Student-Athletes’ Use of Their Performance Data; Evaluating Tech to Regulate Anxiety – UMD iSchool Papers Awarded at CHI 2020
The ACM annual Conference on Human Factors in Computing Systems (CHI) is considered the most prestigious in the field of human–computer interaction.
Congratulations to Dr. Tamara Clegg, Dr. Daniel Greene, Dr. Keith Marzullo, PhD student Nate Beard, and InfoSci student Jasmine Brunson for receiving Honourable Mention awards at CHI 2020!
The ACM annual Conference on Human Factors in Computing Systems (CHI) is considered the most prestigious in the field of human–computer interaction and is one of the top-ranked conferences in computer science. Only the top 5% of conference papers receive Honourable Mentions.
Data Everyday: Data Literacy Practices in a Division I College Sports Context
Tamara Clegg, Daniel M. Greene, Nate Beard & Jasmine Brunson
Abstract: Data analysis is central to sports training. Today, cutting-edge digital technologies are deployed to measure and improve athletes’ performance. But too often researchers focus on the technology collecting performance data at the expense of understanding athletes’ experiences with data. This is particularly the case in the understudied context of collegiate athletics, where competition is fierce, tools for data analysis abound, and the institution actively manages athletes’ lives. By investigating how student-athletes analyze their performance data and are analyzed in turn, we can better understand the individual and institutional factors that make data literacy practices in athletics meaningful and productive—or not. Our pilot interview study of student-athletes at one Division I university reveals a set of opportunities for student-athletes to engage with and learn from data analytics practices. These opportunities come with a set of contextual tensions that should inform the design of new technologies for collegiate sports settings. Read the Full Paper
Evaluating a Personalizable, Inconspicuous Vibrotactile (PIV) Breathing Pacer for In-the-Moment Affect Regulation
Pardis Miri, Emily Jusuf, Andero Uusberg, Horia Margarit, Robert Flory, Katherine Isbister, Keith Marzullo & James J. Gross
Abstract: Given the prevalence and adverse impact of anxiety, there is considerable interest in using technology to regulate anxiety. Evaluating the efficacy of such technology in terms of both the average effect (the intervention efficacy) and the heterogeneous effect (for whom and in what context the intervention was effective) is of paramount importance. In this paper, we demonstrate the efficacy of PIV, a personalized breathing pacer, in reducing anxiety in the presence of a cognitive stressor. We also quantify the relation between our specific stressor and PIV-user engagement. To our knowledge, this is the first mixed-design study of a vibrotactile affect regulation technology which accounts for a specific stressor and for individual differences in relation to the technology’s efficacy. Guidelines in this paper can be applied for designing and evaluating other affect regulation technologies. Read the Full Paper