NSF AI Disclosure Required
NSF requires disclosure of AI tool usage in proposal preparation. Ensure you disclose the use of FindGrants' AI drafting in your application.
AID DCL: Synthesizing Specific Active Learning Strategies in Undergraduate STEM Education Research
NSF
About This Grant
This project aims to serve the national interest by increasing the quality and effectiveness of undergraduate STEM education through improved understanding and instructor use of active learning instructional strategies. Active learning is widely recognized as a high-impact educational approach that has consistently been shown to improve student learning outcomes, engagement, and retention. Yet despite its promise, the field lacks a clear, evidence-based understanding of what constitutes effective active learning in practical, replicable terms. This project addresses that gap by synthesizing and evaluating how specific active learning strategies and tactics are defined and applied across STEM disciplines and institution types. By identifying and organizing a comprehensive set of effective instructional strategies and providing context-specific guidance for their use, the project will generate theoretical and practical insights that can guide researchers, faculty, and educational leaders in enhancing high-quality instruction. The importance of this project lies in its potential to clarify the meaning of "active learning" for the field and broaden its application across STEM education. The expected outcomes intend to include a refined understanding of how instructional strategies align with student needs and learning environments, ultimately supporting student success and fostering lifelong learning. This Engaged Student Learning, Level 1 project aligns with the goals of the IUSE program by supporting the development of tools and practices that improve undergraduate STEM education at scale. The goals of this two-year project are to systematically review and synthesize the current research literature on active learning in undergraduate STEM education in order to 1) identify and describe the full range of active learning strategies and specific instructional tactics in use; 2) determine which strategies are used most frequently and with which course contexts, institution types, and STEM disciplines; and 3) evaluate the extent to which active learning strategies are operationalized in alignment with current ideas in STEM education. The project scope includes a rigorous methodological approach that integrates best practices in systematic literature review and qualitative synthesis. A multidisciplinary team of researchers with expertise in STEM education, educational psychology, and research synthesis methods will lead the project, supported by an Evaluative Advisory Board composed of national experts in discipline-based education research. The project plans to assess the landscape of active learning using transparent coding schemes, cross-context comparisons, and structured reporting. Findings will be disseminated through scholarly publications, practitioner-facing resources, conference presentations, and collaboration with professional societies to reach diverse audiences. This work aims to advance understanding of how active learning is conceptualized and implemented, and under what conditions it is most effective for improving student learning in STEM. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Focus Areas
Eligibility
How to Apply
Up to $400K
2027-09-30
One-time $749 fee · Includes AI drafting + templates + PDF export
AI Requirement Analysis
Detailed requirements not yet analyzed
Have the NOFO? Paste it below for AI-powered requirement analysis.