There is no denying the impact that the coronravirus disease (COVID‐19) outbreak has had on many aspects of our lives. This article looks at the potential impact of COVID‐19 on student learning as schools abruptly morphed into virtual learning environments using data from several instructional, practice, and assessment solutions offered by Renaissance. First, three hypothetical learning scenarios are considered using normative data from Star assessments to explore the potential impact on reading and math test performace. Next, data on Focus Skills are used to highlight which grades may have missed the most foundational math and reading content if instruction was stopped or reduced. Last, data from two of Renaissance's practice tools are used to evaluate whether students were practicing key skills following school closures. The article concludes that academic decline will likely occur but may be tempered by the increased use of practice tools; effects may look different for math and reading; and may impact grades and schools differently. As such, schools may need to leverage decision‐making frameworks, such as the Multi‐tiered Systems of Support/Response‐to‐Intervention (MTSS/RTI) framework, more than ever to identify needs and target instruction where it matters most when school begins in fall 2020.

This article introduces two new classification consistency indices that can be used when item response theory (IRT) models have been applied. The new indices are shown to be related to Rudner’s classification accuracy index and Guo’s classification accuracy index. The Rudner- and Guo-based classification accuracy and consistency indices are evaluated and compared with estimates from the more commonly applied IRT-recursive procedure using a simulation study and data from two large-scale assessments. Results from the simulation study and practical examples suggested that the Guo- and Rudner-based indices tended to produce estimates that were closer to the simulated values and exceeded those from the IRT-recursive-based procedure. However, results did suggest that the Rudner- and Guo-based indices can have some undesirable features that are important to keep in mind when applying them in practice. The values of the classification accuracy and consistency indices appeared to be affected by a number of factors including the distribution of examinees, test length, the placement of the cut-scores, and the proficiency estimators applied to estimate examinee ability. Suggestions are made that an important part of investigations evaluating classification accuracy and consistency indices should be the creation of figures that show the value of the classification accuracy and classification consistency for individual examinees across the range of possible scores as these figures can help provide indications into subtle and important differences between indices.

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