[MUSIC PLAYING]
Hello, and welcome to
another episode of Undercool,
the materials education podcast.
So today, Steve and I are
hanging out in the office.
And we're going to talk
about the acronym that
is no longer an acronym.
It's just a name all by itself.
That's right, ABET.
Maybe once upon a time
it stood for something.
But we'll get into the
history with Steve here.
But today, we're going to talk about
ABET, what it's good for,
what it's useful for, what
it might not be good for,
and how it can inform what we
do in our materials programs
and help us teach better.
So Steve, to kick things
off, why don't you just tell us,
what is ABET and why
should materials departments
care about it?
So ABET used to stand
for the Accreditation
Board of Engineering and Technology.
Now it's just ABET.
And it's because they do
more than just engineering
and technology.
They do applied stuff as well.
But whatever, it's
really the same organization.
It's a federation of 30 to 40
professional societies
that get together and
they make bylaws and set up
all the rules and
regulations for doing accreditation
for engineering schools.
And ultimately, it's about making sure
that the people who are
designing your bridges,
your airplanes, your boats, actually
know something about what
they're supposed to know.
So as a country, as a
society, as the world,
we can actually trust the engineers
that our educational
institutions are putting out.
That's the big reason.
And it's even
sanctioned by the United States
to do accreditation for all US programs,
engineering programs.
But ABET also performs many
international accreditations
at the request of
schools in other countries
because it's become
kind of the gold standard
for engineering accreditation.
And other countries want to have--
I mean, it's kind of funny.
They want to say, we have the same
accreditation as MIT does.
So for whatever
that's good for, but we'll
get into that later on.
So why do MSE programs care about this?
Well, they should care because we usually
have a few students who want
to go into careers where they
must become professional engineers.
And as you know, Tim,
the TMS has a committee
that sets the
professional engineering degree
for materials.
It's really metallurgy mostly.
And although I don't think
more than 20, 30 people a year
take that test, it's
there in case they need to be.
Where would they need to do this?
It would be companies like Exponent.
These are consulting companies that often
do a lot of litigation.
When something goes wrong, in some
company or some person,
who's another company or
another person, goes to court.
And in court, there are all these rules
about who's allowed to
testify as an expert witness.
Anything involving the government,
only people with accredited--
with professional
engineering are going to count.
But it's also to impress the jury.
So the jury is going to want to know
that it's a professional engineer.
And guess what society
is also a member of ABET,
the Society of Professional Engineers.
And a long time ago, they insisted that--
well, hey, they make their own rules.
If you want to be a
professional engineer,
you must have graduated from an
accredited engineering program
or have 11 years of
equivalent experience.
So do we really want our students
to spend 11 years
before they can start a job?
They might want to start
right after in our department.
Maybe 1% or 2% of our
students every year want to do it.
So we do it for them.
And it makes parents and
alums feel really good.
So those are the two main reasons
why we partake in accreditation.
OK, yeah, it definitely makes sense
about wanting to give
students the opportunity
to earn certifications, to have access
to different career
paths, different opportunities
down the road that
ultimately rest on the program
that they graduated
from, having demonstrated
a certain minimum
quality of the education that
is being provided there.
So that seems reasonable.
Now, my understanding
is that to get certified
as an ABET accredited
institution, part of the process
is having ABET, essentially,
inspectors come to the program
and do an on-site physical visit.
What do those visits look like?
And what's the hardest
part of preparing for a visit?
What do you have to do to get ready?
That might not be obvious.
So that's a great question.
And first of all, it's
very important to realize ABET
doesn't accredit
institutions or departments.
They only accredit programs.
So they look at the
degrees that are being granted.
And in our case, we have
one material science degree
that says a bachelor's in material
science and engineering.
That's a program.
And that is what is accredited.
And that's all ABET accredits.
So what you need to do to be accredited,
it's a little different the first time,
but we've been
accredited for so many years.
I wasn't even here when
we first had accredited.
I've been involved in some, but I
think you need to have graduated at least
one student before you
can be accredited.
And when you go for your first
accreditation visit,
you're visited by two
evaluators, not one.
So that's a little different.
But for most of us in most programs,
it's about 125
accredited materials programs
in the country.
And the vast majority of
those have been accredited before.
So what you need to do, your
institution will call up ABET
and say, hey, we're
ready to be accredited.
We're going to start the process.
Here are all the
programs we'd like to do,
because ABET does a
visit for all the programs
at an institution at the same time.
