Data-Driven Decisions: Data-Driven Decisions:

Principal’s WorkshopPrincipal’s Workshop

November 30, 2010November 30, 2010

The End in MindThe End in Mind To summarize and update our district status in To summarize and update our district status in

school level use of DDDM; school level use of DDDM; Introduce Administrators to the Process of DDDM Introduce Administrators to the Process of DDDM To discuss “Next Steps”.To discuss “Next Steps”.

LPSS Data Team Survey LPSS Data Team Survey Results Results n= 29n= 29

20/29 active20/29 active 4/11 middle and high utilizing auxiliary 4/11 middle and high utilizing auxiliary

personnelpersonnel Types of data reported:Types of data reported:

EdusoftEdusoft Grade level failures Grade level failures

DisciplineDiscipline Targeted groupsTargeted groups

AttendanceAttendance Reading levelReading level

Standardized TestStandardized Test Math GLE masteryMath GLE mastery

STAR ResultsSTAR Results DIBELSDIBELS

AIMSWEBAIMSWEB

State Initiative: Dropout Early State Initiative: Dropout Early Warning SystemWarning System

• Attendance (5 absences in last 35 days/ Attendance (5 absences in last 35 days/ 10 absences year- to- date)10 absences year- to- date)

• Behavior (3 discipline referrals in last 35 Behavior (3 discipline referrals in last 35 days/ 5 referrals year-to-date)days/ 5 referrals year-to-date)

• Course Failure (Current GPA <1.0 or Course Failure (Current GPA <1.0 or drop in GPA of .50)drop in GPA of .50)

  

Principal

Leadership Team

School Improvement

Team

Grade/ Dpt. Level Teams SBLC

Data Team

•Professional Development•Parent/ Community Support

RtI•Academic•PBIS

Principal: Roles and Principal: Roles and ResponsibilitiesResponsibilities

1.Align teams2.Communicate team purpose and

outcomes3.Clearly define and communicate

expectations connected to data4.Ensure teams utilization of data5.Provide tools and training

necessary to utilize data

What is Root Cause Analysis?What is Root Cause Analysis?

A Root Cause is “the most basic A Root Cause is “the most basic reason that the problem occurs.” reason that the problem occurs.”

Total Quality Schools Total Quality Schools by Joseph C. Fieldsby Joseph C. Fields

Watch this Ferrari Pit Watch this Ferrari Pit Crew…Crew…

How we do think they relied How we do think they relied on data to solve a problem?on data to solve a problem?

Cause vs. Effect DataCause vs. Effect Data

Effect data: outcomes or resultsEffect data: outcomes or results

Cause data: professional practices Cause data: professional practices that create specific effects or resultsthat create specific effects or results

Cause or Effect?Cause or Effect?

Types of data reported:Types of data reported:EdusoftEdusoft Grade level failures Grade level failures

DisciplineDiscipline Targeted groupsTargeted groups

AttendanceAttendance Reading levelReading level

Standardized TestStandardized Test Math GLE masteryMath GLE mastery

STAR ResultsSTAR Results DIBELSDIBELS

AIMSWEBAIMSWEB

School Team TrainingSchool Team Training

Going on a “DIG”

DIG

Ask Dig Analyze and Prioritize Set SMART Goals Strategize Outcomes Monitor and Evaluate

Roll up your sleeves…

We’re going on a DIG

Ask questions about your data

Use Data Questioning Template

Functional View of School DataFunctional View of School Data

Fishbone AnalysisFishbone Analysis

Steps for Fishbone

1. Fish Head: Use course failure data to formulate your obstacle (i.e. “# of 8th graders with D/F/I):

1st 6 weeks: 60 8th graders with a D/F/I 2nd 6 weeks: 80 8th graders with a D/F/I

Step 2: Fill in Fishbone

4 topics: • Curriculum• Instruction• Student Demographics• System Processes

4 Fish bones Curriculum

• Subjects • Comprehensive Curriculum

Instruction• Teacher Experience • Teacher/Student Relationships• Teaching Methods• Classroom Routines

Demographics• Feeder Schools• Overage• Attendance/Discipline

Processes• Pupil Progression Plan• Collaboration Time• Class size• Progress Reports• Data Teams

Circle fish bones that are internal to a school setting

Be energy efficient

Internal vs. External? Curriculum

• Subjects • Comprehensive Curriculum

Instruction• Teacher Experience • Teacher/Student Relationships• Teaching Methods• Classroom Routines

Demographics• Feeder Schools• Overage• Attendance/Discipline

Processes• Pupil Progression Plan• Collaboration Time• Class size• Progress Reports• Data Teams

Syst

ems

Practices

DataSupportStaff

SupportDecisionMaking

SupportStudents

Outcome: Academic Achievement

Center for Positive Behavior Interventions and Supports (2002)

Steps towards D3MSteps towards D3M

Team TrainingsTeam Trainings

Critical People: WhoCritical People: Who

Critical Elements: WhatCritical Elements: What

Critical Outcome: How Critical Outcome: How

(Train the Trainer)(Train the Trainer)

Critical Elements

Data Team established• Team Composition• Administrator attendance• Set schedule• Designated liaisons to SBLC/RTI, Grade levels and SIP • Meeting agenda/ minutes taken

Basic Data Driven Decision Making skills taught to all staff

Guiding questions developed for each team

Faculty involvement• Sharing data

Frequency• Procedures for getting data to the teams• Procedures for gathering analysis of data from each team

Data Rooms • Maintenance and updating• Source of data

Closing and Final Closing and Final RemarksRemarks

Louise Chargois, Curriculum and Louise Chargois, Curriculum and InstructionInstruction

Thank you!Thank you!