## 6 Results  
### Evaluating Alternative Interpretations Based on the AOIs Examined  
We began computing the Pearson coefficient (refer to Table 2), revealing a significant correlation between scores and question type r (87) = \(-\)0.389, \(p\) < 0.01, the RT r (87) = \(-\)0.313, \(p\) < 0.01, the scan-path length r (87) = \(-\)0.370, \(p\) < 0.01 and the number of AOIs to be checked from the total number of AOIs defined and the scan-path length (AOIs rate) r (87) = 0.941, \(p\) < 0.01. That is, regarding the type of question, the item that did not require a systematic approach (all the AOIs needed to be examined) was more difficult (participants showed lower scores) than those that did require it, and participants also required longer scan-paths and RTs (i.e., the spent more time examining more AOIs, as if they lacked a sufficiently clear understanding of the key issues to be able to solve the task correctly). Furthermore, higher scores correlated with higher AOIs rates, meaning that participants showed a greater knowledge of the subject matter with more focus on the key AOIs than on the other AOIs (not relevant for the task resolution). As part of our analysis, we determined the critical AOIs necessary for successfully completing each task. Table 3 shows the proportion of participants that visited each of the AOI identified. Bolded values indicate invariant areas (visited either by none or by all the participants) that offer no information in the analytical process. A hyphen indicates that the AOI is missing in that task. Each item has specific critical AOIs for resolution. Hence, the analysis must assess participants' visitation to these AOIs. If we had seen that these areas had hardly been considered in the resolution, that would have raised doubts regarding the validity the item. In Item24, all the AOIs were examined with different visit rates. In Item32, DC, B, C, E and F were critical, and all these AOIs had relevant visit rates. In this item, the B AOI was crucial, as one could not answer the question accurately without recognizing the invalidity of the URL. In Item4, D and E AOIs were critical, as the key to solving this item was a spelling mistake in the subject field of the email, and all the participants paid attention to these areas. Thus, data show that the critical AOI in each of the selected items was visited, and so, it is suitable to continue the analytical process. Once we discarded the invariant features (bold values in Table 3), we performed a categorical classification of the participants' behavior, clustering the different behavioral patterns during the experiment, using the visits of each participant to each AOI as classification features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Item Type** & **AOIs Rate** & **Resp. Time** & **Score** & **Sc. Length** \\ \hline Item type & 1 & 0.941 ** & 0.219 * & \(-\)0.389 ** & 0.317 ** \\ (Sig) & & 0.000 & 0.042 & 0.000 & 0.003 \\ \hline AOIs rate & 0.941 ** & 1 & 0.170 & \(-\)0.378 ** & 0.211 * \\ (Sig) & 0.000 & & 0.116 & 0.000 & 0.050 \\ \hline Resp. time & 0.219 * & 0.170 & 1 & \(-\)0.313 ** & 0.699 ** \\ (Sig) & 0.042 & 0.116 & & 0.003 & 0.000 \\ \hline Score & \(-\)0.389 ** & \(-\)0.378 ** & \(-\)0.313 ** & 1 & \(-\)0.370 ** \\ (Sig) & 0.000 & 0.000 & 0.003 & & 0.000 \\ \hline Sc. length & 0.317 ** & 0.211 * & 0.699 ** & \(-\)0.370 ** & 1 \\ (Sig) & 0.003 & 0.050 & 0.000 & 0.000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relationship between the scores and the type of question, RT, scan-path length and AOIs rate. Pearson correlations (* \(p\) < 0.05 (bilateral); ** \(p\) < 0.01 (bilateral); r (87)).