Abstract: Ecology majors often avoid mathematics and statistics courses that could aid them in advancing their understanding in ecological science while preparing them for data-rich careers. The goal of this project was to transform the current ecology curricula at Eastern New Mexico University by introducing technology-enhanced data-centric laboratories in which students apply mathematical and statistical concepts. Students will benefit by learning how to use computational tools to visualize abstract concepts in ecology and by learning the fundamentals of data science. Students’ understanding and ability to apply and interpret data in an ecological context will be enhanced through the introduction of the practical application of transformations and learning how to perform helpful parametric tests like analysis of variance and regression with more complex software. Such improvements will enhance ecological concepts while also advancing quantitative and technological reasoning skills. Specifically, this study implemented a semester long data collection project that used use novel or public data. Students were responsible for asking questions, collecting real world data (including online publicly available data), visualizing their data, and using basic statistics to address their question. Secondly, three labs (3 hours/lab) were dedicated to data related topics and computational tool use (e.g., Excel and Rstudio). This study assessed student learning by comparing pretests and posttests of statistical and ecological data understanding of students enrolled in the General Ecology course at ENMU. Based on preliminary data, students showed significant improvement in understanding of statistical concepts and how to apply them to datasets to better understand ecological concepts. This project will lay the groundwork for institutional STEM education improvements that can be implemented in existing courses enabling adoption by other undergraduate institutions.
