Designing Looms as Kits for Collaborative AssemblyBoth engineering kits and collaboration skills are increasingly prevalent in education and are often used in conjunction, yet little research addresses how kit hardware can be designed to support collaboration. Collaboration skills are best acquired through carefully designed collaborative experiences. While prior work highlights technology's important role in fostering collaboration, less research explicitly explores hardware design. This paper posits that certain design features of kits can promote or hinder collaboration. We present a user study evaluating the collaborative assembly of two loom kits: one for higher education (RoboLoom) and one for individuals (Ashford Loom). We developed a coding scheme from the 3Cs framework (coordination, cooperation, and communication) to analyze which hardware features influenced collaboration. We find five design feature categories that may influence collaboration: repetitiveness, specificity, difficulty, parallelizability, and physicality. This paper presents our findings and recommendations for implementing these features into educational kit hardware design to create opportunities for collaboration.2025SSSamantha Speer et al.Makerspace CultureParticipatory DesignPrototyping & User TestingUIST
"A five-year-old could understand it" versus "This is way too confusing": Exploring Non-expert Understandings and Perceptions of Cybersecurity DefinitionsExperts struggle with explaining cybersecurity in a language and tone appropriate for non-expert audiences. This communication gap may make it difficult for a broad and diverse audience to fully engage in cybersecurity. Fundamental forms of communication, such as definitions, can be for a means for experts to communicate cybersecurity concepts to non-experts. To explore how nonexperts perceive cybersecurity definitions and identify potential areas of misunderstanding and misconception, we performed a semi-structured interview study with 30 non-experts of different generations (ages) and education levels. Our findings reveal that non-experts may have incomplete mental models of cybersecurity, misinterpret terms and concepts commonly used in definitions, and express strong preferences for how cybersecurity is defined. While our study focuses on definitions, our results have broader implications for how cybersecurity should be communicated to a diverse range of individuals.2025LNLorenzo C. Neil et al.North Carolina State UniversityPrivacy by Design & User ControlCybersecurity Training & AwarenessCHI
Analyzing the Impact and Accuracy of Facebook Activity on Facebook's Ad-Interest Inference ProcessSocial media platforms like Facebook have become increasingly popular for serving targeted ads to their users. This has led to increased privacy concerns due to the lack of transparency regarding how ads are matched against each user profile. Facebook infers user interests through their activities and targets ads based on those interests. Although Facebook provides explanations for why a particular interest is inferred about a user, there is still a gap in understanding what activities lead to interest inferences and the extent to which the sentiment or context of activities is considered in inferring interests. To obtain insights into how Facebook generates interests from a user’s Facebook activities, we performed controlled experiments by creating new accounts and systematically executing numerous planned activities. This enabled us to make causal inferences about activities that lead to generating specific interests, many of which were not representative of actual user preferences. We also evaluated which activities resulted in interests and found that very naive activities, such as only viewing/scrolling through a page, leads to an interest inference. We found 33.22% of the inferred interests were inaccurate or irrelevant. We further evaluated the interest inference explanations provided by Facebook and found that these explanations were too generalized and, at times, misleading. To understand if our findings hold for a large and diverse sample, we conducted a user study where we recruited 146 participants (through Amazon Mechanical Turk) from different regions of the world to evaluate the accuracy of interests inferred by Facebook. We developed a browser extension to extract data from their own Facebook accounts and ask questions based on such data. Our participants reported a similar range (29%) of inaccuracy as observed in our controlled experiments. We also found that most of our participants were unaware of the availability of Facebook’s ad preference manager, interest inference process, and even interest explanations.2022ASAafaq Sabir et al.Algorithmic Decision-making; Algorithmic Decision-makingCSCW
