essei 2016 ews final - university of south florida

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PREPARING OUR GRADS TO BECOLLEGE, CAREER & LIFE READY

Amber Brundage & Alyssa Lipinski

9/20/16

Advance Organizer

• Early Warning Systems Overview– Context

• National & Florida Data• Predictive Indicators

– History & Research• High School• Middle School

• EWS Implementation– Determining Heath of the Building

• Disproportionality• Problem Solving

Advance Organizer

EWS Overview

Context for Early Warning Systems

• In order for students to graduate career, college, and life ready they must:– Successfully navigate academic transitions– Acquire academic enabling behaviors

• Attend• Behave• Complete work

• 50% of future non-graduates readily identifiable as early as 6th

grade (Balfanz, Herzog & Mac Iver, 2007)

• Early warning systems provide a mechanism for early identification of those students who signal they are not on-track for on-time graduation (Balfanz & Stenson, 2012)

• Supported by America’s Promise Alliance• My Brother’s Keeper Initiative

The “Promise of Early Warning Systems”

• Early Warning Systems (EWS):– Use readily available data typically collected at the

school-level– Allow educators to hone-in on key pieces of data to

inform decisions– Provide “real-time” data for monitoring– Allow districts to identify patterns, trends and school

effectiveness at keeping students on-track– Identify at-risk students who are likely to experience

adverse outcomes early enough to alter student trajectories (Davis, Herzog, & Legters, 2013)

Early Warning Indicators versusEarly Warning Systems

Early Warning Indicators

• Individual predictors and thresholds utilized to indicate student level of risk or likelihood of a given outcome:– Missing more than 10% of

instructional time– 1+ ISS/OSS– Course performance

Early Warning Systems

• Organized system where:– Struggling learners or students at-

risk are identified– Interventions are provided-

matched to student need with varying intensity levels

– Individual and aggregate-level progress is monitored

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Infrastructure Necessary to Effectively Utilize EWS

• Development of user-friendly/efficient data system

• High quality and accurate data entry• Designated EWS teams with dedicated

meeting time– District– School

• Staff professional development/support for analysis of data

• Resource allocation• MTSS framework• Data-based problem-solving

History and Research on Early Warning Systems

Early Warning Systems High School• 1999 Consortium on Chicago School Research (CCSR): On-Track

Indicator (OTI)accurately predicted 80% of those who would graduate on-time based on 9th grade:

• Number of Fs• Number of credits earned (Allensworth & Easton, 2005)

• Background characteristics (race/ethnicity, SES, previous test scores, age, mobility) only predicted 65% of on-time graduates

• Adding background characteristics to OTI only increased predictive ability by 1% above and beyond Fs and Credits

• Background characteristics important:– Relationship with course performance which impacts course failures and credits

earned

We cannot monitor or impact all the background factors students bring BUT we can monitor and impact course performance

Course Failures & Attendance As Predictors

• When researchers examined why students failed courses:– Student behaviors: attendance & study habits

(engagement indicators) accounted for the majority of course failures

• Consistent across achievement and SES levels

• 2007 CCSR found GPA and attendance as predictive as Fs and credits– Allowed for more timely monitoring

• 2014 CPS graduation rate of 69%– Projected rate 84% for 2018

National High School Center EWS Indicators

• 2008 National High School Center created high school EWS to automatically flag students off-track for graduation based on:

• Earning less than ¼ total credits required for graduation minus 1 per semester

• Less than 2.0 GPA• Missing 10% or more absences• Failing two or more courses (Heppen & Therriault, 2008)

Middle SchoolEarly Warning Systems Background

• Researchers followed a Philadelphia cohort of almost 13,000 6th graders for 8 years to find middle school predictors of non-graduates (Balfanz, Herzog, & MacIver, 2007)

• Based on 2 pronged test- 75+% of 6th graders with indicator didn’t graduate on-time AND identified substantial number of future non-graduates:

• Failure of math or English• 20+% absences• 1 out of school suspension or failing behavior grade

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Middle School EWS Background Continued

• 2011 Baltimore schools replicated the Philadelphia research with cohort of ~8,000 students

• Baltimore researchers used the following indicators that predicted 70+% non-grads:– 10+% absences– Failing English and math or failing average for core

courses– Overage for grade– Suspensions of 3+ days

National High School Center Middle School EWS Indicators

• 2011 National High School Center adapted EWS for middle school utilizing following indicators: – Failing English or math– Locally defined behavior indicators– 20% or more absences per year (Heppen & Therriault, 2008)

