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Early Warning Systems:

Early Warning Systems:. How to Use Data to Identify and Help Middle School and High School Students at High Risk of Dropping Out. There are many underlying reasons for dropping out of school …. But often, students send “distress signals” long before they actually drop out.

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Early Warning Systems:

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  1. Early Warning Systems: How to Use Data to Identify and Help Middle School and High School Students at High Risk of Dropping Out

  2. There are many underlying reasons for dropping out of school ….

  3. But often, students send “distress signals” long before they actually drop out

  4. How can educators“capture the signal” in order to prevent dropout?

  5. Three questions • What are the signals? • How can they be “captured”? • What can schools and districts do once the signals are identified and captured?

  6. What do we mean by “signals”?

  7. 8th grade • Got good grades in elementary school • Feels that his middle school teachers don’t really care about him • Struggles with middle school mathematics, has decided he “can’t do it,” and now doesn’t even try

  8. In 8th grade • Gets average grades • Has an autistic brother who consumes her parents’ attention • Sometimes needs to stay home to take care of her brother

  9. In 9th grade • High school is disorganized and the teaching is weak • Mom is a high school dropout and says she doesn’t care whether her daughter finishes high school • Student skipped school and passed only one class

  10. A good early warning system predicts with: • High accuracy – at least 75% of students with a characteristic will drop out (our definition) • High yield – these characteristics capture most of the dropouts • Easily available and relatively inexpensive

  11. The very best early warning system is customized for your district

  12. Identifying “signals”: An example from Philadelphia

  13. A must-have: Longitudinal data • Data that allows tracking of individual students over time, to the point where the students exit the district through graduation or dropout

  14. A district will never get dropout and graduation rates 100% correct, but it is critical to work to get high-quality data

  15. What is high quality graduation and dropout data? • The list of codes that students can be assigned when they leave the district is clear and complete • Students are actually assigned codes when they leave • There are standards of evidence indicating when students can be coded as “transfers” to another district (e.g. some confirmation from another district)

  16. Philadelphia case study: Who makes up dropouts? Students who are listed as leaving the district, but have no withdrawal code Students who dropped out, according to the district Students whose status simply disappears in district data

  17. Data scan (8th grade on) • Test scores • Report card grades • Attendance • Special education and ELL status • Gender • Age • Race/ethnic background

  18. 75% threshold – why? • Choosing a “strong signal” – students who are at highest risk of dropping out • By not making the net too broad, scarce resources can be targeted at those students who are greatest risk

  19. The Big Four in 6th grade • Failing Math • Failing English • Attendance <80% • At least one poor behavior mark (Balfanz and Herzog)

  20. 8th Grade signals that we found • Three factors gave students at least a 75% probability of dropping out: 1. Failing math in 8th grade 2. Failing English in 8th grade 3. Attending less than 80% of the time

  21. 54% of the dropouts sent one or more of these signals in 8th grade

  22. Had an 8th grade “signal” Did not have an 8th grade signal: Passed 8th grade English Passed 8th grade Math Attended at least 80% of the time

  23. 9th Grade signals that we found • Three factors gave students at least a 75% probability of dropping out: 1. Earning fewer than 2 credits 2. Not being promoted to 10th grade 3. Attending less than 70% of the time

  24. 80% of the dropouts sent one or more of these signals in 8th or 9th grade

  25. Pilot Middle Grades Program

  26. A work in progress

  27. Conceptual frame Whole school interventions More labor intensive More specialized More costly Targeted Interventions Intensive Interventions

  28. Matrix: Interventions by predictors

  29. Example: Attendance Whole School Create a culture of “Attending Every Day Matters!” Every absence brings a response Positive social incentives for good attendance Ongoing attendance tracking at teacher team meetings 2+ unexcused absences/month=Big Deal. Daily check in by an adult. Teacher team invites parent, counselor in to investigate causes. Targeted Intensive Daily one-on-one attention and problem solving Social service or community support engaged

  30. When do students move to the next tier?

  31. Real-time monitoring tool

  32. New developments • Middle schools reorganized to provide teachers with time to address issues with team leaders and intervention providers • Partnering with City Year to provide some targeted supports • Partnering with Cities in Schools to provide intensive supports

  33. Ruth Curran Neild Center for Social Organization of Schools, Johns Hopkins University rneild@csos.jhu.edu

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