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The papers in this issue address the general question of how to add value to educational assessments, particularly in terms of student growth in academic disciplines. In addressing this goal, the papers focus on several recent and emerging model-based methodologies: in particular, learning progressions and cognitive models of learning (Pellegrino, this issue; de la Torre & Minchen, this issue), evidence-centered design (ECD) as a framework for assessment design and development (Zieky, this issue), and cognitively based assessment of, for, and as learning (Deane & Song, this issue; van Rijn et al., this issue). These model-based methodologies involve major developments in how we interpret assessment results and, therefore, they have strong implications for how we evaluate the psychometric quality of the assessments. The model-based interpretations of each student’s assessment results involve relatively complex descriptions of each student’s achievement emphasizing the student’s overall level of sophistication as specified by a list of skills mastered and not mastered (de la Torre & Minchen, this issue), or by a level in a learning progression (Pellegrino, this issue), rather than the student’s standing on a unidimensional scale (or on several scales). The goal is to develop assessments that promote learning by providing information that is useful in teaching and learning, and to generate evidence that supports the proposed interpretation and usefulness of the assessment results. Our main point in this paper is that while grounding assessment design in cognitive theories and model-based methodologies is highly desirable, rigorous evaluation of the resulting scores is still necessary. Specifically, the basic definition of validity in terms of the extent to which the interpretation and use of test scores is supported by appropriate evidence and analysis does not need to change. However, as discussed in more detail later, the structure of the arguments used to support the proposed interpretations and uses of the scores and the evidence needed to evaluate these arguments will need to be adapted to fit the proposed interpretations and uses of the test results. Similarly, the analyses of the precision, or reliability, of the results will need to be reconsidered; for example, to the extent that the focus is on placement in a learning progression rather than on a score on a continuous scale, analyses of precision would focus on consistency of placement (in the progression), rather than on traditional reliability or generalizability coefficients. Learning Progressions and Cognitive Models

Cognitive frameworks for assessment, teaching, and learning: A validity perspective