Transcript Document

Using Data Process
Assumption #1
• http://www.youtube.com/watch?v=_llFlj6FeQ8
• Making significant progress in improving
student learning and closing achievement gaps
is a moral responsibility and a real
possibility in a relatively short amount of
time—two to five years. It is not children’s
poverty or race or ethnic background that
stands in the way of achievement; it is school
practices and policies and the beliefs that
underlie them that pose the biggest obstacles.
Assumption #2
• http://www.youtube.com/watch?v=w6NR0edrgWE
• Data have no meaning. Meaning
is imposed through interpretation.
Conversely, data themselves can
also be a catalyst to questioning
assumptions and changing
practices based on new ways of
thinking.
Assumption #3
• http://www.youtube.com/watch?v=pW8TdqF7Pao
• Collaborative inquiry—a process where
teachers construct their understanding
of student-learning problems and invent
and test out solutions together through
rigorous and frequent use of data and
reflective dialogue—unleashes the
resourcefulness and creativity to
continuously improve instruction and
student learning.
Assumption #4
• http://www.youtube.com/watch?v=9dMzFNQwaqg
• A school culture characterized
by collective responsibility for
student learning, commitment
to equity, and trust is the
foundation for collaborative
inquiry.
Assumption #5
• http://www.youtube.com/watch?v=HbYiXyUynRk
• Using data itself does not improve
teaching. Improved teaching comes
about when teachers implement sound
teaching practices grounded in cultural
proficiency—understanding and respect
for their students’ cultures—and a
thorough understanding of the subject
matter and how to teach it.
Assumption #6
• http://www.youtube.com/watch?v=6d5IWsaDlkE
• Every member of a collaborative
school community can act as a
leader, dramatically impacting the
quality of relationships, the school
culture, and student learning.
State Assessments
Summative
Demographics
Benchmarks
Formative
Common Assessments
Classroom Assessments
Task 1:
Using Data Project Overview
The Using Data Process
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power
of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
The Data Divide
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power
of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
S 1.13
Core Value
Significant improvement in student
learning and closing achievement gaps is
a moral responsibility and a real
possibility in a relatively short amount of
time—two to five years.
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power
of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
S 1.14
Possibility
% of Students Proficient or Above
Poverty, Race/Ethnicity, and Achievement
X
Schools with a majority
of African American,
Latino/a, and/or Native
American students
and/or students living in
poverty are achieving at
this level.
Poverty and Race/ Ethnicity Enrollment
Source: Adapted from Accountability in Action (2nd ed.) by Douglas Reeves, 2004, Denver, CO:
Advanced Learning Press.
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power
of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
S 1.15
Core Value
Collaborative inquiry—school teams
constructing meaning of student-learning
problems and testing out solutions together
through rigorous use of data and reflective
dialogue—unleashes the resourcefulness
of educators to continuously improve
instruction and student learning.
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power
of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
S 1.16
Virtually All Education Researchers
Agree: Collaboration is Key
Deborah Ball
Roland Barth
Carol Belcher
Louis Castenell
Jim Collins
Tom Corcoran
Linda Darling-Hammond
Lisa Delpit
Rick DuFour
Karen Eastwood
Richard Elmore
Susan Fuhrman
Carl Glickman
Asa Hilliard
Anne Lieberman
Dan Lorti
Robert Marzano
Milbrey McLaughlin
Jay McTighe
Fred Newmann
Allan Odden
Doug Reeves
Mike Schmoker
Deborah Shifter
Dennis Sparks
James Stigler
Gary Wehlage
Grant Wiggins
and more…
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power
of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
S 1.17
Building the Bridge Between Data
and Results
Leadership & Collaboration
Data Use
Capacity
Culture/Equity/Trust
Instructional
Improvement
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power
of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
S 1.18
Shifts That Are Evident in Using
Data Schools
Less Emphasis
External accountability,
Culture,
Equity, Trust cultural blindness, little trust
Instructional
Improvement
Data Use
Data to sort,
learning left to chance
Punishment/reward,
avoidance
Collaboration
Top-down, data-driven
decision making
Leadership &
Capacity
Individual charismatic
leaders as change agents
More Emphasis
Internal and collective
responsibility,cultural
proficiency, trust
Data to serve, expanding
opportunities for all
Feedback for continuous
improvement, frequent and
in-depth use by teachers
and students
Ongoing Data-Driven
Dialogue and
collaborative inquiry
Learning communities
with many change agents
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power
of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
S 1.19
Unique Features of the Using Data
Process
• Builds everyone’s knowledge and skills
• Focuses on cultural proficiency, equity, and school
culture
• Focuses on data culture and collaboration
• Includes both long-term and short-cycle improvement
• Is a structured and adaptable process:
– identifies the student-learning problem
– verifies its causes
– uses research and a logic model to design
interventions
– monitors results
• Provides tools for collaboration and sense making
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power
of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
Using Data Process is the
Intersection
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power
of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
New Baseline Year for
Mathematics and Reading
Purpose for Grades 3 – 8 Standard Setting
• Increase rigor by raising standards for Grades 3 – 8 student
achievement on the OCCT as a means to be more competitive
at the national and international levels.
• Align student expectations on the OCCT more closely with
student expectations for the National Assessment of
Educational Progress (NAEP).
• Because tests were implemented over multiple years, there
were outliers.
• Vertically aligning performance standards for students on the
OCCT tests for Grades 3 – 8 allows for consistent expectations.
August 27, 2009
State Board of Education Meeting
2
Oklahoma Performance Index
(OPI)
• A scaled score is derived from the
number correct and is used to place
a student in one of the given
performance levels for each content
area.
• Score ranges vary across content
areas.
• A scaled score of 700 is Satisfactory
or Proficient across all content areas.
2
Standard Setting and Cut Scores
• Standard Setting is the process that allows
experts to make judgments about the
content that a student should know and
be able to do in order to be classified in a
specific performance level.
• Cut scores are necessary for the
categorization of student test scores into
the four performance levels utilized in
Oklahoma: Advanced, Proficient, Limited
Knowledge, and Unsatisfactory.
24
Standard Setting and Cut Scores
• The experts included on the standard
setting committees were educators across
Grades 3-8 with significant experience in
reading or mathematics instruction, as
well as representatives from higher
education and business.
• One panel of experts conducted standard
setting for reading and another panel
conducted standard setting for
mathematics.
25
Oklahoma Core Curriculum Tests
Performance Benchmarks or "Cut Scores”
• The standard setting panelists determined cut
score recommendations based on test content,
performance standards, and test item difficulty,
NOT based on number of test items answered
correctly.
– Raw scores were mapped to scale scores as is
standard procedure with large-scale
assessments such as the NAEP, ACT, or SAT.
– Scale scores provide a consistent
interpretation of assessments across years.
26
Ground Rules
• No blaming students.
• No blaming teachers.
• Data is just information.
• Use data for instructional
purposes.
• “De-emotionalize” data.
2
Median
• The median is the middle score in a set
of ordered scores.
• The median is a better measure of
central tendency than the mean
(average) because it is not affected by
extreme scores.
• Remember: Comparing Median %
Correct does not take into account
difficulty of items across years, yet it
will show general trends in strengths
and weaknesses.
2
Key Points When Analyzing Data
1. What does the data show?
(Factual Information)
2. Why might this be?
(Hypotheses)
3. How should we respond?
(Plan for action)
4. How will you measure success?
2
QUESTIONS
COMMENTS
CONCERNS