By what metric?

Join the Data Journalism Teachers’ Club–a new place for data journalism educators to learn together

An instructor stands in front of a classroom full of students.

Lots of folks in the data journalism community teach, but not many have formal training in how to teach. (Erik Westra photo)

“Where did you learn to teach?”

This is the question that the authors of this post–all recently appointed professors of data journalism–found themselves confronted with after their first semester of teaching data journalism. The question came from enthusiastic students who enjoyed our classes, top performers who seemingly had learned a great deal in their first courses with us. It gave us warm fuzzies, but the question also gnawed at us. Because truthfully, none of us had formally learned pedagogical practices. Like many data journalism professors–and unlike our colleagues in other disciplines who had spent the past several years in academia–we were plucked out of our respective newsrooms and dropped into classrooms.

Our first semesters seemed to have concluded “successfully,” but as data journalists, we were left pondering what the metric of success is. Our students were happy, but was that enough?

We found ourselves in environments that were eager for our data expertise; we received emails from faculty members offering assistance, and, in best case scenarios, were offered access to one-day trainings on teaching during onboarding. We solicited syllabi from our new colleagues and friends at other institutions. We read books like Data for Journalists and Numbers in the Newsroom. We attended conferences like NICAR and picked up some great tips from professors who had been doing this work for decades.

But we had little by way of evidence, evidence beyond anec-data, that students had truly internalized our lessons. A good data journalist is a careful and methodical purveyor of information. We take great pains to ensure that we have met the burden of proof to write each and every sentence, to back up every assertion.

So, how do we back up the assertion that we are practicing good data journalism teaching methods? How can we KNOW we’ve taught well?

It is with this gnawing question in mind that we have decided to launch the Data Journalism Teachers’ Club. This initiative will include three spring sessions—one at NICAR and two online—where educators of any type or level will hear perspectives on teaching from those with specific expertise, and will have time to discuss strategies and ideas with each other.

If you’re a data journalism educator of any kind, we’d love for you to join us. Here’s our upcoming schedule:

  • This month, we’re excited to hear about what you’re looking for via our form;
  • In March, check out our session at NICAR: "Past, present and future of data journalism education,” during which we’ll set aside time to talk about data journalism educator needs;
  • In April, we’ll talk to researcher Anita Sundrani about how math education can inform data journalism; and
  • In May, we’ll hear from investigative journalist and educator Eva Constantaras on teaching data journalism around the globe.

We want to hear from you on what you’d like from a community like this, and we’d love for you to join our sessions!

Among the three of us, we’ve talked extensively about what we’d want out of a community like this, but throughout 2024, we want to hear from all of you and build this network together. Here are some of the conversations we’ve already started having:

Formal structures

When data journalism professors gather at conferences, we often discuss which tools we teach in our classes. We share techniques, adopt one another’s lesson plans, and pass along resources we’ve created. We volunteer to guest speak in one another’s classes and share our syllabi with new professors who are just getting their start. There is a wonderful sense of community, and a network of support from others who have trodden the path from newsroom to classroom.

But rarely do we speak of pedagogical formalisms like learning objectives and assessment mechanisms.

While we bring a degree of rigor and empiricism to our journalism, helping the public to understand what is known about a topic after a thorough review of the relevant academic literature, data journalism educators rarely talk about what “research shows” when it comes to modes of teaching. We don’t have discussions about how one rubric is better than another or what form of feedback is empirically most effective. Many of us run project-based classes where students produce a work of journalism for their final project, but is that sufficient evidence that every student has gained the intended level of mastery over each learning objective for the class?

And what of our own performance? Many of us are evaluated by admin on the quality of our student evaluations, despite research showing that student evaluations don’t always correlate with instructional quality (Esaray & Valdes).

There is no right way to be a data journalist. There is also no right way to teach data journalism. We all teach differently, and that’s great! A diversity of approaches is important. But we don’t have a shared vocabulary to articulate those differences and debate the effectiveness of one approach over another. We need acknowledged and developing best practices which we can discuss within our small community.

