DATA ANALYSIS Tools and Processes -...
Transcript of DATA ANALYSIS Tools and Processes -...
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DATA ANALYSIS
Tools and Processes
The session will begin shortly.
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Welcome
Thank you for joining the webinar
Hawaii Department of Education
Office of Curriculum, Instruction and Student Support
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DATA ANALYSIS
Tools and Processes
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Hawaii Department of Education
Office of Curriculum, Instruction and Student Support
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Hawaii’s Five RTTT Pillars
Improved Student
Outcomes
Data for School Improvement
Longitudinal Data System
Balanced Scorecard
Data Governance
Using data to inform instruction
Common Core Standards
Career & College Ready Diploma
Curriculum Framework
Common Instructional Materials
Formative Assessments
Interim Assessments
Summative Assessments
STEM
Focused support on
lowest-performing schools
-
Zones of School
Innovation
•Flexibility
•Great teachers and great
leaders
•Remove barriers to
learning
Performance-based
evaluation system
New Teacher Induction &
Mentoring
Incentives
Leadership development
Alternative pathways
Systems of Support to enable schools to do their best work – reprioritize and reorganize State resources;
establish Human Resources Unit in Zones of School Innovation; automate
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DESIRED OUTCOMES
An understanding of a process in
which to move from summative state
level data to formative classroom data
in order to provide teachers with
detailed student information.
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ESSENTIAL QUESTION
How might a teacher analyze
available data ranging from
summative assessments to
looking at student work?
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Multiple
Measures Demographics
Perceptions
Student Learning
Processes
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System
School
Classroom
System Data
• NAEP
• State Assessment
Frequency
•Yearly
Data – Three Levels
Classroom Data
•DSI
•Formative Assessments
Frequency
•Daily/ Weekly
School Data
• LDS
•eCSSS
Frequency
•Quarterly/Semester/Yearly
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Types of School Teams
Principal
Counselor
Curriculum Coordinator
GL Reps/Dept Chairs
Kindergarten
First
Second
Teacher Teacher
Science
Math
Language Arts
Teacher Teacher
Leadership Team
Grade Level/
Department
Classroom
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Inverted Data Pyramid
Summative High Stakes Assessment
Other
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N. Love, 2010
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Data Pyramid (types of data): How often
do teams and coaches use these types of
data?
Summative High Stakes
Assessments
Demographic, Process and
Perceptual Data
Benchmark Common Assessment
Formative Common Assessment
Formative Classroom Assessment
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N. Love, 2010
= 2-4 times a year
= Annually
= Quarterly or end of unit
= 1-4 times a month
= Daily - Weekly
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Identifying student
learning needs by
drilling into data
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Levels of Data
Aggregated Data
Disaggregated Data
Strand
Item
Student Work
16 N. Love, 2008
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Identifying a Student-
Learning Problem: Data Findings
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin
Press. All rights reserved.
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“Drilling Down” into Data
Aggregated Data
Disaggregated Data
Strand
Item
Student Work
18 N. Love, 2008
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100 thousand foot view
©2012 Google – NASA,
TerraMetrics
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Why is Aggregated data results
important?
“Headlines” but not the entire “story”
Cause for celebration
Calls attention to areas of further
investigation
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CAUTIONS When examining Aggregated Results
Sampling Error - Different Students! Different Tests!
“The cohort of students in any one year is often very different from
those in previous years, and these differences among student
cohorts cause scores to fluctuate substantially more one year to
the next, even if the effectiveness of the school remains
unchanged.” (Boudett, City & Murnane, 2006, p 35).
Tests Change!
Sample Sizes
Measurement Error
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H S A Score Report
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HSA
2010-12
26% of the 3rd gr. are
proficient in math, which is
a decrease from last year.
Math Grade 3
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When looking at aggregate scores,
we need to be careful about . . .
Moving too quickly to generate solutions
Blaming students
Targeting bubble students
Getting discouraged
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“Drilling Down” into Data
Aggregated Data
Disaggregated Data
Strand
Item
Student Work
25 N. Love, 2008
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10 thousand foot view
©2012 Google – NASA, TerraMetrics
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Why is Disaggregated data
results important?
Goal: Identify who is and who is not
learning
Data that have been separated into groups
based on a criterion.
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CAUTIONS when examining Disaggregated Results
Achievement Gaps
The importance of achievement gaps is to call
attention to the inequities of our educational
system and to take action to address them (N.
Love, 2009)
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HSA
2010-12
40% of the 3rd gr. are proficient
in math, which is a decrease
from last year.
There is a persistent
achievement gap between
the males and the females in
math.
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“Drilling Down” into Data
Aggregated Data
Disaggregated Data
Strand
Item
Student Work
31 N. Love, 2008
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1000 foot view
Contributed by Lauren, Marcelle, Champt10 and
others
©2012 Google – DigitalGlobe, GeoEye
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Strand Data
Multiple years – Patterns and
Trends
Standards in context
“Whole to Parts” perspective
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Look at the test
blue print to
determine how
many items
appear on the test
within each strand.
www.alohahsa.org
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CAUTIONS when examining Strand Data
Drawing Conclusions
Blaming Students and/or
Teachers
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Complex Area
Complex
School
HSA Strand Report
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HSA
2010-12
40% of the 3rd gr. are
proficient in math, which is
a decrease from last year.
