My Efforts, Ideas and Goals with Quantitative Reasoning and Data

Anita Ho, Geology/Geography, Flathead Valley Community College

I teach geology and geography (earth science, physical geography, astronomy, environmental science, physical geology, world regional and human geography) at Flathead Valley Community College in Kalispell, Montana.

My approach. My use of quantitative reasoning and/or data in the classroom has been minimal, which is why this EDDIE workshop appeals to me. I try to show my students that in all fields there exist extensive data from which much of our current understanding of the world is drawn. Most commonly, I show a class data that have already been plotted, often with obvious trends highlighted. Examples include Mauna Loa CO2 data, USGS hydrographs and climographs from the National Weather Service. Students particularly enjoy visualizations that are colorful and animated, such as NASA's videos of global average annual temperatures over the past century.

What I have tried. Outside of class, my students are generally only exposed to small datasets, some of which may not include real data. I require students to have access to Pearson's Mastering online homework platform. Assignable Mastering items include graphing exercises, which step students through plotting and analyzing small sets of data. They answer questions requiring basic analysis and interpretation of the data; however, students are not expected to think critically about the origins of the data or the variation, if any, within it.

I have used a lab on modeling population growth in my environmental science course several times. "Introducing endangered birds to Ulva Island, NZ," a lab on modeling exponential and logistic population growth, was developed by Ben Steele at Colby-Sawyer College and may be found on the SERC teaching portal. This lab requires using Microsoft Excel to model a population, and introduces students to formulas, data generation and graphing. Students gain a basic understanding of carrying capacity as well as exponential and logistic population growth.

I have tried having geography students visualize demographic and other data available in to compare different countries and world regions. The GapMinder Tool allows a user to choose two demographic indicators to plot, then animates colorful bubbles representing different countries changing over time. More commonly, I show a video of animations of these plots narrated by Hans Rosling, the late statistician and founder of GapMinder (e.g., "The River of Myths" at

As I prepared my application for this workshop, I found and decided to try the EDDIE climate change module in my environmental science class. This exercise helped students realize that databases used by many scientists are available to the public. They also learned that large sets of data include a significant amount of noise, and saw how a dataset needs to be large and/or collected over a long time period in order to be analyzed for temporal trends. We downloaded data as text files into Excel, formatted it and chose various data series and time ranges to plot, format and compare. Only one student had previously taken a statistics class, but it was easy for everyone to visualize the meaning of the degree of variance (R2 value) for the linear trends we were looking at, and understand its relevance to our interpretations.

Ideas I would like to try. Just one assignment, exercise or small project on some high-profile data in any class could make a significant impact on a student's understanding about a topic. Aside from other EDDIE modules that would be appropriate in environmental science, I am interested in using data from the Energy Information Administration to help students see trends in energy production and consumption in the US or globally to come to a deep(er) understanding of peak coal/oil/gas and the importance of alternative energy development. I would like to use data to discuss weather events that may (or may not) be related to anthropogenic climate change, such as the intensity and frequency of storms and floods. I could also have geography students work with demographic—census numbers, or data on migration, employment or incomes. Students always seem fascinated by the magnetic field reversals recorded in rocks, and invariably ask about data from present-day monitoring of the planet's magnetic field. For some of these ideas, I am not sure where to find the appropriate datasets, or whether they even exist.

Student learning goals
- I would like students to know that large amounts of real-world data are available for anyone to download and use. They should know there are certain institutions and agencies (e.g., NASA, census bureaus, the UN) that gather, archive and update the "best," or at least the most complete and current, data that exist.
- I hope students will become proficient and confident in their data-management and manipulation skills: able to format, plot, interpret and analyze a dataset of any size. In the process, they must also keep in mind that uncertainty is inherent in all data and datasets.
- I hope students will discover that it is worthwhile to handle data for oneself. This is the only way to avoid relying on interpretations made by a third party, unknown organization, media outlet or advocacy group—which may be biased—and pacify (or fuel) one's skepticism.
- Finally, I feel it is important for students to know that many academics are using the same or similar datasets, which is why sometimes different groups may arrive at the same or similar conclusions in their analysis and interpretation. Likewise, data may also display a high degree of variance, which is likely to lead to less definitive conclusions, or an apparent lack of consensus about their meaning.

- None of my courses have prerequisites, so students' computer, math and reasoning skills vary widely. Many students will have used Excel in high school, but do not remember how to arrange data and plot it. Many of my students are still working through pre-algebra or are currently in an algebra course, and manipulating data with formulas to calculate a new variable can be very confusing. A student who has completed a statistics course, and is already familiar with linear regressions and R2, is rare.
- My own skills and experience with large datasets are limited; I often come across links to data, and I should be more comfortable downloading and using it. I look forward to some practice and confidence-building in working with large datasets during (and after!) the workshop this summer.

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