From time to time, I have featured here the work of Mimi Ito and others from the Connected Learning Research Network. Along with danah boyd, Mimi and I wrote Participatory Culture in a Networked Society and we've collaborated on a broad range of education-related ventures. So, when Mimi flags something to my attention, I listen and respond. Last October, Ito sent me the copy of From Good Intentions to Real Outcomes: Equity by Design in Learning Technologies, a report she had written with Justin Reich, currently in the department of Writing and Comparative Media Studies at MIT. Having featured Ito several times here, I wanted to put the spotlight on Reich, all the more so when I learned he was now teaching through the program I helped to establish at MIT.
What he has to say here gives some provocative glimpses into what these two researchers found, challenging the discourse of technological disruption and inevitability, which shaped so much early thinking about the ways new media would impact education. In a classic meeting between technology and culture, they find that the culture of schools, much more conservative than even many skeptics imagined, wins out most of the time, resulting in a world where lowered expectations and diminished resources for some youth keep them from enjoying the benefits imagined by those who introduce new media tools and platforms. But, what he shares here scratches the surface. There is no substitute for doing what Reich urges at one point: "Read the report!"
Your report identifies three core myths about technology and education. What are they? Each of these seems to boil down to a form of technological determinism. How do we help people to understand the social and cultural forces that shape our relations with technology?
To provoke people’s thinking on edtech and equity, we argue that there are three myths out there that are worth rethinking
The first is that technology disrupts systems, when very often, culture domesticates technology. From Clayton Christensen on down, we have a whole mythology about the power of technology to reorganize human systems, but what we see over and over again is that schools and other learning ecologies are great at taking new technologies and putting them in service of existing goals and intentions. From slate to chalkboard to overhead projectors to document cameras to projectors to smartboards, we’ve had nearly a dozen display technologies in classrooms and overwhelming they are used to display notes that students are supposed to copy or summarize. I was at Google recently and someone involved in the Classroom team was explaining how they were so successful at scaling up so quickly, and the “secret” turned out to be helping the system do everything it was doing anyway. Generally speaking in schools, it’s a good bet that if you introduce a new technology, it will be used to extend existing practices, and it won’t be a catalyst for disruptive innovation.
The second myth is that open equals equitable, but more commonly, free technologies disproportionately benefit affluent folks with the financial, social, and technological capital to take advantage of free innovations. I’ve studied this in several contexts now, at the end of the 00s I was studying classroom uses of wikis, and found they were used more often and for more interesting purposes in affluent schools. In the last few years, I studied MOOCs, and found that U.S. residents lives in neighborhoods about a half of a standard deviation more affluent that typical Americans.
If you want to make a safe bet about any new tech in schools, bet that it will be used to extend existing practices, and most adoption and most of the interesting practices on the margins will happen in affluent schools or in the upper tracks of schools with more affluent kids.
The third myth is that we can close some of these digital divides through expanding technology access. In reality, social and cultural exclusions are much more difficult to overcome. This is an old lesson, but we understand it better with each passing year. I was first exposed to some of these ideas from the sociologist Paul Attewell’s work on the two Digital Divides: the divide of access and the divide of usage. You can wire everyone up the same with the same devices, and young people from more affluent neighborhoods will have more opportunities to use tech for more creative and production-oriented uses with more support from adults and mentors. Henry, your own work on the Participation Gap—the gap between who has access to new technologies and who actually participation as producers in creative networks—is another source of inspiration for this kind of thinking.
One overarching lesson from all this is that if you want to build great edtech, you ought to have folks with social and cultural expertise on your team. The tech is just table stakes, it’s really about the integration into the learning ecology.
I’ve been teaching undergrads at MIT this semester, and most of them are Computer Science concentrators. A big part of how startups encourage developers to think is to focus very closely on a particular and well-defined interaction: think of how Uber tries to create the experience of tapping your phone have having a black car come pick you up and whisk you away like a celebrity. Focusing on a particular interaction makes design tractable, but it also means you aren’t paying attention to the large context and system.
It might be technological determinism, but even if it’s not the result of strictly deterministic thinking—maybe just a kind of techno-optimism—we think there are real limitations to how much technology alone can shape systems.
As to your questions about how we help people understand more about how social and cultural forces shape tech, Mimi and I are starting a whole project related to this. Over the past year, we’ve had three meetings with folks from venture capital, philanthropy, and edtech trying to have a good old-fashioned consciousness raising conversation. I think the research on the challenges we face is pretty stable and robust at this point, and the more exciting work ahead is to figure out how we can learn from the exemplar projects out there that are doing great work to close opportunity gaps.
