In this activity, students will use data collected about two male arctic ground squirrels by researchers at Toolik Field Station in northern Alaska. Each squirrel had a lightlogger to record light intensity (lux) and an implanted data logger to record internal body temperature (°C). Students work individually or in pairs to analyze the data sets and interpret the results.
- Students will use data collected from arctic researchers about arctic ground squirrels.
- Students will learn how scientists use the data they collect to answer their research questions.
- Students should have a good working knowledge of Microsoft Excel. Some background about hibernation and the Arctic is helpful, but is not necessary.
- Photocopy the handout for each student.
- Reserve a computer lab or have students bring in their laptops.
Students should work through the data-processing individually or in pairs, and ask for help as needed. They should make the graphs and either print them out, or sketch them in their notebooks, depending on teacher preference.
Students can use the second data set to compare and contrast the results between two male arctic ground squirrels to look at individual differences.
An excel file with data from two arctic ground squirrels is included, along with the worksheet and an answer key.
Answers can be self-assessed during class discussion, or can be collected and graded.
Jennifer Baldacci, 2017 PolarTREC teacher
International School of Basel
jlbaldacci [at] hotmail.com
Dr. Cory Williams, Researcher
University of Alaska Fairbanks
ctwilliams [at] alaska.edu
SC.912.CS-CP.1.3 Analyze and manipulate data collected by a variety of data collection techniques to support a hypothesis.
SC.912.CS-CS.2.6 Evaluate various data types and data structures.
SC.912.CS-CS.1.3 Explain how data analysis is used to enhance the understanding of complex natural and human systems.
SC.912.N.1.3 Recognize that the strength or usefulness of a scientific claim is evaluated through scientific argumentation, which depends on critical and logical thinking, and the active consideration of alternative scientific explanations to explain the data presented.
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