Middle Grade Indicators of High School and College Readiness

• CCSR released a report in 2014 outlining critical middle grades indicators for high school and college readiness:– Grades/GPA

• Strongest predictor of on-track status in high school and earning high grades

– Only those with GPA greater than 3.0 had moderate chance of earning A’s & B’s in high school

– 61% of 8th graders with GPA of at least 3.5 earn A’s and B’s in 9th– 90% of 8th graders with GPA >3.5 finish 11th grade with GPA

necessary for a somewhat selective college – Attendance

• Improves predictive ability of high school performance beyond grades

• Much more predictive of passing high school classes than test scores

Allensworth, Gwynne, Moore, de la Torre, (2014)

Pasco County District Implementation Process

Brundage, 2013

What did weLearn from the Pasco EWS study?

• Consistency between Pasco and national data. • On-track/off-track status in 9th grade and earlier accurate

predictor.• Attendance is primary and most critical factor explaining drop

out. – Suspension is another predictor– Failures/GPA are other predictors– Yet…standardized tests were not predictors

• Importance of using “multiple data factors”.

Internal systems analysis:• Inconsistent, non-unified, un-standardized data system of

collection and monitoring for all and at-risk students• Variation in problem-solving teaming and practices, planning for

tiered supports, and decision-making at all levels esp. secondary

Challenges and Solutions• PK-12 Solution translatable to SIS/LIIS

• Consensus among staff about transition

• School-Based teams and leaders (WHO)

• Dedicated Common, Standardized, Centralized data system with intentional planning of time and processes for analyzing and using data to inform decisions at district, school, and classroom levels (TOOLS)

• Common approach to problem-solving/data analysis and use that is connected to defined actions for teachers, parents, and students (Inquiry/PS Process)

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Focus on What: Diagnostic PracticesEarly Warning Systems plus Standards

PK-12earlywarningsystems(EWS)alignedwithstandards–usingreadilyavailabledatato:

• Predictwhichstudentsareat-riskfordropping• Targetresourcestosupportoff-trackstudentswhiletheyare

stillinschool,beforetheydropout• Examinepatterns,identifyschoolclimateissues

CompellingwhyforaPK-12SystemWhatdoweknowaboutEWS?50%accurateby3rd

65%accurateby7th90%accurateby9th

EWS Implementation Journey1. Initial indicator study2. Development of district EWS steering committee

1. Vision2. Determination of Indicators

1. Aligned K-123. Development of data system

1. Continuous refinement3. Development of user-group

1. Update & feedback sessions1. Multi-year

4. Targeted professional development1. Multiple groups/methods

5. Data evaluation

Initial Indicator Study

• 4,000 students were followed from 2007/2008-2011/2012 who met the inclusion criteria:– Enrolled in sixth grade during the 2007/2008

school year – Enrolled for four out of five possible years from

sixth through tenth grade (2007-2012) • Included multiple school and student-level

variables to predict off-track status (DV)– Background Factors, Academic and Behavioral Factors,

Previous Off-track Status.

Indicator Study Results: Across Time Points

– Background Factors• SES Level: Eligibility for free or reduced lunch price

increased likelihood of Off-track Status• Racial/Ethnic Designation as Hispanic: Designation as

Hispanic was associated with increased likelihood of Off-track Status

– Hispanic & ODRs– Academic/Behavioral Factors

• GPA: Every 1.0 increase in GPA was associated with decrease in likelihood of Off-track Status

• ODRs: Every one unit increase in ODRs was associated with increased likelihood of Off-track Status

– Off-track Status• Previous Off-track Status: Previous Off-track Status

increased likelihood of Off-track Status

On/Off Track Graduation Outcomes

Grade On-Track Off-Track

On-Time

GED Not On-Time

DO WD On-Time

GED Not On-Time

DO WD

6th 78% 1% 9% 1% 11% 49% 5% 25% 5% 17%

7th 78% 1% 9% 1% 11% 41% 6% 29% 6% 19%

8th 80% 1% 8% 1% 10% 47% 4% 25% 5% 19%

9th 79% 1% 8% 1% 11% 50% 5% 24% 4% 16%

10th 83% 1% 7% .6% 8% 60% 3% 20% 3% 13%

On-Time = 4 yr Grad, Not On-Time= 5 yr Grad, DO= Dropout, WD= Withdrew

Implications for Practice

• Major focus on early intervention for Off-track 6th graders

• Compute GPA every semester in middle school– Every 1 point GPA increase:

• 51-59% reduction in likelihood of Off-track Status in 10th

• 72-82% reduction of the likelihood of Off-Track Status 6th-8th

Grade Off-Track Increased Likelihood of Off-Track Status End of 10th Grade

6th 48%

8th 95%

9th 312%

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District Steering Committee

• Tasked with the development and implementation of EWS district-wide– Vision of one web-based K-12 data system to

identify at-risk and off-track students• Multi-departmental representation

– Student support services– Professional development– Dropout prevention– Information technology– Research and measurement

District Steering Committee

• Investigation of various data systems/tools/platforms in use within the district– Surveyed all building principals

• 4+ data systems/platforms that were in use depending on building level

– Studied the assets, limitations and usability features of each system

– Determined the optimal platform for the district based on EWS vision

EWS Indicators

• Phase 1– Used combination of national benchmarks, local

research and research in other counties to determine indicators and thresholds for each indicator K-12

• Indicator versus graduation requirement (grad progress)

• Phase 2– Added indicators/thresholds based upon FL

Senate Bill 850 requirements for middle grades

Centralized Data System v5.0

High School EWS Indicators

District School Board of Pasco County

At-Risk / Early Warning System:What matters for staying on track and graduating?

On-Track

At-Risk forOff Track

Off-Track

GPA

2.5 or higher

2.0 to 2.49

Less than 2.0

OfficeDisciplineReferrals

0 ODR’sin a quarter

2 or fewerODR’s in a year

1 ODRin a quarter

3 ODR’s in a year

2 or more ODRsin a quarter

4 ODR’s per yearOR 2 ODR’s

in a semester

On-Track Indicators

Per Quarter

Per Year

Per Quarter

Per Year

Per Quarter

Per Year

CoursePerformance

C’s or betterin all classes

1 or more D’sin any class

Failing 1 or more classes (F’s)

Credits

Meeting creditsto move to next

grade level

1 credit behind

2 credits behind

Attendance

0 to 2 absencesin a quarter

4% or lessabsences in a year


5% -9%absences in a year

5 or more absencesin a quarter

10% or moreabsences in a year

Middle School EWS Indicators

*Note(s): GPA is an overall average of current course grades (sum of grade values / #classes) (A=4, B=3, C=2, D=1, F=0)District School Board of Pasco County


On-Track


Off-Track

GPA*

2.5 or higher

2.0 to 2.49

Less than 2.0




1 ODRin a quarter

3 ODR’s in a year



in a semester

On-Track Indicators

Per Quarter

Per Year

Per Quarter

Per Year

Per Quarter

Per Year

CoursePerformance




Attendance







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Elementary School (Grades 3-5) EWS Indicators

District School Board of Pasco County


On-Track


Off-Track




1 ODRin a quarter

3 ODR’s in a year



in a semester

On-Track Indicators

Per Quarter

Per Year

Per Quarter

Per Year

Per Quarter

Per Year

CoursePerformance




Attendance







Differentiate Between EWS DATAGo to Student Data and Student List View to access.

EWS based on Pasco NeedsSchool team uses to identify, problem-solve, track students throughout year.• Catches more students than the

state indicators based on our local study.

• Includes at-risk level for early ID.• Includes Grad Ready Progress Bar.

SB850 Columns for state• Report in SIP in September.

– Based on identified students in prior year.– Reflects trends tied to SB850 definitions.– Assists in targeting tiered strategies.

• Middle schools meet with parents.

Senate Bill 850For intervention strategies, schools asked to:

Identify areas of need and consider goalsProblem-solve at school, grade, student levelsPut in place tiered supports in identified areas

MTSS (courses/75263/pages/mtss-resources)Attendance (courses/75263/pages/attendance)Behavior (courses/75263/pages/behavior)Academics (courses/75263/pages/academics)

Middle Schools required to conduct:Individual Problem-Solving (“child study team” in statute language) meetings to plan interventions for every child with 2 or more indicatorsInvite parents to attend using a 10 day notice

Time, location, and invite to participate in discussion

Graduation Progress BarEncompasses Requirements for Graduation

DEVELOPMENT OF DATASYSTEM/USER GROUPS

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Development of the Data System• Phase 1

• Collaborated with developer of data platform commonly used in middle schools to mirror usability/functionality

• Utilized feedback from user groups regarding desired features and functionality

• Developed Graduation Progress Bar

• Phase 2• Integrated quarterly markers for measuring progress • School Dashboards

• Phase 3• Intervention log• Disproportionality Indicies

User Group Development

• Development of cross-disciplinary user group to provide feedback on functionality and end-user desired features

• District steering committee members attended sessions with the user group to facilitate: – Demonstration of new features– Practice opportunities– Open discussions for ideas/desired features/issues

Entry Points

• Early Warning Indicators/System- Evidence of

consensus/understanding in your District?

