Addressing the Dropout Challenge Dr. Kristal Ayres

Transcription

1 Early Warning Addressing the Dropout Challenge Dr. Kristal Ayres

2 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 1 st grade. DR. KRISTAL AYRES Senior Professional Learning Leader kristal@brightbytes.net 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

3 Who is BrightBytes? Mission-Driven Organization Former Educators Technology Experts Educative, Engaging, and Actionable Tens of Thousands of Schools Nationwide

4 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 DR. MARIAM AZIN President, PRES Associates 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

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6 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.

7 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%

8 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 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. Greater accuracy Minimizes false positives/negatives Timeliness Real-time district data; promotes the effectiveness of existing services and supports

9 Analysis of the factors contributing to dropout risk Behaviors Major Behaviors Minor Disciplinary Referrals Expulsions Suspensions 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

10 Diary of a Teenage Dropout: Summative Data 2.9 GPA 92% attendance rate No behavioral incidents 2.5 GPA 90.5% attendance rate No behavioral incidents 2.2 GPA 88.7% attendance rate Two behavioral incidents, two suspensions 1.9 GPA 79% attendance rate Four behavioral incidents, two suspensions GPA 91% attendance rate No behavioral incidents 2.5 GPA 89.4% attendance rate Two behavioral incidents, one suspension 2.0 GPA 83.6% attendance rate Two behavioral incidents, four suspensions Drops out of high school; before leaves has 0.9 GPA 78% attendance rate Sample size for summative data is 35, th grade students. This represents the profiles of students, at each grade level, who eventually dropped out in 10 th grade.

11 What it is and What it isn t It is: It isn t: Based on historical patterns and current data for earlier and more accurate identification 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 A data repository or reports center An average of colors for overall risk level assignment The same weight for indicators across all grade levels

12 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

13 Scenario: Low/Moderate Risk Across Domains

14 Scenario: Low Assessment Scores

15 Scenario: Underlying Demographic Risk Factors

16 9 th Grader Profile

17 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

18 A General Tour Early Warning Walkthrough Here s what DATA So what REPORTS Now what INSIGHTS

19 QUESTIONS??? For additional information: Dr. Kristal Ayres Thank you!