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The Learning Divide
It was the last day of February 2017, and Amos Winter, an assistant professor of mechanical engineering at MIT, was warning the group of sophomores in his afternoon lab section about the destructive potential of their batteries. Though supposedly safe, in the unlikely event of a sudden discharge, each of the lithium polymer batteries scattered on the conference table possessed enough energy to maim, even kill.
How much energy, exactly? “Go ahead—slam it into a calculator,” he said. After approximately ten seconds, anyone who had worked it out was keeping the answer to herself, so Winter bounded over to a whiteboard. You know the capacity of the battery, he explained, which came labeled in units of milliampere hours. “You basically just add in time to figure out energy in joules,” he said, and in short order, the answer was on the board: 13,320 joules. “That’s the equivalent to lifting a Honda Civic ten meters off the ground,” he said. “Imagine a Honda Civic falling on your hand”—that’s the kind of damage an exploding lithium polymer battery could inflict. If the casing on such a battery begins to bubble, he said, chuck it in one of the lab’s many sand buckets and run in the opposite direction.
In the absence of any such catastrophes, however, class would continue to hum along as it had for the first few weeks of the semester. In addition to the batteries, sitting on the table in front of each student was a simple robot—two wheels and a skid designed to drag along the ground—which would serve as a sort of training vehicle, in anticipation of the more complex robots the class would build later in the semester. On these practice bots, which Winter dubbed “Mini-Mes,” the students would learn mechanical engineering principles ranging from simple to complex. They would start by learning to code a microcontroller (that is, a very small computer) to run an electric motor; later, they would instill in their Mini-Mes the capacity to navigate the world autonomously like rudimentary self-driving cars. Along the way, they would learn not just robotics knowledge and skills, but how to think like designers and engineers. They would come to understand how to approach a task creatively, to spot issues before they become serious problems, and, perhaps most important, to gain a level of trust in their own ability to guide a project from early phase, when there are innumerable paths to a desired solution, to late, when there’s only one best way forward.
That was the learning progression in theory, at least. In practice, some of Course 2.007’s students were coming to it with more engineering experience than others. Some had competed in high-school robotics tournaments. (The best-known extracurricular robotics organization, FIRST Robotics, had actually spun out of MIT’s original version of Course 2.007, back in 1989.) And the rumor mill had already made it known that one student, Alex Hattori, had competed on Battlebots, a televised contest known for its metal-on-metal violence. He and his teammates had sent a buzz-saw-wielding robot the size of a manhole cover into a gladiatorial arena, to wage war on opponents with names like SawBlaze and Overhaul.
To the other 164 students who lacked such head starts, these advantages were cause for real concern. In MIT’s charged academic atmosphere, stress among students is a perennial issue, and unnecessary competition, usually over grades, does not help. Most of the time, the Institute works hard to dampen this instinct—for instance, by abolishing grades in the first semester of freshman year. But Course 2.007 is different. Competition is baked into it at a deep level, and is the reason why it is arguably MIT’s most famous undergraduate offering. At the end of every spring semester, the course culminates in a robotics showdown, which draws hundreds of spectators from across campus and beyond. The winner achieves lifelong bragging rights, entering MIT Valhalla while notching one heck of a résumé bullet point.
Brandon McKenzie’s gaze slid to his lab mates seated around the table. A varsity swimmer who had competed in the Division III national championship as a first-year and would return to the championship series later in the semester, he had thus far maintained a perfect 5.0 GPA despite spending eighteen-plus hours per week in the pool. He was not used to the sense of falling behind, and yet there was no shaking the feeling that others were several lengths ahead of him in the race to build serious, competition-worthy robots. He had come to 2.007 with next to no practical robotics experience, and there were a few others in the same predicament—Amy Fang, for instance, at the other end of the table, and Josh Graves, Brandon’s roommate, teammate, and all-around co-conspirator, at his right elbow. But then there were folks like Jordan Malone, seated directly across from Brandon, whose computer-aided-design prowess Winter would later describe as a “super power.” (And that wasn’t even the most impressive thing about him: Although he never brought it up unbidden, everyone knew that Malone, a short track speed skater, had brought home Olympic medals from Vancouver and Sochi, prior to enrolling at MIT at age thirty.) And there was Zhiyi Liang— Z, for short—a joyful mad-scientist-in-training who seemed to come to class every week having produced a new mechanical marvel in his downtime. Brandon expressed no animosity toward his fellow students; indeed, he would become the lab’s most reliable source of fist bumps and backslaps in the weeks to come. But then again, he didn’t feel any animosity toward his swimming teammates either, and that certainly didn’t stop him from trying to outswim them.
