Recasting Engineers as Economic Drivers

by Dave Meaney

educating engineers

In the aftermath of the presidential election, quite a few experts cited the lack of economic opportunity for many as a primary factor that elevated Donald Trump to the presidency. These changes in economic opportunity did not occur months prior to the election, but they resulted from years of continual changes in the US economy.

For example, manufacturing represented more than 50% of the economic output and jobs after World War II; it now represents only 10% of the economy. Professional services — in finance, health, insurance, education, and similar industries — represented less than 5% of the economy in 1950, while it now captures almost 40% of the economy. Our country went from makers to providers. Many other workplace traditions have also changed; e.g., one often doesn’t work for the same employer for decades, nor do workers have confidence that they will remain in the career they start in their 20s. A physician could become a business owner and then (if we are lucky) a teacher. These changes are causing many of us to ask: What should we be teaching our students for this future?

First, let’s understand how economies can change. One theory in economics puts these job sector shifts as part of Kondriateff waves, which pass through the US economy in (roughly) 50- to 80-year cycles. These “K-waves” reach back to late 18th century and continue to the current day. The economist Joseph Schumpeter reasoned that these waves were triggered by technological revolutions; e.g., the invention of the steam engine and new steel production processes led to a K-wave from 1850 to 1900 that included the development of the railroad system, the settling of the American West, and the emergence of the American economy as a global force. Similarly, the widespread availability of consumer computer power and the invention of the Internet in the late 20th century created a K-wave that began in 1990 and is cresting now with the emergence of alternative media (e.g., cutting the digital cord with online media access), the Internet of Things, and the Big Data wave.

Where Engineers Fit In

As engineers, we are naturally attracted to the idea that technology starts the wave that affects everything else. But this belief raises a question: If technology triggers waves, then how can we predict where the next wave will start? And a second question follows: How do we organize and educate ourselves so that we make the most of these technologies so society can ride this wave effectively, rather than absorb the displacements these waves create? Well, we all know it is hard to predict the future. However, a recent report from the Brookings Institute helps us pinpoint areas of the economy that are most powerful in creating downstream economic output, whether it is additional jobs, more exports, or the forming of completely new industries. Given their potency, it is likely that new economic opportunities will emerge more frequently from this sector than any other.

educating engineersRather than using the traditional categorization scheme that breaks up the economy into bins associated with worker output (e.g., we manufacture, provide financial services, trade energy goods, supply food), the Brookings report asked a slightly different question: Which parts of the economy provide the downstream spark for the rest of us?  If we understood the origin of this spark, we would be much more informed about how to make strategic investments that will have broad economic trickle-down effects on the national economy. The answer? The most potent part of our economy consists of the industries that invest heavily in research and development and contain a high percentage of employees with STEM degrees.  The Brookings report termed these advanced industries. And this part of the economy is indeed potent. It generates 2.7 additional downstream jobs for every job in this sector, far outpacing the highly publicized downstream impact of the manufacturing sector (1.7 downstream jobs per manufacturing job).  Advanced industries contain 8% of the workforce but generate 19% of the national GDP, and advanced industries span everything from communications, defense, and security to health, medicine, and the environment.

Creating Economic Opportunity Waves

Knowing that this is the proverbial spark certainly places a premium on educating scientists and engineers and placing them in these advanced industries.  Some of them could become the next Elon Musk, a Penn alum (SAS ’97) whose vision will eventually electrify the entire fleet of motor vehicles in the US. Others could follow in the footsteps of Carl June, MD, a Penn faculty member who invented a radically new form of cancer immunotherapy that may be the biggest change in cancer treatment in several decades. But what can colleges and universities teach students today to make them thrive in the epicenters of these advanced industries? How can we teach so that our students are ahead of the curve and, in some cases, creating these curves?

educating engineers

We are constantly discussing the content of undergraduate and graduate education here at Penn. In these conversations, it is often easy to fall into the trap of saying “Well, I can’t imagine a degree in X not having a course in Y” or “If I had to learn X, then my students should learn X too.” I think we should step away from specific courses and distribution sequences for a moment and think about the core principles in an engineering education that will allow our graduates to successfully navigate any economic wave that falls across all of us. In the most successful form, we would educate people that successfully create waves to benefit everyone. I suggest focusing on three core principles in an undergraduate’s engineering education toward achieving this goal.

