Collaboratory to Advance Mathematics Education and Learning (CAMEL) for K-12
🏛 U.S. National Science Foundation (NSF)
✓ Free, no account · Source: Grants.gov · Last verified Oct 18, 2026
Can you apply?
This grant is for interdisciplinary research networks focused on advancing K-12 mathematics learning through collaboration between learning scientists, educators, and data scientists.
Eligible applicants include institutions of higher education (public and private 2- and 4-year colleges, including community colleges), nonprofit research organizations (museums, laboratories, professional societies), and federally recognized Tribal Nations. All applicants must be US-based or have a US campus.
Phase I funds creation of new research networks generating high-value datasets on math learning. Phase II (invitation-only) establishes collaborative platforms for sustained community engagement. Proposals must integrate basic learning science, education practice, and data science expertise.
International branch campuses may participate if their involvement provides clear project benefits that cannot be achieved at US locations.
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Program description
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 networks must include researchers who study the basic science of learning, education practitioners, and data scientists. The high value dataset may come from either generating a new dataset or by increasing the value of an existing dataset. Phase II, open only to awardees of Phase I, establishes a “collaboratory,” which is a socio-technological platform that prioritizes community-building and capacity-building to sustain collaborative efforts to advance math learning and education in K-12.
Expanding Participation in STEM, NSF Priorities and Gold Standard Science:
NSF prioritizes cutting-edge discovery science and engineering research, advancing technology and innovation, and creating opportunities for all Americans. NSF has established priorities set forth by Congress, the administration and the NSF director to promoteNSF’s mission. Proposers should review the list ofNSF prioritiesand are encouraged to align their proposals with them, where appropriate. NSF also expects the highest standards of scientific rigor, integrity and adherence to appropriate tenets ofGold Standard Sciencein proposals, as appropriate for the field of science and research modality.
Who can apply
Eligible applicants
How to apply
Application links
Key dates & requirements
Required documents
- NSF PAPPG required forms (SF-424, SUBAWARD budget detail sheets if applicable)
- Project Narrative and budget justification
- Collaboration letters and team commitment documentation
- Data management plan
- Plans for practitioner engagement and sustainability
Program contact
- 👤 U.S. National Science Foundation
- 📧 grantsgovsupport@nsf.gov
- 📞 703-292-4203
Funding track record
Recent awards under CFDA 47.075 from the last 3 years — real organizations that won funding through this same program.
Top 10 Largest Recent Awards
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$38,357,018
-
$18,499,999
-
$13,999,656
-
$10,999,998
-
$8,993,747
-
$8,043,354
-
$5,237,549
-
$5,200,000
-
$5,047,151
-
$4,500,000
Top States by Funding
- MI 9 awards $94.1M
- DC 6 awards $20.7M
- MA 7 awards $18.5M
- NY 9 awards $17.0M
- CA 8 awards $14.8M
Source: USAspending.gov — federal spending transparency. Data covers last 3 years.
Funding history
Annual funding for this program — Federal obligations (CFDA 47.075). How funding has trended year over year.
| 2024 | $292,390,000 | |
| 2025 | $219,410,000 | |
| 2026 est. | $92,200,000 |
FAQ
Who can submit proposals to CAMEL Phase I?
Institutions of higher education, nonprofit research organizations, and federally recognized Tribal Nations located in the US. All applicants must have direct educational or research missions.
What is the funding range for Phase I awards?
Awards typically range from $1,000,000 to $1,500,000 for the Phase I research network development phase.
What must a Phase I proposal include?
Proposals must form interdisciplinary teams combining learning scientists, education practitioners, and data scientists. The team must propose either generating a new high-value dataset or enhancing an existing dataset focused on math learning.
Is cost sharing required?
No, CAMEL does not require cost sharing or matching funds from applicants.
Can Phase I awardees move to Phase II?
Yes, Phase II is invitation-only and open exclusively to successful Phase I awardees. Phase II establishes the collaboratory platform for sustained community-building.
💡 Tips for applicants
- Build your interdisciplinary team early. Include researchers in cognitive science, neuroscience, or psychology; practicing K-12 educators; and data scientists or AI specialists.
- Connect your dataset to real classroom challenges. Funders want datasets that generate actionable insights for improving math instruction at scale.
- Demonstrate existing relationships between researchers and practitioners. Teams showing prior collaboration are more competitive than newly formed partnerships.
- Align with NSF priorities. Explicitly reference how your network advances equity, diversity, and access in STEM education as appropriate for your context.
- Plan for sustainability and community governance. Describe how your research network will operate beyond the funding period and serve as a resource for the broader math education community.
⚠️ Common mistakes
Assembling teams without true integration of learning scientists, practitioners, and data scientists. Proposing datasets disconnected from K-12 instructional challenges. Underestimating the complexity and cost of generating high-quality datasets while underestimating management costs.
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