Winter doled out off-brand Arduinos: microcontrollers that would inform the movements of the class’s Mini-Mes today and, later, their full-fledged, competition-ready robots. That morning’s lecture had concerned the mechanics of brushed, direct-current motors, the simplest type of electric motor. Now, mere hours later, Winter was taking his students’ understanding of DC motors as given and demonstrating how they could be put to work. As Winter blasted through a series of reasonably complicated concepts, Brandon scrambled to take in his words while also adjusting his Mini-Me’s physical wiring and fiddling with his Arduino’s code on his laptop. He sensed he was in danger of sliding even further behind.
“I felt a little discouraged,” he said later. Although Arduino’s programming language, C++, was basically new to him, some of his classmates seemed to know it “like the back of their hand.” He was keeping up for the time being, but he knew that the moment his attention strayed he would find himself stranded. This course had a sink-or-swim quality to it that felt all too familiar. It was as though he’d been chucked into the deep end of a pool but didn’t yet know how to stay afloat. And although there were plenty of instructors looking on, telling him how to keep his head above water, it was up to him to apply that information in a way that actually worked.
Provoking that conceptual shift—from theory to practice, from inert to activated knowledge—is what 2.007, at its core, is all about. The course assumed its modern form in 1970, when a young professor named Woodie Flowers took the reins. In the decades that followed, as the beloved professor became a professor emeritus, he took on local-celebrity status on campus, where he tended to pop up periodically, Stan Lee–like with his trademark mustache and silver ponytail, to speak about learning. Every time, he stressed a single, crucial point: the difference between “learning calculus” and “learning to think using calculus.”
To educators like me, who hope to produce students capable not just of earning good grades but of exerting their knowledge in the wider world, this distinction is of the utmost importance. But for students accustomed to clean borders around their education—the four edges of an eight-by-eleven worksheet, four walls of a classroom, four-year progression through high school, then college—it can be unsettling to step out into the messiness of the real world, even temporarily. And so every year, while some of 2.007’s students exult in the chance to get their hands dirty, others hang back, sometimes for weeks, to get their bearings.
Across the table from Brandon, Amy Fang had already sized up her classmates. Arriving at MIT, she later recounted, she found her fellow students so intimidating that she decided that staying in the middle of the pack was a perfectly worthy goal. “I try to be average,” she said, laughing. For the moment, as she connected her servomotor to her Mini-Me’s breadboard, any sort of noteworthy success in 2.007’s semester-end competition seemed unlikely. A decent grade would be challenge enough: 2.007 had no exams, and success in the final competition itself would have no direct bearing on grades. But the design of the students’ competition robots would eventually come under close scrutiny, and in the meantime the course’s significant homework load felt like a millstone. It came in two parts: written homework—a weekly set of four tricky, multipart engineering problems—as well as a weekly physical challenge that students were charged to accomplish using their Mini-Mes. This week, they had to submit video of their Mini-Mes heading forward a preordained distance, performing a U-turn, returning, and coming to a complete stop—all without any input from a remote control. Later in the semester, some students’ robots would pull off autonomous tasks of greater complexity, such as using a light sensor to follow a line drawn on the ground.
As Winter explained about this path-following strategy, Mo Eltahir, a student from nearby Lexington, Massachusetts, spoke up. “It’s cool because every lecture is stuff you can think of to use for the competition.”
Winter let out a laugh. “Would you go so far as to say you’re learning?”
They were indeed. In the demanding weeks to come, they would etch new memories more deeply than they probably knew—and not just any old memories, either, but highly contextualized ones that would empower them to both understand their world and influence it. To encourage this process, Course 2.007 does away with many of the problems endemic to traditional classrooms and lecture halls, which have hardly budged over the course of more than a century, and replaces them with something better.
