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.
- Implement!
Introduction
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.
The following two slides might be more effective as a live demo or in-class lab activity.
One
of the underlying datasets used by both groups is the statistics provided by
OECD. If you go to the OECD website there are some neat online
tools that let us quickly visualize data in different ways. One of the elements
of the data story told by the Fraser Institute is that individual families pay
too much in taxes. I wondered if there has been any change in the portion of
tax revenue contributed through personal and corporate taxes over the years.
Here is what I found using the OECD website. It seems that more tax is gathered
from personal rather than corporate taxes, but over the past few years the
portions don’t seem to have changed much. This is the default view that shows
trends from 2000 – 2015. If this had fit what I already believed, I suspect I
would have stopped here. But I seem to recall a relative decrease in corporate
taxation over the past few decades so I decided to slide the years covered…
And
this is what I found. If we slide the start date of the
visualization tool back to 1965, it does appear that there has been a relative
increase in tax revenue from personal sources and a relative decrease in tax
revenue from corporate sources. This shows how easy it would be for two people
with different perspectives on what a data trend is likely to be to go to exactly the same dataset and
make a slight change to how the data is visualized to tell two very different
stories.
Kaulfuss
uses OECD data to tell a story about U.S. health care spending on a blog called Beyond Economics. The story is that the U.S. spends two and a half
times the OECD average on health. It doesn’t surprise me that the U.S. spends
more than the OECD average on health, but I am surprised that the difference is
this much. What I found even more intriguing is the author’s claim that U.S.
public spending on health is above the OECD average. Who knew? Disclaimer: what
I am doing here is presenting stories told through data, I have not examined
the data itself so cannot comment on the accuracy of the story.
Wikipedia
has a section called Health Care in
Canada. Here in Canada many of us – I include myself – think highly of our
public health care system, and I think I see this perspective here. This
section states that “most health statistics in Canada are at or above the G8
average” in a paragraph that is followed by the table pictured above. The table draws from a
number of data sources and appears to me to demonstrate above-average data
literacy skills. However…
When you look at the statistics that are presented and calculate
the averages, Canada is above average on 3 of 8 measures. This is not “most”.
This suggests a need for data literacy. If you look at the specific measures
where we are above average, an argument can be made that being above average in
life expectancy is a good thing. However, an above-average infant mortality
rate is probably not such a good thing. We are also slightly above average on %
of government revenue spent on health, but what does this mean and is it a good
thing? Looking at some of the areas where we are below average –such as
the # of doctors & nurses per
population & % of health costs paid by government – might give one reason
to re-consider our narrative that we Canadians are above average in public
health. This illustrates a need for critical data literacy. In other words, our beliefs might be getting in the way of understanding what is our existing data tells us.
Some approaches and suggestions for creating a meaningful introduction
The
reason for the introduction section is because as Tygel and colleagues found there is a
need to start with some explanation about what data is and how people use it.
There are many potential approaches to introducing the topic such as having
guest speakers come to explain how they make use of data and data visualization.
Suggested sample activity
One activity that would fit here is to have students create their own
demonstrations. In the case of tax data, students could do a google search for tax data and limit to
images. This search will yield lots of material to work on. The idea is to have
students find out who created the visualization and what the story behind the
visualization is. If this is done for evaluation purposes, I recommend a
pass/fail approach because student success will depend a lot on which images
are selected. Being there to hear the findings of all the students is
sufficient for this learning exercise. A teacher in an area where computers are
not readily available could bring in copies of materials to work with. This
introductory phase may be more relevant for some student groups than others,
for example university students. If this doesn’t seem to fit, you could skip
this stage.
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.
Investigation
Problematisation
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:
What does this mean? In brief, I have no interest in using intellectual property law to prevent anyone from using or re-using my work with intentions such as furthering the collective knowledge of humanity (truth with justice and compassion), protecting or restoring the environment or making the conditions of life of humanity better. That is what I mean by with love. If your motives in using my work are something other than love, such as making a profit for yourself or a corporation that you work for, subverting truth, justice, or compassion, then note that I reserve all rights under copyright. Please use attribution as appropriate. For example, if you use my work in an academic or journalist context, you need to acknowledge me as author in order to avoid plagiarism (and confusion).
This post is part of the Creative Globalization series.
