Dataland a Block Based Programming System for Learning With Data

About

Dataland is a new block-based programming system designed to help middle and high-school-aged students learn and do data analysis and visualization. Dataland aims to foster data literacies in children by enabling them to write computer programs that analyze and visualize data that connect to their personal interests.

Dataland is currently being developed as a research project at the Department of Human Centered Design & Engineering, University of Washington (UW). Prior to UW, the project was based at the School of Information and Library Science, University of North Carolina at Chapel Hill (UNC Chapel Hill).

Try it out

Dataland is being developed as a set of distinct “microworlds,” and currently has two microworlds, the maps microworld and the plots microworld. Each microworld offers a unique set of visualization possibilities, keeping the core programming grammar and the query language the same. For example, the maps microworld provides a set of programming primitives (e.g., set latitude of map marker to __) that allow for creating geographical visualizations. The plots microworld swaps out the map-related visualization primitives for cartesian-plot-related primitives (e.g., set ‘x’ of plot marker to __). This potentially allows for interest-driven and creative explorations with data that are also constrained to better scaffold the experiences of early-stage learners.

Please note that the version of Dataland available from this site may change significantly without any advance notice. If you are planning this for use in your classroom or similar contexts, please reach out to us at sdg1@uw.edu.

Maps microworld

Plots microworld

Learning resources

Below are the getting started guide and a collection of example learner activities for Dataland, designed with inspiration from the Scratch educational resources.

Getting Started with Dataland

Example Learner Activities

Publications

Dataland is a research project. Below are the papers, posters, and demos that we have published and presented with and about Dataland.

Cheng, R., Dangol, A., Ello, F. M. T., Wang, L., & Dasgupta, S. (2023). Concepts, practices, and perspectives for developing computational data literacy: Insights from workshops with a new data programming system. Proceedings of the 22nd Annual ACM Interaction Design and Children Conference, 100–111. https://doi.org/10.1145/3585088.3589364

Wang, L., & Dasgupta, S. (2022). Dataland: An Informed, Situated, and Critical Approach to Data Literacy. General Proceedings of the 2nd Annual Meeting of the International Society of the Learning Sciences 2022. 2nd Annual Meeting of the International Society of the Learning Sciences 2022, Hiroshima, Japan.

Project Team

Contributors to the Dataland project include Regina Cheng, Aayushi Dangol, Sayamindu Dasgupta (project PI), Frances Marie Tabio Ello, Shivam Hingorani, Chris Lyu, Lingyu Wang, and Mari Woodworth.

Acknowledgements

Financial support for Dataland has come from the US National Science Foundation (awards #1948113, 2230291), the University of Washington, and the School of Information and Library Science, UNC Chapel Hill (as a Frederick and Eleanor Kilgour Research Grant).

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.