WEBVTT

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[Music] Welcome to the overview
on Utilizing Data Systems

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for Dropout Prevention.

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Washington High School has a
significant dropout problem.

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Principal Alvarez
looks at her list

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of incoming ninth-grade students

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and wonders how many will
be on-track for graduation

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at the end of the school year.

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Too many freshmen do not
make a successful transition

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to high school and end up
failing classes, missing school,

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and not earning credits.

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Eventually they drop out.

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Student "dropout"
is a serious problem

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for many schools and districts.

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Even schools that know they
have a problem often have no way

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to understand its
nature or scope,

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let alone form an effective
strategy to deal with it.

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A longitudinal database can help
staff plan dropout prevention

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and credit recovery programs

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that help keep students
in school.

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A data system can help staff
clearly determine how specific

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or widespread the problem is.

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One challenge is getting
accurate enrollment data,

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especially for transfer
students.

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Oftentimes, schools
will record a student

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as enrolled once they
receive a transfer notice,

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but the student may
never actually show up.

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A system that doesn't mark
a student as "in school"

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until they actually attend

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at the new location
can help a school

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or district determine
how best to proceed.

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A student-level data
system can help identify

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which students are
off-track for graduation.

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By tracking factors known
to contribute to dropout,

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an early warning system can
automatically flag students

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who display at-risk indicators,
such as frequent absences,

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grade retention, low
academic achievement,

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and behavioral problems.

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A predictive and comprehensive
system would have data

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on many different factors, both
student- and school-centered,

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known to lead to dropout.

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An effective data system
also needs to gather data

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on why students drop out.

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Simply marking a student as
"withdrawn" is unhelpful,

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so fields need to be created
to provide more information.

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Knowing that a student
isn't showing

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up to class is a good start,
but understanding why is key

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to creating powerful
dropout prevention

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and credit recovery strategies.

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Lastly, a data system
can help identify

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when a student is
likely to drop out.

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Transitions such as moving
from middle to high school

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or transferring between
schools are high-risk periods.

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By regularly reviewing student
transcripts, test scores,

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and discipline referrals for
sudden changes in academic work

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or in behavior, schools
can identify students

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who may be experiencing
stressful life events

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that often lead to dropout.

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Once detailed data have been
gathered, they can be used

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to develop appropriate
schoolwide or student-

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level strategies to
meet the specific needs

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of at-risk students.

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Data can guide resource
allocation and reveal policies

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that are not serving students
and need to be adjusted.

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Schools can determine
the scope of the problem

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and design interventions
accordingly.

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A data-driven, specifically
targeted dropout prevention plan

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such as this can be
much more effective

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than a generalized program.

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For a data system
to achieve all this,

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what features should it have?

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A data system needs to be
standardized so that it can work

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with the systems
of other schools,

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both district and statewide.

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A uniform data structure
allows schools

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to share accurate information
and quickly identify students

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who fall off-track and
need interventions.

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This level of standardization
is not without challenges.

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It requires statewide
coordination

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and adequate infrastructures
in place for all schools

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to gather and transmit data.

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Some "off the shelf"
data systems can be used

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to get started and can help

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in ensuring cross-compatibility
between districts.

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In order for data to be useful,
it must be "longitudinal"

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In other words, it needs
to be data gathered

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over a student's school career--
at the very least, during middle

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and high school years.

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Students will need to be
assigned IDs that are unique

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to them across the entire state,

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so that their data are easily
transferred as they move

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from school to school.

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It goes without saying
that data must be accurate,

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but even the most elegantly
designed data system can fail

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at the point of data entry.

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Standards and checks for
accuracy are essential

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and should not be
taken for granted.

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In order to ensure accurate data
is received, especially data

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that answers the "why"
questions, the reasons for

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and methods of data collection
need to be transparent

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to students, parents,
teachers, and administrators.

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Aggregate results,
trends, and findings need

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to be publicly available,

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although individual
student data must be kept

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strictly confidential.

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These are the types of data
systems that we all need

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to work towards, but in the
meantime, it's important

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to remember that the key data
that these systems will use--

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grades, attendance, test
scores, course failures, etc.--

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are already routinely
collected by schools.

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Using these data, it is possible

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to begin identifying both
the scope of the problem

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and which individual students
are off-track even before

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comprehensive longitudinal data
systems are up and running.

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A year has passed

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since Washington High School
implemented a districtwide

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data system.

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As fall approaches,
Principal Alvarez reviews data

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on the incoming ninth
graders and sees

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that several students
have a history

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of academic failure in math.

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She plans to provide
extra-support classes

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for these students
to keep them on-

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track during their first
year in high school.

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The district has
noticed some trends

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across all its high schools
and is working to implement a

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"multiple pathways" approach in
order to better meet the needs

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of the district's diverse
student population.

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[Music] To learn more about data
systems and dropout prevention,

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please explore the
additional resources

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on the Doing What Works website.