Integrating polar research into undergraduate curricula using computational guided inquiry

Abstract

Polar research plays a vital role in developing our understanding of Earth’s climate system. It is intrinsically interdisciplinary, lending itself to integration into existing undergraduate courses. Here we explore introducing undergraduates to polar research through computational guided inquiry (CGI) modules taught in a variety of courses and disciplines. Students apply course disciplinary techniques to analysis of polar data or research, in the context of climate change, by working through educational modules that include spreadsheets (Excel^TM) or interactive computer programing (Python in a Jupyter Notebook), over a few class or lab periods. The goals of this exploratory curriculum project are to determine instructor perceptions of effectiveness of the educational modules for teaching preexisting disciplinary course objectives, as well as student perceptions of enjoyment and learning. Evaluation consisted of a student questionnaire and interviews with instructors by an external evaluator. Students and instructors overall reported positive experiences with the modules, highlighted the importance of polar data and climate literacy, and noted increases in student understanding of course learning goals and comfort with the computational tools. Professors further reported that students found the modules motivating, fun and engaging. Taken together, this suggests that the modules are an effective means of bringing polar research into undergraduate classrooms while satisfying instructor goals for course learning objectives. Lessons learned include the importance of providing material such as videos to help transition to the topics of polar research and climate change and of supporting widely varying computational fluency.

Keywords:

• Computational guided inquiry
• polar research and data
• climate change
• undergraduate
• Python
• Jupyter Notebook
• excel
• computational literacy
• climate literacy

Acknowledgements

We are grateful to Washington State University’s Social and Economic Sciences Research Center for conducting our external evaluations and to the Science Education Resource Center (SERC) for developing our website and publishing our CGI modules. We thank Erin Cisewski for editing references; Katie Gray, Shreeti H. Patel, and Maxwell Coleman for help testing CGI modules; and Von Walden and Erin Colbert-White for their participation and help with workshops. We also thank the anonymous reviewers and the editors whose many excellent suggestions greatly improved this manuscript.

Additional information

Funding

PR acknowledges NSF award #1712354. SN and LF acknowledge NSF award #1712282. GS acknowledges funding from a Clare Boothe Luce (CBL) professorship and a Cottrell Scholar Award from the Research Corporation for Scientific Advancement.