2020年中心评估成绩:用于调查教师判断偏见的自然实验
1Department of Methodology, London School of Economics and Political Science (LSE), London, UK.
概括
在COVID-19期间,英格兰的教师评估成绩 (CAG) 没有显示绝对偏见. 虽然所有学生的CAG都高于预期,但出现了微妙的相对偏见,特别是在考虑交叉学生特征时.
科学领域:
- 教育研究教育研究
- 量化社会科学 量化社会科学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 2020年,COVID-19大流行扰乱了英格兰的标准考试.
- 学生获得教师评估的成绩 (CAG),而不是考试成绩的进展.
- 了解CAG中的潜在偏见对于教育公平至关重要.
研究的目的:
- 通过使用CAG作为自然实验,对教师判断中的偏见进行因果调查.
- 分析人口和社会经济因素如何影响教师评估.
- 探索学生交叉特征对评分偏差的影响.
主要方法:
- 使用英格兰 (GRADE) 数据集 (2018-2020) 的评分和录取数据.
- 在2018-19年数据上训练机器学习模型,以预测2020年学生的成绩.
- 预测等级和实际CAG之间的计算差异,以评估治疗效果.
主要成果:
- 没有发现对任何学生人口或社会经济群体的绝对负面偏见的证据.
- 所有学生群体的CAG都高于他们预测的考试成绩.
- 观察到不实质性的相对偏差,当考虑学生特征的更高阶相互作用时,变得更加实质性.
结论:
- 在大流行期间,教师评估成绩 (CAG) 没有表现出绝对偏见.
- 证据表明微妙的相对偏见,特别是在交叉学生特征.
- 交叉分析对于强大的定量教育平等研究至关重要.
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