Researchers from the University of Colorado Boulder have
developed a way to detect when students using personalized learning software start to daydream.
By using machine-learning algorithms on recordings of student eye movement, the
researchers were able to figure out which eye patterns were associated with the
mind wandering.
The study found that when students’ eyes matched “zoning
out” patterns, they were less focused on the work than those students who
showed “not zoning out” patterns. It also noted that when students were paying
attention, their eyes bounced around the screen more.
“When you’re zoning out, you’re just fixating,” explained
Sidney D’Mello, leader of the University of Colorado research team. “You’re not
moving on.”
The study could lead to instructional software that
monitors mind wandering in real time. That troubles Jill Barshay, a
contributing editor for The Hechinger Report who writes about education
research and data.
“Do we really want to curb mind wandering?” she asked. “It’s
associated with creativity, and perhaps a bit of mind wandering is needed to
come up with big thoughts.”
Barshay suggested the result might be used better to point
out the places where the computerized learning bores students, instead of creating
prompts to keep them on track.
“But what I find fascinating about this research is how
data scientists have come to a conclusion that contradicts human intuition,”
she wrote. “You often hear teachers say that they don’t need data to tell them
what their students know. Well, this research points out that it’s hard for
teachers to know when students are really absorbing something just by looking
at their faces.”