Hey Alexa, Who Am I Talking to?: Analyzing Users’ Perception and Awareness Regarding Third-party Alexa SkillsThe Amazon Alexa voice assistant provides convenience through automation and control of smart home appliances using voice commands. Amazon allows third-party applications known as skills to run on top of Alexa to further extend Alexa's capability. However, as multiple skills can share the same invocation phrase and request access to sensitive user data, growing security and privacy concerns surround third-party skills. In this paper, we study the availability and effectiveness of existing security indicators or a lack thereof to help users properly comprehend the risk of interacting with different types of skills. We conduct an interactive user study (inviting active users of Amazon Alexa) where participants listen to and interact with real-world skills using the official Alexa app. We find that most participants fail to identify the skill developer correctly (i.e., they assume Amazon also develops the third-party skills) and cannot correctly determine which skills will be automatically activated through the voice interface. We also propose and evaluate a few voice-based skill type indicators, showcasing how users would benefit from such voice-based indicators.2022ASAafaq Sabir et al.North Carolina State UniversityIntelligent Voice Assistants (Alexa, Siri, etc.)Agent Personality & AnthropomorphismPrivacy by Design & User ControlCHI
RMS: Removing Barriers to Analyze the Availability and Surge Pricing of Ridesharing ServicesRidesharing services do not make data of their availability (supply, utilization, idle time, and idle distance) and surge pricing publicly available. It limits the opportunities to study the spatiotemporal trends of the availability and surge pricing of these services. Only a few research studies conducted in North America analyzed these features for only Uber and Lyft. Despite the interesting observations, the results of prior works are not generalizable or reproducible because i) the datasets collected in previous publications are spatiotemporally sensitive, i.e., previous works do not represent the current availability and surge pricing of ridesharing services in different parts of the world; ii) the analyses presented in previous works are limited in scope (in terms of countries and ridesharing services they studied). Hence, prior works are not generally applicable to ridesharing services operating in different countries. This paper addresses the issue of ridesharing-data unavailability by presenting Ridesharing Measurement Suite (RMS). RMS removes the barrier of entry for analyzing the availability and surge pricing of ridesharing services for ridesharing users, researchers from various scientific domains, and regulators. RMS continuously collects the data of the availability and surge pricing of ridesharing services. It exposes real-time data of these services through \textit{i)} graphical user interfaces and \textit{ii)} public APIs to assist various stakeholders of these services and simplify the data collection and analysis process for future ridesharing research studies. To signify the utility of RMS, we deployed RMS to collect and analyze the availability and surge pricing data of 10 ridesharing services operating in nine countries for eight weeks in pre and during pandemic periods. Using the data collected and analyzed by RMS, we identify that previous articles miscalculated the utilization of ridesharing services as they did not count in the vehicles driving in multiple categories of the same service. We observe that during COVID-19, the supply of ridesharing services decreased by 54\%, utilization of available vehicles increased by 6\%, and a 5$\times$ increase in the surge frequency of services. We also find that surge occurs in a small geographical region, and its intensity reduces by 50\% in about 0.5 miles away from the location of a surge. We present several other interesting observations on ridesharing services' availability and surge pricing.2022HKHassan Ali Khan et al.North Carolina State UniversityRidesharing PlatformsSustainable HCICHI
Remote, but Connected: How #TidyTuesday Provides an Online Community of Practice for Data Scientists.Data science practitioners face the challenge of continually honing their skills such as data wrangling and visualization. As data scientists seek online spaces to network, learn and share resources with one another, each individual has to employ their own ad-hoc strategy to practice their data science skills. Given these disjointed efforts, it is crucial to ask: how can we build an inclusive, welcoming online community of practice that unites data scientists in their collective efforts to become experts? Daily hashtags on Twitter are used on specific days and have shown promise in forming a community of practice (CoP) in social networking sites like Twitter, but how do they benefit the community and its members? To understand how daily hashtags benefit data scientists and form an online CoP, we conducted a qualitative study on #TidyTuesday---a daily hashtag project for data scientists using R---using the framework of CoP as a lens for analysis. We conducted semi-structured interviews with 26 participants and uncovered motivations behind their participation in #TidyTuesday, how the project benefited them, and how it cultivated an online CoP. Our findings contribute to the CSCW research on community of practices by providing design trade-offs of using daily hashtags on Twitter, and guidelines on growing and sustaining an online community of practice for data scientists.2021NSNischal Shrestha et al.Expert WorkCSCW
