Teaching Quantitative Reasoning to Promote Transferable Skills

Kimberly Coleman, Center for Earth and Environmental Science, SUNY - Plattsburgh

As an educator, I believe that critical thinking, as well as oral and written communication, teamwork, and cultural competence, are as important as academic content. I refer to these skills as "transferable skills" because I believe that they serve students well throughout their college tenure and beyond. I am interested in teaching quantitative reasoning because I see it as way to deliver these critical transferable skills.

I have tried a range of strategies to teach these skills through use of data and through quantitative reasoning in my courses. Because I also believe in student-centered approaches to delivering content, many of these strategies have involved student projects, often with the goal of students analyzing data or other information in order to make a decision, complete a presentation, or write a paper. For example, the National Science Foundation's National Center for Case Study Teaching in Science maintains a database of case studies by topic; I have used many of these as the basis of in-class group activities. This approach highlights the complexity of a given topic, such as collaborative decision-making in environmental planning contexts, and to help students begin to think critically. These case studies present a range of quantitative and qualitative data for students to analyze, interpret, and consider the implications of. I have found that case study exercises can be particularly effective to help students unpack difficult or uncomfortable topics, such as environmental justice.

In my current role as Assistant Professor of Environmental Planning at SUNY Plattsburgh, I teach a mixed methods research class in which students are analyzing real data from one of my research sites and using it to collaboratively write one paper for submission to a peer reviewed journal. My hope is that this teaching students quantitative reasoning skills, as well as teaching them about the process of science (i.e. how do studies work? How does peer review work?). Ultimately, I hope this makes them more science-literate in their life outside of SUNY Plattsburgh.

I am still early in my career, and I am still learning what strategies work best for helping to develop graduates who can think critically and communicate clearly. The biggest challenge that I have faced thus far is a lack of resources at my institution. For example, in my mixed methods class, my students only have access to older qualitative coding software, which means that they cannot process data from projects that I created in a newer version of the program. Beyond those financial restrictions, my most pressing challenge is evaluation. Throughout my teaching experience, I have come to realize that students will learn what they are tested on. For that reason, I believe in evaluation approaches that test for both skills and content knowledge. I have spent some time thinking about designing exams that I ask to demonstrate quantitative reasoning, (e.g. asking students to analyses data sets or asking them assess a peer reviewed paper and the claims it makes) but I would love more strategies to evaluate the critical thinking that I hope comes from teaching quantitative reasoning.

Finally, I am looking for more teaching activities to incorporate into my classes. How can big data sets be used to teaching both quantitative reasoning and research methods? I would love to see examples of assignments, projects, and modules that others have used. I would also love to see a list of existing data sets or repositories that can be accessed for use in class. I am looking forward to learning all of this and more at the workshop in June!

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