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Knowing What Students Know: The Science and Design of Eduacational Assessment
7 of the 60 students. Students taking an Aristotelian or novice perspective would show the pattern of line 17 (1 student). The rest of the combinations reflect a knowledge-in-pieces understanding. Note that across the four questions, 81 (3 × 3 × 3 × 3) response combinations would have been possible, but students tended to produce certain patterns of responses. For example, line 10 shows that 10 students apparently understood the independence of horizontal and vertical motions (problem parts a and d) without understanding the forces on projectiles (part b) or forces during collisions (part c). (The list of answer combinations in Box 5–7 is not meant to imply a linear progression from novice to Newtonian). Such profiles of student understanding are more instructionally useful than simply knowing that a student answered some combination of half of the test questions correctly.
On a single physics assessment, one could imagine having many such sets of items corresponding to different facet clusters. Interpretation issues could potentially be addressed with the sorts of measurement models presented in Chapter 4. For instance, how many bundles of items are needed for reliable diagnosis? And could the utility of the information produced be enhanced by developing interpretation models that could generate profiles of student performance across numerous topics (facet clusters)? Doing so would require not only cognitive descriptions of developing competence within a topic, but also a model of how various topics are related and which topics are more difficult or build on earlier ones. A statistical interpretation model could articulate these aspects of learning and also help determine how many and which items should be included on the test to optimize the reliability of the inferences drawn.
Once a preliminary set of tasks and corresponding scoring rubrics have been developed, evidence of their validity must be collected. Traditionally, validity concerns associated with achievement tests have tended to center around test content, that is, the degree to which the test samples the subject matter domain about which inferences are to be drawn. Evidence is typically collected through expert appraisal of the alignment between the content of the assessment tasks and the subject matter framework (e.g., curriculum standards). Sometimes an empirical approach to validation is used, whereby items are included in a test on the basis of data. Test items might be selected primarily according to their empirical relationship with an external criterion, their relationship with one another, or their power to differentiate among groups of individuals. Under such circumstances, it is likely that the selection of some items will be based on chance occurrences in the data (AERA et al., 1999).