The federal No Child Left Behind Act has made local school board members and principals more accountable for improving students' academic progress -- a key goal of the law -- a study released by the Public Policy Institute of California (PPIC) concludes.
California voters are more likely to re-elect their local school board members if schools meet goals for student achievement mandated by the law, and districts in California are more likely to demote principals whose schools repeatedly fail to meet the targets. However, there is no evidence that the law has succeeded in making district superintendents more accountable for student achievement.
The No Child Left Behind Act of 2001 (NCLB) is intended to improve students' academic achievement by defining the primary goals of schools and districts -- as measured by standardized test results -- and holding officials accountable for meeting the goals. The PPIC study looks at the re-election rates of school board members and the salaries and turnover rates of principals and superintendents in California to assess how effective these accountability programs have been. It finds:
- In districts that meet NCLB's "adequate yearly progress" targets for student achievement, incumbent school board members are more likely to be re-elected than would have been the case before NCLB.
- In schools that have been sanctioned for repeatedly failing to make adequate yearly progress, principals are more likely to be demoted than would have been the case before NCLB. However, there is no evidence that changes in principals' salaries are linked to the academic achievement of their students.
- Neither the salaries nor retention rates of superintendents are related to student achievement in their districts.
"NCLB gives voters and parents a clear measure of how students are doing and a way to judge schools and districts. The threat of sanctions may also be a factor in increasing accountability," says S. Eric Larsen, PPIC research fellow and author of the study.
Taken together, the study's results point to ways in which NCLB can be improved when Congress considers reauthorizing it. Among the recommendations:
- Improve the information available to voters. Test results reflect the students who live in a district as well as the effectiveness of the officials who run it. Low achievement may be attributable to the low socioeconomic status of a district's students, rather than poor management. An accountability system based on growth in student achievement, rather than on a percentage of students reaching a specific target, would provide voters with better information about the effectiveness of the governing board and administrators.
- Refocus NCLB sanctions on school boards rather than schools and districts. These governing boards have a better understanding of conditions in their districts and are better positioned to decide the most appropriate interventions and sanctions for schools and administrators. Sanctions imposed on school boards would hold these officials -- and the voters who elect them -- accountable for making wise choices.
- Help school boards determine the best way to improve student achievement. NCLB should provide more support to rigorous evaluation of promising interventions.
This report is available at http://www.ppic.org/main/publication.asp?i=789
New science of learning offers preview of tomorrow's classroom
Of all the qualities that distinguish humans from other species, how we learn is one of the most significant. In the July 17, 2009 issue of the journal Science, researchers who are at the forefront of neuroscience, psychology, education, and machine learning have synthesized a new science of learning that is already reshaping how we think about learning and creating opportunities to re-imagine the classroom for the 21st century.
“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.”
“To understand how children learn and improve our educational system, we need to understand what all of these fields can contribute,” explains Howard Hughes Medical Institute investigator Terrence J. Sejnowski, Ph.D., professor and head of the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies and co-director of the Temporal Dynamics of Learning Center (TDLC) at the University of California, San Diego, which is sponsored by the National Science Foundation. “Our brains have evolved to learn and adapt to new environments; if we can create the right environment for a child, magic happens.”
The paper is the first major publication to emerge from a unique collaboration between the TDLC and the University of Washington’s Learning in Informal and Formal Environments (LIFE) Center. The TDLC focuses on the study of learning—from neurons to humans and robots—treating the element of time as a crucial component of the learning process. This work complements the psychological research on child development that is the principal focus of the LIFE Center. Both have been funded as part of the NSF’s Science of Learning initiative.
Among the key insights that the authors highlight are three principles to guide the study of human learning across a range of areas and ages: learning is computational— machine learning provides a unique framework to understand the computational skills that infants and young children possess that allow them to infer structured models of their environment; learning is social—a finding that is supported by studies showing that the extent to which children interact with and learn from a robot depends on how social and responsive its behavior is; and learning is supported by brain circuits linking perception and action— human learning is grounded in the incredibly complex brain machinery that supports perception and action and that requires continuous adaptation and plasticity.
As the only species to engage in organized learning such as schools and tutoring, homo sapiens also draw on three uniquely human social skills that are fundamental to how we learn and develop: imitation, which accelerates learning and multiplies learning opportunities; shared attention, which facilitates social learning; and empathy and social emotions, which are critical to understanding human intelligence and appear to be present even in prelinguistic children.
These and other advances in our understanding of learning are now contributing to the development of machines that are themselves capable of learning and, more significantly, of teaching. Already these “social robots,” which interface with humans through dialogue or other forms of communication and behave in ways that humans are comfortable with, are being used on an experimental basis as surrogate teachers, helping preschool-age children master basic skills such as the names of the colors, new vocabulary, and singing simple songs (see image).
“Social interaction is key to everything,” Sejnowski says. “The technology to merge the social with the instructional is out there, but it hasn’t been brought to bear on the classroom to create a personalized, individualized environment for each student.” He foresees a time when these social robots may offer personalized pedagogy tailored to the needs of each child and help track the student’s mastery of curriculum. “By developing a very sophisticated computational model of a child’s mind we can help improve that child’s performance.”
“For this new science to have an impact it is critical that researchers and engineers embed themselves in educational environments for sustained periods of time,” says coauthor Javier Movellan, Ph.D., co-PI of TDLC’s Social Interaction Network and director of the Machine Perception Laboratory at UC San Diego. “The old approach of scientists doing laboratory experiments and telling teachers what to do will simply not work. Scientists and engineers have a great deal to learn from educators and from daily life in the classroom.” Movellan is collaborating with teachers at the UC San Diego Early Childhood Education Center to develop social robots that assist teachers and create new learning opportunities for children.
What makes social interaction such a powerful catalyst for learning, how to embody key elements in technology to improve learning, and how to capitalize on social factors to teach children better and foster their innate curiosity remain central questions in the new science of learning.
“Our hope is that applying this new knowledge to learning will enhance educators’ ability to provide a much richer and more interesting intellectual and cultural life for everyone,” Sejnowski says.
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