WEBVTT

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My name is Liz City.

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I am the executive
director of the Doctor

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of Education Leadership Program
at the Harvard Graduate School

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of Education, where I am
also a lecturer on education.

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We find it's very helpful
for teachers to use some sort

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of process or cycle
when looking at data.

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Now, which one they
use isn't as important

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as that it essentially
has the elements

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of a problem-solving cycle.

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No matter what name it's under,

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all of the cycles share
some characteristics.

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So they are essentially
problem-solving

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inquiry-based cycles.

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Usually, at the beginning,
you are getting organized.

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You are preparing.

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So you are getting organized
for collaborative work

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because it's essentially
a collaborative practice.

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You are deciding what data
you are going to look at.

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You are making sure that you
understand how to look at data.

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So that is the prepare part.

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Then you move to inquire.

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Inquire is where
you start figuring

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out what questions do we want
to ask, what data would we look

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at to answer those questions.

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And usually, and this is kind
of frustrating, using data leads

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to more questions than answers.

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So you start looking at data,
you generate more questions.

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Then you look at some more data.

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Once you've decided what's the
learner problem that we want

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to focus on, you start trying
to figure out solutions.

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So that gets to the act part
of any data inquiry cycle.

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Figure out, what are we going
to do about this problem?

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What are our solutions?

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Write down the plan.

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Figure out, how are we going
to know if we are successful?

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And then you are essentially
doing an iterative process,

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so you're going to
come around the cycle

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as many times as you can.

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So you prepare, you inquire,
you act, and you come right back

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to inquire-probably don't
need to prepare again.

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And you say, now what have we
learned, are we making progress

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or not, let's inquire again,
figure out what to do next,

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see if it's working,
and come around again.

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One example that I often
like to share with people

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about teachers really using data
to improve their instruction is

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from the Mason School,
in Boston.

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It's an urban public
school, K-8 school,

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and the teachers there decided,

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"We need to really
start using our own data

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in our own classrooms to
drive our instruction."

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So they started with their
standardized test data

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and they said, "What

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over several years is our
data telling us is a problem?"

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And they said, "Well, reading.

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We seem to be making
some strides in math,

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but in reading, we
are very flat.

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We are better than most of the
other schools in the district

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in terms of our performance,
but it's relatively flat.

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Why is that?"

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And then they looked at
their interim assessments

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and they said, "Well, it looks
like students aren't able

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to express their
understanding about their books

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in writing very well in
our interim assessments."

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Then they said, "Well, where
do we do writing about reading

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in our daily curriculum."

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They said, "Ah, we
use reading journals.

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We have students write letters
to us about their reading."

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I did this when I was
a teacher as well.

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So every week, students are
writing a letter to the teacher

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about their book
they are reading,

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and the teacher writes
back-lots of data there.

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They said, "Let's sit down
with some of those notebooks

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and see what we can see."

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So this was a third-, fourth-,
fifth-grade teacher team.

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They each brought
three notebooks:

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one high, one medium, one low.

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They spread them out on
the table, and they looked

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at the notebooks, and
they saw huge variation.

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They also saw that
third-, fourth-,

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and fifth-grade notebooks all
sounded pretty much the same.

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They said, "We are spending
tons of time on this,

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and there is no improvement
really.

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What are we doing?"

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They said, "Well, we
don't think we are clear

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about what a great
letter looks like,

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and we don't think we have very
explicit standards for students

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about what that letter would
look like that they can use

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to improve their own work.

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Let's craft a common
rubric, and let's decide,

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what would we teach about
this writing letters

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for comprehension in third
grade, what would be different

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about fourth grade, and
what would be different

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about fifth grade?"

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So they made a plan.

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They came back together a month
later with their notebooks

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and they said, "Let's look
at the notebooks now."

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And they saw a lot
of improvement

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in the student writing.

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Well, one of the teachers was
very into data, total data geek.

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She had been leading
this effort.

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Her colleagues were okay,
but a little reluctant.

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When they saw how much
student writing had improved

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and how much better
students were able to talk

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about their reading
comprehension,

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everybody got excited.

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And success breeds success.

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So then they took that same
process right over to math,

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and they started
having a conversation

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about what was going on in math.

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And they did all of
this in a month or two,

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and then they came
back six months later,

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looked at those reading
journals again,

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decided how to move
them to the next level.

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So now, over time, they have
two or three years worth

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of data showing how student
notebooks are improving

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as they go through the grades,

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all from a very small
conversation

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that was grounded in data.

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Often when I work with educators
about using data, they say,

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"We like the notion
of using data,

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but we are drowning in data.

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We don't really know how to
be efficient and effective

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and really use data to
drive our instruction.

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We keep getting bogged down."

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So I usually give
them three tips.

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The first one is, start small.

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Think big, think about what
you are trying to do overall,

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but choose something very,
very small to work on.

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Often we are faced with so many
problems, so many challenges,

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we feel we need to bite them
all off at the same time.

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But we don't get the
problem small enough

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that we could actually
see progress on it.

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When you get it small,
you can see some progress,

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which then motivates people
to keep working at it.

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It gives the students
and the teachers feedback

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that what they are doing
makes a difference,

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which is really important.

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So that's the first
one: start small.

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Repetition really helps.

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So the faster and tighter
the improvement cycle,

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the faster the improvement
acceleration.

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So if you are going to take
a year to decide what to do,

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you are not going to
make as much progress

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than if you took a month
to decide what to do;

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you used a bunch
of data sources,

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but you took a month
to decide what to do.

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You tried it and then you
came back a few weeks later

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to see whether it was working.

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You will get through
the cycle five times

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in a year instead of one time.

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The third tip that I give
people is to be audacious.

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John Dewey has a great quote
that "every great advance comes

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from an audacity of
the imagination."

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And I think one of
the challenges

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with using data is it's
inherently backward looking

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because you are problem
solving and so you are reacting

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to something that's
already happened.

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Now, people who are making great
improvement strides are also

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using data prospectively to try

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to anticipate what
students will need.

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But you need to keep
your vision in mind

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and you need it to be bold.

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Often when I am working with
educators on using data,

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I will say, "Rate the
audacity of this plan for me

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on the scale of one to five."

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And they'll look at it and they
say, "Well, it's kind of a two.

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We kind of did it incremental;
we tried a little bit."

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And you need to take
those little steps

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but you need it embedded in a
big vision of what's possible

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for students as learners
and also what's possible

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for teachers

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as practitioners-what
are we capable of?

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So I encourage you to be
audacious as you are using data

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and not use it to
all be reactive

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to what's already happened.

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So go out there, think small,

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get through that
improvement cycle a number

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of times, and be audacious.