Of critical importance to education policy is monitoring trends in education outcomes over time. In the United States, the National Assessment of Educational Progress (NAEP) has provided long-term trend data since 1970; at the state/jurisdiction level, NAEP has provided long-term trend data since 1996. In addition to the national NAEP, all 50 states and United States jurisdictions participate in the state NAEP assessment. Thus, NAEP provides important monitoring and forecasting information regarding population-level academic performance of relevance to national and international goals. However, an inspection of NAEP trend reports shows that relatively simple trend plots are provided; and although these plots are important for communicating general trend information, we argue that much more useful information can be obtained by adopting a Bayesian probabilistic forecasting point of view. The purpose of this paper is to provide a Bayesian probabilistic forecasting workflow that can be used with large-scale assessment trend data generally, and to demonstrate that workflow with an application to the state NAEP assessments.

对教育政策至关重要的是随着时间的推移监测教育成果的趋势。在美国，国家教育进步评估（NAEP）提供了自 1970 年以来的长期趋势数据；在州/辖区层面，NAEP 提供了自 1996 年以来的长期趋势数据。除国家 NAEP 外，所有 50 个州和美国辖区都参与了州 NAEP 评估。因此，NAEP 提供了与国家和国际目标相关的人口水平学业成绩的重要监测和预测信息。但是，对 NAEP 趋势报告的检查表明提供了相对简单的趋势图；尽管这些图对于传达总体趋势信息很重要，我们认为通过采用贝叶斯概率预测的观点可以获得更多有用的信息。本文的目的是提供一个贝叶斯概率预测工作流程，该工作流程通常可用于大规模评估趋势数据，并通过对州 NAEP 评估的应用来演示该工作流程。