Initial Publication Date: May 14, 2019

Quantitative reasoning in the geosciences through data-rich activities and projects

Dan Maxbauer, Geology Department, Carleton College

Understanding how to think about data and to make data-driven interpretations is a learned skill that is important across a range of disciplines and career paths. It follows that integrating quantitative reasoning into geoscience education is critically important for student development at all levels of the curriculum. There are, however, certain pedological challenges with integrating data-rich activities into courses. Here, I will explain two class activities I use at the introductory level to build quantitative reasoning skills.
Both activities are part of an introductory geology course, GEOL 115 Climate Change in Geology, taught at Carleton College. A major focus of the course involves studying local climate archives as a way to understand how paleoclimate proxies work. Two local archives of interest are lake sediment and tree rings. When studying each archive, students begin by making field or lab observations of the physical samples (lake sediments, trees, tree rings, etc). Subsequently, students work to collect various types of quantitative data from each archive to build a dataset they can use to answer a question. For lake sediments, a main focus is placed on studying pollen assemblages in the lake sediments that the class collects. This activity is very hands on and involves learning the full arc of collecting data, organizing a dataset, building an interpretation, and developing figures/visualizations that convey proper information. The tree ring activity involves more analysis of data that has been pre-processed and cleaned prior to students interacting with it.
There are certain strengths and weaknesses to each unit described here. For the pollen unit, students seem to have an ownership of their data and fully understand how and why it was collected. In this sense, the data represent more than just numbers to them and they can intuitively have a sense for how best to interpret the data. In this case, changing plant communities over time relates to changes in climate and environmental conditions. However, a result of having students collect and share their own data (where data represent pollen counts conducted by students) is that the data tend to be fairly messy, which proves challenging to interpret. Often, general trends emerge that are consistent with published results - and some students are able to come to this realization with time. For the tree ring activity, data is more prescribed and I am able to dictate a bit more with what the students should be able to determine given the dataset. This creates a scenario where the final interpretation is more straightforward than the pollen activity. But, a trade off here is that students understanding for what the data actually represent (including understanding of any statistical cleaning of the data that is conducted beforehand) is more limited. Utilization of an online tool for measuring tree ring widths on high-resolution images seems to improve student understanding of the data and their interpretations - which highlights the importance of having students collect and interpret data, rather than just interpret data they are given.
Common to both activities is the challenge at the introductory level of balancing practical instruction on how to plot and analyze data against instruction on understanding the more conceptual/scientific aspects of the project.

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Maxbauer_EDDIE Workshop_Essay (Acrobat (PDF) 55kB May7 19)