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Optional Module Accelerating Sea Level Rise

Initial Publication Date: May 19, 2020

Summary

This is another, optional module, styled after modules 3, 4, and 5. It covers changes in sea levels over many years, and allows students to look at the differences between a linear and quadratic equation and what effects this has over time. This module has not been tested alongside the other modules presented previously.

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Learning Goals

  • Students will learn what causes sea levels to rise
  • Students will be able to describe how ice masses have changed over time in the Arctic and Antarctic
  • Students will be able to differentiate between linearly and quadratically increasing equations

Context for Use

This unit is designed to be taught to undergraduate non-science majors over several 75-minute class periods as part of an introductory marine science course, where students discuss a news article, have a lecture, complete a pair programming exercise with the aid of instructional videos, and conclude with an opportunity to write a response to the author of the news article read at the beginning of the unit. It builds on previous concepts and thus is best taught after the previous modules.

If adapting this entire course, students will practice and expand upon what they've learned in the other modules. They will need to use their narrative interpretation skills as well as their R plotting skills to visualize data and draw conclusions. If this is not being taught after the previous module, then it will need to be adapted.

Description and Teaching Materials


Students should be given the news story ahead of time and should read it and fill out the Elements of a Story form prior to the first class of the module. The form should be submitted before class, and students should come to class ready to discuss the article. The first few minutes of class should be spent discussing students' thoughts on the article, and what they thought the heroes, villains, problems, and solutions were. The handout includes several questions on it which can guide how students should be thinking about the article and data as they move forward.

Following this, the lecture can be given to introduce students to the key properties of sea ice. This can be done in the same class period or may be spaced out over several class periods to limit the time spent lecturing in any class period. Additionally, the lecture can be removed entirely and replaced with another method of information dissemination, such as an in-class reading.

Example lecture topics to introduce the module include:

  • Oceanographic aspects
    • Tides, Ice Mass, Steric Effects, Ice Volume, Topography
  • Statistical aspects
    • Time Series and Quantifying Change
    • Linear Regression

After the lecture, students can watch the two R coding videos for the section and follow along, creating their own R code. The instructor should be ready to provide technical support and assist students if they become confused or lost. This module contains two instructional videos, and it is intended that each video is allotted one class period for students to watch it and for the instructor to aid as students make their code. If students do not complete their code in class, it becomes homework. Students will generate graphs in R, and these can be submitted at the beginning of the next class period for a few points, creating a low-impact assessment that encourages students to stay engaged in the class.

After the videos have finished and students have generated their graphs, the discussion can return to the article to see if students have different viewpoints after looking at the data themselves. This can occur at the end of the class period after watching the second R video or can happen the following class period. The questions on the handout can help facilitate this discussion, and students should now be able to answer all the questions on the sheet except for the last one which requires work outside of class.

Students should then spend time at home finding more data they can analyze that is related to the news story and pen a letter to the editor to respond thoughtfully to the news article. This letter should include graphs of the data covered in class and other data the student finds on their own. This serves as the primary assessment for the module, as the letter will show students' grasp of what the article says, what the data says, and demonstrate the students' ability to critically analyze and draw conclusions from both.

Teaching Notes and Tips

The lecture and assigned textbook readings help students gain an understanding of the scientific concepts behind the data presented in the module. However, an in-depth understanding of these factors is not necessary to interact with the news article and data, and thus both may be eliminated to save time if desired. Alternatively, the material covered in the lecture and textbook may be expanded on to create a longer course on polar science.

Students are still likely to struggle with the heroes, villains, problems, and solutions of the article, so they will need help during the class discussion to ensure everyone understands. While it is best if the students can work through the details as a class, they may need some guiding questions from the instructor to help them reach the correct conclusions. One way to go about this is to first have students list off all the people and things discussed in the article, then categorize them into good, bad, or neutral as presented by the article.

With the R code, if students follow along with the video, they should have little trouble. Where students may struggle is when asked to find their own data sets. This can be particularly daunting for students who have never had to seek out data before. These students may need extra attention and assistance to become accustomed to how to seek out new data sources. An alternative option is for the instructor to provide several suggested data sets for students to access and analyze, as this eliminates the need for students to search for new data but still requires them to choose a data set and work through it on their own.


Assessment

Students should turn in their Elements of a Story form before class, which can be graded. At the end of the module, students will generate a letter to the editor as well as R code with graphs, all of which can be submitted for a grade. The Elements of a Story form is intended as homework to ensure the students read and understand the news article that is the focus of the unit.

As students work through the code for the two videos, they will generate several R plots. Having students submit these at the start of the next class provides further motivation to keep pace during class, and anything they do not finish in class they then finish at home.

The letter to the editor is the product that tests understanding of both the news article and the data set analyzed. It goes a step further by having students bring in an additional data set to their discussion, ensuring that students are not just parroting back concepts learned in class but are actively applying them to something not directly worked through in class.

References and Resources

  • Morrissey et al. Intro to the Biology of Marine Life (Ch. 2, Ch. 14.0 and 14.1)
  • Pereria, S. "Sea levels are surging at faster and faster rates as Antarctica and Greenland melt, Satellite data reveals." Newsweek, February 2, 2018. https://www.newsweek.com/sea-level-rise-has-rapidly-accelerated-1992-melting-ice-and-its-not-803326
  • NOAA/NSIDC; Sea Ice Concentration and Sea Ice Index
    • GSFC. 2016. Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2 Version 4.2 Ver. 4.2 PO.DAAC, CA, USA. Dataset accessed [2018-05-04] at http://dx.doi.org/10.5067/GMSLM-TJ142.
    • Wiese, D. N., D.-N. Yuan, C. Boening, F. W. Landerer, and M. M. Watkins (2016) JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height RL05M.1 CRI Filtered Version 2., Ver. 2., PO.DAAC, CA, USA. Dataset accessed [2018-05-04] at http://dx.doi.org/10.5067/TEMSC-2LCR5.
    • Wiese, D. N., D.-N. Yuan, C. Boening, F. W. Landerer, and M. M. Watkins (2016) JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent
  • R Core Team. 2017. R: A language and environment for statistical computing. Vienna, Austria. URL https://www.R-project.org/

R code files (for instructor reference only)

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