- Opportunity for growth/on ramps?

• District Steering Committee/Leads- Opportunity for focused efforts?

TARGETED PROFESSIONALDEVELOPMENT

Establishing the Vision: How do we do it?

Establish a laser-like focus on the end in mind MAXIMIZE POST-SECONDARY OPTIONS FOR ALL STUDENTS

Align ALL resources towardaccomplishing this goal including the use of:

– Time– Personnel– Space– Materials– Resources

EWS within the Whole System(The What)

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EWS within the Whole System(The Who)

Implementing the Inquiry Cycle Steps to Data Decision Making across PLCs

Are there school trends* that need to be communicated to inform classroom PLCs?

Course Performance(Credits, GPA)

School-wide standards-Based

AssessmentsBenchmark

AssessmentsUnit

AssessmentsClassroom

AssessmentsFormative

Assessments

*by grade, content area, subgroup (students with disabilities, minorities, etc.)

Are students at school?

Are students adhering to

rules, routines, expectations?

Targeted Professional Development

• High school administrators– Rationale– Development– Demonstration– Opportunity to practice with their data– Opportunity to provide feedback– Assigned a case-manager


• Elementary and secondary administrators• Student services teams and support staff

– Rationale– Development– Demonstration– Opportunity to practice with their data– Opportunity to provide feedback


• School based teams as part of 3 session series on PLC’s– Developing school level PLCs and teaching

problem-solving within the context of EWS/MTSS– Creating Functional Teams– Identifying and establishing plans for intervening

and plans for screening/monitoring– Using EWS with other school-wide data to assess

the overall health of a school

Learning Outcomes

• Goal: Use PascoSTAR to access school and individual data in effective, efficient ways. In order to problem-solve most at-risk students.

• Products: Bring back to school team (PS PLC/SLT):– “Health” of school (EWS Scorecard)– Most at-risk kids

• Like groups of students based on need• Students with disproportionate representation

– Scope and root causes of identified problem– Ideas about intervention supports

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Important Data Sources for School TeamsProblem-ID: Health of the School and more

How do I look at the overall School Health?• Beginner: School Data/Profile Page (District, School Users)

– View trends in current year– Compare to individual schools/regions– *Access historical data (back to 2013-14 school year)

• Basic: EWS Scorecard*– View trends across quarters– Identify disproportionate risk groups– Compared individual schools to other schools and district– Monitor progress of schools with graphs

How do I identify the most at-risk students?• Advanced: Student List View (District, School Users)

– View trends by individual students in current year – Group, sort, and filter for any variable (subgroup, program, EWS)– Drill down to class and teacher– Export reports based on problem-solving questions– *Access historial data (back to 2014-15 school year)

School Profile Page1. Click on School Profile in the School Data Menu.2. Select region and/or school from drop down menu. 3. Click on the At-Risk/EWS icon.

School Profile Page4. Drag and drop icons to sort and filter data

Notes: Additional data sources have been added for demographic information.Hover over graphs to view number and percentage by indicators.

5. Click year menu to view and compare prior year data.

Student List View1. Click on School List in the Student Data Menu.2. Select school from drop down menu. 3. Select pre-built filter and/or layouts based on need.

Student List View4. Scroll to desired columns5. Right click or type desired filter (e.g., off-track). 6. Record # of records for that filter7. Review and/or export student names.

EWSScorecardSchoolHealthandRiskRatiosbyQuarter4.ClicktabstoviewRiskRatiosforsubgroupsbyQuarter

RiskRatiosarecalculatedas:

#studentsoff-trackinasubgroup/#studentsoff-track intheschool

dividedbyTotal#studentsinthesubgroup/Total#studentsintheschool.

ForRiskRatios,cellsarehighlightedbasedon:

YellowwhenRiskRatiois1.5-2.49RedwhenRiskRatio2.50+

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EWSScorecardSchoolHealthGraphsbyQuarter5.Clicktabstoview%EWSforeachIndicatorbyQuarter

Application:LookateachdistributionofEWSindicatorsbyquarter.Whataretrendswithinandacrossindicators?Whataretrendsovertimeacrossquarters?

