august 23, 2011. use the “key drivers” as a framework for inquiry for continuous improvement...
TRANSCRIPT
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August 23, 2011
Inquiry to Improve Teaching and Learning
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Use the “Key Drivers” as a Framework for Inquiry for Continuous Improvement
AssessmentCommon Core alignmentAssessment Literacy
AnalysisExamples of the Inquiry CycleFormulating a problem statement Setting goals
ActionEmploying research based strategies to improve teaching
and learningReflecting on outcomes for students
CultureProtocolsAwareness of cultural competenceBuilding and sustaining a collaborative culture review
Today’s Goal
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Culture Assessment
Analysis
Action
Key Drivers
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… and other useful data
Assessment
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PRINCIPLES FOR EFFECTIVE ASSESSMENTS:
COMMON INTERIM:• At least quarterly• Common across all teachers of the same grade level
DEFINE THE STANDARDS—ALIGNED TO:• To state test (format, content, & length)• To instructional sequence (curriculum)• To college-ready expectations
ASSESSMENTS:
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PRINICIPLES FOR EFFECTIVE ASSESSMENTS:
REASSESSES:• Standards that appear on the first interim assessment
appear again on subsequent interim assessments
WRONG ANSWERS: • Illuminate misunderstanding
TRANSPARENT:• Teachers see the assessments in advance
ASSESSMENTS:
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How are “Data Driven” Decisions Different?Scattered staff development programs
Focused staff development programs as an improvement strategy to address documented problems/needs
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How are “Data Driven” Decisions Different?Budgetary decisions based on prior practice, priority programs
Budget allocations to programs based on data-informed needs
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How are “Data Driven” Decisions Different?Staff assignments based on interest and availability
Staff assignments based on skills needed as indicated by the data
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How are “Data Driven” Decisions Different?Reports to the community about school events
Organized factual reports to the community about the learning progress of students
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How are “Data Driven” Decisions Different?Goal setting by board members, administrators, or teachers based on votes, favorite initiatives, or fads
Goal setting based on data about problems and possible explanations
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How are “Data Driven” Decisions Different?Staff meetings that focus on operations and the dissemination of information
Staff meetings that focus on strategies and issues raised by the local school’s data
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How are “Data Driven” Decisions Different?Parent communication via twice-a-year conferences at elementary “open houses” and newsletters
Regular parent communication regarding the progress of their children
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How are “Data Driven” Decisions Different?Grading systems based on each teacher’s criteria of completed work and participation
Grading systems based on common student-performance criteria that report progress on the standards as well as work skills
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Summative District or State Assessments
Other Data
THE INVERTED DATA PYRAMID: NOT RECOMMENDED
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… alone or with some friends
Analysis
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A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
Virtually All Education Researchers Agree: Collaboration is Key
Anne LiebermanDan LortiRobert MarzanoMilbrey McLaughlin Jay McTigheFred NewmannAllan OddenDoug ReevesMike SchmokerDeborah ShifterDennis SparksJames StiglerGary WehlageGrant Wiggins and more…
Deborah BallRoland BarthCarol BelcherLouis CastenellJim CollinsTom CorcoranLinda Darling-HammondLisa DelpitRick DuFourKaren EastwoodRichard ElmoreSusan FuhrmanCarl GlickmanAsa Hilliard
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Create your own inquiry cycle …Using your own professional experience, and/or
what you have heard from us, sketch out a draft of an inquiry cycle. Include at least three necessary elements. (2 minutes)
Share with your table. What do you all have in common? (5 minutes)
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• IMMEDIATE: Ideal 48 hrs, max 1 wk turnaround
• BOTTOM LINE: Includes analysis at question level, standards level and overall—how well did the students do as a whole
• TEST-IN-HAND analysis: Teacher & instructional leader together
• TEACHER-OWNED analysis
• DEEP: Moves beyond “what” to “why”
ANALYSIS:
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Results Now
The 30 Minute Team MeetingA protocol for organizing teams of teachers to
participate in the data inquiry cycle to make meaningful improvements in teaching and learning.
