Browsing by Author "Cafaro, Francesco"

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    aiDance: A Non-Invasive Approach in Designing AI-Based Feedback for Ballet Assessment and Learning
    (2021-12) Trajkova, Milka; Cafaro, Francesco; Bolchini, Davide; Dombrowski, Lynn; Fusco, Judi; Hickey, Daniel; Magerko, Brian; Toenjes, John
    Since its codified genesis in the 18th century, ballet training has largely been unchanged: it relies on tools that lack adequate support for both dancers and teachers. In particular, providing effective augmented feedback remains challenging as it can be limited, not always provided at the proper time, and highly subjective as it depends on the visual experience of an instructor. Designing a ballet assessment and learning tool with the aim of achieving a meaningful educational experience is an interdisciplinary challenge due to the fine motor movements and patterns of the art form. My work examines how we can effectively augment ballet learning in three phases using mixedmethod approaches. First, through my past professional experience as a ballet dancer, I explore how the design and in-lab evaluation of augmented visual and verbal feedback can improve the technical performance for novices and experts via remote learning. Second, I investigate the learning and teaching challenges that currently exist in traditional in-person training environments for dancers and teachers. Furthermore, I study the current technology use, reasons for non-use, and derive design requirements for future use. Lastly, I focus on how we can design aiDance, an AI-based feedback tool that attempts to represent an affordable and non-invasive approach that augments teachers’ abilities to facilitate assessment in the 21st century and pirouette towards the enhancement of learning. With this empirical work, I present insights that inform the HCI community at the intersection of dance and design in addressing the first steps towards the standardization of motor learning feedback.
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    Current Use, Non-Use, and Future Use of Ballet Learning Technologies
    (ACM, 2021-06) Trajkova, Milka; Cafaro, Francesco; Human-Centered Computing, School of Informatics and Computing
    Learning ballet is a complex motor task that can be effectively enhanced by technology. Learning technologies, however, are not typically used for the assessment of ballet technique due to a lack of adequate and non-invasive tools that can be pragmatically adopted. We conducted an interview-based qualitative study with seven expert ballet teachers and six pre-professional dancers to examine their current and future technology use in a ballet technique class. Through inductive and deductive analysis, we identified reasons for technology non-use and derived seven requirements that can inform the design and implementation of ballet assessment technologies including designing for: adaptation to multi-skill/multi-method environments, teacher/dancer skill augmentation, agency, non-invasive design, feedback for gross/fine movements, trust, and proprioception by supporting transformative assessment. We discuss barriers for technology acceptance and unintended consequences that should be considered when designing future technologies for ballet.
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    Data Literacy for Social Justice
    (2020-06) Matuk, Camillia; Matuk, Camillia; Susan Yoon; Polman, Joseph; Amato, Anna; Barton, Jacob; Bulalacao, Nicole Marie; Cafaro, Francesco; Haldar, Lina Chopra; Cottone, Amanda; Cortes, Krista; DesPortes, Kayla; Erickson, Tim; Finzer, William; Taylor, Katie Headrick; Herbel-Eisenmann, Beth; Graville, Cynthia; Gutiérrez, Kris; Higgins, Traci; Himes, Blanca; Lanouette, Kathryn; Lee, Hollylynne; Lim, Vivian; Lopez, M. Lisette; Lyons, Leilah; Milz, Dan; Olivares, Maria C.; Osche, Elizabeth; Parikh, Tapan S.; Philip, Thomas; Rubel, Laurie; Shelley, Joey; Rivero, Edward; Roberts, Jessica; Roberto, Collette; Petrosino, Tony; Rubin, Andee; Shim, Jooeun; Silander, Megan; Sommer, Stephen; Stokes, David; Tes, Marian; Trajkova, Milka; Urbanowicz, Ryan; Vacca, Ralph; Van Wart, Sarah; Vasudevan, Veena; Wilkerson, Michelle; Woods, Peter J.; Human-Centered Computing, School of Informatics and Computing
    The projects in this interactive poster symposium explore ways of engaging learners with social justice issues through the design and study of data literacy interventions. These interventions span classroom to museum contexts, and environmental to social sciences domains. Together, they illustrate research and practice approaches for engaging learners withdata to promote emancipatory activity.
