►
Description
Date: 01/08/21
Presenter: Amanda Herring
Institution: Baylor University
Title: "Navigating Undergraduate Research Projects Online"
http://sbdh-prod.ideas.gatech.edu/resources/newsblog/education-and-workforce-working-group
B
Yeah,
thank
you
renata,
and
the
group
for
organizing
these
this
working
group.
I
know
that
when
I've
been
scheduled
has
allowed
me
to
jump
in
on
these.
I
always
pick
up
a
few
nuggets
that
are
really
interesting
and
I
feel
like
would
be
well
applied
to
our
program
and
what
we're
doing,
and
so
I
wanted
to
really
try
to
focus
on.
I
know
everyone's
needs
are
slightly
different,
depending
on
your
educational
audience
and
sort
of
the
backgrounds
and
levels
of
of
those
students.
B
I'm
the
pi
of
harnessing
the
data
revolution,
data
science,
core
award,
and
I
want
to
just
first
briefly
talk
about
how
that
project
is
organized
and
then
spend
some
time
talking
about
our
our
fully
online
five-week
summer
program
and
how
we
navigated
that
in
2020,
when
it
was
all
on
the
online
and
everyone
in
various
states
and
places
were
in
lockdown,
and
I
think
that
at
the
end
of
the
day,
we
were
able
to
achieve
a
lot
of
our
objectives
through
that
program.
In
spite
of
the
fact
that
we
were
all
in
different
locations.
B
Let's
see
here
we
go.
Here's
our
here's,
everyone
who's
involved
in
our
program
so
like
I
had
on
the
first
slide,
I'm
from
baylor
university,
an
associate
professor
professor
on
statistics,
and
we
have
several
other
co-pis
as
well,
who
are
both
at
colorado,
school
of
mines,
which
is
the
csm
and
at
baylor
university.
So
we
have
a
mix
of
statisticians.
B
Our
project
is,
is
titled
mo
water,
which
stands
for
modernizing
water
and
wastewater
treatment
through
data
science,
education
and
research,
and
I've
really
benefited
from
hearing
just
the
wide
variety
and
diversity
of
focuses
about
foci.
I
should
say
that
all
these
different
projects
have,
and
so
our
focus
is
on
water
and
wastewater
treatment.
Research
and
the
reason
that
we
targeted
this
particular
area
is
that
there
is
such
a
need
for
water,
specifically
getting
it
to
the
places
where
it's
needed
and
in
the
quality
that
it's
needed.
So
you
can
see
in
this
graphic.
B
These
are
just
the
number
of
months
that
out
of
the
year
that
a
region,
experiences,
water
scarcity
and
so
you'll
notice
that
you
know
florida
is
highlighted
and
parts
of
georgia
are
highlighted.
So
it's
not
just
the
arid
western
us.
It's
not
just
the
desert
in
egypt.
There's
lots
of
places
in
the
world
that
you
might
not
necessarily
think
are
short
of
water
at
some
point
during
the
year,
and
so
one
of
the
solutions
to
this
that
we
started
out
with
is
that,
instead
of
having
these
large
centralized
facilities
that
that
treat
water?
B
One
of
the
issues
with
these
decentralized
treatments
is
that
the
quality
of
water
that
comes
into
the
facility
is
much
more
variable
because
it's
not
as
highly
mixed
as
as
you
would
have
in
a
centralized
facility.
So
you
need
to
be
able
to
monitor
online
constantly
to
mitigate
risk
to
to
the
system
itself,
because
the
system
can
go
down
if
it's
not
monitored
carefully
and,
of
course,
to
human
health
and
environmental
health,
and
we
want
to
use
data
to
do
all
of
this
in
a
more
efficient
way.
B
What
we
found
is
that
the
water
and
wastewater
treatment
industries
have
tons
of
data
just
massive
amounts
of
data
that
they
just
don't
use
because
they
don't
know
what
to
do
with
it.
So
these
seem
like
they
would
be
able
to
provide
us
with
a
lot
of
tractable
projects
for
undergraduate
students
to
work
on.
B
So
the
overall
purpose
of
our
program
is
to
get
students
interested
early
on
in
their
undergraduate
career
in
careers
in
data
science,
and
so
what
we
felt
like
in
our
programs
at
colorado,
school
of
mines
in
baylor,
is
that
we're
kind
of
missing
some
of
these
lower
rungs
in
the
latter.
That
would
help
you
reach
a
career
in
data
science.
