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Personalized assessments are of increasing interest because of their potential to lead to more equitable decisions about the examinees. However, one obstacle to the widespread use of personalized assessments is the lack of a measurement toolkit that can be used to analyze data from these assessments. This article takes one step toward building such a toolkit by proposing a validation framework for personalized assessments. The framework is built on the threats‐to‐validity approach. We demonstrate applications of the suggested framework using the AP 3D Art and Design Portfolio examination and a more restrictive culturally relevant assessment as examples. 

In this study, upper-elementary-age students used an interactive reading app to read from a classic children’s novel during a summer program. Students took turns reading with an adult virtual narrator (audiobook). We use process and background data to explore factors that could predict whether a reader will read their next turn or skip it. We find that skipping quickly becomes self-perpetuating, underscoring the need to support the teacher in providing just-in-time personalized intervention to help students avoid the disengagement trap. 

Abstract Culturally responsive assessments have been proposed as potential tools to ensure equity and fairness for examinees from all backgrounds including those from traditionally underserved or minoritized groups. However, these assessments are relatively new and, with few exceptions, are yet to be implemented in large scale. Consequently, there is a lack of guidance on how one can compute comparable scores on various versions of these assessments. In this paper, the multigroup multidimensional Rasch model is repurposed for modeling data originating from various versions of a culturally responsive assessment and for analyzing such data to compute comparable scores. Two simulation studies are performed to evaluate the performance of the model for data simulated from hypothetical culturally responsive assessments and to find the conditions under which the computed scores are accurate. Recommendations are made for measurement practitioners interested in culturally responsive assessments.