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MAA 2012 Dropout Predictors

Using dropout predictors for program targeting and measuring impact

Wendy Tackett

on 11 October 2012

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Transcript of MAA 2012 Dropout Predictors

Wendy Tackett, Ph.D. and Kelley Pasatta, Ed.M. Using dropout predictors
for program targeting and measuring impact Discussion: what does your evaluator do? To strictly adhere to the evaluation contract? To share findings with the greater community? To look beyond our contract and identify research and studies that can benefit our client? High School Dropout Predictors
Missing 10 or more days of school
Being suspended 1 or more times
Having a GPA of less than 2.5
Earning an E/F in 1 or more core classes Chicago Study (2007 & 2009)
Identified factors that can help predict on-time graduation after freshman year
getting an E/F in no more than 1 core course
missing less than 10 days of school
having a GPA of 2.5 (C+) or higher Denver Study (2009)
Validated Chicago study
Expanded predictors to middle school
missing less than 20 days of school
no suspensions
failing no core courses Our Dropout Predictors for 21st CCLCs
Missing 10 or more days of school
Getting a GPA of less than 2.5 (C+)
Failing 1 or more core classes
Getting suspended 1 or more times What Matters for Staying On-Track and Graduating in Chicago Public High Schools, report by The Consortium on Chicago School Research at the University of Chicago (2007). Dropouts in the Denver Public Schools: Early Warning Signals as Possibilities for Prevention and Recovery, report by Johns Hopkins University (2009). Why calculate dropout predictors?

Determine need for program - indicating what percentage of students at risk are being served and what number still need to be served (great for grants!) What data do you need?
Number of student school day absences, by child
Grades in core classes for each term (English/language arts, social studies, mathematics, science)
Overall GPA by year
Number of days suspended, by child CRITICAL to build RELATIONSHIPS with school district personnel so they understand how data will be used and how it could also benefit the regular school day (while adhering to FERPA) www.michigan.gov/21stcclc The Family Educational Rights and Privacy Act (FERPA) (20 U.S.C. § 1232g; 34 CFR Part 99) is a federal law that protects the privacy of student education records. The law applies to all schools that receive funds under an applicable program of the U.S. Department of Education. More information about FERPA can be found at: http://www2.ed.gov/policy/gen/guid/fpco/ferpa/index.html.

While FERPA regulations are designed to protect the privacy of students, the same regulations also allow external evaluators to have access to student data if the evaluation is designed to “conduct studies for, or on behalf of, educational agencies or institutions for the purpose of developing, validating, or administering predictive tests, administering student aid programs, and improving instruction, if such studies are conducted in such a manner as will not permit the personal identification of students and their parents by persons other than representatives of such organizations and such information will be destroyed when no longer needed for the purpose for which it is conducted, and contractual partners with (Name of District) schools.”

It is critical that the evaluator use data in a manner that aligns with FERPA: storing data in password protected files, not sharing data with any unauthorized sources, and reporting data in unidentifiable ways (i.e., only in aggregate form). Calculating risk predictors:
ATT=1 if student missed 10 or more days
FAIL=1 if student got an E/F in 1 or more core classes
GPA=1 if GPA is less than 2.5
SUSP=1 if student was suspended 1 or more days
Sum four risk categories. The higher the number, up to 4, the more risk the student has Our next step:
start connecting with data from the National Student Clearinghouse to connect middle and high school risk predictors to college success OUR GOALS TODAY:

1. Discuss evaluator responsibility related to contract & beyond

2. Understand dropout predictors

3. Understand how to use dropout predictors for program improvement & targeting

4. Discuss next steps in evaluation strategies Important detail: can NOT store identifiable student data on Dropbox or any other unsecured cloud storage service. Why do we focus on students who may drop out? Dropouts are much more likely than their peers who graduate to be unemployed, living in poverty, receiving public assistance, in prison, on death row, unhealthy, divorced, and ultimately single parents with children who drop out from high school themselves. High school dropouts, on average, earn $9,200 less per year than high school graduates, and about $1 million less over a lifetime than college graduates. Students who drop out of high school are often unable to support themselves; high school dropouts were over three times more likely than college graduates to be unemployed in 2004. Dropouts are twice as likely as high school graduates to slip into poverty from one year to the next. There seems to be a correlation with education and good health: at every age range, the more education, the healthier the individual. The Silent Epidemic Perspectives of High School Dropouts. A report by Civic Enterprises in association with Peter D. Hart Research Associates for the Bill & Melinda Gates Foundation. March 2006 Identify students in need of support for enrollment in program Determine impact of programming on increase or decrease in risk predictors Identify type of individualized support needed for each student Help focus entire school on meeting goals of on-time, on-track graduation
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