Note:Thebargraphsreflect%ofstudentson-track,at-risk,andoff-trackbasedonquarterly indicatordefinitionsincourseperformance,attendance,anddiscipline.(thisalignswiththeQ1,Q2,Q3,Q4columnsontheStudentListView.)

EWSScorecardSchoolHealthGraphsbyQuarter6.Clicktabstoview%on-track foreachIndicatorbyQuarterandYear

Application:Lookat%ofstudentson-trackbyquarter.Whatarethetrendswithinandacrossindicators?Howdothequarter%comparetotheyear%?

Note:Thefirst4barsinthegraphreflect%ofstudentson-trackbasedonthequarterly indicatordefinitions incourseperformance,attendance,anddiscipline. Thelastbarreflects%ofstudentson-trackbasedontheEWSindicatordefinitions fortheyear(pleaseseeslide3).ThequarterbarsshowEWSdatachunkedbyquartertimepointsandareintendedtotrackgrowth.Theyearbarisarunning%intendedtoprojectendofyearstatus.

EWSScorecardSchoolHealthGraphsbyQuarter7.Clicktabstoview%off-trackforeachIndicatorbyQuarterandYear

Application:Lookat%ofstudentsoff-trackbyquarterWhatarethetrendswithinandacrossindicators?Howdothequarter%comparetotheyear%?

Note:Thefirst4barsinthegraphreflect%ofstudentsoff-trackbasedonthequarterly indicatordefinitions incourseperformance,attendance,anddiscipline. Thelastbarreflects%ofstudentson-trackbasedontheEWSindicatordefinitions fortheyear(pleaseseeslide3).ThequarterbarsshowEWSdatachunkedbyquartertimepointsandareintendedtotrackgrowth.Theyearbarisarunning%intendedtoprojectendofyearstatus.

Student Interaction Log Uses

• The Student Interaction Log can be used for:– Recording positive behavior occurrences– Recording minor discipline incidents– Submitting online discipline referrals– Documenting intervention plans (such as TBIT/SBIT)

• Also may upload documents such as 504 and IEPs.– Monitoring outcomes of tiered interventions– Recording and monitoring parent communication

• Specifically for tracking SB850 requirements– Any other event that incorporates problem-solving

and intervention plans at the individual student level– Submitting discipline referrals for online processing

Note: all data are dummy data.

Student Interaction LogExample Print Out for Records

• For SB850, acts as a record of parent conference with strategies to target identified areas.

• For all other uses, acts as documentation for history of areas of need and matched interventions, minor and/or positive behavior incidents, parent communications and associated graphs/forms (such as for 504, SBIT, etc.)

Note: all data are dummy data.

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Student Interaction Log to Process Discipline or DetentionThen click the Save and Submit to buttons.

Student Interactions GridTrends in school interactions can be sorted/filtered many ways.

• Filter and sort by type of intervention and then by type of response (see right example).

• Can also filter by any interaction field and then by demographic information in the same list (see below example).

EWS within the whole system: Making Connections

• Consider systems- Making connections to current work

• Professional Development - Targeted selected groups- Systematic roll out of PD

• Common approach to problem-solving/data analysis?

• Next Steps?

Creating Supportive LearningEnvironments For All Students

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Pasco County SchoolsRisk of being Off-Track in the Area of Discipline (2014-15)

For Students who are Black and White

For all Students with Disabilities

For Students with Disabilities who are Black and White

Black

Yes

Black

White

No

White

Pasco County SchoolsRisk of being Off-Track in the Area of Discipline (2014-15)

For students who are Black and White

For all 504 students

For 504 students who are Black and White

Black

Black

White

White

Yes No 0.87

0.63

1.32

1.01

2.03

0

0.5

1

1.5

2

2.5

Race

Pasco County SchoolsOut of School Suspension Data

2014-15 School YearRisk Ratio by Race/Ethnicity

White Asian Mixed Hispanic Black

Pasco County Schools Drop Out Rates

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5 Whys Approach to Problem-Analysis

PROBLEM-SOLVING WITH THE DATA

5 Whys• Analysis to

uncover root causes

• Ask why 5 times to deeply understand the problem

Thewrongitemwaspulledfrominventory

Theitemwepulledfrominventorywasmislabeled

Oursuppliermislabeledtheitempriortoshippingittoourwarehouse

Theindividualapplyinglabelstoourproductatthesupplierplacedthewronglabelontheproduct

Labelsfordifferentordersarepre-printed,anditiseasytoapplythewronglabel

SAMPLE:5-WhyAnalysis– ABCDistributionCenter

ProblemStatement:Wrongitemshippedtocustomer

Why?

