Engaging students with large publicly accessible datasets

Strong quantitative reasoning is a critical skill for both conducting science and applying scientific information. Working with large, authentic, and publicly available data can enhance the development of quantitative skills as well as democratize science, while also engaging with scientific conceptual frameworks. We describe how working with large publicly accessible datasets uniquely contributes to the identification and redirection of misconceptions and improves learning in science. The pedagogical approach outlined by Project EDDIE (Environmental Data-Driven Inquiry and Exploration) also allows students to participate in discovery science, where the outcomes are unknown and not prescribed in advance. Although this uncertainty can be uncomfortable for instructors, this approach replicates much of our own process in working with large datasets as scientists and can create a fun, collaborative environment in the classroom. We outline the key components necessary for this approach to be effective. We describe how instructors can adapt to teaching open-ended data-based activities and the best practices for working with students in the classroom or online. Project EDDIE also provides scaffolded, flexible modules in a variety of science content areas that can be modified for a wide range of courses and student levels to facilitate the use of this approach. Incorporating large, publicly accessible data into our teaching is a critical tool for improving learning in science.

Primary Presenter: Catherine O'Reilly, Illinois State University (oreilly@ilstu.edu)

Authors:

Rebekka Darner, Illinois State University  (rldarne@ilstu.edu)

Dax Soule, Queens College CUNY  (dax.soule@qc.cuny.edu)