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[no dialogue]
>>Rebecca Throneburg: Alright, good morning.
This is our final workshop in the workshop series.
It's our new learning goal: quantitative reasoning.
This is the goal that was added from the basis of the
learning goals study.
When we noticed we were studying what other universities were
doing, the majority did have something related to
quantitative reasoning.
When we talked to the various curriculum committees
and got feedback across campus, again, there was some
positive feedback that, you know, perhaps EIU should
go in this direction, as well.
So, we do; we have formally passed the new
learning goals; quantitative reasoning is one of them,
which will be officially adopted in the fall.
So, this group, Wesley was part of the group who wrote the goal
for us, starting last spring when we were drafting it.
But other than that, this group has had a shorter time frame,
because the other learning goals had a year and a half
to study the learning goals and were ongoing learning goals,
and such like that.
This group had a new learning goal that just had a goal
that was written with no resources.
So, this group has been scrambling a little bit more
than the other groups, but we appreciate their efforts.
And again, all of these workshops are just a beginning
first step in developing resources for the campus
that we'd like to build upon.
Wesley and the group has said that some of you have to
come and go for meetings and classes, and that's fine.
They said if a few of us would like to join them at the table,
they would like that.
[laughs]
So, when I go back to my seat, I will move up and
join the table; if any of you would like to join me, as well,
I'll move from my corner back row over there where
I had sat, as well.
So, anyway, we'll welcome Wesley, and he can introduce
the rest of his group and tell us how the program
will be structured.
>>Wesley Allen: Thank you very much, Rebecca.
Good morning, everybody, and thank you very much for
being here this morning for the quantitative reasoning
learning goal workshop.
We're very excited to talk about what we're going
to be doing today.
And as Rebecca very kindly pointed out, you know,
we have been a little bit behind the other groups because
this is a new goal, so please do keep that in mind.
But at the same time, we're really excited about what we
put together here, in terms of resources and in terms of ideas
for all of you to take back to your classrooms.
I'd like to introduce the rest of the group.
Some of the people can't be here today, so I do want to
go ahead and put up there the full list of everybody that's
in the Learning Goals Committee, starting with me,
Alejandra Alvarado is here, and she'll be talking about
best practices in teaching.
Marita Gronnvoll from communication studies is here,
and she'll be talking about how to implement this in the
classroom, giving some applied ideas.
Michael Menze from biological sciences is here.
Mary Konkle can't be here today; she's from chemistry.
Kevin Savage is our student member.
He's from, a graduate student dean, and unfortunately
he can't be here today either.
Krishna Thomas from Faculty Development is here.
Thank you, Krishna.
And finally, Larry White from the School of Business,
who helped a lot with putting together the assessment portion
of our learning goals.
So, now that I've introduced everybody, I'd like to talk
just a little bit about today's schedule and what we'll be
talking about today.
We're going to start off, first of all, by talking about the
quantitative reasoning learning goal, a little bit about
how it came to be, and a little bit about kind of where do we
stand in the field.
What do we know about quantitative reasoning,
how is it defined, how are we going to be measuring it here,
and also how are students doing at EIU, in terms of
quantitative reasoning?
We'll move on then and talk about principles and
best practices in the teaching of quantitative reasoning,
and then assessment issues.
And then, finally we'll wrap up by talking about how to do this
in the classroom.
And so, we'll have some small group discussions and
activities planned to help all of you get some ideas
about how you can do this in your classroom.
And I just want to make sure that, you know,
we really want this to be an informal workshop today,
so please do jump in with any questions you have,
any comments.
If any of you would like to talk about at any point
during the time how you're doing this in your classroom,
we would definitely love to hear from all of you.
So, I want to start off, first of all, by just a brief
definition of what is quantitative reasoning.
You might also see here in the literature,
use terms such as quantitative literacy, or numeracy,
or a variety of other terms.
It all refers to, you know, kind of essentially
the same idea here.
When we're talking about QR, quantitative reasoning,
we're talking about a habit of mind, competency and comfort
in working with numerical data.
And so, it really goes beyond just knowing about numbers,
understanding how to work with them, but it's really comfort
with them, being able to reason with them, being able to
analyze with numbers, talk about numbers, etc.
So, individuals with strong quantitive literacy skills are
able to do things such as, they can reason and solve
quantitative problems from a wide array of authentic
contexts and everyday life situations.
So, this idea of context is really going to play a big role
throughout everything we talk about today.
Really giving students context for the numbers that
they are given.
So, not just giving them, you know, numbers to solve,
calculations to perform, etc., but giving them real contexts
in which to put those numbers in and talk about them.
So, they're able to talk about numbers within some kind of
context, within their discipline, etc.
They also can understand and create sophisticated arguments
supported by quantitative evidence.
And then, finally they can also clearly communicate
those arguments in a variety of formats, using things such as
tables, charts, graphs, using technology, and so forth.
So, that's kind of a basic definition of what quantitative
reasoning or quantitative literacy looks like.
Let's turn next and talk about our learning goal.
As Rebecca pointed out, this is the newest goal,
and it actually came about in kind of an interesting
way, I thought.
The first four goals were already developed and were
sent out for feedback to various departments.
And Rebecca and Stephen Lucas went around and
talked about it at lots of different places,
lots of different committees and so forth, spent quite a bit of
time doing that.
And we actually got feedback from the
College of Arts and Humanities that we lack the
quantitative reasoning goal.
And so, this was not driven by the College of Sciences,
which I thought was really interesting, but actually from
arts and humanities.
So, I think it really points out the importance of, you know,
having this across lots of different disciplines.
This is not just something that in the sciences is important,
but it's really important across all different disciplines.
So, what is the specific learning goal?
Well, it's "EIU graduates will produce, analyze, interpret,
and evaluate quantitative material," and we have this
specifically kind of parceled out into different portions.
So, for example, they are able to perform basic calculations
and measurements.
They are able to apply quantitative methods
and use the resulting evidence to solve problems.
They can read, interpret, and construct tables,
graphs, charts, etc.
They can critically evaluate quantitive methodologies
and data.
They are able to construct cogent arguments utilizing
quantitative material.
And they are able to use appropriate technology to
collect, analyze, and produce quantitative material.
So again, this is going beyond numbers, not just being able to
work with numbers, but it's really, again, this comfort
with numbers, this comfort of mind.
Being able to talk about numbers in more complex ways.
[no dialogue]
So, that's the learning goal.
And a little bit, I just want to talk a little bit about what
drove this learning goal and what makes this
really important.
So, one of the things that we need to be aware of, obviously,
in higher education today is this whole idea of benchmarking
and the common core in primary education.
So, we know this is coming, and we know it's probably
coming very soon.
And so, one of the focuses of this is to make sure that,
if students are coming in at a certain level,
that we're meeting that level, that we're able to exceed that
level, able to contribute to the education of the students
who are coming into our university.
So, I just included up here just for an example the standards
for mathematical practice.
So, you can see that, you know, mildly different standards
for mathematical practice, and this is what, you know,
what graduates of high school are expected to do.
They look very similar to a lot of our learning goals.
So, being able to reason abstractly and quantitatively,
for example.
So, we hope that within, you know, within a certain
time period high school students are going to be
coming into college with these skills.
And so, again, it's really important that we have these
learning goals to be able to document that they're
coming in with these skills, and that we're adding to it,
more importantly.
So, again, these are just some of the different ones.
Models mathematics, using appropriate tools
strategically, and so forth.
And again, you can see a lot of these look a lot like
our learning goal.
So, we feel very good looking at the common core and seeing
where our students are expected to be, and then where we're
hoping to take them text.
So, that leads then to the question of where are we now,
both in terms of college students in general,
the public in general, and also EIU students.
So, there are a couple of different surveys
I just want to briefly touch upon.
The first is the National Assessment of Adult Literacy.
This was a 2003 survey.
And they found that only 13% of adults ages 16 and over
demonstrated proficiency in quantitative literacy.
So, the numbers are very low, and we see these kinds of
numbers all the time.
Every time you turn on CNN, you see another report coming out
about how poorly Americans are doing in mathematics,
and so forth.
So, it's not a surprise, unfortunately.
Quantitative literacy disadvantages:
most happen among females and economically
disadvantaged minorities.
And we know this is a big problem because, you know,
increasingly these are the students who are coming to EIU.
And so, we know we need to be able to address these problems
as they come into EIU, and are behind where they should be.
Another survey came out of the Organization for
Economic Cooperation Development, and this was a
2013 survey of adult skills, including numeracy.
And they found that young adults 16 to 24 years scored lowest.
Now, this is not surprising that they would score the lowest
among different age groups, but among developed countries,
Italy, Cyprus, and the United States scored the lowest.
You know, so this is the list we don't want to be on, obviously.