There is a persistent
achievement gap between
the males and the females in
math. For two years, the lowest
%age of students were
proficient in the
measurement strand for
math
DSI
2011-12
This year, the standards within
the measurement strand
showed the most difficulty for
all students.
Of the students who scored
2 and below, 52% were
male, and 33% were female.
65% of all 3rd graders
scored a rubric rating of 2
and below
Math 3
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“Drilling Down” into Data
Aggregated Data
Disaggregated Data
Strand
Item
Student Work
40 N. Love, 2008
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100 foot view
Contributed by kaipaniolo, Champt10, heidi and
others
©2012 Google – DigitalGlobe, GeoEye
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Connecting HSA to formative
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Video – Mililani Ike Data Team
Please click on play button to start.
To view video separately or after the webinar
go to - http://vimeo.com/channels/rtttdatacoaches/40615808
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Analyzing Item Level Data
Information about student learning
Discussion points in data teams
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Item-Level Data
Four approaches to analyze Item-Level Data
1. Percentage Correct
2. Distractor Patterns
3. Task Deconstruction
4. Open-Response Item Analysis
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Open
Response
Multiple
Choice
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Cautions with
Item Level Data
Test-Item Quality
Content Knowledge
Task Manageability
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Student 1
Student 2
Student 3
Student 4
Student 5
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HSA
2010-12
26% of the 3rd gr. are
proficient in math, which is a
decrease from last year.
Classroom DSI
2011-12
There is a persistent
achievement gap between
the males and the females
in math.
For two years, the lowest
%age of students were
proficient in the measurement
strand for math
NA (no state data
provided)
NA (no state data
provided)
Students performed poorly
on tasks that require area
and perimeter.
65% of all 3rd graders
scored a rubric rating of 2
and below
Of the students who scored
2 and below, 52% were
male, and 33% were female.
The students performed
poorly in standards within
the measurement strand
Math 3
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“Drilling Down” into Data
Aggregated Data
Disaggregated Data
Strand
Item
Student Work
49 N. Love, 2008
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Street View
©2012 Google
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Why is Student Work
important?
Student misconceptions and misunderstandings.
Student understanding of the concept, skills, and
knowledge within content areas.
Patterns observed in the item level data.
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CAUTIONS when examining Student Work
Stay Objective
Keep to the criteria selected
Stick to “just the facts”
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HSA
2010-12
26% of the 3rd gr. are
proficient in math, which is
a decrease from last year.
Classroom
DSI
2011-12
There is a persistent
achievement gap between
the males and the females
in math. For two years, the lowest
%age of students were
proficient in the
measurement strand for
math
NA (no state data
provided)
NA (no state data
provided)
Cannot distinguish
between area and
perimeter when presented
together. Have trouble with
the vocabulary.
65% of all 3rd graders scored
a rubric rating of 2 and below
Of the students who scored 2
and below, 52% were male,
and 33% were female.
Classroom
Assessment
The students performed
poorly in standards within
the measurement strand
2011-12
Students performed poorly
on tasks that require area
and perimeter.
Cannot distinguish
between area and
perimeter when
presented together.
Have trouble with the
vocabulary.
Math 3
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Student Involvement
Students should look at their own work in
order to determine their needs.
Teachers may use a variety of templates
and strategies to involve students.
rubrics,
student self-assessments
student analysis of their own
performance
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Student Reflection Sheets
56
Chappuis, Jan. Seven Strategies of Assessment for Learning. MA: Pearson Education, Inc., 2009
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Student Reflection Sheets
57
Chappuis, Jan. Seven Strategies of Assessment for Learning. MA: Pearson Education, Inc., 2009
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Principles of Effective Data
Use Go visual with the data
Use data to build understanding and ownership of problems
Take time to learn as much as possible from the data (the first solution may not be the best one)
Separate observation from inference
Pay attention to the process
Assure that diverse voices are brought into the analysis
Data-Driven Dialogue (Wellman & Lipton, 2004)
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Data “Safety
Regulations”
Don’t use data to punish
Don’t use data to blame students or
their circumstances
Don’t jump to conclusions without ample
data
Don’t use data as an excuse for quick
fixes. Focus on improving instruction
N. Love, 2008
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Resources Chappuis, Jan. Seven Strategies of Assessment for Learning. MA:
Pearson Education, Inc., 2009
Love, Nancy, The Data Coach’s Guide to Improving Learning for All
Students. CA: Corwin Press, 2008.
Symonds, Kiley Walsh, “After the Test: Closing the Achievement Gaps
with Data” Learning Point Associates, 2004.
www.alohahsa.org
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Questions and
Answers
Answering Chat
Questions
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Thank you for joining us!
A recording of this webinar will be posted on the
Standards Toolkit website.
If there are any questions, please e-mail:
Dewey Gottlieb, Mathematics Specialist
Monica Mann, Acting Administrator
Petra Schatz, Language Arts Specialist, or
Derrick Tsuruda, Science Specialist
Dan Miyamoto, DSI Project Manager
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