HILT Grants: Flipped classroom prep – Flash Talk
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HILT Grants: Flipped classroom prep – Flash Talk


My name is Barbara Cockrill. I’m the Director of
Faculty Development at HMS, and I’m going to
talk to you just in this little 5-minute
slot about some work we’ve done on optimizing preparation
for the flipped classroom using efficiency metrics. And I really want
to give a shout out to Henrike Besche, who’s the
first person up here, who’s really done most of this
work, but unfortunately she can’t be here today. So what we have done at HMS
in 2015, we launched the– completely blew
up our curriculum and started over again. And in med school, for those
of you who are unfamiliar, our students are
spending the first 14 months in the classroom. And that’s all been done in
a flipped classroom pedagogy. And then they spend years
two, three, and four on the wards in the hospital. So I’m focusing on what they
do in the first 14 months. And we used a flipped
classroom pedagogy that we call case-based
collaborative learning. So I know that I
don’t need to talk to this group about exactly
what flipped classroom is, but there’s two
things that you need to know about the way
we’ve done it that is important to this project. Number one, like most, or many,
students get their prep work the night before. And then they also, before
they come into class, have to do a quiz. And that’s a multiple-choice
quiz that we track. The second point that
is different than a lot is that our students
come in, and they work in groups of four. So they are assigned
to groups of four. They come in, and in
that group of four, they work through the prep work
from the last night before. And then they also apply
it to new problems. So something came from it that
we learned is number one– we were all experienced
faculty, who had been preparing and
teaching Harvard Medical students for quite a few years. And we had been assigning
the same amount of prep, kind of, that we’d
always been assigning. But what we found was
that our students actually in these groups of
four, they wanted to be prepared for
themselves, but there was also a lot of peer pressure. Because part of the
contract here is you work with this group of four,
and you learn from each other. And there’s time in the
classroom to do that. So if you show up unprepared,
it’s not just on you, but it’s on– it ruins the morning for
the rest of the group. So what we learned was our
students were doing the prep, and it was way too much. So we’d been assigning
what we thought was appropriate amount
of prep, but our problem was that this was
taking forever. It was too much. It was too hard. And so what we
wanted to try to do is figure out how do
we most efficiently assign that prep, because the
students are now doing it, to optimize learning
in the classroom. So we used a simple approach. A student– a
learner-centered approach, where we asked the
students about their prep. And we were asking them
not, is this too long, but, is this efficient
and effective preparation for your learning? And then we compared
it to the scores that they did on their
readiness assessment quizzes ahead of time. And what we were
trying to work toward is what we are calling
an efficiency graph. So on this axis– and
I’m going to show you some data next– is the level
of difficulty as perceived by the learner. And then on this axis is
the successful learning, like how well they
scored on that quiz. So we want things
to be in this area, like it was effective learning,
and they did well on the quiz, or maybe it was really
hard, but they still did well on the quiz. We wanted to avoid this down
here where things were just super hard, and it
wasn’t effective. So this helped to– one of the things is
many of our courses are run by a number of
different content experts. So you’ll have 90
sessions in the class, and five sessions are the
genetics content expert. The other five are the
biochemistry content expert. So it was really
important for us to try to identify
that expert blind spot so that faculty weren’t
giving things that– you know how this goes. It’s so easy. Why is this so hard
for the student? But we forget how difficult it
can be for the novice learner. So here I’m going to
show you how our data– and this is primarily
for faculty development and determination
of best practices. So remember, difficulty
here and quiz score here. This is a summary
of one course, which has 10 different disciplines
within that course. Each one of these
arrows tells you where they ended up for
all of their content. What we did was we picked apart
each one of those disciplines. So it’s not surprising to
us that anatomy is up here. It’s hard. It’s typically the hardest one. Any med students in the room? It’s very hard. But we picked it
apart and looked at each of the 90
sessions and identified some that were up here,
some that were here, some that were here,
some that were here. What we were then
planning to do is take this back to the
faculty, have them look at it, try to understand it. And we’re going to
look at this group up here to identify
best practices. So what allows these sessions
to be very effective learning. And by the way, it wasn’t
just the length of time. Some of these sessions
up here in the best area were the parts that took
the students the longest. So it wasn’t just that
they were working. It was effective
learning material. And then the things
that show up down here are the ones that we want
to target and figure out what it is about those that– what can we change to
make it more effective. So our future goals– in progress, we’re scaling up. We’re evaluating alignment
between faculty and students. Faculty can’t tell. They think it’s easy, and
the students think it’s hard. So we’re not very good
at identifying that. We’re engaging faculty
in conversations about desirable
level of difficulties across all courses. So hitting it in that
good area up there. And then our next steps will
be to identify best practices and improve instructional
design, figuring out why they’re up in
that top-left area, and then to automate this data
collection so all faculty have access to it. So you can go in
and look and see where your session falls in. So thank you to HILT. I
think I’m a little over time. But what HILT did for us
is helped fund, really, the statistics
and people helping with all of the computer
programming to put this up. And without them, we
couldn’t have done it. So thank you.

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