In the suggested group investigation (see "Student Instructions"), students work in teams to plot data over 20-year intervals. Decide how groups will be assigned their data sets. Each group first looks at their own time frame to decide on possible patterns and hypotheses to explain the data. In the final step, groups line up all their figures to see long-term trends. Hopefully students will also gain an appreciation for the value of long-term data sets.
There are several additional steps in these instructions that you can include or not, depending on time. Before working with the data, students are asked to discuss the usefulness of ice-off, ice-on, and duration data in studies of global change. Also, after working with their data, students are asked to pair with another group to compare findings before looking at the whole data set.
On the Excel file for faculty, the ice duration data are plotted for 20-year intervals so that you can easily see the type of figures students will make. In addition, there are separate figures for the full 150-year period for ice duration, ice-off date, and ice-on date. Students can work with any of these three measurements or several of them, depending on how much time you want to devote to this.
Students can work with the “ice-off,” “ice-on,” or “ice duration” data. If you have time, the class could decide as a whole which type of data to examine. A note about the duration calculations: in a few isolated cases the total number of days of ice cover is not simply a subtraction of the ice on day from the ice off date. Rather, the number of days of ice cover is the total number of continuous days of ice cover. For example in 2001, even though the lake first froze on 1/2/02 (day 2) and last thawed on 3/15/02 (day 74), it was only continuously covered with ice for 21 days that season. In that particular year, it thawed after the initial freeze and then refroze a few weeks later.
If your students have minimal experience with data sets like this, it would be a good idea for them to first plot the data by hand. There are several advantages in doing so. First, you don't need computers and students don't need to know how to use Excel. Second, for a simple data set like this (20 years) students will pay more attention to the pattern because they are actually drawing the trend by hand and take more time making the figure. If you decide to go directly to use of Excel, be sure that they look carefully at the year-to-year variation.
If your students are not familiar with Excel, you can also use this exercise to teach them how to use this spreadsheet. There are several good Excel tutorials on line for your students (see the Resources section in the Overview). This would have to be done in an additional session before the lake data exercise.
Making Predictions Through Comparison of Three Lakes (see Extra Data Set Excel file)
The excel file “Extra Data Set” contains data for three lakes in Madison: Mendota (labeled ME), Monona (MO), and Wingra (WI). The Lake Mendota and Lake Monona data are from 1853-2002 and the Lake Wingra data from 1877-2002. Note: the Lake Wingra data set is incomplete with several years of missing data. This data set provides opportunities for students to try to make sense of incomplete data. You may modify the activity by focusing on temporal changes alone for one or more of the lakes.
In groups of 3-5 students, students should make predictions of ice cover for these three different LTER study lakes in Madison, Wisconsin. Students should discuss and identify how differences in the physical characteristics of the three lakes might result in differences in trends of changes in ice cover. Groups should use the following lake information in their discussions.
Table 1. Physical Characteristics of Madison-area study lakes.
|Lake||Surface Area (ha)||Mean Depth (m)||Max Depth (m)|
Predicting Beyond 2001
The lake ice data excel spreadsheets include data through the winter of 2001. Faculty should note that these spreadsheets were developed from a data catalog that adds new data each year. These may be found at http://lterquery.limnology.wisc.edu/abstract_new.jsp?id=PHYS which holds the data catalog for physical limnology of North Temperate Lakes Primary Study Lakes. The physical limnology data catalog includes the dataset North Temperate Lakes LTER: Ice Duration Madison Area Lakes. To access this dataset you will need to set up a username and password before querying the dataset and identify how you plan to use the data (e.g., education materials). Identify the fields that you wish to retrieve, lake id or name of the lake, the winter season, ice duration, ice on, or ice off and specify the output (Excel spreadsheet, screen, or comma-delimited file).
Because these datasets include new data each year, faculty may wish to consider an extension of the main activity, and may choose to ask students to predict ice cover (e.g., duration of ice cover and / or date of freezing and thawing) from 2001 to the current year. Students should explain why they made their particular prediction and identify factors they considered when making their prediction. They can then compare their prediction with actual observations. This extension of the main activity is an effective way for students to develop and evaluate a simple predictive model from their analyses and interpretation of the pattern that emerges from the long-term dataset. This could be used as an interesting assessment that can help determine if and how students apply their analyses and interpretation.
Additional Assessment Notes:
Margaret Waterman and Ethel Stanley (http://serc.carleton.edu/introgeo/icbl/assess.html) suggest that the following be considered when assessing students engaged in problem-based learning activities such as this one:
Cathryn Manduca and David Mogk (Carleton College), Using Data in Undergraduate Science Classrooms, (http://serc.carleton.edu/research_education/usingdata/report.html) provides an excellent background on using data and assessment ideas. See also http://serc.carleton.edu/research_education/usingdata/ for additional information and resources.