教师增值评价的理念发展、模型变迁与效果评估研究

Exploring the Concept Development,Model Transition and Effectiveness Evaluation of Teacher Value-Added Evaluation

随着教育评价改革的稳步推进与逐渐深入,教师增值评价对反映学生真实进步和教师实际教学效能、激励教师成长及助推学校变革的重要性日益凸显。教师增值评价的理念从早期的保障评价客观、服务绩效管理,转变为促进教育公平、引导师生全面发展。教师增值评价的建模方法从基于线性回归的简单预测分析、基于方差分析的混合效应分析,逐步发展到融合机器学习的智能算法分析。教师增值评价的实践模式主要分为基于学业成绩的教师个人评价、基于学生素养的教师集体评价以及基于终身发展的全体师生评价。教师增值评价的未来发展趋向包括多元模式、指标融合、数据互联、模型优化、智能平台5个方面。

With the gradual deepening and steady promotion of education evaluation reform,the role of teacher value-added evaluation in reflecting students'real progress and teachers'actual teaching effectiveness,stimulating teacher growth,and promoting school reform is becoming increasingly prominent.The concept of teacher value-added evaluation has shifted from ensuring objective evaluation and serving performance management to promoting education equity and leading the comprehensive development of teachers and students.Its essence is to separate the impact of non-teacher factors on educational output and quantify the impact of teacher factors on educational output.The model of teacher value-added evaluation relies on three functions:data conversion,function analysis,and performance output.Analyzing the relationship between independent variables,covariates,and random variables can obtain the"net effect"of teachers.The modeling methods of teacher value-added evaluation have developed from simple prediction analysis based on linear regression and mixed effect analysis based on variance analysis to intelligent algorithms analysis with machine learning.The practical models of teacher value-added evaluation are mainly divided into individual evaluation based on students'academic performance,collective evaluation based on students'competencies,and evaluation of all teachers and students based on lifelong development.The evaluation of the effectiveness of their practice needs to be analyzed from three aspects:perception,process,and result.We propose the future development trends of teacher value-added evaluation from five aspects:multi-mode,indicator fusion,data interconnection,model optimization,and intelligent platform.