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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.

Bayesian probabilistic forecasting with large-scale educational trend data: a case study using NAEP