analogies, fill-in-the-blank and synonym item types. CATs typically have some form of item exposure constraints, to prevent the most informative items from being over-exposed. Also, on some tests, an attempt is made to balance surface characteristics of the items such as gender of the people in the items or the ethnicities implied by their names. Thus CAT exams are frequently constrained in which items it may choose and for some exams the constraints may be substantial and require complex search strategies (e.g., linear programming) to find suitable items. A simple method for controlling item exposure is the "randomesque" or strata method. Rather than selecting the most informative item at each point in the test, the algorithm randomly selects the next item from the next five or ten most informative items. This can be used throughout the test, or only at the beginning. Another method is the Sympson-Hetter method, in which a random number is drawn from U(0,1), and compared to a ki parameter determined for each item by the test user. If the random number is greater than ki, the next most informative item is considered. Wim van der Linden and colleagues have advanced an alternative approach called shadow testing which involves creating entire shadow tests as part of selecting items. Selecting items from shadow tests helps adaptive tests meet selection criteria by focusing on globally optimal choices (as opposed to choices that are optimal for a given item). Multidimensional Given a set of items, a multidimensional computer adaptive test (MCAT) selects those items from the bank according to the estimated abilities of the student, resulting in an individualized test. MCATs seek to maximize the test's accuracy, based on multiple simultaneous examination abilities (unlike a computer adaptive test – CAT – which evaluates a single ability) using the sequence of items previously answered (Piton-Gonçalves and Aluisio, 2012). See also References Additional sources Drasgow, F., & Olson-Buchanan, J. B. (Eds.). (1999). Innovations in computerized assessment. Hillsdale, NJ: Erlbaum. Piton-Gonçalves, J. & Aluísio, S. M. (2012). An architecture for multidimensional computer adaptive test with educational purposes. ACM, New York, NY, USA, 17-24. Piton-Gonçalves, J. (2020). Testes adaptativos para o Enade: uma aplicação metodológica. Meta: Avaliação 12(36):665-688 Van der Linden, W. J., & Glas, C.A.W. (Eds.). (2000). Computerized adaptive testing: Theory and practice. Boston, MA: Kluwer. Wainer, H. (Ed.). (2000). Computerized adaptive testing: A Primer (2nd Edition). Mahwah, NJ: ELawrence Erlbaum Associates. Weiss,