The first method of conditional inference is that the observer influences the observational setting (the assessment task presented to the student) or the conditions that precede the observation in ways that ensure a certain task-examinee matchup. This method is demonstrated by the design of the control-of-variables study just described.
A second way to condition the extraction of information from student performances is to obtain relevant background information about students from which to infer key aspects of the matchups. In the Klahr et al. (in press) example, this approach would be appropriate if the researchers could only give randomly selected post-test tasks to students, but could try to use curriculum guides and teacher interviews to determine how each student’s post-test happened to correspond with his or her past instruction (if at all).
A third method is to let students choose among assessment tasks in light of what they know about themselves—their interests, their strengths, and their backgrounds. In the control-of-variables study, students might be shown several tasks and asked to solve one they encountered in instruction, one a great deal like it, and one quite dissimilar (making sure the student identified which was which). A complication here is that some students will likely be better at making such decisions than others.
The forms of conditional inference described above offer promise for tackling persisting issues of equity and fairness in large-scale assessment. Future assessments could be designed that take into account students’ opportunity to learn what is being tested. Similarly, such approaches could help address issues of curriculum fairness, that is, help protect against external assessments that favor one curriculum over another. Issues of opportunity to learn and the need for alignment among assessment, curriculum, and instruction are taken up further in Chapter 6.
The design of high-quality classroom and large-scale assessments is a complex process that involves numerous components best characterized as iterative and interdependent, rather than linear and sequential. A design decision made at a later stage can affect one occurring earlier in the process. As a result, assessment developers must often revisit their choices and refine their designs.
One of the main features that distinguishes the committee’s proposed approach to assessment design from current approaches is the central role of a model of cognition and learning, as emphasized above. This model may be fine-grained and very elaborate or more coarsely grained, depending on the purpose of the assessment, but it should always be based on empirical