An underlying argument is that despite our high hopes and best intentions, “evidence is mounting that these new technologies tend to be used and accessed in unequal ways, and they may even exacerbate inequity.” What are some of the indicators supporting this claim?
I mentioned two of my studies on this, about wikis and MOOCs. Let me describe for a minute some commonalities of both of these studies. First, these technology platforms operate at a global scale and collect massive amounts of data. There are many serious privacy concerns about this kind of data collection, but if you want to understand edtech and inequality, you need to gather enough data to understand how subgroups use technology indifferent ways. In both of these studies we connect log data from the platforms with national datasets about demographics—in the case of schools we use school level data from the National Center on Education Statistics and for the MOOC study we used data derived from the Census.
For the wiki study, we found publicly-viewable, education related wikis used in U.S., K-12 schools, and measured where they were created, how long they were used, and how rich and collaborative the learning experience was. We then gathered socio-economic status data about the schools themselves, so we could compare how wikis were used differently in school serving different populations. We found that wikis were more likely to be created in schools serving affluent kids, that wikis created in affluent schools were used longer and with more student involvement.
For the MOOC study, we had all of the data about HarvardX and MITx enrollments and course completions, and we had folks’ addresses, which we could use to identify their census block group, a neighborhood of about 1200 individuals. If you know something about someone’s neighborhood, you can make a good guess about their own level of affluence, There, we found that people who register for MOOCs live in neighborhoods about ½ standard deviation more affluent that typical Americans, and for young people who register, students from more affluent neighborhoods are more likely to complete courses.
There is lots of previous research on edtech and inequality, Paul Attewell did observational studies in homes and schools. Other researchers have used surveys; Harold Wenglinsky used NAEP surveys in the 90s to identify that Black and low-income students were more likely to use computers in math class for drill and practice than for more cognitively complex math work. For the methods nerds, the observational work had great validity, but problems with generalizability, and the surveys probably had low validity, but good generalizability. The virtue of some of the newer work examining whole systems is that it has high validity, since we can peer closely at exactly what people do, along with the generalizability that comes from massive, international platforms. But all this work points in the same direction- people with more financial, social, and technical capital have a greater ability to take advantage of new innovations, even free ones.
This is a rather dire finding for people who have spent the last few decades trying to bring new media platforms and practices into schools. I can imagine it was hard won. Has it force you to rethink some of your earlier work in this space?
Hard won, for sure: I started working on this is 2008, and 2017 was when I felt confident to get together with Mimi and say “Look, we know what’s going to happen when the next piece of edtech comes out, and we have to start avoiding some of the same mistakes.” Each little brick takes years to stack up on the foundation, but at this point we have thirty years of work with computers, and 100 years of work on signals technology going back to radio—we can make good bets about how edtech will affect equity when in context. .
I started my work in edtech in affluent private schools as a history teacher, and I thought teaching in 1-1 environments there was fabulous—16 kids, computers for everyone, batteries always charged, networks always working. When I started into research, I was pretty sure that the things that worked great for me in the world’s best teaching environment weren’t going to work other places. But that was the real start of the Web 2.0 era, and there were all kinds of calls that social media and peer production tools were going to democratize education, my instinct was that wasn’t going to happen because even though the tools were “free”, the infrastructure to make them valuable was very expensive. So I was right from the beginning.
What are some of the factors that result in this reproduction of unequal relations?
My favorite story about this comes from an observation in a school in rural New Hampshire. The teacher was preparing a lesson using wikis, and all the kids had laptops, the batteries were charged, the broadband was coming into the building, the internet was reaching the wireless access points and connecting to the computers, the projector had a bulb, and the introductory slides were all ready to go. The teacher went to plug in the projector, and the electrical outlet fell behind the dry wall, and the teacher needed to rethink everything. Getting technology working in schools requires the maintenance of a complex logistical infrastructure, that includes outlets, wires, wireless access, power, batteries, policy, and pedagogy. It takes a big investment in staffing to keep all that running, and it’s easier for affluent schools to make those investments.
Mimi’s student Matt Rafalow has some great research about how cultural perspectives at schools also reproduce structural inequalities. To oversimplify, when rich white kids play around with technology, they are treated as hackers, and when poor black and brown kids play around with technology, adults treat them as slackers. Adults can treat very similar behaviors differently based on the demographics of the students engaging in the behavior.
