Browsing by Subject "Sensemaking"

Now showing 1 - 7 of 7
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    Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations
    (Association for Computer Machinery, 2019) Choi, In Kwon; Childers, Taylor; Raveendranath, Nirmal Kumar; Mishra, Swati; Harris, Kyle; Reda, Khairi
    Visualization tools facilitate exploratory data analysis, but fall short at supporting hypothesis-based reasoning. We conducted an exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data. We report on how participants leveraged these unique affordances for visual analysis. We found that a majority of participants articulated meaningful models and predictions, utilizing them as entry points to sensemaking. We contribute an abstract typology representing the types of models participants held and externalized as data expectations. Our findings suggest ways for rearchitecting visual analytics tools to better support hypothesis- and model-based reasoning, in addition to their traditional role in exploratory analysis. We discuss the design implications and reflect on the potential benefits and challenges involved.
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    Coordinating the Uncoordinated Giant: Applying the Four Flows Model of Communicative Constitution of Organizations to the United States Weather Enterprise
    (2019-10) Rothrock, Matthew Carter; Parrish-Sprowl, John; Goering, Elizabeth; Sandwina, Ronald
    The US weather enterprise includes academia, the private weather industry, and government-funded forecasting, research, and dissemination agencies. While not an organization in its own right, the enterprise behaves like an organization of organizations. This thesis applies the communicative constitution of organizations, and McPhee and Zaug’s four flows model in particular, to the US weather enterprise. Each organization in the weather enterprise behaves like individual members of an organization would, which extends this theory to a conceptualization of organization that increases innovation, collaboration, and coordination. The weather is a constitutive force which calls the US weather enterprise into being. Finally, CCO is extended to other collaborative, coordinated efforts among the public and private sectors, indicating the possibilities of CCO as an attractive answer to the great organizational questions of the 21st century and beyond. Future research areas are considered, including how the US weather enterprise manages the unexpected and reduces uncertainty organizationally. Also, considerations as to how CCO can be applied to the incident command structure, often called forward during high-impact weather events, will be made.
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    Dentist-patient communication: How do patients make sense of oral health information and translate it into action?
    (2016-01) Laorujiralai, Kamolchanok; Goering, Elizabeth M.; Brann, Maria; Bute, Jennifer Jo
    Purpose: Patient-provider communication has been studied extensively in the last two decades, and many researchers have confirmed the importance of communication between patient and provider in medical contexts. In spite of increased research in patient-provider communication in dentistry, dental care providers still report that patients often do not accurately follow oral health recommendations. Thus, there is the need for additional study on how patients make sense of the oral health information they receive and how they translate that information into action. This study aimed to obtain insight into how dental care patients perceive and make sense of the information they receive from their dentist and how they translate that information into action. Methods: 16 patients and 8 dentists from Indiana School of Dentistry’s (IUSD) Graduate Prosthodontic Clinic in Indianapolis, Indiana were included. Two in-depth interviews, one immediately following the dental visit and one 7-10 days later, were conducted with the patients, and one short interview was conducted with each patient’s dental care provider. Interviews were audio taped and transcribed. Results: The results show both patients and providers perceived the interaction during consultation positively. The majority of patients were able to accurately recall information they received from their dentists and made sense of new information through the lens of their previous experiences. Four additional factors that explain patients’ adherence with health advice were also found in addition to the previous studies. Conclusions: Successful dentist-patient interaction could be thought of as a match between what dentists think patients need to know, what patients think they want/need to know, and what patients actually know. Thus, some barriers that can keep dentists and patients from reaching information equilibrium are discussed. The study concludes by offering practical and theoretical implications.
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    Pause But Not Panic: Exploring COVID-19 as a Critical Incident for Nonprofit Workers
    (Sage, 2023-01-16) Kuenzi, Kerry; Stewart, Amanda J.; Walk, Marlene; O'Neill School of Public and Environmental Affairs
    Critical incidents often have significant impacts on workers, sometimes causing disruptions to career pathways and a re-evaluation of past career decisions. This article seeks to explore the impact of the COVID-19 pandemic on nonprofit workers and their commitment to the sector using a critical incidents lens. In-depth interviews with nonprofit workers provided insights on the pandemic’s impact on workers’ personal and professional lives and how they made sense of these. Changes to work including flexibility and work-from-home options were often viewed positively, yet workers expressed a loss of connection with their colleagues, mental health and well-being challenges, as well as challenges to adapt to new ways of working. In making sense of these changes, commitment to the sector was mostly sustained; however, respondents also noted a shift in priorities and expressed a desire for better balance between their personal and professional lives.
