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Building a Data-Driven School Hilary Shea September 26, 2006.

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Building a Data-Driven School Hilary Shea September 26, 2006
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Page 1: Building a Data-Driven School Hilary Shea September 26, 2006.

Building a Data-Driven School

Hilary SheaSeptember 26, 2006

Page 2: Building a Data-Driven School Hilary Shea September 26, 2006.

Words to the Wise

• Building a data driven school takes a lot of time

• Real change does not happen overnight

• It is not enough for a plan to look good on paper

• Best data system means nothing if data is not used

• Making data-based decisions impacts all parts of school life, not just instruction

• It is essential to set realistic and attainable goals

Page 3: Building a Data-Driven School Hilary Shea September 26, 2006.

The Big Picture

• Interlocking system of teams

• Efficient data management

• Effective meetings

• Meaningful peer observation

• Real internal accountability

Page 4: Building a Data-Driven School Hilary Shea September 26, 2006.

InterlockingSystem of Teams

• Time to meet

• Strong Instructional Leadership Team

• Communication between teams

Page 5: Building a Data-Driven School Hilary Shea September 26, 2006.

EfficientData Management

• Data coordinator

• Data inventory

• Data binder

Page 6: Building a Data-Driven School Hilary Shea September 26, 2006.

Effective Meetings

• Careful planning

• Use of protocols

• Teacher leadership

• Action-oriented

Page 7: Building a Data-Driven School Hilary Shea September 26, 2006.

MeaningfulPeer Observation

• Frequent visits

• Shared language for discussing instruction

• Beyond “culture of nice”

Page 8: Building a Data-Driven School Hilary Shea September 26, 2006.

RealInternal Accountability

All staff accountable to

each other for:

• Productive meetings

• Learning together

• Improving practice

Page 9: Building a Data-Driven School Hilary Shea September 26, 2006.

Zooming In

Efficient

Data

Management

Page 10: Building a Data-Driven School Hilary Shea September 26, 2006.

Four steps to successful data collection

1. Create a data inventory

2. Assemble and maintain a data binder

3. Develop data templates for recording raw data from each assessment

4. Create data displays that summarize information from raw data

Page 11: Building a Data-Driven School Hilary Shea September 26, 2006.

1. Data Inventory

Answers the questions:

• Which assessments do we use?

• Who is tested?

• When?

• Why?

• What happens to the data?

Page 12: Building a Data-Driven School Hilary Shea September 26, 2006.

2. Data Binder

• Choose level: school, grade, class, student

• Choose organization: by subject, assessment, and/or teacher

• Plan maintenance: decide what roles data team, teachers will play

Page 13: Building a Data-Driven School Hilary Shea September 26, 2006.

3. Data Templates

• Start with big picture: what do you want to know?

• Address details: what info should you include on form?

• Plan structure: make it easy to use and to read

Page 14: Building a Data-Driven School Hilary Shea September 26, 2006.

What Not To Do…

TOO MUCH DATA GOING ON!

Page 15: Building a Data-Driven School Hilary Shea September 26, 2006.

Example 1:Guided Reading Template

Grade 2Instructional Reading Levels

Sep-05 C D E F G H I J K L M N O

Jan-06 C D E F G H I J K L M N O

Page 16: Building a Data-Driven School Hilary Shea September 26, 2006.

The Form Alone Provides a Helpful Visual

The gray column denotes the end-of-year grade level benchmark.

The two arrows highlight one student’s progress from the fall assessment to the winter assessment.

At the classes current rate of improvement the majority of students will be at or above grade level by the Spring.

Page 17: Building a Data-Driven School Hilary Shea September 26, 2006.

Looking at Charts from Year to Year

The yellow boxes highlight two students progress from kindergarten to second grade. Both students were placed in Reading Recovery in 1st Grade.

Both students finished second grade at nearly benchmark level.

Page 18: Building a Data-Driven School Hilary Shea September 26, 2006.

Example 2:Writing Prompt Template

6 Trait Rubric

Fall, Winter, and Spring Scores

Page 19: Building a Data-Driven School Hilary Shea September 26, 2006.

4. Data Displays

• Visual representation of specific aspects of the raw data

• Can be generated from the data entered into Excel templates

• Can inform classroom-level and school-wide discussions

Page 20: Building a Data-Driven School Hilary Shea September 26, 2006.

