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item response theory (IRT) provides us not only the abilities of
examinees but also the difficulties of items (problems), and it
is believed that the results are fairer and more accurate than
those by the classical test methods. Adaptive testing using the
IRT selects the most appropriate items to examinees automatically,
resulting more accurate ability estimation and more efficient test
procedures, where the term gadaptiveh means the adequate item selection.
The difficulties of the items are determined somehow, e.g., by
using the monitor test, in advance. Such adaptive testing fits
well online test systems. However, as the number of examinees is
growing in online testing, the difficulty values measured by the
monitor test will possibly be different from those assessed by
the new examinees. Then, calibration of the difficulty values may
be required. For such conditions, we use the dually adaptive online
IRT testing system, where gdually adaptiveh means that one is targeted
to the adequate item selection and the other is targeted to the
adjustment of the difficulty values for items. The monitor tests
are not necessarily required for the newly added items in the proposed
system. We have investigated how this system works using the simulation
study, and we applied this method to the high-school mathematics
testing case and university mathematics case. This system can be
applicable to many fields such as medical area, human resource
division, a variety of tests. In this paper, we focus on the large
scale data. |
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