Mike Schmoker
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• IDENTIFY ROLES: Timer, facilitator, recorder (2 min) • IDENTIFY OBJECTIVE to focus on (2 min or given)
• WHAT WORKED SO FAR (5 min)• [Or: What teaching strategies did you try so far]
• CHIEF CHALLENGES (5 min)
• BRAINSTORM proposed solutions (10 min)• [See protocol on next page]
• REFLECTION: Feasibility of each idea (5 min)
• CONSENSUS around best actions (15 min)• [See protocol on next page]
• PUT IN CALENDAR: When will the tasks happen? When will the teaching happen? (10 min)
ACTION: RESULTS MEETING 50 MIN TOTAL
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RESULTS MEETING STRUCTURE:PROTOCOLS FOR BRAINSTORMING/CONSENSUS
PROTOCOL FOR BRAINSTORMING:
• Go in order around the circle: each person has 30 seconds to share a proposal.
• If you don’t have an idea, say “Pass.” • No judgments should be made; if you like the idea, when it’s
your turn simply say, “I would like to add to that idea by…”• Even if 4-5 people pass in a row, keep going for the full
brainstorming time.
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RESULTS MEETING STRUCTURE:PROTOCOLS FOR BRAINSTORMING/CONSENSUS
PROTOCOL FOR REFLECTION:
• 1 minute—Silent personal/individual reflection on the list: what is doable and what isn’t for each person.
• Go in order around the circle once: depending on size of group each person has 30-60 seconds to share their reflections.
• If a person doesn’t have a thought to share, say “Pass” and come back to that person later.
• No judgments should be made.
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RESULTS MEETING STRUCTURE:PROTOCOLS FOR BRAINSTORMING/CONSENSUS
PROTOCOL FOR CONSENSUS/ACTION PLAN:• ID key actions from brainstorming that everyone will agree to
implement• Make actions as specific as possible within the limited time• ID key student/teacher guides or tasks needed to be done to
be ready to teach—ID who will do each task • Spend remaining time developing concrete elements of lesson
plan:• Do Now’s• Teacher guides (e.g., what questions to ask the students or
how to structure the activity)• Student guides• HW, etc.
NOTE: At least one person (if not two) should be recordingeverything electronically to send to the whole group
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• Let the data do the talking
• Let the teacher do the talking (or get them to!)
• Always go back to the test and back to specific questions
• Don’t fight the battles on ideological lines (you’re going to lose)
• There’s a difference between the first assessment and the third
• You’ve got to know the data yourself to have an effective meeting
• Make sure it’s connected to a concrete plan that you can verify
TIPS FOR EFFECTIVE ANALYSIS MEETINGS:
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IES Practice GuideUsing Student Achievement Data to Support Instructional Decision Making
Make data part of an ongoing cycle of instructional improvement
Teach students to examine their own data and set learning goals
Establish a clear vision for school wide data use
Provide supports that foster a data-driven culture within the school
Develop and maintain a district wide data system
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Student-Focused LearningBy Eon Verrall
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Phase I What is it that these students can’t do but need to be able to do? And how do we know that?
Phase II What are we going to do to ensure they learn it, and how are we going to prove that it worked?
Phase III What are the best next steps to make sure that our school continues to improve, and how will we prove that our next steps are working?
The Inquiry Cycle
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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 Using Data Process
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A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
Data-Driven Dialogue Detail
Adapted from Wellman, B., & Lipton, L., 2004. Data-Driven Dialogue: A Facilitator’s Guide to Collaborative Inquiry. Sherman, CT: MiraVia, LLC. Used with permission.
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A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by
Corwin Press. All rights reserved.
Phase 1: Predict Starters
I predict… I assume… I wonder… I’m expecting to see…
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A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
Data-Driven Dialogue Detail
Adapted from Wellman, B., & Lipton, L., 2004. Data-Driven Dialogue: A Facilitator’s Guide to Collaborative Inquiry. Sherman, CT: MiraVia, LLC. Used with permission.
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A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by
Corwin Press. All rights reserved.
BECAUSE
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Made by the five sensesAre quantitative and qualitativeContain no explanations
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by
Corwin Press. All rights reserved.
Observation Reminders
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A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by
Corwin Press. All rights reserved.