So,
for
example,
in
statistics,
you'll
start
out
with
a
probability
statistics
class
and
those
tend
to
be
kind
of
boring.
B
So
we
wanted
classes
that
would
inspire
students
to
proceed
their
make
their
way
up
the
ladder
and
take
some
of
these
more
advanced
courses.
So
we
have
first,
a
class,
that's
designed
to
fill
that
gap
on
the
ladder.
So
it's
prerequisite
free.
It's
a
sophomore
level
class
and
we
just
take
anyone
who
wants
to
come
to
teach
them
about
data
science.
B
Everything
has
real
data
sets
and
real
problems
that
we
introduce
all
the
concepts
of
data,
not
maybe
not
all
the
concepts
of
data
science,
but
a
lot
of
the
concepts
of
data
science
through
real
data
sets
and
the
second
component
of
our
project
beyond
the
class
is
a
data
science
fellows
program
and
that's
what
I'll
focus
the
rest
of
the
talk
on.
It's
a
five-week
paid
summer
research
program
and
we
solicit
projects
from
stakeholders.
B
B
So
one
of
the
first
challenges
that
we
face
is
is
soliciting
projects
from
stakeholders,
and
so,
when
we
scope
out
these
problems,
we
want
them
to
be
tractable
for
beginner
data
scientists.
I
mean
it
can't
be
really
really
complicated,
but
they
also
need
to
be
important
and
relevant
and
we
have
to
get
both
the
data
from
the
stakeholder
and
we
have
to
have
some
sort
of
time
committed
commitment
because
it
takes
some
time
to
meet
with
the
stakeholders,
identify
them,
email
them
meet
with
them
and
then
talk
to
them
about.
B
You
know
how
much
interaction
we
would
hope
to
have
from
them
over
the
course
of
the
summer,
and
when
we
ask
for
data
from
these
stakeholders,
some
of
the
things
that
we
that
we
talk
to
them
about
are
things
like.
We
often
will
get
data
in
excel
spreadsheets,
and
so
what
asks
them
to
save
these
in
into
csv
format
and
to
not
do
a
ton
of
formatted
formatting
in
these
excel
spreadsheets.
B
You
know
we
don't
want
one
sheet
talking
to
another
sheet,
really
complicated
formulas,
and-
and
so
that's
one
of
the
things
that
we
ask
for
we
ask
if
they
avoid
averaging
or
interpolating
data,
and
we
just
really
want
the
raw
observations,
and
then
we
decide
to
average
or
interpolate.
That's
something
that
we'll
have
the
students.
Do
we
often
get
two
different
types
of
data
process,
data
which
is
data
monitored
in
an
online
setting
from
the
facility
and
then
lab
data
where
they
collect,
grab
samples
of
water
and
take
it
back
to
the
lab?
B
B
Another
thing
that
we
felt
was
going
to
be
really
important
important
in
an
online
setting
is
how
to
find
teams.
So
before
the
students
started
the
five-week
program,
we
administered
a
survey
to
them.
With
these
10
questions,
we
had
some
other
questions
as
well,
but
these
are
the
10
questions
that
we
used
to
form
teams
and
so
each
student
ranked
themselves
on
a
scale
of
1
to
10,
with
10
being
I'm
really
strong.
B
B
B
At
least
one
who
write
themselves
highly
in
one
of
these
questions
and
one
who
write
themselves
here
and
I
think
that
what
we
found
is
that
we
avoided,
for
example,
getting
two
really
really
strong
programmers
in
the
same
group,
because
every
group
was
having
to
do
programming
and
we
wanted
to
make
sure
that
each
group
had
someone
who
was
very
good
at
programming
facilitating
online
interactions.
So
what
are
all
the
the
sort
of
software
and
online
programs
that
we
use
to
organize?
B
We
had
different
channels
for
the
six
different
projects,
and
so
we
assigned
students
to
their
channel
and
then,
of
course,
the
faculty
and
graduate
student
tas
who
are
also
involved,
could
also
participate
in
each
of
these
channels.
But
it
was
where
we
would
instead
of
emailing.
You
know
it's
just
tough,
to
email
everything
and
expect
students
to
see
everything
in
an
email.
But
here
it
was
like
everything
corresponding
to
this
five
week.
Summer
program
was
all
posted
in
one
central
location.
I
thought
that
was
really
a
nice
thing.
B
Stakeholders,
one
of
the
things
that
we
were
really
concerned
about
in
having
an
online
program
is
how
do
we
facilitate
team
building?
