About

Sayamindu Dasgupta is an Assistant Professor in the Department of Human Centered Design & Engineering at the University of Washington. His specialization includes constructionism, data literacy, digital media and learning, and human-computer interaction. His research focuses on understanding and designing digital media and learning environments, emphasizing data literacy and constructionist approaches to technology use. He collaborates across disciplines to explore how digital media can enhance learning experiences and foster data literacy, contributing to the development of innovative educational tools and methodologies.

Research topics

• Sociology
• Computer Science
• Political Science
• Social Science
• Knowledge management
• Pedagogy
• World Wide Web
• Psychology
• Geography
• Management science
• Engineering
• Public relations
• Library science
• Epistemology
• Data science
• Engineering ethics
• Mathematics education
• Linguistics

Selected publications

• From Data Activism to Activism in a Time of Data-Centrism: Affirming Epistemological Heterogeneity in Social Movements

  Proceedings of the ACM on Human-Computer Interaction · 2025-05-02 · 4 citations

  articleOpen accessSenior author

  In this paper, we seek to understand how grassroots activists, operating within the hegemony of data-centrism, are often disempowered by data even as they appropriate it towards their own ends. We posit that the shift towards data-driven governance and organizing, by elevating a particular epistemology, can pave over other ways of knowing that are central to social movement practices. Building on Muravyov's [102] concept of ''epistemological ambiguity,'' we demonstrate how data-focused activism requires complex navigations between data-based epistemologies and the heterogeneous, experiential, and relational epistemologies that characterize social movements. Through three case studies (two drawn from existing literature and the third being an original analysis), we provide an analytical model of how generative epistemological refusals can support more value-aligned navigations of epistemological ambiguity that resist data-centrism. Finally, we suggest how these findings can inform pedagogy, research, and technology design to support communities navigating datafied political arenas.

  PublisherDOI

• The Social Construction of Visualizations: Practitioner Challenges and Experiences of Visualizing Race and Gender

  2025-04-24 · 3 citations

  preprintOpen accessSenior author

  Data visualizations are increasingly seen as socially constructed, with several recent studies positing that perceptions and interpretations of visualization artifacts are shaped through complex sets of interactions between members of a community. However, most of these works have focused on audiences and researchers, and little is known about if and how practitioners account for the socially constructed framing of data visualization. In this paper, we study and analyze how visualization practitioners understand the influence of their beliefs, values, and biases in their design processes and the challenges they experience. In 17 semi-structured interviews with designers working with race and gender demographic data, we find that a complex mix of factors interact to inform how practitioners approach their design process, including their personal experiences, values, and their understandings of power, neutrality, and politics. Based on our findings, we suggest a series of implications for research and practice in this space.

  PublisherDOI

• Reading AI and Reading the World: Using an Interactive AI System to Promote Children’s Understanding of AI Bias

  ACM Transactions on Computer-Human Interaction · 2025-09-08 · 1 citations

  articleOpen accessSenior author

  AI technologies, despite having well-documented biases and shortcomings, are becoming increasingly pervasive across various aspects of society. AI biases often reflect and interact with broader societal biases, underscoring the need to support children in understanding these biases so that they can identify when they (or others) are being discriminated against by an AI-based system. To explore this learning through a new methodology, we built an interactive system called CLIP4KIDS. We conducted four classroom sessions with 28 fifth graders in the United States and examined our data using qualitative thematic analysis. Students frequently described AI biases in terms of “assumptions” and “stereotypes” and drew connections between historical injustices and present biases in AI models. This work contributes a novel tool for learning about AI biases, an empirical account of children’s experiences, and a theoretical analysis incorporating Vossoughi and Gutiérrez’s framework of critical pedagogy and sociocultural theory.

  PublisherOA PDFDOI

• "Even Though I Went Through Everything, I Didn't Feel Like I Learned a Lot": Insights From Experiences of Non-Computer Science Students Learning to Code

  2025-04-24 · 3 citations

  articleOpen accessSenior author
  PublisherDOI

• Concepts, practices, and perspectives for developing computational data literacy: Insights from workshops with a new data programming system

  2023-06-14 · 8 citations

  articleOpen accessSenior author

  In this paper, we present a new visual block-based programming system designed for children to process, analyze, and visualize data. We introduce the system and describe how it was used during a series of 7 workshops with 27 children. During the workshops, children played the role of investigators and followed a storyline as part of the system to conduct data analyses to help the story’s protagonist locate a missing family member. We present our findings as a framework of computational data literacy that builds on the dimensions of Computational Thinking proposed by Brennan and Resnick [8], with a focus on aspects that are specific to using programming for data processing, analysis, and visualization. We conclude with a series of recommendations for future designers of systems to support the development of computational data literacy.

