# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 4 Model Performance and Results Analysis
### Overall Findings  
The 1 PL-IRT model remains the most stable, generalizable, and interpretable framework for ability estimation. The DTA-IRT model provides a slight improvement in specific cases but does not significantly outperform the standard model. Specifically, the DTA-IRTmodel demonstrated robust performance across key evaluation metrics in the third computational approach. It achieved an AUC value of 0.804, which was the highest among all models tested, indicating superior classification accuracy in distinguishing between correct and incorrect responses. In terms of model fit, DTA-IRT also recorded one of the lowest AIC (1410.343) and BIC (1573.265) values, as presented in Table 1, reflecting its strong balance between goodness-of-fit and model complexity. Table 2 further supports these findings by showing that the DTA-IRT model yielded consistently favorable log-likelihood scores and balanced item fit values across all three computational approaches. Furthermore, as shown in Table 3, the correlation between the DTA-IRT model's ability estimates (\(\theta\)) and those of the baseline 1 PL-IRT model was 0.992, with a mean absolute error (MAE) of 0.216. This high correlation confirms that DTA-IRT preserves the examinee ranking consistency observed in standard models while incorporating response-time sensitivity. The observed trade-off lies in the model's subtractive approach to time adjustment: while it successfully penalizes disproportionately long response times promoting efficiency and fairness it may slightly moderate ability estimates for high-performing students who invest more time per item. Thus, the combination of low AIC/BIC values, high AUC, and strong agreement with 1 PL-IRT in \(\theta\) estimates provides empirical justification for the model's stability and interpretability, even if its advantage over the baseline is context-dependent rather than universal. Multiplicative transformations (TWD-IRT, NRT-IRT, ART-IRT) consistently degrade model performance, as evidenced by higher AIC/BIC values, lower AUC, and unstable correlation scores. The third computational approach (cross-validation + regularization) proved to be the most effective methodology, ensuring better generalization and robustness in parameter estimation. These results highlight that while response time can be incorporated into IRT models, its integration must be theoretically justified and empirically validated to avoid unnecessary complexity that reduces model reliability. Table 1 compares the AIC, BIC, and AUC values of different IRT models across three computational approaches. The 1 PL-IRT model consistently demonstrates the best performance, achieving the lowest AIC/BIC values and high AUC scores. TP-IRT and DTA-IRT models also perform well, showing stable results across all approaches. In contrast, TWD-IRT and ART-IRT models exhibit poor performance, particularly in the second approach, with significantly lower AUC values. The third approach outperforms the other two because it incorporates five-fold cross-validation and regularization techniques. This method provides more reliable parameter estimates, reduces overfitting, and improves model generalization. The lower AIC/BIC values and more balanced AUC scores indicate that the third approach offers a more robust methodology for model selection and performance evaluation.