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Reinventing Scientific Talent

What is the compelling question or challenge?

As the pace of scientific discovery accelerates exponentially, how will scientists, educators, and other STEM workforce professionals meet the demand for career-long learning?

What do we know now about this Big Idea and what are the key research questions we need to address?

The National Science Foundation (NSF) has made significant investments in developing a larger and more diverse pool of STEM talent. However, many of the practices of STEM preparation implicitly assume that professional preparation ends at a terminal degree. While being a life-long learner is an academic ideal, in practice this aspiration has not alleviated many of the skill gaps we see in science and science education today. No matter how much the training behind a degree is updated and improved, the shelf-life of skills is getting shorter in a rapidly changing, more interdisciplinary world. We must learn how to combine the deep and slowly acquired expertise of a degree with novel approaches to training and learning that can enable individuals in the STEM workforce to refresh and reinvent themselves over the course of their careers.

In biology, at least the last 10 years have been a transition into a “Big Data” science. While the technological obstacles of this transition were significant, the highest barrier continues to be building computational and data skills within the science workforce. Barone et.al. showed that currently, the top 3 of 13 unmet needs of investigators funded by NSF's Directorate for Biological Sciences (BIO) were all forms of computational training. Computational and data science skills are a case study of how a discipline can quickly become dependent on methodologies only wielded successfully by the hands of a few. Disruptive but critical methodologies fuel progress, but at the cost of enlarging the skill gap scientist face. Will scientists in the 21st century have time to wait for transformative approaches to make it into the classroom? How will experienced researchers be on-boarded when skills emerge from outside of their discipline, and when the luxury of sabbatical study is inaccessible?

Only time (helped along by an interested and diverse community) can pinpoint all the relevant research questions about how we will learn what we have yet to discover. Two exemplar questions include:

What is the best way to disseminate a new skill into a STEM community of practice?

Again using computational skills in biology as an example, Feldon et.al. frames the current problem. Their analysis of 294 PhD students in life sciences from 53 US institutions concludes that despite more than $28 million in investment in training in the form of boot camps and other short formats, student outcomes don’t demonstrate sustained impact. While this claim may seem shocking, many of the conclusions of this paper would have been anticipated by experts in cognitive science and andragogy. How did we come to invest so much in a process that may have produced very little? We need systematic dissemination of evidence-based learning approaches and implementation science to address impacting the STEM workforce at scale.

How can curriculum for rapidly evolving methodologies be developed and delivered at scale?

It is plausible that there is one or a few optimal ways to teach a particular subject. Yet countless hours are spent creating and recreating educational resources that span a continuum – from low to high quality – in terms of their validity, accessibility, and assessability. How can we create the circumstance for the development of authoritative resources for the dissemination of skills? For example, the US Agency for Healthcare Research and Quality’s National Guidelines Clearinghouse was a single resource for physicians to obtain authoritative treatment protocols. An educational repository created by communities of practice in the sciences could save thousands of hours of curriculum development as new methods emerge. Interestingly, The Carpentries (Software Carpentry and Data Carpentry) (Teal et.al. 2015) presents the only global example of how a volunteer organization (of more than 1,600 researchers serving as instructors) can do this at scale. The Carpentries has reached more than 38,000 researchers in 46 countries and assessment data supports the conclusion they are achieving sustainable impact.

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