And
I
don't
know
about
you,
but
in
transitioning
a
lot
of
teaching
online.
That
was
one
of
the
things
that
I
was
really
concerned
about,
and
so
you
know,
if
you
just
google
icebreaker
questions.
B
You
know
that
the
zoom
interactions
can
be
stilted
and
a
little
bit
awkward,
and
so
this
allows
you
to
spend
a
little
bit
of
time,
making
sure
you're
able
to
talk
with
each
student
over
the
course
of
the
semester
and
call
them
out
and
also
have
an
informal
mentoring.
You
know
one
of
the
things
that
I
do
when
I'm
teaching
in
person
is,
I
go
to
class
early,
walk
around
the
room
and
talk
to
students,
and
this
activity
takes
the
place
of
that
a
bit.
B
We
had
very
clearly
communicated
scheduling
so
every
week
we
would
make
a
schedule
of
what
we
would
we're
doing
each
week,
what
were
the
overall
goals
and
then
within
each
day
exactly
what
was
expected.
So
if
we
were
going
to
do
a
training
session
to
teach
students,
for
example,
how
to
use
lasso
or
how
to
use
dancova,
then
we
would
build
that
into
the
schedule,
and
this
is
a
schedule
for
a
couple
of
days
near
the
end
of
the
end
of
the
program.
B
And
so
you
see,
we
have
a
weekly,
a
weekly
kickoff
on
the
first
day
and
then,
each
day
after
that,
we
have
a
big
group
meeting.
Everyone
gets
together
each
team
sort
of
has
a
report
out
on
what
they
plan
to
do.
For
that
day,
it
helps
everybody,
get
oriented,
say
hello,
we're
all
awake,
we're
all
on
board
and
and
helps
kick
off
the
day.
B
Maintaining
stakeholder
engagement
was
one
of
the
things
that
I
was
a
little
worried
about,
and
so
what
we
did
was
at
the
end
of
each
week
we
had
a
youtube
presentation
for
each
team
and
the
stakeholders
are
welcome
to
join
that.
But,
of
course,
these
are
busy
professional
people
there
they
may
not
have
you
know
that
specific
time
locked
out
on
friday
afternoon
where
they
can
attend.
B
So
when
we
put
the
we
recorded
the
presentations
and
post
them
on
youtube
that
actually
post
a
link
directly
to
where
their
specific
project
would
start
in
the
say
two
hour,
usually
about
a
two
hour,
long
video,
but
they
don't
want
to
have
to
search
through
that.
So,
there's
a
way
to
find
the
specific
location
in
the
youtube
presentation
that
you
can
link
to
where,
if
you
click
that
link
it
goes,
and
it
starts
at
that
minute.
B
Second
time
stamp
in
the
middle
of
the
program,
we
also
planned
a
one-hour
meeting
between
the
stakeholder
and
each
individual
team,
and
that
was
really
great.
The
students
loved
doing
that.
They
they
wanted,
wanted
to
do
another
another
stakeholder
meeting,
but
we
were
being
a
little
conservative,
at
least
this
first
time
through
with
stakeholder
time
and
but
at
the
conclusion
of
the
program.
The
stakeholders
themselves
said
that
they
also
would
like
to
have
more
interaction
with
the
student
teams,
and
so
in
the
future.
B
B
If
they
have
internships
that
are
upcoming
for
upcoming
summers,
will
we
can
advertise
those
to
all
the
students
so
part
of
what
they
get
out
of
the
interaction
with
our
program?
Is
the
access
to
students
that
they
might
be
able
to
use
as
internship
employees
or
full-time
permanent
employees
and
finally,
the
recommendation
letter?
B
One
of
the
things
that
we
did
at
the
very
end
of
the
program
was
ask
each
student
to
write
their
own
letter
of
recommendation
and
because
one
of
the
things
we
want
to
do
is
is
see
these
students
go
on
to
do
internships
or
other
research
programs
in
in
the
future
or
apply
to
graduate
school,
and
so
by
doing
this,
it
does
a
couple
of
things
one
it.
It
forces
the
students
to
think
about
how
to
communicate
professionally
and
effectively.
B
We
can
look
back
at
these
letters
and
see
where's.
Were
the
students
accurate
in
their
own
assessments
of
themselves
in
terms
of
their
contributions
to
the
project
it
helps
the
students
really
think
critically
about
what
did
I
contribute
to
my
team
and
to
spell
that
out
and
so
then,
ultimately,
it
lets
us
very
quickly
and
efficiently
respond
to
requests
for
letters
of
recommendations.
