Learning How We Learn
“You know too much; it’s time to kill you.” In Russian, the phrase rhymes. The captain raised his glass, steering an inebriated wink my way.
I glanced down at the table, littered with bottles of every persuasion—vodka, whisky, tequila, champagne. I had learned Russian during a stint in the Army in the 1970s and subsequently happened upon a seasonal job as a Russian translator on Soviet trawlers up on the Bering Sea and on the Pacific Northwest coast (below). I’d spend three to six months out at sea every year and the rest of the time studying to retrain myself to become an electrical engineer.
The captain was joking about killing me, but what wasn’t so funny was how extraordinarily compartmentalized knowledge was in the former Soviet Union. Ordinary Russians who happened to mention an inconvenient truth in front of the wrong person could disappear—become liquidated, in the terminology of the time. When I would tell my shipmates of life in the United States, where it was perfectly possible to buy milk, eggs, and butter without waiting for hours in line, most Russians simply stared at me in disbelief. They’d been told all their lives in newspapers, by teachers, and by their government that Soviet lifestyles were the best in the world—what I was saying ran so counter to what they’d heard that it simply did not compute: I had to be lying.
Fast-forward about 30 years. Today, I’m a professor of engineering, with a special interest in neuroscience, cognitive psychology, and education—including the impact of these disciplines, not only on learning in the science, technology, engineering, and mathematics (STEM) disciplines but on learning in general . The Soviet Union, of course, is no more—imploded in the wreckage of an unworkable system built, in part, on avoiding and suppressing the mention of inconvenient realities.
But the mind-sets I saw manifest in the Soviets live on and have profound effects—particularly in education.
The Hard Sciences Look at Learning
It might surprise you to learn that some of the most innovative, groundbreaking work being done involving learning and education today is being done by bioengineers and closely allied hard scientists who are building on bioengineering-related breakthroughs.
For example, physicist Danielle S. Bassett is the youngest winner of the 2014 MacArthur Fellow Award, a no-strings-attached US$625,000 prize commonly known as the “Genius Grant” (above). Bassett has found her interdisciplinary home in the Department of Bioengineering at the University of Pennsylvania, where she is the Skirkanich Assistant Professor of Innovation. Her group studies biological, physical, and social systems by using and developing tools from network science and complex systems theory—her knowledge of math and physics allows her to understand the brain in ways of which conventional neuroscientific approaches aren’t capable.
One of Bassett’s major interests focuses on the brain’s interconnections and how learning causes that connectivity pattern to change over different time scales, from seconds to minutes to years. How, she wonders, does the brain reconfigure itself to adapt during learning? Bassett and her colleagues recently discovered that people who learn very well are people who have very flexible brain connectivity patterns—connections in different parts of the brain, in other words, can reconfigure in very short timescales. But people who are less able to learn are people who are less flexible in their brain configurations (Figure 3) .
These types of findings could prove invaluable in helping people to learn more effectively. It may be that certain parts of the brain can be gently nudged to help enhance the brain’s flexibility—in other words, to chivvy the brain into better learning.
Another researcher who exemplifies the contributions of “hard” scientists to our knowledge of learning is Guang Yang (Figure 4), first author of a recent seminal Science paper, “Sleep Promotes Branch-Specific Formation of Dendritic Spines after Learning,” revealing how sleep after learning promotes the growth of dendritic spines, the tiny protrusions where neurons connect and communicate with each other (Figure 5). Yang’s undergraduate and graduate degrees are biochemistry and biology, respectively, while her postdoc training was in neuroscience. Yang works in conjunction with Wenbiao Gan’s Lab at the Skirball Institute of Biomolecular Medicine at New York University Langone Medical Center, which investigates how sensory and learning experiences lead to changes of synaptic connectivity in the living mouse brain.
Bassett, Yang, Gan, and their colleagues are part of a new trend in education-related research, where individuals from widely varied backgrounds, with expertise in theoretical and/or biological physics, applied mathematics, engineering, network science, complex systems, systems neuroscience, cognitive and clinical neuroscience, neuroimaging, psychology, computational biology, computer science, and social networks, can all make significant contributions to advancing our knowledge of how learning takes place. In fact, as can be seen in Figure 6, it is clear that many levels of learning-related research fall squarely into the areas of expertise of many bioengineering and hard-science-oriented researchers.
The Temporal Dynamics of Learning Center, headquartered out of the University of California, San Diego, is also spearheading work to further our understanding of how humans learn and the critical element of time in learning. Through projects that study various aspects of learning, including the development of expertise, the improvement of face-reading skills in kids with autism, and the role of musical training in brain development, the 40 individuals at 17 partner research institutions in three countries and several San Diego schools are serving as a focal point for scientific research and translation of that research to students.
