Geospatial Patterns of Science Teacher Shortages

• In Event: AERA Roundtable Session Saturday 9:50 am Four Seasons Ballroom 4
  In Roundtable Session: Teacher Shortages and Policy Responses (Table 13)

Abstract

This paper focuses on the spatial and temporal trends of science teacher shortages. Using data including every school over a 30 year period in Texas (1990-2020), we conduct a longitudinal geospatial point density analysis to identify regions of greatest shortage, and employ OLS regression and LASSO machine learning models demonstrate the significance and magnitude of school conditions on shortages. Results demonstrate that general shortages have decreased while teacher churn has increased; lower salary is associated with higher shortages; and urban schools along the southern corridor with higher ESL populations are more likely to experience shortages. Our aim is to estimate where shortages may be the most the prevalent so that policymakers can better ensure equitable access to science education.

Authors

• Andrew Pendola, Auburn University

• Bryan Mann, University of Kansas