Didd 2014

Walter L. Burt, Ph.D., WMU Associate ProfessorJ. Mark Rainey, Ed.D., Education ConsultantW a l t e r L. B u r t, Ph. D.

J. M a r k R a i n e y, Ed. D.

DATA - INFORMED DECISION – MAKING for PRINCIPALS and ASPIRING PRINCIPALS

Module I : ACL Six Dimensions of Leadership

A C L – S I X D I M E N S I O N S o f L E A D E R S H I P

(1) Engage in data-informed decision-making;(2) Manage safe and orderly school operations;(3) Develop Teacher leaders;(4) Redesign the organization;(5) Establish a coherent and rigorous instructional program; and(6) Lead the continuous school renewal process.

Learning ObjectivesAs a consequence of participating in this module, participants will:• Understand and experience the importance of data in a continuous

improvement cycle;• Become familiar with MDE-supported data mining tools that will equip

participants with an understanding of the type of data collected and how they can be used to support teaching and learning;

• Learn from other practicing and aspiring school leaders about their use of data to improve student achievement;

• Develop and implement a renewal activity in a high priority content area that is designed to improve the school improvement process.

3DIDML E A R N I N G O B J E C T I V E S

4DIDMDUNCAN - US SECRETAR Y OF ED

“Data systems to me are at the heart of this reform effort…we need comprehensive data systems that do three things:

1. Track students throughout their educational trajectory.2. Track students back to teachers so we can really shine a

spotlight on those teachers who are doing a phenomenal job of driving student achievement.

3. Track teachers back to their schools of education so..we’ll understand which schools…are adding value with their graduates.”

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Arne DuncanUnited States Secretary of Education

http://en.wikipedia.org/wiki/File:DuncanArne.jpg

5DIDMWMU-ELRT TRAINING WITH DIDM

• WMU/KRESA Principal Leadership Institute, 2003-2005

• The Wallace Foundation, 2003-2010

• USDE: Learning-Centered Leadership Development Program for Practicing and Aspiring Principals, 2010-2015

• USDE: Achievement-Centered Leadership Development Program for Practicing and Aspiring Principals, 2013-2018

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6DIDMWALLACE FINDINGS - PERCEPTIONS

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Wallace Findings: Principals’ Perception Regarding the Use of Data

• Of Teachers Teachers uncomfortable with data Teachers cannot read data Data has meaning to classroom Do not know what to do with data Data not part of teacher training Lack of knowledge data-instruction No data link to teaching practices

• Of Themselves Do not understand data use What data do you use Teacher collection of data Need systematic disaggregation Find better assessment tools PD for teachers and principals

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7DIDMWALLACE FINDINGS: TIME CONSTRAINTS

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Wallace Findings: Principals’ Perception of Time Constraints to Analysis Data

Time to complete tasks Data vs. classroom duties Limited instructional time Time to analyze data Time for collaboration

Time to monitor teacher use Time in getting test results Time in getting data back A year behind-results Holistic approach in working with

teachers

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WALLACE FINDINGS: PERCEPTIONS04/10/2023

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Wallace Findings: Principals’ Perception of Teacher and Student Issues

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No relevance to individual students

Results do not reflect current students

Needs to make sense Students mirror teacher attitude Too much student testing Teacher buying into data Unsure if data use beneficial Quality of instruction No consistence in teacher use of

tools

Teacher cooperation in assessment

Teacher- team cynicism Teachers see data as important Teacher-staff cooperation in data

assessment Inconsistent teacher collection of

student data Student do not take testing

seriously A few teachers see testing as a

fad Utility of data

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What is Data?

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NumbersOpinionsObservationsEssaysScience projectsDemonstrationsand …….


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EXAMPLES OF DATA:


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Data Can Answer These Questions

1. How are we doing?2. Are we serving all students well?3. In what areas must we improve?

Other Questions

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State-Wide Infrastructure to Support Data Informed Decision Making


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The “Data Wise” Improvement Process

Adapted From: Boudett, K.P., Clay, E.A., & Murnane, R. J. (2013). Data wise: A step-by-step guide to using assessment results to improve teaching and learning. Cambridge, MA: Harvard Educational Press. Revised and Expanded Edition.


