Science of Learning (SL)
NSF has published a News Release on awards supported by the Science of Learning in 2017: https://www.nsf.gov/news/news_summ.jsp?cntn_id=243658&org=NSF&from=news
Frequently Asked Questions (FAQs) for the Science of Learning Program are now available: https://www.nsf.gov/pubs/2017/nsf17041/nsf17041.jsp
|Soo-Siang Lim-Pgm Dirfirstname.lastname@example.org||(703) 292-7878|
|Cori Jacildone-Pgm Specialistemail@example.com||(703) 292-8740|
Apply to PD 16-004Y as follows:
For full proposals submitted via FastLane: standard NSF Proposal & Award Policies & Procedures Guide proposal preparation guidelines apply.
For full proposals submitted via Grants.gov: the NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov Guidelines applies. (Note: The NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: http://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide)
Important Information for Proposers
A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 19-1), is effective for proposals submitted, or due, on or after February 25, 2019. Please be advised that, depending on the specified due date, the guidelines contained in NSF 19-1 may apply to proposals submitted in response to this funding opportunity.
Full Proposal Deadline Date
January 15, 2020
Third Wednesday in January, Annually Thereafter
July 8, 2020
Second Wednesday in July, Annually Thereafter
In addition to the Full Proposals above, the Science of Learning Program also accepts proposals for Workshops, EArly-concept Grants for Exploratory Research (EAGER), Rapid Response Grants (RAPID), and Supplements to existing awards. PIs must contact the NSF program officer prior to submission of an EAGER or RAPID proposal.
The Science of Learning program supports potentially transformative basic research to advance the science of learning. The goals of the SL Program are to develop basic theoretical insights and fundamental knowledge about learning principles, processes and constraints. Projects that are integrative and/or interdisciplinary may be especially valuable in moving basic understanding of learning forward but research with a single discipline or methodology is also appropriate if it addresses basic scientific questions in learning. The possibility of developing connections between proposed research and specific scientific, technological, educational, and workforce challenges will be considered as valuable broader impacts, but are not necessarily central to the intellectual merit of proposed research. The program will support research addressing learning in a wide range of domains at one or more levels of analysis including: molecular/cellular mechanisms; brain systems; cognitive affective, and behavioral processes; and social/cultural influences. The program supports a variety of methods including: experiments, field studies, surveys, secondary-data analyses, and modeling.
Examples of general research questions within scope of the Science of Learning program include:
• How does learning transfer from one context to another or from one domain to another? How is learning generalized from specific experiences? What is the basis for robust learning that is resilient against potential interference from new experiences? How is learning consolidated and reconsolidated from transient experience to stable memory?
• How does the structure of the learning environment impact rate and efficacy of learning? For example, how do timing, content, learning context, developmental time point and type of engagement (e.g., active learning, group learning) impact learning processes and outcomes?
• How can we integrate research findings and insights across levels of analysis, relating understanding of cellular and molecular mechanisms of learning in the neurons to circuit and systems-level computations of learning in the brain, to cognitive, affective, social, and behavioral processes of learning? What concepts, tools, or questions will provide the most productive linkages of across levels of analysis?
• How can insights from biological learners contribute and derive new theoretic perspectives to computational learning systems, neuromorphic engineering, materials science, and nanotechnology? Biological and non-biological systems and social systems can all display learning. What can integration across these different domains contribute to a general understanding of learning?