“That’s a table with one row with three cells. Get it? Anyone have any questions?” I look around the room and tu to Amairani, a quiet Latinx student with bright observant eyes, to whom I had just demonstrated the same task using a web design program called Dreamweaver. I opened the explanation up to the entire class, using hand code and drawing on the board rather than having the software create tags for them. I ask Amairani if this approach is more confusing than using the software and she says no —in fact, it’s simpler than figuring out the extra tags automatically added by Dreamweaver. With that, the class sets to work in pairs—one hand codes and types while the other reads off content to add into the tables.
I look around the room. Given the demographics of science, technology, engineering, and mathematics (STEM) education and occupational fields, everyone here could be viewed as an unlikely participant. This disparity is particularly true in computer science, where sexist, racist, and classist assumptions about “natural” differences in technological competence and “ambient” cues about who belongs in these spaces shape participation. Although not by design, we are all female. We are all from low-income backgrounds, my students much more so. Almost all of my students are first- or second-generation immigrants and come from homes where their first language is something other than English, primarily Spanish. The school and neighborhood have limited resources—which isn’t to say it isn’t a lively, thriving place. But based on the gender, racial, and socioeconomic makeup of my class, the school, and the neighborhood, we should (supposedly) not be engaged in what we are doing—playing with computers and coding—given that research shows we are the least likely people to be included in these aspects of STEM.
Still, here we are—contrary to the statistics and what stereotypes might suggest—and here I am, a new teacher in a relatively new field, trying my best to lead my students toward an ill-defined end goal:technological competence. With so much potential around the room—the kind that I later come to understand as systematically unseen and undervalued—I want them to be able to pursue any form of technology leaing to get them to wherever they want to go. But I don’t know how to support them. I need to know:What does it mean to be good with technology, how can I help my students achieve it, and how can I ensure that others recognize their potential?
This book offers an answer to these questions, based on what became a decade long jouey to understand what successful technology leaers do, including a way of completely rethinking what it means to be good with technology. The project started out as a way to better understand and support my students and other students like them. That remains a goal:to provide tools for parents and educators to help students lea. But I also want to shake up our broader cultural assumptions about “natural” technological ability—assumptions that devalue the talent of low-income, minoritized, and female students and push them out of STEM. We do that by redefining technological competence as something that can be leaed. Tech skills and literacies are not natural gifts. Instead, there are leaing habits that help skills and literacies grow. These habits are especially important as technologies and the skills and literacies needed to use them change. So, what it really means to be good with technology is to develop skills, literacies, and technology leaing habits…
When we reinterpret technological “instinct” as a set of leaing habits and systematically describe these habits, we can then peer into what science and technology scholars call the “black box” of scientific and technical work. Black boxing hides what scientists and technologists actually do and makes it seem as though being tech savvy involves no leaing at all but is instead the lucky ability of the talented few. When we are unclear about what it means to be good with technology, popular opinion, public policy, and even many scientists default to the cultural myth that being good with technology is natural or instinctual. This idea of natural ability is easily linked to other things considered natural or biological, such as age, race, and gender. In the tech world at this historical moment, assumptions about good “instinct” are often attached to young, white or Asian, and male bodies—an idea that disadvantages women and other racial and ethnic groups. These assumptions make it harder for us to see what real technological ability is, they make it harder to teach, and they make it harder to fight against inequities in tech.
In this book, I use an interdisciplinary approach building on research in the leaing sciences, communication, social psychology, and sociology to explain what being good with technology really is. And I counter assumptions about natural ability with in-depth descriptions of the five habits that help people lea new technologies, highlighting in particular the habits of low-income, Black, Latinx, Native American, and female teens. I also show how to measure and build these habits—and demonstrate that many teens historically marginalized in tech already use the habits. In other words, they may be more ready for advanced technological skill development than assumptions about instinct might suggest, building on a growing body of research demonstrating the many missed opportunities for socioeconomic, gender, and racial equity. Unpacking “instinct” in this way is essential not only for the reconceptualization of the goals of STEM education to better support students’ readiness to respond to technological change but also to contest ideas about natural technological ability to combat inequities in STEM.