Another important survey came out in 2010,
and this was the Hart Research Survey of Employers.
And so, they asked employers across the country
essentially what are you looking for in employees?
And 63% reported they seek employees who can understand
and work with numbers and do statistics.
And this wasn't just in the kinds of fields we might
think of that would, you know, want somebody very strong
in math; this was across all fields.
You know, increasingly it's important to have these
basic kinds of quantitative reasoning skills in lots of
different disciplines.
55% reported that colleges should place more
emphasis on this.
And when they looked at 17 different learning outcomes,
employers stated that the two most important were
critical thinking and analytical reasoning, and complex
problem solving analysis.
You know, obviously, both of these are involved in
quantitative reasoning.
So again, you know, we're not just failing in terms of
the students that are being turned out across colleges
across the country, but you know, unfortunately employers
are not happy with it either.
So, there's definitely that problem.
So, this is the data from the National Survey of
Student Engagement.
This is one of the national tasks that's given to
a lot of universities to assess things,
including quantitative reasoning.
And what they do is ask students how often
they use quantitative reasoning in their coursework.
And they don't just say quantitative reasoning;
obviously, they give some specific skills.
And I'll go over some of those specific skills, excuse me,
when I talk about the EIU data.
This is the national survey, and so these are the results
from nationally.
And you can see that if we look at different disciplines,
different majors, as you would expect, majors such as
engineering, physical sciences, biology, etc., they report
using quantitative reasoning more often in their courses.
So, this is not surprising.
But you see that the numbers go down,
you know, quite dramatically.
You know, so when you get down to some of the lower ones,
you know, the numbers are, you know, half are lower than
the top numbers.
And again, remember, employers are reporting they want
these kinds of skills, not just in engineers,
but across disciplines.
So, one of the outstanding leaders in the field,
Derek Bok, has asserted that, "Thus, quantitative material
needs to permeate the curriculum, not only in
the sciences, but also in the social sciences,
and in appropriate cases, in the humanities,
so that students have opportunities to practice
their skills and see how useful they can be in understanding
a wide range of problems."
Here's some more information from the Nessie, and this is
quantitative reasoning among freshmen and seniors.
And so, again, what they do is they ask freshmen and seniors
how often they have to use quantitative reasoning
in their classes.
So, you can see there's three basic questions here.
The first question, I think I can use the laser pointer,
or maybe not.
Okay, well I, okay.
The first one you can see is reach conclusions based on
your own analysis of numerical information, so using numbers,
graphs, statistics, etc.
Second question up here is using numerical information
to examine real world problems or issues.
And the third is evaluate what others have concluded from
numerical information.
So, again, these are the national data.
And you can see here that these focus on students who report
often or very often; it's on a four point scale.
So, the top two levels.
And what you can see is that freshmen and seniors, you know,
the numbers don't necessarily go up quite that much
from freshman year to senior year.
So, what's going on?
Well, probably what's going on at a lot of colleges is what's
probably going on here, which is students get a lot of this
in their general ed courses, but then not again.
So, people who are teaching higher level courses or
discipline specific courses think, well they got
quantitative reasoning in gen ed; I don't really need to
teach that, that's not my job.
So, I really want to challenge all of you to think about
how can we do that more, how can we not just rely on gen ed,
but how can we do that across disciplines.
And I think the reason it's so important is I'm really
reminded of my graduate work, where first year we had our
two stats courses, and then the next year we did our thesis,
and then later dissertation.
And as a lot of you probably remember, it was a really
different experience learning in the classroom, and then later
having to apply it while doing your thesis
or your dissertation.
And if you don't have much experience in between,
it's a real struggle.
So, it's the same for students.
If they get it only their freshman year, and not again,
it's a real struggle for them.
So, we really need to further develop those skills and
further elaborate on those skills as they go along.
And then, here are the EIU data.
And our data look very similar, as you can see, to competitors,
other people in the same class, and then all of the Nessie data.
So, you have the three different questions again across
freshmen and seniors.
And you can see that, you know, for most of them, you know,
again we look very much like our competitors,
which is good news.
The bad news is the numbers don't really go up very much.
And I'd like to thank CASA and Carla for giving me these data.
So, you can see that, you know, across freshman and
senior year, for example, the top two columns,
the often and very often, you know, go up here from 49 to 49;
they stay the same.
And so, you see a lot of these kind of level pegging, or go up
a few percentage points across the three different questions.
Here's the second question.
[no dialogue]
And then, here's the third question.
And remember, this has just asked them whether or not
they do this in courses.
This isn't testing whether or not they can really do it,
or testing anything else; that's a different question.
And so, just the fact they're reporting they don't even
do this in courses that often, or it doesn't increase much
from freshman to senior year I think is a big problem.
So, that's a little bit of background about the goal.
I want to turn it over next to Alejandra, who's going to
talk about practices and principles of teaching.
[no dialogue]
>>Alejandra Alvarado: Good morning.
So, I'm going to talk a little bit about principles and
best practices in teaching.
But first, I'm going to start off with this true story that
happened last year when I was out to dinner with some friends.
And it wasn't here; it was in another state, so
this was not an EIU student.
So, we went out to eat, and our bill came out to about
48 dollars, and we were going to split this bill among
six people, but my friend was going to pay for me.
So, one person paid, let's see, so 48 divided by six,
that should be about eight dollars.
And then, so my friend was going to pay 16,
and then the remainder of the bill, the remaining bill was
32 dollars that was going to be split amongst the
four other people.
Well, when the server went to, I forget exactly what order
this went in, but when she went to get the bill,
and she brought the bill back to us, even after my friend
had already paid his portion and our portion, she was just
adamant that everybody owes 12 dollars.
And we tried to explain to her the reasoning,
but she just didn't want to hear it.
She said, well no, the computer said, or the cash register
said this and this.
And we're like, but no, you don't understand.
So, instead of arguing, she decided to go get her manager
to help her out.
And the manager went, and she realized what was happening.
And you know, in the end, everything worked out fine.
But really, this should not have happened.
She could have easily, this was a small enough calculation that
she could have done on her own and figured it out
right away without, you know, getting so flustered.
So, here's a reason why we need quantitative
reasoning in the classroom.
[laughter]
Maybe.
Okay, so I'm going to say a little bit about these four
interrelated aspects of quantitative reasoning,
and this is coming from a really nice article by
Christopher Wolfe, titled "Quantitative Reasoning Across
a College Curriculum."
So, he discusses these four aspects that are
very much related.
And so, really, as Wesley was saying earlier, was what we
want our students to know is to have, or to have this
wide range of mental ability developed.
For example, being able to just do some
small estimations or a sense of scale.
We're not asking for them to have skills in something beyond
calculus; we're just asking for some basic understanding
of measurements and maybe some probability and statistics.
Just, you know, just enough is what we're asking for here.
So, the first one that I'm going to talk about is
learning from data.
And here's a nice picture.
This picture is coming from an article on the
carleton.edu website, titled "How Students Engage the Data."
And what it's showing us is this continuum of how students
can engage the data from, anywhere from watching,
all the way down to open ended discovery, where we mean
these are the various ways in which students engage data,
from guided direction to independent thinking.
So, when I say learning from data, what we mean
are the skills associated with collecting and analyzing data,
particularly in the natural and social sciences.
This reminds me of a recent comment that I heard
in my class.
So, I'm teaching calc I this semester, and the students
recently had an exam.
And one of the things I also do is I have a
mid-semester survey.
I figure, why wait until the end of the semester,
when by then it's too late?
I can't help them if there were any issues that they wanted
to address, so I do something around spring break
or winter break.
And one of the comments that I got was, well, the questions
on the exam don't look like the questions from the homework
or the examples in the book.
And I addressed that in class, and I said you know,
some of them may, but some of them may not.
You can't expect, right, they can't expect to be in the
section over there under watching; they have to be
able to, if they really understand the material,
they really have to be able to ask questions to test
whether they understand the material, not just ask the
same question with a different value.
Okay.
[no dialogue]
By quantitative expression, I mean the ability to use and
comprehend quantitative language in a variety of contexts.
A common mistake that I see, even myself, is this
conversion error.
All dogs are animals to some people may imply
all animals are dogs.
Well, it's not the same thing, right?
Or another example would be an American youth is more likely
to become president than to die on an airplane flight.
Alright, so what does this mean, right?
What sort of percentage or thinking can we get from this?
[no dialogue]
Evidence and assertion allows one to comprehend
which conclusions we may reasonably draw from,
draw from a body of evidence.
So, here's an example, where did I get it from, Reuters,
or Krishna found this article on reuters.com, titled
"A Plunge in U.S. Preschool Obesity?
Not So Fast, Experts Say."
And what this article was saying is that there was this plunge,
but a lot of people were really confused by this.
