This OER was developed for presentation at the Data Power 2017 conference held at Carleton University, Ottawa, Ontario June 22 – 23. This is primarily a framework for how to go about teaching critical data literacy in the student-centered tradition of Freire, supplemented by the work of Tygel and colleagues. A sample introduction developed for Canadian university students, and a few references, are included. My definition of critical data literacy as used in this OER is:
critical data literacy is the ability to understand and critique how the beliefs and values of people and groups (including government) influence what data is created, how it is shared and how it used by to tell compelling stories by storytellers whose beliefs and values shape the kind of stories they choose to tell and how they tell the stories. Critical data literacy also means having the ability to create and tell one’s own stories using data.
This OER is released under the terms of copy and share – with love, my latest statement on sharing which can be found at the bottom of this post. The Freire tradition of popular education involves starting with the lived experience of students. In this context, following is what I recommend for anyone who wishes to develop a full critical data literacy program based on the framework. I think that this framework could be adapated for teaching at any level, from community-based learning (led by community groups or organizers or as a participatory action research project) to graduate classes (that’s where I teach). Some of the details would change. For example, if you are teaching at a university, some parts of the process are likely to involve formal evaluation (marking), but if you are teaching to the general public or a community group, this would not make sense. Please adjust as needed for your own context.
The overall approach:
- Identify your student group. Think about what kinds of issues or problems they might have that could potentially be helped by data, the kind of data stories they might be familiar with.
- Develop an introduction to critical data literacy. Tygel and colleagues (2015, 2016) found that this was necessary. One way to think about the difference between critical data literacy and basic literacy (reading) is that people who do not know how to read in recent history are likely to be aware of the existence of reading as something that other people do. Data literacy / critical data literacy is not at this point in time as broadly understood as reading.
- Plan the 3 phases of the framework that follow directly from the Freire tradition: investigation, thematisation, and problematisation. In these phases, students should lead the learning process (active learning), pursuing problems and questions of their own devising. The teacher’s role is to provide support.
- Plan a systematisation (synthesis) wrap-up approach that makes sense for your student group. In some cases this might be left for the students to decide the approach, and the teacher only helps to guide the students towards this closure. In a formal educational setting, this might involve a pre-determined assignment.
The 5 phases are: introduction, investigation, thematisation, problematisation, and systematization (synthesis). Details follow. The introduction section is the most fully developed as this is the only teaching portion that involves imparting knowledge; all others begin with the student.
As noted above, it will not be obvious to everyone what data literacy or critical data literacy is or why they should learn about it, as discovered by Tygel and colleagues (2015, 2016). For this reason, an introduction to the topic may be helpful. In this phase one might invite in guest speakers from the community who use data in their storytelling and/or to provide examples of data storytelling. This is also where definitions of critical data literacy could be introduced. In addition to my definition (see above), I like this definition of data literacy from the Data Journalism Handbook because it includes the element of critical thinking; not every definition that I have seen includes this, to me a significant omission.
data literacy is the ability to consume for knowledge, produce coherently and think critically about data [emphasis added] (Grey, Bounearu & Chambers (2012)
Following is a sample introduction developed for an audience of Canadian university students. If you are teaching a different type of student group, I recommend that you develop your own introduction tailored to your group. If you do and you are willing to share this with others, please send me a link (via e-mail to Heather dot Morrison at uottawa dot ca) or as a comment to this post and I will include a link to your work in this post. If you would like to use this introduction as is, please see the link to the full presentation.
Introduction slide 1
This slide presents two conflicting stories that are told using basically the same underlying data. One of these (tax freedom day) will be very familiar to the audience, while the other will not as it is relatively new.
This slide illustrates two very different perspectives on taxation in Canada. On the left, we see the Fraser Institute’s Tax Freedom Day. The Fraser Institute, a right-wing think tank, uses data to tell their story of over-taxed Canadians, working more than half the year for the government before earning a dime for themselves. The idea of tax freedom day has been very effective in Canada over the past few decades. On the right, we see one of the images from the Broadbent Institute’s report The Brass Tax which was published very recently. The left-wing Broadbent Institute challenges the numbers behind the Fraser Institute’s analysis, argues that Canadian taxation is pretty reasonable compared to other countries, and presents a different picture. In this case this graph illustrates Canada’s progressive approach to taxation and makes the point that people with little to no income pay no income tax and only a small percentage of Canadians age 25 to 54 are in the top income tax bracket, paying more than 30% of income in taxes. These are 2 groups of people with a different vision of what society should be like, using the same underlying data to tell 2 very different stories. If we go directly to the data source, will this eliminate the impact of the storyteller? Let’s see.
Investigation, Thematisation & Problematisation
Two key points to keep in mind in these 3 phases: 1) the core focus should be lived experience not imparting abstract knowledge and 2) teaching involves helping people seek and find answers. This is important because in teaching data literacy one might be tempted by starting with the data, teaching people how to understand and work with data. Keynote speaker Gwen Phillips (and BC First Nations data activist) at the Data Power 2017 provided a brilliant example of why not to start with the data: the existing data might not be what is wanted at all. As Gwen said, we should measure what do want (e.g. youth vitality) not just what we don’t want (e.g. teen suicide). This introduces a challenge to develop new metrics, but one that seems worthy of pursuit. If we start by teaching about existing data we risk missing the opportunity to identify gaps like this.
Disclosure: in understanding the following 3 phases, it may be helpful to know that although I teach at a university and am very engaged in pedagogy, I do not have an education degree and do not consider myself an expert on pedagogy. If you would like to know more about how to teach in the Freire tradition, I suggest starting with the Tygel references below and if desired supplementing with general educational books and articles covering the Freire tradition. My contributions below are limited to providing a very quick introduction and making the connection with critical data literacy.
After thematisation, with some back-and-forth, comes problematisation. This is where we get into research on what kinds of data actually exist that is relevant to the problem, who collects the data and why. Some examples of the types of data sources students might look into at this point if they choose to focus on taxation and spending:
- Canada Revenue Agency
- Federation and provincial budgets
- Academic Research
- NGO / Think Tank research (e.g. Fraser Institute and Broadbent Institute)
Fraser Institute (n.d.). Tax freedom day calculator. Retrieved June 9, 2017 from https://www.fraserinstitute.org/tax-freedom-day-calculator
This post is part of the Creative Globalization series.