Since I started research back in 1999 as an undergraduate, I’ve always been intrigued by the goal of helping people write code more productively. Sometimes I ran studies that tried to identify barriers to their productivity. Sometimes I made tools that helped them navigate faster, debug faster, test faster, and communicate faster. Every one of my research efforts was aimed explicitly at speed: the more a developer can do per unit time, the better off the world is, right?
Then something changed. I founded a software startup, and led it’s technical and product endeavors. And I watched: how much did developer productivity matter? What influenced the quality of the software? What was the real value of faster developers?
In my experience, faster development didn’t matter much. Developers’ speed mattered somewhat, but only to the extent that we made effective product design choices based on the valid understanding of customer, user, and stakeholder needs. It didn’t matter how fast they were moving if they were moving in the wrong direction. And developer tools—whether a language, API, framework, IDE, process, or practice—mattered only to the extent that developers could learn to use these tools and techniques effectively. Many times they could not. Rather than faster developers, I needed better developers, and better developers came through better and faster learning.
Furthermore, I couldn’t help but wonder: what part of this job is fulfilling to them? It’s certainly wasn’t writing a lot of code. There was always more code to write. In fact, it was the moments they weren’t coding—when they were reading about a new framework, picking up a new language, trying a new process—that they enjoyed most. These were moments of empowerment and moments of discovery. These were moments of learning. Around the same time, my student Paul Li was beginning to investigating software engineering expertise, and finding that, much as I had experienced, it was the quality of a developer’s code, and their ability to learn new coding skills, that were critical facets of great engineers. Better learning allows developers to not only be more productive and more effective, but also more fulfilled, Better learning makes better developers, who envision better tools, better processes, and better ideas. As obvious as it should have been to someone with a Ph.D. in HCI, it was the human in this equation that was the source of productivity, not the computer. Like most things computational, developer tools are garbage in, garbage out.
After I stepped down as AnswerDash CTO and begin my post-tenure sabbatical, it became clear I had to pivot my research focus. No more developer tools. No more studies of productivity. I’m now much less interested in accelerating developers’ work, and much more interested shaping how developers (and developers-in-training) learn and shape their behavior.
This pivot from productivity to learning has already had profound consequences to my research career. For a long time, I’ve published in software engineering venues that are much more concerned with productivity than learning. That might mean I have less to say to that community, or that I start contributing discoveries that they’re not used to reading about, evaluating, or prioritizing. It means that I’ll be publishing more in computing education conferences (like ACM’s International Computing Education Research conference). It means I’ll be looking for students that are less interested in designing tools that help them code faster, and more interested in designing tools to help developers of all skill levels code better. And it means that my measures of success will no longer be about the time it takes to code, but how long it takes to learn to code and how well someone codes.
This pivot wasn’t an easy choice. Computing education research is a much smaller, much less mature, and much less prestigious research community in computing research. There’s less funding, fewer students, and honestly, the research is much more difficult than HCI and software engineering research, because measuring learning and shaping how people think and behave is more difficult than creating tools. Making this pivot means making real sacrifices in my own professional productivity. It means seeing the friends I made in the software engineering research community less often. It means tackling much trickier, more nuanced problems, and having to educate my doctoral students in a broader range of disciplines (computer science, social science, and learning science).
But here’s the upside: I believe my work will be vastly more important and impactful in the arc of my career. I won’t just be making an engineer at Google ship product faster, I’ll be inventing learning technologies and techniques that make the next 10,000 Google engineers more effective at their job. I’ll be helping to eliminate the hundreds of thousands of horrific experiences that people have learning to code into more fulfilling and empowering experiences, potentially giving the world an order of magnitude more capable engineers. Creating a massive increase in the supply of well-educated engineers might even slow down some of the unsustainable growth of software engineering salaries, which are at least part of the unsustainable gentrification of many of our great American cities. And most importantly, I’ll be helping to give everyone that learns to code the belief that they can succeed at learning something that is shaping the foundational infrastructures of our societies.
I’ll continue to be part of the software engineering research community. But don’t be surprised if my work begins to focus on making helping better developers write better code than simply writing code faster. I’ll continue to be part of the HCI research community, but you’ll see my work focus on interactive learning technologies that accelerate learning, promote transfer, and shape identity. And for now, you’ll see me invest much more in building the nascent community of computing education researchers, helping it blossom into the field it needs to become to transform society’s ability to use and reason about code as it weaves itself deeper into our world.
I’m so excited to engage in this new trajectory, and hope to see many of you join me!