Information Studies Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2780
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Item Designing Human-Centered AI Tools with Interactive Visualization(2024) Hoque, Md Naimul; Kraus, Kari; Information Studies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Human-centered AI (HCAI), rather than replacing the human, puts the user in the driver's seat of so-called human-centered AI-infused tools (HCAI tools): interactive software tools that amplify, augment, empower, and enhance human performance using AI models. In this dissertation, I discuss how interactive visualization can be a key enabling technology for creating such human-centered AI tools. Visualization has already been shown to be a fundamental component in explainable AI models, and coupling this with data-driven, semantic, and unified interaction feedback loops will enable a human-centered approach for bridging AI models and human users. To validate this approach, I at first interviewed HCI, AI, and Visualization experts to define the characteristics of HCAI tools. I then discuss the design and development process of four HCAI tools powered by visualization. I conclude by outlining the design guidelines learned from the design process of the tools and research directions for designing future HCAI tools.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 Improving Selection of Analogical Inspirations with Chunking and Recombination(2023) Srinivasan, Arvind; Chan, Joel; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Innovation is vital in various fields, and analogical thinking is a powerful tool for gen- erating creative solutions to complex problems. However, recognizing analogies can be time- consuming, and successful recognition doesn’t guarantee their adoption in innovation. In this thesis, A novel computational support system for analogical innovation is proposed that employs the cognitive mechanisms for chunking and recombination as mediums of interaction. Chunking involves identifying and extracting meaningful chunks or segments from a design problem into interactive tiles called magnets while recombination involves combining these magnets to gener- ate insightful questions that elicit divergent thinking. In this way, the proposed system aims to streamline the process of recognizing and selecting analogical inspirations for innovation while avoiding premature rejection and design fixation.To evaluate the effectiveness of the system, a within-subjects study involving 23 participants was conducted, comparing the proposed interface with a baseline. The study found that using chunking and recombination as interactive mechanisms helped prevent premature rejection of useful analogical leads, resulting in 4 times fewer ignored analogical leads. Participants were also found to make 12 times fewer changes to their decisions, given a minor increment in processing time in the order of 1.5 minutes. Overall, these results suggest that our proposed intervention is an effective tool for facilitating the selection of beneficial analogies, fostering analogical innovation through computational support.Item “I FEEL LIKE I’M TEACHING IN A GLADIATOR RING”: BARRIERS AND BENEFITS OF LIVE CODING(2023) Berger, Caroline Palma; Elmqvist, Niklas; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Live coding—synchronously writing software in front of students for the purpose of teaching—can be an effective method for engaging students and instilling practical programming skills. However, not all live coding sessions are effective and not all instructors are successful in this challenging task. We present results from an interview study involving university instructors,teaching assistants, and students identifying both barriers and benefits of live coding. We also designed and collected participant feedback on a prototype live coding tool to better facilitate learner engagement with the live coding pedagogical practice. Finally, we use this feedback to propose guidelines for how to design tools to support effective live coding in the classroom. This work advances our understanding of the benefits and challenges of live coding in university computer science instruction and highlights potential future work on the design of tools to better support this productive instructional practice.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.Item Reasons and Rationalizations for Bedtime Procrastination in University Students(2021) Patrick, Matthew David; Choe, Eun Kyoung; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)University students are often known for their poor sleeping habits, which have ill-effects on various aspects of their lives. However, causes for this lack of proper sleep are underexplored. In this thesis, I focus on understanding reasons and rationalizations for delaying one’s bedtime when there are no direct external circumstances preventing a person from doing so. I investigate what types of activities students engage in before bedtime, their own reflections on these activities, and their sleep the following night. To this end, I completed a two-week survey and debriefing interview to uncover reasons and rationales for bedtime procrastination. I conclude that screen-time, attention-maintaining activities, and social expectations are key factors in delaying one’s bedtime, and that minimal effort assigned to self-tracking and reflection may enable better sleeping habits, which were found desirable by all participants.Item Design And Evaluation of A Conversational Agent for Mental Health Support: Forming Human-Agent Sociotechnical And Therapeutic Relationships(2021) Liao, Yuting; Vitak, Jessica; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Many people with mental health disorders face significant challenges getting the help they