The first thing you need
to do once all that-- that's
all Dean stuff, so
don't worry about that.
First thing a program needs to do
is start six years before that moment,
because they really need to
be preparing for this right
from the day after their last visit.
And I'll get back to that.
So what you need to do is create what's
called a self-study.
And ABET gives you a template to fill in.
ABET is all focused
around their criteria.
They have eight criteria.
I'm not going to go through all of them.
The most important
ones are the first few.
So criteria one is
all about the students.
Where the students come
from, how are they advised,
what kind of tracking do you do, how do
you handle mental health
issues, how do you do
admissions, all that stuff.
And the second
criteria is all about program
educational objectives.
And these are those statements that
talk about what a graduate
should look like a few years
after they graduate.
What's our aspiration for that?
And those don't even need to be measured,
because some of our aspirations are we
want them to be creative.
We want them to solve
the world's problems.
And how do you
actually measure those things?
But it's important to
have high aspirations,
because that kind of
drives the whole thing.
I actually believe that the objectives
are the most valuable part of the ABET
process for a program.
It's also one of the easiest
parts to be compliant with,
because all you need to do is consult
with your constituencies
at least once every six years, and ask
them, are our objectives as written,
meeting your needs as
a constituent group?
Our program has three
constituencies, the students,
the faculty, and our alums.
Some programs get crazy, and they say,
their constituents are the universe.
Well, how are you
going to ask the universe
what their needs are?
You know, we just can't
get to some of those planets.
So that would be a very
unwise thing for a program to do.
It's great to just have three.
It works.
But it's so important
that you meet with them
and document that thing.
That's actually easy to do.
The third criteria is the simplest,
because ABET says, here
are the student outcomes, one
through seven.
And you can add more,
but what crazy programs
can add more and add
more work to their plate?
So just do what ABET asks you to do.
You just list it.
Those are your outcomes.
I think there's a table.
You show how they're
related to your objectives.
That's easy to do.
So that's easy.
It's the fourth criteria
that usually stumps everybody.
And that's the criteria
for continuous improvement.
In that-- and it's very
short if you read the words
the criteria.
It's not much language.
But it says that you have
to have a outcomes assessment
process that is performed regularly.
And in ABET, that
means at least two cycles
during the six years
between when you started
to when you get accredited.
And it must be appropriate.
And that's a big catch word.
That could mean literally anything.
Yes, it can.
So those are the two words that usually
catches most programs.
And so it really all comes down to,
how are you going to assess
the outcomes for the students?
How are you going to do it
in a way that is regular?
And how is it appropriate?
So after being on many ABET visits
and going to many ABET symposiums
and even being on the board of directors
for ABET for a while,
I've gotten a good sense of what they
actually mean by that,
even though it's not
explicitly written down.
So my takeaways for a lot of this
are that what ABET
really cares deeply about--
and you'll get this from
anyone from ABET you talk to--
they care deeply about
continuous improvement.
This all came from ISO 9000.
And it turned into the EC2000, ABET,
all of a sudden, with these words.
But basically, they
changed ABET dramatically
around the year 2000, where they wanted
to be more like what industry does
for their continuous improvement.
And there's a lot of good ideas in there.
I have my own personal beliefs.
I don't believe students in education.
It's like a product
that's on an assembly line.
So it's a little harder to measure
students' achievement
of outcomes than it is
to measure quality control
on the dimensions of a
part, the hardness of the metal,
all those things.
Those are very easy to measure.
Measuring learning is really hard.
And actually, none of us really know how
to do it in a scalable way.
So that's the hardest thing.
But what ABET cares
about is they at least
have a process that makes a
very serious attempt to measure.
And what ABET cares about
is you must, in the criteria,
it says that you must
measure the extent to which
the graduates of your
program have achieved the seven
outcomes.
Now, let's unpack that.
The extent to which ABET doesn't say
anything about that everyone
has to have an A. They
just say your program needs
to know how well your
graduates are doing so they can use
that information if you need to to
improve your program.
It's a diagnostic.
The other thing is the word "graduates."
They're not looking for
learning about what students
at the first or second year are doing,
because as you know, it's hierarchical.
You build on the knowledge.
And the kinds of things
and the outcomes are--
the outcome one is to solve science and
engineering problems,
complex engineering
problems, which usually
means open-ended problems.
That's not something we
expect our freshmen to do.
It's something we expect
our seniors to be able to do.
So it really makes-- because of that,
it makes little sense to do
assessment in the first or second
year if all you care about is
meeting the objectives of ABET.
