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| When students learn online, every mouse click is tracked. Harness this wealth of data and we can create the ultimate in personalised lessons. |
One day, Sebastian Thrun ran a simple and surprising experiment on a
class of students that changed his ideas about how they were learning.
The
students were doing an online course provided by Udacity, an
educational organisation that Thrun co-founded in 2011. Thrun and his
colleagues split the online students into two groups. One group saw the
lesson’s presentation slides in colour, and another got the same
material in black and white. Thrun and Udacity then monitored their
performance. The outcome? “Test results were much better for the
black-and-white version,” Thrun told Technology Review. “That surprised me.”
Why
was a black-and-white lesson better than colour? It’s not clear. But
what matters is that the data was unequivocal – and crucially it
challenged conventional assumptions about teaching, providing the
possibility that lessons can be tweaked and improved for students.
It
was an early example of a trend promising to transform online education
– the exploitation of huge amounts of data about how people actually
learn. Artificial intelligence underpinning online courses can log every
click and keyboard stroke a student makes, and this is revealing
patterns of learning behaviour that are difficult, if not impossible,
for teachers to see in a traditional classroom. Equipped with this
information, course designers can adapt their materials, and deliver the
ultimate in targeted teaching. Could this lead to the perfect,
personalised lesson?
This wealth of data is only available thanks to the recent rise in
popularity of Moocs (massive open online courses), which offer anyone
with access to the internet the chance to sign up for university courses
and study them for free. These online courses, hosted by the likes of
Udacity, Coursera and edX, have been the subject of much hype in recent
months, as institutions debate whether this will save or endanger the
traditional university degree. But arguably the real novelty they offer
has been missed. Many critics dismiss Moocs as simply online videos of
lectures, and so nothing new. Yet Moocs greatest impact may come from
what they can teach the teachers: offering a unique opportunity to
monitor student behaviour during lessons in unprecedented detail.
You
can even monitor mouse clicks. “We collect tracking data such as
whether they press pause or play at certain parts of a video,” says
Chuong Do, a software engineer and leader of the data analytics team at
Coursera.
For starters, such data helps Coursera group
participants into different types of student, such as those who watch
all the lectures and complete all the assignments, others who lose
interest over time, and those that like to watch the videos but have no
interest in completing any homework. Perhaps surprisingly, Coursera has
discovered there is also a group of students who complete all of the
homework assignments without watching any of the lectures. “This was
unexpected, but maybe there are people who are really interested in
earning a Coursera certificate, or who have read the material already
and are just using it as a way of brushing up.”
Such information
will allow people to adapt courses for different sub-groups of students.
In particular, it provides clear and sometimes surprising signals about
the presentation style that works best for students, as Udacity’s trial
with black-and-white slides revealed.
Motivation exercise
According to Rene
Kizilcec, a PhD student at the Lytics Lab at Stanford University, the
style of presentation on a computer screen can make a big difference to
learning. For example, the lecture videos that make up the bulk of
teaching in Moocs often contain an inset of the instructor in one corner
of the slides. Kizilcec wondered whether these inset videos, which are
expensive to produce, actually help students to learn, or are simply a
distraction.
Kizilcec looked at whether the video of the instructor should be
placed in the corner of every slide, or if it students would be equally
happy if it disappeared and reappeared intermittently. By monitoring
over 21,000 participants on a Coursera course over a ten-week period, he
found that students fell into two camps. Those participants who had
previously expressed a preference for learning visually – with an
emphasis on text and graphics – experienced less mental effort and were
less likely to drop out of the course when the instructor’s face
appeared intermittently. But those students who preferred to be taught
verbally were much better off with the instructor’s face permanently in
one corner of the screen. “What this result suggests is a need for
adaptive systems,” says Kizilcec.
Mooc data is also revealing how
to best motivate students online. Joseph Jay Williams and other
researchers at Stanford University, alongside Jascha Sohl-Dickstein at
non-profit online education provider Khan Academy, added messages above
mathematics problems on the KhanAcademy.org website to keep students
motivated while undertaking assignments. They found that positive
messages such as "this might be a tough problem, but we know you can do
it," had little effect on student performance. But when they added notes
emphasising that intelligence can be improved with effort, such as
"remember, the more you practice the smarter you become," they found
that students attempted a greater number of problems and were more
likely to get them right.
A similar attempt by Coursera to encourage students to finish their
course by reminding them of what homework assignments they had yet to
complete, actually led to a drop in student retention when participants
felt harassed, says Do. But the company got a much better response when
they “slipped in” information within an email that focused more on the
positive achievements students had made that week, with a chart showing
what percentage of assignments they had completed and how many lectures
they had watched.
If this trend continues, then, students could
soon be receiving the ultimate in personalised teaching, with unique
lessons targeted exactly to their needs, motivations and learning style.
The education technology start-up Knewton, for example, has developed
an adaptive learning system that instantaneously alters the way it
presents information to students based on what it gleans about their
individual learning style as they interact with it. It’s also possible
that student behaviour and progress could be monitored in even more
detail than today. For example, some researchers are working on using
facial recognition to identify – via webcam – whether students are following the lesson or frowning in confusion.
All
of which promises a future in which teachers can adapt at a glance to
how different students respond to everything from string theory to
Shakespeare – whether they are in a classroom or not. How students may
feel about this level of monitoring, however, is less clear.
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