Engaging undergraduate students in ecological investigations using large, public datasets: lessons-learned by a ‘teaching with big-data’ working group

Background/Question/Methods

Starting in 2009, ESA, NCEAS, and NEON sponsored the ‘Engaging Undergraduate Students in Ecological Investigations Using Large, Public Datasets’ distributed seminar. Faculty from five colleges worked together and with colleagues at the sponsoring organizations to develop, implement, assess, and disseminate classroom exercises using publically-available ecological data sets.  Our objectives were to create exercises that, by working with real data, enhance students' ecological content knowledge and critical thinking skills, engage students in the synthesis of ecological knowledge, and instill in students an understanding of the value of publically-available archived data. We also intended these exercises to introduce 21st century ‘ecoinformatic’ skills in data mining, processing, and analysis; and be practical to use at any undergraduate institution and with any population of undergraduate ecology students. Five exercises were developed and tested on undergraduate classes. We administered pre and post surveys to our students that measured content knowledge, attitudes about the exercises, and general perceptions about using publically-available data in ecology. We also brought two students from each participating institution to a meeting of the working group at NCEAS, to provide their perspectives on the exercises we developed, and to share and help synthesize the distributed seminar’s results.

Results/Conclusions

We were surprised at how difficult it was to develop exercises that met our objectives and were doable in an undergraduate course. Our assessments indicated, however, that students gained a lot in terms of ecological content knowledge and process skills from our exercises, and they liked doing them. Students valued the opportunity to explore real data about ecological issues, stating that doing the exercises was more like authentic research. They appreciated the tangible connections with other course content. However, there was a diversity of students’ attitudes about using publically-available ecological data, and some views were quite surprising and problematic. Overall, we judged the exercises to be highly worthwhile, despite the time commitment required. We are now convinced that exercises such as these will become an essential component of 21^st century ecology instruction. They teach skills that ecology students must acquire to be successful in their eventual careers. In the future, big data exercises may be incorporated into traditional courses, or else as an ‘ecoinformatics lab’ in addition to the traditional ‘ecology lab’. Ecology educators need to develop and critically evaluate more data-intensive exercises, so we can document student outcomes and ‘best practices’ in the development and delivery of such exercises.