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    Sensemaking during the use of learning analytics in the context of a large college system
    (2017-04-05) Morse, Robert Kenneth; Brady, Erin; Bolchini, Davide; Boling, Elizabeth; Hook, Sara
    This research took place as a cognitive exploration of sensemaking of learning analytics at Ivy Tech Community College of Indiana. For the courses with the largest online enrollment, quality standards in the course design are maintained by creating sections from a course design framework. This means all sections have the same starting content and the same framework for assessment. The course design framework is maintained by the curriculum committee composed of program chairs who oversee the program to which the course belongs. This research proposed to develop a learning analytics dashboard to elicit the best practices in instantiating a course design framework from the perspective of the program chair. The Instructional Design Implementation Dashboard, IDID, was designed to address the sensemaking needs of program chairs. The program chairs were asked to make sense of IDID built around the data collected from the course management system and the student information system. IDID leveraged metrics from the user activity and the learner performance from the learning management system, combined with data about the student demographics captured from the student information system. IDID was used to identify highly successful sections and examine the instructor behaviors that might be considered best practices. Data Frame Sensemaking theory was confirmed as an accurate description of the experience of program chairs when using IDID. A revised model of Data Frame Sensemaking theory was developed to explain the interaction of those using the IDID platform.
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    Techniques for Improving the Robustness of Visual Analytics
    (2024-08) Koonchanok, Ratanond; Reda, Khairi; Chakraborty, Sunandan; Cafaro, Francesco; McCabe, Sean
    Interactive visualization systems, such as Tableau, are integral parts of the data analysis workflow. While such tools were built to help analysts perform exploratory data analysis with minimal effort, analysts have also been using them to make statistical inferences (e.g., predicting future trends) based on patterns revealed by the dataset. However, in addition to revealing true patterns, visualizations can also surface noise and other random fluctuations in data, which could lead to spurious discoveries. The latter poses a threat to the trustworthiness of analyses, especially given the increased reliance on visualizations across various domains. My central thesis is that it is possible to reduce the incidence of false discovery by introducing lightweight user interface elements in visualization tools. In particular, I propose eliciting and incorporating analyst beliefs into visualizations as an approach for guarding against spurious patterns and reducing the risk of analysts “overfitting” the data. To study how analysts would respond to such intervention, I first designed an interactive tool that combined visual belief elicitation with traditional visualization functionalities. In a qualitative study with data analysts, the tool appeared to allow users to operationalize their working knowledge into analyses, nudging them to adopt normative analysis practices (e.g., specifying hypotheses before peeking at data). I then conducted a crowdsourced experiment to investigate if this design could indeed help reduce the incidence of false discovery. Compared to a control condition, participants who used our intervention made significantly more accurate inferences and reported fewer false discoveries. Lastly, I investigated the capability of human intuition by comparing inferences from participants against those generated by statistical machines to understand the advantages and limitations of each. Overall, my thesis paves the way toward the development of a robust visual analytics system that facilitates collaborative decision-making processes, leveraging the complementary abilities of humans and machines.
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    Understanding Informational Practices and Exploring Data Collection Approaches for Quality of Life in Brain Injury Illness Management
    (2023-07) Masterson, Yamini Lalama Patnaik; Brady, Erin; Miller, Andrew D.; Toscos, Tammy; Hong, Youngbok; Gunter, Tracy D.
    Brain injury, a combination of medical injury, chronic illness, and impairment, affects more than 3.5 million people in the United States every year through an interplay of physiological, psychological, environmental, and cultural factors spanning clinical recovery, illness management, and personal recovery phases. The lack of collaborative and integrated understanding from healthcare and accessibility communities led to treating brain injury as a localized damage rather than individual response to ever-changing impairment and symptoms, focusing primarily on clinical recovery until recently. While self-tracking and management technologies have been widely successful in measuring individual symptoms, they have struggled to facilitate sensemaking and problem solving to achieve a consistent biopsychosocial awareness of illness. My dissertation addresses this gap through three aims: (1) investigate the current informational practices of individuals undergoing post-acute brain injury recovery, (2) explore technology-agnostic approaches for data collection and their impact on sensemaking processes and conceptual understanding of brain injury, and (3) develop guidelines for designing data collection tools that facilitate sensemaking in brain injury self-management. I achieve this through two longitudinal studies – an interview study that introduced participants to the framework on quality of life after traumatic brain injury (QoLIBRI) and a narrative study that used QoLIBRI framework to do structured journaling and co-design individualized data collection tools. The goal of this work is to improve self-awareness of individuals with brain injury enabling them to anticipate or recognize the occurrence of a challenge caused by impairment and then, utilize assistive technologies to bypass the limitation. It also has implications for involving neurodiverse populations in research and technology design.