Early Warning
Addressing the Dropout Challenge
Dr. Kristal Ayres
Welcome & Introduction
Welcome
This 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
DR. KRISTAL AYRES
Senior Professional
Learning Leader
kristal@brightbytes.net
• 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?
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Mission-Driven Organization
Former Educators
Technology Experts
Educative, Engaging, and
Actionable
• Tens of Thousands of Schools
Nationwide
A Partnership between BrightBytes & Mazin Education
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;
DR. MARIAM AZIN
President, PRES Associates
mazin@presassociates.com
• Federal SS/HS grants
• Project Aware
• School Climate Transformation Grants
• PBIS/MTSS
• Dropout Prevention
• Early Warning Systems
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%
How does predictive analysis work?
State-of-the-art predictive
analytics
Draws upon multiple data points spanning
the domains of academics, attendance,
behavior, and demographics
Customized, flexible
One size does not fit all
Earlier identification
Middle and elementary
Greater accuracy
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.
Minimizes false
positives/negatives
Timeliness
Real-time district data;
promotes the
effectiveness of existing
services and supports
Analysis of the factors contributing to dropout risk
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Behaviors – Major
Behaviors – Minor
Disciplinary Referrals
Expulsions
Suspensions
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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
Diary of a Teenage Dropout: Summative Data
• 2.5 GPA
• 2.9 GPA
• 92% attendance rate
• No behavioral incidents
3
4
• 2.6 GPA
• 91% attendance rate
• No behavioral incidents
• 90.5% attendance rate
• No behavioral incidents
5
• 2.5 GPA
• 89.4% attendance
rate
• Two behavioral
incidents, one
suspension
6
• 2.2 GPA
• 88.7% attendance
rate
• Two behavioral
incidents, two
suspensions
7
• 1.9 GPA
• 79% attendance rate
• Four behavioral incidents,
two suspensions
8
• 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.
9
10
Drops out of high school;
before leaves has
• 0.9 GPA
• 78% attendance rate
What it is and What it isn’t
It is:
Based on historical patterns and current
data for earlier and more accurate
identification of students showing
signs of risk
It isn’t:
A data repository or reports center
Efficient targeting of school and
program resources to where they
are needed most
An average of colors for overall risk
level assignment
Actionable supports to connect
students to research-based
interventions
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 areas
of concern and progress monitor implementation of
interventions
A General Tour
Early Warning
Walkthrough
Here’s what
So what
Now what
DATA
REPORTS
INSIGHTS
QUESTIONS???
For additional information:
Dr. Kristal Ayres
Kristal@brightbytes.net
239-398-1770
Thank you!