Collaboratory to Advance Mathematics Education and Learning (CAMEL) for K-12
U.S. National Science Foundation
Who can apply
*Who May Submit Proposals: Proposals may only be submitted by the following: -Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities. -Institutions of Higher Education (IHEs): Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members. Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of sub-awards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus. -Tribal Nations: An American Indian or Alaska Native tribe, band, nation, pueblo, village, or community that the Secretary of the Interior acknowledges as a federally recognized tribe pursuant to the Federally Recognized Indian Tribe List Act of 1994, 25 U.S.C. §§ 5130-5131.
About this opportunity
The Collaboratory to Advance Mathematics Education and Learning (CAMEL) for K-12 initiative aims to advance mathematics learning and education through purposeful collaboration that draws on the interdisciplinary Science of Learning (including neuroscience; cognitive, developmental, and social sciences; computer science; machine learning; engineering; and education research), deep experiences in education practice and teaching, and innovations in the use of data science, AI and technology. Through an agreement with philanthropic partners, including the Walton Family Foundation (WFF), CAMEL consists of two phases. Phase I invites proposals for the creation of new research networks to support the generation of high value datasets that aim to advance math learning and education. These research...