Education is on the cusp of a transformation because of recent scientific findings in neuroscience, psychology, and machine learning that are converging to create foundations for a new science of learning.

Writing in the July 17 edition of the journal Science, researchers report that this shift is being driven by three principles that are emerging from cross-disciplinary work: learning is computational, learning is social, and learning is supported by brain circuits linking perception and action that connect people to one another. This new science of learning, the researchers believe, may shed light into the origins of human intelligence.

"We are not left alone to understand the world like Robinson Crusoe was on his island," said Andrew Meltzoff, lead author of the paper and co-director of the University of Washington's Institute for Learning and Brain Sciences. "These principles support learning across the life span and are particularly important in explaining children's rapid learning in two unique domains of human intelligence, language and social understanding.

"Social interaction is more important than we previously thought and underpins early learning. Research has shown that humans learn best from other humans, and a large part of this is timing, sensitive timing between a parent or a tutor and the child," said Meltzoff, who is a developmental psychologist.

"We are trying to understand how the child's brain works – how computational abilities are changed in the presence of another person, and trying to use these three principles as leverage for learning and improving education," added co-author Patricia Kuhl, a neuroscientist and co-director of the UW's Institute for Learning and Brain Sciences.

University of California, San Diego robotics engineer Javier Movellan and neuroscientist-biologist Terrence Sejnowski are co-authors. The research was funded by the National Science Foundation and the National Institute of Child Health and Human Development. The National Science Foundation has funded large-scale science of learning centers at both universities.

The Science paper cites numerous recent advances in neuroscience, psychology, machine learning and education. For example, Kuhl said people don't realize how computational and social factors interact during learning.

"We have a computer between our shoulders and our brains are taking in statistics all the time without our knowing it. Babies learn simply by listening, for example. They learn the sounds and words of their language by picking up probabilistic information as they listen to us talk to them. Babies at 8 months are calculating statistically and learning," Kuhl said.

But there are limits. Kuhl's work has shown that babies gather statistics and learn when exposed to a second language face to face from a real person, but not when they view that person on television.

"A person can get more information by looking at another person face to face," she said. "We are digging to understand the social element and what does it mean about us and our evolution."

Apparently babies need other people to learn. They take in more information by looking at another person face to face than by looking at that person on a big plasma TV screen," she said. "We are now trying to understand why the brain works this way, and what it means about us and our evolution."

Meltzoff said an important component of human intelligence is that humans are built so they don't have to figure out everything by themselves.

"A major role we play as parents is teaching children where the important things are for them to learn," he said. "One way we do this is through joint visual attention or eye-gaze. This is a social mechanism and children can find what's important – we call them informational 'hot spots' – by following the gaze of another person. By being connected to others we also learn by example and imitation."

Infants, he said, learn by mixing self-discovery with observations of other people for problem-solving.

"We can learn what to do by watching others, and we also can come to understand other people through our own actions," Meltzoff said. "Learning is bi-directional."

The researchers believe that aspects of informal learning, the ways people, particularly children, learn outside school, need to be brought into the classroom.

"Educators know children spend 80 percent of their waking time away from school and children are learning deeply and enthusiastically in museums, in community centers, from online games and in all sorts of venues. A lot of this learning is highly social and clues from informal learning may be applied to school to enhance learning. Why is it that a kid who is so good at figuring out baseball batting averages is failing math in school?" said Meltzoff.

Even though it appears that babies do not learn from television, technology can play a big role in the science of learning. Research is showing that children are more receptive to learning from social robots, robots that are more human in appearance and more interactive.

"The more that interacting with a machine feels like interacting with a human, the more children – and maybe adults – learn," said Kuhl. "Someday we may understand how technology can help us learn a new language at any age, and, if we could, there are countless schools around the world in which that would be helpful."

"Science is trying to understand the magic of social interaction in human learning," said Meltzoff. "But when it does we hope to embody some of what we learn into technology. Kids today are using high-powered technology – Facebook, Twitter and text messaging – to enhance social interaction. Using technology, children are learning to solve problems collaboratively. Technology also allows us to have a distributed network from which to draw information, a world of knowledge."

