. 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.
Aniko Apro, T. Tajti· Annales Mathematicae et Info...· 0 citations
. Social media sentiment analysis faces a persistent aggregation problem: lexicon-based and transformer-based models often produce inconsistent outputs for the same short, informal, and stylistically heterogeneous texts. This paper introduces ADRTW (Adaptive Dynamic Reliability-Trig-gered Weighting), an interpretable sentiment fusion framework that combines heterogeneous sentiment estimators using rule-guided reliability weights derived from textual cues, inter-model disagreement, and consistency patterns [5, 8]. The framework is evaluated on a Reddit dataset containing 1,577 posts, 354,050 comments, and 187,666 authors collected between 2017 and 2025, together with a controlled synthetic benchmark for aggregation comparison. The results show that ADRTW remains competitive with static averaging in controlled settings while preserving context-sensitive local variation in large-scale discourse analysis. Beyond sentiment fusion, the ADRTW-derived signal supports complementary analyses of online discussions, including temporal trend inspection, toxicity-aware interpretation, and participation-based clustering. Overall, the proposed framework provides a transparent and reusable basis for examining emotional dynamics in social media discourse.
Aniko Apro, L. Sasi· Annales Mathematicae et Info...· 0 citations
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