Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Abstract

Environmental research requires understanding nonlinear ecological dynamics thatinteract across multiple spatial and temporal scales. The analysis of long-term andhigh-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 trainingprograms in ecology. Despite its importance, however, computational literacy-thatis, the ability to harness the power of computer technologies to accomplish tasks-israrely taught in undergraduate ecology classrooms, representing a major gap in training students to tackle complex environmental challenges. Through our experiencedeveloping undergraduate curricula in long-term and high-frequency data analysisand simulation modeling for two environmental science pedagogical initiatives,Project EDDIE (Environmental Data-Driven Inquiry and Exploration) andMacrosystems 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 useof 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 increasestudents' experience and comfort levels with advanced computational tools. Thesetypes of modules in turn provide great potential for empowering students with thecomputational literacy needed to ask ecological questions and test hypotheses ontheir own. As continental-scale sensor observatory networks rapidly expand theavailability 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 indata science, and will help advance the future of open, reproducible science inecology.