“Research shows”

Over the past several months, we’ve worked to take a look at some of the more formal research we’ve collected in existing academic journals, like Journalism & Mass Communication Educator, which we’re hoping to share more of at NICAR this year.

While other professions like medicine and law have research and journals dedicated to generating empirical insights about the advancement in pedagogical approaches to training practitioners, including dedicated journals for various sub-disciplines, that is not so much the case in journalism.

Some of the existing thoughts around data journalism education as a whole have been explored by thoughtful folks in our field through whitepapers like Teaching Data and Computational Journalism by Charles Berret and Cheryl Phillips and a few academic papers like a recent one by Bhaskaran et al that we found in the process of preparing this post. We were particularly interested in their work because while they didn’t systematically review what they called “gray literature”, like whitepapers and blog posts where admittedly much of the conversation on these topics has taken place, they did a comprehensive review of the academic literature around English-language data journalism pedagogy globally. They conclude that a large portion of their corpus is rooted in anecdotal case studies or one-off suggestions from other instructors: “This shortcoming reflects in the general lack of methodological diversity and absence of references and frameworks from allied areas of theory or research.”

There has been even less written on the journalistic environment in which data reporters work, and how to discuss these nuances with students who are just starting out. The ideas that data and numbers are not inherently objective, that our missing datasets are “missing” on purpose in order to serve power, that a huge part of our work is agitating to release data and documents to the public rather than just working within the confines of clean data to which we have easy access – these are not ideas that inherently exist in our classrooms. There is a shared understanding that teaching data journalism is a lot more than teaching technical tools. Despite our “focus on the use of technology”, we are teaching something deeper, a “journalistic practice at the core” (Bhaskaran Et. Al.). But what exactly that journalistic practice consists of, which deeper learning objectives are most important, and how we can assess whether our students have learned them are moving, and often unarticulated, targets.

We were plucked from our newsrooms primarily because of our journalistic aptitude, understanding of the incredibly applied nature of our profession. But knowing how to do something well and knowing how to teach it well are two overlapping, yet very distinct, skill sets. Our hope is to create a year-long dedicated space towards cultivating the latter.

If you’re also an educator who, like us, never went to “teacher school” but are interested in joining our discussion and study group about pedagogy in the context of data journalism, we’d love to hear your thoughts. Please fill out this quick form—we’ll reach out to you about ways to connect.


  • Nausheen Husain

    Nausheen Husain is a reporter and researcher focused on Muslim communities and the past and present “War on Terror.” She is currently an assistant professor at Syracuse University’s S.I. Newhouse School Of Public Communication, teaching journalism with an emphasis on data analysis, underreported communities and civil rights. Among other outlets, she has written for the HuffPost, the Chicago Tribune, and Oakland North.

  • Dhrumil Mehta

    Dhrumil Mehta is an Associate Professor of Journalism at Columbia University and the Assistant Director of the Tow Center for Digital Journalism. He helps to run the Columbia’s Computer Science + Journalism Dual M.S. program and is also a Visiting Associate Professor in Public Policy at the Harvard Kennedy School. Dhrumil teaches classes at the intersection of journalism, statistics and computation. Previously, Dhrumil was a Database Journalist at FiveThirtyEight, where he built databases, scrapers, bots, and interactive graphics alongside reporting and writing about elections, public opinion and media.

  • Aarushi Sahejpal

    Aarushi Sahejpal is a data scientist and journalist exploring inequality and accountability. He is a professor of quantitative methods and data journalism at American University’s School of Communication, and Data Editor at the Investigative Reporting Workshop at American University. He did cool things, and learned a lot, at The COVID Tracking Project at The Atlantic, Reveal from The Center for Investigative Reporting, Education Week, Cisco Systems, and The White House Office of Science and Technology Policy.


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