Improving Data Literacy in Undergraduate Biology Education

Eastern New Mexico University Main Campus, Portales NM

Investigators

Abstract

This project aims to serve the national interest by increasing data science literacy for life science undergraduate students using new teaching materials that will incorporate data analysis and visualization using computational tools. Undergraduate biology laboratory exercises will be improved by requiring greater critical and quantitative thinking in order to complete the exercises. These data-centric laboratory enhancements will occur at all levels of student learning. While the exercises will still illustrate the same biological concepts, data science will be integrated with the exercises by including data, descriptive statistics, spreadsheet applications, and data visualization tools in the learning activities. In the first and second years of the biology program, students will learn to manage, interpret, and visualize data. Students will be trained in using basic computing tools. As students progress through their biology training, the modules will integrate programming languages as well as more sophisticated data analytic activities. The exercises will facilitate the development and use of quantitative literacy and critical thinking that will also help students gain a deeper understanding of the foundational biological concepts. Findings from this project will guide further curricular improvements in undergraduate biology courses and can potentially be adapted by other STEM fields. Biology majors often avoid mathematics and statistics courses that could aid them in advancing their understanding in biological science while preparing them for data-rich careers in the biotechnology industry. The goal of this project is to transform the current biology curricula 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 biology and by learning the fundamentals of data science. Such changes will include the introduction of the concept of scale transformation, as biology often occurs on a log scale, as observed in bacterial growth, viral spread, the pH scale, and gene expression. Students will learn that log transformations can normalize these data for basic statistics. Students’ understanding and ability to apply and interpret data in a biological 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 biological concepts while also advancing quantitative and technological reasoning skills. The study will assess student learning by comparing pretests and posttests of biological and data understanding within sections, among sections, and among years so that student progression can be tracked as they advance through the curriculum. This project will lay the groundwork for institutional STEM education improvements that can be implemented in existing courses enabling adoption by other undergraduate institutions. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.