WEBVTT

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[Music]

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My name is Ellen Mandinach.

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I am at WestEd and
was a panelist

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on the IES practice
guide dealing

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with data entitled Using
Student Achievement Data

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to Support Instructional
Decision Making.

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The primary goal for training

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around data-driven
decision making should be

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that it is actionable.

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Data in the abstract
are meaningless.

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They have to be transformed
into information and ultimately

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to actionable knowledge.

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So for a teacher, it means that
the data must be transformed

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in a way that makes sense to
inform instructional practice.

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An example around actionable
information might be something

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to do with taking
formative test results.

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If a teacher gets
formative data,

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he or she then must understand
that it's not just numbers

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that they are using, but
that those numbers represent

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information that can be
transformed into how he

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or she defines the
kinds of instruction

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that a student may need.

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Around the topics of
professional development

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on data-driven decision
making, there are a couple

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of things to keep in mind.

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One of the most important is
that there are certain skills

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and content knowledge that
a teacher may need to have

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around data-driven
decision making.

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Those would include cognitive
skills like how to collect data,

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how to organize, summarize,
analyze, prioritize data.

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It really becomes
a feedback loop.

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So these are fundamental
cognitive skills

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that a teacher will need
to bring to bear as he

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or she is analyzing data.

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The same with an
administrator as well.

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But other topics that are
important include what one might

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call high-capacity
data strategies.

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These are topics such
as what do you do

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to differentiate instruction
based on data, what do you do

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about using multiple
sources of data.

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So you are using
formative assessments,

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summative assessments,
benchmarks, locals,

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classroom assessments, classroom
assignments-how do you take all

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of those disparate data
sources, bring them together

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to make sense, and then use them

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from which you can then
make an actionable decision.

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In terms of format
and structure,

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one of the important
things is the alignment

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of the professional development
to the needs of the school,

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both in content and
in subject matter.

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So for example, in
mathematics, you may want

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to structure the
professional development

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around the content for,
say,elementary mathematics

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or high school algebra.

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You may want to align
it to the standards

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that the school is using.

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One of the foremost
issues is whether you do it

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on an ongoing basis
or an ad-hoc basis.

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The literature firmly
states that it is important

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to make sure that it's
continuous, that it is tied

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to the needs of the
educators, and that they see

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that the outcomes
will be effective

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for their own practice.

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If they don't understand
the ramifications

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of the professional
development around data,

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then they are not
likely to use it.

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Another format and
structure that is important is

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that the timing of
it is vitally tied

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to when they are
going to use it.

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It's a "use it or
lose it" paradigm.

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So if you do summer training
and then the teachers go back

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to school and they
are not examining data

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for several months, then
they may forget everything

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that the professional
development has invoked to them.

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For example, a project on which
I am working is using a model

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of professional development that
consists of several sequences

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of training sessions,
interspersed

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across several months, where
the teachers will be trained,

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have workshops, go into their
classrooms, try things out,

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come back, think about
what needs to be done,

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ask the professional
development providers

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for technical assistance,
and it's an iterative model.

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Another issue that is

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to be considered is the
model of collaboration.

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The teaming around data-driven
decision making is very,

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very much important.

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So the structure of professional
development should be

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for schools to bring
data teams to the table

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to be trained together.

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So that model of
having multiple people

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from the school working together
and, on an intermittent basis,

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getting professional
development,

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working through in
their school the issues,

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learning to use data,
and then coming back

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around the professional
development is a model

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that has been shown
to be very successful.

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