“When I went to school, I watched TV,” Yim reflects. “You went to lectures, you learned things by someone telling you. It’s what I call the push model: teachers just push the information into you. Students don’t do that now. They grew up on the Internet, on videogames—they look at and do what they want to do. They go on the web and click something … This is called the pull model: students pull what they want. So we have to change the way we teach to suit those kids, because that’s the way they’re brought up. The old ways don’t work as well.”

The best place to see the new ways is in one of the lab modules Yim and Koditschek have refashioned. Although the two professors work in separate realms, many of their innovations have run along parallel tracks.

Engineering-class labs used to be built around what Yim calls “giant setups” whose physical infrastructures were sufficiently expensive to make it difficult to change them from year to year. Students would observe phenomena their textbooks had prepped them for and write a report more or less by the numbers. Compared to the research-grade work their professors spent the rest of their time on, these exercises were, in a word, boring, but at least made for easy scoring. Individual students were likely graded on a curve, and they trusted that after a few years of learning by rote, everything would add up to a set of marketable skills.

As Joel Weingarten, whom Koditschek largely credits with reinventing the freshman ESE lab, puts it, “Traditional engineering education sort of just treats students like filing cabinets. It fills them up with lots of data, they spit it back out on tests, and they do that on 20 or 30 disjoined core courses.” For the approximately two-thirds of engineering majors who don’t drop out before reaching the end, he adds, “that was great—people can design circuits or build bridges. But those jobs have mostly been automated or sent overseas.

“So the question we started with,” he adds, “was, What does it mean to be a 21st-century engineer?

What they came up with was a substantially different picture than the old stereotype of engineer as technician and organization man. “Leaving aside what the kids are interested in or willing to do,” Koditschek says, “what we think they need to be able to do is create wealth, create knowledge.” His goal, in other words, is to produce entrepreneurs ready to join what economist Richard Florida has dubbed “the creative class.” From product design to next-generation computer hardware, tomorrow’s engineering jobs are as likely to be based in a one-car garage as at a Boeing plant. NASA’s recent decision to turn to amateur inventors—like Peter Homer, an underemployed community-center director who nabbed a $200,000 prize for building a better space glove last year (he commandeered the family living room in addition to the garage)—is one indication of how traditional engineering career paths are changing.

In the redesigned lab modules, out went the “giant setups.” Indeed, to some extent, out went the setups altogether. “I’m putting more of the infrastructure on the students,” says Yim. “I’ll say, Here’s the project, you have to build it.” In addition to satisfying the undergraduates’ thirst for immediate application, this maximizes the professors’ flexibility to keep up with new technology from one year to the next.

Another thing that went by the wayside was the single-minded emphasis on individual performance. Group collaboration has taken its place. No doubt this plays to the expectations of students who came of age as the team-learning trend became widespread in American primary education, but it’s not just a capitulation. “Individual inspiration and insight is still crucial,” says Sampath Kannan, associate dean of SEAS, “but successful research and innovation increasingly happens through joint efforts across diverse teams, often across cultures and time zones.”

This is changing the way students are graded. In Yim’s class, there’s no explicit curve. Some years everyone works hard and a lot of A’s end up on transcripts. Other years, there may be very few. Yim even pushes some of the evaluation itself back onto the students. In one class, for example, teams had to figure out how to mount a camera on a moving bicycle such that the bouncing and jostling could be neutralized to create a steady video picture. When different teams developed different strategies, Yim made each pursue its chosen path—even the ones he knew to be fruitless. Learning to recognize and learn from failure, he believes, is an essential skill. Not all the students appreciated the exercise—“They hated it,” Yim recalls—but for many the initial disgruntlement turned into enthusiasm when at the end they had to appraise the reports generated by other teams.

“I expected them to read [the reports] and then just rank them: one, two, three,” Yim says. “But they all went in depth and said, This group is good because of x, y, and z, and I ranked this guy better because of this and that. It actually turned out to be a really valuable experience for the students who graded.” Eventually, Yim would like to enable his students (and/or recent graduates) to vote on the curriculum itself, getting them more invested in the whole process.

Yet there’s more to the story than converting lab modules from the old, formulaic model to an inquiry-based, you-build-it approach. This is where Koditschek’s introductory ESE course comes in. When Koditschek came to Penn from the University of Michigan in 2005, with Weingarten and Komsuoglu in tow, they brought along a remarkable robot. Its name was RHex, it was inspired by a cockroach, and it had just attracted a $5 million grant from the National Science Foundation (NSF).

RHex is a six-legged device whose computer “brain” is on par with a consumer-grade laptop. Its 20-pound body is a few inches longer than a college dictionary, but about as wide and as thick. Its design incorporates some basic features of everyone’s least favorite kitchen invader. As Komsuoglu points out, “Cockroaches are a very simple machine by itself and can demonstrate high mobility over highly unstable, unstructured environments. And they achieve that not by really intelligently thinking about where they put their feet and how they propel themselves, but by relying on their passive mechanical infrastructure, along with some well-trained excitation signals.” RHex is an electromechanical version of that biological model. With the right programming, it can navigate varying terrain under its own command without requiring physical modifications as the topography changes. The robot can walk, run, leap, tumble, slide, travel upright on two legs, and even swim.

“This was a machine,” says Koditschek, “that up until two years ago, was at the very far reaches of the research enterprise in our laboratory and a few labs across the country.” For freshmen ESE students at Penn, it became something else: a next-generation textbook that was the focal point of their lab work.

In terms of immediate gratification, it’s hard to imagine something with greater potential to motivate the Millennial Generation. Couple that with the demotion of the actual textbook to mere recommended status, and you’re talking about a considerable break with convention. “It’s an interesting situation,” Koditschek observes. “Events in the research domain moved enough in parallel with the needs of very early undergraduate curriculum, that this solution for research was, magically, exactly what we thought would be appropriate and useful for undergraduate laboratories.”

Under Weingarten’s supervision, students start by programming a version of RHex to perform simple tasks. (Komsuoglu developed the educational platform that makes much of this possible.) Gradually their projects become more and more involved. Last year their robots scaled the steps of the Philadelphia Art Museum in homage to Rocky. Student teams also programmed dance routines, choreographing as many as 2,000 individual leg movements to tunes ranging from Michael Jackson’s “Thriller” to the raps of Kanye West. They wrote optimization codes aimed at getting the robot to teach itself how to move faster—an artificial-intelligence endeavor—and turned $20 budgets at Home Depot into a boggling variety of alternative leg designs.

“There’s a lot we don’t understand about these machines—in fact, far more that we don’t than we do,” says Koditschek. “There’s a tremendous amount of empirical work that needs to be done. What would happen if you change this shape? What would happen if you change this compliance or that compliance? We don’t have the answers to those questions, so if the undergraduates are interested, they can actually come to the horizons of research very quickly, without a lot of tools.

“Kids don’t want to just listen to us talk about what mathematics is useful and what programming techniques are useful,” he continues. “They want to actually see if it’s true, or see how it’s true. If you confront people early on with challenging laboratory environments, where it’s clear that there’s no right answer—that we don’t know what the right answer is—I think it sets the whole framework for four years of inquiry-based education, critical thinking, lifelong learning skills, and collaborative interdisciplinary work.”

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