Critical Pedagogy as tool for data literacy
Teaching everyone to be social researchers
Rather than trying to defy the trend to use the term data, which at this point looks to be (read: is) a losing battle, I argue that social researchers can play a proactive role by intervening in how everyday citizens interpret their own definitions of what counts as data, as these definitions get negotiated through their own lived experience within digitally-saturated environments. This can be achieved by helping people become researchers of their own experience, to identify how identities, events, and cultural formations are quantified, and more importantly, to interrogate the ways personal behaviors and activities and relationships–in the form of information—might be harvested, stored, and eventually commoditized for sale in the corporate or governmental marketplace. Data literacy includes reflexive awareness of the systems of digitization, datafication and computation, which involves the many ways data are defined, created, and used, along with an ability to understand the greater systems within which data play a role.
Taking on this pedagogue/researcher role requires that we academics reach outside the isolation and protection of the typical academic production cycle to teach as many people as possible to use our well-honed critical tools for thinking about their lives in this digital data epoch. After all, who better than well-trained sociologists, phenomenologists, media and information scholars, data scientists, and ethnographers to teach citizens to be researchers of their own lived experience? As we interact on formal and informal levels with audiences beyond the academy, we have the opportunity to help others analyze the way data flows in the situations they live in, how digital platforms and algorithmic logics constrain and enable citizen actions, how social media conversations and networked information function rhetorically to frame basic definitions of the self, relationships, politics, and institutional systems and dynamics.
Italian social theorist Antonio Gramsci also highlighted the importance of workers teaching themselves to be conscious of the conditions of structural oppression that hide beneath the surface of everyday institutional practices, a condition he labeled hegemony, or control through consent.
Tips for critical digital pedagogy
1) Teach more critical theory, without the baggage terminology of critical theory
Critical theory can be a daunting subject for students ensconced in neoliberal mindsets of constant consumption and consumerism. I have found that broaching the topic gradually and through examples and experiential experiments helps students reach for a critical mindset, rather than accept one I’m trying to force feed them. I learned a trick from years of teaching organizational communication to students who were resistant to critical approaches: Build cognitive dissonance about a situation until the students insist on a critical perspective. For example, I would read aloud to the students a shortened version of Shirley Jackson’s short story The Lottery as a way of shifting from ‘thick description’ to ‘critical interrogation’ (a technique I got from Rachel Pekora, a fellow graduate student at Purdue University) As the story goes, the townspeople gather once a year in very ritualistic fashion for a lottery. The mystery of the story is that the reader doesn’t have any idea what the lottery is until the very end. So at first, it seems like a straightforward, if quirky, example of the richness of everyday life. Taking a cultural perspective on organizing, students are instructed to consider the story from the perspective of an ethnographer, to think about “what is going on here?” from a nonjudgmental perspective. After the story, I would ask for comments and students would inevitably address the wrongness of the situation. The dialogue would go something like this:
“I hear you,” I reply, and I try to look sympathetic to soften my response, “But you know that’s not really an analytical assessment. We’re supposed to be ethnographers here. Maybe focus on some of the elements that could make this an organizational ‘culture’. What sort of material objects or behaviors seem to point to a strong or cohesive culture?”
After some thought, another student says, “Well, there is the old saying that everyone seems to repeat, “lottery in June, corn be coming soon.”
“Good,” I reply, “Anything else?”
Another student says, “They carry out the old box. It’s tattered, but they treat it like it’s a precious relic, so obviously it has significance in the culture.”
I reply, “So does the box tell us anything about their cultural values?”
Another student erupts, “What do you mean, does the box tell us anything, Dr. Markham! What about the villagers stoning someone to death? That would tell us something about their values!”
I reply, “Now, we agreed we wouldn’t judge, but simply observe. Does anyone else have a comment?”
“But how can we ignore that the lottery ‘winner’ is killed by their neighbors and family?!”
I repeat my stance, asking, “Well, if we’re just observing a cultural practice, who are we to evaluate or critique?”
After awhile, I stop trying to play devil’s advocate. I say, “I get the feeling that some of you are not satisfied with being value-neutral or simply descriptive. Maybe we should change our rules?”
