Testing and Diversity in Postsecondary Education: The Case of California

The past several years have seen numerous efforts to scale back or eliminate affirmative action in postsecondary admissions. In response, policymakers and postsecondary institutions in many states are searching for ways to maintain the diversity of student populations without resorting to a prohibited focus on race. In response to these changes, this study used data from California and a simplified model of the University of California admissions process to explore how various approaches to admissions affect the diversity of the admitted student population. "Race-neutral" admissions based solely on test scores and grades were compared with the results of actual admissions before and after the elimination of affirmative action. A final set of analyses explored the effects on diversity of alternative approaches that take into account factors other than grades and scores, but not race or ethnicity. Replacing the former admissions process that included preferences with a race-neutral model based solely on GPA and SAT-I scores substantially reduced minority representation at the two most selective UC campuses but had much smaller effects at the other six, less selective campuses. SAT-I scores contributed to but were not the sole cause of the underrepresentation of African American and Hispanic students. A race-neutral model based solely on GPA also produced an underrepresentation of minorities, albeit a less severe one. None of the alternative admissions models analyzed could replicate the composition of the student population that was in place before the termination of affirmative action in California. The only approach that substantially increased the representation of minority students was accepting most students on the basis of within-school rather than statewide rankings, and this approach caused a sizable drop in both the average SAT scores and the average GPA of admitted applicants, particularly among African American and Hispanic students. Although admissions systems differ, the basic findings of this study are likely to apply at a general level to many universities and underscore the difficulty of providing proportional representation for underserved minority students at highly selective institutions without explicit preferences

An Item Selection Procedure for Testlets in CAT An Adaptive-within-Testlet Item Selection Method with Both Testlet Level and Test Level Content Balancing in CAT An Item Selection Procedure for Testlets in CAT An Item Selection Procedure for Testlets in CA

Abstract Testlets are items bundled together based on related context such as a common stimulus, passage, theme, or scenario. The most important essentials of testlets in computerized adaptive testing (CAT) are content balancing, which secures the test validity, and administering items adaptive to the test taker's ability, which ensures the test reliability. For testlets, it is not uncommon that the number of items within a testlet is large and therefore only a partial set of the testlet should be administered. Therefore, the purpose of this study is to propose a heuristic item selection procedure, which selects a testlet and a subset within the selected testlet to be administered with consideration of content balancing and being adaptive within the testlet. This proposed heuristic procedure forms appropriate subsets for each testlet beforehand, where those subsets satisfy specific constraints at the testlet level. Those subsets are used as the unit for item selection. To deal with content balancing at the test level, the concept of the shadow test is applied to assemble a test with several subsets that contains all items previously administered to the test taker and has no content constraint violation. Within the assembled shadow test, a testlet associated with the free items is selected. Based on this selected testlet, a subset of items associated with this testlet is adaptively reselected at each item selection level, which contains the items previously administered from this selected testlet and free items within this selected testlet as well. Based on the reselected subset, one free item is randomly selected to be administered. Simulations were conducted to evaluate the performance of this proposed heuristic procedure. The evaluation criteria include bias, mean square error (MSE), and conditional standard error of measurement (CSEM) for measurement precision, and item usage rate distribution and maximum item exposure rate for pool usage. The results presented the evidence An Item Selection Procedure for Testlets in CAT 1 that the proposed heuristic is an efficient algorithm regarding the measurement precision and pool usage for administering large-size testlets in CAT

Educational policy analysis archives

https://digitalcommons.usf.edu/usf_EPAA/1100/thumbnail.jp