Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/11727/4809

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    Can Computerized Adaptive Testing Work in Students' Admission to Higher Education Programs in Turkey?
    (2017) Berberoglu, Giray; Kalender, Ilker
    Admission into university in Turkey is very competitive and features a number of practical problems regarding not only the test administration process itself, but also concerning the psychometric properties of test scores. Computerized adaptive testing (CAT) is seen as a possible alternative approach to solve these problems. In the first phase of the study, a series of CAT simulations based on real students' responses to science items were conducted in order to determine which test termination rule produced more comparable results with scores made on the paper and pencil version of the test. An average of 17 items was used to terminate the CAT administration for a reasonable reliability level as opposed to the normal 45 items. Moreover, CAT based science scores not only produced similar correlations when using mathematics subtest scores as an external criterion, but also ranked the students similarly to the paper and pencil test version. In the second phase, a live CAT administration was implemented using an item bank composed of 242 items with a group of students who had previously taken the exam the paper and pencil version of the test. A correlation of.76 was found between the CAT and paper and pencil scores for this group. The results seem to support the CAT version of the subtests as a feasible alternative approach in Turkey's university admission system.
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    Sparsity-driven weighted ensemble classifier
    (2018) Erdem, Hamit; Ozgur, Atilla; Nar, Fatih
    In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights directly affect classifier accuracy. In the proposed method, ensemble weights finding problem is modeled as a cost function with the following terms: (a) a data fidelity term aiming to decrease misclassification rate, (b) a sparsity term aiming to decrease the number of classifiers, and (c) a non-negativity constraint on the weights of the classifiers. As the proposed cost function is non-convex thus hard to solve, convex relaxation techniques and novel approximations are employed to obtain a numerically efficient solution. Sparsity term of cost function allows trade-off between accuracy and testing time when needed. The efficiency of SDWEC was tested on 11 datasets and compared with the state-of-the art classifier ensemble methods. The results show that SDWEC provides better or similar accuracy levels using fewer classifiers and reduces testing time for ensemble.