An Introduction to Field Data Collection - Part 2
John Nevins Crandon High School Crandon, Wisconsin
We attempted to answer the questions by a sampling method which selected sites of one square kilometer that were located in representative locations through a region that went from the foot hills of the Brooks Range North to Deadhorse and West to Barrow Alaska.
At each site the thaw depth was probed at 100 meter intervals in the selected square kilometer and at 10 meter intervals within a randomly selected hectare within the site. In addition, each site had a set of temperature probes mounted on a mast that would record temperature in the soil, at the surface of the soil and in the air above the ground. Additional thermal probes were located in other parts of the site and all these were serviced and down loaded in spring and fall.
I have been looking for ways to show my students what it is like to do research and it occured to me that an example procedure might help. Our school is built on an 80 acre site in North central Wisconsin and includes athletic fields, woodland that has been logged in the last 20 yrs and some that is older as well as a transition area from the mowed lawns to the forest biome. In keeping with my need to try out something new, I decided to ask a question in class one day and see where it might lead us. How does the temperature change as we go from the athletic fields into the woods?
In the discussion that followed, each of my 3 classes established their own hypothesis and a null hypothesis. We discussed what factors might influence the measurements and decided to keep a record of the temperatures at 3 levels (this sounds a little like my research experience doesnít it?) As well as a record of the number, kinds, and height of vegetation.
I decided to cheat here a little as the time I had available was short. I set up lines for a series of line transects going down the hill from the athletic fields, across the region of succession created when the farm field that became the school grounds was abandoned, and into the woods. On each of the lines, I had the students make measurements of temperature and counts of plant numbers at 10 meter intervals over a line that was 100 meters long. Vegitation ranged from mowed lawn at one end to trees at the other end.
The students were divided into teams and each member of the team had a job. One was responsible for recording the teamís data, one measured temperature of the soil, the air at the surface of the ground and at 1.5 meters above ground, and another counted the number and kinds of plants in 1 square meter as well as measuring heights when possible. (Tree heights required an estimate or else a bit of trig. to calculate.)
When we returned to the classroom, each team supplied a copy of their data to the other teams and I held a copy for the other classes which I assembled into one handout for use by the other classes. By the end of the day, we had data that ran from early morning into the afternoon.
The next day I supplied each group with a copy of the data from the other classes so that each group had a sample set from the whole day. Once again I elected to save a little time by graphing a set of sample data from one of the research groups. We graphed the three temperatures measured at each of 11 points on the 100 meter transect on the same graph for easy comparison and drew a conclusion about how the results compared to the hypothesis. (I did not try to ensure that each team or class had the same hypothesis and null hypothesis selected though I did lead each class into picking a temperature relationship.)
Next, we tried to draw a temperature map of the study area from the data gathered by the class. I decided again to save time and started the process for the students with an example set of class data displayed as a map of data points and lines connecting equal temperatures (isotherms) between each transect line. (Our data surprised the students because it was possible to tell where there was shadow and how the shadow moved during the day as we had done our transects from East to West down a hill into the trees.)
Finally, I had each student graph and map their class data and write a report that provided a statement of their hypothesis, method of data collection, graphic analysis of class data, and a conclusion which included what errors they think their group may have made, was their hypothesis correct, and each student was expected to suggest another hypothesis that they could examine. For extra credit they could analyze the data from the other classes and/or run another experiment.
I have used versions or variations on this theme in AP Bio, Field Ecology and other classes that needed an introduction to scientific method and data collection.
Elaboration (Polar Applications)
Generally, I discuss how the logging companies figure out how much timber is on a parcel and then I talk about sampling methods. This activity came out of a wisecrack in class one day when one of my students was looking up at the ceiling. The outcome of the discussion was that we decided (I lead into this track) to find out.
I assigned different lines on the floor in my classroom for each group of students to walk along and set a sampling interval for them to use. Each group cut a 10 x 10 cm window in a file folder that would mark out a quadrat to be counted, and each student group moved along their transect stopping at the assigned interval to make a count overhead. They were not allowed in this instance to move off of a light fixture or a tile support.(I wanted to be able to use the data to figure out the number of lights and support track)
(An interesting thing happened the first time I tried this one. Our superintendent came through with a board member and several guests on a tour of the department just in time to see my students on stools and lab tables counting the holes in the ceiling. Itís a good thing they are not easily shocked by me. In fact, they acted like nothing unusual was happening in my class.)
When the students had completed their counts along the transect, I had them post their results on the board so that all the students could copy the results and then we went through a sample analysis with a part of the data. In that analysis we calculated the average number of holes in a square meter and multiplied that value by the area of the ceiling.
Another thing that can be figured out from the data is the number of lights in the room based upon how often the count was zero. When this is done it has poorer results with individual transects than it does with the whole data set from the class. This can help illustrate the degree of accuracy that can be achieved with this method.
For example data and calculations see below.(insert table here)