## 5 Discussion  
As computer-based assessments, or more broadly digital assessments, have become more prevalent over the last decade, the availability of process data (e.g., item response times) recorded by computers has motivated researchers to leverage such data to gain further insights into various aberrant response behaviors such as cheating, item preknowledge, hint abuse, and idle responding (e.g., [3; 12; 55; 56]). In the context of low-stakes assessments with little to no formal consequences for students, rapid guessing emerges as a common type of aberrant response behavior that may jeopardize the validity of decisions and inferences made from the results of such assessments [6; 9]. Researchers proposed various statistical methods to detect and eliminate rapidly guessed answers from the response data (e.g., [4; 5; 7]). These methods typically rely on the assumption that a particular RT threshold separates students with rapid guessing from those with solution behavior. Therefore, each method aims to identify the most accurate (or optimal) RT threshold that could help identify rapid guessing, while avoiding false negatives (i.e., flagging responses that are actually not rapid guesses) and false positives (i.e., missing responses that are truly rapid guesses). In this study, we proposed a data-driven threshold-setting method that iteratively searches for the most optimal RT threshold for each item within a search space defined by the researcher. Our method works under the assumption that the presence of rapid guesses distorts the quality of response data, leading to weaker model-data fit and lower reliability for the psychometric model underlying the assessment (e.g., a 2 PL IRT model). We combined two optimization algorithms (random search and genetic algorithm) with the existing threshold-setting methods, namely the NT method [5] and the CUMP method [4], to build a single search function. This function looks for the RT threshold that could maximize the adjusted deviance difference between the baseline model (based on the original response dataset) and the adjusted model (i.e., after rapid guesses have been removed based on the identified RT threshold). In addition to the deviance difference, the function uses empirical reliability as a secondary criterion to be maximized. To demonstrate how the proposed method works, we used real data from a low-stakes, computer-based assessment designed for measuring and monitoring elementary students' progress in math throughout the school year. We examined if the proposed approach could produce viable RT thresholds for detecting rapid guessing in low-stakes assessments and also compared its performance with that of the NT method. Our findings showed that the proposed approach could produce viable RT thresholds for identifying rapid guesses in the items. As we removed the responses identified as rapid guesses, the proportion-correct values for all items increased, reducing the impact  
Figure 5: Pairwise relationships between the ability estimates obtained from the IRT models. of the inflation caused by incorrect responses associated with rapid guessing. A similar result was also obtained when we reviewed the item parameters from the baseline and adjusted IRT models (i.e., lower difficulty parameters after removing rapid guesses). The two optimization algorithms, random search and genetic algorithm, yielded very similar results in terms of the RT thresholds for most of the items in the assessment. However, the computational time required for the genetic algorithm was much higher than that of random search, due to having a more complex mechanism for sampling random RT values within the search space. Since the assessment consisted of constructed-response items requiring short answers rather than selecting a response option, the proportion-correct values were either zero or very close to zero for the students flagged for exhibiting rapid guessing behavior.