Browsing by Author "Trajkova, Milka"

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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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    "Alexa is a Toy": Exploring Older Adults' Reasons for Using, Limiting, and Abandoning Echo
    (ACM, 2020-04) Trajkova, Milka; Martin-Hammond, Aqueasha; Human-Centered Computing, School of Informatics and Computing
    Intelligent voice assistants (IVAs) have the potential to support older adults' independent living. However, despite a growing body of research focusing on IVA use, we know little about why older adults become IVA non-users. This paper examines the reasons older adults use, limit, and abandon IVAs (i.e., Amazon Echo) in their homes. We conducted eight focus groups, with 38 older adults residing in a Life Plan Community. Thirty-six participants owned an Echo for at least a year, and two were considering adoption. Over time, most participants became non-users due to their difficulty finding valuable uses, beliefs associated with ability and IVA use, or challenges with use in shared spaces. However, we also found that participants saw the potential for future IVA support. We contribute a better understanding of the reasons older adults do not engage with IVAs and how IVAs might better support aging and independent living in the future.
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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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    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.
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    Exploring The Effect Of Visual And Verbal Feedback On Ballet Dance Performance In Mirrored And Non-Mirrored Environments
    (2016-05) Trajkova, Milka; Cafaro, Francesco; Bolchini, Davide; Mannheimer, Steve
    Since the 1800s, the ballet studio has been largely unchanged, a core feature of which is the mirror. The influence of mirrors on ballet education has been documented, and prior literature has shown negative effects on dancers’ body image, satisfaction, level of attention and performance quality. While the mirror provides immediate real-time feedback, it does not inform dancers of their errors. Tools have been developed to do so, but the design of the feedback from a bottom-up perspective has not been extensively studied. The following study aimed to assess the value of different types of feedback to inform the design of tech-augmented mirrors. University students’ ballet technique scores were evaluated on eight ballet combinations (tendue, adagio, pirouette, petit allegro, plié, degage, frappe and battement tendue), and feedback was provided to them. We accessed learning with remote domain expert to determine whether or not the system had an impact on dancers. Results revealed that the treatment with feedback was statistically significant and yielded higher performance versus without the feedback. Mirror versus non-mirror performance did not present any score disparity indicating that users performed similarly in both conditions. A best fit possibility was seen when visual and verbal feedback were combined. We created MuscAt, a set of interconnected feedback design principles, which led us to conclude that the feasibility of remote teaching in ballet is possible.
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    Move Your Body: Engaging Museum Visitors with Human-Data Interaction
    (ACM, 2020-04) Trajkova, Milka; Alhakamy, A’aeshah; Cafaro, Francesco; Mallappa, Rashmi; Kankara, Sreekanth R.; Human-Centered Computing, School of Informatics and Computing
    Museums have embraced embodied interaction: its novelty generates buzz and excitement among their patrons, and it has enormous educational potential. Human-Data Interaction (HDI) is a class of embodied interactions that enables people to explore large sets of data using interactive visualizations that users control with gestures and body movements. In museums, however, HDI installations have no utility if visitors do not engage with them. In this paper, we present a quasi-experimental study that investigates how different ways of representing the user ("mode type") next-to a data visualization alters the way in which people engage with a HDI system. We consider four mode types: avatar, skeleton, camera overlay, and control. Our findings indicate that the mode type impacts the number of visitors that interact with the installation, the gestures that people do, and the amount of time that visitors spend observing the data on display and interacting with the system.