Received: by alpheratz.cpm.aca.mmu.ac.uk id VAA02160 (8.6.9/5.3[ref pg@gmsl.co.uk] for cpm.aca.mmu.ac.uk from fmb-majordomo@mmu.ac.uk); Fri, 10 Mar 2000 21:07:58 GMT Subject: Fwd: Looking Inside of Learning Date: Fri, 10 Mar 2000 16:03:34 -0500 x-sender: wsmith1@camail2.harvard.edu x-mailer: Claris Emailer 2.0v3, Claritas est veritas From: "Wade T.Smith" <wade_smith@harvard.edu> To: "memetics list" <memetics@mmu.ac.uk> Content-Type: text/plain; charset="iso-8859-1" Content-transfer-encoding: quoted-printable Message-ID: <20000310210402.AAA23717@camailp.harvard.edu@[128.103.125.215]> Sender: fmb-majordomo@mmu.ac.uk Precedence: bulk Reply-To: memetics@mmu.ac.uk
Looking Inside of Learning
http://www.news.harvard.edu/gazette/2000/03.09/ed5connel.html
By Catherine Walsh
Michael Connellıs fascination with "neural networks"computer programs
that simulate the activity of brain cells or neurons and actually learn
over timestems in no small part from a "crystallizing moment" he
experienced in ninth-grade trigonometry.
His teacher brought in a mathematics journal to show students a picture
of a spiraling flower made up of numbersthe result of mapping a
sophisticated trigonometric equation. Curious about this phenomenon,
Connell copied the equation, took it home, and tried to plot it on his
Atari computer. "It took me eight hours to figure it out," he recalls
with a rueful laugh. "But when I did, it was one of those thrilling
experiences, one of those eureka moments that changed my life."
Connell went on to study computer science and electrical engineering at
MIT, as well as psychology and cognition. He even enrolled in MITıs
doctoral program in computer science, until a course on learning
environments provided him with another eureka experience.
"We talked about problems in understanding and problems in knowledge. We
spent weeks at a time creating computer simulations of the ways that
people come to understand what causes traffic jams or why the moon goes
through phases," says Connell. "I didnıt know anyone could get paid to do
this."
Inspired to study human learning processes further, Connell made his way
to GSEafter a stint at Microsoftand began designing mathematical
computer models to explore human cognition. Now a third-year doctoral
student, he uses software to explore how the human brain thinks and
learns.
"Take learning physics, for example," he says. "What if there are a small
number of conceptual holes into which people fall or get stuck? Can we
map those holes and thereby help people to avoid falling into them?
Thatıs what Iım trying to find out."
His long-term vision is simple, says Connell. If neural networks can be
used to understand how the brain processes information, "educators can
design curriculum more effectively."
Connell has long been fascinated by artificial intelligencethe field
that studies how machines can be designed to replicate intelligent human
behavioras the potential means by which everything from education to
life in the workplace can be improved. Now he is using the tools of
artificial intelligence to maximize human potential rather than the
capacities of machines.
Instead of approaching learning problems from a hypothetical scientific
premise in search of solutions, as he did at MIT and Micro-soft, Connell
uses the needs of the child or the adult as a starting point. "Many
engineers never get to see the real applications of what they do," he
muses.
"While I donıt expect to solve the problems of cognition in my lifetime,
I might come up with an adaptable software system for a child with a
learning disability or help other educators better understand under- and
overachievement in kids. And thatıs pretty exciting."
Education, adds Connell, serves as an important nexus between the
sciences and human values. "Science can tell us how best we should use
the brain, or how kids differ from one another, but it canıt tell us what
we should teach."
Copyright 2000 President and Fellows of Harvard College
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