Now, there's lots of people who might
want to do assessment
earlier for their own purposes,
and that's fine.
And in fact, the mantra--
at least it used to be--
I hope it still is-- at the TMS
Accreditation Committee,
used to be improve your
program for yourself first
and worry about ABET later.
Because if you're doing a good job
improving your program
for yourself, you should have no problem
documenting your processes
and showing ABET that you did it.
The converse is sort of
a fool's errand, right?
To just do it for compliance alone is
kind of wasting your time.
If you're going to do
something, make it meaningful.
So that's what you
should really be doing.
But at any rate, this is
the number one criteria
that shortcomings are
delivered to that
there either is no good process or the
program only did it once
instead of at least twice,
or that it's not appropriate.
And what they mean by
that is either doing it just
running through the paces, but it's not
giving them any information.
The last part of the criteria says
that you must use the results of the
measurements, the assessment,
as input to your
continuous improvement process.
And you must have a continuous
improvement process as well.
The last line of that
criteria is the most valuable.
It says you can also use as input
anything else to improve your program.
Now, this has been
debated by many people at ABET,
but ABET has made it exceedingly clear
that the word input is
there and not output.
So if you do your assessment and you
analyze your results
and you show that everyone is doing great
above whatever threshold
you decide is important,
then you may not be able to use the data
you collected to
actually improve your program.
And that's OK.
Because then you can
bring in those other inputs,
information from your advisory board or
from industry partners
to get that information instead.
And so we have really good students.
They come in, they all take our intro
engineering course, Engin 100,
which is really a fantastic course.
It's really an English course, a
communications course,
but it's cast in a framework of a design,
build, test environment,
teaching freshmen how to do engineering
design right from the get-go.
So our students who take that, they learn
how to work in teams,
they learn about ethics, they learn about
design, they learn
about doing experiments.
They do everything that our
ABET outcomes ask us to do.
So by the time they come in our
department, in Michigan,
they don't join our program
until they've had at least one term.
Wow, they're amazing.
And I remember the days before Engin 100
when it wasn't that way.
I'm sure they were dark times.
Yes. And so things have really gotten
better in the sense of at
least outcomes two through seven.
Outcome one is the toughest outcome.
Outcome one is using engineering, math,
and science to solve
complex engineering problems.
That's difficult.
That's the one we beat
on the students hard.
We have very high expectations for them.
And so the scores for that outcome are
always lower college-wide
for all of these things.
So all that's kind of cool.
So at any rate, you know, that's what's
the most difficult part.
And so we've developed a whole new
approach to doing this.
We started this six years ago.
We actually started in our department
earlier, but we've rolled it out and are
now doing it on a college-wide basis,
automated, trying to follow some basic
principles to make sure our process is
completely sustainable.
And so this is the process for measuring
the student outcomes.
Yes.
OK, so from the instructor side, I can
see my perspective on it.
But I want to actually come back to
something else you said earlier, which is
the priority order of what to do for
improving the program
versus what to do for compliance.
And when I'm thinking about making
changes, hopefully improvements to my
courses, I'm always saying, well, what do
I really want to do with my class?
And then looking at the ABET outcomes
and saying, is there an outcome that this
change I'd like to make
happens to be well tied to?
And if so, to me, that's a good indicator
that it's something worth pursuing
because most of the ABET outcomes are
pretty transparently things that I think
we should want our students to do.
We should want them to consider societal,
economic, environmental implications of
their engineering work.
We should want them to design experiments
that have scientific validity.
These are just good things to do anyway.
So as I'm thinking about my courses,
anytime I can point to an ABET outcome
and say, by the way,
I'm also achieving this.
In addition to just doing what I believe
is good teaching, that's always my anchor
for how to make those changes.
And that's great.
There's kind of two viewpoints of this.
There's the ABET viewpoint that they
believe that they're driving all of our
education by mandating these outcomes.
But, you know, their
outcomes are kind of motherhood.
It's kind of obvious.
And I'd like to think that our program
actually has many more outcomes beyond
just what ABET requires.
And I think any good program will, of
course, do all the things ABET wants.
But the real improvement of the programs,
at least my experience here in Michigan,
has never really come from doing the
assessment of the outcomes.
The real improvement comes from people
just like yourself having that good
attitude of trying to do what's best for
our students in a broad range of
areas.
So we have a very robust undergraduate
committee that reviews
constantly our curriculum.
