Funded by the U.S. National Science Foundation in partnership with the Bill & Melinda Gates Foundation, Schmidt Futures, and the Walton Family Foundation

This free, online-only event brings together expertise in the Science of Learning, education research, data science and AI to engage a community of interdisciplinary scientists, education researchers and education practitioners, with shared goals of advancing K-12 mathematics learning and education through innovative use of data science and AI.

WE WANT TO HEAR FROM YOU! Insights from this workshop will inform future funding opportunities to be supported by NSF in partnership with philanthropic organizations.

Workshop Overview

This workshop will explore major research questions about learning and teaching mathematics that would benefit from the building of a mathematics research and education data-sharing ecosystem. In particular, we are interested in research topics and new approaches that can be advanced through innovative use of datasets and AI

The workshop will inform future funding priorities to catalyze innovations in research and practice of K-12 math learning and education, and participants will be invited to collaborate on a synthesis report of the workshop discussions.

Opportunities and Challenges

Scientific knowledge about how people learn, including how people learn mathematics, has the potential to drive innovations in K-12 mathematics learning and education. There is also potential to more fully engage education practitioners as partners in innovation.

One challenge is that insights and scientific evidence about human learning are fragmented across disciplinary lines. There is a critical need to integrate these findings and provide usable information that can be taken up by practitioners. A related challenge is that the researcher and practitioner communities do not usually attend the same meetings.

This partnership between NSF and the named philanthropies seeks to create an active collaboration of researchers and education practitioners to overcome the above-mentioned challenges. We seek to create new pathways for the convergence of knowledge and effort through innovative use/reuse of datasets and associated tools to advance math learning and education.

A key focus is to improve access to quality research and education for all, with particular attention to historically underrepresented and underserved students. To this end, the project’s focus on the creation, production, and ecosystem-building around K-12 datasets will build the capacity of researchers and educators to leverage data science, technological advances and AI for new avenues of scientific investigations and education practice.

Desired Outcomes

This workshop will identify the major research questions and issues for the advancement of math learning and education. It will identify opportunities and challenges inherent in building a mathematics research and education data-sharing ecosystem. Discussions will also inform priorities for human capacity building and infrastructure necessary for the Science of Learning (neuroscience; social, behavioral and cognitive sciences; computer science and engineering) and education research communities to capitalize on cutting-edge data science and AI advances; this includes addressing key challenges in generation of high quality and trustworthy data, data sharing, use/reuse, and machine learning applications within the context of research on mathematics learning and mathematics education.

The primary tangible output of the event will be a synthesis report that outlines the key research issues in math learning and education that can be advanced with existing datasets or those that can serve as compelling drivers for the creation of new datasets and new analytical tools. To these ends, and to the potential use of the datasets for machine learning input, there will be suggestions for how to build capacity of the research and education communities in the context of advancing math learning and education.

To facilitate the open access of scientific evidence and research findings, the workshop organizers will create a catalog of any datasets that the participants would like to share with the community. The registration form includes space to submit the details of these data sets.