WEBVTT

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I am Laura Hamilton.

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I am a senior behavioral
scientist

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at the RAND Corporation
in Pittsburgh,

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and I am the panel chair
of the Practice Guide

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on Using Student
Achievement Data

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to Inform Instructional
Decision Making.

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The Practice Guide includes
five recommendations

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that are intended to form a
framework that examines data use

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at all levels of the system.

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And so some

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of the recommendations are
geared toward district staff

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who need to make decisions about
what kind of technology to put

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in place; what kinds of
assessments to purchase;

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what professional development to
offer, down to the school level

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where school leaders
need to think

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about how they might create and
foster that data-driven culture

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in the school; what kinds

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of scheduling changes
they might want to make

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to facilitate the collaboration
that's necessary for teachers

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to work together to review data;

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what kinds of changes they might
want to make to their curriculum

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to support student data
use within the classroom,

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and then down to the
classroom level, where teachers

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and students will be
working together to look

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at both aggregate and
individual student data

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and make decisions
about instruction.

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Our first recommendation
focuses on making data part

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of an ongoing cycle of
instructional improvement.

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The first step in that is to
really put in place the data

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that will support
good decision making.

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The decisions that can be made
on the basis of data are only

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as good as the data
that are provided.

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So district, schools,
teachers need to think

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about what are the assessment
systems that they might want

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to have in place to
provide information,

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what are the other sources
of data that teachers use,

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things that they might collect
from their own assessments

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or even from their oral
questioning of students.

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So all of that counts as data,

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and that's all part
of the first step.

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So this first step in
the cycle is to collect

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and prepare a variety of data

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to support instructional
decision making.

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The second step is to
review the data and to come

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up with some hypotheses

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about what might be the next
most appropriate instructional

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steps to address what you find
when you examine the data.

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And then the third
step in the cycle is

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to test those hypotheses

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by implementing those
instructional improvements.

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And as a result of that,

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it's likely that more
data will be collected

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and further hypotheses
will be developed,

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and so that's why we
think about it as a cycle.

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Students are really partners
in their own education,

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and teaching students to
use data-to examine it

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and develop their own learning
goals from it-can be one way

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to foster that partnership
and help motivate students,

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make them really feel
like they are part

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of the educational process.

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There are several steps
that need to be taken

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to do this effectively.

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First, it's important
that students be aware

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of what the expectations are.

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They need to understand
specifically what's expected

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of them; the expectations
need to be very transparent.

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Students, once they are
given an opportunity

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to demonstrate what they know
and could do, it's important

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to provide feedback to
them that's timely, clear,

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and very constructive.

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And so while providing a
worksheet that's graded

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with a percent correct on
it provides some information

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to students, it's
much more useful

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if teachers can identify
specifically what are the areas

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in which students
were really strong

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and where were their weaknesses.

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And also the teachers,
to the extent possible,

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should help students
understand what they can do,

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what are those next steps
that they can take in order

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to address those weaknesses.

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Schools and districts really
need to take a number of steps

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in order to create the
conditions that are necessary

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to support data use and
to establish a vision

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that everyone can get behind
in terms of how they want

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to use data in the school.

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We have two specific
recommendations.

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One is to develop a written plan
for data use that reflects input

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from all of the different
groups of people

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who would be using data,

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and so that would include
teachers obviously,

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curriculum coaches,
school leaders,

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might even include
students or parents.

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But all of these users
have different needs,

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and any written plan should
reflect what their needs are

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and how they plan
to use the data.

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Then in addition, we recommend
creating a data-wide team

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in each school, a schoolwide
data team that, again,

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would include representatives
from the different groups

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who are going to use the data.

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And this team can
serve as a liaison

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between the district
and the school staff.

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So they can communicate from
the district to the school staff

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about what are the changes
that are being considered,

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what new systems might
be coming down the line.

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They can also communicate back
up to the district to advocate

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for things that teachers
might decide they need.

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So it may be that they really
need more grade-level planning

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time, and that's something

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that this data team can
communicate to the district.

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As districts are planning for
a system to support data use,

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it's important for them
to collect input from all

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of the different
users of that system.

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So there will be some
district-level users

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who will have certain needs,
but there will also be users

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at the school level and
at the classroom level

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who may have different
needs for the data.

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And so up front, districts
should make sure that they talk

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with each of those
user groups to find

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out exactly what they'll
need from the system in terms

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of analysis and reporting
access and so forth.

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Districts and schools are now
receiving large volumes of data,

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and the use of data to promote
instructional decision making is

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becoming a key school
improvement strategy that's

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being adopted all
over the country.

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It's important, though, that
that be done effectively

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and that it be done in a
way that doesn't result

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in the data overly
driving the goals

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that the schools have
for their students.

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So the goals should come first.

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We should think about what it
is that we want our students

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to achieve and to get
out of their education,

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and then we should use
the data as appropriate

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to inform those goals, rather
than letting the goals be driven

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by what happens to be in
the data that we collect.