not been translated into sets of tasks that can be used for assessment purposes. Even in subject domains for which characteristics of expertise have been identified, the understanding of patterns of growth required for assessment purposes, which would enable one to identify landmarks on the way to competence, is often lacking.
To develop assessments that are fair—that are comparably valid across different groups of students—it is crucial that patterns of learning for different populations of students be studied. Much of the development of cognitive theories has been conducted with restricted groups of students (i.e., mostly middle-class whites). In many cases it is not clear whether current theories of developing knowledge and expertise apply equally well with diverse populations of students, including those who have been poorly served in the education system, underrepresented minority students, English-language learners, and students with disabilities. While there are typical learning pathways, often there is not a single pathway to competence. Furthermore, students will not necessarily respond in similar ways to assessment probes designed to diagnose knowledge and understanding. These kinds of natural variations among individuals need to be better understood through empirical study and incorporated into the cognitive models of learning that serve as a basis for assessment design.
Sophisticated models of learning must be paired with methods of eliciting responses from students that effectively reveal what they know, as well as tools for comparing and scoring those responses. Current measurement methods offer greater potential for drawing inferences about student competence than is often realized (National Research Council, 2001c). It is possible, for example, to characterize student achievement in terms of multiple aspects of proficiency rather than a single score; chart students’ progress over time, instead of simply measuring performance at a particular point in time; deal with multiple paths or alternative methods of valued performance; model, monitor, and improve judgments based on informed evaluations; and model performance not only at the level of students, but also at the levels of groups, classes, schools, and states.
Much remains to be done, however, to improve the use of assessment in practice. Iterative cycles of research and development will be required to capture critical dimensions of knowledge in assessment tools and protocols that can be used effec-