Lynn S. Fuchs

Standards have been at the heart of state and federal efforts to improve education for several decades. Most recently, standards-based reforms have evolved with a focus on more ambitious "college- and career-ready" (CCR) standards. This paper synthesizes the results of a seven-year national research center focused on the implementation and effects of CCR standards. The paper draws on evidence from a quasi-experimental longitudinal study using NAEP data, a cluster-randomized trial of an alignment feedback intervention, and detailed implementation data from state-representative surveys and case studies of five districts. Situating our work in a "policy attributes theory," we find important gaps in the theory of change underlying current standards-based reform efforts. We conclude that the CCR standards movement is not succeeding in achieving its desired outcomes. We make specific suggestions for improving instructional policy, including a) providing more specific instructional guidance, b) reconceptualizing professional learning, c) building buy-in through the involvement of trusted leaders, d) providing better supports for differentiation, and e) devoting attention and guidance to the intersection of content and pedagogy, and f) addressing persistent deficit thinking among educators. 

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Despite policy relevance, longer-term evaluations of educational interventions are relatively rare. A common approach to this problem has been to rely on longitudinal research to determine targets for intervention by looking at the correlation between children’s early skills (e.g., preschool numeracy) and medium-term outcomes (e.g., first-grade math achievement). However, this approach has sometimes over—or under—predicted the long-term effects (e.g., 5th-grade math achievement) of successfully improving early math skills. Using a within-study comparison design, we assess various approaches to forecasting medium-term impacts of early math skill-building interventions. The most accurate forecasts were obtained when including comprehensive baseline controls and using a combination of conceptually proximal and distal short-term outcomes (in the nonexperimental longitudinal data). Researchers can use our approach to establish a set of designs and analyses to predict the impacts of their interventions up to two years post-treatment. The approach can also be applied to power analyses, model checking, and theory revisions to understand mechanisms contributing to medium-term outcomes.

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