Maybe one other important point is that there are some sectors where introducing technology does lead to certain kinds of reducing of inequalities. I’ve seen data about agricultural prices in rural parts of southeast Asia where before cell phones, prices are very volatile, and after the widespread introduction of phones, prices stabilize dramatically. Or even something as basic as cameras, which were the provenance of the elite for many years, but recently have played a crucial role in documenting police violence and so forth. So I understand why people might have an intuition that free technologies would be particular good for people without a lot of resources, and certainly sometime they can be, but it’s unusual in edtech for new technologies to disproportionately benefit low income students. When it happens, it happens because designers are very intentional about that as a goal.
Even when educational materials are free and open to all online, they tend to draw the most use from those who are already educational and informational haves. I can imagine frustrated designers and educators throwing up their hands and saying, What more can we do? What steps can we take to decrease or even reverse this process of inequality in educational opportunity? Do you have some good exemplars of what this better practice looks like?
So that’s the second part of our paper: From Good Intentions to Real Outcomes. There is great work that’s happening out there, and terrific researchers, developers, and funders and finding out all kinds of important strategies for making technology work for the students furthest from opportunity.
There are a number of great strategies that folks have identified. Ricarose Roque’s Family Creative Learning and Boston’s TechGoesHome both get families involved in learning more about tech so they can support their kids learning… if it takes a village to raise a child, then let’s teach the village. The folks at OpenStax at Rice University realized that there were something like 20 college courses in the US that were responsible for over half of all enrollments in universities: Calculus I, U.S. History, etc. So they got donors to fund the development of really great open source textbooks books on these topics that they target at the community college market, where textbook costs are a substantial burden on student budgets. This seems to be a case where free things do the greatest benefit for the students furthest from opportunity.
In the paper, we offer four types of strategies to get people started. First, co-design with learners and communities. Make sure that your development teams include people and have close relationships with the learners you most want to serve. Second, align home, school, and community—get parents and families involved and build their capacity alongside students. Third, building on all the great work in the Connected Learning community, leverage the interests that students bring from their cultures and backgrounds. Fourth, measure the impact of new technologies on different kinds of learners, and really try to understand how innovations get picked up differently by different communities. There is much more in the paper we released about each of these strategies, but what they have in common is the call for people to think about the context of edtech, not just the tech.
Here’s one thought that I’ve been playing around with in teaching my undergraduates: one question that edtech developers and advocates might ask is: “What is the human-human interaction that you hope results from the technology that you are developing? Before, during or after an interaction with edtech, what kind of conversation will a kid have with an adult or with another kids because of the technology.” That might be a simple way to get people to start thinking more about the broader context of edtech.
What advice do you have for people trying to develop ed-tech for use in the current cultural and educational climate? What should they do differently if they want to be part of the solution rather than part of the problem?
Read the report! I guess that’s sort of a boring researcher answer, but we wrote the darn thing to help people find their first steps.
We think that step one is getting a handle on the basic findings of 30 years of research into education technology and equity. If you are working on a project that’s trying to make education more equitable using tech, there is a long history to suggest that it’s really hard to do that.
Step two is looking out there at the great examples out there, many of which we describe in the paper, that are finding creative and clever ways of partnering with learners and other stakeholders to build equitable edtech.
Step three is getting your team together and saying, “OK, we haven’t done as well as we wanted to as a field on this over the last 30 years. From our own vantage, what could we be doing in the next 30 days or 30 years to make some improvements.” This New Gilded Age that we are in is a very difficult place to finds ways of connecting innovation and equity, but the challenge that we face shouldn’t dim our hopes. Education is a great place for people who maintain hope in the face of structural adversity.
What are the next steps for you and the other researchers on this team?
Mimi and I have some schemes that we’re working on. We’d like to continue to find ways of engaging the venture capital, philanthropic, developer, researcher, and practitioner communities around this. There aren’t that many people in the US who are gatekeepers to what kinds of edtech projects get started and what gets adopted. If we could educate and engage a good portion of those folks, I think we could start a new conversation across many different actors in the field.
While we have some good early exemplars of how to think about edtech and equity in sophisticated ways, there is much, much more work to be done. We’re hoping to find a way to have the technology industry come together to fund some of that research collaboratively, so it’s not just something coming out of one foundation or one research lab, but it’s something that the edtech industry takes on itself to better figure out how to serve all kids, especially those who need us most.
Justin Reich is an educational researcher interested in the future of learning in a networked world. He is an Assistant Professor in the Comparative Media Studies/Writing department at the Massachusetts Institute of Technology, an instructor in the Scheller Teacher Education Program, a faculty associate of the Berkman Klein Center for Internet and Society, and the director of the MIT Teaching Systems Lab. The Teaching Systems Lab investigates the complex, technology-rich classrooms of the future and the systems that we need to help educators thrive in those settings.