  PublisherOA PDFDOI

• Constructionist approaches to critical data literacy: A review

  2023 · 25 citations

  Senior authorCorresponding
  • Computer Science
  • Sociology
  • Political Science

  Increased technological capacity to collect and use data has created both new possibilities for benefiting individuals and societies, and critical questions of what is acceptable and just [31]. Because early definitions of data literacy have often excluded aspects of power, equity, empowerment, and emancipation, children’s learning experiences have focused more on the potential benefits compared to the critical questions. In this review article, we examine the importance of teaching critical data literacy to children as a key aspect of developing fluency with data. Using constructionist principles [67] as a guiding framework, we synthesize 48 educational research and design approaches that engage youth with data projects. We describe how these projects provide students with information about data’s origins and perspectives, and assist them in identifying, analyzing, and presenting data. Finally, we provide design implications and concrete examples on how constructionist approaches can be utilized for teaching critical data literacy.

  PublisherOA PDFDOI

• Designing for Critical Algorithmic Literacies

  The MIT Press eBooks · 2023-06-27 · 3 citations

  book-chapterOpen access1st authorCorresponding

  As pervasive data collection and powerful algorithms increasingly shape children's experience of the world and each other, their ability to interrogate computational algorithms has become crucially important. A growing body of work has attempted to articulate a set of "literacies" to describe the intellectual tools that children can use to understand, interrogate, and critique the algorithmic systems that shape their lives. Unfortunately, because many algorithms are invisible, only a small number of children develop the literacies required to critique these systems. How might designers support the development of critical algorithmic literacies? Based on our experience designing two data programming systems, we present four design principles that we argue can help children develop literacies that allow them to understand not only how algorithms work, but also to critique and question them.

  PublisherOA PDFDOI

• Design Values in Action: Toward a Theory of Value Dilution

  2023-07-10 · 18 citations

  articleOpen accessSenior author

  Designing for values has been a focus of human-computer interaction research, but what happens when value-laden design artifacts are put into practice? Do they exercise their commitment to stated design values? We present four case studies that suggest a gap between the values that technologies set out to support and their performance toward supporting these values in practice. By critically analyzing these case studies, we theorize the phenomenon of value dilution—technical artifacts moving away from values they committed to embody. We hypothesize two significant methodological gaps contributing to value dilution—the static framing of stakeholders and a lack of engagement with politics of values. We argue that addressing value dilution needs to be a long-term and ongoing task in the design and use of technology as values in design are not only embodied, but also they are dynamic, subject to change in how they are enacted.

  PublisherOA PDFDOI

• Taking Stock of Concept Inventories in Computing Education: A Systematic Literature Review

  2023-08-07 · 18 citations

  articleOpen accessSenior author

  Background and context. Concept inventories (CIs) are a widely used tool in STEM education that can help instructors identify specific misconceptions students hold about key concepts. Over the past several years, much research has been published contributing to CIs in computer science education.

  PublisherOA PDFDOI

• How Interest-Driven Content Creation Shapes Opportunities for Informal Learning in Scratch: A Case Study on Novices’ Use of Data Structures

  CHI Conference on Human Factors in Computing Systems · 2022-04-28 · 13 citations

  articleOpen access

  Through a mixed-method analysis of data from Scratch, we examine how novices learn to program with simple data structures by using community-produced learning resources. First, we present a qualitative study that describes how community-produced learning resources create archetypes that shape exploration and may disadvantage some with less common interests. In a second quantitative study, we find broad support for this dynamic in several hypothesis tests. Our findings identify a social feedback loop that we argue could limit sources of inspiration, pose barriers to broadening participation, and confine learners’ understanding of general concepts. We conclude by suggesting several approaches that may mitigate these dynamics.

  PublisherOA PDFDOI

Frequent coauthors

• Benjamin Mako Hill

  22 shared

• Jason Spingarn-Koff

  Massachusetts Institute of Technology

  3 shared

• Lining Yao

  Carnegie Mellon University

  3 shared

• Ostap Rudakevych

  3 shared

• Hiroshi Ishii

  Chiba Cancer Center

  3 shared

• Nadia Cheng

  3 shared

• Ruijia Cheng

  Apple (United States)

  3 shared

• Aaron Shaw

  Northwestern University

  2 shared

Education

• PhD, Program in Media Arts and Sciences

  Massachusetts Institute of Technology

  2016

• Masters, Program in Media Arts and Sciences

  Massachusetts Institute of Technology

  2012

• BTech, Computer Science and Engineering

  West Bengal University of Technology

  2008