Forging on-campus connections to enhance undergraduate student reasoning, writing, and research skills

ABSTRACT

Research and writing are critical components of an undergraduate education. Partnerships between economics faculty and campus resources can improve student research and writing skills. Here, the authors describe programs at three different campuses that bridge department and campus resources: the Empirical Reasoning Lab at Barnard College, the Writing Program at Carleton College, and the Library's Data Lab at the University of California, Berkeley. The authors describe each program's mission and structure, provide examples of its impact on student learning, and discuss administrative factors (and hurdles) to consider in implementing similar programs elsewhere.

KEYWORDS:

• General economics
• library science
• quantitative reasoning
• teaching
• undergraduate research
• writing center

JEL CODE:

• A22

Notes

1. We distinguish between empirical and quantitative reasoning because in the context of general education requirements, the latter term can often refer to formal analytical (e.g., mathematical) analysis. For a provocative view on the meaning of quantitative reasoning, see Lutsky (2014). Empirical reasoning is similar to the notion of “quantitative literacy” found, for example, in the AAC&U (2010) VALUE—Valid Assessment of Learning in Undergraduate Education—rubrics.

2. For information on Barnard's T&L Center, see http://barnard.edu/about/teaching-learning-center. The ERC will join two other innovative learning spaces to constitute a “digital commons.”

3. For just one example of the challenge we face in this regard, the online version of a New York Times (Zimmer 2012) article includes a correction for misstating the probability of a virus mutation as “roughly one in a thousand billion billion” instead of “one in a thousand trillion trillion.” Thanks to Mija Van Der Wege for sharing this example with us.

4. Akers and Doty (2013) describe the role of the data librarians at Emory University, noting the key differences by discipline in faculty expectations and understandings of research data management needs.