Why?

Why?

Why?

Why?

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Why? Thewrongitemwaspulledfrominventory

“Retrain our stock pickers” – almost no benefit – this solution has nothing to do with the true cause

Theitemwepulledfrominventorywasmislabeled

“Inspect our inventory” –minimal benefit – applies to current stock only

Why?

OursuppliermislabeledtheitempriortoshippingittoourwarehouseWhy?

“Have the supplier sort their stock to contain the problem” – very limited long-term benefit

TheindividualapplyinglabelstoourproductatthesupplierplacedthewronglabelontheproductWhy?

“Conduct training at the supplier” – limited long-term benefit

Labelsfordifferentordersarepre-printed,anditiseasytoapplythewronglabelWhy?

“Mistake-proof the label printing and application process” –highly effective

Studentswereinadvertentlydeniedanopportunitytomasterwritingskills

Thewritingblockwaseliminatedtofullyintegratewritingacrossthecurriculum,resultinginfeweropportunitiesforstudentfeedbackandrevision.

TeachersreportednotbeingsureHOWtoprovidefeedback,giventhetransitiontoFLstandards

TeachersreportedthatPDregardingintegratingwritingacrossthecurriculumwasmisaligned

Principal,district,DAteam,andteachersallhaddifferentexpectations

5-WhyAnalysis– SampleElementarySchool

ProblemStatement:Studentwritingproficiencydeclineddramaticallybetween2012and2013.

Why?

Why?

Why?

Why?

Why?

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Root cause linked to Intervention Take Aways

• Long/short term goals?- End in mind- What can you bring back

tomorrow? Refinements?

Ingredients for Success• Cross-department representative EWS steering committee• Collective commitments

– Aligned with district strategic plan and school improvement plans• Shared vision, mission, values, goals• Feedback loop with stakeholders

– District and school (user group)– Collaborative with key district personnel/departments– Communication-Connectivity with School Board

• Targeted and systematic professional development– High schools– All school leaders– Alignment with SIP– All schools-Problem-Solving PLC

• Use of PLCs and blended learning models to communicate resources• Intentional connections with MTSS, Problem-Solving, SIP, Marzano Framework

• Common, standardized data system – ongoing development• *MTSS calibration to include expectations and monitoring

Evidences of ChallengesIn Skills• Varying skill levels of staff to access and use the data

– Managing and supporting transitions from prior systems• Professional development (especially at secondary) on MTSS

– Knowledge, beliefs, and supports to establish MTSS and integrate EWS with Standards-Base– High School: relevance of Attendance and Discipline to Grad Req

• Students urgency about postsecondary activities (motivation)– Supporting schools in student engagement factors and strategies

In Performance• Identifying, supporting, and monitoring progress for students in eSchool.• Inconsistencies in data coding and reporting.• MTSS infrastructure to support the use of the data.• Trust in the system. Worry about the reliability of the data.

In Systems• Centralized data system with standardized implementation• Fidelity in data access, use, and problem-solving/decision-making• *Monitoring outcomes at school and district levels: students and implementation

Additional Readings

Balfanz, R., Herzog, L., MacIver, D., (2007). Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: Early identification and effective interventions. Educational Psychologist, 42(4), 223-235.

Balfanz, R., Stenson, T. (2012). Using data to build early warning systems [Webinar]. United States Department ofEducation School Turn-around Learning Community. Retrieved from http://vimeo.com/37739265

Brundage, A. (2013) Middle and high school predictors of off-track status in early warning systems. (Unpublished doctoral dissertation). University of South Florida, Tampa.

Heppen, J. B., & Therriault, S. B. (2008). Developing early warning systems to identify potential high school dropouts. Washington, DC: National High School Center, American Institutes for Research. Retrieved from http://www.betterhighschools.org/pubs/ews_guide.asp

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Contact Information

Amber BrundagePK-12 Alignment Unit CoordinatorFL PS/RtI Project at [email protected]

Alyssa LipinskiProgram Coordinator, Student ServicesDistrict School Board of Pasco [email protected]

essei 2016 ews final - university of south florida

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