Wait a second, there's all these other stories and statistics
and data that say, wait a second, that's not the case.
And come to find out that this study did not have that many,
or the sample pool was not very large.
For example, the last bullet here, there was a WIC program
in California's Los Angeles County, where researchers found
that the problem worsened from 2003 to 2011,
and their sample size was 200,000.
The sample size for this data, let me see if, I don't know
if I put it up there, it looks like 9,000.
No, no, no, I'm sorry.
Maybe I didn't put it in there, but I remember it being
a very small sample size, where you really couldn't
draw much conclusion.
So, the point here is that, you know, students,
by the time they graduate, they should be able to
read an article and try to figure out whether it
makes sense or not, and whether there's enough proof or
enough evidence to say.
[no dialogue]
And the last part here is quantitative intuition.
Right, so what I mentioned earlier, an appropriate
sense of scale.
A feel for numbers and other quantitative concepts.
So, maybe instead of asking, if you were to ask a student
how old someone is in years, can they estimate how old are they
in the number of days?
Or what I recently asked in class, what's your best guess
for the distance from Charleston to Seattle?
Anybody want to take a guess?
[laughter]
One of my students got it.
He was off by about a hundred, which is pretty good.
What's that?
>>Attendee: 2,400.
>>Alejandra Alvarado: 2,400?
Pretty good.
Google Maps says 2,142.5.
So, good job.
[laughter]
So, at this point, I'm going to stop and hand the mic
over to Krishna, and she will finish off the rest.
Thank you.
>>Krishna Thomas: Thank you.
Well, good morning.
Just to continue from the best practices, when we were
looking at the research and preparing for this workshop,
we noticed that there is a plethora of information
out there on quantitative reasoning,
particularly in different institutions, their different
regional organizations, national organizations.
Some of the things that readily come to mind are the
National Numeracy Network, which is actually fantastic for
faculty because it also has sample assignments and rubrics
attached to these assignments, so it might be something worth
looking at, and we referenced that in the handout.
But from our overview of the research, a couple of
best practices stood out to us, and one of those was using
real world applications, using real world data, big data,
and using active learning to actually have the students
engage this information.
For example, using a case study, having your students,
assigning your students a role in the case study.
We will be talking about spreadsheets
across the curriculum.
I know, I guess across the curriculum is the buzz word,
but, and interestingly enough, there are a number of
assignments, too, that use different
kinds of spreadsheets.
But anyway, with these spreadsheets you can also
ask your students to play with the data.
I don't think any of this data is particularly sensitive,
otherwise it wouldn't be on the internet, right?
But using 'what if' analyses to actually
engage this information.
Other ways that you can use real world applications:
involving students in collecting the information.
One of the most significant experiences in my undergrad
was actually helping a faculty member collect data
for political trials.
And so, that type of quantitative exposure, I guess,
I think will lead to some further foundations.
The next one actually, and I'm really excited that
Fern is here, yay, because it talks about pairing
quantitative reasoning with writing, and with storytelling
and critical reading.
Came across this graphic from Bridgewater State University
about how to conceptualize a relationship between the three.
So, on the far right corner you have a symbolic problem,
at the bottom corner you have an abstract solution,
and the relationship between them formal reasoning,
you have contextual solution, and then story problem.
And trying to see how that all plays out with reading,
writing, and quantitative reasoning.
Just an interesting diagram, you know, for all of us that
like visual diagrams.
In your handout, you'll have a short and simple guide
to quantitative writing.
And the reason it's short and simple is because a lot of this
stems from writing across the curriculum.
John Bean, who the writing across the curriculum folks
referenced in their earlier workshop, has a new edition
of his book, and a number of his assignments or
sample assignments in that book pertain specifically
to quantitative writing.
I think what's probably one of the more important takeaways
from pairing quantitative reasoning with writing and
critical reading is that it all has to be both critical,
it has to be conceptual, and contextual.
And I think that's one of the main things that we're
talking about in this workshop, is that quantitative reasoning
is not so much doing math, it's not necessarily a
mathematics course, but providing a context
for the numbers.
And so, a couple of other best practices for quantitative,
for this interaction right here is also using backwards design
when you design your assignments, as well.
I know we talk about it so much more with courses, but actually
using that, as well in your assignments.
[no dialogue]
And of course, it would be the day that we have the EIU,
just because it's so important, right.
And... [laughs]
And so, I guess one of the, another takeaway is
using technology in relation to quantitative reasoning.
I think we can all agree, at least because we're here,
that in some form or fashion quantitative reasoning and
technology almost go hand in hand to promote
some more logical thinking.
One of the best practices here is that using what's
readily available on campus.
And for us here, I know we have access to MS Excel,
MS Access, Microsoft Excel, Microsoft Access, we have SPSS,
we have Qualtrix for survey software.
And then, there are those that are specific
to your disciplines.
And then, here I'd like to put a plug in for spreadsheets
across the curriculum.
In your handout, there are some sample assignments,
and the last one of those is from sociology/anthropology,
talking about the average rate of high school dropouts.
And this spreadsheet is given to the students,
I think the data is from 1973 to 2003, and asking the students
to calculate an average rate of change.
Which, to be quite honest with you, I didn't know
until I looked at the assignment.
[laughs]
Second, another thing that the students were asked to do
in this assignment was to actually plot the average
rate of change using Excel.
Which, again, was a little difficult to understand.
But the outline of the assignment actually had a
step-by-step process.
Now, I know here we're not actually asking our faculty
to sit down and do step-by-step tutorials for Excel, right?
But if there is something there that you can use, just
look it up; it's spreadsheets across the curriculum.
[no dialogue]
The fourth best practice is collaborative instruction
and group work.
On the table here, which I know is so far back from
all the people in the audience, there is a book here on
collaborative learning.
And one of the reasons that this made it to the table was
because, let's face it, when you have a quantitative reasoning
assignment, you're not only asking your students to do the
math, right, your'e also asking the students to apply it in the
context and ask a question specific to your discipline.
So, that's a lot of moving parts you have.
For example, one of the assignments in the handout,
really you're asking them to do an internet search for
big data, for real world data, you're asking them to
manipulate the data, you're asking them to present
the data to you.
So, you're asking, there's a lot of
moving parts in this assignment.
And so, one of the best practices here is actually
using groups, having a person within each group responsible
for part of the assignment, so that the, so that in a sense,
the instructor, the faculty member himself, in the end
you're not responsible necessarily for remediating
skill levels in math.
So, it's just a way of breaking things up that make it
a little more doable.
And so, here's a quote from a couple of professors who did
group work on social science and quantitative reasoning.
[no dialogue]
The fifth best practice is pedagogy that is sensitive to
cultures and learning styles.
A couple of years ago, Faculty Development hosted a speaker
here on universal design.
And one of the components of universal design is providing
multiple means of representation.
So, giving your students different ways to access
the information.
Now, a lot of the research states that
quantitative reasoning skills are significantly weaker maybe
for females and for minorities.
And so, this is one way of actually
addressing the problem.
We talked about collaborative learning.
Under universal design for learning, you can ask
your students to, you can present your students
information visually.
You can present it in, you can present it as,
in terms of videos, for example.
I know one of our, one of the, one of the examples
that I did not write in the handout was the use of
Kahn Academy in supplementing instruction in, say,
chemistry or...
Although, for some reason I think Kahn Academy also has
foreign language tutorials at this point now, too.
So, it might be the next best thing.
I don't know.
And another best practice is we also assume that the students
know how to find this information.
I mean, honestly, I don't know if I would be finding
information on average high school dropouts if I wasn't
asked to in an assignment.
So, teaching them how to find it, collaborating with the
library, for example, to find out how to explain to
the students where to go and how to manipulate this data.
And finally, we're talking about scaffolding.
On page 25 of your handout is actually a pretty interesting
scaffolding handout from also Bridgewater State University.
If I can find it real quick.
[no dialogue]
But what is interesting about this is that it is not only
specific to a course, but you could adapt it to an assignment
and do, for example, you could do you're introducing
this material, you're developing it, and you're mastering it.
So, page 25 of your handout is really the best one
I've seen so far, in terms of scaffolding for
quantitative reasoning assignments and courses.
I think what's also interesting here is that we are developing
quantitative, this learning goal in tandem with,
not in tandem with, I guess, maybe exemplified from the
writing across the curriculum information.
And so, part of that is all the revision,
providing your students enough time to revise the assignment,
providing them feedback.
And even if they don't get it right the first time,
still having an element of revision in that component.
And so, I believe I am done.
And so, I'm going to pass this on.
And so, here's just a quick recap.
I thought I was done.
Here you go.
[no dialogue]
>>Wesley Allen: Thank you very much, Krishna.
