Designing data journalism courses: reflections on a decade of teaching


Students from the MA Data Journalism join conference attendees in a session at the Data Journalism UK conference

In this second extract from a commentary for Asia Pacific Media Educator I reflect on the lessons learned from a decade of teaching dedicated data journalism courses. You can read Part One — on teaching one-off data journalism classes — here.

In contrast to the one-off classes involving data journalism, courses and modules that focus on data journalism skills present a different type of challenge.

These courses typically attract a different type of student, and provide more time and space to work with.

My own experience of teaching on such courses comes from three contexts: in 2009 I launched an MA in Online Journalism at Birmingham City University with an explicit focus on data-driven techniques (the term “data journalism” was yet to be popularised). A year later I acted as an advisor to the MA in Interactive Journalism that City University London were then developing (delivering guest classes in data journalism for the following 5 years as a visiting professor). Finally, in 2017 I replaced the MA in Online Journalism with a dedicated MA in Data Journalism at Birmingham City University.

In this post I talk about the factors that shaped course design, and how student output compared to the objectives of the course.

Course design: fitting it all in

If the challenge in teaching ‘fast’ data journalism is how to boil it down to the essentials and motivate word-oriented students, teaching ‘slow’ data journalism brings a very different challenge: how to do justice to the vast diversity of the field.

This diversity is what makes data journalism qualitatively different from its antecedent Computer Assisted Reporting: to spreadsheets and databases, data journalism has added visualisation and mapping, interactivity and coding, networks and APIs.

There are broader forces at work, too: changes in journalism as a whole mean data journalists are not exempt from the requirement to learn how to tell stories with video, audio, text and images; they must be able to work within a range of newsrooms and routines that are increasingly networked, collaborative, and informed by analytics.

Other journalism courses struggle too with such a proliferation of change in the industry — but data journalism courses in particular must manage the same competing tensions while attempting to remain at the forefront of the next wave of change.

At the time of writing that is a wave of innovation driven by ‘big data’ — automation, algorithms and AI — alongside a new and expanding body of literature attempting to address the ethical, legal and broader critical issues raised by data-driven practice.

Within this context I believe it is important to design a syllabus that is flexible enough both to respond to the pace of change and accommodate the different professional backgrounds and objectives of those participating.

Use flexible (universal) learning outcomes that continue to apply as the field develops

Learning outcomes tied to universal skills (that can be adapted to different technologies) help in this respect: two learning outcomes that have underpinned my teaching throughout the past decade are, firstly, gathering information and, secondly, communicating it to an identified audience within a particular professional context.

These outcomes apply regardless of whether the students are being taught to gather their information with Freedom of Information laws or scraping; whether they communicate the results through a video package or a mobile app.

What’s more, they emphasize that students must explicitly consider what are the right tools for their particular editorial challenge: not every data journalism story can be found using an Excel spreadsheet.

Alongside these fundamentals there is a third, somewhat catch-all, outcome: demonstrating an understanding of strategy, distribution, law, ethics and other critical issues.

In terms of strategy, for example, data journalists increasingly operate in an environment as networked as the data they deal with: from international inter-organisational collaborations such as the Panama Papers investigations (described as “An evolving division of labour that prioritizes inter-organisational networked journalism relationships”), to crowdsourcing-driven data projects and open source investigations by organizations as diverse as the UK’s Bureau Local and ProPublica in the US.

Within these contexts an understanding of community management and practice is a skill in short supply, and students are asked to think about how those techniques can be used to better inform their work from pre-production onwards.

Thankfully there is an increasing body of work in this field, from the Reuters Institute’s edited collection on the rise of collaboration in investigative journalism to Seth Lewis and Nikki Usher’s work on the Hacks/Hackers network.

Communities of practice and lifelong learning

A common anxiety experienced by those starting out in data journalism (and indeed modern journalism more generally) is the worry of having so many things that you feel you should be learning about.

It is wonderful to have access to almost infinite knowledge — and yet it is also oppressive. Learning how to manage what Kierkegaard described over a century ago as the “dizziness of freedom” has taken on a new and urgent importance in education.

