ArticlePloS one2023
Detection of hotspots of school dropouts in India: A spatial clustering approach.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed, 12 citations in OpenAlex.
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2 authors at 1 institution in 1 country.
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Abstract
School dropout is a significant concern universally. This paper investigates the incorporation of spatial dependency in estimating the topographical effect of school dropout rates in India. This study utilizes the secondary data on primary, upper primary, and secondary school dropout rates of the different districts of India available at the Unified District Information System for Education plus (UDISE+) for the year 2020 to contemplate the impact of these dropouts from one region to different regions in molding with promotion rate and repetition rate. The Global Moran's I, Univariate and Bivariate Local Indicators of Spatial Association, and spatial models are utilized to investigate the geographical variability and to find the possible relationship between dropout rates and the school-level factors at the district level. The outcomes provide clear spatial clustering and precisely highlight the hot zone dropout regions with high repetition and low promotion rates. Based on this study's results, educational administrators can make evidence-based decisions to reduce dropout rates in hot zones of various regions of India. Furthermore, futuristic studies focusing on linking spatial hot zones with causal factors will add consistent data in assisting policymakers in taking necessary measures to develop a sound education management system.
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