Since its discovery just a few years ago, graphene has climbed to the top of the heap of new super-materials poised to transform the electronics and nanotechnology landscape. As N.J. Tao, a researcher at the Biodesign Institute of Arizona State University explains, this two-dimensional honeycomb structure of carbon atoms is exceptionally strong and versatile. Its unusual properties make it ideal for applications that are pushing the existing limits of microchips, chemical sensing instruments, biosensors, ultracapacitance devices, flexible displays and other innovations.

In a recent issue of Nature Nanotechnology Letters, Tao describes the first direct measurement of a fundamental property of graphene, known as quantum capacitance, using an electrochemical gate method. A better understanding of this crucial variable should prove invaluable to other investigators participating in what amounts to a gold rush of graphene research.

Although theoretical work on single atomic layer graphene-like structures has been going on for decades, the discovery of real graphene came as a shock. “When they found it was a stable material at room temperature,” Tao says, “everyone was surprised.” As it happens, minute traces of graphene are shed whenever a pencil line is drawn, though producing a 2-D sheet of the material has proven trickier. Graphene is remarkable in terms of thinness and resiliency. A one-atom thick graphene sheet sufficient in size to cover a football field, would weigh less than a gram. It is also the strongest material in nature—roughly 200 times the strength of steel. Most of the excitement however, has to do with the unusual electronic properties of the material.

Graphene displays outstanding electron transport, permitting electricity to flow rapidly and more or less unimpeded through the material. In fact, electrons have been shown to behave as massless particles similar to photons, zipping across a graphene layer without scattering. This property is critical for many device applications and has prompted speculation that graphene could eventually supplant silicon as the substance of choice for computer chips, offering the prospect of ultrafast computers operating at terahertz speeds, rocketing past current gigahertz chip technology. Yet, despite encouraging progress, a thorough understanding of graphene’s electronic properties has remained elusive. Tao stresses that quantum capacitance measurements are an essential part of this understanding.

Capacitance is a material’s ability to store energy. In classical physics, capacitance is limited by the repulsion of like electrical charges, for example, electrons. The more charge you put into a device, the more energy you have to expend to contain it, in order to overcome charge repulsion. However, another kind of capacitance exists, and dominates overall capacitance in a two-dimensional material like graphene. This quantum capacitance is the result of the Pauli exclusion principle, which states that two fermions—a class of common particles including protons, neutrons and electrons—cannot occupy the same location at the same time. Once a quantum state is filled, subsequent fermions are forced to occupy successively higher energy states. As Tao explains, “it’s just like in a building, where people are forced to go to the second floor once the first level is occupied.”

In the current study, two electrodes were attached to graphene, and a voltage applied across the material’s two-dimensional surface by means of a third, gate electrode. Plots of voltage vs. capacitance can be seen in fig1. In Tao’s experiments, graphene’s ability to store charge according to the laws of quantum capacitance, were subjected to detailed measurement. The results show that graphene’s capacitance is very small. Further, the quantum capacitance of graphene did not precisely duplicate theoretical predictions for the behavior of ideal graphene. This is due to the fact that charged impurities occur in experimental samples of graphene, which alter the behavior relative to what is expected according to theory.

Tao stresses the importance of these charged impurities and what they may mean for the development of graphene devices. Such impurities were already known to affect electron mobility in graphene, though their effect on quantum capacitance has only now been revealed. Low capacitance is particularly desirable for chemical sensing devices and biosensors as it produces a lower signal-to-noise ratio, providing for extremely fine-tuned resolution of chemical or biological agents. Improvements to graphene will allow its electrical behavior to more closely approximate theory. This can be accomplished by adding counter ions to balance the charges resulting from impurities, thereby further lowering capacitance.

The sensitivity of graphene’s single atomic layer geometry and low capacitance promise a significant boost for biosensor applications. Such applications are a central topic of interest for Tao, who directs the Biodesign Institute’s Center for Bioelectronics and Biosensors. As Tao explains, any biological substance that interacts with graphene’s single atom surface layer can be detected, causing a huge change in the properties of the electrons.

One possible biosensor application under consideration would involve functionalizing graphene’s surface with antibodies, in order to precisely study their interaction with specific antigens. Such graphene-based biosensors could detect individual binding events, given a suitable sample. For other applications, adding impurities to graphene could raise overall interfacial capacitance. Ultracapacitors made of graphene composites would be capable of storing much larger amounts of renewable energy from solar, wind or wave energy than current technologies permit.