I want to move on next and talk about best practices
and principles in assessment.
And you'll see that hopefully this maps on nicely with
what they've been talking about in terms of teaching.
So, what I want to do is talk about some different
universities and some different programs that exist out there.
Fortunately, we're at the stage now where many universities
have started to look at quantitative reasoning,
and they develop specific programs in order to teach it
and to also develop quite a bit in terms of assessment.
So, a lot of these programs do have really good websites.
Krishna, for example, mentioned Carleton College.
There are some others.
So, I'll talk about some of these different programs.
The first one I wanted to highlight is Bowdoin College
in Maine.
They've done quite a bit, in terms of how to do assessment
of quantitative reasoning.
They have a QR exam, and they have a number of different
suggestions they have that can be used in any classroom,
in terms of trying to assess quantitative reasoning.
The first suggestion they have is, instead of using procedural
questions, so questions that just have, you know,
right or wrong number answer and have a certain calculation
that needs to be used, instead we should have questions that
require more involved reasoning and critical thinking skills.
Likewise, they request that students interpret tables and
charts, rather than providing the information for them.
So, instead of giving them a story problem, for example,
and giving the numbers to them, have a chart or a table.
So, they have to first of all read the chart or table,
and then see what kinds of numbers they need from it,
and then do the calculation.
So, it's kind of, you know, secondary processes
that are going on.
Likewise, they also suggest focus on using numbers
in context, rather than simple computation math skills.
I'll talk a little bit more about this idea of context,
but again, this is something, you know, Krishna in the
teaching group mentioned, and it comes up again
in terms of assessment.
This really seems to be one of the keys that people are really
focusing on now.
And then finally, they suggest asking students to postulate
potential explanations for statistics.
So, not just getting the right number, or how did they derive
that number, but also explain it.
So, give me some theory, explain how they
came to that number, etc.
Because, we can often see that maybe they're on
the right track, in terms of their reasoning,
but how they got to it is maybe incorrect.
So, it's a very different story, obviously, then somebody who
just does the problem completely differently,
doesn't understand the problem, etc.
Another really good program is at Hollins University
in Virginia.
And what they really have focused on is developing
clear rubrics for assessing quantitative skills.
And again, you know, usually when we correct or
give feedback about quantitative skills and so forth,
we usually focus on the right answer, you know,
using the correct computation, etc.
But instead, what they really focus on are a lot of the other
kinds of steps that go into that, and a lot of other things
that go into quantitative reasoning.
And they developed this based on the AAC&U value rubric.
And what they do is they assess and grade students based on
interpretation of the problem, representation, calculation.
So, this is just a small portion of it; it's not, you know,
again, it's not the main focus of their assessment.
Analysis and synthesis, assumptions, and communications.
How does a student communicate about the statistics.
How do they communicate about
the quantitative reasoning, etc.
So again, you see we're going beyond the numbers here.
Another really good program that was mentioned earlier
is Carleton College in Minnesota.
And one of the faculty there is Grawe, he's done a number of
different studies and has written extensively on
quantitative reasoning, especially assessment of
quantitative reasoning.
And one of the things he says is that because QR itself
is grounded in context, our assessment tools
must be, as well.
So again, this focus on context is really one of the keys here.
Complex QR skills such as the ability to construct arguments
with quantitative evidence, obviously they can be
best assessed, not just by coming up with the number,
but by having students do things such as essay tests,
oral presentations, etc.
Now, obviously some of you probably teach courses that are
too large to do that.
You can't have, you know, essay tests all the time
if you teach a hundred students.
And so, you know, that is certainly knowledge
in this area.
And so, again, you can do multiple choice exams.
But again, then the options need to be context rich.
And so, you might, for example, have multiple choice items
where maybe the numbers are the same in each of the different
options, but the reasoning to get there is different,
or the context is different.
So, the student really has to pick that out and focus
on that, rather than just the number.
Another thing they focus on is that QR is multifaceted.
And so, because of that, it's going to require multifaceted
assessment, as well.
And so, they really focus on using multiple tools.
Now, we'll talk about some of the different kinds of tools
that are used, again, not just to look at
quantitative reasoning, in terms of right or wrong answers,
but in particular things like attitudes and beliefs.
So, here's an example of a context rich question,
and how you would assess it.
So, what they do is they give a student an article,
and this article is about the U.S. Postal Service.
And the full article is linked below, at the New York Times.
And what they do is they give them a little bit of a blurb,
and they have them read the article.
But most of the information that you need for the questions here
are in this particular little blurb above, but they do have
more information in the article.
So, they'd have students read the article,
and then answer questions from it.
So, things such as the graph next to the article says
the standard mail in 2010 amounted to 3.3 pounds for
every adult in the U.S.
Verify this figure.
Likewise, having to take the 2010 figure and knowing that it
fell by 6.6%, and then calculating the
2009 percentage.
So again, you have to look at this, you know, try to pick out
the relevant numbers, and then do the calculations.
So, it's much more complex than just a story problem that
has the information.
And then, you know, likewise doing some more complex,
so just looking at the cost.
You know, which is, you really have to pick out
multiple things from this article.
And then, here's the type of rubric that you might use.
So, you see that each of the different kind of steps,
each of the rubric components matches onto
the specific questions.
So, the student has to demonstrate the ability to
identify and extract relevant data, verify textual claims,
aptly perform the backwards percentage calculation,
and then perform the complex calculation involving
estimation, multiple unit conversions.
So, you see again it's not just yes or no, do they do this,
but it's a three-point scale.
And so, again, calculating, you know, really kind of
taking into account the fact that students might miss
the question, but still have good reasoning for missing it.
Another thing that's really important to keep in mind with
assessment is that many programs posit the importance of
assessing, not just their quantitative reasoning skills,
but also their attitudes and beliefs about it.
My training as a clinical psychologist, yeah...
[unclear dialogue]
Right, right.
[unclear dialogue]
I was actually just talking to Karla about that before.
And as far as we know, I don't think there's really
anything out there quite yet.
The Nessie does look at some things about that, but...
[unclear dialogue]
Yeah, it's a really, definitely a really important question.
And so, definitely the, you know, what they're talking
about in terms of the CLA is going to be a good
thing, in terms of having that and being able to compare
nationally to how we look at other universities.
But right now, you know, what most these programs are doing
is developing their own assessments.
You know, which we know is not the best way to do it,
is kind of piecemeal every university do this,
but that's kind of where the field is right now for a lot of
these places and a lot of these programs.
So, that's largely what we'll have to do immediately,
is to do that, you know, here for EIU.
And you know, likewise faculty in different departments and
different disciplines are going to have to figure out
how do they assess in their courses, as well.
One, I think it was Carleton College, had a really good
article about how they had small groups of professors from each
department, five to six from each department meet and
have kind of subcommittees, talk about assessment
and how it will look in their department,
and come in with ideas.
And so that, you know, maybe, perhaps something like that
where, you know, we start off with, you know, kind of
small numbers of people trying to come up with that, you know,
would probably be one of the text steps.
So, that's an excellent question.
Thank you very much.
So, as I was talking about, you know, in addition to looking at
quantitative reasoning skills, kind of the basic skills that
students do have, you know, one of the things that people
have been talking about quite a bit in the area is also
looking at their attitudes and beliefs.
My training as a clinical psychologist, my
research actually focused on social anxiety,
which includes math anxiety, that's one of the reasons
I was interested in being on this committee.
So, I have students all the time come to me
very anxious about math.
In psychology, one of the courses we have is a
statistics course; they have to take that before they can take
research methods.
They have to take that before they can take their
capstone course.
So, it's kind of a three-semester sequence,
and what you see is students waiting until the very last
moment to take it, even though they're bright students.
You know, they have the capability;
they're just terrified of taking statistics.
I see this again and again.
Even our graduate students, who come in, you know,
with very good GRE scores, they, you know, really don't like
statistics, they really fear it.
So, addressing those attitudes and beliefs can be, you know,
a very important thing.
Because, these can be definitely stumbling blocks
to students' success.
They don't think they're going to do well in a course,
they're probably not going to do well in the course,
no matter how well designed the course is.
So, understanding this, I think, is really important
for all of us, as well.
Another thing to keep in mind is that we should expect that
students are going to make the most progress
in discipline specific skills.
So, there's been quite a bit of research out there now
looking at across different disciplines, where do students
improve the most in their quantitative reasoning skills.
And not surprisingly, people, students in the social sciences
improve the most in things like statistics,
where as conditional logic is shown the greatest increases
in students who are natural science and humanities majors.
Now, this is probably reciprocal interaction here.
The fact that they are probably better able to teach
these things in each discipline, and likewise students are
probably more interested in it because it's relevant to their
discipline, and also faculty are going to be using
discipline specific techniques,
discipline specific examples, etc.
