Predictive GIS-Based Decision Support System for Forecasting Teacher Demand and Optimizing Educational Human Resource Allocation A practical study to achieve the target profit for the distribution diversity of educational materials
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Abstract - Unavoidably, educators world over are struggling to address the issue of supply and demand of teachers in geographically dispersed regions. The conventional workforce planning methods are based on reactive, manual and non-spatial forecasting processes which in most cases leads to shortage of teachers in the rural sections and excesses in the urban centres. In this paper, the author suggests a Predictive Geographic Information System (GIS)-Based Decision Support System (DSS) to combine machine learning forecasting, spatial analytics, and optimization modeling to improve the efficiency of teacher demand prediction and allocation. The suggested framework will use historical data on enrollments as well as demographic data, records of teacher attrition, and geospatial data to project future staffing needs. Random Forest and Gradient Boosting models are compared in terms of forecasting performance and geographic differences are defined by spatial clustering and hotspots analysis. The optimization model of an integer linear programming minimizes the cost of staffing imbalance and allocation with regulatory and budget constraints. The results of the experiment indicate higher accuracy in prediction (R^2 = 0.92) and 27% lowering of the staffing imbalance than the standard allocation procedures. The suggested system offers a data-driven and spatially intelligent planning tool of strategic educational workforce planning in the hands of policymakers.
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