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The package containing data from last spring’s mandatory state exam landed with a thud on principal Roger Bolton’s desk. The local newspaper had already published an article listing Franklin High as a school “in need of improvement.”

17Adapted From: Boudett, K.P., Clay, E.A., & Murnane, R. J. (2013). Data wise: A step-by-step guide to using assessment results to improve teaching and learning. Cambridge, MA: Harvard Educational Press. Revised and Expanded Edition.

Now this package from the state offered the gory details. Roger had five years of packages like this one, sharing shelf space with binders and boxes filled with results from the other assessments required by the district and state. The sheer mass of paper was overwhelming. Roger wanted to believe that there was something his faculty could learn from all these numbers that would help them increase student learning. But he didn’t know where to start.



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Table Activity

Where do you start?

How do you make time for the work?

How do you build your faculty’s skill in interpreting data sensibly?

How do you build a culture that focuses on improvement, not blame?

How do you maintain momentum in the face of all the other demands at your school?

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Adapted

From: Boudett, K.P., Clay, E.A., & Murnane, R. J. (2013). Data wise: A step-by-step guide to using assessment results to improve teaching and learning. Cambridge, MA: Harvard Educational Press. Revised and Expanded Edition.


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The “Data Wise” ProcessThe “Data Wise” process includes eight distinct steps

school leaders can take to use their student assessment data effectively, and organized these steps into three phases:

1. Prepare2. Inquire3. Act

19Adapted From: Boudett, K.P., Clay, E.A., & Murnane, R. J. (2013). Data wise: A step-by-step guide to using assessment results to improve teaching and learning. Cambridge, MA: Harvard Educational Press. Revised and Expanded Edition.



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“Data Wise” Improvement Process

The “Data Wise” Improvement Process graphic shown in next slide illustrates the cyclical nature of this work.

Initially, schools prepare for the work by establishing a foundation for learning from student assessment results.

Schools then inquire - look for patterns in the data that indicate shortcomings in teaching and learning—and

subsequently act on what they learn by designing and implementing instructional improvements.

Then you cycle back through inquiry and further action in a process of ongoing improvement.

20Adapted

Adapted From: Boudett, K.P., Clay, E.A., & Murnane, R. J. (2013). Data wise: A step-by-step guide to using assessment results to improve teaching and learning.

Cambridge, MA: Harvard Educational Press. Revised and Expanded Edition.


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“Data Wise” 8-Step Improvement Process

Adapted



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The “Data Wise” Improvement Process is an arrow curving back on itself

• Once you get to the “end” of Step 8 “Act and Assess” phase, you continue to repeat the cycle with further inquiry

• But now, Continuing the process at Step 3 “Creating Data Overview”

• Now you go deeper into the work, asking tougher questions, setting higher goals, and involving more people in using data wisely

22Adapted.



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The “Data Wise” District

What can district administrators do to support schools in becoming “data wise”?1. Set Up a Data System2. Create Incentives3. Support New Skills4. Find the Time5. Model the Work

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Table Activity

Question 1:As a school leader, what steps can you take to engage faculty and others in the use of data to improve student achievement?

Question 2:What support can central office, teacher leaders, and others provide to help in the study of data?

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Data Analysis for Contiguous School ImprovementMultiple Measures of Data


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Demographics

Enrollment, Attendance,Drop-Out Rate

Ethnicity, Gender,Grade Level

StudentLearning

Standardized Tests

Norm/Criterion-Referenced Tests

Teacher Observations of Abilities

Authentic Assessments

School Processes

Description of

School Programsand Processes

Over time, demographic data indicates changes in the context of the school.

PerceptionsPerceptions of

Learning Environment

Values and BeliefsAttitudes

Observations

Over time, perceptions can tell us about environmental improvements.

Over time, student learning data give information about student performance on different measures.

Tells us: The impact of student perceptions of the learning environment on student learning.

Tells us: The impact of the program on student learning based upon perceptions of the program and on the processes used.

Tells us: If a program is making a difference in student learning results.

Tells us: What processes/programs work best for different groups of students with respect to student learning.

Over time, school processes show how classrooms change.

Tells us: Student participation in different programs and processes.

Allows the prediction of actions/processes/programs that best meet the learning needs of all students.