So, you know, I think this is one of the biggest takeaway
messages here, is you know, if you don't know stats,
if that's not part of your discipline, you're not going
to be teaching stats obviously.
So, don't worry about trying to cram every single,
you know, quantitative reasoning skill into your course.
But what is your area of expertise?
You know, what can you bring to your classroom,
and what can you teach students?
I think, you know, really focus on that, is the goal.
Another really good program is at the University of
Massachusetts at Boston.
And what they recommend is that student self-assessment
should also be done.
So, asking students how well they've learned certain skills,
in terms of quantitative reasoning.
And also assess attitudinal change towards it, as well.
So, this is an example of one of their questions for a class.
And you can see in question 12 they ask them, as a result of
the course, my ability to do certain kinds of things is
about the same, is improved, is much improved.
You know, you can see it's a variety of different things
for different classes.
So, this particular class had things like attach documents
to email, and much more complex things such as draw conclusions
from data sets, work with formulas, etc.
Then, they also asked them very polite questions.
So, things like, do you find that you now read newspaper
or magazine articles that contain data, charts, or graphs
more critically?
And then, also how comfortable are they doing these
different things.
Because, again, we know that their comfort level, their
attitudes about this, etc., are really very important here.
And then finally, one of the important things, again this is
going back to, you know, the writing across the curriculum
idea, is that we need to require that students
write about data.
You know, we know that thinking about how to translate
numerical information into words, and especially be
able to do that without relying on a lot of jargon,
is very difficult for students.
One of the things I always do with my graduate students is,
when they're working on their thesis, I tell them you
have to have a cocktail party explanation of your thesis.
You need to be able to take in two sentences or less
without using any jargon what your thesis is on,
so when you go home, you know, your parents ask you about
your thesis, your grandparents ask you, or you go to a
cocktail party with non-professionals,
they want to know what your thesis is on, they don't want
a lot of jargon.
Tell them just two sentences.
And even my graduate students struggle to do that.
You know, they start throwing out there,
well this was correlated with this at 0.5 level.
And okay, no, no, stop.
You know, let's get rid of all that jargon.
What did you actually find, and why is it important?
And so, that's, you know, again, that's very difficult
for students.
So, it's very important that we try to measure
those kinds of things.
So, those are the things I wanted to talk about,
in terms of assessment.
I want to know turn it over to Marita, who's going to
talk about how to use this in an applied manner.
>>Marita Gronnvoll: Thank you.
[no dialogue]
Well, good morning, everybody.
We have now reached the discussion, brainstorming,
and application portion of our program.
So, one of the things that I wanted to start with is just an
acknowledgement how, what a challenge this particular
learning goal might be, particularly for those,
for across the curriculum, particularly for those in the
arts and humanities.
That we may have some reservations about how we're
going to apply this learning goal.
So, what we're going to do for the rest of our time together
is actually show some solid examples of how this can
work in very different types of learning environments.
And for most of us, particularly those of us who are in the
humanities as I am, we've often heard the narratives
from students, and I would argue from faculty alike,
that I'm just not good at math; I'm not good at math,
that's why I'm in the humanities.
I'm not good at math, right?
So, numbers are frightening to a lot of them.
And I can really sympathize with that.
My own view when I was asked to join the quantitative
group here, I really thought, are you sure you want me?
It seems like I'd do more harm than good.
I seriously work with textual based analysis in my research.
I teach classes in rhetoric and popular culture.
How can I possibly help?
But then, as we started to do a lot of this research,
I realized how often I actually do use quantitative based
evidence in my classrooms, and now have actually
thought of ways that this can be applied to assignments.
And so, there are some very specific ways
this can be applied.
So now, I have actually a conscious plan for how to
apply some of this.
But we thought today what we'd do is at least,
now for maybe the next 10, 15 minutes is get you talking
to each other a little bit and brainstorming a little bit.
So, one of the things that we'd like you to do is just take
a few minutes and answer a couple questions amongst
yourself, talking amongst yourselves.
Like, what challenges do you believe that you could face
or have faced in implementing quantitative reasoning
in your classroom, and/or, because some of you are
already experts at this, and we sure want to hear form you,
how have you already implemented quantitative reasoning
in your classroom?
Yeah.
>>Dagni Bredesen: Can I throw in another question?
Because, I am one of the remedial math people,
I mean that I hate it.
And I just had a student come up to me saying,
in a panic, saying I'd like to major in English before I
have to do math.
And evidently, she will.
So, [unclear dialogue],
can you also talk about what kind of support
faculty may need?
You know, what kind of programming
[unclear dialogue]
>>Marita Gronnvoll: Sure, and I think that's a
really fantastic question that can come out of some of these
small group breakout sessions that we want to do right now,
and also some of the specific applied examples that we
provided for you today may help to answer some of those
questions, as well.
Yeah.
>>Attendee: You know, being part of the
discussion in the group is going to depend on what
quantitative reasoning is.
And in philosophy, I hear you guys say numbers a lot,
comfort with numbers, but your examples
are saturated with logic.
And so, my department certainly thinks logic
belongs right here.
And I'm sure you do, too.
But I don't know, I guess just maybe more explicit
acknowledgement of that, that it's not just numbers, right?
I mean, you talk about comfort with numbers in context,
and things like that.
But logic isn't numbers.
It's different, but I would consider it part of your
goal here, a big part.
>>Marita Gronnvoll: Yes, yes.
And I think that's going to come out of the individual
discussions a little bit, and then some of the examples,
applied examples we provided for how numbers can be used
to make actual, real social problems come to life
a little bit.
So, they can aid in logic, but they are not logic
in and of themselves, right?
They can aid in coming to conclusions.
So, if you all wouldn't mind just talking to each other
for a few minutes, maybe groups of three or four,
just the people around you,
about these particular questions.
And then, we'll brainstorm a little and see what came out
of these groups.
[no dialogue]
So, we asked you to think for just a few minutes,
talk for just a few minutes about some of the challenges
that can come up with implementing this
learning goal.
So, I thought maybe we could just create a bulleted list
or two, and brainstorm a little bit about how we can deal with
some of these challenges.
So, what did you talk about in your groups?
>>Attendee: We talked about, in my case,
I'm teaching music literacy, which is [unclear dialogue]
>>Marita Gronnvoll: Okay, so numbers conceptually,
as opposed to computation.
[unclear dialogue]
Okay, okay.
I think that's a good one.
And that was one of the issues that we talked about
as a group, is like how this could apply particularly to
people who are in the fine arts and music, yeah, in particular.
I saw a hand.
Oh, I'm sorry, I'm sorry.
I will do that.
I'll just get my exercise running around here
a little bit.
Anyone else?
>>Attendee: Well, in the visual arts,
especially in the graphic...
In the visual arts, in the graphic design area,
they do, sometimes do problems where they have to convert
information into charts and graphs for, as such.
I mean, that's one way.
And lots of times that problem is set up to force students to
go research something, and then take that information
and make it visual.
Another problem that they oftentimes do in a
graphic design program is an annual report, as such.
>>Marita Gronnvoll: Okay, so to conceptualize this
into a bullet point; can you do that for me?
>>Attendee: No.
[laughter]
Well, I think it's going, I guess you call it
critical thinking, it's taking those elements and
putting them together.
In terms of, you know, it's a challenge.
Oh, you know, you need to write really darker, better,
because I can't.
>>Attendee: Is it synthesis?
>>Marita Gronnvoll: Knowledge is synthesis.
Okay.
>>Attendee: Yeah, visualization of data's
really what it is.
The other thing in the fine arts is many things are
what I would call recipe driven.
In ceramics, all students need to know how to make glazes.
That's all portions of this, that, and another.
And then, how long to fire it, when to turn down the kiln.
And they're not, they're math skills,
but they're not overt sort of math skills.
>>Marita Gronnvoll: Anyone else?
[unclear dialogue]
[laughter]
>>Dagni Bredesen: I can see ways to introduce
math, different kinds of numerical problems,
problem solving, in like our composition classes,
and especially our argument driven composition classes.
I can also see ways of using it in, say, digital humanities,
topics where we're going into the databases.
But I think for someone like me, who really is math anxious,
and I don't even know what questions to begin asking.
I don't know how to frame the problems numerically,
because I haven't thought in that way.
And I was thinking what Jonelle said.
I really, you know, don't know the sort of logic, the
frameworks of logic that would help me create these problems.
So, that was, and Michael and I were both noting that our
departments are, you know, there are people that are, you know,
would not even think about, I mean, being here because it
doesn't concern them.
And I don't know if Fern would agree with that.
So, there's a split at least, you know, a fairly deep rift
between people that are open to applying, you know,
you would have to use technology, as well.
So, it's the technology, it's the numbers; there's a whole...
