Demand structure prediction and adaptation path of rural teachers in western China under population change.

Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE

Original Publication: San Francisco, CA : Public Library of Science

Rural Population* ; School Teachers* ; Population Dynamics*; China ; Humans ; Male ; Child, Preschool ; Schools ; Female ; Child ; Forecasting ; Workforce

The construction of a rural teaching workforce is a key factor in promoting educational equity and the development of high-quality education. We employ the Leslie model and the age-specific grade progression rate method to estimate a population forecasting model. Using data from the seventh national census and regional statistical yearbooks and accounting for educational centralization and population trends, we forecast rural preschool, primary, and junior high teacher demand in 12 western Chinese provinces (2024-2040) on the basis of school-age population changes. The results show that (1) population changes have led to a decline in the allocation of rural education resources in western China; (2) the decrease in the school-age population has led to a continuous contraction of the teacher team structure, with a more pronounced decline in the preschool education stage; for example, rural student numbers in Gansu are projected to decline by 35% between 2024 and 2040, contributing to a 28% drop in primary teacher demand under constant policy conditions; and (3) the decline in teacher demand at all educational stages has exacerbated the imbalance in the allocation of educational resources. To address shrinking school-age populations and rural teacher imbalances, we recommend adjusting staffing standards on the basis of demographic forecasts, strengthening cross-stage professional development with clear pathways and incentives, and modernizing training through blended mentoring and data-driven coaching to ensure sustainable education quality.
(Copyright: © 2025 Lv et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The authors have declared that no competing interests exist.

Erratum in: PLoS One. 2025 Dec 19;20(12):e0339499. doi: 10.1371/journal.pone.0339499.. (PMID: 41417720)

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Date Created: 20250918 Date Completed: 20250918 Latest Revision: 20251219

20260130

PMC12445470

10.1371/journal.pone.0331907

40966217