Sayamindu Dasgupta

Moore/Sloan & WRF Innovation in Data Science Postdoctoral Fellow

University of Washington

Curriculum Vitæ
Research Statement
Teaching Statement

I am a postdoctoral fellow with the eScience Institute at the University of Washington, where I am also affiliated with the Community Data Science Collective and the Human-Centered Data Science Lab.

My research focuses on developing tools to support data science education for youth and on using data science in the service of education. I design, build, and study systems that engage young people in doing data science. I also use (mostly) quantitative methods to advance theories of how young people learn.

I received my doctorate degree from MIT in 2016, where I was a part of the Lifelong Kindergarten Research Group, and my work was centered around the Scratch programming language and online community.

My research has received recognition and awards at a number of human-computer interaction conferences (CHI, CSCW, VL/HCC, IDC). In 2014, I was selected as a member of the Forbes 30 under 30 list for Education.


Peer-reviewed publications

  • How “wide walls” can increase engagement: Evidence from a natural experiment in Scratch

    Sayamindu Dasgupta and Benjamin Mako Hill

    Conditionally accepted for ACM CHI 2018

  • Scratch Community Blocks: Supporting Children as Data Scientists

    Sayamindu Dasgupta and Benjamin Mako Hill

    Honorable mention ACM CHI 2017 [ACM DL ][Blog Post]

  • Youth Perspectives on Critical Data Literacies

    Samantha Hautea, Sayamindu Dasgupta, and Benjamin Mako Hill

    ACM CHI 2017 [ACM DL ][Blog Post]

  • Learning to Code in Localized Programming Languages

    Sayamindu Dasgupta and Benjamin Mako Hill

    ACM Conference on Learning @ Scale (L@S) 2017 [ACM DL][Blog Post]

  • Children’s Civic Engagement in the Scratch Online Community

    Ricarose Roque, Sayamindu Dasgupta, and Sasha Costanza-Chock

    Social Sciences. 2016; 5(4):55 [MDPI ]

  • Skill Progression in Scratch Revisited

    J. Nathan Matias, Sayamindu Dasgupta, and Benjamin Mako Hill

    ACM CHI 2016 [ACM DL ]

  • Remixing as a Pathway to Computational Thinking

    Sayamindu Dasgupta, William Hale, Andrés Monroy-Hernández, and Benjamin Mako Hill

    Honorable mention ACM CSCW 2016 [ACM DL ][Blog Post]

  • Extending Scratch: New Pathways into Programming

    Sayamindu Dasgupta, Shane M. Clements, Abdulrahman Y. idlbi, Chris Willis-Ford, and Mitchel Resnick

    Best short paper IEEE VL/HCC 2015 [pre-print PDF][IEEE DL]

  • Engaging Novices in Programming, Experimenting, and Learning with Data

    Sayamindu Dasgupta and Mitchel Resnick

    ACM Inroads (2014) [ACM DL]

  • Surveys, Collaborative Art, and Virtual Currencies: Children Programming with Online Data

    Sayamindu Dasgupta

    International Journal of Child-Computer Interaction (2014) [ScienceDirect]

    Extended version of IDC paper listed below.

  • From Surveys to Collaborative Art: Enabling Children to Program with Online Data

    Sayamindu Dasgupta

    IDC 2013 [ACM DL]

    Extended version invited and published in IJCCI as part of “IDC 2013 best papers” section.

  • Rope Revolution: Tangible and Gestural Rope Interface for Collaborative Play

    Lining Yao, Sayamindu Dasgupta, Nadia Cheng, Jason Spingarn-Koff, Ostap Rudakevych, and Hiroshi Ishii

    ACE 2011 [ACM DL]

Extended abstracts, posters, and workshop papers

  • Measuring Learning of Code Patterns in Informal Learning Environments

    Sayamindu Dasgupta and Benjamin Mako Hill

    Poster for the ACM SIGCSE Technical Symposium on Computer Science Education (2017) [ACM DL (abstract)] [Poster]

  • Learning With Data: Designing for Community Introspection and Exploration

    Sayamindu Dasgupta and Benjamin Mako Hill

    Position paper for Developing a Research Agenda for Human-Centered Data Science (a CSCW 2016 workshop). [PDF]

  • Block-based Programming with Scratch Community Data: A Position Paper

    Sayamindu Dasgupta

    Position paper for Blocks and Beyond: Lessons and Directions for First Programming Environments (a VL/HCC 2015 workshop). [IEEE DL]

  • RopePlus: Bridging Distances with Social and Kinesthetic Rope Games

    Lining Yao, Sayamindu Dasgupta, Nadia Cheng, Jason Spingarn-Koff, Ostap Rudakevych, and Hiroshi Ishii

    ACM CHI 2011 Extended Abstracts (alt-chi) [ACM DL]

  • Multi-jump: Jump Roping over Distances

    Lining Yao, Sayamindu Dasgupta, Nadia Cheng, Jason Spingarn-Koff, Ostap Rudakevych, and Hiroshi Ishii

    ACM CHI 2011 Extended Abstracts (CHI work-in-progress poster) [ACM DL]

  • Interactive Ebooks: Experiments on the OLPC XO-1 Book-reading System

    Sayamindu Dasgupta

    International Conference on Designing for Children - With focus on ‘Play + Learn’ (2010) [PDF]


  • Children as Data Scientists: Explorations in Creating, Thinking, and Learning with Data

    PhD Thesis, MIT (2016)

  • Learning with Data: A Toolkit to Democratize the Computational Exploration of Data

    Masters Thesis, MIT (2012)

Selected Projects

Tools to support data science education for youth

I design, build, and study the use of computer programming toolkits that enable children to programmatically analyze data from various sources—empowering them to be data scientists in their own ways, and enabling them to learn through making. One of the systems that I have designed, implemented, and studied is Scratch Community Blocks. With this system, children in the Scratch community not only created programs that analyzed and visualized data about their and their peers’ learning and participation; but also, they engaged in critically thinking about the implications of data access and analysis. In contrast to the dominant trend in educational technology where data is collected from learners and rarely given back, this project’s goal was to empower young learners with access to their own data, as well as with “meta-tools” to analyze it.

Data science in the service of education

Drawing from theories in education and the broader social sciences, I use data collected from online informal learning communities to empirically identify factors that support and foster learning. For example, in one study, we looked at the relationship between remixing in Scratch and learning outcomes of more than 170,000 community members who participate in remixing. To our knowledge, this is the first large-scale empirical study that provides support for theories which suggest that young people learn through remixing.


Unpacking Impact: Reflecting As We Make

In this course, we took a reflective and critical look into the wider implications of technologies in society, developing intentional awareness on those implications in our design, making, and research. I co-designed and co-taught this course along with two other PhD students at the Media Lab—Ricarose Roque and J. Nathan Matias.

MIT, Fall 2015
[Course website][Reflection blog post]

Learning Creative Learning

Learning Creative Learning was an introduction to ideas and strategies underlying the design of new technologies to support creative learning experiences, with special focus on technologies from our Lifelong Kindergarten research group. This course was taught by Mitchel Resnick, Natalie Rusk, and Philipp Schmidt. I was a teaching assistant in this course.

MIT, Spring 2013
[Course website]


The best way to contact me is over email. I’m not very good about monitoring my social media accounts, so Twitter DMs, Facebook messages, LinkedIn messages, etc. are most likely to go unanswered.