Unravel: A Fluent Code Explorer for Data WranglingData scientists have adopted a popular design pattern in programming called the fluent interface for composing data wrangling code. The fluent interface works by combining multiple transformations on a data table---or dataframes---with a single chain of expressions, which produces an output. Although fluent code promotes legibility, the intermediate dataframes are lost, forcing data scientists to unravel the chain through tedious code edits and re-execution. Existing tools for data scientists do not allow easy exploration or support understanding of fluent code. To address this gap, we designed a tool called Unravel that enables structural edits via drag-and-drop and toggle switch interactions to help data scientists explore and understand fluent code. Data scientists can apply simple structural edits via drag-and-drop and toggle switch interactions to reorder and (un)comment lines. To help data scientists understand fluent code, Unravel provides function summaries and always-on visualizations highlighting important changes to a dataframe. We discuss the design motivations behind Unravel and how it helps understand and explore fluent code. In a first-use study with 14 data scientists, we found that Unravel facilitated diverse activities such as validating assumptions about the code or data, exploring alternatives, and revealing function behavior.2021NSNischal Shrestha et al.Interactive Data VisualizationComputational Methods in HCIUIST
Morphaces: Exploring Morphable Surfaces for Tangible Sketching in VRThis pictorial documents our inquiry into the design and utility of morphable surfaces to provide tangible feedback while sketching in Virtual Reality (VR). We explored materials and various structures that could enable a surface to morph. We designed and implemented the Morphace ecosystem that includes 3D printed accessories that enable handheld and desk-mounted pen-and-surface interaction for the Oculus Quest VR device. We present this preliminary exploration with the hope that this will be explored further by the design and broader HCI community.2021PPPayod Panda et al.Shape-Changing Interfaces & Soft Robotic MaterialsImmersion & Presence ResearchVR Medical Training & RehabilitationC&C
Anchorhold Afference: Virtual Reality, Radical Compassion, and Embodied PositionalityThis work situates the potential of empathy and affective application in VR systems - as well as explore the role of gamified spaces through digital humanities and critical making. We argue that the material infrastructure of VR technologies make Anchorhold Afference, a virtual reality model of Julian of Norwich’s anchorhold created by Author 1 with Unity and Oculus, an especially vivid experience. In a time when VR is conflated with video games and in which games are most traditionally associated with conquest, winning, and mastery, Anchorhold Afference opposes this and instead fosters radical compassion, as aligning with feminist media and data understandings, to invite users to an embodied experience. This work considers how VR technology can allow us to discover and evaluate the embodiment and materiality of isolation and confinement through a singular, unified and gamified experience, while also retrospectively considering the rhetorical emergence evoked through this process.2021KDKelsey Virginia Dufresne et al.Social & Collaborative VRTechnology Ethics & Critical HCIInteractive Narrative & Immersive StorytellingC&C
Data Analysts and Their Software Practices: A Profile of the Sabermetrics Community and BeyondFor modern data analytics, practices from software development are increasingly necessary to manage data, but they must be incorporated alongside other statistical and scientific skills. Therefore, we ask: how does a community recontextualize software development through the unique pressures of their work? To answer this, we explore the analytic community around baseball, or sabermetrics. To discover software development's place in the search for robust statistical insight in sports, we interview 10 participants in the sabermetric community and survey over 120 more data analysts, both in baseball and not. We explore how their work lives at the intersection of science and entertainment, and as a consequence, baseball data serves as an accessible yet deep subject to practice analytic skills. Software development exists within an iterative research process that cycles between defining rigorous statistical methods and preserving the flexibility to chase interesting problems. In this question-driven process, members of the community inhabit several overlapping roles of intentional work, in which software development can become the priority to support research and statistical infrastructure, and we discuss the way that the community can foster the balance of these skills.2020JMJustin Middleton et al.Data WorkCSCW