[no sound]
>>Attendee: I think there's a siloing,
but a siloing not just between disciplines,
but within disciplines.
I'm not going to out any of my colleagues in history,
but it's very clear that there's a group of us,
and a very small group, who are willing to do this.
But the large majority of my colleagues don't,
and don't want to do this.
And we're now going through a process, which I don't
want to be cynical, but I see where a lot of people,
and we've got all these subcommittees based on classes,
and I'm getting back which learning goals they're going
to do in each of the classes.
And QR is not showing up, except where David, Newton, or I teach.
So, you know, there's this problem within the
discipline itself, this resistance of change.
And QR, I think, is the one learning goal that,
in the humanities particularly, is the one where there is a
great deal of resistance to change.
And so, one of the things I suggest to the committee is
you make a big stress in your presentation about employers.
I would suggest you also make a stress about graduate school.
Because, one of the things I try to do with my students,
in terms of my work is to say, you can learn GIS
very basically with me.
But when you apply to graduate school, you can at least say,
where a lot of people don't have MA's, I've done GIS,
and that makes me a little bit better than the other guy from,
let's say Southern or Central Michigan, or wherever.
So, we've got two problems.
We've got the siloing problem within the discipline,
and I'm not sure how to address that.
Because, it also goes to the question of assessments.
You talk about assessments by department.
I just, it gave me stomach churn.
No, seriously, I don't know how to do it.
And I don't know how to do it, I'm not trained to do it.
And I can see all kinds of pushback when the time comes
to try to tell my colleagues to accept assessment by
whatever I come up with.
And I would really want some support from the university
about how to do that assessment stuff,
because I don't feel competent.
You know, how do convince people who don't want to
do the topic to begin with that the assessment
I've come up with, which they know I know in my
heart of hearts I'm not competent to come up with,
they should accept.
I mean, it's like a recipe for disaster.
So, I think that's part of the problem we have,
and I'm not sure how to shift.
But it's got to shift somewhere in the next nine months.
Okay.
[no dialogue]
>>Attendee: I have actually two suggestions,
under the caveat that I actually teach statistics
in the Sociology Department.
And my students don't want to be statisticians;
they either want to be cops or social workers.
And so, one of the things that I try to do is bring in
the integrative learning, and show them how that
this information that they are now going to spend 16 weeks
struggling with is actually applicable to their lives
in some way, shape, or form.
And so, I bring in a lot of government reports on,
you know, uniform crime reports, and I bring in a lot of
social work types of things, because I also have my MSW.
And so, I can make those connections to the real world.
And then, for the students who don't want to either be a cop
or a social worker, I try to bring in other materials.
So, I bring in the Journal Gazette everyday and show them
how there's numbers in there, or even have gone and bought
a Men's Health magazine or two to show them all those
silly little surveys that are in there, and how you can
make sense of a pie chart.
And so, and I think we had a conversation in our group
about how these numbers are essentially everywhere,
and we wouldn't have a society structured
as it is if it didn't.
But one of the things that I think was missing from the
presentation and I think is a very important point is that
statistics is not math.
Statistics uses math, and math is about certainty;
statistics is about dealing with uncertainty.
And all the students that I encounter are afraid of
statistics because they think it's math.
But they're really uncomfortable with being uncomfortable,
which is what statistics is about.
And so, I think that there's a really big difference
that needs to be filled in our students' minds
that statistics is not math; we just happen to use it.
And you can actually get away with taking nothing more
than a square root and be fine in statistics.
But not understanding that, and sort of feeling like there's
all this pressure, and now there's all this pressure
in this learning goal to be very certainty, quantitative based
vs. what I think really needs to be filled in is that we have to
deal with this uncertainty with numbers, as well,
which is equally important.
So, thank you.
[no dialogue]
>>Marita Gronnvoll: I just want to add something
really quick.
Coming from communication studies, I'll absolutely out
my department as being split in this area because
half of the faculty, actually more than half of the faculty,
they do a lot of social science research;
they're very comfortable with using numbers and
quantitative reasoning.
Our students are not as comfortable by far.
And I think that one of the things that I've been hearing,
correct me if I'm wrong, is that there is kind of the concern
about privileging of numbers, so privileging of data.
And one of the ways that I think we need to address that is that
numbers on their own say nothing, that there has to be
the critical component of this, the critical reasoning
component of this, and I think that's one of the things that
we're trying to do with this learning goal, is to not
privilege numbers, which many of us in particularly in the
humanities may be fearful of, I know I certainly have been,
but to realize that the numbers on their own say nothing,
that there's much more to it than that.
Okay, was there any other...?
Yeah.
[no dialogue]
>>Rebecca Throneburg: Well, I guess a couple things
just within the university and within our disciplines.
I mean, Wesley mentioned that, you know, given the choice,
the majority of psychology majors will put off the
three-sequence courses.
You know, you have to take the research class before you
take the capstone, and you have to take the statistics course
before you take the research course.
Then, three semesters before the end of college is when you will
sign up for the statistics course, because you don't
want to do it any sooner than you absolutely have to.
Our math gen ed course on campus doesn't have to be
taken until graduation.
I mean, there's one point for the university of, you know,
if we're saying it's a building block, you know,
and if students who don't like math are going to put it off
until the very end when they have to, again, we've got a
chunk of people who are scared of math and numbers who
aren't taking their gen ed math class until the last year
that they're here either.
Something to consider.
I know in our discipline, we personally haven't done much
quantitative ourselves until grad school.
We have our research methods class and our statistics base
within research methods is all, it's the first semester of
graduate school.
But I mean, I would say my average undergrad person
can't read a journal article well.
They skip the numbers.
When they're in undergrad, they're going to read the
introduction, and they're going to read the discussion.
And I mean, the beginning grad student says yes,
I skip the results.
I don't want to look at the tables and figures;
I don't understand them.
So, I mean, I think, again, we'll be one of those
departments as well saying where is it going to go
more systematically in our undergraduate curriculum?
Because, okay, it's nice it's for our grad students,
but we are graduating people with a bachelor's degree
that we're not systematically trying to build these skills,
other than maybe some tests and measurement numbers at the
undergraduate, and evaluation class.
But other than that, we're going to be talking about it,
as well, and where is it going to go, and which pieces
are going to be responsible for it.
And Fern, I know again there's some overlap in
all of these goals, I know the critical thinking group,
some of their websites and resources that they have,
there are books and there are websites of a lot of these
problems that exist, that they can have right and
wrong answers, and that, you know,
make the wrong argument with numbers.
And they have to figure that out.
So, I know you guys have found some, and critical thinking
has found some.
And it's nice that, again, we don't, we could search
newspapers ourselves, but there's also lots of resources
out there that, you know, other instructors have also found
these types of things helpful and have put them together
in some key resources for us, so.
[no dialogue]
>>Marita Gronnvoll: Which is absolutely the
perfect segue for our next portion of our program,
so thank you for that.
Thank you, Wesley.
We thought what we would do, these are all such,
this is such a great list, hopefully somebody wrote it
down in our group.
We thought what we would do is just a couple of examples
of assignments that can be taught in the class,
and these are, we have this all the time in different
conventions and conferences that we go to, great ideas
for teaching, GIFTS.
So, the first one that I have here is something that I
actually found as part of the research is Dingman and
Madison, an article that I found as we were doing our research
that had this really wonderful idea.
And it's one that I plan to use in a lower division class.
So, this fall I'll be teaching argumentation and
critical thinking; it's a 2000 level class.
So, this is something where you could apply
quantitative reasoning, but there is the critical component
to it that I think we're all concerned about.
So, it's not necessarily about numbers, but it's about
how to critically reason through numbers.
So, I'm just going to show you how this could possibly work
in a classroom.
So, and this is part of your handout,
so if you have the slides handout, you'll have this.
So, as you see, for this assignment you find a
media news article that contains quantitative data in support
of its claims.
So, here it's providing quantitative evidence.
And what they will do either individually or in small groups,
they'll summarize the central claims of the article,
convert the quantitative data into meaningful charts
or graphs that bring the numbers to life for the class,
evaluate the quantitative evidence, so where does
the data come from, are the study's polls reliable,
and this last step is interactive with the class
members, they have their smart phones, their laptops,
their notebooks.
This is one time when you actually want them to have
their phones out, in this case.
And they'll research the data to research the reliability
of where this information comes from.
And then, they''ll come to a conclusion about the strengths
and weaknesses of the article.
So, one of the handouts that you received is from,
on student loan debt statistics.
It's a rather lengthy article.
This is something that I felt would be fine to use
for this workshop, but typically you would ask students to
find something much, much shorter.
The reason I chose this for this workshop is for one thing,
the topic is just right on point, the sorts of things
that students would be concerned about,
student loan debt.
