pearson copyright 2007 successful literacy coaching: using data to enhance literacy instruction ira...

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Successful Literacy Coaching: Using Data to Enhance Literacy

Instruction

IRA

May 6, 2008

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Objectives

• Discuss barriers to developing a culture of data driven decision making

• Identify the purpose of different categories of assessments

• Investigate a collaborative process for data driven decision making

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Agenda

I. The Numbers Game

II. What is a Culture of Data Use?

III. The Importance of Collaboration

IV. What Data Should We Use?

V. Collaborative Data Analysis

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Where Do We Start?

“What are we supposed to do

with all this stuff ???”

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What is a Culture of Data Use?

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Data-Driven Decision MakingIdentify Issues

“What do we need to know more about?”Identify Issues

“What do we need to know more about?”

Collect Data “What data do we need?”

“Go get it!”

Collect Data “What data do we need?”

“Go get it!”

Analyze Data“Look at it!”Analyze Data“Look at it!”

Develop GoalsWhy is this happening?

“What do we want to do about it?”

Develop GoalsWhy is this happening?

“What do we want to do about it?”

Design Action Plan“How are we going

to do it?”

Design Action Plan“How are we going

to do it?”

Implement Action Plan“Go do it!”

Implement Action Plan“Go do it!”

Monitor/Assess/Revise“How are we doing?”

Monitor/Assess/Revise“How are we doing?”

an integrated, collaborative, and iterative process

At appropriate times …Communicate Results

“Whom should we inform and how?”Participant Guide, p. 3

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Data-Driven Decision MakingIdentify Issues

“What do we need to know more about?”Identify Issues

“What do we need to know more about?”

Collect Data “What data do we need?”

“Go get it!”

Collect Data “What data do we need?”

“Go get it!”

Analyze Data“Look at it!”Analyze Data“Look at it!”

Develop GoalsWhy is this happening?

“What do we want to do about it?”

Develop GoalsWhy is this happening?

“What do we want to do about it?”

Design Action Plan“How are we going

to do it?”

Design Action Plan“How are we going

to do it?”

Implement Action Plan“Go do it!”

Implement Action Plan“Go do it!”

Monitor/Assess/Revise“How are we doing?”

Monitor/Assess/Revise“How are we doing?”

an integrated, collaborative, and iterative process

At appropriate times …Communicate Results

“Whom should we inform and how?”

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What makes it so difficult to build this culture?

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Cognitive Dissonance

Cognitive dissonance theory tells us that to reduce stress, human beings strive for congruence between their behavior and beliefs.

Psychologists call the tension a person feels when actions are not consistent with beliefs cognitive dissonance.

Do your existing school processes leverage our inherent desire for cognitive congruence as a force for positive change?

Participant Guide, p. 4

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Barriers to Effective Data Use

Cultural Barriers Technical Barriers

Political BarriersWelcome to Data Driven Decision

Making!

Participant Guide, p. 5

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Barriers to Effective Data UseCultural Barriers

– Personal metrics for judging teaching differ from metrics of external parties

– Decisions are based on experience and intuition, rather than on systematically collected information

– Disagreement about which student outcomes and data are important

– Teachers may disassociate own performance from student performance

(Ingram, Seashore & Schroeder, 2004)

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Barriers to Effective Data Use

Technical Barriers– Data teachers want is rarely available and

difficult to measure

– Inadequate time to collect and analyze data

Political Barriers– Data have often been used politically, leading

to mistrust of data and data avoidance

(Ingram, Seashore & Schroeder, 2004)

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Tips on Using Data Safely

• Do not use data primarily to identify or eliminate poor teachers.

• Inundate practitioners with success stories that include data.

• Collect and analyze data collaboratively and anonymously by team, department, grade level, or school.

• Allow teachers, by school or team, as much autonomy as possible in selecting the kind of data they think will be most helpful.

(Schmoker, 1999)

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The calibration process allows stakeholders to explore personal positions on important questions such as, “what should students learn” and “how will we know learning has happened,” with the stated aim of arriving at a group (e.g., team, school, or district) set of common standards and definitions.

(Springfield & Wayman, 2006)

The Foundation of the Culture

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The calibration process allows stakeholders to explore personal positions on important questions such as, “what should students learn” and “how will we know learning has happened,” with the stated aim of arriving at a group (e.g., team, school, or district) set of common standards and definitions.

The Foundation of the Culture

(Springfield & Wayman, 2006)

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What Data Should We Use?

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Data Domains

Demographic

Perceptual

Student Learning

SchoolProcesses

Information about who we are

Information about how we work Information about how we think

Outcomes

(Bernhardt, 2003)

Participant Guide, p. 6

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Four Categories of Reading Assessments

Outcome: Provide a bottom-line evaluation of the effectiveness of the reading program

Screening: Determine which students are at risk for reading difficulty and who will need additional intervention

Diagnostic: Help teachers plan instruction by providing in-depth information about students’ skills and instructional needs

Progress Monitoring: Determine if students are making adequate progress or need more intervention to achieve grade-level reading outcomes

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Four Categories of Reading AssessmentsOutcome: AIMS (Arizona Instrument to Measure

Standards), TerraNova, District Benchmarks, DIBELS (Dynamic Indicators of Basic Early Literacy Skills)

Screening: DIBELS, KIST (Kindergarten Individual Screening Test), Reading Series Phonics Screening

Diagnosis: DRA2 (Developmental Reading Assessment), Words their Way Qualitative Spelling Inventory, QRI (Qualitative Reading Inventory), teacher-created assessment

Progress Monitoring: DIBELS, DRA2, district-created prescriptive assessments

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Reading Assessment

Categorize the assessments used in your school.