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    Designing embodied interactions for informal learning: two open research challenges
    (ACM, 2019-06) Cafaro, Francesco; Trajkova, Milka; Alhakamy, A’aeshah; Human-Centered Computing, School of Informatics and Computing
    Interactive installations that are controlled with gestures and body movements have been widely used in museums due to their tremendous educational potential. The design of such systems, however, remains problematic. In this paper, we reflect on two open research challenges that we observed when crafting a Kinect-based prototype installation for data exploration at a science museum: (1) making the user aware that the system is interactive; and, (2) increasing the discoverability of hand gestures and body movements.
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    Designing for Ballet Classes: Identifying and Mitigating Communication Challenges Between Dancers and Teachers
    (ACM, 2019-06) Trajkova, Milka; Cafaro, Francesco; Dombrowski, Lynn; Human-Centered Computing, School of Informatics and Computing
    Dancer-teacher communication in a ballet class can be challenging: ballet is one of the most complex forms of movements, and learning happens through multi-faceted interactions with studio tools (mirror, barre, and floor) and the teacher. We conducted an interview-based qualitative study with seven ballet teachers and six dancers followed by an open-coded analysis to explore the communication challenges that arise while teaching and learning in the ballet studio. We identified key communication issues, including adapting to multi-level dancer expertise, transmitting and realigning development goals, providing personalized corrections and feedback, maintaining the state of flow, and communicating how to properly use tools in the environment. We discuss design implications for crafting technological interventions aimed at mitigating these communication challenges.
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    E Pluribus Unum: Using Conceptual Metaphor Theory to Explore and Support Mixed-Ability Workplaces
    (ACM, 2021-10) Cafaro, Francesco; Brady, Erin; Chandra, Sow Mya; Patil, Ulka; Saxena, Abhijeet; Human-Centered Computing, School of Informatics and Computing
    Even when they are able to secure employment, people with cognitive disabilities typically encounter significant difficulties in the workplace. In this paper, we focus on Mixed-Ability workplaces: work settings in which people without disabilities and with different types of disabilities collaborate on a daily basis. The case study for our exploratory research is a university library that has been able to support a mixed-ability work setting for over four years. We describe how a theory from cognitive linguistics (Conceptual Metaphor Theory) can be used to explore the challenges that people encounter in mixed-ability workplaces, identify the cognitive processes that differ between neurotypical team leaders and workers with cognitive disabilities, and translate these findings into design recommendations for embodied technologies that support mixed-ability workplaces.
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    End-User Needs of Fragmented Databases in Higher Education Data Analysis and Decision Making
    (2019-05) Briggs, Amanda; Cafaro, Francesco; Dombrowski, Lynn; Reda, Khairi
    In higher education, a wealth of data is available to advisors, recruiters, marketers, and program directors. However, data sources can be accessed in a variety of ways and often do not seem to represent the same data set, presenting users with the confounding notion that data sources are in conflict with one another. As users are identifying new ways of accessing and analyzing this data, they are modifying existing work practices and sometimes creating their own databases. To understand how users are navigating these databases, the researchers employed a mixed methods research design including a survey and interview to understand the needs to end users who are accessing these seemingly fragmented databases. The study resulted in a three overarching categories – access, understandability, and use – that affect work practices for end users. The researchers used these themes to develop a set of broadly applicable design recommendations as well as six sets of sketches for implementation – development of a data gateway, training, collaboration, tracking, definitions and roadblocks, and time management.
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    End-User Needs of Fragmented Databases in Higher Education Data Analysis and Decision Making
    (MDPI, 2021) Briggs, Amanda; Cafaro, Francesco; Human-Centered Computing, Luddy School of Informatics, Computing, and Engineering
    In higher education, a wealth of data is available to advisors, recruiters, marketers, and program directors. These large datasets can be accessed using an array of data analysis tools that may lead users to assume that data sources conflict with one another. As users identify new ways of accessing and analyzing these data, they deviate from existing work practices and sometimes create their own databases. This study investigated the needs of end users who are accessing these seemingly fragmented databases. Analysis of a survey completed by eighteen users and ten semi-structured interviews from five colleges and universities highlighted three recurring themes that affect work practices (access, understandability, and use), as well as a series of challenges and opportunities for the design of data gateways for higher education. We discuss a set of broadly applicable design recommendations and five design functionalities that the data gateways should support: training, collaboration, tracking, definitions and roadblocks, and time
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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.