B
Of
course
it's
not
required,
but
we
strongly
encourage
it,
especially
if
they're
going
to
go
on
to
do
other
things
and
five
of
those
students
have
already
requested
letters
of
recommendation
and
instead
of
I'm
a
slow
writer
instead
of
taking
two
hours.
It
only
takes
about
one
hour
now
for
me
to
put
a
letter
of
recommendation,
because
I've
got
some
template
to
start
from.
B
I'm
just
going
to
very
briefly
talk
a
little
bit
about
our
feedback
from
the
program.
These.
We
ask
these
same
10
questions
to
the
students
at
the
end
of
the
program,
and
these
ones
that
are
highlighted
are
those
that
have
significant
p
values
right.
So
that
means
there's
been
some
significant
change
over
the
course
of
the
program.
So
we
saw
that
there
was
a
significant
increase
in
their
comfort
and
experience
level
in
programming
with
r
and
presenting
and
speaking
with
each
others
and
in
terms
of
their
creativity.
B
One
of
the
interesting
things
that
we
saw
was
how
it
was
when
we
asked
them
to
rate
what
they
believe
they
know
about
statistical
methods,
there's
actually
a
negative
change,
and
so
our
evaluator
said
that
this
tends
to
happen
when
students
overestimate
how
much
they
know,
and
then
they
get
into
a
program
and
they
learn
a
lot
and
they
realize
I
didn't
know
as
much
as
I
think
that
I
did
that.
There's
a
whole
big
world
out
there
of
methods
and
data
science
and
computer
science
that
I've
just
not
been
exposed
to
yet.
B
B
I
like
this
one
it
felt
to
me
like
we
were
working
in
the
real
world,
where
I
imagine
you're,
always
working
with
other
people
in
different
locations
and
having
video
chats.
That's
exactly
the
experience
that
we
wanted
to
create,
and
so
I
think
that
the
the
method
that
we
used
to
build
teams
was
effective
and
and
that
we
got
students
in
groups
that
worked
well
together.
B
I
think
having
those
teammates
was
really
helpful
overall
in
building
community
in
the
overall
program,
so
I'm
gonna,
I'm
gonna
end
it
there.
I
think,
thanks
for
your
attention,
I'm
happy
to
take
questions
or,
if
you
have
other
icebreakers
put
them
in
the
chat
that
would
be
great
to
see
as
well,
because
those.
A
This
has
been
very
helpful.
I
think
that
there
are
going
to
be
questions.
We
might
be
able
to
look
at
one
today,
but
the
structure
of
the
program
is
very
important.
Everybody
is
thinking
about
how
to
do
this
with
students,
and
so,
if
there
are
other
people
that
have
done
ice
breakers
or
even
I
wrote
down
managing
groups
what
software
you
use,
you
know
how
you
evaluate
the
teams
are
really
good.
So
there's
one
question
in
the
chat
for
you,
you
know:
do
you
introduce
students
to
our
programming
and.
B
B
So
it's
a
one
week,
self-guided
tour
of
our
that
we
made
available
to
those
students
who
had
not
had
the
introduction
of
data
science
class,
and
so
we
also
made
sure
to
to
pair
those
students
who
hadn't
taken
the
class
and
who
are
less
familiar
with
r
in
a
group
with
someone
else
who
one
was
very
good
at
our
programming
and
also
sort
of
a
patient
explainer.
Not
everyone
is
a
patient
explainer,
and
so
we
wanted
that's.
B
We
made
sure
that
those
students
were
in
a
group
where
they
would
be
a
little
bit
more
nurtured
and
did
any
of
the
groups
investigate
the
same
research
question
the
answer.
There
is
no.
We
had
six,
we
had
six
teams
and
we
had
six
unique
research
projects
and
it's
certainly
we
could.
Some
of
the
data
sets
were
really
large.
So
we
could
have
had
multiple
teams
working
on
the
same
data
set
to
investigate
different
questions,
but
this
year
we
just
we
just
had
six
different
projects.
A
Thank
you
thank
you
and,
if
so,
we're
to
switch
to
adam,
thank
you
mandy
and
as
they
switch.
If
people
have
additional
questions,
please
put
them
in
the
ether
pad
under
those
notes.
Mandy.
If
you
you
are
in
there,
you
can
respond
to
people
or
if
you
have
additional
suggestions
like
she
said
without
icebreakers
or
any
of
these
other
things
you
can
put
them
in
there
as
well.