If I injected this reading at the right point in the semester, it worked flawlessly to shock bachelor students (in the U.S.) into not just accepting but insisting on the importance of a critical theory perspective in responding to a situation gone wrong, which could not be analyzed from a value-neutral perspective.
Ever since I learned this technique, I’ve used it in almost every course I’ve taught. The trick is not in the story but in letting students approach critical theory through watching or having an experience rather than reading about an abstract theoretical framework. There are many techniques for accomplishing this, but for me, it requires letting go of the baggage terms that go along with such approaches, such as interpellation, hegemony, and systematically distorted communication. Initially, I don’t even use softer terms like disenfranchisement, disempowerment, and marginalization. Only later, once our everyday explanations fail to articulate an adequate accounting, can we (they) take up discipline strengthened vocabularies and concepts to further develop the analytical accounts or build the frameworks or strategies for social action.
Even as I minimize the terminology associated with critical theory, I treat everything as a concern that requires a critical theory approach (more Foucault than Marx). This means every situation contains power imbalance and a struggle over meaning that privileges certain people and perspectives over others. Therefore, nothing is a neutral object to simply observe and describe. Likewise, there is no such thing as a value-neutral research project, which means that one’s effort to address it ethically must take a stance in addition to a perspective. Combining standpoint theory and future-oriented approaches invites students to consider where structures come from but further, helps them recognize that the aim of research need not be to simply describe, but to raise awareness and build arguments that have, or could have, impact on some future outcome.
This critical theory stance forms the foundation for anything labeled literacy, once we move beyond its classic definition of knowing how to read and write. Literacy is now understood to indicate competence, which in the internet era means more than simply being able to surf the internet, use various platforms, carry out basic searches, and produce content.
A critical theory stance begins with the idea that there’s something wrong and works to investigate the who, what, where, when, and how of this wrongness. Applied deliberately and consistently to everyday lived experiences, this stance is a strong response to datafication, as it questions everything from the perspective of “who benefits and who loses?”—a question in sharp contrast to the more typical open ended qualitative question, “What is going on here?” The latter does not preclude a critical conclusion, but the initial focus prompts a descriptive answer rather than a critical investigation. The descriptive stance is much more vulnerable to the comfort and shortcuts of ideologically laden commercial, political, and populist discourses.
Young people are constantly criticized for being overly obsessed with consumption and consumerism. Simultaneously, they face limited job prospects and shrinking economies, forcing them to find a way of thriving in part time and nomadic labor markets without a safety net. This precarity is not mitigated by thrusting critical theory upon them but by encouraging them to be deeply reflexive of their own experience, to question how larger structures come into being and end up dominating them in ways that may not be fair. I don’t mean to say we should soft peddle critical theory—well, actually, that’s exactly what I’m arguing–
There’s a strong need for critical theory yet a strong resistance to it from students, aging populations, or conservatives. Soft peddling it –by allowing it to emerge through the dialectical tensions within experiential and everyday inquiry (or qualitative research projects) –is one way to move these concepts into front burner territory while minimize their feelings of alienation or belittlement.
We can’t stop machine learning, nor should we want to, but we can question both the source of the data and the simplification of human experience that often accompanies practical big data projects (Grinter, 2014; Markham, 2013). This can only be accomplished by understanding how big data calculations work, which means understanding computational logics behind computer programming, the connections between software and everyday infrastructures for communication and interaction, how algorithms function, how data is created, transformed, transferred, and manipulated, how basic math and statistics underlie all data visualizations, and what arguments are being made through particular data combinations.
I don’t teach all of this myself, but as part of a longterm research project I started in 2012, I’m training youth (approximately 20-30 years old) to do just this: to dive deep into self-reflexive ethnographic analysis of their own social media experiences.
This project incorporates techniques from ethnography, auto-ethnography, and phenomenology, and is inspired by experiments and exercises that loosely follow the logic of Garfinkel’s (1967) ethnomethodological breaching experiments, and help reveal tacit norms, assumptions, practices and feelings about an area of life that is discursively overregulated by overly anxious and overly hopeful rhetoric since the early 1990s.