We hear about it all the time,
and it's in the news constantly.
And it is also loaded with information about
where this data comes from, so it's easy for them to pull out
their laptops, pull out their smart phones and actually
trace where this information came from,
and then assess the reliability.
So, if this information is good.
So, just to show you really quickly how this could be done
in a classroom, one of the pieces of information here
if you turn to, let's see, page two, at the bottom of page two,
this is student loan debt statistics.
And I've done this sort of thing before when I've taught
a long time ago, and I have taught public speaking,
where numbers, when students just get up and start
reciting them, they just don't mean anything.
You can hear these numbers, and they are
absolutely meaningless.
But if you put them into a chart, suddenly
they make sense.
Suddenly, you have the visual component, and they make sense.
So, if you see on the bottom of page two and the top of
page three, it goes into who is borrowing, so who is actually
taking, borrowing on their student loans.
So, as you can see, there are no numbers attached here.
You could actually attach numbers, but for the purposes
of this assignment, you don't really need to.
You could actually recite these numbers, and the graph
brings the numbers to life.
So, for example, those who are borrowers under
the age of 30 are 292 billion dollars in debt,
borrowers under the age of 30.
Then, you have the borrowers in this range right here,
30 to 39, which are 307 billion dollars in debt.
40 to 49, 154 billion.
50 to 59, 106 billion.
60 plus, 43 billion.
For a total of 902 billion dollars.
So, what the graph does is translate those numbers into
something that's a little bit more meaningful to the class.
They can find exactly where they are.
And for me, the terrifying part of this is that people who are
approaching retirement age are still horribly,
horribly in debt.
So, that's one of the things that, meaningful that can
come out of this.
Also, and just to use one more example of how you can do this,
in the same article it has, in fact,
this is on the bottom of page three and the top of page four,
it talks about who struggles the most in repaying debt.
And in this case, just using a very simple chart on how,
and this becomes, again, meaningful because there's
the visual component here.
So, if you'll see, again, the numbers on their own
don't really mean much, but when you see it in a chart
it can make a difference.
So, we can see that borrowers who are in their 30s,
there's a 6% delinquency rate, which means that
more than 90 days past due, 6%.
And then, it doubles, borrowers in their 40s
have a 12% delinquency rate.
Okay, and that stays relatively high.
Borrowers in their 50s, 9.4.
Borrowers in their 60s, 9.5 delinquency rate.
So again, the point here is that the numbers coming
at the students orally don't really make any sense,
but when they are converted visually, they can help a lot.
>>Attendee: So then, you show them
these two graphs.
You then say to them, okay, write a me a little one-page
or two-paragraph response.
Tell me what the conclusions you draw from this are.
>>Marita Gronnvoll: The students create this;
I wouldn't.
So, this is part of their assignment.
I'm just showing you how it would look.
So, the students would actually create the graph.
They would take the data and convert it into something
that's visually meaningful, and then as a class they would
verify the numbers themselves.
So, where did this data come from,
and then assess it, critically assess it.
So, is this data reliable.
[unclear dialogue]
The method...
[unclear dialogue]
They present, yes.
Yes, I'm sorry.
It's argumentation and critical thinking in
communication studies, so it's an oral presentation.
So, the presentation component.
So, that would be my assessment as the instructor of the class.
Yes.
>>Attendee: Does it matter how students
create [unclear dialogue]
>>Marita Gronnvoll: Yeah, Excel creates it.
No, they can create it.
I'm sorry?
[unclear dialogue]
It doesn't matter, no.
And actually, this is one of those things where technology
is actually incredibly useful here.
The PowerPoint program can use either Excel,
it defaults to Excel, to use Excel spreadsheet to
create these, but you can also use a Word program
if you're more comfortable with that.
And in my experience, most of my students come to my class
already knowing how to do thi; they know how to do it.
And if they don't, it's actually a relatively simple workshop
you can do, probably in about 15 minutes in class
to show them how it is done.
And...
[unclear dialogue]
Yes, yes.
Yes, that's correct.
So, the creation of the graph, at least for my class,
it would not be something that I would assess them on.
It would be on how they interpret it.
Yeah, yes Fern.
I'm sorry.
Because they can't hear you.
[unclear dialogue]
>>Fern: Well, one of our goals is not
visual literacy, but maybe something we want to pick up.
And it seems, well it seems very much related to this,
because if you see people taking the same data and
presenting it in different ways, and then even reading it
in different ways.
Because, I was thinking, it's very scary to me,
these people in their 40s with all the debt.
But you know, it wasn't 100% clear to me, I thought that was
a little bigger than another one, but I wasn't 100% sure,
or you know.
So, I mean, there are, so I think that's what,
there's a visual literacy component, which I just
wanted to point out.
The other thing is I think we have to be careful about
that's not something I would assess them on.
Because, that's, well I would just say
that's part of what I feel is siloing.
Like, so I'm teaching an English class, so I don't assess them
on, you'll enjoy this, how they present their material orally,
there you go, just on the content.
Because, this isn't that kind of class.
You know, I mean, if we're going to be integrative,
we have to assess them on all of the things we expect them
to do, because it is just part of sending the message,
that's not what we actually do here.
You know, we can borrow that, we need this right now,
but it's not really part of what we do here.
[unclear dialogue]
Right, I mean, I think there's going to have to be something
attached to, did you successfully,
you know, create this kind of thing.
And there are better and worse ways of doing it.
So, I mean...
>>Marita Gronnvoll: Sure, I think what my
point was that obviously, this is going to be for
a presentation in class.
So, it needs to be persuasive, so that means that the
visual component is important.
And so, how they create it would be important.
But not, for our purposes, not whether they use Excel or Word.
Yeah, that it wouldn't matter for us whether they
use either one.
>>Wesley Allen: I was just going to add to that,
you know, like for example, the way I might use this is
to have some of the rubrics I was talking about earlier,
and having one that might be creation of this chart,
but that might be a very small portion of this.
Likewise, the calculation might be very small.
But you know, then other things might be more important,
including the speaking component.
You know, certainly the members of the speaking learning goal
committee would argue that would be important also.
So, I definitely, I like the idea of the
integrative component.
You know, and kind of the eye opening part of this
for me was the use of the rubrics, which I don't usually,
never really associated with quantitative reasoning before,
but makes a lot of sense once you start thinking about
all these different components that go into it.
>>Marita Gronnvoll: Yeah, and just to quickly
go back to one of the reasons that at least I have
used this a lot when I've taught argumentation and
critical thinking is because students will often come to my
class with that privileging of numbers.
Or they may see, they want stats as they always say,
I want stats to back something up.
And in this case, well okay, fine.
Here are your statistics, but are these reliable statistics?
That you can't just stop at presenting a statistic,
that there needs to be the critical thinking component.
So, I think this is very nice, it's nice and integrative.
I like this assignment a lot.
I'll try it this fall and see how it works, but...
Okay, I'm sorry.
Did you have a...
Wait, wait, wait.
[laughter]
>>Attendee: I was just curious,
since there's representatives from Faculty Development here,
whether or not there would be any workshops or anything
on these kinds of things for people across the faculty,
across the campus, who are interested in integrating this.
I don't even know if you've thought about it
yet or not, but...
>>Dagni Bredesen: [unclear dialogue]
I don't think about it because I'm not good at it.
I don't, and I mean, this is my question.
Where do we go from here?
I could see that people in the math departments who are
experts, if they're anything like my high school teachers,
they teach to the people who can already do it.
And what is there for remediation, for students
who come in who are math phobic?
You know, where do they go, who do we refer them to
if they come into our classes?
So, that would be something I would like personally
more information on.
And then, I mean, it seems to me this has to be at least
two-tiered, because there are those faculty, like myself,
who really, when I said I don't know how to frame
the questions, I don't know, where do we start from.
Like, in terms of shifting our mindsets.
So, it seems to me that there are people like me who need
something even more basic than, you know, getting your students
to use charts or something like that, right?
Like how do you start thinking in a mathematical way
about your discipline.
So I think, I don't, but I don't even know how you would
structure a class like that.
And then, the other thing is for people that are the experts,
how do you make, how do you work math concepts into your
classes in a way that is really responding to universal design
and working with people at all levels.
But I would welcome thoughts on this, and I need help in
formulating programming that would meet people in this way.
[unclear dialogue]
>>Attendee: I think we were talking about
scaffolding in our courses, and I think we should really
consider that across the curriculum.
So, to hear that math, a general math class doesn't have to be
taken until you graduate, I mean then it's certainly too late
in some cases.
So, I would say we have to critically think about that,
and that would be something a freshman should be exposed to.
And I also think that sometimes, there are some kinds of
misconceptions and miscommunications.
