Early Warning

Addressing the Dropout Challenge

Dr. Kristal Ayres

Welcome & Introduction

DR. KRISTAL AYRESSenior Professional

Learning Leaderkristal@brightbytes.net

WelcomeThis is an interactive session to share the

components of BrightBytes’ Early Warning System

using predictive analytics to identify students that are

showing signs of dropping out of school as early as

the 1st grade.

Introduction• 26+ years in the field of education & research

• Doctorate in Educational Administrative

Leadership

• Educator: elementary, middle, high & university

• District Administrative Positions

• College Board Consultant and National Trainer

• BrightBytes Senior Professional Learning Leader

Who is BrightBytes?

• Mission-Driven Organization

• Former Educators

• Technology Experts

• Educative, Engaging, and

Actionable

• Tens of Thousands of Schools

Nationwide

DR. MARIAM AZINPresident, PRES Associates

mazin@presassociates.com

RESEARCHER – APPLIED

• 20+ years of work experience in the field of research and evaluation;

• Multiple research studies reviewed by the What Works Clearinghouse (WWC) -- all receiving the highest quality ratings possible.

• President of PRES Associates (Planning, Research & Evaluation Services) – national research firm

• Principal investigator on numerous national, statewide, and local evaluation efforts related to at-risk learners, such as;

• Federal SS/HS grants • Project Aware• School Climate Transformation Grants• PBIS/MTSS• Dropout Prevention• Early Warning Systems

A Partnership between BrightBytes & Mazin Education

Early Warning Checklist Approach*

* Everyone Graduates

Center – Johns Hopkins

University: Based on

numerous research

studies across a number

of different states and

districts, a consistent set

of triggers have been

identified.

Predictive Analytics

Early Warning System 2.0 ~ Second Generation Predictive Analytics

Accuracy• Accuracy of Checklist Model is around 48%

• Accuracy of BrightBytes Predictive Analytics is over 85%

Customized, flexibleOne size does not fit all

Earlier identificationMiddle and elementary

Greater accuracyMinimizes false

positives/negatives

TimelinessReal-time district data;

promotes the

effectiveness of existing

services and supports

State-of-the-art predictive

analyticsDraws upon multiple data points spanning

the domains of academics, attendance,

behavior, and demographics

Customized to districts and grade

levels Looks at actual dropouts in the district and,

using available data across all domains, fits

the best predictive models that would have

predicted those dropouts. Such predictive

models are then applied retroactively to

students still in the district.

How does predictive analysis work?

Analysis of the factors contributing to dropout risk

• Assessments – District

• Assessments – State

• Credits Earned Annually

• Academic Indicator – All Courses

• Academic Indicator – Core Academic Courses

• Grade Retention

• Pass Rate – All Courses

• Remedial Courses

• Attendance – First 30 days

• Attendance – Total

• Tardies

• Behaviors – Major

• Behaviors – Minor

• Disciplinary Referrals

• Expulsions

• Suspensions

Diary of a Teenage Dropout: Summative Data

• 2.9 GPA

• 92% attendance rate

• No behavioral incidents

• 2.6 GPA

• 91% attendance rate

• No behavioral incidents

• 2.5 GPA

• 90.5% attendance rate

• No behavioral incidents

• 2.5 GPA

• 89.4% attendance

rate

• Two behavioral

incidents, one

suspension

• 2.2 GPA

• 88.7% attendance

rate

• Two behavioral

incidents, two

suspensions

• 1.9 GPA

• 79% attendance rate

• Four behavioral incidents,

two suspensions

Drops out of high school;

before leaves has

• 0.9 GPA

• 78% attendance rate

3 4 5 6 7 8 9 10

• 2.0 GPA

• 83.6% attendance rate

• Two behavioral

incidents, four

suspensions

Sample size for summative data is 35,000 10th grade students. This represents the profiles of students, at each grade level, who eventually dropped out in 10th grade.

What it is and What it isn’t

It is:

Based on historical patterns and current data for earlier and more accurateidentification of students showing

signs of risk

Efficient targeting of school and program resources to where they

are needed most

Actionable supports to connect students to research-based

interventions

It isn’t:

A data repository or reports center

An average of colors for overall risk level assignment

The same weight for indicators across all grade levels

Surprising Data

What you see will surprise you

Predictive Analytics identifies students that may not be on your radar because the algorithm analyzes hundreds of data points simultaneously to provide greater accuracy for student risk predictions

Scenario: Low/Moderate Risk Across

Domains

Scenario: Low Assessment Scores

Scenario: Underlying Demographic Risk

Factors

9th Grader Profile

Critical Components

1. Predictive algorithm vs. threshold models

2. Risk prediction colors on the dashboard do not average

3. EW supports educators to drive core initiatives – connects to:* School improvement plan of increasing student achievement* District and state goal of increasing graduation rates* Supports the identification and intervention process (RTI/MTSS/PLS)

4. Focus on your top 3 areas of concern on main dashboard* District level* School level* Student level

5. Administrators: download, disseminate, delegate student data for areasof concern and progress monitor implementation ofinterventions

A General Tour

Here’s what

DATASo what

REPORTSNow what

INSIGHTS

Early Warning

Walkthrough

For additional information:

Dr. Kristal Ayres

Kristal@brightbytes.net

239-398-1770

Thank you!

QUESTIONS???