Back

Distribution of the National Science Foundation's Advancing Informal STEM Learning Awards (AISL) between 2006-21

Houzenga, H. M.; Muindi, F. J.

2022-02-23 scientific communication and education
10.1101/2022.02.18.480415 bioRxiv
Show abstract

The COVID-19 pandemic tested many fundamental connections between science and society. A growing field working to strengthen those connections exists within the informal STEM learning (ISL) community which provides diverse learning and engagement environments outside the formal classroom. One of the largest funders of ISL initiatives is the National Science Foundation (NSF) which runs the Advanced Informal STEM Learning (AISL) program in the United States. The AISL program supports initiatives through six categories that include pilots and feasibility studies, research in service to practice, innovations in development, broad implementation, literature reviews, syntheses, or meta-analyses, and conferences. However, a number of questions remain unanswered with respect to the distribution and ultimately the broad impact of the awards. In this study, we analyzed publicly available awardee information across a 15-year period (2006-2021) to provide a preliminary analysis of how the AISL grants are distributed across individual states, organizations, and the principal investigators. Several states, organizations, and principal investigators stood out as prolific awardees. Massachusetts and California represented the largest share of awards at 14% and 13% respectively during that time. WGBH Educational Foundation located in Massachusetts and the Exploratorium located in California, received the largest number of awards during the 15 year period. Notably, 67% of the AISL awards list at least one co-principal investigator. Our report brings to light a number of new questions and charts new paths of exploration for future studies.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.