Opportunity Information: Apply for 13 565
Apply for 13 565
- The National Science Foundation in the science and technology and other research and development sector is offering a public funding opportunity titled "Data Intensive Research to Improve Teaching and Learning An Ideas Lab to Foster Transformative Approaches to Teaching and Learning" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 47.076 Education and Human Resources.
- This funding opportunity was created on Sep 12, 2014 and posted on Aug 13, 2013.
- Applicants must submit their applications by This opportunity has been archived.. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- The funding agency has allocated a total of $5,000,000.00 to eligible and selected applicants.
- The number of recipients for this funding is limited to 10 candidate(s).
- Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).
- PI Limit An individual may be included as a Co PI or senior investigator on multiple fullproposals.
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Opportunity Summary:
This National Science Foundation (NSF) grant opportunity, titled "Data Intensive Research to Improve Teaching and Learning: An Ideas Lab to Foster Transformative Approaches to Teaching and Learning" (NSF 13-565), was designed to spark bold, cross-disciplinary research that uses large-scale educational data to produce actionable insights for improving STEM learning. The central aim was twofold: generate practical improvements to STEM teaching and learning environments in the medium term (across both formal settings like classrooms and informal settings like museums or online communities), and lay the groundwork for longer-term changes that could fundamentally reshape how people learn. NSF emphasized that the work should be genuinely transformative, potentially high-risk, and capable of moving the field forward quickly rather than incrementally.
A defining feature of the opportunity was its focus on integrating expertise that is rarely combined in deep, sustained collaboration. NSF anticipated teams that brought together learning scientists and educational researchers with STEM disciplinary experts, computer scientists, statisticians, and database/data experts, especially those who build and study learning environments. The expectation was that blending these perspectives could unlock new ways to improve student engagement and learning outcomes, enable more effective personalized instruction, and support faster, evidence-based decision making so educators can respond to the needs of individuals and groups of learners in real time and across different contexts.
The solicitation made clear that "data intensive" meant more than just using a big spreadsheet. It encouraged approaches that could leverage and connect data at multiple levels. At the micro level, this could include fine-grained data about individual learners (for example, clickstreams, problem-solving traces, or other interaction data). At the online level, it could include data from large-scale platforms such as MOOCs. At the meso level, it could involve classroom-based data that informs both students and teachers about how learning is progressing, potentially supporting timely instructional adjustments. At the macro level, it could include broader school, district, state, and national data, including information from federal science and policy agencies. The underlying idea was that new research approaches could emerge from combining and interpreting these varied data sources to generate knowledge that can be acted on in authentic learning environments.
At the same time, the opportunity drew an important boundary: it was not meant to fund education data infrastructure development focused primarily on database design and development. In other words, the emphasis was on novel research approaches, analytic methods, and learning innovations enabled by data, rather than building new standalone database platforms as the main deliverable.
This program used an "Ideas Lab" model rather than a traditional proposal-only process. Interested individuals first had to submit a preliminary application by August 19, 2013 (due by 5 p.m. local time) to be considered for participation in the Ideas Lab. Selected participants then attended an intensive five-day residential workshop where they formed multidisciplinary teams, refined big ideas into research concepts, and developed collaborative proposals through a real-time, iterative review process. Only teams invited after the workshop were expected to submit full proposals, which were due December 9, 2013.
In terms of funding specifics, NSF anticipated making about 10 awards with an estimated total program budget of $5,000,000. Awards had a floor of $300,000, and there was no cost sharing requirement. The funding instrument type was a grant, categorized under Science and Technology and other Research and Development, and it was administered under CFDA 47.076 (Education and Human Resources). Eligibility was listed broadly as "Others (see text field entitled Additional Information on Eligibility for clarification)," and a key participation rule noted that an individual could serve as a Co-PI or senior investigator on multiple full proposals (with PI limits addressed in the full solicitation). The opportunity was posted on August 13, 2013, later archived on September 12, 2014, and is no longer open.
For reference and official details, NSF directed applicants to the full announcement (NSF Publication 13-565) and provided support contacts through Grants.gov assistance (grantsgovsupport@nsf.gov), along with standard NSF linking/help contacts.
Frequently Asked Questions (FAQs)
What is the title of this NSF grant opportunity?
The opportunity is titled "Data Intensive Research to Improve Teaching and Learning: An Ideas Lab to Foster Transformative Approaches to Teaching and Learning" (NSF 13-565).
What was the main purpose of NSF 13-565?
The purpose was to spark bold, cross-disciplinary research that uses large-scale educational data to generate actionable insights for improving STEM learning, while also laying groundwork for longer-term changes that could reshape how people learn.
