Addressing the Issue on Teacher Shortage: Bridging the Gap on Public School Teachers and LEPT Passers

Abstract

This paper seeks to contribute to an evidence-based discussion of this question by developing predictive models that translate the number of licensed teachers into actual hiring trends of the Department of Education (DepEd). Using longitudinal data from School Year 2010–2011 to School Year 2022–2023, the study uses the GAIMME modeling framework and the Trendline Function of Microsoft Excel to formulate the following models: (1) to predict the number of public-school teachers to be hired over; (2) time to estimate the number of Board Licensure Examination for Professional Teachers (LEPT) passers at the elementary and secondary levels; and (3) to calculate the hiring of teachers per given year as a function of LEPT passers. A variety of models (linear, logarithmic, exponential and polynomial functions) were compared according to coefficient of determination (R²), sum of square errors (SSE) and simplicity. The best model to predict the number of employed teachers was relatively different, revealed by the logarithmic model, while linear and logarithmic models could approximate to forecast LEPT passers. Power functions were found to be the best for predicting teacher employment from LEPT output. These models can offer policymakers actionable insights and demonstrate the value of incorporating predictive analytics into workforce planning. The models should be updated frequently and disaggregated by region to enhance predictive validity. Although data limitations were presented, the research shows promise of a basic use of Excel-based mathematical modeling as a decision support tool for teacher deployment and its potential to contribute to reaching quality education as part of SDG 4.