We do curricular reviews apart from ABET
because we think it's important.
We try to have meetings where we put
together people who teach Thermo and
Kinetics and ask, are you getting the
right kind of
background in your students?
What's missing?
And you know this better than anyone
because you're teaching a math course
because we're not getting students coming
into our Thermo and our
Kinetics courses with enough math.
And so we're going to tailor the math
they need to supplement from what the
math department gives them so they can
perform better in our Thermo
and in our Kinetics courses.
Another example of massive improvements
in our system of our program didn't come
out again from ABET but came by the work
you did that we talked in another podcast
about your alloy
design module in the lab.
You worked with the people who taught
Thermo because while they're taking
Thermo, our students are taking your lab,
whether you're learning how to use tools
like ThermoCalc and design their own
materials, their own alloy.
Using those thermodynamic principles
they're learning in Thermo actually make it
pour dog bone test specimens and test it
and then try to understand why it didn't
work because it rarely works.
But you know it will once you get really
good at it but you've
got to start somewhere.
So how is that captured
in our assessment results?
You need to go way beyond just
assessment. You need to you know be
creative and innovative and you need to
inspire a culture in a program of the
faculty caring about
their undergraduate students.
And I am so happy to say I think we've
got an amazing culture here
at Michigan doing just that.
So the way I try to document that I know
that every faculty member innovates in
every single course they teach.
And so when it comes time to write the
self-study I put a note out to the
faculty asking them to write me half a
page to a page of what they're most proud
of in developing in
the previous six years.
And that's the I think the best part of
our self-study because it's honest it's
from the heart. It's the real actual
improvements that all
these individuals do.
Often with the help and support of the
undergraduate committee or other things.
But what a great way I think to document
all of that and to really demonstrate
that we're doing massive improvements to
our program all the time.
And it's coming from what the students
tell us because we have we have town hall
meetings every year with
our students to hear comments.
We heard a comment last term that the
BioMed course is actually too biological
and not enough material science.
And guess what we're acting on that.
And so Brian Love who also agrees with
that assessment even though he's taught
the course before is actively trying to
change that course now.
We also get comments from our external
advisory board who are people in industry
who are giving us heads
up about things they need.
We get information from our alums with
whatever careers they followed.
So it's you know it's a lot of good stuff
that we use to improve our program.
But I view this outcomes assessment
process which we're mandated to do is
actually a critical part of this process.
And the way I think about it is these are
the diagnostics that if we don't do we
can get ourselves in serious trouble just
like engineers design that little
material inside your brake pads.
To start squealing when
the brake pads get small.
You need that there even if you change
your brake pads
religiously and never hear it.
You want to know before something bad
happens that it's about to happen.
And that's how I view our outcomes
assessment at the very end.
I talk about the
future of what we can do.
I think we can even learn more from our
outcomes assessment.
Well let's get to nuts and bolts for a
minute here because we have this big
picture aspirational vision of here's
what all this assessment accomplishes.
Right. Here's what it helps
our program get better at.
But there is still
that implementation layer.
The actually doing it which might not be
obvious especially if a program is trying
to step up their game in terms of having
an easier more efficient time with
checking the ABET boxes.
While doing great teaching.
So how do you actually
do outcomes assessment.
How is that
implemented at a practical level.
That is absolutely the critical question.
We all know the biggest problem with
doing outcomes assessment is
getting our faculty to do it.
The only people who can really probe the
students in an efficient way where we
distribute the load of the work is to
have every single
instructor of any course.
That we're using to assess our students.
They have to do the work.
So it comes down to some fundamental
concepts to make a build a process that
everyone will participate in in the
easiest possible way so
that it becomes sustainable.
And we do it all the time.
So the first thing is although a bit you
know they don't tell you how to do this.
They just tell you have to do it.
I've seen a lot of suggestions and I
don't like most of them.
Like ABET, symposia.
When they say oh well you could do
outcomes one and two this term and
outcomes three and four the next term and
then new outcomes five and six the term
after that and do that and then cycle and
by the time you're done six
years you've done it two times.
The problem with that is everyone forgets
what they were supposed to be doing
because we're creatures of habit.
So we need to do all of the outcomes
every single term in my opinion and find
a way to make it as easy as possible for
our instructors to actually do the work.
So that's what I first make it easy for
the faculty and instructors
so that actually gets done.
The next thing you know we need to do is
how do we do this and
also make it meaningful.
So what we've done is we built and a lot
of programs do this by the way I think
people have learned over the years.
