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

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.

Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

“…, Touchon and McCoy , Klug et al. , Farrell and Carey ), we suggest that one previously overlooked benefit to such training is that computational ecologists are more likely to recognize the benefits of, and successfully engage in, collaborations with computer scientists. Consequently, in addition to a strong curriculum in mathematics, we recommend that undergraduate ecology students learn basic skills in a programming language (e.g., R, Python, C++, and Java).…”

An introduction to agent‐based models as an accessible surrogate to field‐based research and teaching

“…Here, NetLogo comes into its own as it has a graphical user interface that a student can interact with or without needing to change the underlying code. At the same time, it is important to recognize that quantitative methods and computer literacy are core skill sets of modern ecologists in research and opportunities for exposure to these skills in the undergraduate level are limited (Farrell & Carey, 2018; Read et al., 2016). Teaching computer literacy and code language skills to students who have no experience is a challenge in ecological teaching, especially as these are important skills for a graduate research study (Farrell & Carey, 2018).…”

“…At the same time, it is important to recognize that quantitative methods and computer literacy are core skill sets of modern ecologists in research and opportunities for exposure to these skills in the undergraduate level are limited (Farrell & Carey, 2018; Read et al., 2016). Teaching computer literacy and code language skills to students who have no experience is a challenge in ecological teaching, especially as these are important skills for a graduate research study (Farrell & Carey, 2018). By embedding these concepts in ecological practical classes using an ABM, students are more likely to engage and increase experience and literacy in computer science (Carey & Gougis, 2016).…”

A CURE for physiological characterization of bacterioplankton in liquid culture

“…This would allow for more robust data-sharing and collaboration between students. Lastly, students could be exposed to the bioinformatic side of the workflow in a more comprehensive way by using techniques and surveys highlighted in Farrell and Carey 2018 (23).…”