recommendations offer a roadmap for future work in building the next generation of value set repository platforms and authoring tools.Item TRANSITIONING VISUALLY IMPAIRED USERS TO UTILIZE ACCESSIBILITY TECHNOLOGY(2024) Jo, Hyejin; Reitz, Galina; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In a world increasingly driven by visual information, this research develops the Transition Experience Interface (TEI), dedicated to supporting individuals adapting to visual impairments with advanced accessibility technologies. TEI features a user-centric design with a mobile user interface that includes tutorials, updates on new features, a voice command guide, and a progress dashboard. These components aim to reduce dependency on visual cues, enhancing digital inclusivity and promoting independence by encouraging the use of built-in accessibility features on smartphones. TEI educates users on their devices’ capabilities and fosters habitual use of these features, preparing them to rely less on vision and more on voice commands and other settings. This proactive approach helps users operate their smartphones confidently and independently as their visual function changes, bridging the gap between traditional tools and user needs, and highlighting the potential of inclusive design.Item Change Detection: Theoretical and Applied Approaches for Providing Updates Related to a Topic of Interest(2024) Rogers, Kristine M.; Oard, Douglas; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The type of user studied in this dissertation has built up expertise on a topic of interest to them, and regularly invests time to find updates on that topic. This research area—referred to within this dissertation as "change detection"—includes the user's process of identifying what has changed as well as internalizing the changes into their mental model. For these users who follow a specific topic over time, how might a system organize information to enable them to update their mental model quickly? Current information retrieval systems are largely not optimized for addressing the long-term change detection needs of users. This dissertation focuses on approaches for enhancing the change detection process, including for short documents (e.g., social media) as well as longer documents (e.g., news articles). This mixed methods exploration of change detection consists of four sections. First, this dissertation introduces a new theory: the Group-Pile-Arrange (GPA) Change Detection Theory. This theory is about organizing documents relevant to a topic of interest in order to accelerate an individual's ability to identify changes and update their mental model. The three components of this theory include: 1. Group the documents by theme; 2. Pile the grouped documents into an order; and 3. Arrange the piles in a meaningful way for the user. These steps could be applied in a range of ways, including using approaches driven by people (e.g., a research librarian providing information), computers (e.g., an information retrieval system), or a hybrid of the two. The second section of this dissertation includes the results of a survey on users' sort order preferences in social media. For this study, change detection was compared with three other use cases: following an event while it happens (experiential), running a search within social media, and browsing social media posts. Respondents recognized the change detection use case, with 66% of the respondents indicating that they perform change detection tasks on social media sites. When engaged in change detection tasks, these respondents showed a strong preference for posts to be clustered and presented in reverse chronological order, in alignment with the "group" and "pile" components of the GPA Change Detection Theory. These organization preferences were distinct from the other studied use cases. To further understand users' goals and preferences related to change detection, the third section of this dissertation includes the design and prototype implementation of a change detection system called Daybreak. The Daybreak system presents news articles relevant to a user's topic of interest and allows them to tag articles and apply tag labels. Based on these tags and tag labels, the system retrieves new results, groups them into subtopic clusters based on the user's tags, enables generation of chronological or relevance-based piles of documents, and arranges the piles by subtopic importance; for this study, rarity was used as a proxy for subtopic importance. The Daybreak system was used for a qualitative user study, using the framework method for analyzing and interpreting results. In this study, fifteen participants engaged in a change detection scenario across five simulated "days." The participants heavily leveraged the Daybreak system's clustering function when viewing results; there was a weak preference for chronological sorting of documents, compared to relevance ranking. The participants did not view rarity as an effective proxy for subtopic importance; instead, they preferred approaches that enabled them to indicate which subtopics were of greatest interest, such as pinning certain subtopics. The fourth and final component of this dissertation research describes an evaluation approach for comparing arrangements of subtopic clusters (piles). This evaluation approach uses Spearman's rank correlation coefficient to compare a user's ideal subtopic ordering with a variety of system-generated orderings. This includes a sample evaluation using data from the Daybreak user study to demonstrate how a formal evaluation would work. Based on the results of these four dissertation research components, it appears that the GPA Change Detection Theory provides a useful framework for organizing information for individuals engaged in change detection tasks. This research provides insights into users' change detection needs and behaviors that could be helpful for building or extending systems attempting to address this use case.Item Stable Science and Fickle Bodies: An Examination of Trust and the Construction of Expertise on r/SkincareAddiction(2023) DeCusatis, Cara Maria; Sauter, M.R.; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)While there is considerable research on the topic of trust when it comes to health information or news media, there is less work examining how trust and expertise are conceptualized for information that may straddle both subjective and objective approaches to knowledge. In this thesis, I