Good a&ernoon. My goal for this presenta5on is to make an argument that there is an urgent need for data literacy and cri5cal data literacy and to present an approach for teaching data literacy. This presenta5on is intended as an open educa5onal resource that anyone who wishes to teach data literacy can take apart and reconfigure to fit their own teaching needs.

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There are a few defini5ons of data literacy and cri5cal data literacy. T his one from the Data Journalism Handbook fits what I am talking about today, because it includes the concept of cri5cal thinking about data.

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This is my defini5on of cri5cal data literacy, A key point is understanding that while data is neutral, it is collected by people for a reason and that stories using data are told by human storytellers who have par5cular beliefs and values that consciously or unconsciously influences how they see and explain data.

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The approach of this OER draws from the tradi5on of Paulo Freire and the work of Tygel and colleague, who have done and reported some data literacy training using this approach. I have not yet tested this. The reason I am sharing this untested framework is my convic5on that the need for the development of cri5cal data literacy is urgent. If anyone else can make use of this framework to develop cri5cal data literacy educa5on at any level, from popular educa5on with oppressed group to academia, I encourage this. The OER that I have developed is available at:

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Here is the key concept that draws from the tradi5on of Freire. Rather than teaching cri5cal data literacy as an abstract subject, the idea is to start with the lived experience of the student and then to figure out how data might be used to help solve real-world problems of concern to the student or public.

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There are 5 phases to this framework. The first is an introductory sec5on that goes beyond a tradi5onal Freire approach; I will soon be demonstra5ng one example of how to do an introduc5on. This is inspired by Tygel et al who found that some explana5on was necessary in order to teach data literacy. The next 3 stages are from Freire’s popular educa5on approach with the oppressed, inves5ga5on, thema5sa5on, and problema5sm. Finally there is a systema5za5on stage, an innova5on of Tygel and colleagues.

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The following sample introduc5on was designed for use in Canada and is likely more relevant for some groups of students than others. For example, I teach informa5on policy at the graduate level and this intro would be suitable for my students. Other teachers might wish to subs5tute this with an introduc5on more suited to their own students.

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This slide illustrates two very different perspec5ves on taxa5on in Canada. On the le&, we see the Fraser Ins5tute’s Tax Freedom Day. The Fraser Ins5tute, 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 effec5ve in Canada over the past few decades. On the right, we see one of the images from the Broadbent Ins5tute’s report The Brass Tax which was published very recently. The le&-wing Broadbent Ins5tute challenges the numbers behind the Fraser Ins5tute’s analysis and argues that Canadian taxa5on is pre\y reasonable compared to other countries, and presents a different picture. In this case this graph illustrates Canada’s progressive approach to taxa5on and makes the point that people with li\le 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.

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One of the underlying datasets used by both groups is the sta5s5cs 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 Ins5tute is that individual families pay too much in taxes. I wondered if there has been any change in the por5on 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 por5ons 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 rela5ve decrease in corporate taxa5on over the past few decades so I decided to slide the years covered…

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And this is what I found. If we slide the start date of the visualiza5on tool back to 1965, it does appear that there has been a rela5ve increase in tax revenue from personal sources and a rela5ve decrease in tax revenue from corporate sources. This shows how easy it would be for two people with different perspec5ves on what a data trend is likely to be there to go to exactly the same dataset and make a slight change to how the data is visualized to tell two very different stories.

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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 5mes 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 presen5ng stories told through data, I have not examined the data itself so cannot comment on the accuracy of the story.

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Wikipedia has a sec5on 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 perspec5ve here. This sec5on states that “most health sta5s5cs in Canada are at or above the G8 average” in a paragraph that is followed by this table. The table draws from a number of data sources and appears to me to demonstrate above-average data literacy skills. However…

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When you look at the sta5s5cs 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 # doctors & nurses per popula5on & % of health costs paid by government – might give one reason to re-consider our narra5ve that we Canadians are above average in public health. This illustrates a need for cri5cal data literacy.

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The reason for the introduc5on sec5on is because as Tygel and colleagues found there is a need to start with some explana5on about what data is and how people use it. There are many poten5al approaches to introducing the topic such as having guest speakers come to explain how they make use of data and data visualiza5on. One ac5vity that would fit here is to have students create their own demonstra5ons. 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 visualiza5on and what the story behind the visualiza5on is. If this is done for evalua5on 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.

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The inves5ga5on phase is the first of 3 phases that follow the Freire tradi5on. 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 par5cipatory ac5on research project, this is closest to the classic Freire scenario and the best example of a pure inves5ga5on stage. To modify this for an educa5on sepng, students could either choose problems or issues of direct interest to them, for example student debt, or they might brainstorm a par5cular target group whose problems they are familiar with such as First Na5ons, a salient issue here in Canada as many of us struggle to implement the recommenda5ons of our T&R commission. Classroom ac5vi5es could include a brainstorm session, individual or small group reflec5on, and/or presenta5on of the results of the inves5ga5on stage.

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Thema5sa5on is the first analy5c 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 taxa5on and health spending could include learning what sorts of taxes are collected and by which governments, or comparing public collec5ve health spending with individual spending.

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A&er thema5sa5on, with some back-and-forth, comes problema5sa5on. 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. Listed are some examples of the types of data sources students might look into at this point if they choose to focus on taxa5on and spending. One ques5on that might be raised is whether the exis5ng 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.

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Finally, in the systema5za5on stage we put what we have together to come up with an ac5on plan. The nature of the ac5on plan might vary quite a bit depending on the students. An ac5vist community group might want to develop an ac5on campaign or an infographic or other data story to facilitate an exis5ng ac5on campaign. One approach to ac5on could involve ci5zen data collec5on. In a graduate class on informa5on policy, developing a policy briefing and recommenda5ons might make sense.

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Thank you for listening! A copy of the OER developed for this session can be found here. I look forward to your comments and ques5ons a&er our next speaker.

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