Testing the validity of value‐added measures of educational progress with genetic data
Testing the validity of value‐added measures of educational progress with genetic data
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用遗传数据测试教育进步增值措施的有效性
DOI：
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发表时间：
2018
影响因子：
2.3
通讯作者：
G. Smith
中科院分区：
文献类型：
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作者：
T. Morris;N. Davies;D. Dorling;R. Richmond;G. Smith
教育研究人员和政策制定者已经使用教育进步的增值指标来评估教师和学校的表现，并根据绩效来决定工资和学校排名。它们旨在控制学生之间的所有潜在差异，因此应提供公正的衡量学校和教师对学生进步的影响的措施，然而，其有效性受到质疑。我们利用来自英国出生队列的遗传数据来研究增值措施如何成功地控制学生之间的遗传差异。我们使用原始附加值、背景附加值（额外控制背景特征）和教师报告的附加值指标，这些指标基于11岁、14岁和16岁的数据。分析的样本量从4,600到6,518不等。我们的研究结果表明，学生之间的遗传差异解释了原始附加值测量的微小差异，但解释了高达20%的上下文附加值测量差异（95% CI = 6.06%至35.71%）。根据教师评价的能力建立的增值测量指标，学生之间的遗传差异解释了更大比例的差异，其中36.3%的横截面差异在统计上由遗传解释（95% CI = 22.8%至49.8%）。相比之下，每个年龄段原始测试成绩的遗传差异解释了更大比例的差异，至少为47.3% （95% CI: 35.9至58.7）。这些发现提供了证据，表明教育进步的增值措施可能受到学生之间遗传差异的影响，因此可能提供了一种有偏差的学校和教师表现衡量标准。我们包括对在教育研究中使用遗传数据感兴趣的教育研究人员的遗传术语表。
Value‐added measures of educational progress have been used by education researchers and policy‐makers to assess the performance of teachers and schools, contributing to performance‐related pay and position in school league tables. They are designed to control for all underlying differences between pupils and should therefore provide unbiased measures of school and teacher influence on pupil progress, however, their effectiveness has been questioned. We exploit genetic data from a UK birth cohort to investigate how successfully value‐added measures control for genetic differences between pupils. We use raw value‐added, contextual value‐added (which additionally controls for background characteristics) and teacher‐reported value‐added measures built from data at ages 11, 14 and 16. Sample sizes for analyses range from 4,600 to 6,518. Our findings demonstrate that genetic differences between pupils explain little variation in raw value‐added measures but explain up to 20% of the variation in contextual value‐added measures (95% CI = 6.06% to 35.71%). Value‐added measures built from teacher‐rated ability have a greater proportion of variance explained by genetic differences between pupils, with 36.3% of their cross‐sectional variation being statistically accounted for by genetics (95% CI = 22.8% to 49.8%). By contrast, a far greater proportion of variance is explained by genetic differences for raw test scores at each age of at least 47.3% (95% CI: 35.9 to 58.7). These findings provide evidence that value‐added measures of educational progress can be influenced by genetic differences between pupils, and therefore may provide a biased measure of school and teacher performance. We include a glossary of genetic terms for educational researchers interested in the use of genetic data in educational research.