Information Studies Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/2780

Browse

Start date: 2010Subject: search.filters.subject.Information technology

Search Results

Now showing 1 - 10 of 28
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • No Thumbnail Available
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    Item
    USING SOCIAL MEDIA AS A DATA SOURCE IN PUBLIC HEALTH RESEARCH
    (2022) Sigalo, Nekabari; Frias-Martinez, Vanessa; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Researchers have increasingly looked to social media data as a means of measuring population health and well-being in a less intrusive and more scalable manner compared to traditional public health data sources. In this dissertation, I outline three studies that leverage social media as a data source, to answer research questions related to public health and compare traditional public health data sources to social media data sources. In Study #1, I conduct a study with the aim of developing, from geotagged Twitter data, a predictive model for the identification of food deserts in the United States, using the linguistic constructs found in food-related tweets. The results from this study suggest the food-ingestion language found in tweets, such as census-tract level measures of food sentiment and healthiness, are associated with census tract-level food desert status. Additionally, the results suggest that including food ingestion language derived from tweets in classification models that predict food desert status improves model performance when compared to baseline models that only include socio-economic characteristics. In Study #2, I evaluate whether attitudes towards COVID-19 vaccines collected from the Household Pulse Survey can be predicted using attitudes extracted from Twitter. The results reveal that attitudes toward COVID-19 vaccines found in tweets explain 61-72% of the variability in the percentage of HPS respondents that were vaccine hesitant or compliant. The results also reveal significant statistical relationships between perceptions expressed on Twitter and in the survey. In Study #3, I conduct a study to examine whether supplementing COVID-19 vaccine uptake forecast models with the attitudes found in tweets improves over baseline models that only use historical vaccination data. The results of this study reveal that supplementing baseline forecast models with both historical vaccination data and COVID-19 vaccine attitudes found in tweets reduce RMSE by as much as 9%. The studies outlined in this dissertation suggest there is a valuable signal for public health research in Twitter data.
  • Thumbnail Image
    Item
    A MORE-THAN-HUMAN PERSPECTIVE ON OLDER ADULTS’ USE OF AND PARTICIPATION IN DESIGN OF EMERGING TECHNOLOGIES
    (2022) Pradhan, Alisha; Lazar, Amanda; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Till date, research on aging in HCI has largely adopted human-centered approaches, such as user-centered and participatory design. However, recent research is beginning to question this “humanistic” focus in aging. Through this dissertation, I provide a case of adopting posthumanist entanglement perspective to understand the ‘more-than-human’ aspects of aging. Posthumanist entanglement perspectives [71]—previously adopted by HCI researchers in different contexts varying from creative design to technical areas such as machine learning and neural networks—attunes to the agency of nonhuman world in addition to the human world to account for how humans and their socio-material worlds are entangled. In Study 1, I investigate older adults’ ontological perceptions with respect to a popular emerging technology to examine the phenomena of “ontological uncertainty” (here, ontologies refer to how things exist and what categories they belong to). Although some researchers adopting entanglement perspectives in HCI argue that ontological uncertainty is posed by emerging technologies such as AI, IoT [71], we lack an understanding of when this uncertainty emerges, and why this matters. Here, the first study of my dissertation focuses on older adults’ use of emerging AI-based voice assistants, and contributes by providing an empirical understanding of the different factors that contribute to ontological uncertainty (e.g., location in house, time, user’s desire for companionship), and provides recommendations for designing voice technologies with ontological categorization in mind. In the next two threads of my work, I attune to the agency of nonhuman entities and how they shape reality associated with older adults’ use of emerging technology (Study 2), and when older individuals engage in designing emerging technology (Study 3). My analysis from Study 2 reveals how nonhuman actors such as materials and norms play a role in shaping older adults’ preference and use of voice technologies. My findings also reveal the salient ways in which voice assistants play an active role in mediating relations between older adults and their larger social world. These mediations are shaping our social practices around what it means to live alone, to give company, or to give and receive care. Finally, my analysis from Study 3— which adopts a posthumanist perspective to understand older adults’ engagement in design workshops— reveals the nuanced ways in which designs materials (both expected and unexpected) act in relation to older adults: from facilitating creative brainstorming, to limiting creative brainstorming, to leading to clashing of ideas, and contributing to non-participation in the design activity. My findings also reveal how older adults went beyond focusing on just the technology idea to account for the physical objects or the environment associated with both technology use and non-use, thus bringing to attention that technology cannot be seen, used, or designed in isolation, and exists within specific configurations of actor-networks. Overall, my thesis contributes by providing insights on the new directions that HCI researchers working on aging can take in terms of: a) taking into account the ways in which the nonhuman entities act and hold them accountable for undesired realities, b) designing emerging technologies that support meaningful relationships between older adults and their world, and c) move beyond designing technology in isolation to instead purposefully situate older adults in designing meaningful configurations of human and nonhuman entities (including technology).
  • Thumbnail Image
    Item
    Situated Analytics for Data Scientists
    (2022) Batch, Andrea; Elmqvist, Niklas E; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Much of Mark Weiser's vision of ``ubiquitous computing'' has come to fruition: We live in a world of interfaces that connect us with systems, devices, and people wherever we are. However, those of us in jobs that involve analyzing data and developing software find ourselves tied to environments that limit when and where we may conduct our work; it is ungainly and awkward to pull out a laptop during a stroll through a park, for example, but difficult to write a program on one's phone. In this dissertation, I discuss the current state of data visualization in data science and analysis workflows, the emerging domains of immersive and situated analytics, and how immersive and situated implementations and visualization techniques can be used to support data science. I will then describe the results of several years of my own empirical work with data scientists and other analytical professionals, particularly (though not exclusively) those employed with the U.S. Department of Commerce. These results, as they relate to visualization and visual analytics design based on user task performance, observations by the researcher and participants, and evaluation of observational data collected during user sessions, represent the first thread of research I will discuss in this dissertation. I will demonstrate how they might act as the guiding basis for my implementation of immersive and situated analytics systems and techniques. As a data scientist and economist myself, I am naturally inclined to want to use high-frequency observational data to the end of realizing a research goal; indeed, a large part of my research contributions---and a second ``thread'' of research to be presented in this dissertation---have been around interpreting user behavior using real-time data collected during user sessions. I argue that the relationship between immersive analytics and data science can and should be reciprocal: While immersive implementations can support data science work, methods borrowed from data science are particularly well-suited for supporting the evaluation of the embodied interactions common in immersive and situated environments. I make this argument based on both the ease and importance of collecting spatial data from user sessions from the sensors required for immersive systems to function that I have experienced during the course of my own empirical work with data scientists. As part of this thread of research working from this perspective, this dissertation will introduce a framework for interpreting user session data that I evaluate with user experience researchers working in the tech industry. Finally, this dissertation will present a synthesis of these two threads of research. I combine the design guidelines I derive from my empirical work with machine learning and signal processing techniques to interpret user behavior in real time in Wizualization, a mid-air gesture and speech-based augmented reality visual analytics system.