5 Questions with Matthew H. Rafalow, author of “Digital Divisions”
It seems that the effects of COVID-19 persist in each and every arena of our lives. With its emergence, the unjust systemic stratifications of resources, distribution, and access became more apparent than ever. One such area is education. With back to school season upon us again, we must think critically about the divides driving education and schools. In his new book, Digital Divisions: How Schools Create Inequality in the Tech Era, Matthew H. Rafalow explores how different student body demographics receive starkly contrasting responses to their interests and implementations of technology.
What lead to you this subject? Were there any particular elements that you were drawn to learning more about?
I have always been fascinated by how schools work. Since my parents worked in education, dinner table conversations centered on stories about students. But they were also big supporters of my interests in computers, even though a lot of my peers saw it as rather geeky. As an adult, I watched as the world adopted all sorts of new digital technologies. I wondered if kids’ experiences with technology today were similar or different from my own. I also was curious about what school would be like if everyone liked using technology in some fashion. I combined these interests with my love of social science and sought to understand how new technologies were used at school to different effects.
What information or insight were you most surprised by when conducting your research and analysis?
I think I was most surprised by how important faculty workplaces are in shaping how they construct their students in both racialized and classed ways, with or without technology—it was a real puzzle to figure out. Research from as early as the 1960s suggests that the predominately White, middle-class population of school teachers here in the US likely do exhibit biases (racial or classed stereotypes) that shape their beliefs about their particular student demographic. This work argues that these beliefs, then, would lead to different teaching approaches with their students. But a couple things stood out to me in the field that were eyebrow-raisers. Teachers at each of the schools indeed shared racialized and classed beliefs about their students with striking color and consistency. But stereotypes varied, even when coming from the same teachers. Teachers all had multiple stereotypes about Asian-American students; variably as either “model minorities” or “Tiger Mom-raised hackers.” They also had multiple stereotypes about Latinx students; variably as “hard-working immigrants” or “future gang members.” But in describing the students at their present school, teachers believed that only one of the two stereotypes for each racial-ethnic group was applicable—and had a hard time explaining why.
Teachers ultimately helped me figure out this puzzle. As they discussed these beliefs about their students, they also simultaneously unloaded what life was like for them on the job as a teacher. What I learned really opened my eyes to a considerable gap in research: teachers have their own workplace cultures, and these workplaces shape how teachers perceive their students. Teachers at the school serving mostly middle-class, Asian-American students described their faculty workplace as “every man for himself.” It was hostile. Teachers would publicly humiliate and bully one another. They saw each other as threats. In turn, I found that teachers carried this day-to-day “threat” orientation into the classroom. Rather than see their Asian-American students as “model minorities,” they saw them as “Tiger Mom-raised hackers” who would do anything to get ahead. I unpack a similar interaction between teachers’ workplaces and student perceptions at the other two schools. But interestingly, since White students had no available stereotype for teachers to draw on, they mostly skirted by without enduring the effects of stereotyping. Their achievements and failures were seen as individual, and did not carry the additional weight of a collective assumption about their entire racial-ethnic group.
Your book addresses how the race and class of students affects and effects how teachers interpret student’s relationship with technology in an educational setting. What, if any, role do parents or the students themselves play in enforcing or challenging teacher biases?
One critical piece of this story is the significant role that parents have at the private school that I studied. Since the cost of admission is so high, parents really could really wield a lot of power over how teachers did their jobs. To that end, they dramatically shaped the teacher workplace culture. It resulted in a workplace environment where the priority was to treat students like elites. I observed how teachers would link this workplace ideology to available racialized imagery of their students of color. What that meant was that they constructed the few Asian-American students there as “model minorities” rather than the other “hacker” stereotype I observed at a different school. Parents could have wielded their power in many other ways—like encouraging diverse enrollment, offering scholarships for students to attend, and so much more—but they didn’t. Here I’d suggest folks read others’ work, too, on the role of even well-meaning parents in reproducing similar inequities, especially Lewis and Diamond’s Despite the Best Intentions (2015).
I actually didn’t observe students directly resisting teacher biases. Part of this is because the predominately White teachers in this study went out of their way to hide or minimize the fact that they had racial biases, which is consistent with Bonilla-Silva’s work on colorblind ideology. But students did directly engage with punitive reality teachers set up for students at one of the schools—resisting by hacking teachers’ accounts or using tools to “ghost,” or hide from school surveillance because they felt like it wasn’t fair. What’s interesting is that this then led faculty to, behind the scenes, talk about these acts of resistance as further fodder to support their racist stereotyping.
With back-to-school season occurring during a global pandemic, the disparities of resources, including access to technology and other tools, is a major point of concern. How does the at-home or remote schooling occurring due to the pandemic exasperate or display these divisions?
Many of us who studied digital technology adoption pre-COVID have been thinking quite a lot about this topic, as you can imagine. As we look to the fall, it’s hard to not think that teachers at most public schools, particularly schools serving less affluent students of color that already lack financial and infrastructural support, will be working under less than ideal conditions. Teachers are already rallying for possible strikes against their employers at the prospect of risking their lives or teaching remotely without needed training and support. If these are the workplace dynamics, then I expect this teacher stress will extend to students through online instruction, as well. It will not be collaborative, learning-centered, and fun, to say the least, as I’ve seen how some online learning experiences can actually be.
I’d echo other scholars in arguing that schools should focus on continued closure of digital divides to ensure the most disadvantaged don’t encounter further setbacks: reducing gaps in both digital access and skills. But I add that we need to ensure that teachers are safe and supported, otherwise we will see unequal uses of digital tools, even similar ones, for instruction. Federal, state, and district priority must be to protect faculty health, whether in-person or not. Teachers need to be treated as collaborators, not downstream executors, of digitized instruction—and they need technical support and training. Further, just because school members are increasingly going online doesn’t mean racial and class biases are left to analog. Teachers, like the rest of us, are subject to racism and racist bias and need continued professional development opportunities to grow and mitigate these effects on instruction.
Further, the recent emergence of teaching pods (small, quarantined groups of students with hired teaching help) really underscores how bad things are; the largely privileged families who will create them will likely reify digital divisions between students. These already advantaged families are seeing the smoke before the fire. They know that, as of right now, most schools aren’t positioned to do right by students. They’ll be able to hire teachers and wave their cash, much like the wealthy parents in my study did, to ensure their children get the best instruction no matter what—digital or not, pandemic or not. Meanwhile, other children will be left behind.
What’s next for you? Do you have any upcoming projects that you’re jazzed about?
Yes! I’m now drawing on data I’ve been collecting over the last few years at YouTube to better understand this “influencer” space. Specifically, I’m analyzing a mixed methods dataset on live streaming content creators and their viewers. I’m really curious about why people create and share themselves in this way and why people tune in. I have a hunch it will teach me something about new media and the contemporary public sphere.
Matthew H. Rafalow is a visiting scholar at the University of California, Berkeley’s Center for Science, Technology, Medicine, and Society and a social scientist at Google.