  1. Introduce the uncertainty of research to counterbalance the certainty of formal didactic instruction. For engineering, teaching the fundamentals makes the world a safer place, whether we are teaching safety factors, repeatability, or design standards. But the advanced industries are at the bleeding edge of uncovering knowledge not in textbooks. And this new knowledge eventually creates something useful and interesting. Yet there is always a major transition for students when they realize that technological advances never come from a script in a textbook. Many will ask, “How can I learn anything that isn’t known?” Historically, we would use undergraduate education to teach what is known, and graduate education to answer the unknown. But if creating new ideas in advanced industries requires one to determine some of the unknowns, we shouldn’t restrict research experiences to just graduate education anymore.

    Research forces one to learn the inexact science of breaking down a complex problem into more manageable parts, finding out which of these parts is most critical in solving the problem, and the finding a solution. Research uses failure as a mechanism to learn, and teaches persistence and patience. These are good things to learn if you want to be in industries that are searching for the Next Big Idea. In many ways, research experiences resemble learning a foreign language — the first language (research experience) is a real bear, but they get easier as you learn more of them (additional experiences). Jumping across different fields would parallel the learning of more than one foreign language and would be a good primer for a career in the advanced industries. If more of us became comfortable with uncertainty and failure, we would accelerate the creation and filtering of new ideas and products, in turn creating more opportunities for everyone in the economy.

  2. Teach invention, as it will continue to drive economic development. Over a decade ago, the American university system was recognized for its almost unique ability to educate students who would thrive as innovators over their careers. American higher education was sought after by students around the world, and world universities started to tweak their own models of education, inspired by the US success story. Much of what was written about the ‘secret sauce’ for American higher education was the magical ingredient of innovation that existed on college campuses in the US. However, we are overlooking the one critical ingredient upstream of innovation that makes the innovation engine go: inventing new ideas. So much activity surrounding innovation involves how to package ideas for marketplace needs or how to use marketplace needs to filter through existing technologies to create new products.

    Our science and engineering infrastructure is driven by inventing technologies and algorithms that appear years to decades later in innovative products. And we are sorely overlooking how to best educate to invent, e.g., the classroom environment that forms the best ideas, or the best methods to teach the abstraction of several seemingly unrelated problems into a common group of invention challenges that will serve hundreds of innovations. Just as philosophy class in college can shape people’s views of morality for the rest of their lives, the practical experience of conceiving and executing a new idea for a market can leave a lifelong impression on a college student for seeing and creating opportunity in the world. Many students graduate nowadays with a much better idea about how to take ideas and commercialize them into products. Adding the teaching of invention will replenish the ideas that feed the future of these innovation pipelines.

  3. Include the economists, artists, and philosophers. Jason Silva has a wonderful quote about engineering: “The scientist and engineers who are building the future need the poets to make sense of it.” I couldn’t agree more. Artists and philosophers have an interesting reflection role in society, whether it is to challenge one’s perception of the ordinary or to make the ordinary unusual (artist) or to provide a more holistic view of a human’s purpose (philosopher). Likewise, economists can explain how technology can drive development locally and globally and the subsequent changes expected in the workforce. In other words, they all provide different optics on the same idea.

    Engineering may enjoy a sterling reputation as creating a world that others do not see, but we are sometimes too enamored with this vision to ask a very simple question: If we can do it, should we do it? Technologists can cite several inventions in the past as drivers of economic change that pushed society forward (see K-waves, above) and never backward. The mechanization of the agriculture industry coincided with the emergence of manufacturing and heavy industries in the US and elsewhere in the 19th century, and this advanced the world. People moved from working on farms to working in factories, and the urbanization movement swept across the country. In a similar manner, artificial intelligence could cause a similar shift in the services sector today and create a supply of highly educated people to tackle the world’s next big problem. For this reason, they can help engineers understand the impact of their ideas even before they are implemented.

