Optimization of multi-objective feature regression models for designing performance assessment methods in college and university educational reform.

The evaluation of teacher performance in higher education is a critical component of educational reform, requiring robust and accurate assessment methodologies. Multi-objective regression offers a promising approach to optimizing the construction of performance evaluation index systems. However, conventional regression models often rely on a shared input space for all targets, neglecting the fact that distinct and complex feature sets may influence each target. This study introduces a novel Multi-Objective Feature Regression model under Label-Specific Features (MOFR-LSF), which integrates target-specific features and inter-target correlations to address this limitation. By extending the single-objective stacking framework, the proposed method learns label-specific features for each target and employs cluster analysis on binned samples to uncover underlying correlations among objectives. Experimental evaluations on three datasets-Education Reform (EDU-REFORM), Programme for International Student Assessment (PISA), and National Assessment of Educational Progress (NAEP)-demonstrate the superior performance of MOFR-LSF, achieving relative root mean square error (RRMSE) values of 0.634, 0.332, and 0.925, respectively, outperforming existing multi-objective regression algorithms. The proposed model not only enhances predictive accuracy but also strengthens the scientific validity and fairness of performance evaluations, offering meaningful contributions to educational reform in colleges and universities. Moreover, its adaptable framework suggests potential applicability across a range of other domains.