The design takes seriously and literally Freire’s (1968) idea that respectful dialogue connects the personal experience to broader knowledge spheres. I know more than the participants about the impact of social media, about ethnographic methods, about techniques of self-exploration through auto ethnography, but none of my efforts would work if the participants didn’t also help design of all the elements of the project. The participants decide which technologies to study, how to track and document their digital media use, how to study the infrastructures within which and through which their personal data flow. They determine how to analyze the algorithmic elements that filter their experience. They also choose which genres and media to formulate their reflections in. They decide how far to dive into the self. They decide what tools they’ll use to document, describe, and share their experiences. Finally, they test different tools associated with inductive and emergent approaches (Hesse-Biber & Leavy, 2010) to find an approach that resonates with their own habitual and skilled ways of knowing.
Each set of participants meets regularly for 10-12 sessions. The setup of each session is experimental and based in the foundations of what we generally mean by emergent or inductive approaches. More specifically, I use the vocabulary of remix, a framework I use to reconceptualize possible vocabularies for research methods that allow people across disciplines to talk more effectively about what they are actually doing without getting stuck in disciplinary terminology.
While flexible and varying with each cohort of participants, the basic process has always (during the past 6 years) included the following elements:
- Tracking the extent of use for 48 hours, using different forms of tracking and logging techniques and tools.
- Taking a “media fast” for 24 hours to experience disconnectivity. During this time, participants kept audio/video/text diaries to reflect on what it means and how it feels to be disconnected.
- Reflecting: After each stage, participants wrote or recorded reflections in the form of brain dumps, drew situational maps building on the basic premises and techniques of Adele Clarke’s (2003) situational analysis, and interviewed themselves and one other person.
- Following this intensive ‘generating’ phase, they played around with different possible answers to questions like, “So what?” or “What does it mean?” or “Who cares?”. Finally, some of them produced more refined multimedia narratives.
In six years, I’ve collected around 1,200 narrative accounts in multi-media format. As a whole, they are highly reflexive and show clear signs of consciousness raising. Most, if not all the participants used a critical lens to analyze their social media use, which for a short time made them feel negative about themselves. Some believed they should stop using social media altogether. Most of this self-negativity wore off, as the complexity of the lived experience and the normative, commercial, and ideological discourses surrounding the internet came into sharper relief. But the critical lens that accompanied these shifts in sensemaking appears to remain as a tool to think about other situations beyond their own personal interactions with and through digital and networked media. By the end of each experiment, which included many debriefing sessions and discussions about the inherent good/bad sides of media use, they could articulate a significantly more nuanced understanding of the continual struggle to find balance (Tiidenberg, et al., 2017).
This is, accidentally, one of the strongest models for critical data literacy building I’ve encountered. Iteratively developed and honed for six years, it has become a standard part of my own curriculum and has been taken up elsewhere with similarly strong outcomes. The point is not to gather data about lived experience, although that is an ancillary benefit. Rather, the point of the research is to do the research, and the findings are not for me, as the facilitator, but for the users who become researchers themselves. What they do with these findings is up to them. In other words, once this experiment ends, my job is done.
In this, I am functioning as a pedagogue more than an empirical researcher. The research outcome for me is not to provide answers but to raise questions and cause a chain reaction whereby participants raise their own questions and ask their parents, siblings, friends, and colleagues to also raise questions.In the past six years, participants remind me time and again that they are aware of being tracked, watched, surveilled, sold to. They knowingly carry on personal and intimate activities in public digital spaces. For the most part, they take this for granted, accepting it as the price of free access and convenience of targeted advertisements, better prediction about their likes and dislikes, and faster directions for getting somewhere or locating and connecting with friends. They tend to define privacy differently than older generations, enacting novel techniques to increase their privacy or at least their sense of privacy. Increasingly, their Facebook accounts are not <Firstname Lastname>, and they are very careful about which pictures they post to which platform, using a complex set of criteria related to their understanding of privacy, publicity, friendships, self-branding, and surveillance.
While they know many strategies for working around tracking and data protection concerns, they are less aware of the larger implications, beyond their personal sphere. So rather than try to have them accept the view that big data is bad, or wrong, or only a partial representation of a limited sample–which it is, I shift to a different register or level. By allowing them to learn for themselves that they are being tracked and calculated as data in ways they cannot see or don’t notice, they begin to pay attention to how this might serve others’ interests more than their own. Then, they can begin to see the flaw in the idea that we simply trade privacy for convenient access.
