Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses. Issue 16 (30th July 2018)

• Record Type: Journal Article
• Authors: Farrell, Kaitlin J.; Carey, Cayelan C.
• Is Part Of: Ecology and evolution. Volume 8:Issue 16(2018)
• Page Start: 7744
• Page End: 7751
• Publication Date: 2018-07-30
• Subjects: active learning -- big data -- computer programming -- data science -- education -- R software -- sensor data -- teaching modules; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.4363
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: Environmental research requires understanding nonlinear ecological dynamics that interact across multiple spatial and temporal scales. The analysis of long‐term and high‐frequency sensor data combined with simulation modeling enables interpretation of complex ecological phenomena, and the computational skills needed to conduct these analyses are increasingly being integrated into graduate student training programs in ecology. Despite its importance, however, computational literacy—that is, the ability to harness the power of computer technologies to accomplish tasks—is rarely taught in undergraduate ecology classrooms, representing a major gap in training students to tackle complex environmental challenges. Through our experience developing undergraduate curricula in long‐term and high‐frequency data analysis and simulation modeling for two environmental science pedagogical initiatives, Project EDDIE (Environmental Data‐Driven Inquiry and Exploration) and Macrosystems EDDIE, we have found that students often feel intimidated by computational tasks, which is compounded by the lack of familiarity with software (e.g., R) and the steep learning curves associated with script‐based analytical tools. The use of prepackaged, flexible modules that introduce programming as a mechanism to explore environmental datasets and teach inquiry‐based ecology, such as those developed for Project EDDIE and Macrosystems EDDIE, can significantly increase students' experience and comfort levels with advanced computational tools. These types of modules in turn provide great potential for empowering students with the computational literacy needed to ask ecological questions and test hypotheses on their own. As continental‐scale sensor observatory networks rapidly expand the availability of long‐term and high‐frequency data, students with the skills to manipulate, visualize, and interpret such data will be well‐prepared for diverse careers in data science, and will help advance the future of open, reproducible science in ecology. Abstract : Ecology increasingly requires the use of advanced computational techniques including simulation modeling and the analysis of high‐frequency data, yet teaching these techniques in undergraduate ecology courses is not common. Here, we synthesize our experiences developing and assessing teaching modules that bring "big data" into undergraduate ecology courses. These experiences have emphasized the importance, challenges, and opportunities of explicitly embedding computational literacy skills (e.g., data manipulation and visualization using the R programming language) into undergraduate ecology curricula, and we use data from student assessments to support our observations.