## 6 Results
### Evaluating Alternative Interpretations Based on the AOIs Examined
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** \\ \hline Item24 & 0.37 & 0.44 & 0.68 & 0.65 & **1.00** & 0.72 & 0.51 & - & - \\ \hline Item32 & **0.00** & 0.63 & 0.83 & 0.90 & 0.80 & **1.00** & 0.30 & 0.76 & 0.76 \\ \hline Item4 & 0.92 & 0.82 & 0.85 & **1.00** & **1.00** & 0.60 & 0.42 & 0.57 & - \\ \hlineWe employed the OPTICS algorithm to determine an appropriate clustering regarding the participants' behavior. Tables 4 and 5 show the results of the clustering evaluation. Even if clustering analysis is a valuable tool for identifying patterns, Tables 4 and 5 evidence the lack of clear clustering results. Table 4 shows the existence of several unclassified elements, especially for Items24 and 32. Table 5 shows that even the classified elements merge into very fragmented small size clusters. The absence of distinct clusters suggests a high degree of variability and complexity in participant responses. This variability indicates that participant behavior is multifaceted and may not conform to predefined patterns. This complexity could arise from various factors, such as individual differences or the variability of approaches engaged to overcome the tasks, consistent with the unguided nature of the experimental process. Table 6 shows the results from the Kruskal-Wallis test regarding the efficiency of the behavioral pattern identified in the clustering process. The use of this test was preferred over the ANOVA test, as the normality of data cannot be assumed. The results indicate that the clustering process was not able to detect significant differences in behavioral patterns across clusters. However, considering that the process led to very fragmented small size clusters and many unclassified elements, there is no ground to establish that the participants' behavior was uniform among clusters. The clustering analysis based solely on the visited AOI shows no significant ability to predict the outcome of each task. However, some items may be more predictable to these features than others, and show a greater correlation to certain AOIs. Looking to assess the predictability of the items based solely on the visited AOIs, we designed a predictive Decision Tree classifier and trained it over 15 random stratified splits. Figure 4 shows the results of this process. We can conclude that the distributions of the values for these items are relatively similar, with Item24 and Item32 exhibiting slightly higher central tendencies compared to Item4. Notably, the median values observed in the boxplot are relatively small, indicating that solely the visitation of AOIs is not a very efficient feature for use in predicting the outcome of the answer. \begin{table}
\begin{tabular}{c c c} \hline  & **Partial Score** & **Overall Competence** \\ \hline Item24 & 0.616 & 0.602 \\ \hline Item32 & 0.844 & 0.530 \\ \hline Item4 & 0.685 & 0.542 \\ \hline \end{tabular}
\end{table} 
Table 6: \(p\)-value of the Kruskal–Wallis test considering the partial result and the global performance. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Total Elements** & **Classified Elements** & **Unclassified Elements** & **Number of Clusters** \\ \hline Item24 & 29 & 15 & 14 & 5 \\ \hline Item32 & 30 & 16 & 14 & 5 \\ \hline Item4 & 28 & 23 & 5 & 6 \\ \hline \end{tabular}
\end{table} 
Table 4: Description of the clustering results for each item. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Cluster \#1** & **Cluster \#2** & **Cluster \#3** & **Cluster \#4** & **Cluster \#5** & **Cluster \#6** \\ \hline Item24 & 5 & 4 & 2 & 2 & 2 & - \\ \hline Item32 & 5 & 4 & 3 & 2 & 2 & - \\ \hline Item4 & 7 & 5 & 4 & 3 & 2 & 2 \\ \hline \end{tabular}
\end{table} 
Table 5: Element distribution along the identified clusters for each item. To further clarify the classifier's performance, we present Table 7, with detailed metrics including accuracy, precision, recall, and F1 score. These metrics provide a comprehensive overview of the classifier's effectiveness across different items.