It’s May 2004 and I’m standing next to a whiteboard at the front of a technology class in a small public middle school called Urban Promise Academy (UPA, or “ooo-pah”). The school is located in the predominantly low-income Latinx Fruitvale neighborhood of Oakland, California. Walking to UPA from the Fruitvale Bay Area Rapid Transit (BART) station, I pass clusters of men in dusty work boots—day workers—waiting for the trucks that pick them up and women selling tamales from red coolers on the corner. Vibrant colors adorn most buildings, street signs, and even the trash cans on the street. Music calls out from busy pupuserias and panaderías, the delicious scent of Salvadoran stuffed corn cakes and Mexican sweet breads filling the air, and families smile and wave as they notice neighbors across International Boulevard.

It’s the second of the six years I will teach technology classes (web design and robotics) at UPA, and I’m close to completing a master’s degree from Stanford’s Learning Design and Technology program. I’m standing at the whiteboard, chattering away at a breakneck pace, scribbling hypertext markup language (HTML) tags on the board to explain how to format pages of a yearlong web project celebrating the school’s first graduating class. We’ve collected photos and quotes from each eighth grader about their experiences at UPA and are now formatting the individual student pages and populating the website with content. We are close to completing the project, my students are almost ready to move on to high school, and I am just about to graduate as well. It’s an exciting moment.

Around me, the class munches on snacks. I explain how to hand code to structure each page, including where to add <table> tags. I say, “First you need the opening <table> and closing </table> tags, then you add code for the row, and each cell within that row like this...” I scribble on the board with dry-erase marker:
 
<table>
<tr>
<td></td>
<td></td>
<td></td>
</tr>
</table>
 
“That’s a table with one row with three cells. Get it? Anyone have any questions?” I look around the room and turn 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 learning 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 journey to understand what successful technology learners 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 learn. 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 learned. Tech skills and literacies are not natural gifts. Instead, there are learning 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 learning habits…

When we reinterpret technological “instinct” as a set of learning 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 learning 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 learning 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 learn 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.