Phase 3: Observe Starters
I notice that…
I see that…
I am struck by….
I am surprised
that…
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A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by
Corwin Press. All rights reserved.
Data-Driven Dialogue Detail
Adapted from Wellman, B., & Lipton, L., 2004. Data-Driven Dialogue: A Facilitator’s Guide to Collaborative Inquiry. Sherman, CT: MiraVia, LLC. Used with permission.
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What are some inferences we are drawing about these data?
What are some implications to consider as we prepare to look at student-learning and other data about our school?
What questions do we have now?
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by
Corwin Press. All rights reserved.
Phase 4: Infer/Question
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A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by
Corwin Press. All rights reserved.
Phase 4: Infer/Question Starters
A possible explanation is…
That may be because…
A question I have now
is…
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_________ % of students tested at _________ School passed the _____________ exam. A performance gap of ______ percentage points was noted between __________ students and ____________ students.
A weak strand is ________________, particularly in ________________________________.
Student-Learning Problem Statement Template
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Brainstorm Possible Causes
Possible categories to consider…
CurriculumInstructionAssessmentEquityCritical Supports
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Prioritize Causes
You believe you have control to change
Has the greatest impact on student learning
Eager to investigate through research and local data
$Have time and resources to address
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A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by
Corwin Press. All rights reserved.
Verify Causes Tree
Adapted from Paul G. Preuss, Root Cause Analysis: School Leader’s Guide to Using Data to Dissolve Problems. 2003. Larchmont, NY. Eye on Education. Used with permission.
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Analyze Data…
Investigate root causes of all skill gaps
Use research based instructional strategies to address the weakness at the skill level
Continually evaluate the success of the strategies used
Race to the Top Inquiry Teams
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… how to get from here to there
Action
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• PLAN new lessons based on data analysis
• ACTION PLAN: Implement what you plan (dates, times, standards & specific strategies)
• LESSON PLANS: Observe changes in lesson plans
• ACCOUNTABILITY: Observe changes classroom observations, in-class assessments
• ENGAGED STUDENTS: Know end goal, how they did, and what actions they’re taking to improve
ACTION:
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What do data teams do?Collect and analyze a variety of types of school dataDevelop or adapt common assessment instrumentsCommit to norms of collaboration and to examining
data from an equity perspectiveIdentify student-learning problems, verify causes,
generate solutions, and monitor resultsConsult research to investigate problems, causes,
and best practiceDevelop data-supported action plansCommunicate with staff and key stakeholders about
findings and plans
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What do data teams do?Oversee the implementation of plans and/or
implementing instructional improvement in their own classroom
Share successes and challenges from their own classrooms
Engage a broader group of stakeholders to gain their input, involvement, and commitment
Coordinate with other school or district initiatives and leaders
Develop their data literacy and collaborative inquiry knowledge and skills; content knowledge, generic pedagogical knowledge, and pedagogical content knowledge; cultural proficiency; and leadership and facilitation skills
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Some tasks for teacher “Data Teams” that are possible in a short time period.
Set a short term goal based on instructional data.
Agree on a common assessments to monitor student progress towards the short term goal.
Monitor assessments and improve instruction.
Create or evaluate Common Core lessons.
Data Team Meeting
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S specificM measurableA attainableR results basedT time bound
Setting a SMART Goal
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Specific: The goal identifies what will happen and with whom.
Measurable: The goal includes clear indicators of success.
Attainable: The goal can be accomplished with the strengths, abilities, and resources available.
Relevant: There is a documented need for the goal and it is something you want to do.
Time-Bound: The goal includes the time frame for when it will be met.
SMART Goals
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SMART Goal Template
Area to improve : specific and measurable
Amount to Improve :attainable results
Target Date :time-bound
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Learner-Centered ProblemA statement of the skills or knowledge that
students lack.
Problem of PracticeA statement of the instructional
challenge(s) related to the learner-centered problem.