Because of graphene’s planar geometry, it may be more compatible with conventional electronic devices than other materials, including the much-vaunted carbon nanotubes. “You can imagine an atomic sheet, cut into different shapes to create different device properties,” Tao says.

Since the discovery of graphene, the hunt has been on for similar two-dimensional crystal lattices, though so far, graphene remains a precious oddity.

As more and more news appears on the Internet as well as in print, it becomes possible to map the global flow of news by observing it online. Using this strategy, Cornell computer scientists have managed to track and analyze the "news cycle" -- the way stories rise and fall in popularity.

Jon Kleinberg, the Tisch University Professor of Computer Science at Cornell, postdoctoral researcher Jure Leskovec and graduate student Lars Backstrom tracked 1.6 million online news sites, including 20,000 mainstream media sites and a vast array of blogs, over the three-month period leading up to the 2008 presidential election -- a total of 90 million articles, one of the largest analyses anywhere of online news. They found a consistent rhythm as stories rose into prominence and then fell off over just a few days, with a "heartbeat" pattern of handoffs between blogs and mainstream media. In mainstream media, they found, a story rises to prominence slowly then dies quickly; in the blogosphere, stories rise in popularity very quickly but then stay around longer, as discussion goes back and forth. Eventually though, almost every story is pushed aside by something newer.

"The movement of news to the Internet makes it possible to quantify something that was otherwise very hard to measure -- the temporal dynamics of the news," said Kleinberg. "We want to understand the full news ecosystem, and online news is now an accurate enough reflection of the full ecosystem to make this possible. This is one [very early] step toward creating tools that would help people understand the news, where it's coming from and how it's arising from the confluence of many sources."

The researchers also say their work suggests an answer to a longstanding question: Is the "news cycle" just a way to describe our perception of what's going on in the media, or is it a real phenomenon that can be measured? They opt for the latter, and offer a mathematical explanation of how it works.

The research was presented at the Association for Computing Machinery Special Interest Group on Conference on Knowledge Discovery and Data Mining Conference June 28-July 1 in Paris.

The ideal, Kleinberg said, would be to track "memes," or ideas, through cyberspace, but deciding what an article is about is still a major challenge for computing. The researchers sidestepped that obstacle by tracking quotations that appear in news stories, since quotes remain fairly consistent even though the overall story may be presented in very different ways by different writers.

Even quotes may change slightly or "mutate" as they pass from one article to another, so the researchers developed an algorithm that could identify and group similar but slightly different phrases. In simple terms, the computer identified short phrases that were part of longer phrases, using those connections to create "phrase clusters." Then they tracked the volume of posts in each phrase cluster over time. In the August and September data they found threads rising and falling on a more or less weekly basis, with major peaks corresponding to the Democratic and Republican conventions, the "lipstick on a pig" discussion, rising concern over the financial crisis and discussions of a bailout plan.

The slow rise of a new story in the mainstream, the researchers suggest, results from imitation -- as more sites carried a story, other sites were more likely to pick it up. But the life of a story is limited, as new stories quickly push out the old. A mathematical model based on the interaction of imitation and recency predicted the pattern fairly well, the researchers said, while predictions based on either imitation or recency alone couldn't come close.

Watching how stories moved between mainstream media and blogs revealed a sharp dip and rise the researchers described as a "heartbeat." When a story first appears, there is a small rise in activity in both spheres; as mainstream activity increases, the proportion blogs contribute becomes small; but soon the blog activity shoots up, peaking an average of 2.5 hours after the mainstream peak. Almost all stories started in the mainstream. Only 3.5 percent of the stories tracked appeared first dominantly in the blogosphere and then moved to the mainstream.

The mathematical model needs to be refined, the researchers said, and they suggested further study of how stories move between sites with opposing political orientation. "It will be useful to further understand the roles different participants play in the process," the researchers concluded, "as their collective behavior leads directly to the ways in which all of us experience news and its consequences."

A new statistical analysis technique that identifies and removes systematic bias, noise and equipment-based artifacts from experimental data could lead to more precise and reliable measurement of nanomaterials and nanostructures likely to have future industrial applications.

