Jennifer B. Esty
The results from this curriculum unit will be in the form of a lab report. Any decent lab report addresses New Haven Science Standard 1.1f. The specifications for the report are a standard in every class I teach. The report requirements are found in an appendix at the end of this unit. All students, even the ones at home will be required to write a report detailing their findings. The format of the lab report helps student to organize their thoughts into a logical flow of information. The practice is especially useful in high school because the format is so similar to a five paragraph essay and is a good basis for writing research papers, which all of our students do. I generally give extra credit for typing the reports; it makes them easier to read and encourages the students to practice using the computers.
The results to be generated for this lab report consist primarily of two types of data analysis. The first is mathematical. The second is more analytical. The mathematical part of the data analysis is mechanical. It is simply applying formulae to a collection of data and generating more numbers. The more thought provoking pursuit is explaining what those numbers tell a reader about the data that was collected.
In this case, there are several mathematical models to follow, some or all of which could be used depending on the level of the class or of individual students. In the end, all methods should yield generally similar results; the differences being in the degree of detail yielded by the data. The simplest approach is to have the students compose a bar graph showing the different numbers of species found in the different habitats. This will show a general trend in the data, but make it difficult to derive any mathematical relationships between the species diversity and the land management technique. Furthermore, it does not give much information about the richness or density of various species. A slightly more sophisticated approach would be to create a line graph showing the relationship again between the number of species and an approximate degree of human interference in a given habitat. This approach will yield something more nearly resembling a mathematical relationship between the two quantities. However, it again lacks the information about species richness. A more sophisticated approach might use weighted averages to show the abundance of some species and the scarcity of others. These data could be plotted against the land management techniques to show how different organisms fare under different land management styles. For example, one might expect to find corn very prevalent in a corn field, but corn borers also ought to be present in large numbers. The variability of data analysis techniques are really only limited by the mathematical abilities of the students in a particular class.
For my classes, I expect students to answer the fundamental question of "what do all these numbers mean"? So, if the student finds ten species in the first landscape, five species in the next and only two in the third, I expect not only a description of the trend, but also a possible explanation for the results. Furthermore, I expect my students to describe the accuracy of their data. If, for example, all the students entered the study area in a noisy wave, I expect an explanation for why there were no animals found in a particularly favorable location and whether they expect to get similar results in a different trial.
For many students the mathematics is challenging, but it is the interpretation of the results that is most difficult. Instinctively, they know what the numbers mean, but they have trouble converting numbers to what they saw when collecting their data.