## 4 Results  
Research Question 1: Does the Proposed Data-Driven Approach Produce Viable RT Thresholds for Detecting Rapid Guessing in Low-Stakes Assessments? To address our first research question, we compared the results of the random search and genetic algorithms with those from the baseline model (i.e., the 2 PL IRT model based on the original response dataset) in terms of item parameters and model-level statistics. As expected, both random search and genetic algorithm successfully produced an optimal RT threshold for each of the 40 items included in the assessment. Table 1 presents the average RT and proportion-correct values based on the entire sample of students for the baseline model and the adjusted models, where rapid guesses were removed based on the RT thresholds identified by random search and genetic algorithm. The results show that the average RT values in the baseline model increased after removing the identified rapid guesses based on random search and genetic algorithm. This was an expected finding because eliminating rapid guesses for each item removed students with very short RT values and retained those who spent sufficient time to read, understand, and answer the items. The average RT values for random search and genetic algorithm were very close, suggesting that the two methods selected similar RT thresholds for the items. Unlike the average RT values, the proportion-correct values from the baseline  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{5}{c}{**Average Response Time (s)**} & \multicolumn{5}{c}{**Proportion-Correct (\%)**} \\ \cline{2-7}  & **Baseline** & **Random Search** & **Genetic Algorithm** & **Baseline** & **Random Search** & **Genetic Algorithm** \\ \hline
1 & 3.602 & 3.790 & 3.790 & 61.576 & 64.155 & 64.155 \\
2 & 4.689 & 5.099 & 5.099 & 50.688 & 55.859 & 55.859 \\
3  
model decreased after removing rapid guesses identified by the random search and genetic algorithm methods, with genetic algorithm generating higher proportion-correct values than random search. This was also an expected finding because rapid guesses are more likely to be incorrect than correct, which inflates item difficulty (i.e., lowering the proportion-correct values). Figure 2 demonstrates the RT thresholds (as percentages of the average RT value) for each item. For most of the items, the two methods yielded very similar RT thresholds. Although there were a few items (e.g., items 12, 34, and 39) for which either random search or genetic algorithm yielded relatively higher RT thresholds, there was no clear pattern in the results. Both random search and genetic algorithm seemed to have identified RT thresholds within the range of 28% and 30% of the average RT values. This is an interesting finding because the RT thresholds selected by both methods were close to the 30% boundary, although the search space for the RT thresholds was set up within the range of 10% and 30% of the average RT values for the items. This finding also suggests that, compared with the NT approaches (e.g., NT10 with the 10% of the average RT value), the proposed data-driven approach with random search and genetic algorithm tends to yield more liberal RT thresholds for detecting rapid guessing, despite using both deviance difference and empirical reliability as optimization criteria. These comparisons are discussed in more detail in the next section. To have a closer look at the impact of rapid guessing on the items, we also computed the average RT and proportion-correct values for each item based on the sample of students who were flagged for exhibiting rapid guessing by each optimization method (see Table 2). Overall, the average RT values for both methods were quite short (i.e., within the range of 0.224 s and 0.904 s), indicating the presence of rapid guessing response behavior among these students. The two algorithms also produced similar low proportion-correct scores for the items, except for item 1.