Using Large datasets to teach quantitative reasoning and analytical writing in a global climate change coursesubmitted by
This is a partially developed activity description. It is included in the collection because it contains ideas useful for teaching even though it is incomplete.
Higher Order Thinking Skills:
developing sci. arguments
Role of Activity in a Course:
Data, Tools and Logistics
paper which presents the data, the data analysis and a coherent and substantiated conclusion
NIEMITZ, JEFFREY W., Dept. of Geology, Dickinson College, Carlisle, PA 17013, firstname.lastname@example.org
Many undergraduate students cannot adequately interpret large, complex datasets even when presented in graphical form. The need to improve our student's quantitative reasoning and analytical writing skills has lead to the development of a series of integrated exercises in our introductory global climate change course. Global climate datasets are excellent resources for helping students improve their quantitative reasoning skills and understand of temporal and spatial interactive global processes. In an effort to provide formative assessment for student progress in both these critical skills, labs start with simple data extraction from newspapers and hand graphing and culminate in large and complex database analyses using Excel with computer graphing skills and basic statistics integrated into short written assignments. In advance of the first exercise, students gather a week's worth of data from their hometown newspapers. Then the students find their state climatologist's website and download the same data from the year before. They graph these data for both time periods, compare them, and turn their data and reasoned interpretations into a two-page paper. The following week a few students' examples are highlighted to show the range of weather and climate change. By analyzing student results anonymously all learn the kinds of misinterpretations that can result and the depth of analysis that can be done even with a small dataset. Dataset size and complexity increases in subsequent labs using climate phenomena such as ENSO, monsoon intensity, and drought to explore the relationships between global climate change and local manifestations of those changes over time. Datasets come from the websites including NCDC climate, USGS stream gauge, and ITRR tree ring records. Besides learning the basic functions of Excel, students' data analyses include regression and basic spectral analysis. Improved quantitative and written skills do translate to other courses and, hopefully, the quantitative literacy all citizens need in the 21st century.