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.
Investigation
The
investigation phase is the first of 3 phases that follow the Freire tradition. The idea
is to begin with lived experience, with real-world problems. If this approach
is used for self-teaching by community groups independently
or with an academic consultant as a participatory action research project, this
is closest to the classic Freire scenario and the best example of a pure investigation
stage. To modify this for an education setting, students could either choose
problems or issues of direct interest to them, for example student debt, or
they might brainstorm a particular target group whose problems they are
familiar with such as First Nations, a salient issue here in Canada as many of
us struggle to implement the recommendations of our Truth & Reconciliation commission.
Classroom activities could include a brainstorm session, individual or small group
reflection, and/or presentation of the results of the investigation stage.
Thematisation
Thematisation is the first analytic stage. Before searching for what data
is available, the idea is to focus on the real-world issue and figure out what
kind of data might help to understand or resolve the issue. Examples based on
today’s case studies on taxation and health spending could include learning
what sorts of taxes are collected and by which governments, or comparing public
collective health spending with individual spending.
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
- OECD
- Federation and provincial budgets
- Academic Research
- NGO / Think Tank research (e.g. Fraser Institute and Broadbent Institute)
One question that might be
raised is whether the existing data is actually sufficient or not, that is, the
scope of the inquiry is not focused just on understanding what data is
available. but rather what is needed to understand and resolve the problem of
interest.
Systematization
Finally, in the systematization stage we put what we have together
to come up with an action plan. The nature of the action plan might vary quite
a bit depending on the students. An activist community group might want to
develop an action campaign or an infographic or other data story to facilitate an existing action
campaign. One approach to action could involve citizen data collection. In a
graduate class on information policy, like the classes that I teach at the University of Ottawa's School of Information Studies, developing a policy briefing and
recommendations for evaluation as academic work might make sense.
References
Fraser Institute (n.d.). Tax freedom day calculator. Retrieved June 9, 2017 from https://www.fraserinstitute.org/tax-freedom-day-calculator
Fraser Institute (n.d.). Tax freedom day calculator. Retrieved June 9, 2017 from https://www.fraserinstitute.org/tax-freedom-day-calculator
Grey, J., Bounegru, L., & Chambers, L. (2012). Data
Journalism Handbook. OKFN. (as cited in Tygel & Kirsch 2016)
Kaulfuss, R. (2017). Health care: human right or expensive
entitlement? Beyond economics. Retrieved June 15, 2017 from https://beyondeconomics.org/2017/03/15/health-care-human-right-or-expensive-entitlement/
OECD (2017), Tax revenue (indicator). doi:
10.1787/d98b8cf5-en (Accessed on 15 June 2017)
Shillington, R. & Shaban, R. (2017). The brass tax:
busting myths about overtaxed Canadians. Ottawa: Broadbent Institute. Retrieved
June 9, 2017 from http://www.broadbentinstitute.ca/the_brass_tax
Tygel, A.; Campos, M.; De Alvear, C. (2015). Teaching open
data for social movements: a research strategy. The Journal of Community
Informatics 11:3. Retrieved June 19, 2017 from http://ci-journal.net/index.php/ciej/article/view/1220/1165
Tygel, A.; Kirsch, R. (2016). Contributions of Paulo Freire
for a critical data literacy: a popular education approach. The Journal
of Community Informatics 12:3 pp. 108 – 121. Retrieved June 19, 2017 from http://ci-journal.net/index.php/ciej/article/view/1296.
Wikipedia (n.d.). Healthcare in Canada. Retrieved
June 15, 2017 from https://en.wikipedia.org/wiki/Healthcare_in_Canada
Terms: Please copy and share with love.
What does this mean? In brief, I have no interest in using intellectual property law to prevent anyone from using or re-using my work with intentions such as furthering the collective knowledge of humanity (truth with justice and compassion), protecting or restoring the environment or making the conditions of life of humanity better. That is what I mean by with love. If your motives in using my work are something other than love, such as making a profit for yourself or a corporation that you work for, subverting truth, justice, or compassion, then note that I reserve all rights under copyright. Please use attribution as appropriate. For example, if you use my work in an academic or journalist context, you need to acknowledge me as author in order to avoid plagiarism (and confusion).
This post is part of the Creative Globalization series.
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