So it's pretty standard now.
We build a matrix of we look at all of
our required courses and all of our
elective courses and we assign we try to
assign no more than one
outcome to any given course.
Now we can't do this for all courses
because we lean very heavily into our
design and our lab courses because those
are the courses where it's more
appropriate to measure things about
teams, communications, design designing
experiments, ethics.
So those are outcomes two through six,
two, three, four, five and
six. That's five outcomes.
But luckily we've got two lab courses and
we have two design courses.
So we're able to break it up so they only
have three outcomes to do.
Some programs wait until the last year
and dump this all, all seven outcomes on
the capstone design course, which makes
sense because you're measuring with our
graduates the extent to
which they've learned it.
But it makes it
unsustainable in my opinion.
Right. It's even more work for the person
who's already doing the hardest class.
Exactly. And that's just not fair.
So we've broken it up.
So our idea is let's collect a relatively
sparse data set, but do it every single
term so that we end up with a massive
amount of data after six years.
We do this 12 terms.
And I have to say it kind of works well.
So that's the first thing.
And we also only do assessment in our
junior and senior classes.
We assess outcomes one
through six in our required courses.
So regardless of path, every student is
assessed in our required courses because
they all have to take it.
Then our elective courses, because we
want to distribute the load again, we
have outcome seven is done in all of our
which is lifelong learning, learning,
different methods of learning, all that.
And so we have all of our elective
courses measure that outcome.
So regardless of which elective courses
our students take,
they're being assessed.
So we do assessment of all outcomes
across regardless of path
for all of our students.
So once we've done that,
we need one more thing.
How can we make this even easier for our
for our instructors?
And that has to do with the actual
mechanics of how they
first they have to create.
We expect our faculty to create the
assessment measures.
We have a choice.
We could hire assessment professionals
and have them tell us what
the assessment should be.
Or we can ask our faculty.
I happen to think our faculty are better
positioned because
they're teaching the course.
They know what they want
their students to learn.
And after all, don't we want all of our
students to be able to solve complex,
open ended problems?
And all of our faculty do that.
So they should find something within the
context of their course.
So it's meaningful for them for their
course parameters, but use that to
measure the particular outcomes.
So what I've done is I
made a series of videos.
One short video for every single outcome,
explaining to the faculty good, good
approaches to build
an outcome assessment.
And that assessment measure, this measure
for the assessment, it
can be a homework problem.
It can be an exam problem.
It can be an activity that's not even
graded for the course, whatever the
faculty member wants to do.
But if it's outcome one, for instance, it
must involve a complex problem.
And I show them the definition of what a
complex problem is to ABET.
And simply say, if you look at that, it's
really just an open ended problem.
And our faculty are great at
writing open ended problems.
So every faculty member, and they usually
only have to do this once, because if
they teach the same course over and over,
they can keep using the
assessment tool if it's good.
And they can talk to other faculty if
they're inheriting a
course and borrow theirs.
That's OK.
But in each case, we have some faculty
member to make a document where first
they write down what is the
outcome they're assessing.
Because you want it first
and foremost in their minds.
Then what is the actual assessment?
And they write down that problem in great
detail, exactly what the student would
see, what you're
asking the student to do.
And then finally, after that, they write
a short little paragraph explaining why
they believe this is
an excellent outcome,
a measure of the outcome
they're trying to measure.
And again, that's just to make sure that
it's present in their minds and they've
actually thought about it.
Once they do that, they put that in a
document and make a PDF.
And at the end of the term,
they upload that document.
So they'll get a link.
This comes from our college because we've
developed this system
across all of our 12 programs.
And the college has our matrix and the
every program has an ABED coordinator on
the ABED coordinator for our program.
So every term I review the list of who's
teaching what courses that
are being used for assessment.
I confirm that with the college.
They send a special link to each
instructor with everything pre-filled.
So what term it is, what course they're
teaching, their name, all that stuff
that's all in there.
It's done for the faculty member.
All the faculty member has to do is
upload the PDF of what was the metric.
And they have to upload
the scores of the students.
This is where it gets tricky.
So we want to know the actual unique
name, the student
identifier for every score.
This is going to be critical for another
onerous thing that ABED has made us do.
ABED demands that we
disaggregate the data.
How does that mean?
That means that we can only consider
students who are in our program when we
do the analysis of our results.
They don't want to contaminate it by a
graduate student is in the class, by a
student from another department.
I still don't understand
why, but we have to do it.
If we know the unique names of every
student, we know that information.
