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Shri Harini Ramesh

Publications and source records attributed to Shri Harini Ramesh.

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Input Visualizations to Track Health Data by Older Adults with Multiple Chronic Conditions

Older adults living with multiple chronic conditions (MCC) can considerably benefit from collecting and reflecting on their health data. Many older adults collect their health data using various approaches, such as digital tools or handwritten notebooks. However, in these approaches, the act of collecting data does not itself yield insights; sensemaking and reflection happen only if individuals later review their accumulated records. The daily process of data collection thus offers limited opportunity for individuals to actively engage with their data or find the process personally meaningful and enjoyable. Personal data input visualizations using physical tokens offer a promising solution that can help individuals recognize evolving patterns while collecting data and discover meaningful insights more serendipitously and engagingly. Yet, there is a limited understanding of whether and how older adults living with MCC might adopt physical input visualizations to collect data and reflect on their health, and how the tangible, expressive, and personalizable nature of this process supports their sensemaking and reflection. In this paper, we present the results of our interview and diary studies in which older adults living with MCC inputted health data using physical tokens over two weeks. Our findings highlight the diverse and unique needs of older adults for tracking personal health data, illustrating how they adapt strategies and personalize physical input visualizations to align with their individual needs. We demonstrate how older adults integrated input visualizations into daily routines and leveraged tangible markers to reflect on patterns and behaviors, while enjoying the process of tracking and focusing on personal expression and meaningful reflection. Finally, we provide design considerations for supporting older adults with MCC when inputting health data through physical tokens.

cs.HC

Pulli Kolam: A Traditional South Indian Craft Practice for Representing Data

This paper introduces Pulli Kolam, a traditional South Indian craft, as a medium for physical data representation. Grounded in its cultural meaning and embodied practice, Pulli Kolam follows structured geometric rules while allowing creative variation. We identify five mapping strategies within Kolam (dots, patterns, fills, lines, and color) that can be used for representing data physically. without disrupting traditional practice. Through an illustrative scenario of daily well-being tracking, we demonstrate how data representation can be embedded within routine craft practice. We conclude by outlining potential material adaptations that extend Kolam beyond its ephemeral form while maintaining its embodied and ritual qualities.

cs.HC

Metacognitive Demands and Strategies While Using Off-The-Shelf AI Conversational Agents for Health Information

As Artificial Intelligence (AI) conversational agents become widespread, people are increasingly using them for health information seeking. The use of off-the-shelf conversational agents for health information seeking could place high metacognitive demands (the need for extensive monitoring and control of one's own thought process) on individuals, which could compromise their experience of seeking health information. However, currently, the specific demands that arise while using conversational agents for health information seeking, and the strategies people use to cope with those demands, remain unknown. To address these gaps, we conducted a think-aloud study with 15 participants as they sought health information using our off-the-shelf AI conversational agent. We identified the metacognitive demands such systems impose, the strategies people adopt in response, and propose considerations for designing beyond off-the-shelf interfaces to reduce these demands and support better user experiences and affordances in health information seeking.

cs.HC

Challenges and Opportunities of Teaching Data Visualization Together with Data Science

With the increasing amount of data globally, analyzing and visualizing data are becoming essential skills across various professions. It is important to equip university students with these essential data skills. To learn, design, and develop data visualization, students need knowledge of programming and data science topics. Many university programs lack dedicated data science courses for undergraduate students, making it important to introduce these concepts through integrated courses. However, combining data science and data visualization into one course can be challenging due to the time constraints and the heavy load of learning. In this paper, we discuss the development of teaching data science and data visualization together in one course and share the results of the post-course evaluation survey. From the survey's results, we identified four challenges, including difficulty in learning multiple tools and diverse data science topics, varying proficiency levels with tools and libraries, and selecting and cleaning datasets. We also distilled five opportunities for developing a successful data science and visualization course. These opportunities include clarifying the course structure, emphasizing visualization literacy early in the course, updating the course content according to student needs, using large real-world datasets, learning from industry professionals, and promoting collaboration among students.

cs.HC