As the data that we collect dramatically increases in both quantity and complexity, all college graduates will need more quantitative and computational skills to be productive and successful members of society. Ecology students are no exception. Instructors are often looking for efficient and exciting (or at least non-off-putting) ways to introduce undergraduates early in their careers to the powerful data analysis tools that they will learn and use later, such as the statistical programming language, R, without overwhelming introductory students. Our question was: what is the most accessible and effective framework for teaching quantitative and computational skills in introductory ecology classes that will meet our learning goals and prepare students for continued development in statistics and advanced courses?
Results/Conclusions
We have developed inquiry based, active learning materials in Jupyter Notebooks to teach coding and quantitative skills to undergraduate students at several different levels. We will present our materials and discuss our experience using this open source system as an effective and accessible tool to teach quantitative and computational skills in introductory ecology. Our materials address basic calculation and graphing skills, reproducible analysis, and basic statistics using R or Python. We will discuss the advantages of Jupyter Notebooks for teaching and learning over using more basic tools such as Excel or systems with a steep learning curve such as Rstudio. The Jupyter Notebook system is relatively easy to install or use in the cloud, and combines text, code, and output all in one place that is easily exportable to HTML and PDF. It is also easy to guide students through solving scaffolded problems with code that they can adapt and modify, and to evaluate students’ thought processes as they work through everything from simple exercises to complex data analysis projects. We see Jupyter Notebooks as an easily accessible tool to get students at various levels engaged in doing ecological data science.