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Writing With Numbers, and How to Teach It


Photo by Carl Socolow '77.

Inaugural Faculty Learning Community on Writing focuses on data literacy

by MaryAlice Bitts-Jackson

It’s been called “the oil of the 21st century,” and the guiding force behind important emerging trends. We’re speaking about big data, that superabundant resource that increasingly drives decision making at businesses and organizations the world over. And the more data we use, the greater the demand for professionals who can not only wrangle data, but also communicate what it all means.

Enter the Writing With Numbers learning community. Held during the 2018-19 academic year, Writing With Numbers brought professors from different departments together to share how they’ve incorporated writing instruction into data-rich courses and discuss best practices in teaching students to write about data effectively. As they brainstormed, the professors discovered new ways to weave data literacy into their courses and help sharpen students’ skills in this high-demand area.

Writing With Numbers was the first installment of the Writing Center’s new Faculty Learning Community on Writing initiative. (The second round, going on now, focuses on the First-Year Seminar.) Six faculty members, representing the departments of computer science, economics, international business & management, mathematics, political science and sociology, participated, and four Norman M. Eberly Writing Center tutors also took part, offering their insiders’ perspectives on the student side of the equation.

During monthly meetings, the group discussed common readings and held “teaching mirror” exercises; they also shared links, articles and teaching materials throughout the year. A highlight was a two-day workshop with Eric Gaze, president of the National Numeracy Network. And at the year’s end, the participants shared how they’d apply what they learned.

“It was quite interesting to see how differently we attack similar topics,” said Professor of International Business & Management Steve Erfle, who fine-tuned the way he teaches students in his Applied Empirical Data Analysis class as a result. Sarah Bryant, visiting assistant professor of mathematics and computer science, agreed. She used the working group to discuss and refine new assignments for her Elementary Statistics class, including a letter-to-the-editor exercise that helps students learn to identify and address misleading statistics. 

Sarah Niebler, assistant professor of political science, applied key points from the seminar into three classes she taught last year. These included a senior seminar, in which students participated in the creating, implementing and analyzing an exit poll of Cumberland County, Pennsylvania, voters. Based on group discussions in the learning community, Niebler reduced the amount of class time she spent on data analysis and increased her instruction on how to write effectively about that data, including how best to describe graphics that present quantitative information.

“I have done versions of this assignment in many classes, but the response papers that resulted from this iteration where some of the strongest I have received to date,” said Niebler, who also tweaked her current Research Methods class so that it emphasizes skills in communicating with percentages and proportions, and her First-Year Seminar, Ouija Boards to Big Data, which now includes a public presentation.


Published November 4, 2019