## 5 Discussion
### Limitations and Future Research  
While the random search and genetic algorithm methods provide an effective solution to identify rapid guessing in low-stakes assessments, some limitations should be recognized. First, the dataset utilized in this study did not contain a \"ground truth' regarding which responses were truly rapidly guessed and which ones were valid attempts, because such a ground truth would be very hard to generate (e.g., using teacher observations or self-reports) in practice. Therefore, the methods presented in this study for detecting rapid guesses should be considered \"exploratory\" rather than \"confirmatory\" to promote informed decision making, instead of advocating for empirical interventions targeting rapid guessing. Second, given that the random search and genetic algorithms offer optimized RT thresholds informed by the data themselves, they require a large dataset (e.g., \(N>1000\)) to obtain a stable threshold for each item. Future studies can evaluate the performance of the random search and genetic algorithms for detecting rapid guessing in relatively smaller datasets. Third, the search function utilized in this study considers each item separately to find the local maxima (i.e., maximum deviance difference). Future research may benefit from considering all items simultaneously to find the maxima at both the local (i.e., individual items) and global level (i.e., the whole test). Fourth, although the genetic algorithm performed the best among all the methods, it is the most computationally intensive method, requiring longer times to optimize the threshold selection. This also means that, in assessments with larger item banks, the genetic algorithm may not be an efficient approach for looking for the RT threshold for identifying rapid guessing. This may imply that researchers and practitioners may opt for random search, which is an efficient and yet reliable method for finding the RT threshold for detecting rapid guessing. Finally, our search function for finding the RT threshold was based on two criteria (i.e., the deviance difference between the baseline and the adjusted IRT models and empirical reliability). Future research may benefit from involving other criteria based on process data (e.g., actions or clickstream data) in the prediction of rapid guessing on static or interactive test items using predictive models [60].  
Conceptualization, O.B.; methodology, O.B., G.G. and T.W.; software, O.B. and G.G.; validation, O.B. and G.G.; formal analysis, O.B. and G.G.; investigation, O.B.; writing--original draft preparation, O.B., G.G., T.W. and B.T.; writing--review and editing, O.B., G.G., T.W. and B.T.; visualization, G.G.; supervision, O.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data analyzed in this study are subject to copyright. Therefore, they cannot be discussed or shared by the researchers in any form or format.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
AIC Akaike's Information Criterion  
BIC Bayesian Information Criterion  
Cumulative proportion method  
HQ Hannan-Quinn criterion  
ICC Item characteristic curve  
IRT Item response theory  
MLN Mixture log-normal method  
NT Normative threshold method  
RT Response time  
SABIC Sample-size-adjusted BIC  
2 PL Two-parameter logistic model