Thankfully, within the field we have a useful parable to illustrate this: we use the term ‘unicorn’ to refer to the person who can do everything.

When anyone asks, “Why do you call them unicorns?” we reply: “Because they don’t exist.”

I talk about this paradox with students at the very start of the course, and return to it throughout: the student should never expect to know or learn everything.

They will spend their lives learning new skills, and that’s part of the fun. Just as the traditional journalist might one week be expected to interview a lottery winner, and the next attend a crime scene, so the data journalist can be learning spreadsheet skills one week, and text analysis the next.

The real data journalism skill is to get better and better at learning new things — and always curious.

To this end, it is important to draw on networks of support both for guidance and the sorts of collaboration outlined above. To encourage this, within my courses I have for some years now adopted an explicit emphasis on identifying and engaging with such ‘communities of practice’.

Perhaps the best-known example of a community of practice within data journalism is the NICAR-L mailing list, on which data journalists and CAR practitioners around the world share questions, answers, tips, experiences, and opportunities (including jobs and internships).

But beyond that mailing list there are hundreds of other networks which journalists – and aspiring journalists – can benefit from participating in, from Slack channels (such as those of the Bureau Local and Stories With Data) and ‘civic coding’ mailing lists (coders who want to create tools for public good), to language-specific resources such as the R-Journalists mailing list, subject-based communities, and real-life events found through platforms like Meetup and Eventbrite, hackdays and conferences.

Where events do not exist, it is an opportunity to create them: we established a Hacks/Hackers meetup group in Birmingham, and worked with the BBC data unit in the city to hold an annual Data Journalism UK conference in the city, with a pre-conference hackday to get students engaging with that wider industry and practice.

Students are encouraged not only to attend these events — taking part in the “trading zones” of data journalism — but also to organize and speak at them, too: every September a Hacks/Hackers meetup is held where incoming students can hear what the graduating class made for their final projects, while it provides a perfect excuse for students to invite their favorite speakers from the industry.

Coding – and computational thinking

There are two common complaints that you will hear from employers in the industry looking to hire data journalists: applicants either have impressive technical skills, but few ideas around how to spot and tell stories — or the opposite problem: a sound news sense, but a lack of ability to realize those ideas technically, masked through a frustrating tendency to ‘bluff’ about their technical prowess.

The challenge for a university seeking to offer data journalism education is this: how do you develop both editorial and technical skills in data journalism graduates when there is a lack of skilled educators (see also Davies & Cullen 2016)?

Sending journalism students to computing classes is not, I believe, the answer: we do not outsource teaching of media law to the Law Faculty, or ask broadcast engineering staff to teach video journalism, after all.

From having to operate within a content management system and coding within newsroom timescales, to the ethics of visualization or legal issues relating to scraping, data journalism has its own set of priorities and constraints.

And then there’s the language problem: in data journalism we are not training programmers in a particular language, but rather giving them the ability to adapt to any number of languages that might be used in the industry.

This was a particularly tough problem to tackle when designing the MA in Data Journalism: should I teach one language or many? Which should I teach where?

Ultimately, I came back to the idea that data journalists should have the confidence to be able to learn a range of new skills relevant to editorial problems, rather than be given a limited range of skills frozen in the time that their education took place.

The key to this approach came with the concept of computational thinking. This provides a framework for breaking down challenges into manageable chunks (decomposition), abstracting those problems, recognizing patterns, and compiling some sort of workflow – an algorithm – to accomplish those.

I decided to introduce this concept in the second week of the course – as part of a second and final session on spreadsheets. I had already chosen to spend only two classes on spreadsheet techniques before moving on to other data journalism techniques, and my reasoning was this: once you understand fundamental spreadsheet techniques, most data journalism work consists of breaking down an editorial problem into separate steps, and searching for the functions that will accomplish that.

This then laid the foundations for the remaining classes, where a range of coding techniques (R, JavaScript, command line and SQL) were introduced as ways of exploring key concepts in the field, from responsive design and interactivity to APIs and dealing with large datasets.

