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- For most institutions, we're still struggling
with how we improve first year retention
especially for students who are often
our least well served usually low income
and students of color.
And we're looking at ways that we can actually improve
the teaching and learning process both for them.
So those courses have been stubborn
about seeing improvements and that's where we really think
the power in adaptive courseware lies right now.
We do see a future that will include
and you already have organizations thinking
about doing adaptive across the program
so that students might get through a program faster
or get deeper into their learning
but we're really focused on that introductory course level
so that we can see first year retention go up.
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- One of the frustrations we've had over the years
is this idea that the course is a black box
and you don't really know whether the students are learning
the material until after the first exam
or after the end of the course.
And what we're trying to do is accelerate
the learning process.
So the ability to get insight into what the students know
and don't know early in the class is very valuable.
- The data that universities can get
from adaptive learning platforms can help them
in so many ways.
So at the institutional level,
it can help them track student progress.
If they're in one platform, it's easier
but they can have multiple platforms
speaking to each other but they can get that data across
the institution to check student progress
and see how they're doing
with these new learning initiatives.
At the programmatic level, again especially if students
are in the same platform,
departments can see programmatically where are the gaps,
where are students, where they're loosing students,
where might they be retaining, what are they doing right,
where they're retaining students
so that's really valuable information.
Also for accreditation purposes,
that's very useful information.
And then at the course level,
it's really helpful for faculty
and I can still segue into the third use of it as the tool
to manage high enrollment classes.
It's useful for facility to see patterns
in student learning behavior.
But also use that data to do interventions
with students who might be at risk.
So rather than waiting for the first high stakes exam
which could be four or five weeks into the semester,
they can check how students progressing
in the courseware one week, two weeks into the semester
and do those interventions early
when they really matter.
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- The other thing that's critical in our mind
is section variability.
So a lot of community colleges
even smaller colleges will have multiple faculty
teaching a course.
And if a student walks into a course
that they've been assigned to,
not getting the choice
of just picking their professor.
And one course has a pass rate of 80%
and another one has a pass rate of 50%,
how is that a good model for us?
We've got to decrease section variability.
So what that means is the other support
is really getting faculty to work together
in these intro courses.
And depending upon your institution,
that may actually be a easy lift
but too many of our institutions,
people teach one section at a time,
and there's not a lot of collaboration.
And that collaboration should extend
not just choosing a textbook, right
or having learning outcomes that are similar, right.
It should be about what are we gonna do
in the classroom that is working for you, right
so that we're sharing those best practices.
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