Multiple Measures of Data:

Tells us: If groups of students are “experiencing school” differently.

Tells us: What processes & programs different groups of students like best.

Tells us: The impact of demographic factors and attitudes about the learning environment on student learning.

Adapted From: Bernhardt, V.L. (2013). Data Analysis for Continuous School Improvement. Larchmont, NY: Eye on Education. 3rd Edition 27


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School ProcessesDemographics Perceptions Student Learning

Demographics- Gender- Grade- Teacher- Age- Time in Building- Behavior- Attendance- Poverty Level- Racial/ethnic- Socioeconomic- Single Parent- Siblings in household- Free/Reduced Lunch

Parental Involvement- Preparedness- Transience- Out of school

experiences

Community Support- Programs e.g., Head Start- Services e.g, FIA

Opportunity to Learn- Current Offerings- Extra Curricular Activities

Teacher quality- Qualifications & Credentials- Instructional Practices- Professional Development- Collective Efficacy - Learning Communities- Professional Affiliations

Leadership- Vision, Mission, Goals- Staff Engagement & Perceptions- Parent Engagement &

Perceptions- Supervision Practices- Professional Affiliations

Resource Allocation- Budget Allocation- Staffing Patterns - Professional Development- Facility Usage/Maintenance- Technology Distribution

Results Data (Static Data)- MEAP/MME- ACT- AP Testing- District Benchmark Assessments- Standardized Assessments- Graduation Rate- Postgraduate Follow-up

Process Data (Real-Time Data)- Instructional Strategies- Classroom Assessments- Instructional Time on Task- Behavioral Referrals- Books- Writing Samples- Homework

Assigned/Completed- Positive Parent Contacts

Perception Data- Student Engagement- Student motivation- Student perceptions of

success- Values- Beliefs- Culture- Attitudes- Observations

32Adapted From: Bernhardt, V.L. (2013). Data Analysis for Continuous School Improvement. Larchmont, NY: Eye on Education. 3rd Edition

Data Steams Examples


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Michigan School Data (MI School Data)


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Using MI SCHOOL DATA to Identify School Improvement Goals for

MEAP/MME• For this training all data collected will come from MI School Data website at: https://www.mischooldata.org/• Look this site carefully. It has a wealth of data. The data is found in the “Student Testing” Tab on left side of Home Page. Use MEAP/MME

data from our school for this activity. Be sure an select appropriate templates: MEAP or MME. Depending on configuration for grade levels in your school. You may need to use combination of MEAP and MM Templates.

• Use Data Proficiency Templates provided to record data and answer all questions associated with each template.• After collecting data, develop 1 to 3 Data Narrative Statements based on data collected. (This will be explained before Data is collected)• This training will guide you through the data mining process focused on the question:

What can we learn from our MEAP/MME data?The intent of this training is to build the capacity within your school to ask questions of the data and support for continuous school improvement . After data is collected on your school and recorded on the Data Proficiency Templates and have analyzed data, answer the following questions:– Where are we in relation to our AYP/Annual Measurable Objectives (AMOs) targets?– How do we analyze our MEAP/MME data to identify strengths and challenges?– What questions do our data raise for us?– How do we use this data to identify school improvement goals and annual measurable objectives?– How do we engage staff in the data analysis process?– How do standards change expectations for teachers?– What are the limitations of state assessment data?

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https://www.mischooldata.org/


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Data Narrative Statement

37Adapted from State of Michigan MI School Data Web Site https://www.mischooldata.org

The Data Narrative Statements will not be created until after all data is collection and analysis using MI School Data process.


Data Narrative Statement Criteria:

1. Are objective statements of FACT about the school data2. Represent student achievement, demographics, school

programs, school processes, and stakeholder perceptions3. Communicate a SINGLE idea4. Are clear and concise – written in sentences or phrases5. Describe the data; they do not evaluate the data!6. They do not require the data source to accompany them

in order to be understandable.