(In a second semester module the pattern continued, where students added a further language – Python – as they explored scraping within a more investigative module that provided an important space for students to further apply and develop the technical skills developed in that first semester.)

My objective was not that students become masters of all these languages and techniques, but rather than they not feel intimidated by any of them.

The analogy that I presented every week was this: I was hoping to open a series of doors for them, one door each week. In their independent study, project work and newsroom activity they would need to choose which doors to walk through and explore further, in order to solve their own editorial challenges — in the process developing important problem-solving techniques.

And in future, when they encountered different editorial challenges, they could return to the other open doors and feel confident that they could go through those too, just as they had learned new skills before.

This was data journalism as a set of practices, a collection of habits, a toolkit of problem-solving techniques, which are adapted to each new problem.

To my delight, it worked.

At the end of the module, the students submitted their work. It covered a wide range of techniques and skills: one student used the JavaScript library D3 to create distinctive cartograms; others used third party tools. Some used spreadsheets to find their stories; others used R.

In other words, each had chosen a door to walk through and explore further in relation to their chosen editorial problems (ranging from reactive news stories and election coverage to investigations and explainers) — and they had done well: the average mark was around ten percentage points above the typical average for a Masters module; none scored lower than a Merit (the middle of three bands used to score work at that level in the UK).

The feedback for the module from students was equally positive: they felt well prepared for the challenges they would face, and, importantly, not overwhelmed.

The data newsroom

The launch of the new MA in Data Journalism alongside another new MA in Multiplatform and Mobile Journalism at Birmingham City University — with a combined live newsroom — provided an opportunity for data journalism students to reflect on the organization of data teams and how those fit into the wider newsroom in which it operates.

Data journalism teams can take a number of shapes, from the one-person operation to the dedicated investigations team: not only do we need to prepare our students for those, but we also need to prepare them for the inevitable reorganizations and tensions that data journalists face as they negotiate relationships with their colleagues in the wider news organisation.

Students from the MA in Data Journalism, then, are asked to form a data team alongside the Multiplatform and Mobile Journalism students. Decisions must be made about allocating time and expertise across platforms and stories: do the data journalists operate entirely separately on longer-term projects? Or do they operate as a service department, providing complementary material such as visualization and interactivity for the day’s news?

This question provides an opportunity to explore literature on the organization of both multiplatform newsrooms and interactive teams —Uskali & Kuutti’s framework provides a useful overview of the options, while Nikki Usher’s book Interactive Journalism provides further ethnographic detail.

During the period of the newsroom students might be coached to vary their approach, increasing or reducing their independence from the rest of the team, in order to get a varied experience of the different dynamics of different arrangements.


Across two posts I have outlined two different approaches to data journalism teaching, based on the time and dedication being provided by the students involved: ‘fast’ (one-off classes for general audiences) and ‘slow’ (dedicated courses and modules for specialist groups).

In delivering ‘fast’ data journalism teaching, it is important to begin with the contexts that the students situate themselves within, challenging preconceptions and focusing on the practices relevance to the student’s professional aspirations.

A pedagogical process that incorporates data journalism as a problem-solving activity rather than a self-contained practice can also help break down barriers for students who see it as outside of their sphere.

In teaching data journalism across a range of contexts and with students who arrive with different experiences, interests and motivations, I have found it is important to begin first with those motivations.

In delivering ‘slow’ data journalism teaching, however, it is important to acknowledge that the scale and speed of development of the field will always surpass any space or time that can be devoted to it within a classroom setting.

Perhaps more than any other form of journalism, data journalism encapsulates a networked and dynamic mode of production and learning which requires both a lifelong learning approach, and developing strategies of problem-solving and networks of support that foster that ongoing professional development.

Conceptual frameworks, such as computational thinking and communities of practice, can be useful in this regard, while course and assessment design which is flexible enough to accommodate different editorial challenges can ensure that students are given the freedom to develop different technical skills that fit relevant editorial demands, rather than the other way around.

This post is adapted from a commentary for Asia Pacific Media Educator. You can read the final edited version of the paper here.


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