Innovative, highly interdisciplinary approaches to research on learning are also being fostered through initiatives such as the Latin American School for Education, Cognitive, and Neural Sciences, which will hold its fifth meeting in March 2015 in San Pedro de Atacama, Chile. Each year, participants in the school spend two weeks together working with world-class researchers, generating project proposals potentially relevant for the development, design, and implementation of effective science-based educational practices.
A Need for Integrative Translational Research
There is an enormous need for integrative translational research about learning at college levels, particularly in the STEM disciplines. For example, previous research has revealed the importance of chunking and “retrieval” in gaining conceptual understanding and expertise, but little of this information has made it into conventional college classrooms. (Retrieval, it should be noted, is a semisynonym for “memorization”—a dirty word in today’s U.S. educational community, unlike some other educational communities around the world.)
In yet another area of research, neuroscientific findings by psychologists Ian Lyons and Sian Bielock have revealed how the brain’s pain centers are activated when a person considers a disliked topic such as mathematics. Yet, despite the obvious bearing of this research on the important issue of procrastination in studying, little work has yet been done in bringing forth to students the practical implications of these findings. It seems that knowledge about how to learn effectively has come to be seen as primarily the concern of professors, teachers, and educators, rather than something that students themselves should know.
One approach to begin dealing with this integrative translational problem has been developed by Terrence Sejnowski, the Francis Crick Professor at the Salk Institute, and myself in the construction of “Learning How to Learn,” a new massive open online course (MOOC) developed for Coursera, an education platform that partners with top universities and organizations worldwide to offer free, public online courses (Figure 7). This course is directed at adult learners of all types and provides a fast-paced recap of relevant research in learning from a wide variety of disciplines and covers the practical implications of this research for high school and college students. The course has quickly become one of Coursera’s most popular offerings; over 500,000 students worldwide have taken or are currently registered to take the course, despite the fact that it has been available for only a few months. (Sadly, students in certain countries have restricted access to the course due to sophisticated Internet censorship—an all-too-modern version of the knowledge compartmentalization I experienced with the Soviets in the early 1980s.)
Working Toward a Comprehensive Understanding of Learning
For many years, research about how people learn has often been compartmentalized within the field of “education sciences.” As revealed in a recent high-profile study by Matthew Makel and Jonathan Plucker, “Facts Are More Important than Novelty: Replication in the Education Sciences,” this parochialism has created challenges that have lessened the credibility of the discipline. New efforts are being formed to help bridge the many different fields and their relationships between education, learning, cognitive psychology, neuroscience, and the various hard sciences. But these bridges are themselves often too narrow—involving the creation of new journals linking education and the cognitive sciences, for example, or connecting education with neuroscience. And these efforts do not solve the issue of translational efforts to move research results into the classroom and into improving students’ own knowledge–base about learning.
As the seminal philosopher–historian of science Thomas Kuhn noted in The Structures of Scientific Revolutions, there is a rich tradition in science of “outsiders”—people trained in other disciplines—making enormous advances when they turn their gaze sideways and begin working in a new field. Bioengineering offers a special, more broadly encompassing framework for “outsiders” from many different disciplines to view learning and the many fields it can encompass. Approaches centered from bioengineering offer an ideal framework not only for research but also for outreach to students.
It is often hard for people to realize that compartmentalization of knowledge can have an omnipresent and pernicious influence—that it can affect not only entire societies, as with my experiences with the former Soviet Union, but also have an outsized effect on entire fields of study and their practical import and impact. Such compartmentalization doesn’t just take place due to governmental fiat—people, and disciplines, can find themselves, sometimes for the best of reasons , associating only with those with whom they feel most comfortable—often people with whom they share doctrinal training and approaches.
In learning, in particular, it is critical to avoid the shackles and strictures of compartmentalization. We’re in a modern world now—a world where “You know too much; it’s time to kill you,” can instead become “We all know a little—it’s time to share.” Bioengineering—a discipline that is by its very nature both deeply scientific and highly interdisciplinary—gives a great living room for that sharing.
- B. A. Oakley, A Mind for Numbers: How to Excel at Math and Science (Even If You Flunked Algebra). New York: Penguin-Random House, 2014.
- D. S. Bassett, N. F. Wymbs, M. P. Rombach, M. A. Porter, P. J. Mucha, and S. T. Grafton, “Task-based core-periphery organization of human brain dynamics,” PLoS Comput. Biol., vol. 9, no. 9, p. e1003171, Sept. 2013.
- B. A. Oakley, “Concepts and implications of altruism bias and pathological altruism,” Proc. Natl. Acad. Sci. U.S.A., vol. 110, pp. 10408–10415, 2013.