1. Outcome

2. Screening

3. Diagnosis

4. Progress Monitoring

Participant Guide, p. 7

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Collaborative Data Analysis

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Looking at Data

• Predict – What do we think we’ll see?

• Observe– What do we see?

• Infer– What does this mean for

us now and in the future?

Predict

Observe

Infer

Participant Guide, p. 8

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Predict

Observe

Infer

Predicting

• What are our assumptions?• What do we predict we will

see?• What questions will we ask?

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Predict

Observe

Infer

Observing

• What important information “pops out?”

• What patterns and trends do you see?

• What seems surprising?• What seems odd or

confusing?

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Predict

Observe

Infer

Inference

• What inferences or explanations can we make?

• What questions are raised?

• What additional data should we explore?

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Let’s try it…

Observe

Predict

Infer

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Prediction

I expect to see growth in enrollment over the four years of data that I have.

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What Do We See?

• Review the data.

• Make an observation.

• Share observations with colleagues.

Observe…Participant Guide, p. 9

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Riverview School District Enrollment

Grade Level 2000-2001

2001-2002

2002-2003

2003-2004

Kindergarten 1,696 1,709 1,790 1,819

Grade 1 1,249 1,297 1,320 1,385

Grade 2 1,142 1,221 1,275 1,299

Grade 3 1,128 1,209 1,284 1,309

Grade 4 1,208 1,138 1,197 1,233

Grade 5 1,142 1,238 1,194 1,248

Grade 6 1,103 1,176 1,232 1,224

Grade 7 1,282 1,354 1,428 1,541

Grade 8 1,055 1,235 1,290 1,359

Grade 9 1,450 1,619 1,930 2,134

Grade 10 1,061 1,226 1,302 1,499

Grade 11 759 790 934 1,024

Grade 12 902 982 957 1,065

District Total 15177 16194 17133 18139

Observe…

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Riverview School District Enrollment

Grade Level 2000-2001

2001-2002

2002-2003

2003-2004

Kindergarten 1,696 1,709 1,790 1,819

Grade 1 1,249 1,297 1,320 1,385

Grade 2 1,142 1,221 1,275 1,299

Grade 3 1,128 1,209 1,284 1,309

Grade 4 1,208 1,138 1,197 1,233

Grade 5 1,142 1,238 1,194 1,248

Grade 6 1,103 1,176 1,232 1,224

Grade 7 1,282 1,354 1,428 1,541

Grade 8 1,055 1,235 1,290 1,359

Grade 9 1,450 1,619 1,930 2,134

Grade 10 1,061 1,226 1,302 1,499

Grade 11 759 790 934 1,024

Grade 12 902 982 957 1,065

District Total 15177 16194 17133 18139

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Guidelines for Making Inferences

• Review your observations to see if there are any commonalities or patterns.

• Write any new questions that arise from reviewing your observations.

• Make inferential statements:– “I wonder if _________ is the cause of ________.

– “I think _________ might be happening because of _____.

• Your inferences and questions may lead to the next analysis, or can be tabled for later analysis.

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Questions to Guide Analysis

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Questions to Guide Analysis

• Is the assessment valid and reliable?• What is the purpose of the assessment?• What information does the data provide? • Is the information adequate to make a decision?• Do we need additional information? How can we

get the information we need?• What does the data tell us about student

learning? What does the data tell us about instruction?

• Next steps?

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How Does This Apply to a Classroom?

• Teachers can research what failing (or high achieving) students have in common.

• Teachers can monitor discipline issues and patterns with a simple data collection plan.

• An observer can gather data about which students (or parts of a classroom) get the most attention or instruction.

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How Does This Apply to a Classroom?

Teachers collect more useful data in a day than they may realize:

• Every classroom assessment should provide useful data.

• Teachers can use standards based assessments to monitor individual student progress toward meeting objectives.

• Grade level teams can use common assessments to provide useful data about the progress of all of their students.

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Taking it Back to School

• What can you apply from today’s session immediately upon your return?

• What additional information would you like to have?

• How will you share the strategic approach with your teachers?

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References

• Bernhardt, V. (2003). Using data to improve student learning. Larchmont, NV: Eye on Education.

• Ingram, D., Seashore Louis, K., Schroeder, R. (2004). Accountability Policies and Teacher Decision Making: Barriers to the Use of Data to Improve Practice Teachers College 106: 6, 1258-1287.

• Schmoker, M. (1999). Results: The key to continuous school improvement, 2nd Ed. Alexandria, VA: ASCD.

• Stringfield, S. & Wayman, J.C. (2006). Data Use for School Improvement: School Practices and Research Perspectives. American Journal of Education 112:4, 463-468.

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Debbie Fast, Instructional Specialist, Chandler Unified District

fast.debbie@chandler.k12.az.us

Angee Lewandowski, Literacy Coach, Chandler Unified District lewandowski.angee@chandler.k12.az.us

Alesha Henderson, Literacy Specialist, Pearson

alesha.henderson@pearson.com

623.399.0478

Carey Regur, Director of Instructional Services, Pearsoncarey.regur@pearson.com714.323.0779