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Measurement and Fairness Issues The experience of the early entrants also suggests that there will be nontrivial measurement and fairness issues For those K-12 agencies that plan to offer the same tests on paper and computer, comparability will be a concern, especially for high-stakes decisions Although comparability has often been supported (Bridgeman, 1998; Mead & Drasgow, 1993; Schaeffer, Bridgeman, Golub-Smith, Lewis, Potenza, & Steffen, 1998; Schaeffer, Steffen, GolubSmith, Mills, & Durso, 1995), in some instances it does not hold, as when examinees are tested in a mode different from the one in which they routinely work (Russell & Plati, 2001) Further, while it is desirable from a fairness perspective, comparability may limit innovation by preventing the computer-based version from exploiting the technology to broaden measurement beyond what traditional methods allow Where feasible, the wise choice may be to eliminate the problem entirely by offering only a computerbased instrument or by delivering in the two modes for as short a period as possible Regardless of whether the test is delivered solely on computer, there is a second comparability concern This concern is for platform comparability From one school to the next (and even within the same school), monitor size, screen resolution, keyboard layout, connection speed, and other technical characteristics may vary, causing items to appear differently or to take more time to display (Display-time variations may occur for the same machine as a function of time of day) Any of these variations may affect scores unfairly For instance, Bridgeman, Lennon, and Jackenthal (2001) found that lowering the screen resolution, and thus increasing the need for scrolling, diminished test performance on reading comprehension items by a small (but nontrivial) amount Similarly, Powers and Potenza (1996) presented evidence to suggest that essays written on laptops might not be comparable to those written on desktops having better keyboards and screen displays A third measurement issue is differential computer familiarity Although physical access to computers at school differs little by income and racial group, home-access disparities are still substantial (US Department of Commerce, 2002) Thus, there may be group (or individual) differences in computer experience that affect test performance in construct-irrelevant ways For multiple-choice tests, the research to date suggests that differences in computer experience have little, if any, effect on test scores (see, eg, Bridgeman, Bejar, & Friedman, 1999; Taylor, Jamieson, Eignor, & Kirsch, 1998) However, as electronic tests incorporate more performance tasks, the complexity of the mechanisms for responding and the demands on computer facility could well increase Finally, for States wanting to use online assessment in connection with the accountability requirement of No Child Left Behind, there is uncertainty about the appropriateness of adaptive testing NCLB requires all students in a grade to be assessed against the same standards The US Education Department (ED).



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Figure 76 Neighbor location where the node estimates its location as the centroid of the locations of anchors that it hears

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40 years old) Given that women appear to have greater computer anxiety than men, we need to examine the effect of gender on test scores Finally, if computer anxiety is related to test scores, researchers should determine whether there are any strategies for addressing this problem Will a brief training and practice session, for example, help reduce anxiety and eliminate problems that interfere with maximum performance on the test Such questions are potentially of great practical importance

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Based on the above research and theory, this section outlines some possible practical suggestions for improving examinee reactions to Internet-based testing

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Test-takers should be informed of the purpose of the testing program and why the data are being gathered (eg to help predict job performance) Tests should be designed to have the maximum face validity (appearance of job relatedness) possible

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shortening the path and hence a much smaller hop count), removing or displacing some nodes (this will modify the path), and so on 72122 Neighbor Location This is a range-independent, proximity-based technique based on leveraging the location of neighbors [152] In this case, given a set of locator nodes, a wireless device can localize by calculating the centroid of the locations of anchors that they hear The implicit assumption here is that the various nodes are uniformly distributed The main advantages of this scheme are its simplicity, low overhead and ease of implementation The drawback is that it can lead to very coarse-grained location determination The accuracy of localization can be improved using different power levels, thereby changing the transmission range of the locators This will change the set of locators in the neighborhood of a system node This approach has been proposed in [153] Note that an adversary can modify the effective radio region In addition, the adversary can make use of jammers placed strategically to bias the location estimate 72123 Region Inclusion A representative example here is APIT (approximate point in triangle) This is a range-independent scheme [154] In this case the area is divided into several triangular regions with anchor nodes forming the vertices of the triangles The anchor nodes transmit beacons A system node that seeks to determine its location determines the triangles in which the node resides using the transmitted beacons The test used to determine this is called the APIT test In this test, a node chooses three anchors from the list of anchors whose beacons it could hear and tests whether it is inside the triangle formed by connecting these three anchors The test consists of the node checking the signal strengths at all its neighbors of the three locators that form the vertices of the triangle A node is determined to be further away if its signal strength is smaller Now in APIT, if no neighbor of the node is further from/closer to all the three anchors simultaneously, then the node assumes that it is inside the triangle Otherwise, the node assumes that it is outside the triangle We illustrate the working of APIT in Figure 77 In (a) node M assumes that it is inside the triangle since none of its neighbors.

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