Exploratory Visual Analysis of Transcripts for Interaction Analysis in Human-Computer InteractionTranscripts are central to qualitative research in HCI, particularly for researchers using methods of Conversation Analysis (CA) and Interaction Analysis (IA) who study the socially situated nature of human-computer interaction. However, CA and IA researchers continue to highlight the significant need for more dynamic ways to visualize transcripts to support interaction analysis. This need is particularly evident in HCI, where transcripts as a form of data have received little attention. In this article, we make three contributions to HCI research. First, we present Transcript Explorer, an open-source visualization system that integrates three visualization techniques we have developed to interactively visualize transcripts linked to videos: Distribution Diagrams, Turn Charts and Contribution Clouds. Second, we present findings from a qualitative analysis of focus group interviews with three different qualitative research groups who engaged with this system to analyze common transcript data. Finally, we expand upon transcripts as a unique form of data for HCI research and propose directions for future research.2025BSBen Rydal Shapiro et al.Georgia State University, Learning SciencesInteractive Data VisualizationPrototyping & User TestingCHI
The Interaction Geography Slicer: Designing Exploratory Spatial Data Visualization Tools for Teachers' Reflective PracticeResearchers in HCI and teacher education have long recognized the potential of visualization to support teachers' reflective practice. Despite much progress however, teacher educators continue to highlight the need for more dynamic classroom data visualizations to better support teachers' reflective practice, particularly about spatial dimensions of their pedagogy. In response, this article makes three contributions. First, we build on prior work to present the Interaction Geography Slicer (IGS), an open-source tool to dynamically visualize movement, conversation, and video data over space and time in settings such as classrooms. Second, we share findings from a participatory design-based research project involving 11 experienced high school mathematics teachers who used the IGS over one year to support their reflective practice. Finally, we propose new directions for exploratory spatial classroom data visualization.2025BSBen Rydal Shapiro et al.Georgia State University, Learning SciencesInteractive Data VisualizationGeospatial & Map VisualizationCollaborative Learning & Peer TeachingCHI
When Workers Want to Say No: A View into Critical Consciousness and Workplace Democracy in Data WorkIn this paper we describe and reflect upon the development of critical consciousness and workplace democracy within an experimental workplace called DataWorks. Through DataWorks, we hire adults from communities historically minoritized in computing education and data careers, and train them in entry-level data skills developed through work on client projects. In this process, workers gain a range of skills. Some of these skills are technical, such as programming for data analysis; some are managerial, such as scoping and bidding projects; others are social, perhaps even political, such as the ability to say “No” to projects. In this paper, we describe a workshop series developed to build the workers’ critical literacy and consciousness about their data work, specifically regarding the use of data in machine learning and artificial intelligence systems. After that, we describe a data project the workers questioned and resisted because they determined the work to be harmful. In that process, they demonstrated and enacted a critical consciousness towards data and machine learning. Reflecting on this enactment of data-focused critical consciousness, we identify themes that characterize a democratic workplace, describe the work of designing for organizational action and institutional relations, and discuss how worker and researcher positionality affects this work. In doing so, we argue for enabling workers to resist and refuse harmful data work and challenge the standard power structures of academic research and data work.2024CDCarl DiSalvo et al.Session 1a: Work and TechnologyCSCW
iScore: Visual Analytics for Interpreting How Language Models Automatically Score SummariesThe recent explosion in popularity of large language models (LLMs) has inspired learning engineers to incorporate them into adaptive educational tools that automatically score summary writing. Understanding and evaluating LLMs is vital before deploying them in critical learning environments, yet their unprecedented size and expanding number of parameters inhibits transparency and impedes trust when they underperform. Through a collaborative user-centered design process with several learning engineers building and deploying summary scoring LLMs, we characterized fundamental design challenges and goals around interpreting their models, including aggregating large text inputs, tracking score provenance, and scaling LLM interpretability methods. To address their concerns, we developed iScore, an interactive visual analytics tool for learning engineers to upload, score, and compare multiple summaries simultaneously. Tightly integrated views allow users to iteratively revise the language in summaries, track changes in the resulting LLM scores, and visualize model weights at multiple levels of abstraction. To validate our approach, we deployed iScore with three learning engineers over the course of a month. We present a case study where interacting with iScore led a learning engineer to improve their LLM's score accuracy by three percentage points. Finally, we conducted qualitative interviews with the learning engineers that revealed how iScore enabled them to understand, evaluate, and build trust in their LLMs during deployment.2024ACAdam Coscia et al.Explainable AI (XAI)Interactive Data VisualizationIUI
