studies of learners in a domain. Ideally, the model of learning will also provide a developmental perspective, showing typical ways in which learners progress toward competence.
Another essential feature of good assessment design is an interpretation model that fits the model of learning. Just as sophisticated interpretation techniques used with assessment tasks based on impoverished models of learning will produce limited information about student competence, assessments based on a contemporary and detailed understanding of how students learn will not yield all the information they otherwise might if the statistical tools available to interpret the data, or the data themselves, are not sufficient for the task. Observations, which include assessment tasks along with the criteria for evaluating students’ responses, must be carefully designed to elicit the knowledge and cognitive processes that the model of learning suggests are most important for competence in the domain. The interpretation model must incorporate this evidence in the results in a manner consistent with the model of learning.
Validation that tasks tap relevant knowledge and cognitive processes, often lacking in assessment development, is another essential aspect of the development effort. Starting with hypotheses about the cognitive demands of a task, a variety of research techniques, such as interviews, having students think aloud as they work problems, and analysis of errors, can be used to analyze the mental processes of examinees during task performance. Conducting such analyses early in the assessment development process can help ensure that assessments do, in fact, measure what they are intended to measure.
Well-delineated descriptions of learning in the domain are key to being able to communicate effectively about the nature of student performance. Although reporting of results occurs at the end of an assessment cycle, assessments must be designed from the outset to ensure that reporting of the desired types of information will be possible. The ways in which people learn the subject matter, as well as different types or levels of competence, should be displayed and made as recognizable as possible to educators, students, and the public.
Fairness is a key issue in educational assessment. One way of addressing fairness in assessment is to take into account examinees’ histories of instruction—or opportunities to learn the material being tested—when designing assessments and interpreting students’ responses. Ways of drawing such conditional inferences have been tried mainly on a small scale, but hold promise for tackling persistent issues of equity in testing.
Some examples of assessments that approximate the above features already exist. They are illustrative of the new approach to assessment the committee advocates, and they suggest principles for the design of new assessments that can better serve the goals of learning.