Known as sequential profile adjustment by regression (SPAR), the technique could also reduce the amount of experimental data required to make conclusions, and help distinguish true nanoscale phenomena from experimental error. Beyond nanomaterials and nanostructures, the technique could also improve reliability and precision in nanoelectronics measurements – and in studies of certain larger-scale systems.

Accurate understanding of these properties is critical to the development of future high-volume industrial applications for nanomaterials and nanostructures because manufacturers will require consistency in their products.

"Our statistical model will be useful when the nanomaterials industry scales up from laboratory production because industrial users cannot afford to make a detailed study of every production run," said C. F. Jeff Wu, a professor in the Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. "The significant experimental errors can be filtered out automatically, which means this could be used in a manufacturing environment."

Sponsored by the National Science Foundation, the research was reported June 25, 2009 in the early edition of the journal Proceedings of the National Academy of Sciences. The paper is believed to be the first to describe the use of statistical techniques for quantitative analysis of data from nanomechanical measurements.

Nanotechnology researchers have long been troubled by the difficulty of measuring nanoscale properties and separating signals from noise and data artifacts. Data artifacts can be caused by such issues as the slippage of structures being studied, surface irregularities and inaccurate placement of the atomic force microscope tip onto samples.

In measuring the effects of extremely small forces acting on extremely small structures, signals of interest may be only two or three times stronger than experimental noise. That can make it difficult to draw conclusions, and potentially masks other interesting effects.

"In the past, we have really not known the statistical reliability of the data at this size scale," said Zhong Lin Wang, a Regents' professor in Georgia Tech's School of Materials Science and Engineering. "At the nanoscale, small errors are amplified. This new technique applies statistical theory to identify and analyze the data received from nanomechanics so we can be more confident of how reliable it is."

In developing the new technique, the researchers studied a data set measuring the deformation of zinc oxide nanobelts, research undertaken to determine the material's elastic modulus. Theoretically, applying force to a nanobelt with the tip of an atomic force microscope should produce consistent linear deformation, but the experimental data didn't always show that.

In some cases, less force appeared to create more deformation, and the deformation curve was not symmetrical. Wang's research team attempted to apply simple data-correction techniques, but was not satisfied with the results.

"The measurements they had done simply didn't match what was expected with the theoretical model," explained Wu, who holds a Coca-Cola chair in engineering statistics. "The curves should have been symmetric. To address this issue, we developed a new modeling technique that uses the data itself to filter out the mismatch step-by-step using the regression technique."

Ideally, researchers would search out and correct the experimental causes of these data errors, but because they occur at such small size scales, that would be difficult, noted V. Roshan Joseph, an associate professor in the Georgia Tech School of Industrial and Systems Engineering.

"Physics-based models are based on several assumptions that can go wrong in reality," he said. "We could try to identify all the sources of error and correct them, but that is very time-consuming. Statistical techniques can more easily correct the errors, so this process is more geared toward industrial use."

Beyond correcting the errors, the improved precision of the statistical technique could reduce the effort required to produce reliable experimental data on the properties of nanostructures. "With half of the experimental efforts, you can get about the same standard deviation as following the earlier method without the corrections," Wu said. "This translates into fewer time-consuming experiments to confirm the properties."

For the future, the research team – which includes Xinwei Deng and Wenjie Mai in addition to those already mentioned – plans to analyze the properties of nanowires, which are critical to the operation of a family of nanoscale electric generators being developed by Wang's research team. Correcting for data errors in these structures will require development of a separate model using the same SPAR techniques, Wu said.

Ultimately, SPAR may lead researchers to new fundamental explanations of the nanoscale world.

"One of the key issues today in nanotechnology is whether the existing physical theories can still be applied to explain the phenomena we are seeing," said Wang, who is also director of Georgia Tech's Center for Nanostructure Characterization and Fabrication. "We have tried to answer the question of whether we are truly observing new phenomena, or whether our errors are so large that we cannot see that the theory still works."

Wang plans to use the SPAR technique on future work, and to analyze past research for potential new findings. "What may have seemed like noise could actually be an important signal," he said. "This technique provides a truly new tool for data mining and analysis in nanotechnology."