Teaching

In my 10+ years of teaching, I’ve designed and led writing courses for all levels of undergraduate students. These have ranged from courses targeted to students in specific fields, like engineering or data science, to courses introducing students across the humanities and sciences to emergent frameworks like algorithmic rhetoric. I’ve co-taught seminars in data analysis for humanities scholars and professionals.

At Emory, I regularly teach a course on Technical Writing for Data Science, seminar courses on topics like disinformation and citation, and the gateway and capstone courses for our Rhetoric, Writing, and Information Design minor.

With my Center for the Future of Trust co-director Jo Guldi, I co-designed and co-taught an Introduction to Text as Data, a survey course for data science students on humanistic data analysis that uses analog activities to teach them how computational text analysis methods work.

My Approach to Teaching

My teaching combines some threshold concepts from writing studies with a rhetorical approach to data analysis.

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Writing practices vary with and shape community. Writing practices—like how, when, and where to cite sources—vary from field to field, shaped by and continually reshaping what each community considers "good" writing. My data science students learn practices in coding style, version control, documentation, and environment management as conventional forms of collaborative writing, rather than rigid technical requirements.
Translating genre knowledge is more effective than learning rules. My students test the conventions they've learned in previous experiences and adapt them for new audiences and uses. They move from inheriting formulaic models toward grounding their analysis in rhetorical principles. They increase their repertoire of genres with ones that bring in narrative techniques. They write code that restructures data collected for one purpose to serve another, translating between forms.
Writing is a technology for thinking through ideas. My courses treat writing not as the communication of settled knowledge but instead as a technology for working through ideas and walking readers through inquiry that is often non-linear and iterative. My students engage in data analysis that serves to ask questions as much as answer them.
The best writers know when to show their thinking. I want my students to know that their choices are meaningful and to look for opportunities to reveal and draw attention to those choices. In my data courses, that means telling stories with data that account for motive and method. In my course on disinformation, that means learning to evoke an experience they co-create with readers rather than explaining it, while weighing the ethics of representing others. That reflexive habit is also what can distinguish their writing from AI-generated text.
Friction is part of learning. My pedagogy draws on anthropologist Anna Lowenhaupt Tsing's idea of propulsive "friction" between ways of knowing. I bring seemingly unlike materials together from my different disciplinary backgrounds so that students can put new terms to habitual concepts and see them anew. In my disinformation course, this looks like rhetorical listening: learning to understand others' views on their own terms without the goal of assent or changing minds. It teaches them to situate knowledge from multiple traditions, and to negotiate between representing others' work accountably on its own terms and finding what's useful in it for their own purposes.

Courses at Emory

For earlier courses at Brandeis and Northeastern, see my CV.

Writing Facilitation and Mentorship

At Northeastern University’s Writing Center, I facilitated working groups for PhD students in the disciplines, especially the sciences, coaching them on revising drafts, developing their composition processes, and giving each other peer feedback. I also held one-on-one consultations, in person and online, with writers ranging from undergraduates to faculty.