Mapping the Development of K-12 STEM Education Research Over Time

About This Grant

Too often, valuable STEM education research knowledge and products fail to reach classrooms. Improving the translation and diffusion of education research knowledge requires improving the understanding of how to map the movement of people, ideas, and products along a continuum between basic research and practice. Until recently, such mapping of a field was time-consuming and technically difficult. This study will develop and evaluate an AI-assisted approach for mapping complex relationships in educational research in ways that are both meaningful and efficient across a large number of studies. The study will characterize the movement of people and knowledge across a sample of approximately 2,200 K-12 STEM research projects supported by NSF through the ECR and DRK-12 funding programs between 2013-2027. Ultimately, this study's findings will contribute to improved strategies for organizing education research to enhance connectivity across study types, topics, and researchers. The blend of broad AI-assisted approaches with more qualitative network analyses will offer a methodological framework that can be applied to other fields of education research, enabling broader insights into how scientific knowledge grows, evolves, and informs practice, especially as it moves from fundamental research towards more applied research and development and eventually studies of interventions at scale. The study will characterize the movement of people and knowledge across a sample of approximately 2,200 K-12 STEM research projects supported by NSF through the ECR and DRK-12 funding programs between 2013-2027. The researchers will use AI-assisted text, network, and bibliometric analyses. In Phase 1, they will use human-in-the-loop natural language processing to retrieve data (e.g., project information, abstracts, publications) from publicly available sources to build a dataset that captures key characteristics of the projects — such as researchers, study types, focal topics, and related publications. Additional data, derived from surveys, interviews, and study documents, will support refinement and validation of computer-assisted methods. This will ensure that human expertise and insight is combined with computer-based efficiency at scale through iterative cycles of computer and human coding, interpretation, and model refinement. In Phase 2, they will conduct network analysis to visualize how study characteristics, people, knowledge, and products are interconnected across the sample, revealing large-scale patterns and disconnects, evolution over time, and pathways for idea diffusion. They will apply bibliometric mapping techniques to analyze related publications, examining collaboration networks (e.g., relationships among authors), conceptual networks (e.g., connections between study characteristics), and citation networks (i.e., how articles cite one another). The findings have the potential to advance theories of research diffusion and translation, inform future study designs, and guide the strategic approaches to accelerating the impact of K-12 STEM research on teaching and learning. This project is jointly funded by the Translation and Diffusion (TD) program that supports research that advances the science of translation and diffusion between research and practice in STEM education, the EDU Core Research (ECR) program, which supports fundamental research that generates foundational knowledge to advance the research literatures in STEM learning and learning environments, the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in STEM and information and communication technology careers, and the Directorate for Technology, Innovation and Partnerships (TIP) which advances use-inspired and translational research in all fields of science and engineering. 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.