boone central data training
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Boone Central Data Training. November 28, 2011 Toby Boss ESU 6. My Background. Goals. To increase knowledge about the use of data to influence instruction To form a consistent vocabulary for the use of data - PowerPoint PPT PresentationTRANSCRIPT
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Boone Central Data Training
November 28, 2011Toby Boss
ESU 6
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My Background
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Goals
• To increase knowledge about the use of data to influence instruction
• To form a consistent vocabulary for the use of data
• To build a common practice for analyzing data through organization, management and use
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Looking at Data
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Goal
• To increase knowledge about the use of data to influence instruction
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Why is data important?
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“Without data, you’re just another person with an opinion.”
Scott Ebbrecht, Principal in Westernville, Ohio
18
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Always keep the kid in the chair
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School Improvement
Safe Schools
School-to-WorkStaff Development
Assessments
Empowerment
Standards
NCLB
Accountability
Site Based Decision Making
What is required to focus our efforts?
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Aim of theDistrict
The Big Arrow
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District Goals& Measures
Aim of theDistrict
Random Acts of Improvement
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District Goals & Measures
Aim of theDistrict
Aligned Acts of Improvement
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Teachers were asked “To what attribute do you attribute the lack
of achievement of your students?”
• Some teachers indicated characteristics of students, lack of motivation, their parents, socioeconomic status, etc.
• Some teachers indicated things that teachers do, looking at their skills.
• Students taught by teachers who were concerned about teacher skills achieved 3 times higher than those whose teachers cited student characteristics.
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The Real Question
• What is it that WE are doing that might contribute to these results?
• The objective here is to reflect about our
practices and determine where WE can
improve what WE do.
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Problem or Condition
• A “problem” is something we can do something about, so we can focus time and energy in that direction.
• A “condition” is something that we cannot do anything about—we acknowledge it and go around it, but we do not waste time trying to change it.
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For Example…
• Through disaggregation we find students from single parent families are not reading at grade level by 3rd grade.
• Coming from a single parent family is a condition.• Reading below grade level is a problem.• Strategies and interventions can be implemented
based on the condition.– Establish a mentor program.– Create a before or after school program.
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What are some other “conditions”?
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What are some other “problems”?
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Some Ground Rules
• Don’t blame kids; it is not their fault.• Don’t use kids as excuses.• Don’t blame teachers.• Data is just information about the current state of
affairs.• These are our students.• The real question is “What are we going to help our
students learn.”
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continuous improvement
•members of an organization acquire and use information to change and implement action
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use data to:
•ask the right questions•define needs•plan interventions•evaluate progress
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Data should inform teams about how to improve learning.
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Why is data important?
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Goal
• To form a consistent vocabulary for the use of data.
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Measures
• Norm Referenced
• Criterion Referenced
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Norm Referenced
• Measure student performance against a group (called a norm)
• A common way to present information is in the form of a bell curve.
• Norm referenced guarantees a set number of students in each performance category – based on how well they perform against each other.
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NRT
•terra nova•Itbs•nwea•stanford•explore•plan
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national stanines
– normalized test scores that divide the normal curve into broad intervals ranging from 1 to 9
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scale score
– mathematically transformed raw score
– depend on test taken– range is 1-999– scales span all levels
and grades of test
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national percentilenp
– norm referenced– range from 1-99– specify the percentage
of students in a national norm group whose scores fall below the given students test score
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normal curve equivalent nce
– normalized test scores on an equal interval scale
– range from 1 - 99 with one point change same throughout
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limitations
– extent to which the test captures and covers the domain it is trying to measure
– not sufficiently aligned to curricular standards and instructional emphasis
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Criterion Referenced
• Student performance is judged against a criteria.
• No set number of students in performance categories.
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Student Performance Data
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view
•program•cohort•individual student
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Program DataGrade 4 Math Problem Solving
0.0%20.0%40.0%60.0%80.0%
100.0%
2000-01
2001-02
2002-03
2003-04
2004-05
2005-06
Math problem
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Cohort DataWriting Scores Class of 2006-07
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
2nd 3rd 4th 5th 6th 7th 8th
writing
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Demographic Data
• Defines student characteristics– Male– Female– ELL– Free and Reduced Lunch– SPED
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Perceptual Data
• Perceptions about school related topics – a very powerful data source.
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Example
• Complete the survey.
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Levels of Data Analysis
• Step 10 – Intersection of 4 measures over time
• Step 9 – Intersection of 4 measures
• Step 8 – Intersection of 3 measures over time
• Step 7 – Intersection of 3 measures
• Step 6 – Intersection of 2 types of measures over time
• Step 5 – Intersection of 2 types of measures
• Step 4 – Two or more variables within measures over time
• Step 3 – Two or more variables within same area
• Step 2 – Snapshots over time
• Step 1 – Snapshots
Bernhart, V. L. (2004). Data Analysis for Continuous School Improvement (2nd ed.) Larchmont, NY: Eye on Education, Inc.
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Goal
• To build a common practice for analyzing data through organization, management, and time.
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Data Driven Dialogue
• Phase I: Predictions• Phase II: Observations• Phase III: Inferences
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Utilize the Protocol
• State of the Schools Report 2010-11
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NWEA
• Logins and Teacher Reports – view by goal descriptors
• Class Rosters - choose:– Spring 2011– Spring to Spring– Both text and graph
• Class by RIT– Click on subject for a breakdown of skills
• Dynamic Reports – provides levels and growth between two testing times
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Action Plans
• Decide what to do based on the data.• What are we going to change?
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Closing
• Next Steps