In the winter of 2011, a handful of software engineers landed in Boston just ahead of a crippling snowstorm. They were there as part of Code for America, a program that places idealistic young coders and designers in city halls across the country for a year. They'd planned to spend it building a new website for Boston's public schools, but within days of their arrival, the city all but shut down and the coders were stuck fielding calls in the city's snow emergency center.
In such snowstorms, firefighters can waste precious minutes finding and digging out hydrants. A city employee told the CFA team that the planning department had a list of street addresses for Boston's 13,000 hydrants. "We figured, 'Surely someone on the block with a shovel would volunteer if they knew where to look,'" says Erik Michaels-Ober, one of the CFA coders. So they got out their laptops.
Screenshot from Adopt-a-Hydrant Code for America
Now, Boston has adoptahydrant.org, a simple website that lets residents "adopt" hydrants across the city. The site displays a map of little hydrant icons. Green ones have been claimed by someone willing to dig them out after a storm, red ones are still available?500 hydrants were adopted last winter.
Maybe that doesn't seem like a lot, but consider what the city pays to keep it running: $9 a month in hosting costs. "I figured that even if it only led to a few fire hydrants being shoveled out, that could be the difference between life or death in a fire, so it was worth doing," Michaels-Ober says. And because the CFA team open-sourced the code, meaning they made it freely available for anyone to copy and modify, other cities can adapt it for practically pennies. It has been deployed in Providence, Anchorage, and Chicago. A Honolulu city employee heard about Adopt-a-Hydrant after cutbacks slashed his budget, and now Honolulu has Adopt-a-Siren, where volunteers can sign up to check for dead batteries in tsunami sirens across the city. In Oakland, it's Adopt-a-Drain.
Sounds great, right? These simple software solutions could save lives, and they were cheap and quick to build. Unfortunately, most cities will never get a CFA team, and most can't afford to keep a stable of sophisticated programmers in their employ, either. For that matter, neither can many software companies in Silicon Valley; the talent wars have gotten so bad that even brand-name tech firms have been forced to offer employees a bonus of upwards of $10,000 if they help recruit an engineer.
In fact, even as the Department of Labor predicts the nation will add 1.2 million new computer-science-related jobs by 2022, we're graduating proportionately fewer computer science majors than we did in the 1980s, and the number of students signing up for Advanced Placement computer science has flatlined.
Code.org
There's a whole host of complicated reasons why, from boring curricula to a lack of qualified teachers to the fact that in most states computer science doesn't count toward graduation requirements. But should we worry? After all, anyone can learn to code after taking a few fun, interactive lessons at sites like Codecademy, as a flurry of articles in everything from TechCrunch to Slate have claimed. (Michael Bloomberg pledged to enroll at Codecademy in 2012.) Twelve million people have watched a video from Code.org in which celebrities like NBA All-Star Chris Bosh and will.i.am pledged to spend an hour learning code, a notion endorsed by President Obama, who urged the nation: "Don't just play on your phone?program it."
So you might be forgiven for thinking that learning code is a short, breezy ride to a lush startup job with a foosball table and free kombucha, especially given all the hype about billion-dollar companies launched by self-taught wunderkinds (with nary a mention of the private tutors and coding camps that helped some of them get there). The truth is, code?if what we're talking about is the chops you'd need to qualify for a programmer job?is hard, and lots of people would find those jobs tedious and boring.
But let's back up a step: What if learning to code weren't actually the most important thing? It turns out that rather than increasing the number of kids who can crank out thousands of lines of JavaScript, we first need to boost the number who understand what code can do. As the cities that have hosted Code for America teams will tell you, the greatest contribution the young programmers bring isn't the software they write. It's the way they think. It's a principle called "computational thinking," and knowing all of the Java syntax in the world won't help if you can't think of good ways to apply it.
Unfortunately, the way computer science is currently taught in high school tends to throw students into the programming deep end, reinforcing the notion that code is just for coders, not artists or doctors or librarians. But there is good news: Researchers have been experimenting with new ways of teaching computer science, with intriguing results. For one thing, they've seen that leading with computational thinking instead of code itself, and helping students imagine how being computer savvy could help them in any career, boosts the number of girls and kids of color taking?and sticking with?computer science. Upending our notions of what it means to interface with computers could help democratize the biggest engine of wealth since the Industrial Revolution.
So what is computational thinking? If you've ever improvised dinner, pat yourself on the back: You've engaged in some light CT.
There are those who open the pantry to find a dusty bag of legumes and some sad-looking onions and think, "Lentil soup!" and those who think, "Chinese takeout." A practiced home cook can mentally sketch the path from raw ingredients to a hot meal, imagining how to substitute, divide, merge, apply external processes (heat, stirring), and so on until she achieves her end. Where the rest of us see a dead end, she sees the potential for something new.
If seeing the culinary potential in raw ingredients is like computational thinking, you might think of a software algorithm as a kind of recipe: a step-by-step guide on how to take a bunch of random ingredients and start layering them together in certain quantities, for certain amounts of time, until they produce the outcome you had in mind.
Like a good algorithm, a good recipe follows some basic principles. Ingredients are listed first, so you can collect them before you start, and there's some logic in the way they are listed: olive oil before cumin because it goes in the pan first. Steps are presented in order, not a random jumble, with staggered tasks so that you're chopping veggies while waiting for water to boil. A good recipe spells out precisely what size of dice or temperature you're aiming for. It tells you to look for signs that things are working correctly at each stage?the custard should coat the back of a spoon. Opportunities for customization are marked?use twice the milk for a creamier texture?but if any ingredients are absolutely crucial, the recipe makes sure you know it. If you need to do something over and over?add four eggs, one at a time, beating after each?those tasks are boiled down to one simple instruction.
Much like cooking, computational thinking begins with a feat of imagination, the ability to envision how digitized information?ticket sales, customer addresses, the temperature in your fridge, the sequence of events to start a car engine, anything that can be sorted, counted, or tracked?could be combined and changed into something new by applying various computational techniques. From there, it's all about "decomposing" big tasks into a logical series of smaller steps, just like a recipe.
Those techniques include a lot of testing along the way to make sure things are working. The culinary principle of mise en place is akin to the computational principle of sorting: organize your data first, and you'll cut down on search time later. Abstraction is like the concept of "mother sauces" in French cooking (b?chamel, tomato, hollandaise), building blocks to develop and reuse in hundreds of dishes. There's iteration: running a process over and over until you get a desired result. The principle of parallel processing makes use of all available downtime (think: making the salad while the roast is cooking). Like a good recipe, good software is really clear about what you can tweak and what you can't. It's explicit. Computers don't get nuance; they need everything spelled out for them.