need, including the costs of obtaining psychological counseling or psychiatry services, as well as fear of being stigmatized. As a way of addressing these barriers, text-based conversational agents (chatbots) have gained traction as a new form of e-therapy. Powered by artificial intelligence (AI) and natural language processing techniques, this technology offers more natural interactions and a “judgment-free zone” for clients concerned about stigma. However, literature on psychotherapeutic chatbots is sparse in both the psychology and human computer interaction (HCI) fields. While recent studies indicate that chatbots provide an affordable and effective therapy delivery method, this research has not thoroughly explained the underlying mechanisms for increasing acceptance of chatbots and making them more engaging. Don Norman (1994) has argued the main difficulties of utilizing intelligent agents are social—not technical—and particularly center around people’s perceptions of agents. In exploring the use of chatbots in psychotherapy, we must investigate how this technology is conceptually understood, and the thoughts and feelings they evoke when people interact with them. This dissertation focuses on two types of relationships critical to the success of utilizing chatbots for mental health interventions: sociotechnical relationships and therapeutic relationships. A sociotechnical relationship concerns technology adoption, usability, and the compatibility between humans and chatbots. A therapeutic relationship encompasses people’s feelings and attitudes toward a chatbot therapist. Therefore, this dissertation asks: What are the optimal design principles for a conversational agent that facilitates the development of both sociotechnical and therapeutic relationships to help people manage their mental health? To investigate this question, I designed an original conversational system with eight gendered and racially heterogeneous personas, and one neutral robot-like persona. Using a mixed-method approach (online experiment and interviews), I evaluated factors related to the adoption and use of conversational agents for psychotherapeutic purposes. I also unpacked the human-agent relational dynamics and evaluated how anthropomorphism and perceived racial similarity impact people’s perceptions of and interactions with the chatbot. These findings contributed to the wider understanding of conversational AI application in mental health support and provided actionable design recommendations.Item Student Experiences with Diversity and Inclusion in Technology Design Courses(2020) Fitzgerald, Shannon; Kules, Bill; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Technology design education does not yet teach students how to effectively avoid embedding their unconscious social and cultural biases into artifacts they design and build, despite widespread critical examination of the social impact of technology. Unintended consequences that exclude or discriminate against people as they use technology reflect an inability to acknowledge diversity and inclusion topics as integral to technology design. Through a national survey, this exploratory study examined the attitudes of 115 students studying Computer Science, Information Science, User-Centered-Design and related disciplines, yielding insights into their classroom experiences; receptiveness to and concerns about engaging in discussions of equity, diversity and inclusion; and interest in addressing these issues in their own designs. These findings inform a set of proposed curricular interventions that incorporate ethics, equity, and bias into technology design courses as a supplement to traditional lectures introducing basic diversity and inclusion concepts.Item USERS’ PERCEPTIONS OF DATA OWNERSHIP, DATA STORAGE, AND THEIR LOCUS OF CONTROL OVER DATA GENERATED BY SMART PHONE APPLICATIONS(2020) Rogers, Lisa; Mazurek, Michelle; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)When users don’t understand how their data is stored, managed, and deleted in the cloud, it can leave that data vulnerable to hacking, privacy breaches, and other losses. How aware are users of what data they have in their cloud or other locations? This thesis examines how centralized remote storage affects participants’ knowledge of and ability to control and delete their phone app data using qualitative semi-structured interviews with 16 adults in the Washington, DC area. Results indicate that many users, especially Android users, don’t know what data they have backed up, and don’t feel they have control or understanding of their cloud account. Some participants thought they could better control their data if they learned more technical skills, but felt too intimidated to try. These results have implications for designing more usable cloud storage, recovery and deletion for mobile devices.Item USER INTERFACE CHANGES IN VIRTUAL ENVIRONMENTS AFFECT THE PERCEIVED RESPONSES OF INDOOR CYCLISTS(2018) Stone, Rebecca; Golbeck, Jennifer A.; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Virtual reality is becoming mainstream in areas such as entertainment, medicine and training. However, the affect on a user’s perceived states are still to be fully understood. This study aims to add to the existing body of research by examining changes in user interfaces and the affect on perceived responses. Subjects in the study were exposed to two virtual environments, while undertaking a physical exercise task. Their perceived responses were captured through a combination of interviews, observations, and surveys. This differs from previous studies in that it is capturing the perceived differences between the environments themselves. The results highlighted that the content of the environments resulted in a variety of interesting, and unexpected, perceived responses.