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Data Narrative Statement Criteria:

Do these Statements Meet the Narrative Criteria?1. Spring 2013 MME Math Assessment shows that our girls do slightly better than the boys. Yes: ___ No: ___

2. The Spring 2013 MME Math Assessment shows that 20.5% of our 11th grades students were proficient and 79.5% were not. Yes: ___ No: ___

3. The MME Math Assessment shows that 0% of our students with IEPS were not proficient. This works out to be 0 of our 7 students with IEPS were not proficient. Yes: ___ No: ___

4. The Spring 2013 MEAP Math Assessment shows that we really need a new math series. Yes: ___ No: ___

5. In 2012-13, 21.4 % of all our students taking the MME Math Assessment are proficient; while 20.5% of our 11th graders are proficient and 33.3% of our 12th graders are proficient. Yes: ___ No: ___

6. Parents do not like the math program. Yes: ___ No: ___


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Mi School Data: Data Proficiency Template (MEAP/MME) Handout

Adapted from State of Michigan MI School Data Web Site https://www.mischooldata.org


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MEAP Handouts


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Michigan Annual AYP Objectives


Mi School Data: Data Proficiency Template (MEAP)

Hand Out


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Mi School Data: Data Proficiency Template (MEAP) Hand Out



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Mi School Data: Data Proficiency Template Mi School Data: Data Proficiency Template (MEAP) Hand Out



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MME Handouts


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Using MI SCHOOL DATA to Identify School Improvement Goals for MEAP/MME

• All data collected will come from MI School Data website at: https://www.mischooldata.org/• Look this site carefully. It has a wealth of data. The data we will be using is found in the “Student Testing” Tab on left

side of Home Page. Use MEAP/MME data from their school for this activity.• Use the Data Proficiency Templates provided to record data and answer all questions associated with each template.• After collecting data, develop 1 to 3 narrative statements based on data collected.• This activity will guide you through the data mining process focused on the question:

What can we learn from our MEAP/MME data?The intent of this activity is to build the capacity within your school to ask questions of the data and support for continuous school improvement . After data is collected your school on the Data Proficiency Templates and have analysis data, answer the following questions:– Where are we in relation to our AYP/Annual Measurable Objectives (AMOs) targets?– How do we analyze our MEAP/MME data to identify strengths and challenges?– What questions do our data raise for us?– How do we use this data to identify school improvement goals and annual measurable objectives?– How do we engage staff in the data analysis process?– How do standards change expectations for teachers?– What are the limitations of state assessment data?



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https://www.mischooldata.org/

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Michigan Annual AYP Objectives


Hand Out


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Mi School Data: Data Proficiency Template (MME) Hand Out



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Mi School Data: Data Proficiency Template Mi School Data: Data Proficiency Template (MME) Hand Out



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Mi School Data: Data Proficiency Template Mi School Data: Data Proficiency Template (MME) Hand Out



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WHAT IS A SCHOOL RENEWAL ACTIVITY?

• Based on the current status of the school, it is a best practice that upon implementation will increase student achievement (Shen, 2013).

R E N E W A L A C T I V I T Y

Examples for Renewal Activities Establish a “Data Team” with roles, responsibilities,

and timelines in data-informed decision-making; Develop “Data Walls” that indicate the progress of

students based on standards and adjust instruction; Have “Data Meetings” among parents, teachers, and

administrators to improve student achievement; Design a “Data Intersection” process to connect

student background data to the school process data to the student achievement data;

Develop “School Dashboard” and communicate with constituents


Renewal Activities

• Must, to the extent possible, be linked to the building and district school improvement plans;

• Can be either a new activity or an enhancement of an existing activity;

• Co-developed by the principal/aspiring principal and other school stakeholders;

• Can either be “stand alone” for one dimension or connected to other dimensions;

• Not simply the “Research, Development, Dissemination and Evaluation” (RDDE) model; rather, the “Dialogue, Decision, Action and Evaluation” (DDAE) model.


LEARNING ACTIVITY # ??: CAPTURE YOUR INITIAL DIDM SCHOOL RENEWAL ACTIVITY: 20 Minutes

• On a piece of paper, jot down what you think might be a DIDM opportunity for a school renewal activity in your building.

• Remember, this school renewal activity must support your school improvement planning efforts.

• Share your thoughts with other colleagues at your table.

• WMU staff will randomly select 4 to 5 participants to share their initial thoughts with the total group.


Didd 2014

Education

Transcript of Didd 2014