    Creating new technologies without a thoughtful mulling about how they could really change the world seems irresponsible to me, given how some of these technologies could completely change large parts of the economic landscape quickly. And it could lead to other societal crises — e.g., do we really want to interrupt nature’s evolutionary clock without considering the impact of editing our own genome? Similar questions exist when we start to understand how our minds work and the principles by which we can (and should) study and influence the human traits of identity, reasoning, and self. One of our faculty recently wrote about the ethical constructs by which we should view these advances in understanding how we think, and how they can influence the science of mind control. Broadly speaking, initiating these conversations in advance will help engineers realize that these technologies should not be created in a vacuum, and they must be developed in parallel with conversations about the impact of their use.

A Mirror, Not a Trigger

All of this brings us back to the beginning. The election wasn’t the trigger but the mirror, and we must answer the call to think about engineering education to create future economic opportunity instead of passively watching it happen. We now know that advanced industries are the most powerful part of our economy for generating downstream economic output. We are fortunate that engineers are a central part of these industries. And we now know the dramatic changes in the demographics of opportunity among the electorate that occurred in the past two decades. By re-emphasizing core principles to impress upon our engineering students, we can be part of a future that focuses more on opportunities for the society rather than the individual. And we can use this new mindset to tackle some of the most pressing problems we see in front of us (e.g., affordable health care, energy, climate change) and those problems that we don’t see yet.

Students Receive Awards for the Year

students receiving awards
Students in the BE Department have received several awards

Every year the Penn Bioengineering Department presents several awards to students. In addition to the Senior Design Awards, which will be featured over the course of the month, students were awarded for their service, originality, leadership, and scholarship.

The Hugo Otto Wolf Memorial Prize, endowed more than a century ago by the Philadelphia architect Otto Wolf, in memory of his son, was given to Margaret Nolan and Ingrid Lan. The Herman P. Schwan Award, named for a former faculty member in Bioengineering, was given to Elizabeth Kobe and Lucy Chai.

The Albert Giandomenico Award, presented to four students who “reflect several traits that include teamwork, leadership, creativity, and knowledge applied to discovery-based learning in the laboratory,” was given to Justin Averback, Jake Budlow, Justin Morena, and Young Shin.

In addition, Sushmitha Yarrabothula received the Bioengineering Student Leadership Award and four students — Hayley Williamson, Amey Vrudhula, Jane Shmushkis, and Ikshita Singh, won the Penn Engineering Exceptional Service Award.

Finally, the Biomedical Applied Science Senior Project Award, presented annually to the students who have “best demonstrated originality and creativity in the integration of knowledge,” was awarded to Derek Yee and Andrea Simi.

“These awards recognize many aspects of our students: their high academic achievement,  exceptional collaborative spirit, and leadership abilities,” said BE department chair David Meaney. “However, these traits are not limited to the only these students. Every single one of our undergraduates at Penn pushes themselves well beyond the classroom and into the community to make a unique difference.”

Creativity, Curiosity, and Engineering

by Dave Meaney and Dani Bassett

James Dyson
James Dyson

One can easily see that many of the world’s greatest challenges — producing enough food for the world population, providing each person with a set of fundamental human rights, or creating a sustainable environmental footprint as our societies move forward — must tap into two uniquely human traits: creativity and curiosity. In the fields of science and engineering, one can look at history and easily find creative and curious pioneers who ranged from Leonardo de Vinci (pioneered the field of human physiology), Grace Hopper (invented computer compilers), and Sir James Dyson (brought elegance to common household tools – the vacuum cleaner, the fan, the hand dryer, and the hair dryer).

Grace Hopper
Grace Hopper

Although we can look around and identify creative people, a natural question would be: What events in these individuals’ lives led to this creativity? We may see people around us who are creative and curious, but we often simply shrug and say ,“Wow, pretty ingenious person there.” Maybe we even think of this with a bit of yearning: “Boy, I wish I could think of things like that.”  We often make the observation and get back to our daily lives, accepting that creative people are born or “just happen.” In other words, we are either struck by lightning, or we are not. Nothing could be further from the truth.

Leonardo da Vinci
Leonardo da Vinci

Creative and curious people are not genetically wired differently than others. Curiosity and creativity are not rare skills conferred by serendipity. Instead, creative and curious people have benefited from mentors who pushed them to ask “Why?” at the right time in their lives: perhaps being in the right science class with the right teacher in middle school or reading a novel that made them imagine a world they could not see.

