The simple average of student growth scores is often used in accountability systems, but it can be problematic for decision making. When computed using a small/moderate number of students, it can be sensitive to the sample, resulting in inaccurate representations of growth of the students, low year-to-year stability, and inequities for low-incidence groups. An alternative designed to address these issues is to use an Empirical Best Linear Prediction (EBLP), which is a weighted average of growth score data from other years and/or subjects. We apply both approaches to two statewide datasets to answer empirical questions about their performance. The EBLP outperforms the simple average in accuracy and cross-year stability with the exception that accuracy was not necessarily improved for very large districts in one of the states. In such exceptions, we show a beneficial alternative may be to use a hybrid approach in which very large districts receive the simple average and all others receive the EBLP. We find that adding more growth score data to the computation of the EBLP can improve accuracy, but not necessarily for larger schools/districts. We review key decision points in aggregate growth reporting and in specifying an EBLP weighted average in practice.

中文翻译：

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使用来自两个州的数据探索改进的综合学生成长措施

问责制中经常使用学生成长分数的简单平均数，但它可能会给决策制定带来问题。当使用少量/中等数量的学生进行计算时，它可能对样本敏感，导致学生成长的不准确表示、年际稳定性低以及低发病率群体的不平等。旨在解决这些问题的另一种方法是使用经验最佳线性预测 (EBLP)，它是其他年份和/或受试者的增长得分数据的加权平均值。我们将这两种方法应用于两个全州范围的数据集，以回答有关其性能的经验问题。EBLP 在准确性和跨年度稳定性方面优于简单平均值，但其中一个州的非常大的地区不一定会提高准确性。在这样的例外情况下，我们展示了一个有益的替代方案可能是使用混合方法，其中非常大的地区接受简单平均，所有其他地区接受 EBLP。我们发现在 EBLP 的计算中添加更多的增长分数数据可以提高准确性，但不一定适用于较大的学校/地区。我们审查了总体增长报告中的关键决策点，并在实践中指定了 EBLP 加权平均值。