use the subreddit r/SkincareAddiction as a field site to examine how users construct skincare expertise and position skincare expertise in relation to formalized bioscience and experiential knowledge. Building on Science and Technology Studies’ theories of lay expertise and embodiment, I investigate how users interpret, share, and enact skincare and subreddit competence, discern trustworthy information, and negotiate the boundaries of science. Through a grounded theory analysis of subreddit posts and comments, I argue that r/SkincareAddiction users engage in forms of boundary work to preserve the expertise of medical professionals and the perceived infallibility of science. I argue that such delineations both uphold formalized systems of expertise and make space for alternative, community-specific forms of skincare expertise. This community-specific expertise is reified through community norms and agreed upon beliefs, such as the understanding that “your mileage may vary” and “everyone’s skin is different”. I situate these community beliefs within feminist understandings of embodied knowledge and argue that these beliefs are what afford users participation in “expert” conversations from which they might otherwise be excluded.Item Platform Design Strategies and Implications for User Behaviors(2023) Mudambi, Maya; Viswanathan, Siva; Business and Management: Logistics, Business & Public Policy; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This work examines how the design, features, and moderation policies of online platforms impact user behavior in myriad ways and have significant externalities on society at large. The first two studies examine the effectiveness of different content moderation policies adopted by user-generated content platforms to address issues related to misinformation and verbal aggression, respectively. The third study examines how the design of financial incentive structures affects the behaviors of users on a crowdsourcing platform. The studies produce theoretical implications regarding human behavior on online platforms, from the spreading of misinformation to interpersonal verbal aggression, to the behavioral response to monetary rewards. I additionally make recommendations for practitioners regarding optimal platform design and policies.Item Exploring remote service provision in adult day centers during the COVID-19 pandemic(2023) Marte, Crystal; Lazar, Amanda; Vanderheiden, Gregg; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The COVID-19 pandemic profoundly impacted the long-term services and supports (LTSS) sector, necessitating a rapid shift from in-person services to remote. Adult day service centers (ADSCs) – a type of LTSS – offer in-person community-based programs comprised of health and wellness services to historically underserved populations, such as communities of color, low-income, and older adults. Based on data collected from 23 semi-structured interviews with 22 providers from eight ADSCs across a Mid-Atlantic state, this thesis explores the experiences of ADSC providers – such as directors, activity staff, and nurses – as they navigated pandemic-related closures. To ensure uninterrupted services, centers leveraged their existing infrastructure and adapted to a remote service model. An intricate interplay of technical (e.g., access to devices, internet) to non-technical (e.g., digital literacy, sociocultural context, limited staff) variables affected the overall success of remote services. Simultaneously, ADSCs grappled with limited reimbursement for remote services – which directly impacted their operations and the sustainability of remote services. These findings offer insights into the challenges and adaptations providers experienced amidst an unprecedented crisis, shedding light on the systemic issues throughout this period. The study seeks to inform future interventions that promote the sustainability of remote services in ADSCs, with a specific focus on preventing service disruptions for historically underserved populations.Item WHITHER WONDER WOMEN? ESSAYS ON GENDER DIVERSITY IN IT-ENABLED PROFESSIONAL AND CREATIVE DOMAIN(2023) Wang, Yifei; Ramaprasad, Jui; Gopal, Anandasivam; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Working towards equality and inclusion around gender and race in society is critically important. Despite the increasing number of conversations around these issues, more work is needed to evaluate the causes of unequal participation of men and women in organizations, markets, and economies. In particular, the lack of equity in terms of representation and participation within the important information technology (IT) sector has often been viewed as an ongoing problem. My dissertation focuses on this specific sector and explores potential remedies to enhance the participation and representation of women in specific segments of IT-enabled work, albeit in three different empirical contexts. In my first essay, I investigate the unequal participation of women in IT labor markets and whether they are less willing to compete for complex and risky IT projects. Through multiple experiments on technically trained students, I find that women in the IT industry are more willing to participate in bidding for riskier projects, and their bids are higher than those of men. My second essay studies the issue of unequal representation by women within the digital music industry, where inequitable representation has been clearly noted. Women artists are often faced with less attention, respect, and market share. The study shows that TikTok dance challenges offer a low-cost, effective way to promote artists and increase visibility. The challenges are particularly beneficial for women music artists. My third essay examines the intersection of gender and race in digital music consumption after Floyd's death. I explore whether music can raise awareness of social justice issues and the role of Black artists as sensemaking agents. I find that hip-hop listeners increased after Floyd's death, particularly in less racially diverse cities. Black artists received more listenership across all genres, but consumption was skewed towards Black men artists, highlighting the underrepresentation of women in Black-dominated music genres. Collectively, the findings from these studies in my dissertation will provide valuable theoretical contributions, practical insights, and actionable solutions to bind the gender gap and make the digital markets more diverse and inclusive.