Initial Publication Date: May 14, 2019

Helping students develop quantitative reasoning skills via classroom engagement with data

Joseph T. Zume, Geography & Earth Science, Shippensburg University of Pennsylvania

I am a firm believer in the experiential learning doctrine. I believe that, one of the most efficient ways to help students develop quantitative reasoning skills, is by exposing them to hands-on, research-based, or enquiry-driven learning opportunities. Such can be through field data collection, analyses, and interpretation or in the classroom using available secondary data. At my institution (Shippensburg University of Pennsylvania), there is high expectation on faculty to provide high-impact educational practices (HEPs) for students. My department (Geography and Earth Science) strongly emphasizes the development of critical thinking, written, verbal, and quantitative skills for our students. The means to achieve these goals however, are in the hands of the individual faculty but, overall, we pride ourselves as a hands-on learning-oriented department. Below, I summarize some example approaches that I use in my regularly-taught classes to help students develop problem-solving skills. I use problem solving skills to encompass the set of abilities that enable students to link theory and practice in the context of exploring solutions to real-world problems.
Introduction to the atmosphere
In this general education intro meteorology course, I select a number of exercises from a lab manual by Greg Carbone, that I use to engage students in quantitative problem solving-with the kinds of application that enhance their quantitative reasoning skills. Additionally, for homework assignments, I have students download, for example, temperature and precipitation data from the Cooperative Observer Network, for stations in the local area which they analyze and interpret patterns to answer questions of practical significance (e.g. with respect to climate variability or change). At this GenED level, the challenge I commonly run into, bothers on "quantitative phobia" on the part of many students. Some are unwilling to embrace the learning opportunity offered through the quantitative lab exercises. Similarly, because I insist that all charts be made with excel and not by hand, several students get frustrated due to their unfamiliarity with excel. I am particularly interested in learning through this workshop, other strategies for developing quantitative reasoning skills for students in general education classes.

For this intermediate-level course, I strive to help students develop problem solving skills via both field exposure and use of secondary data sources. However, I will focus on the use of data here. Nation-wide hydrologic data, both for surface and ground water, are readily available through the U.S. Geological Survey (USGS) database and other local agencies such as the Susquehanna River Basin Commission (SRBC). For homework assignments and in-class learning exercises, I have students download several datasets including meteorological datasets from the Pennsylvania State Climatologists database, for analyses and interpretation. We perform several statistical analyses on streamflow, groundwater, and climatological datasets. The goal is to have students use available data to explore relationships between different hydroclimatic variables as well as solutions to a number of practical concerns. I believe strongly that when students are aided to investigate and answer a practical problem using data, their learning is enriched. It is one of the most efficient ways to help students build quantitative reasoning skills. I am interested in learning more about other strategies that colleagues elsewhere may be using.
Applied Geophysical Imaging
This is an upper-level course for our Geoenvironmental Studies students. As an adaptation to our student population, usually with limited physics and mathematics exposure, the course is approximately 50% field-based and research learning. Specific field methods used in this course are: Electrical Resistivity (ER), Ground Penetrating Radar (GPR), and Electromagnetic Induction (EMI) methods. Students are introduced to theories behind these methods and thoroughly prepared on the basic quantitative relationships that govern the functionality of each method before field exposure. The class is assigned a real-life environmental problem in the local community where students work both collaboratively and independently to collect data, analyze, interpret, and present their findings. It is my belief that the integration of theory and practice, through practical field investigations and problem solving, is great for helping students develop quantitative reasoning skills. I can discuss more and also learn from others on their specific approaches.

In conclusion, I have found the hands-on approaches in the manner described above effective. However, there are always challenges in getting some students out of their comfort zones. For a department like mine, where students come with less exposure to the core sciences from high schools, extra efforts are needed to motivate them to embrace quantitative learning.

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