Predicting Early Fall Student Enrollment in the School District of Philadelphia. REL 2022-124

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  • Additional Information
    • Availability:
      Regional Educational Laboratory Mid-Atlantic. Available from: Institute of Education Sciences. 550 12th Street SW, Washington, DC 20202. Tel: 202-245-6940; Web site: https://ies.ed.gov/ncee/edlabs/regions/midatlantic/index.asp
    • Peer Reviewed:
      Y
    • Source:
      12
    • Contract Number:
      EDIES17C0006
    • Education Level:
      Elementary Education
    • Subject Terms:
      Enrollment; Enrollment Projections; School Districts; Statistical Analysis; Data Analysis; Student Mobility; Class Size; Accuracy; Predictor Variables; Elementary Schools; Neighborhood Schools
    • Subject Terms:
      Pennsylvania (Philadelphia)
    • Abstract:
      Predicting incoming enrollment is an ongoing concern for the School District of Philadelphia (SDP) and similar districts with school choice systems, substantial student mobility, or both. Inaccurate predictions can disrupt learning as districts adjust to enrollment fluctuations by reshuffling teachers and students well into the fall semester. This study compared the accuracy of four statistical techniques for predicting fall enrollment at the school-by-grade level, using data from prior years, to assess which approach might be the most useful for planning school staffing in SDP. The predictions differ little in accuracy: predicted cohort size differs from actual cohort size by roughly six students across all methods. The statistical techniques leave much student mobility unaccounted for. Even under the best prediction approach, students and teachers in 22 percent of incoming grade levels within schools might have to be reassigned because of unexpected student mobility and district rules on maximum class size. Predictive accuracy is not meaningfully different in schools with larger proportions of Black students, economically disadvantaged students, or English learner students. Of the 259 predictors analyzed, 4 stand out as the most important: prior cohort sizes, in-school suspensions, out-of-school suspensions, and absences. [For the Study Snapshot, see ED615007. For the appendixes, see ED615008.]
    • Abstract:
      As Provided
    • IES Funded:
      Yes
    • IES Publication:
      https://ies.ed.gov/ncee/edlabs/projects/project.asp?projectID=4648
    • Publication Date:
      2021
    • Accession Number:
      ED615006