I’ve now been teaching data journalism for over a decade — from one-off guest classes at universities with no internal data journalism expertise, to entire courses dedicated to the field. In the first of two extracts from a commentary I was asked to write for Asia Pacific Media Educator I reflect on the lessons I’ve learned, and the differences between what I describe (after Daniel Kahneman) as “teaching data journalism fast” and “teaching data journalism slow”. First up, ‘teaching data journalism fast‘ — techniques for one-off data journalism classes aimed at general journalism students.
Like a gas, data journalism teaching will expand to fill whatever space is allocated to it. Educators can choose to focus on data journalism as a set of practices, a form of journalistic output, a collection of infrastructure or inputs, or a culture (see also Karlsen and Stavelin 2014; Lewis and Usher 2014; Boyles and Meyer 2016). Or, they might choose to spend all their time arguing over what we mean by ‘data journalism’ in the first place.
We can choose to look to the past of Computer Assisted Reporting and Precision Journalism, emerging developments around computational and augmented journalism, and everything that has happened in between.
In this commentary, I outline the different pedagogical approaches I have adopted in teaching data journalism within different contexts over the last decade. In each case, there was more than enough data journalism to fill the space — the question was how to decide which bits to leave out, and how to engage students in the process. Continue reading