I find myself talking about qualitative research all the time when I teach or give workshops and talks about methods, but I find myself using the term ‘qualitative’ less and less, because it glosses the epistemological.
Without the baggage of disciplinary research methods terminology, participants tend to use what seasoned methods teachers would call reflexive interpretive methods to explore their own uniqueness, relationality, and the complexity of human decision making. They easily discuss how they know a friend is a real friend, a Facebook friend, or a Frenemy, or what makes relationships happen, and then they can be taught to examine what evidence they’re using to reach these analytical conclusions. They can deconstruct the idea of the “quantified self” and articulate how certain data is useful and other data would be impossible to gather. They raise questions about whether big data or data science can be truly objective and while my qualitative sensibilities cringe that they would even ask such questions, I let them carry this discussion through to the end, where they, like many of us, reach the same conclusion I would have started with, only they own the knowledge in ways my telling could never allow.
When I do this as part of a class, the more theoretical definitions come only at the end, when we decide we should put a label on the experiments, reflections, and the approaches toward studying their lived experience. Then, we begin to use terms like phenomenology, discourse analysis, grounded theory, ethnography, or critical theory. We start to borrow from relevant scholars to add depth and larger insights to their explanations and help them make arguments that are accepted as currency within the academic institution. The questions of “So what?” or “who cares?” broaden out from the focus on the self and inevitably, we start to recognize and address larger matters about how our worlds are in the making—something we could label “matters of concern” (Latour, 2003), “matters of care” (Barad, 2007), or much earlier, “worlding” (Peirce, 1958).
The point of this effort is to allow qualitative research sensibilities to seep into everyday sensemaking. This seems a ridiculous statement, since it accurately describes what everyday sensemaking actually is. Still, we find ourselves in an epoch where it has become (again) necessary to promote a non-quantitative approach as a counterbalance to the rapid turns toward quantification, datafication, and computation. Citizens have become encouraged to become citizen scientists who use quantitative logics and methods to make sense of their physical exertions, diet, relationships, and reputation through data-driven metrics and statistical analysis. Many social scientists now use the label data scientists. There is, in short, a widespread assumption that quantitative logics are the core of human experience and assessment, which we can critique as incomplete, but cannot counter. Machine learning only grows more accurate in its predictive abilities, which proves the quantitative logic over and over. Qualitative approaches provide the only lens to explore uniqueness, anomalies that cannot be explained numerically, and most importantly, to interpret, or give meaning to, the amazing patterns that can be found through (big) data analytics.
Building critical literacies
This blogpost is really more of a manifesto. It suggests that we should stop simply rejecting the concept of data. Instead, we (academics, teachers, scholars) should use our long training in pedagogy and teaching and our knowledge of interpretive and inductive/emergent methods of analysis to create better literacies about what data can mean. We can help people find modes and means of critically examining and understanding the contexts within which they are drawn into a neoliberal position through the seemingly innocuous practices of such things as making and sharing images, clicking on links, turning on the smartphone’s GPS. By tugging on one or two threads of this tangle, we can trouble (and help others trouble) what a particular part of someone’s life might mean, in terms of privileging certain people and marginalizing others, commoditizing someone’s creativity without pay, unknowingly narrowing and biasing one’s view of news, tracking one’s activities for no valid or valuable reason, or collecting data because it might be used in the future, without any exchange value, etc.
Once we give people the tools to analyze one or two threads, they can find and examine other threads for themselves later, because they have discovered and trained within themselves the capacity to ask specific kinds of questions. They can help their friends, parents, and children do the same. This is how the agency part of the structuration process works, if we follow Giddens notion of the dynamic relation of structure and agency (Giddens, 1971). Or, using Gramsci, it can also be seen as a process of organic ideology, whereby people within a hegemonic system become aware of the deep structures (Mumby, 1988) and act as social agents to build alternate ideologies (Ramos, 1982). Regardless of which conceptual history we use to explain this process, this is how critical data and digital literacy works. Through a process of first intellectualizing and then enacting other possibilities, people can become more capable of making more conscious choices. With a stronger intellectual framework undergirding their analyses, they can resist in ways that do not oversimplify the way that the sociotechnical economies of the digital age work. Academics have a lot to add to these conversations when we turn our attention outward.