This research addresses the challenges and opportunities faced by undergraduate biology educators in preparing students for participation in data-intensive environmental research. Recent work by Hampton et al. (2016) has identified a set of five capstone skills that provide a “roadmap” for the training required to fully participate in science using modern data and analysis resources. We look within these broadly defined skills 1) data management, 2) analysis, 3) software skills, 4) visualization, and 5) communication, to identify opportunities for engaging undergraduates with foundational competencies and associated research like tasks. Our goal was to determine if there were existing models for undergraduate biology education that could be mapped to the development of these advanced research skills and articulate pathways that could be used to promote success among students pursuing data intensive research. We completed our analysis by reviewing existing curricula, policy documents, and educational research to evaluate the support for various pathways to the desired research skills. Sources included in this review spanned literature on teaching and learning statistics, data management skills, computational thinking, mathematics, and modeling.
Results/Conclusions
Our analysis found strong support for the existence of opportunities to enhance undergraduates’ preparation to engage in data-intensive environmental research. While the depth of the knowledge base and richness of exemplary curricula across the targeted skills was uneven, there was clear evidence that successful programs exist in each area. For several of the desired research skills we were able to describe and exemplify pathways that extend the learning trajectory an undergraduate may pursue through various competent skills and attitudes necessary to achieve research level competency. The two most significant contributions to developing data-intensive science skills come from the recently broadening adoption of course based undergraduate research experiences (NAS, 2017) and the continued growth of teaching with large public datasets (Langen, et al., 2014). Despite the existence of many high quality resources addressing the desired skills, there was little evidence of integration across them. Furthermore, it is clear that the adoption of effective pedagogical strategies for building data research skills is not widespread. Despite an increasing emphasis on having undergraduate students work with research data there has been less progress made in adopting strategies that explicitly build data research competencies.