In 1902, the educational psychologist and philosopher John Dewey (“America’s foremost philosopher of his time,” his New York Times obituary would eventually read) laid out the “typical evils” of classrooms he’d observed. Such incorporeal subjects as mathematics, separated from the objects and processes that numbers represent, and geography, divorced from geological and historical events, lacked “any organic connection with what the child has already seen and felt and loved,” he wrote in his book, The Child and the Curriculum. As passive recipients of knowledge-made-inert, students went through the motions of school without ever feeling truly motivated to learn, mainly because the stuff they were being taught didn’t relate to their day-to-day concerns and goals. Today, even when schools manage to better contextualize what they’re teaching, the same central assumption still persists: Students are expected to learn for the benefit of their future, not present, selves.
Course 2.007 isn’t like that. First, thanks to its sink-or-swim nature, students don’t have the luxury of a lazy, memorization-based approach to studying. Rather, any theory taught in class can and must be immediately applied—after all, if you don’t apply it, your opponents will. In part because students thrill to the experience of seeing their knowledge translate into real-world engineering powers, and in part because there’s glory on the line, the course’s ability to motivate learning is second to none. Even students with a long history of doing the bare minimum wind up inspired despite themselves, spending far more time in the lab than is strictly necessary, screwdriver in hand, notebook computer open. In the space of a single semester, the course launches experienced hobbyists and the uninitiated alike toward professional mechanical engineerdom in a way that is sometimes imitated but never, in my completely biased opinion, exceeded.
Today, when I contemplate the task before me, my thoughts never stray far from 2.007. At minimum, any education scheme worth its salt must not only deliver knowledge, but do so in a way that is highly engaging—and then activate that knowledge, so its owner can do real work in the world. Course 2.007 vaults these bars with plenty of clearance.
The problem is a matter of access. Course 2.007 is extremely expensive to offer, costing MIT far more per student than each would pay in tuition, even without financial aid factored in. This owes in great part to the costs of keeping the laboratory up with the cutting edge of fabrication technologies while still safe for novices, which equates to a lot of highly trained personnel.
Pragmatically speaking, anyone hoping to provide an educational experience of this caliber to vast numbers of people either must be mind-bogglingly rich or else find a creative way to pull it off at scale. After all, you can’t put a billion people through Course 2.007; even if you somehow found enough teachers and built enough laboratories, according to one back-of-envelope calculation I’ve done, it would still cost more money than exists in the world.
It’s certainly possible, however, to disseminate knowledge on that order of magnitude for far less: Wikipedia alone has done that, for instance, to great and deserved acclaim. So has MIT, in our own, smaller, way: Starting in 2001, through our OpenCourseWare initiative, we’ve made essentially all of our course materials freely available to anyone with an internet connection. But making information available is not the same as providing an education. And so you might reasonably ask: Can a scaled-up education scheme ever replicate what a skilled teacher in a traditional classroom can achieve, let alone the motivating, contextualizing effect of Course 2.007? Will its students ever truly make the jump from understanding calculus to thinking using calculus?
Teaching Machines, Teaching Humans
Would-be innovators have dreamed of distilling and mass-producing the secret sauce of education for well over a century. As early as 1912, the psychologist E. L. Thorndike, already well on his way to reshaping how America thought about learning, mused: “If, by a miracle of ingenuity, a book could be so arranged that only to him who had done what was directed on page one would page two become visible, and so on, much that now requires personal instruction could be managed in print.” In 1953, the Harvard psychologist B. F. Skinner, in many respects Thorndike’s intellectual descendant, attempted to realize this science-fictional notion by building a series of “teaching machines.” One of them can still be found at Harvard, tucked away on the ninth floor of the university’s William James Hall. The wooden, rectangular machine would have covered most of a student’s desk while she worked at it, making her way through the series of questions printed around the edge of the paper disk inside. A small rectangular window in the machine’s bronzed lid displayed a single question at a time as well as the answer to the previous question, and a nearby aperture let the student scrawl her answer longhand on a strip of paper tape that emerged briefly from the machine’s innards before plunging back in. She would compare each written response to the correct answer and, by pulling a lever, mark herself correct or incorrect. (Teachers could check students’ answer tapes for inconsistencies after the fact.) Once she’d answered every question on the disk, it would then spin more freely, stopping only to re-pose those questions she had initially gotten wrong, a process that would continue until she had answered every question correctly. Students would move along at their own pace, advancing from one disk to the next. The education revolution, Skinner believed, would thus be personalized. As one student memorably put it, “The eggheads don’t get slowed up; the clods don’t get showed up.”
Copyright © 2020 by Sanjay Sarma. All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
