Teacher evaluation reform under China's "Double Reduction" policy: a quasi-experimental study based on the multi-source process evaluation model

Xie Yang

doi:10.55056/cte.1190

Authors

Xie Yang Hebei University https://orcid.org/0009-0004-1395-1638

DOI:

https://doi.org/10.55056/cte.1190

Keywords:

Double Reduction policy, teacher evaluation, Multi-Source Process Evaluation Model, quasi-experimental study, distributive justice

Abstract

China's "Double Reduction" policy aims to alleviate the burden on compulsory education, yet significant subject-based discrimination persists in teacher evaluation, with bonus proportions for non-main subject teachers approaching zero. This study integrates critical policy analysis with organizational justice theory to construct the Multi-Source Process Evaluation Model (MSPEM) and conducted a quasi-experimental study involving 277 teachers at a typical secondary school. The research employed mixed methods, integrating AHP-entropy combined weighting and SoftMax nonlinear allocation to realize multi-dimensional process evaluation across six dimensions (core teaching, social-emotional, technology integration, home-school collaboration, professional development, and student outcomes). Results show that MSPEM significantly weakened the association between subject attributes and bonuses (r = -0.116, 95% CI [-0.22, -0.01], p = 0.053), raising the bonus proportion for non-main subject teachers from 0% to 4.25%. The Gini coefficient decreased to 0.1744 (95% CI [0.168, 0.181], below the 0.20 fairness reference value), with an effect size d = -0.162 (small effect). This indicates improvement in distributive justice, though procedural justice and interactional justice still have room for enhancement. Robustness tests demonstrated high model stability. This study provides empirical support for teacher evaluation reform under the ``Double Reduction'' policy and offers a practical, actionable reference framework for educational evaluation practice. Future work can enhance the model's generalizability through multi-center validation.

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