Penny M. Rowea* , Lea Fortmannb, Timothy L. Guascoc , Aedin Wrightd, Amy Rykene , Emma Sevierf, Grace Stokesg, Amanda Mifflind , Rachel Wadeh, Haiyan Chengi, William Pfalzgraffj , Justin Beaudoink, Isha Rajbhandarib, Kena Fox-Dobbsf & Steven Neshybad a NorthWest Research Associates, Bellevue, Washington 98052b Economics, University of Puget Sound, Tacoma, Washington 98416c Chemistry, Millikin University, Decatur, Illinois 62522d Chemistry, University of Puget Sound, Tacoma, Washington 98416e Education, University of Puget Sound, Tacoma, Washington 98416f Geology, University of Puget Sound, Tacoma, Washington 98416g Chemistry & Biochemistry, Santa Clara University, Santa Clara, California 95053h Physics, Edmonds Community College, Lynnwood, Washington 98036i Computer Science, Willamette University, Salem, Oregon 97301j Chemistry, Chatham University, Pittsburgh, Pennsylvania 15232k Interdisciplinary Arts and Sciences, University of Washington Tacoma, Tacoma, Washington 98402

The authors of this article introduce two teaching modules that aim to increase climate literacy and active learning in undergraduate economics courses through the incorporation of real-world data and modeling. These modules are based on the concept of computational guided inquiry (CGI), which combines a guided inquiry approach within a computational framework, such as Excel. In one module, students estimate and graph expected marginal damages due to regional sea level rise for various polar ice melt scenarios. In the second module, students partially replicate a journal article estimating the total economic value of ecosystem services in the Arctic. These modules have been used in urban, environmental, and climate change economics courses, and are ready to be implemented with minimal upfront cost to instructors.

This paper describes a method of teaching image processing in a computer science (CS) course in which students obtain and analyze polar data through a computational guided inquiry (CGI) module. In CGI, the instructor guides the students in the process of learning, through the use of a computational tool: for this course, a Jupyter Notebook is used, consisting of alternating text and blocks of Python code that the students can modify as needed and execute. The students obtain images of polar ice and use them to learn about image processing while increasing their climate literacy. Students demonstrated learning of course disciplinary objectives through assessments built into the CGI module. Pre- and post-module surveys indicate increases in student self-reporting of comfort with Python and exposure to polar data. Over half of students indicated increased interest in learning more about polar research, and students overall rated the CGI modules positively. Improvements in climate literacy were tested through asking students to ask a question about a visual representation of polar data; results of this assessment were inconclusive. Future work will focus on strengthening the connection between goals, activities, and assessment, in order to better understand whether the goal of improved climate literacy was achieved.