Data+Art Literacy Curriculum Co-Design

Overview

This project explores an art-based perspective on data literacy to promote student relevance, accessibility, engagement, reasoning, and meaning-making with data. Data literacy, or interpreting and reasoning about data, is an essential skill set to be able to inform decisions and actions in today’s society. Collaborating with In collaboration nwith with middle school art and math teachers, we are co-designing curriculum that leverages diverse representational forms, ways of knowing and understanding data.

Research Questions Explored

The work examines:

1. How do we support effective co-design of data literacy units among art teachers, mathematics teachers, and researchers?
2. How can we develop educational materials and technology to leverage the representational opportunities across artistic and mathematical practices?
3. How can we build synergistic curricula for art and math to conceptually support one another?

Answers to these questions will build an understanding of how to support interdisciplinary curriculum design collaborations among researchers and teachers. They will also show how art-integrated, maker-oriented activities can support middle school learners’ data literacy development; and how to design technologies that are accessible and powerful to teachers and learners in these interdisciplinary environments.

Research Team

The work is a collaborative project across New York University, Education Development Center, and Fordham University.

• Anna Amato - PhD Student Educational Communication & Technology @ NYU
• Marian Tes - PhD Student Educational Communication & Technology @ NYU
• Camillia Matuk - Assistant Professor @ NYU (PI)
• Kayla DesPortes (Co-PI) - Assistant Professor of HCI and the Learning Sciences @ NYU
• Ralph Vacca - Assistant Professor @ Fordham (Co-PI)
• Megan Silander - Research Scientist @ EDC (Co-PI)
• Veena Vasudevan - Post-Doc @ NYU
• Peter Woods Consultant PhD Candidate @ University of Wisconsin-Madison

Funders

This project has been funded by the National Science Foundation (DRK12 1908557).