We have a big data
warehouse that's got all that stuff.
And in the back end, we can easily filter
the students who are in our program and
not making a faculty
member do that is insane.
Because if you have 60 people in your
class, you don't know who they you don't
know what program they're in.
And you don't even want to
know what program they're in.
You want to treat them all equally.
You don't want to have a bias.
Oh, they're graduate students.
They should do more work or oh, these are
students from, you know, from.
Well, I won't say the name, but that
other major that we like to pick on, you
know, that we know we're going to you
don't want to know that you want to know
that there are students in your class.
So and plus, it's really hard.
A faculty member is going to have to
download, you know, look up
what program they're all in.
What a pain.
It's easier to just do your
whole class and dump it in.
And now we're
launching a new way to do this.
So if you have a spreadsheet, you just
highlight the two columns, the unique
names and the score.
And we ask all faculty to put in a score
normalized zero to 100 so we can combine
the scores with other courses to see how
they're doing across an outcome.
They just highlight
those things and copy.
Go to a line. I never knew you could do
this in a Google form right in the line.
You just paste it and it's kind of like
comma separated values.
But in the back end, they use regular
expressions to parse the data to put it
back into a spreadsheet.
We're doing this because these
spreadsheets we were getting from faculty
in the past were all over the map and it
became really ugly for the back end
people to deal with this data.
So this should be
much better at any rate.
The way the reason it works, because the
first thing you might think of is, oh, my
God, that's a FERPA violation.
That's student records. You can't make
that public. And we don't.
But the only way it works is because our
university signed a legal agreement with
Google to make sure that the instance of
Google that we have is FERPA compliant.
So we're allowed to use Google mail from
the university to talk about student
issues and all of that.
If your university doesn't have that
agreement, you'll have
to find another solution.
But it works really well for us. And that
student identifier is incredible, as
you'll see in a few minutes.
So that's all. But from a faculty's point
of view, they just give that assessment,
collect that data, upload a PDF of their
metric and upload the student data.
And they're done. It takes very little
time. And believe me, I remind everybody
to make sure they do that assessment so
they have that data.
And then it works. And after we've done
this now for six and a half years now, we
have 100 percent compliance, except the
very first year, one of our 80 year old
faculty members who didn't understand
Google Forms didn't do it.
But that's pretty good. Yeah, I have to
say on the user side.
Being tasked with three outcomes per
semester due to the lab class, it takes
me an hour, maybe an hour and a half tops
to do this once a semester.
And as you said, I just I
have my assessment items.
They're already in our
learning management system.
So I just grab the student scores,
normalized to 100, grab the student IDs,
paste columns in a sheet,
upload to our collection form.
And I'm done. It's really quite painless.
Yeah. And from the ABET coordinator
point of view, I just go to the Google
spreadsheet that they build and I can
instantly see who's
done it and who hasn't.
I can click on the documents
and check them very quickly.
And if there's a problem with a faculty
member, I just go visit the faculty
member and talk to them
and help them make it better.
So it's great. So the really cool thing
is when it's all done, there's this
program called the A BET.
It's called Tableau that
I'm sure many places use.
It's, you know, a database program that
ingest spreadsheets and you can have you
can build a user UI that does different
filtering on what you see.
But the data that we care about, what we
want to know is we want to be able to see
a histogram of the actual values of the
scores that our students got,
where every single piece of
data is on that histogram.
In the old days, we used to report an
average and we would say if the average
is above this number, we're good.
And of course, A-BET evaluators through
no fault of ABET, but because evaluators
like to come up with new stuff, started
saying, yeah, well, what
about the standard deviation?
And what about the modality? And it's
like, yeah, well, what about it?
And, you know, eventually it's going to
become part of ABET evaluator lore that
if this isn't done, you're going to get a
shortcoming and there's
nothing you can do about it.
So how do you protect yourself against
what I call the ABET virus that rapidly,
you know, because, you know, 13 people,
visitors on a team hear
about some great idea,
which has nothing to
do with the real thing.
And then the next year they go to 13
different programs and they
infect all those new teams.
Well, when I did this, it was and all of
a sudden it's growing
exponentially like a virus.
So how do you protect
yourself against that?
Well, you know, we're scientists.
We know that the best thing to do always
is just to look at all the data.
And so what we plot is the histogram of
all the data so we instantly can see what
the standard deviation is, what the
modality is, if there's skewness, we can
look at the tails, you know, all of that.
We just see in an instant.
