# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 5 Limitations and Future Work  
Despite the promising results obtained in this study, there are several limitations that need to be addressed in future research. First, the dataset used in this study consisted of 150 students and 30 test items, which, while sufficient for initial comparisons, may limit the generalizability of the findings. Larger and more diverse datasets, incorporating students from different educational backgrounds and testing environments, are necessary to validate the robustness of the proposed models. Additionally, the relatively short 30-item test may not provide enough data to accurately estimate student abilities (\(\theta\)) and item difficulties (b), particularly for more complex models that include time-based parameters. Future research should explore the application of these models to longer tests or datasets with a greater number of students to ensure stable and reliable parameter estimation.  
Another key limitation is that item difficulty parameters (b) were fixed in some approaches (e.g., MLE-based estimation). While this simplification reduces computational complexity, it may introduce bias if the initial b estimates are not entirely stable. Allowing b parameters to be optimized iteratively rather than precomputed could lead to more accurate and adaptable models. Additionally, this study focused solely on One-Parameter Logistic (1 PL) IRT models, which assume that all test items have the same discrimination power. However, higher-dimensional models, such as Two-Parameter (2 PL) and Three-Parameter (3 PL) IRT models, could offer deeper insights by incorporating item discrimination and guessing parameters. Exploring the integration of time effects within these more complex models could enhance our understanding of the relationship between response time and ability estimation.  
Furthermore, the response time data used in this study was limited to the total time spent on each question. However, test-taking behavior is influenced by various cognitive and strategic factors, such as rapid guessing, distraction, and strategic time allocation. More detailed response time data--such as time spent reading versus solving a question or patterns of revisiting previous questions--could help refine the models' ability to distinguish between different student behaviors. Future research should consider incorporating behavioral time data to improve the interpretability and predictive accuracy of response-time-enhanced IRT models.  
The time adjustments in this study were modeled using linear transformations (subtractive and multiplicative effects). However, the relationship between response time and ability is likely nonlinear, and alternative transformations such as logarithmic, exponential, or hybrid models may provide a better representation of how time affects test performance. Investigating nonlinear models and their impact on ability estimation could lead to more precise and theoretically sound representations of time effects in IRT.  
Additionally, in future implementations, we plan to investigate adaptive item selection algorithms that jointly consider both difficulty and expected response time, which could improve the alignment between item selection and the response time-aware modeling framework.