What kinds of outcomes was the program aiming for?
NSF described a two-part aim: (1) practical improvements to STEM teaching and learning environments in the medium term (in both formal and informal settings), and (2) longer-term changes that could fundamentally transform learning.
What does NSF mean by "transformative" and "high-risk" in this program?
The solicitation emphasized work that is genuinely transformative, potentially high-risk, and capable of moving the field forward quickly rather than producing only incremental advances.
What fields of expertise were expected to be integrated on teams?
NSF anticipated multidisciplinary teams that combine learning scientists and educational researchers with STEM disciplinary experts, computer scientists, statisticians, and database/data experts, especially those who build and study learning environments.
Why did NSF emphasize cross-disciplinary collaboration?
NSF expected that blending perspectives that are rarely combined in deep, sustained collaboration could unlock new approaches to student engagement and learning, support more effective personalized instruction, and enable faster evidence-based decision making for educators.
What does "data intensive" mean in the context of this opportunity?
"Data intensive" was framed as more than using a large spreadsheet. The solicitation encouraged approaches that leverage and connect educational data across multiple levels (micro, online, meso, and macro) to generate knowledge that can be acted on in authentic learning environments.
What are examples of micro-level data mentioned in the opportunity description?
Examples of micro-level data included fine-grained data about individual learners such as clickstreams, problem-solving traces, or other interaction data.
What online-level data sources were referenced?
The opportunity referenced data from large-scale online platforms, including MOOCs.
What is meant by meso-level data in this solicitation?
Meso-level data refers to classroom-based data that informs students and teachers about how learning is progressing, potentially enabling timely instructional adjustments.
What is meant by macro-level data in this solicitation?
Macro-level data includes broader school, district, state, and national data, including information from federal science and policy agencies.
Was the program focused on combining different levels of data?
Yes. A key idea was that new research approaches could emerge by combining and interpreting varied data sources across levels to produce actionable knowledge in real learning environments.
What kinds of settings were considered relevant for improving STEM learning?
The opportunity included both formal settings (such as classrooms) and informal settings (such as museums or online communities).
What was explicitly not the focus of this opportunity?
The solicitation drew a boundary that it was not meant to fund education data infrastructure development focused primarily on database design and development. The emphasis was on novel research approaches, analytic methods, and learning innovations enabled by data, rather than building new standalone database platforms as the main deliverable.
What proposal process did NSF use for this opportunity?
This program used an Ideas Lab model rather than a traditional proposal-only process.
What is an Ideas Lab in the context of this NSF opportunity?
In this program, an Ideas Lab involved submitting a preliminary application to be considered for participation, followed by an intensive five-day residential workshop where selected participants formed multidisciplinary teams, refined ideas into research concepts, and developed collaborative proposals through a real-time, iterative review process.
Was a preliminary application required?
Yes. Interested individuals had to submit a preliminary application to be considered for participation in the Ideas Lab.
When was the preliminary application due?
The preliminary application was due August 19, 2013, by 5 p.m. local time.
Who submitted full proposals under this Ideas Lab model?
Only teams invited after the workshop were expected to submit full proposals.
When were full proposals due?
Full proposals were due December 9, 2013.
How many awards did NSF anticipate making?
NSF anticipated making about 10 awards.
What was the estimated total program budget?
The estimated total program budget was $5,000,000.
What was the minimum award size?
Awards had a floor of $300,000.
Was cost sharing required?
No. The opportunity stated there was no cost sharing requirement.
What was the funding instrument type?
The funding instrument type was a grant.
How was the opportunity categorized?
It was categorized under Science and Technology and other Research and Development.
What CFDA number was associated with this program?
The program was administered under CFDA 47.076 (Education and Human Resources).
Who was eligible to apply?
Eligibility was listed broadly as "Others (see text field entitled Additional Information on Eligibility for clarification)." The description also notes a participation rule related to serving on multiple proposals.
Were there rules about how many proposals an individual could be part of?
Yes. A key participation rule noted that an individual could serve as a Co-PI or senior investigator on multiple full proposals, with PI limits addressed in the full solicitation.
When was this opportunity posted?
The opportunity was posted on August 13, 2013.
Is this funding opportunity still open?
No. It was later archived on September 12, 2014, and is no longer open.
Where can applicants find the official announcement and full details?
NSF directed applicants to the full announcement, NSF Publication 13-565, for official requirements and details.
Who should be contacted for Grants.gov submission assistance?
NSF provided a Grants.gov assistance contact: grantsgovsupport@nsf.gov.
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