EDITOR
Allison Druin
allisond@umiacs.edu
Data Mining For
Educational “Gold”
Shalom M. Fisch
MediaKidz Research & Consulting | mediakidz@lycos.com
Richard Lesh
Indiana University | ralesh@indiana.edu
Elizabeth Motoki
Indiana University | elmotoki@indiana.edu
Sandra Crespo
Michigan State University | crespo@msu.edu
Vincent Melfi
Michigan State University | melfi@msu.edu
Two eight-year-old girls are playing a computer
game in which they have to fill gaps in a railroad
track with different-size pieces of track:
“I think we’re supposed to use the 1 and then the 10.”
“Uh-oh. Can [we] subtract?”
“This is too confusing.”
They clear the pieces from the screen, then start
again with a different strategy:
“This time, we’ll start with the mini-pieces…”
[ 1] Mayer, R.E., and
Moreno, R. “Nine ways
to reduce cognitive load
in multimedia learning.”
Educational Psychologist
38 (2003): 43–52.
None of us is born with a separate part of the brain
devoted exclusively to playing computer games.
While playing games, we apply the same sorts of
knowledge, inferences, and cognitive skills that we
use in our offline lives too. Researchers who study
human-computer interaction sometimes draw on
broader theories of human cognition to explain
users’ thinking while playing games, or note simi-larities between online and offline thinking and
behavior [ 1, 2]. Indeed, research even shows that
users’ interactions with machines are influenced
by the same sorts of social rules that govern their
interactions with other people—regardless of
whether the machine is an animatronic doll or a
desktop computer [ 3, 4].
When children play educational computer
games, we might expect their reasoning to follow
the same sorts of paths that they use to figure
out similar educational content in real (offline)
life. If so, this would not only help us understand
children’s use of technology, but also present a significant opportunity for research. Successful educational games have a tremendous reach among
children: For example, the mathematics-based
Cyberchase website ( www.pbskids.org/cyberchase)
has logged more than one billion page views to
date. Given the countless bits of data generated
while playing a game, data mining could yield a
vast pool of data for investigating applied reasoning
during naturalistic play.
As part of a major study of children’s learning from Cyberchase, our research team has
been exploring the possibility of using online
Cyberchase games—not only as instructional tools,
but as a means of simultaneously assessing children’s problem solving too. Of course the field of
computer-assisted instruction (CAI) has long used
games to teach and assess knowledge [ 5]. However,
unlike traditional types of CAI, the Cyberchase
games were not originally designed for assessment.
In addition, whereas assessment in CAI frequently
focuses on measuring the state of users’ knowledge
or skills, we were more interested in observing the
evolution of children’s strategies and mathematical
thinking over the course of a game.
[ 2] Moreno, R.
“Learning in high-tech and multimedia
environments.”
Current Directions in
Psychological Science
15 (2006): 63–67.
[ 3] Reeves, B., and
Nass, C. The Media
Equation: How People
Treat Computers,
Television, and New
Media Like Real People
and Places. New York:
Cambridge University
Press, 1996.
Piloting an Approach
Does gameplay reflect children’s understanding
of educational content and strategies for problem solving? If so, is data mining sufficiently rich
[ 4] Strommen, E.F.
“Interacting With People
Versus Interacting With
Machines: Is There a
Meaningful Difference
From the Point of View
of Theory?” In Fisch,
S.M., Theoretical
Approaches Toward
Integrating Cognitive
and Social Processing
of Media. Symposium
presented at the biennial meeting of the
Society for Research
in Child Development,
Tampa, FL., April 2003.
September + October 2009
