| Tartalmi leírás: |
Computerized adaptive testing (CAT) combines ability estimation
with an item-selection rule that usually favors the currently most informative
item. Although randomization is often used for exposure control,
security, or robustness, the probability distribution used for item sampling is
rarely treated as an explicit design variable. This paper studies this choice
in a Bayesian adaptive-assessment framework in which posterior updating is
fixed, while the next item is sampled from a distribution over information-ranked
candidates. Five kernels are compared: uniform, binomial, normal,
exponential, and Poisson. Using interaction logs from 33 test sessions completed
by 18 participants, we analyze observed session-level accuracy, test
length, early stopping, and posterior uncertainty reduction. The results indicate
that the selection kernel affects operational behavior: concentrated
kernels tend to produce more stable accuracy and reduce interaction variability,
whereas flatter kernels increase exploration and may prolong sessions.
The study contributes a compact system-level formulation of distribution-aware
item selection and shows how the exploration–exploitation trade-off
appears in deployable Bayesian CAT systems.
|