“Moment to Moment”: A View From the Front Lines with Computing Ethics Teaching AssistantsThe HCI research community has long centered ethics in HCI research and practice. This interest has persisted as scholars highlight the need for more situated understandings and deeper integration of ethics into HCI. In parallel, HCI scholars and students have become increasingly involved in teaching computing ethics across many different university contexts, bringing in valuable perspectives informed by the connections between HCI and the socio-technical subject matter of computing ethics. Yet explicitly bringing these two threads together – examining the teaching of ethics through an HCI research lens – remains nascent. This paper integrates work in HCI and computing education to focus on the role and experience of computing ethics teaching assistants (CETAs), who are increasingly involved in ethics instruction and whose perspectives are predominantly missing in existing literature spanning HCI and computing education. Drawing on HCI theories and methods, our qualitative study of eleven CETAs at two American universities makes three contributions to the HCI literature. First, we build an understanding of who these TAs are with respect to the unique position of teaching computing ethics. Second, we characterize how CETAs’ teaching and learning is situated and shaped within different communities and institutional contexts. Finally, we sug- gest several implications for the design of ethics instruction within undergraduate computing programs. More broadly, our work can be viewed as a call to action, encouraging HCI scholars to play a more significant role in studying and designing the teaching and learning of computing ethics.2023CZCass Zegura et al.unaffiliatedTechnology Ethics & Critical HCIParticipatory DesignUser Research Methods (Interviews, Surveys, Observation)CHI
Interrogating Data Work as a Community of PracticeWe apply Lave & Wenger’s construct of a community of practice to identify and position members of the data work community of practice, focusing on members on the periphery who have received less attention – as compared to full practitioners (e.g., data scientists). Reporting on results of interviews with 19 civic workers who perform data work as their main task, we identify an atypical relationship between subject-domain experts (such as our interviewees) and full members of the data work community. Our interviewees may have less computational skill in data work, but they have extensive and varied practices to engage in data contextualization that data scientists and other full community members could learn from. In identifying the attributes of data workers on the periphery, we also hope to call attention to the challenges they face in performing data work in low resources institutions (e.g., governmental, non-profit). Our findings contribute to the larger conversations in human-centered data science about who performs data work and how they go about it, in order to addresses questions of power, fairness, and bias in data-intensive systems.2022ARAnnabel Rothschild et al.Data Work; Data WorkCSCW
Exploring Approaches to Data Literacy Through a Critical Race Theory PerspectiveIn this paper, we describe and analyze a workshop developed for a work training program called DataWorks. In this workshop, data workers chose a topic of their interest, sourced and processed data on that topic, and used that data to create presentations. Drawing from discourses of data literacy; epistemic agency and lived experience; and critical race theory, we analyze the workshops’ activities and outcomes. Through this analysis, three themes emerge: the tensions between epistemic agency and the context of work, encountering the ordinariness of racism through data work, and understanding the personal as communal and intersectional. Finally, critical race theory also prompts us to consider the very notions of data literacy that undergird our workshop activities. From this analysis, we offer a series of suggestions for approaching designing data literacy activities, taking into account critical race theory.2021BJBritney Johnson et al.Georgia Institute of TechnologyAlgorithmic Fairness & BiasGender & Race Issues in HCICHI
Spaces and Traces: Implications of Smart Technology in Public HousingSmart home technologies are beginning to become more widespread and common, even as their deployment and implementation remain complex and spread across different competing commercial ecosystems. Looking beyond the middle-class, single-family home often at the center of the smart home narrative, we report on a series of participatory design workshops held with residents and building managers to better understand the role of smart home technologies in the context of public housing in the U.S. The design workshops enabled us to gather insight into the specific challenges and opportunities of deploying smart home technologies in a setting where issues of privacy, data collection and ownership, and autonomy collide with diverse living arrangements, where income, age, and the consequences of monitoring and data aggregation setup an expanding collection of design implications in the ecosystems of smart home technologies.2019SKSandjar Kozubaev et al.Georgia Institute of TechnologySmart Home Interaction DesignSmart Home Privacy & SecurityCHI