About this Project
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What are PENGUIN modules?
PENGUIN modules provide a computational framework to allow students to work actively with polar research and data through conduct guided inquiry. Working through a Jupyter Notebook or Excel Sheet, students analyze polar research and data: creating figures, performing calculations, asking questions, and thinking critically about what the data shows.
Why use PENGUIN modules in your classroom?
PENGUIN modules allow you to meet learning outcomes for your course while at the same time introducing students to polar research and data. The context of climate change motivates students, who are often deeply concerned about this topic.
What courses and disciplines are right for PENGUIN modules?
We have made modules for courses in Economics, Computer Science, Physics, Quantum Mechanics, Thermodynamics, and Environment Science Tools and Methods. If you teach one of these classes, or a similar class, and would like a real-world example to bring student inquiry to life, please check out our modules.
The following table, reproduced from Rowe et al. 2020, summarizes the modules, including the courses and course topics covered.
Table 1. Computational Guided Inquiry (CGI) modules and corresponding goals related to course topics, climate and polar literacy, and polar data used.
| Module | Course Topical Goals | Climate/Polar Literacy | Polar data |
| Economics: Arctic EV (Excel) |
Be able to apply the total economic valuation framework. Understand the impact of assumptions on estimated values. Learn how to adjust for inflation, convert currency and organize data. | Understand the value of lost ecosystem services in the Arctic due to climate change. Engage in academic research on climate and polar regions. | Research papers on polar ice melt. |
| Economics: Sea Level Rise (Excel) |
Develop skills with tools used to apply decision-making given uncertainty in sea level rise and flooding. Be able to calculate and graph marginal damage curves. | Connect sea level rise due to ice melt in the polar regions to local impacts (at the nearest coastal city). | Polar ice melt scenarios |
| Quantum Mechanics: Polar Spectra (Python) |
Know shapes of spectral features due to ro-vibrational transitions. Model populations of rotational states according to degeneracy and temperature (T) to infer T. Understand the Planck function, its variation with T, and Wien's Law. | Develop a basic understanding of the greenhouse effect and the role of gases and T, the uniqueness of polar regions, and the importance of water vapor. | Polar down-welling infrared radiance spectra |
| Thermodynamics: Ice Melt (Python) |
Be able to construct a phase diagram & compute heat needed for melting ice. Apply enthalpy, the Clapeyron equation, Raoult's Law & freezing point of sea ice in equilibrium with seawater. | Be aware of Arctic observatories and datasets. Understand how climate change affects Arctic ice volume, area, and depth and climate change. | Arctic ice volume, area, and depth |
| Physics: Permafrost (Python) |
Develop skill in analyzing heat flow through a medium, using a numerical derivative technique, as well as heat flux, thermal diffusivity, heat capacity, and thermal conductivity. | Learn what permafrost is, how it responds to a warming climate annually & over multiple years, & consequences for the Arctic. | Temperature profiles through permafrost |
| Computer Science: Arctic Images (Python) |
Be able to load, manipulate & plot images, extract RGB components, & apply colormaps. Gain experience with noise removal and edge detection. | Learn about the ice-albedo effect, trends in Arctic sea ice related to climate change, and Earth observing satellites. | Satellite images of the Arctic |
| Tools in Env Scia: Ice Cores (Python) |
Be familiar with Milankovitch Cycles, Dansgaard Oeschger Events, and glacial-interglacial cycles and how they are evinced in ice core data. | Know that polar ice cores record past T & reveal correlations between CO2 and T over millions of years. | Ice core record |
| Statistics: Ice Cores (Python) |
Perform linear regression to reconstruct the 800,000-year temperature record from the isotopic record in ice cores. | Learn about polar amplification and how temperature and CO2 are correlated on different time scales. | Temperature and ice core records |
aTools in Environmental Science.
Funding Sources
This project is supported by funding from the National Science Foundation, Division of Undergraduate Education and Polar awards 1712354 and 1712282. NSF PLR 1543236 also helped fund development of the Polar Spectra module.