1. DATA REVIEW A Comprehensive Needs Assessment For School Improvement Planning Susan Wright, Program Evaluator Northside ISD
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2. What will we do in this session?
Discuss the process.
Conduct a data review.
Share the product,
“Promising Practices,”
that arose from data review
discussions.
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3. What is Data Review? formal process of structured communication
Data review is a
formal process of
structured communication
designed to
increase student achievement.
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4. Why Data Review? Comprehensive needs assessment
is crucial to campus success.
Campuses need assistance
and staff development in
collecting and analyzing data.
District staff and campus staff need
to communicate about specific needs and expectations.
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5. Providing Decision Support
Data-driven decision making
improves performance.
Instruction
Staff
Dev
Funding
DATA
Federal
Titles
Special Programs
Account-ability
Testing
Planning
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6. School Improvement Planning Team
Instruction Executive Director (Chair)
Administration Assistant Superintendent
Compensatory Programs Director
Special Education Executive Director
Bilingual Education/ESL Director
Title 1 Supervisor
At-Risk Coordinator
Program Evaluator
Instructional Supervisors for Subjects
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7. Campus Representatives
Principal
Grade Level Team Leaders
Reading and Math Specialists/Coordinators
Others
Vice Principal or Academic Dean
Counselor
Special Program Campus Coordinators
Other Leaders
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8. Meeting Agenda Introductions Purpose of the Meeting
Orientation to the Data Forms
Data Review
Discussion of Other Factors
Not Covered
Discussion of Strengths and Weaknesses
Discussion of School Improvement Plan
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9. Data Collection State Tests National Tests
TAKS, SDAA, RPTE
National Tests
CogAT, ITBS
Primary Achievement Measures & Standards
IRI, TPRI, Local Benchmark, etc.
Other Measures
Retention, F/R Lunch, Suspensions,
Stability, Attendance, etc.
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10. Orientation to the Data Forms
Distribute forms and highlighters.
Walk through the data.
Examine the relationships.
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11. Data Review Measures 1 District MME/TLI 2 Grade Level/Program Trends
What percentage of students are passing?
How do grade levels do from year to year?
How do groups of students grow as they move through the school?
What subgroups are struggling?
How do subgroups do compared to District?
How about higher level achievement?
What other factors impact achievement?
1 District MME/TLI
2 Grade Level/Program Trends
3 Cohort Analysis
4 Substantial Subgroup Variance
5 District Comparison
6 TAAS Excellence Indicators
7 Other Factors
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12. Data Analysis Compare to targets/goals.
Did campuses meet their targets?
Should targets be adjusted?
Compare to district, state and nation.
Compare to last year.
Do cohorts grow as they move through school?
Are subgroups experiencing problems?
Triangulate with multiple measures.
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13. We’re from Central Office and we’re here to help.
Let the data speak for itself.
Ask campus to provide context
for interpretation.
Recognize campus efforts.
Be positive and supportive.
Find ways to improve support.
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14. Discussion Gains higher than expected: Gains lower than expected:
Document successful strategies.
Gains lower than expected:
Reasons? Low cohort? Next grade ready?
Successes on other campuses?
Staff concerns
Trained in programs? Program consistency?
Staff development? New staff supported?
Curriculum alignment? Grade level planning?
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15. Grade 3 performance – a critical measure
How does primary program support 3rd?
Are TAAS/TAKS scores consistent with other measures, i.e. aptitude?
Low subgroups
Are special strategies in place? Did they work? Are safeguards in place?
How are at-risk students targeted?
How are special programs operating (DOI)?
Special factors
Are there boundary changes, overcrowding, high mobility, staff factors (high turnover, etc.)?
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16. School Improvement Plan
Were district initiatives addressed?
Were campus goals addressed?
Were needs uncovered in data review addressed?
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17. Promising Practices Successful practices are discussed on each campus.
Examples and documents are collected.
Books are produced and disseminated to each campus at the end of the process.
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18. Remember… “Not everything that can be counted counts, and
not everything that counts
can be counted.”
Albert Einstein
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