Dongwook Yoon

Assistant Professor

Research Classification

Research Interests

Computer Science and Statistics
design
Educational Technologies
Augmented reality
computer supported cooperative work (CSCW)
Educational technology
Human-computer interaction (HCI)
Interaction and interface design
Multimodal interaction
Speech, touch, multitouch, stylus, gesture, mixed-reality, 3d interaction
Virtual Reality

Relevant Degree Programs

 
 

Research Methodology

Design
Systems study
Ethnography
Qualitative methods, quantitative methods, and mixed methods
quantitative research

Recruitment

Master's students
Doctoral students
Any time / year round

My research lies at the intersection of human-computer interaction (HCI), computer-supported cooperative work (CSCW), computer-mediated communication (CMC), and educational technology. My primary research focus is to build rich collaboration systems that offer expressive multimodal interactions, i.e., interactions through multiple communication channels (e.g., speech, gesture, and grasp). My design approach translates natural human interactions into novel combinations of input modalities that serve as building blocks for fluid, rich, and lightweight interfaces. My evaluation approach deploys and evaluates high-fidelity systems in real world contexts (e.g., classrooms), from which we can obtain ecologically valid user data.

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Graduate Student Supervision

Master's Student Supervision (2010 - 2021)
"@alex, this fixes #9": analysis of referencing patterns in Pull Request discussions (2021)

Pull Requests (PRs) are a frequently used method for proposing changes to source code repositories. When discussing proposed changes in a PR discussion, stakeholders often reference a wide variety of information objects for establishing shared awareness and common ground. Previous work has not considered how the referential behavior impacts collaborative software development via PRs. This knowledge gap is the major barrier in evaluating the current support for referencing in PRs and improving them. We conducted an explorative analysis of ~7K references, collected from 450 public PRs on GitHub, and constructed taxonomies of referent types and expressions. Using our annotated dataset, we identified several patterns in the use of references. We found that despite a prevalent use of references in PR discussions, GitHub's interface lacks the support for referencing the majority of information types. We provide qualitative descriptions of how different contextual factors shape the use of references in discussions. We also discovered distinct referencing patterns in merged and closed PRs which signifies a potential ground for future research to establish a relationship between reference use and PR outcomes. These findings suggest that what is and is not referenced within a PR discussion has an important impact on the software development process, and warrants continued platform support and research. We conclude with design implications to support more effective referencing in PR discussion interfaces. Supplementary materials available at: http://hdl.handle.net/2429/77435.

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Diagnosing bias in the gender representation of HCI research participants: how it happens and where we are (2021)

In human-computer interaction (HCI) studies, bias in the gender representation of participants can jeopardize the generalizability of findings, perpetuate bias in data driven practices, and make new technologies dangerous for underrepresented groups. Key to progress towards inclusive and equitable gender practices is diagnosing the current status of bias and identifying where it comes from. In this mixed-methods study, we interviewed 13 HCI researchers to identify the potential bias factors, defined a systematic data collection procedure for meta-analysis of participant gender data, and created a participant gender dataset from 1147 CHI papers. Our analysis provided empirical evidence for the underrepresentation of women, the invisibility of non-binary participants, deteriorating representation of women in MTurk studies, and characteristics of research topics prone to bias. Based on these findings, we make concrete suggestions for promoting inclusive community culture and equitable research practices in HCI. Supplementary materials available at: http://hdl.handle.net/2429/77303.

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Digital social interaction in older adults during the COVID-19 pandemic (2021)

Throughout the COVID-19 pandemic, older adults have been encouraged to stay indoors and isolated, leading to potential disruptions in their social activities and interpersonal relationships. We conducted an interview study (N=24) to examine older adults' technology adoption and communication practices in light of new circumstances related to the pandemic. Our interviews revealed that the pandemic motivated many older adults to learn new technology and become more tech-savvy in an effort to stay connected with others. However, they also reported challenges related to the pandemic that were major impediments to technology adoption. These were: (1) lack of access to in-person technology support under physical distancing mandates, (2) lack of opportunities for online participation due to negative age stereotypes and assumptions, and (3) increased apprehension to seek help from family members and friends who were suffering from pandemic-related stresses. This study extends technology adoption literature and contributes an up-to-date examination of the "grey digital divide" (the gap between older adults who use technology and those who do not). Our findings demonstrate that despite the rapidly increasing number of tech-savvy seniors, a digital divide not only persists, but has been exacerbated by the transition to virtual-only offerings. We reveal the challenges and coping strategies of older adults who remain separated from technology, and propose actionable solutions to increase digital access during the COVID-19 pandemic and beyond.

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Designers characterize naturalness in voice user interfaces: their goals, practices, and challenges (2020)

With substantial industrial interests, conversational voice user interfaces (VUIs) are becoming ubiquitous through devices that feature voice assistants such as Apple’s Siri and Amazon Alexa. Naturalness is often considered to be central to conversational VUI designs as it is associated with numerous benefits such as reducing cognitive load and increasing accessibility. The literature offers several definitions for naturalness, and existing conversational VUI design guidelines provide different suggestions for delivering a natural experience to users. However, these suggestions are hardly comprehensive and often fragmented. A precise characterization of naturalness is necessary for identifying VUI designers’ needs and supporting their design practices. To this end, we interviewed 20 VUI designers, asking what naturalness means to them, how they incorporate the concept in their design practice, and what challenges they face in doing so. Through inductive and deductive thematic analysis, we identify 12 characteristics describing naturalness in VUIs and classify these characteristics into three groups, which are ‘Fundamental’, ‘Transactional’ and ‘Social’ depending on the purpose each characteristic serves. Then we describe how designers pursue these characteristics under different categories in their practices depending on the contexts of their VUIs (e.g., target users, application purpose). We identify 10 challenges that designers are currently encountering in designing natural VUIs. Our designers reported experiencing the most challenges when creating naturally sounding dialogues, and they required better tools and guidelines. We conclude with implications for developing better tools and guidelines for designing natural VUIs.

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Designing CAST: a computer-assisted shadowing trainer for self-regulated foreign language listening practice (2020)

Speech shadowing, i.e., listening to some audio and simultaneously vocalizing the words, is a popular language-learning technique that is known to improve listening skills. However, despite strong evidence for its efficacy as a listening exercise, existing software tools do not adequately support listening-focused shadowing practice, especially in self-regulated learning environments with no external feedback. To bridge this gap, we introduce CAST, a shadowing system that makes self-regulation easy and effective through four novel interface features — contextual blurring for inducing self-reflection on misheard portions, in-situ annotations for self-monitoring progress through tracking and visualization, embedded recordings for post-practice self-evaluation, and adjustable pause-handles for self-paced practice. We base CAST on a formative user study (N=15) that provides fresh empirical grounds on the needs and challenges of those who practice shadowing using conventional software tools. We validate our design through a summative evaluation (N=12) that shows learners can successfully self-regulate their shadowing practice with CAST while retaining focus on listening.

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