Active Projects » Learning with Data

Scratch Data Blocks

An increasing number of computational activities in the real world revolve around the collection, retrieval and manipulation of large sets of data. This project explores how young programmers can program, learn, and understand their world with data using toolkits like Scratch.

For my masters thesis, I introduced a learning framework for the computational exploration of online data, a system that enables children to program with online data, and then finally described a study of children using the system to explore wide variety of creative possibilities, as well as important computational concepts and powerful ideas around data.

For my PhD dissertation, I designed, deployed, and studied the use of a project that enable Scratch users to retrieve and analyze data about their own social participation and learning trajectory in Scratch. This achieved by enabling read-only access to the Scratch API from Scratch programming blocks, through a system called Scratch Community Blocks. This project was funded by a National Science Foundation grant from 2014-2016.

In pilot tests with Scratch Community Blocks, children have used data and programming to answer their own questions about learning and social behavior within the Scratch community. They used the system not only to create with data through stories and games, but also to think with data by engaging in self-reflection about their own learning and social participation, and through critical conversations about the role of data within the culture of the Scratch community.

Other projects such as (MapScratch, Scratch Extensions, etc.) also fall within the larger scope of the Learning with Data project.


Advisers & Collaborators

Mitchel Resnick, Natalie Rusk, Benjamin Mako Hill, Hal Abelson, Samantha Hautea, Brian Silverman, John Maloney.