Engaging Students with Instructor Solutions in Online Programming HomeworkStudents working on programming homework do not receive the same level of support as in the classroom, relying primarily on automated feedback from test cases. One low-effort way to provide more support is by prompting students to compare their solution to an instructor's solution, but it is unclear the best way to design such prompts to support learning. We designed and deployed a randomized controlled trial during online programming homework, where we provided students with an instructor's solution, and randomized whether they were prompted to compare their solution to the instructor's, to fill in the blanks for a written explanation of the instructor's solution, to do both, or neither. Our results suggest that these prompts can effectively engage students in reflecting on instructor solutions, although the results point to design trade-offs between the amount of effort that different prompts require from students and instructors, and their relative impact on learning.2020TPThomas W. Price et al.North Carolina State UniversityHuman-LLM CollaborationProgramming Education & Computational ThinkingPrototyping & User TestingCHI
A Field Study of Teachers Using a Curriculum-integrated Digital GameWe present a new framework describing how teachers use ST Math, a curriculum-integrated, year-long educational game, in 3rd-4th grade classrooms. We combined authentic classroom observations with teacher interviews to identify teacher needs and practices. Our findings extended and contrasted with prior work on teachers' behaviors around classroom games, identifying differences likely arising from a digital platform and year-long curricular integration. We suggest practical ways that curriculum-integrated games can be designed to help teachers support effective classroom culture and practice.2019ZPZhongxiu Peddycord-Liu et al.North Carolina State UniversityProgramming Education & Computational ThinkingK-12 Digital Education ToolsCollaborative Learning & Peer TeachingCHI
Tangible Landscape: A Hands-on Method for Teaching Terrain AnalysisThis paper presents novel and effective methods for teaching about topography--or shape of terrain--and assessing 3-dimensional spatial learning using tangibles. We used Tangible Landscape--a tangible interface for geospatial modeling--to teach multiple hands-on tangible lessons on the concepts of grading (i.e., earthwork), geomorphology, and hydrology. We examined students' ratings of the system's usability and user experience and tested students' acquisition and transfer of knowledge. Our results suggest the physicality of the objects enabled the participants to effectively interact with the system and each other, positively impacting ratings of usability and task-specific knowledge building. These findings can potentially advance the design and implementation of tangible teaching methods for the topics of geography, design, architecture, and engineering.2018GMGarrett C. Millar et al.North Carolina State UniversityGeospatial & Map VisualizationData PhysicalizationSTEM Education & Science CommunicationCHI
Selfies as Social Movements: Influences on Participation and Perceived Impact on StereotypesA new kind of online movement has emerged on social media: identity hashtag movements, through which individuals share "selfies" and personal stories to elucidate the experiences of marginalized social groups. These movements have the potential to counteract bias and enable social justice, but are enacted in a forum rife with identity and boundary management concerns. To understand this type of movement, we present a qualitative study of #ILookLikeAnEngineer, a hashtag created to challenge engineering stereotypes. We interviewed 32 people, including participants and non-participants of the movement, about their experiences with the hashtag. We found that personally identifiable participation promoted feelings of empowerment and strengthened connections within the marginalized community. At the same time, the personal and professional identity focus raised ambiguity about the boundaries of the collective and the movement's ability to change stereotypes. We discuss implications for online collective action and the use of social media for addressing stereotypes.2018FLFannie Liu et al.Hashtags and Social MovementsCSCW
"We Don't Do That Here": How Collaborative Editing with Mentors Improves Engagement in Social Q&A CommunitiesOnline question-and-answer (Q&A) communities like Stack Overflow have norms that are not obvious to novice users. Novices create and post programming questions without feedback, and the community enforces site norms through public downvoting and commenting. This can leave novices discouraged from further participation. We deployed a month long, just-in-time mentorship program to Stack Overflow in which we redirected novices in the process of asking a question to an on-site Help Room. There, novices received feedback on their question drafts from experienced Stack Overflow mentors. We present examples and discussion of various question improvements including: question context, code formatting, and wording that adheres to on-site cultural norms. We find that mentored questions are substantially improved over non-mentored questions, with average scores increasing by 50%. We provide design implications that challenge how socio-technical communities onboard novices across domains.2018DFDenae Ford et al.North Carolina State UniversityCommunity Collaboration & WikipediaParticipatory DesignCHI