And in a way, it makes the analysis of
the data very straightforward because you
just look at it and you can tell.
So what we do is for each outcome, some
way of a spreadsheet and the
spreadsheet has little boxes.
And you first you choose what department.
And when you choose the department, you
can choose which
outcome you want to look at.
Then you choose which terms and you can
check boxes and choose, you know, like
one term or one academic year or two
academic years or
three, whatever you want.
And you'll pull in all the data.
Then you'll see for that particular
outcome, here's this
curve, which is a histogram.
And there's no curve drawn.
It's just the actual data.
And it gives you an average.
We have to filter out zeros sometimes
because when students drop a class and it
still shows up in the
instructors grade books.
So the zeros usually are not meaningful.
Sometimes they are, but whatever.
And then you do the magic because, like I
said, ABIT doesn't accredit departments,
they accredit programs.
And your department might have a course
like we have our structures course MSE
350 that has a huge number of students
who weren't in our program in that course
because we offer a minor.
And one of the requirements for the minor
is our structures course.
So we have students from biomedical
engineering, from aerospace, chemical
engineering, from mechanical engineering
all over the place that
are not in our program.
So when we have another checkbox that
says which program do
you want to filter by?
And we can choose our material science
and engineering undergraduate program.
And you'll want you click that button and
you watch the numbers drop because now
you're only getting the
students from your program.
And it allows us to then prepare these
histograms so that I can go to a faculty
meeting and I do this once a year.
And I show the entire faculty how are our
students doing on outcomes one through
seven for the last academic year of just
students in our program.
And we look at the data,
we discuss it and off we go.
And that's continuous improvement.
We've used it as input if
it's not going to help us.
That's OK. At least we've considered it
as input to our program at
the level that's appropriate.
So that's what we do.
Yeah. Well, the process sounds great.
I love. I actually enjoy that annual
faculty meeting where we look at the data
is we're visually interrogating this
database of student performance in a
variety of different ways.
And it's quite interesting to see where
the program is doing particularly well
and where there are
improvements to be made.
So I'm a fan, but that's just my opinion.
Would you say that this method is working
from the ABET point of view?
Are they satisfied with the process that
you've described here?
Yeah. Well, we just had our ABET review
last fall and we have 12 programs that
are accredited and 10
of those 12 got NGR.
No general. I forgot.
NGR is perfect. No shortcomings.
And so, yeah, I think it
worked really, really well.
Were there problems? Yeah.
That's why the two programs that got
dinged for shortcomings got dinged.
And guess what? They deserve to be dinged
because they just adopted our process.
But forgot a really important part.
They forgot to document how they did
continuous improvement of their program.
They just said, we did this
and it's not going to help.
That's not OK. You must show continuous
improvement of your program.
And they forgot to do
that. Now, they had done it.
They just didn't document it.
So they had to go back.
And of course, both of these programs had
pretty major
curricular reviews and changes.
And they just didn't
document it or write about it.
So it made them complacent because it
worked too well, I would say.
So don't get complacent.
You still have to make sure that you
document and
continuously improve your program.
And this is not going to
get rid of that part at all.
Well, it sounds like the process works as
long as you follow the
process, which sounds almost
tautological. I'll have to
logic that one out for a minute.
Well, how I think about
that, let me ask this.
You've mentioned that we sort of started
this process six years
ago, seven years ago now.
What do next steps look like for our
continuous improvement of our process for
continuous improvement?
How are we going to get better at
inoculating ourselves against ABET?
So when I look at these histograms, I
think when anyone
looks at these histograms,
although we have something like 90% of
our students are above
our threshold for minimal
acceptance, you can graduate from the
University of Michigan with a 2.0 GPA.
That's like a 60% for the
way most of us grade exams.
And so that's our minimum level because
that's just being honest,
because a 2.0 can graduate you.
But when you look at the data, there's
always a few stragglers in
the tails that are below that.
And you have to ask
yourself, what about them?
And so this has actually been a good
process by looking at
these histograms over and over.
It's made me think about what about that
group? Even at our
university level, we are
very, very proud of our graduation rate
at University of Michigan.
Our five-year graduation rate is like
93%. That's the envy of the whole world.
Only a very few schools can say that. I
remember going on college
visits with my daughter and the
people standing up and saying, "We are
very proud of our five-year
graduation rate. It's 65%."
And I'm like, "What? How can you be proud
of that?" And I came
back, and when I talked to
some of the deans about that, they said,
"Yeah, we're unusual."