What does all of this have to do with engineering?  Well, some research suggests that many U.S. engineering undergraduates are weaker than their international counterparts in divergent and convergent thinking, which are two critical ingredients for creativity. These two thinking modalities may be propelled by different sorts of curiosity. Assessment tests for creative thinking traits often measure the ability to synthesize ideas, observations, and other information to make something new. From many possibilities, only one emerges as the ideal solution. This process is generally referred to as convergent thinking.  A second creativity trait is the raw ability to generate ideas, given a particular problem.  For example, one could be asked to generate as many possible uses of a brick that one can think of, and the resulting ideas are scored — both in terms of the number of ideas generated and the distinctiveness of each idea separately. This assessment, known as the alternative use test, measures divergent thinking. Ideally, engineers would have high ability in both divergent and convergent thinking, which would mean that they could both think of many possible solutions and pick the best among them.  However, one study performed almost a decade ago showed that half of the engineering undergraduates in the U.S. showed deficiencies in both convergent and divergent thinking — troubling, to say the least.

Adapted from an image in: Hany EA, Heller KA, “Entwicklung kreativen Denkens im kulturellen Kontext,” in Entwicklung und Denken im kulturellen Kontext, Mandl H, Dreher M, Kornadt H-J, eds (Toronto: Hogrefe Verlag für Psychologie, 1993), pp. 99-118. Reprinted with generous permission of Hogrefe Verlag.

However, all is not lost. Many changes have occurred over the last decade for engineering education in the U.S. We embraced the laboratory as a platform for problem-based learning, which cultivates the ideation phase of creativity and the convergence to a solution. We have also ‘tipped’ and ‘flipped’ the classroom to introduce more methods of open-ended problems as teaching tools, again using this change to reinforce that there are many ways and, rarely, one best way to solve a particular problem.

Yet with all of these very positive changes, we still don’t have a good road map for how ideas form in the mind, how we trade off one idea versus another, and how we decide which is the best idea. Our tools for creativity are based on countless efforts to try different methods, measure whether they have an effect, and take the most successful empirical methods and transform them into practice. Until recently, we had no idea what was going on in the mind during the creative process.

Fortunately, we now have ways to both interrogate and model how the mind works when we think and create.  Inspired by the principle that blood flow will increase to areas of the brain with high neural activity (side note: the brain is a remarkable energy hog for the body, representing less than 3% of body mass but consuming nearly 20% of its energy resources), researchers are measuring how flow to different areas of the brain change when people are asked to perform specific tasks. Early work showed these beautiful, color-coded images of how one task would increase blood flow to one area, while another task would increase blood flow to a different area.

Patterns of connectivity in the brain can be represented as
dynamic networks, which change in their configuration as
humans change mental states or cognitive processes while
performing a task.

However, scientists began to realize, that instead of looking at one pattern of brain activation at one time, we needed to study how the pattern changed over time. Analyzing these changes over time allowed us to estimate the brain areas that activated simultaneously with another during a mental task. If they activated together frequently, we assumed that they would have a functional connectivity between them. Simply put, areas that fire together are wired together, metaphorically speaking.  Very quickly, we saw maps of the brain’s own functional network emerge when volunteers would work on math problems, navigate a maze, and even when they were asked to just daydream.

Where does this lead us? Well, we stand on the cusp of learning and predicting the coordinated steps that our mind takes when we imagine different ideas and pick one as ‘the best.’  Not only can we map this process in real time, but we can also develop new theories about how to ‘steer’ from one brain network state to another. We can also apply this new knowledge to individuals on a case-by-case basis, rather than relying on the one-size-fits-all approach that is the current and common practice in cultivating divergent and convergent thinking.  In practice, this means that we would move away from prescribing the same creativity training exercise for everyone — with a large variation in the results — to a far more customized, efficient cognitive exercise. In fact, we could directly test the possibility that some of these exercises work for some people and not others because of an individual’s brain wiring map.  Science fiction? Nope, just modern day bioengineering at work.

David F. Meaney, Professor of Bioengineering and Neurosurgery

Danielle S. Bassett, Associate Professor of Bioengineering and Electrical and Systems Engineering