Displays Allow Data to Drive Instruction

Word Choice

0123456789

10

1 2 3 4 5

Score

Nu

mb

er o

f S

tud

ents

FALL:

WINTER:

SPRING:

Charts are created for each trait

Charts are used to identify areas of strength and weakness across grades and across the schools

The data is used to inform mini-lessons, the focus of CCL, and identifying needs in the scope and sequence between grades.

Page 21: Building a Data-Driven School Hilary Shea September 26, 2006.

Helpful Hints

• Hold teachers accountable for submitting their classroom data on time.

• Use the data in meaningful and relevant ways…you can’t have data-driven instruction without using the data!

• Go electronic so data is recorded on a standardized form and automatically stored in multiple places.

Page 22: Building a Data-Driven School Hilary Shea September 26, 2006.

Questions?

Page 23: Building a Data-Driven School Hilary Shea September 26, 2006.

Data in Action:The Development of an Action Plan to Improve Reader’s Notebooks

©Hilary Shea, June 2006

Page 24: Building a Data-Driven School Hilary Shea September 26, 2006.

Data on Learner-Centered Problem

4th Grade ELA MCAS from last two years: average score on open response questions is 2.3 out of 4.

Reader’s Response Notebooks: entries are weak in organization and thesis development.

Guided Reading Assessments: students perform worse on the open response question than on the oral portion of the test

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 25: Building a Data-Driven School Hilary Shea September 26, 2006.

4th Grade ELA MCAS 2004-05

Average Open Response Score for Students Who Received a Grade of Proficient

22.5

3

2

32.5 2.5

2

3

2

3

43.5

0

1

2

3

4

Stude

nt A

Stude

nt B

Stude

nt C

Stude

nt D

Stude

nt E

Stude

nt F

Stude

nt G

Stude

nt H

Stude

nt I

Stude

nt J

Stude

nt K

Stude

nt L

Stude

nt M

Students

Sco

res

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 26: Building a Data-Driven School Hilary Shea September 26, 2006.

Learner-Centered Problem

Students are unable to consistently respond

and reflect when writing about texts they have read

independently or with the class.

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 27: Building a Data-Driven School Hilary Shea September 26, 2006.

Data on Problem of Practice

Reader’s Response Notebooks

Fall CCL reflection on practice: teachers identified ineffective use of reader’s notebooks as a problem

Ongoing Observation: principal and coach found teachers not effectively using mini-lessons as a means to teach written responses to text.

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 28: Building a Data-Driven School Hilary Shea September 26, 2006.

Problem of Practice

Teachers are not effectively using the Reader’s Notebook as a forum for students

to respond and reflect in writing about what they read.

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 29: Building a Data-Driven School Hilary Shea September 26, 2006.

Statement of Solution

Devote this spring’s intermediate CCL to supporting teachers in

using Reader’s Notebooks more effectively.

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 30: Building a Data-Driven School Hilary Shea September 26, 2006.

What will we do?

Create statement of purpose for Reader’s Notebook

Use student letters as the primary data source

Create rubric for assessing letters

Create grade-level expectations for letters

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 31: Building a Data-Driven School Hilary Shea September 26, 2006.

What will we do? (continued)

Model lessons on different types of letters Prompted Non-prompted Topic cards

Model mini-lessons related to improving letters

Generate exemplars for all grades

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 32: Building a Data-Driven School Hilary Shea September 26, 2006.

What will we see in classrooms?

Effective mini-lessons

High-quality student letters (from students of all ability levels)

Notebooks that include both non-prompted and prompted letters

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 33: Building a Data-Driven School Hilary Shea September 26, 2006.

What will we see in classrooms? (continued)

High-quality teacher responses to letters, that have the purpose of improving their content

Use of the rubric and exemplars in all classes

Use of notebooks as means of assessment

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 34: Building a Data-Driven School Hilary Shea September 26, 2006.

How will we confirm that classroom changes are occurring?

Peer observations

Principal and coach learning walks

Focus groups

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 35: Building a Data-Driven School Hilary Shea September 26, 2006.

Where will we look for evidence of improved student learning?

Reader’s Notebooks: Prolific and high-quality letters from all students

Classroom reading response assessments: Well-organized, well developed answers (teachers can use students’ actual letters as the means of assessment)

4th Grade ELA MCAS: Improvement on open response questions

©Hilary Shea, June 2006©Hilary Shea, June 2006

Page 36: Building a Data-Driven School Hilary Shea September 26, 2006.

Potential Pitfalls

Keeping up the momentum following the spring CCL

Effectively using student letters during looking at student work sessions

Monitoring teachers’ use of the rubric and exemplars after this year

©Hilary Shea, June 2006©Hilary Shea, June 2006


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