MADISON,
Wis. -- Human learning is a complex, sometimes mysterious process. Most of us
have had experiences where we have struggled to learn something new, but also
times when we've picked something up nearly effortlessly.
What
if a fusion of computer science and psychology could help us understand more
about how people learn, making it possible to design ideal lessons?
That
long-range goal is moving toward reality thanks to an effort led by professors
in the University of Wisconsin-Madison departments of computer sciences,
psychology and educational psychology. Their collaborative research aims to
break new ground in what computer scientist Jerry Zhu calls "machine
teaching"-- a twist on the more familiar concept of machine learning.
"My
hope is that machine teaching has an impact on the educational world. It's
quite different from how people usually think about education," says Zhu.
"It will give us optimal, personalized lessons for real, human
students."
Machine
learning is a well-established subfield of computer science in which experts
develop mathematical tools to help computers learn from data and detect
patterns. The machine learner (the computer) is like a student. The goal of
machine learning is to develop models that will prove useful in the future when
dealing with large, often unwieldly data sets. Practical tasks like speech
recognition are aided by machine learning.
Machine
teaching turns this concept on its ear. Rather than dealing with pools of data
and not knowing at the outset what patterns might be revealed through analysis,
the researcher in a machine teaching arrangement already knows what knowledge
he or she wants to impress upon the learner.
Machine
teaching uses sophisticated mathematics to allow researchers to model actual
human students and devise the best possible lessons for teaching them. While
the definition of "best" in a particular setting is up to the
teacher, one example could be identifying the smallest number of exercises
needed for a particular student to grasp a concept. Or, as Zhu puts it, "Can
five really good questions teach the material, rather than 20?"
While
this work is still in its early stages, it has immense potential to impact
education.
Timothy
T. Rogers, a professor of cognitive psychology at UW-Madison and one of Zhu's
collaborators, explains how computer science and psychology come together.
"In
order for the machine teaching approach to work, it needs a good model of how
the learner behaves -- that is, how the learner's behavior changes with
different kinds of learning or practice experiences," Rogers says.
"Also, the model needs to be computational; it has to be able to make
concrete, quantitative predictions about the learner's behavior."
"Ultimately,
we hope that the work can be used to help teachers develop lesson plans and curricula
that promote learning in a wide variety of fields," Rogers says, citing
math, science and reading as examples. "And, just as important, the effort
to bring cognitive models of learning to bear on real-world problems is bound
to lead to important new advances in our understanding of how people learn
generally."
Zhu
presented some of his research earlier this year in Austin, Texas, at the 29th
annual Conference on Artificial Intelligence, organized by the Association for
the Advancement of Artificial Intelligence.
A
two-year seed grant from the UW-Madison Graduate School currently supports this
work. Future funding from outside sources will be sought.
"With
machine teaching, it's conceptually easy, but quite challenging to implement in
the real world. It's a major undertaking," says Zhu.
In
addition to Zhu and Rogers, the UW research team includes computer science
professors Michael Ferris, Bilge Mutlu and Stephen Wright; engineering
professor Rob Nowak; psychology professor Martha Alibali; and educational
psychology professors Martina Rau and Percival Matthews.
Machine
teaching probes fundamental mathematical and scientific concepts. In part
because of that, the team's research is open-ended at this stage.
Source | http://www.eurekalert.org/
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