DESIGNING OF COMPUTERIZED ADAPTIVE TESTS IN THE ABSENCE OF TESTING STATISTICS
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Keywords

computerized adaptive test
analytic hierarchy process
experts’ conclusion
decision tables
a network of decision tables

How to Cite

[1]
V. E. . Bondarenko, “DESIGNING OF COMPUTERIZED ADAPTIVE TESTS IN THE ABSENCE OF TESTING STATISTICS”, ITLT, vol. 73, no. 5, pp. 101–115, Oct. 2019, doi: 10.33407/itlt.v73i5.2520.

Abstract

A Computerized Adaptive Test proposes items according to the student's knowledge level. Therefore, the number of items, which are given to students, is reduced. Besides, the ending of such test is determined by the student's knowledge level, which allows an instructor to reduce testing time. As usual, construction of such tests is based on the Item Response Theory (IRT). This theory gives models which use statistical data about the student's knowledge level and difficulty of items. We do not have such statistics for new tests. In such cases, this paper proposes to estimate the complexity of items on the basis of the experts' conclusions. These conclusions are based on the analytic hierarchy process (AHP) which was modified. The modification allows experts to estimate the complexity of items with the help of the collection of the items characteristics. This modification can remove the expert's inadequate estimates of items or their characteristics. This method allows experts to classify all items in clusters according to their complexity in the first stage of the testing when statistics of items use is absent. A test constructor, on the basis of a decision tables network, realizes the algorithm of the items' selection from different clusters. In the future, tutors will have tested a sufficient number of students' groups. They record statistics of the test using. A test constructor receives such statistics, which will allow them to use the models of the Item Response Theory for estimation of the test items' complexity. The assessment of the knowledge level of students is made with the help of an adaptive test, which is based on a network of decision tables. This network determines the algorithm of using items from different clusters for the testing. The adaptive test is built on the basis of the network of decision tables as a computer system. This system is constructed on the Java platform with the help of the programming environment Android Studio. It has the interface suitable for students as well as for a constructor, which allows the constructor to change the algorithm of using items if received statistics of items use shows such necessity.

PDF (Ukrainian)

References

L. Crocker, J.Algina, Introduction to Classical and Modern Test Theory. Mason, Ohio, USA, Cengage Learning. 527 p, 2006. (in English).

Embretson S.E., Reise S.P. Item response theory for psychologists. London, LEA.370 p. 2013. (in English).

T. L. Saaty, Axiomatic foundation of the analytic hierarchy process. Management Science, Vol. 32, №7: p. 841-855, 1986 .(in English).

L.A. Lombardi, A general business oriented language based on decision expression. Communications of the ACM, 7 (2): p. 104-111, 1964. (in English).

E. Humby, Programs from decision tables. London, Macdonald and Co.; New York, American Elsevier, 91 p., 1973. (in English).

R.A.Karayev, N.Y.Sadikhova, Production-Tabular Knowledge. Bases Tools for Assessing and Checking of Correctness, Middle-East Journal of Scientific Research, 21 (9): p.1659-1662, 2014. (in English).

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