Data Wise Terms
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Brainstorm Possible Causes
Possible categories to consider…
CurriculumInstructionAssessmentEquityCritical Supports
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… what it really takes
Culture
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• VISION: Established by leaders and repeated relentlessly
• TRAINED LEADERSHIP TEAM: “real” leaders and formal leaders involved in process
• CALENDAR: Calendar in advance with built-in time for assessments, analysis & action
• PROFESSIONAL DEVELOPMENT: Aligned
DATA-DRIVEN CULTURE:
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Assumptions of the Concerns-Based Adoption Model (CBAM)
CHANGE IS…
A PROCESS, not an event;
made by INDIVIDUALS first, then institutions;
a highly PERSONAL experience.
Change entails DEVELOPMENTAL growth in feelings and skills.
INTERVENTION must be related to…
the PEOPLE first,
the INNOVATION second.
Gene E. Hall & Shirley M. Hord, Implementing Change: Patterns, Principles, and Potholes (2nd ed.). 2006. Boston: Allyn and Bacon. Used with permission.
TS 2
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Even in the most well behaved group, it can still be difficult to talk data….
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• Don’t use data to punish (administrators, teachers, students, schools).
• 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!
Some Data “Safety Regulations” to Guide the Use of Data
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1. Pausing2. Paraphrasing3. Probing for Specificity4. Putting ideas on the table and pulling them
off5. Paying attention to self and others6. Presuming positive intentions7. Pursuing a balance between advocacy and
inquiry
Seven Norms of Collaboration
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Stay engaged: Staying engaged means “remaining morally, emotionally, intellectually, and socially involved in the dialogue”
Experience discomfort: This norm acknowledges that discomfort is inevitable, especially in dialogue about race, and that participants make a commitment to bring issues into the open. It is not talking about these issues that creates divisiveness. The divisiveness already exists in the society and in our schools. It is through dialogue, even when uncomfortable, that healing and change begin.
Speak your truth: This means being open about thoughts and feelings and not just saying what you think others want to hear.
Expect and accept nonclosure: This agreement asks participants to “hang out in uncertainty” and not rush to quick solutions, especially in relation to racial understanding, which requires ongoing dialogue. A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative
Inquiry © 2007 by Corwin Press. All rights reserved.
The Four Agreements of Courageous Conversations
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A protocol is a tool that facilitates a complicated discussion in an efficient manner. (Beth Dryer)
Protocols
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A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
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.
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Narrowing the gaps between the highest- and lowest-performing students; and
Eliminating the racial predictability and disproportionality of which student groups occupy the highest and lowest achievement categories…”
“Equity is not a guarantee that all students will succeed. Rather, it assures that all students will have the opportunity and support to succeed. In an equitable system, the barriers that inhibit student progress are removed.”
A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
“Educational Equity is raising the achievement of all students while…
Source: Singleton & Linton, Courageous Conversations About Race. 2006, pp. 46–47. Thousand Oaks, CA: Corwin. Reprinted with permission.
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A Data Coach’s Guide to Improving Learning for All Students: Unleashing the Power of Collaborative Inquiry © 2008 by Corwin Press. All rights reserved.
Cultural Proficiency
Honoring the differences among cultures, viewing diversity as a
benefit, and interacting knowledgeably and respectfully
among a variety of cultural groups.
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Cultural Proficiency Continuum
Adapted from Randall B. Lindsey, Kikanza Nuri Robins, & Raymond D. Terrell. Cultural Proficiency: A Manual for School Leaders, 2003, p. 85. Thousand Oaks, CA: Corwin. Used with permission.
TS 1
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… now the fun begins
Conclusion
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Inquiry Team Members Data Coaches Instructional Specialists Teachers Students
People – How will they participate in using data to improve learning and teaching?
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Protocols Agendas Scheduling
Meetings – Are there procedures in place for effective collaboration? Who will monitor?
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State Local Interim
Assessments – What do you have already, what do you need?
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Goals Implementation Steps Timelines
Action Plans – Are these district, school, team or individual plans?
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APPR Internal Sharing External Sharing
Accountability – What will motivate your faculty? How will you celebrate success?
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Collect baseline data (Implementation Rubric for Data Driven Instruction and Assessment)
Practice Inquiry CycleFormulate a student learning problem
statementDevelop a related SMART goal
Afternoon Goals
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