COLLEGE PARK, MD (January, 2015) Dr. Kevin Dunbar, director of the Laboratory for Thinking, Reasoning, and Educational Neuroscience at HDQM, has received the prestigious Faculty of Education Visiting Scholar award from the University of Hong Kong. In addition to two seminar lectures at the Sciences of Learning Winter Institute in Hong Kong, Dr. Dunbar will also deliver a keynote lecture at the Scientific Discovery in the Social Sciences conference at the London School of Economics this January.
At the heart of the first lecture in Hong Kong, "Educating the Scientific Brain and Mind: Insights from the Science of Learning and Educational Neuroscience," are the ways in which people interact with and learn about scientific concepts every day. "Is there something special about science and scientific thinking?" Dr. Dunbar asks. "How do children acquire scientific concepts? Is the human brain and mind wired for science?" Bringing in multiple methods, including modern neuroimaging and behavioral research, this lecture will plumb how people learn and think, spanning contemporary science, phenomena in the world around us, science education, and human development. In the second lecture, "Analogy, Causality, and Discovery in Science: The Engines of Human Thought," Dr. Dunbar will focus on how people use previously acquired knowledge to create new knowledge more specifically, educators' processes with and uses of analogy. He will report on his lab's discoveries about key situations and brain mechanisms underlying analogical thought especially causal reasoning that are crucial to learning.
Dr. Dunbar's lecture in London, "From Big Data to Big Theory: The End of the Hypothesis As We Know It?, shows how new work in biology challenges basic assumptions about how science is conducted and taught. Emerging from the laboratories of a few scientists to the front pages of the popular press, "Big Data" has been embraced by various research domains, but nowhere has the use of large and complex datasets been as important as in molecular biology. This field's incredible advances from the discovery of DNA structure to the invention of sequencing methods to the plethora of "omes" (genomes, proteomes, connectomes) have led some to suggest that Big Data heralds the end of theories and hypotheses in science. Using analyses of lab meetings and interviews with scientists, Dr. Dunbar's lecture will show that the idea that hypotheses would no longer be necessary was key to the invention of DNA microarrays and the Big Data they generate. He will narrate some of the history of microarrays and reflect on what it reveals about the nature of scientific discovery and the role of hypotheses in the age of Big Data.
Professor Dunbar is renowned for his research on the ways that scientists think, reason, and interact while making discoveries and inventing new technologies. He uses three converging methodologies to explore scientific thinking: naturalistic observation, experimentation, and neuroimaging research. Over the past twenty-five years, he has explored the heuristics scientists use as well as the development of scientific thinking skills in children, specifically focusing on reasoning strategies.
Dr. Kevin Niall Dunbar is Professor in the Department of Human Development and Quantitative Methodology and the director of the Laboratory for Thinking, Reasoning, and Educational Neuroscience. His research has pioneered the fields of educational neuroscience and the science of learning, and he was a co-founder of the Center for Cognitive and Educational Neuroscience at Dartmouth College. In addition to many academic forums, his work on discovery has been featured in such publications as WIRED and the Washington Post, and he has served on the editorial boards of the Journal of Experimental Psychology, the Journal of Cognitive Psychology, and the Canadian Journal of Experimental Psychology.
Click here to learn about the Sciences of Learning Winter Institute at the University of Hong Kong and here to learn about the Scientific Discovery in the Social Sciences conference at the London School of Economics.
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