Even with our amazing 93%, and by the
way, some of the 7% are
accounted for by people who dropped
out of engineering but still ended up
graduating from other
colleges at our university,
there's still the stragglers of the 3% to
5% that do leave our university.
I just went to a second provost seminar
on teaching where the
provost stood up and
talked about this and said, "What can we
do about that 3%? How can we
make sure that they graduate?
That should be our focus." And why
shouldn't that be our focus in our
program? And maybe we can use
the ABET data we're collecting to help us
understand that
because guess what? We know
who those people are. We haven't accessed
that data yet because
it's a little complicated.
You still, I believe, we would need IRB
approval to actually do a
study. I'm not sure. I'm going
to have to find out the rules. But a new
tool just came available
that might let us easily do this
without hiring education researchers or
data specialists. And that tool is MAIZY.
MAIZY is U of M's generative AI bot.
That's a private
generative AI bot system.
So it means we can look at that data
without exposing student
data to the world. We can stay
FERPA compliant. MAIZY has a way to link
to a SQL database, which is what our
whole data warehouse
is built on. And so I've been talking to
those folks and we're
going to see, can we ask the
generative AI bot questions like, the
students in the tails for outcome one,
you know, can we do a longitudinal study?
Because we have several
years of data from when there are
sophomores until they graduate. Do we see
them improving? Do they
graduate? What happens to
those students? Can we talk about race
and gender? Can we talk about first
generation students,
Pell Grant students? What can we learn
from the data of the
students in our tails?
That could significantly help us improve
our program. And I'll
tell you what, if we did it,
I'm not putting this in our self-study
because I'm not doing this
for ABET. I'm doing this for
ourselves because of what I said way
back. First, we want to improve our
program for ourselves and
worry about ABET later. But I think it's
a wonderful exercise, a
wonderful way to use our data.
I should also mention that our Center for
Research on Learning and Teaching,
CRLT, which is solely focused on
improving education in Michigan, when
they saw our system,
they came and asked me, can we use your
system where we put in,
you know, our own questions?
Because they're doing curricular revision
pilots with a few
different programs or departments
because they do care about departments.
And so they're going
to be using our system
for their purposes, even though it has
nothing to do with ABET. So I think it's
really, really cool.
We have a lot of data. We've got data
from, you know, 13,000
students in this last pass.
In any given term, we're looking at 3,000
students. That's a lot
of data. And it's only
growing because I think other programs
have learned how valuable
this is. So that's what we do
for our ABET outcomes assessment. And
it's made getting
through criterion four a breeze.
I really love how this program
essentially started as, let's make sure
bridges don't fall down.
And now we're at a place with it where we
can interrogate this
really rich, robust data set
to ask questions about equity in our
programs and to say, how can
we better serve the students
who we are not currently serving well
enough? I think that's
fantastic. Well, that is all the
time that we have for today, but that was
a pretty good day one deep dive into
ABET. I think we might
have to revisit this topic in the future
now that we're asking interesting
questions about what we
can do with everything that we've learned
from these self-studies and
how we can use it exactly,
as you said, to make our own programs
better for ourselves
and for our students.
And I should mention, so, you know, I
agree, Tim. I'd like to,
you know, maybe bring on,
you know, some people from ABET. I'd like
to see if Jeff Fergus
can talk to us. Jeff Fergus
has probably been one of the most
influential people from the
materials community at ABET.
And I really think his principles and
values align closely with
mine. You know, Jeff was the
head of the EAC, the Engineering
Accreditation Commission. He's been in
charge of training for
ABET. So he's very, very well known at
ABET. And so I think it'd be
great to get his ideas because,
you know, one of the reasons we're doing
this podcast is to try to
share best practices with
our materials community. And to that end,
I hope everybody comes to
the North American Materials
Education Symposium. There's the plug.
It's a plug because on
Friday, I'm going to be doing a
free workshop on, I will show everybody
our outcomes assessment
stuff in detail. You get
to play with our Tableau system and I'm
available to give, you
know, to answer any questions,
not from a official ABET standpoint,
because I don't have any
official standing in ABET,
but just from a colleague in materials
who would love to help all of our
programs get through ABET
with a minimal amount of work and do a
maximal job on their ABET.
So come to our symposium,
get your chair to send you and we'll help
you with ABET for free.
Excellent. Well, thanks for being in the
hot seat today, Steve, and
to everyone else out there in
the world. We'll see you next time on
another episode of
Undercooled. See you later.