Browsing by Author "Vedak, Sanika"

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    A measurement of faculty views on the meaning and value of student privacy
    (Springer, 2022-06-04) Jones, Kyle M. L.; VanScoy, Amy; Bright, Kawanna; Harding, Alison; Vedak, Sanika; Library and Information Science, School of Computing and Informatics
    Learning analytics tools are becoming commonplace in educational technologies, but extant student privacy issues remain largely unresolved. It is unknown whether faculty care about student privacy and see privacy as valuable for learning. The research herein addresses findings from a survey of over 500 full-time higher education instructors. In the findings, we detail faculty perspectives of their privacy, students’ privacy, and the high degree to which they value both. Data indicate that faculty believe privacy is important to intellectual behaviors and learning, but the discussion argues that faculty make choices that put students at risk. While there seems to be a “privacy paradox,” our discussion argues that faculty are making assumptions about existing privacy protections and making instructional choices that could harm students because their “risk calculus” is underinformed. We conclude the article with recommendations to improve a faculty member’s privacy decision-making strategies and improve institutional conditions for student privacy.
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    Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges
    (MDPI, 2020-09) Trajkova, Milka; Alhakamy, A'aeshah; Cafaro, Francesco; Vedak, Sanika; Mallappa, Rashmi; Kankara, Sreekanth R.; Human-Centered Computing, School of Informatics and Computing
    Social networking sites such as Twitter have been a popular choice for people to express their opinions, report real-life events, and provide a perspective on what is happening around the world. In the outbreak of the COVID-19 pandemic, people have used Twitter to spontaneously share data visualizations from news outlets and government agencies and to post casual data visualizations that they individually crafted. We conducted a Twitter crawl of 5409 visualizations (from the period between 14 April 2020 and 9 May 2020) to capture what people are posting. Our study explores what people are posting, what they retweet the most, and the challenges that may arise when interpreting COVID-19 data visualization on Twitter. Our findings show that multiple factors, such as the source of the data, who created the chart (individual vs. organization), the type of visualization, and the variables on the chart influence the retweet count of the original post. We identify and discuss five challenges that arise when interpreting these casual data visualizations, and discuss recommendations that should be considered by Twitter users while designing COVID-19 data visualizations to facilitate data interpretation and to avoid the spread of misconceptions and confusion.
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    Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges
    (MDPI, 2020-09) Trajkova, Milka; Alhakamy, A’aeshah; Cafaro, Francesco; Vedak, Sanika; Mallappa, Rashmi; Kankara, Sreekanth R.; Human-Centered Computing, School of Informatics and Computing
    Social networking sites such as Twitter have been a popular choice for people to express their opinions, report real-life events, and provide a perspective on what is happening around the world. In the outbreak of the COVID-19 pandemic, people have used Twitter to spontaneously share data visualizations from news outlets and government agencies and to post casual data visualizations that they individually crafted. We conducted a Twitter crawl of 5409 visualizations (from the period between 14 April 2020 and 9 May 2020) to capture what people are posting. Our study explores what people are posting, what they retweet the most, and the challenges that may arise when interpreting COVID-19 data visualization on Twitter. Our findings show that multiple factors, such as the source of the data, who created the chart (individual vs. organization), the type of visualization, and the variables on the chart influence the retweet count of the original post. We identify and discuss five challenges that arise when interpreting these casual data visualizations, and discuss recommendations that should be considered by Twitter users while designing COVID-19 data visualizations to facilitate data interpretation and to avoid the spread of misconceptions and confusion.