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To teach STEM content to K-12 students and to recruit talented and diverse K-12 students into STEM, many outreach programs at universities in the United States rely on STEM undergraduates. While the design of such outreach typically focuses on the K-12 students who are taught or recruited, an important but often overlooked consideration is the effect of the outreach on the professional development of the STEM undergraduates themselves. This proposed EAGER project seeks to determine which outreach programs in the United States provided the most transformative professional development of the participating STEM undergraduates. This project then seeks to capture the essence what practices in those programs provided transformative professional development. Next, the project seeks to disseminate these practices to a network of institutions doing outreach. Supporting this project is the NSF EArly-concept Grant for Exploratory Research (EAGER) program. In this first year of the project, we performed a systematic review of literature and university websites with follow-up survey data to identify outreach programs that may be transformative for STEM undergraduates. This review yielded a matrix of about 100 college-based outreach programs. We then invited these programs to attend one of the following workshops: a March workshop held at Tufts University in Boston or an April workshop held at the University of Nebraska in Lincoln. Nine institutions sent representatives to the Boston workshop, and five institutions sent representatives to the Lincoln workshop. In addition, we held conference calls to gather information from an additional six institutions. The purpose of the workshops and conference calls was two-fold: (1) determine best practices for outreach that used STEM undergraduates, and (2) determine what in those programs provided the most transformative development of the participating STEM undergraduates. This paper presents preliminary results from these workshops and conference calls. 

Developing useful intelligence on scientific and technological emergence challenges those who would manage R&D portfolios, assess research programs, or manage innovation. Recently, the U.S. Intelligence Advanced Research Projects Activity Foresight and Understanding from Scientific Exposition Program has explored means to detect emergence via text analyses. We have been involved in positing conceptual bases for emergence, framing candidate indicators, and devising implementations. We now present a software script to generate a family of Emergence Indicators for a topic of interest. This paper offers some background, then discusses the development of this script through iterative rounds of testing, and then offers example findings. Results point to promising and actionable intelligence for R&D decision-makers. 

https://doi.org/10.1016/j.techfore.2018.04.016 Summary: Indicators of technological emergence promise valuable intelligence. We present an implemented algorithm to calculate emergence scores (EScores) for topical terms from abstract record sets. We offer a family of emergence indicators. Primary emergence indicators identify “frontier” terms based on their EScores. We then tally organizations, countries, or authors especially active in publishing (or patenting) on high EScore topics in a target R&D domain. We can score research fields on relative degree of emergence. This paper illustrates EScoring for Nano-Enabled Drug Delivery, Non-Linear Programming, Dye Sensitized Solar Cells, and Big Data. 

https://doi.org/10.1080/09537325.2018.1480013 published online 

Indicators of technological emergence promise valuable intelligence to those determining R&D priorities. We present an implemented algorithm to calculate emergence scores for topical terms from abstract record sets. We offer a family of emergence indicators deriving from those scores. Primary emergence indicators identify “hot topic” terms, then we use those to generate secondary indicators that reflect organizations, countries, or authors especially active at research frontiers in a target domain. We also flag abstract records (papers or patents) rich in emergent technology content, and we score technological fields on relative degree of emergence. We show illustrative results for example topics -- Nano-Enabled Drug Delivery, Non-Linear Programming, Dye Sensitized Solar Cells, and Big Data.