School-by-grade enrollment prediction analysis for the School District of Philadelphia¶

Element · All elements
Evidence · 4 claims (4 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 4 claims rest on one study

Description¶

A comparative analysis that used "data from prior years" to predict fall enrollment "at the school-by-grade level" in the School District of Philadelphia, a district with school choice and substantial student mobility. It assessed "which approach might be the most useful for planning school staffing in SDP," comparing four statistical techniques and analyzing 259 candidate predictors drawn from district administrative data.

Design Implications¶

Context¶

Requirements¶

• Prior-years district administrative data at the school-by-grade level, including cohort sizes, suspensions, and absences

Constraints¶

• Predictions leave much student mobility unaccounted for; even the best approach may leave 22 percent of incoming grade levels needing reassignment

Target Learners¶

• K-12 students in districts with school choice systems or substantial student mobility

Target Learning Goals¶

• District planning of school staffing to avoid mid-semester reshuffling of teachers and students

Claims¶

• Of 259 predictors analyzed for enrollment prediction, four stand out as most important: prior cohort sizes, in-school suspensions, out-of-school suspensions, and absences [+W]
• Predictive accuracy of enrollment forecasts is not meaningfully different in schools with larger proportions of Black students, economically disadvantaged students, or English learner students [+W]
• Even under the best prediction approach, students and teachers in 22 percent of incoming grade levels within SDP schools might have to be reassigned because of unexpected student mobility and maximum class size rules [+W]
• Four statistical techniques for predicting school-by-grade fall enrollment in SDP differ little in accuracy, with predicted cohort size off by roughly six students across all methods [+W]

Related Elements¶

• School District of Philadelphia administrative dataset on teacher employment and student–teacher linkages

Examples¶

• Use prior-years administrative data to forecast school-by-grade enrollment for staffing planning in districts with choice or mobility

Key Sources¶

• Sean Tanner, Jenna Terrell, Emily Vislosky, Jonathan Gellar, Brian Gill. (2021). Predicting Early Fall Student Enrollment in the School District of Philadelphia. Regional Educational Laboratory Mid-Atlantic, Institute of Education Sciences. https://www.mathematica.org/publications/predicting-early-fall-student-enrollment-in-the-school-district-of-philadelphia