Because, when I talk with people that say, well I don't really
know how to use quantitative reasoning, your idea about
common sense is probably pretty much what we mean with
quantitative reasoning.
So...
[unclear dialogue]
[laughter]
And I think there we have to come all together from
different disciplines, and see what do we expect to be
common sense of our students, what part of that is
quantitative reasoning, and how do we integrate that across a
curriculum while they're going through their EIU experience.
>>Wesley Allen: And I want to address the
math phobia part, because that's something I certainly have
a big interest in.
Obviously, one of the resources we have one campus is the
Counseling Center, and then also the Department of Psychology
has just developed a new psychological services clinic.
And we're actually going to be working together,
and that's one of the things I want to talk about the
Counseling Center, is how do we address this, you know,
along with things like public speaking anxiety,
which a lot of our students have, as well.
I know they do a lot of really good service
already about that.
And that's something to talk to them more specifically about,
you know, in conjunction with these learning goal committees.
Because, it is a really serious problem.
And you know, as you know, a lot of students come in,
and they seriously do, you know, it's to the point where they're
not taking tests, they're, you know, not doing their
presentations if they have that public speaking anxiety, etc.
So, that's something that can be pretty easily addressed,
and people can come into classrooms and
talk about that, etc.
[unclear dialogue]
They are absolutely, yeah.
>>Dagni Bredesen: I mean, and that seems,
the administration...
[laughter]
To, you know, I mean, yeah.
It does seem to me that we need an integrative and holistic
[unclear dialogue]
>>Attendee: Let's get a task force.
>>Fern Kory: Well, I mean all these things
just seem to be connected.
I mean, I can certainly see a Faculty Development workshop
for, I mean, we're going to have to address this for faculty
and for students before it can get to students, right.
I mean, the first thing we have to put in place is a curriculum
before we assess whether they're doing this better
all of a sudden, for no particular reason.
You know, we have to get things in place,
we have to get people on board.
I do think administratively that the sequencing is a big issue.
My colleague Paul Kory teaches math, and so he teaches
sometimes the business calculus course, which is required
of business majors.
I don't know if all, or certain flavors.
But they all take it last because, probably it's
required for accreditation purposes, but none of the
course is integrated.
So, they all take it last.
So, they take it when it's the longest time since they've
taken any math.
And he says you'd be astounded how many people can
aim at C minus and hit it.
[laughter]
But not everybody hits it.
So, you know, I mean really thinking about this
structurally in that way.
And you know, if now that, I mean this is an opportunity,
right, I mean now that quantitative reasoning is
officially something we're doing, maybe it's not okay
for people to put off that class, right.
You know, we say first year writing is a prerequisite
to all English classes or, you know, people don't
take that later.
So, you know, we really do have to think about those
sorts of things, that we are doing what we said
we were going to do.
[no dialogue]
>>Jeffrey Stowell: I mean, I realize this is
kind of counter to what we're trying to do, but we have to
realize that there are experts in quantitative reasoning.
And we're not a jack of all trades.
And some of us are going to be able to do it
better than others.
What we might be able to do is sprinkle into the curriculum
enough courses that require this that our students still get
what they need, without having to require it in every course.
[unclear dialogue]
>>Dagni Bredesen: This might be a private
conversation with Fern that I need to have.
But the thing is that I think a lot of our courses,
Michael brought this up in our small group,
could have a methods of inquiry, right.
Like we have methods of teaching and research methods.
But, and our research methods certainly, in our course,
and I don't know about history, but some of these other courses
this could be a place that we could bring these kinds of
components in.
But anyway.
[unclear dialogue]
[laughter]
>>Attendee: The question I think, though,
I agree with you, Jeff, It can't be in every course.
It shouldn't be in every course.
The problem is do departments stand back and look
at their full curriculum, and say, if there's choices
students have.
And if you're math phobic, or you see the term QR,
students being who they are will find a way to navigate.
And is there a way we can ensure, and I hate to say trap,
but we ensure that there's, the doors are there,
that they can't get out, they can't pull back
out of the cul-de-sac.
And if you, if as a department,
we are ourselves resistant to it and saying we don't want to
have it in my class, then that becomes problematic about
how many doors, you know, the doors that are open to students
to avoid the problem.
Their term, not mine.
Avoid the skill set.
And that's really, I mean, I know in my department
that's true, and I suspect in other departments,
maybe in English, it could also be true.
And that's where the conversation becomes hard.
[no dialogue]
>>Attendee: But I think part of that,
I in no way disagree, because it seems to assume that students
have certain attitude to avoid math or avoid
quantitative reasoning.
So, I think certainly, one thing that should be part of
conversation is how we're going to help students buy into
this whole idea.
So, instead of forcing them into certain courses that
deal with this quantitative reasoning, or making sure that
all courses have some component of quantitative reasoning,
if students are ready to buy into this I think our life is
going to be a lot easier.
So, somehow I think we have to figure out how to do that.
[laughter]
>>Dagni Bredesen: Well, I can see with journalism,
like that is such an obvious place, right?
We can start with the DEN and...
[laughs]
And you know, numbers are sexy in reporting;
they're necessary.
But I think there are other fields that it's
really a challenge.
But I think your'e right; you have to lure them in.
I just, this cartoon from Peanuts that's come to me,
Charlie Brown's hitting his head and saying
"I'm doing new math with the old math mind."
And those of you who came in under new math, you know,
I mean that happened in a transition.
I think that's where I fell off.
And if I could give a testimonial here, I mean,
when I went to take my GRE, they had the
quantitative reasoning session.
And this guy presented the math concepts in such a way
that was so fun.
And I'm going, this is great, I can do it.
And the next day, he came back, and he said people are
complaining that this is too remedial.
And so, they started back up at the level that I was not.
So, I think, you know, like how, I'm just sure there's a lot of
faculty and students like me, who are not avoiding it
on purpose, but because they never, it was not
presented to them in a way that is fun,
fun with numbers, you know.
>>Attendee: I have plenty of students
who are just, I'm here because I don't like math.
>>Marita Gronnvoll: Right, right.
Just in the interest of time, I'm going to show you
one last assignment.
And this one is also in your pack, but just so you can
have some idea how this can be applied again.
This is, and I'm sorry, that's sliding right off
the top there of the slide.
But what this assignment, I would say work quite well
in an upper division course, this is one that
I intend to use on teaching next spring, I'm teaching a
course that's communication in class in American society.
So, and again, I do textual analysis.
I don't do statistics, I don't do a
lot of quantitative reasoning in the course.
But this is something I could imagine working quite well.
So, the title of the assignment is by, On the Margins:
Perspectives on Power, Opression, and Liberation,
and this one came from Wolfe.
So, and again, this is in your handout.
But what the purpose of the assignment, as you can see,
is to illustrate how students can use quantitative reasoning
to gain insight into the difficulties presented by
living in poverty.
So, students are given information about the family
composition and income for a hypothetical poverty
level family of four living in Coles County, Illinois.
So, and the information, you can get that information from
a lot of different places, both state and federal,
what poverty level is.
There are a lot of different organizations that keep track
of that information.
And then, from there students would determine where this
family could live, work, shop, and travel from place to place.
Students develop a monthly budget for the family,
including food, housing, transportation, utilities,
and medical expenses.
And the whole idea here is that it puts kind of on the ground
the difficulties of living in poverty,
the difficulties of class.
Which, again, for my course, it's not a course in sociology,
so we aren't focusing on a lot of data.
But this is something that makes poverty real
for a lot of the students.
They'll understand how difficult it would be for a family
who is living in it.
And then, also that it's local, I think is really cool, too,
that it's Coles County, Illinois.
Okay.
[unclear dialogue]
Well this, yeah, this would be an assignment that I could
conceptualize this being a group assignment or something that
we would, like different parts of different groups would
work on different components here, about where they would
shop, where they would travel, where they could possibly work
with this amount of income.
And this I think would be a very nice oral presentation.
That's something that we obviously focus on
quite heavily in communication studies,
so I could see this being a nice, an oral presentation
for different groups.
But I see this as being an entire class assignment,
something that we work together on, so.
[unclear dialogue]
I, well for this, I might not bring writing in for this;
I might bring in the presentation component.
[unclear dialogue]
Yes, for this I would use this as a presentation.
[unclear dialogue]
Writing, yeah, yeah.
That's definitely something that I think is,
I think one of the things that we've already talked about,
is translating data into writing.
That's another challenge that obviously we would face,
which our English people no doubt will help us with.
So, yes.
Okay, well we have just a few more minutes if anyone has
any other questions or concerns.
Otherwise, this is us, and this is our email addresses
for questions or concerns that could come up later.
I'm sure that Wes would be happy to answer them.
[laughs]
Alright, well thank you all for coming.
We appreciate it.
[applause]
[no dialogue]