## 7 Conclusions
We tried to answer two questions, as follows:  
First, which AOIs within the items were examined, and whether specific patterns of AOIs visited might undermine the claim that the items required the same cognitive processes that are required in real-world tasks, according to the assessment criteria previously defined. We could not identify alternative patterns in terms of visit rates of AOIs that might undermine the assessment criteria defined. All the AOIs were examined with different visit rates, but we could not find any response pattern with an unexpected visit rate that was difficult to explain, which would have raised doubts as to whether the question was generating the expected RPs. Additionally, we carried out a clustering of the responses in terms of AOIs visited, applying an unsupervised OPTICS classification algorithm to examine whether specific patterns of AOIs visited to solve the task predicted higher success rates in the overall performance (the global score considering tasks related to the same DC of task in question), and in a way that is not inconsistent with expectations for each item. We could identify clusters with higher success rates, but nevertheless, the results were far from significant, and thus, it was not possible to conclude that any of the behavioral patterns were more efficient than the rest. It would be very interesting to carry out this analysis again, with a higher number of participants. Secondly, we examined the scan-paths of the items in more depth, and considered the order of processing the different AOIs when solving them. We employed the Levenshtein method, facilitated by the ScanGraph tool, to ascertain that the unsuccessful group exhibits greater variance than the successful group. The significant variability and lack of clear patterns in participant behavior, as suggested by the clustering analysis and the variability within groups in the Levenshtein method analysis, might reflect the complex nature of DC assessment. In order to explain this variability, it would be interesting to incorporate additional variables not accounted for in this analysis (e.g., individual differences in cognitive strategies). Then, we went deeper into cognitive processes and problem-solving strategies, calculating the common scan-paths of only the successful participants whose variance was smaller. Despite the limitations of the position-based weighted model applied, we obtained interesting insights into the performances of the successful participants, which could not have been obtained in traditional TEAs. As a result, we acquired a visual representation enabling us to easily verify that participants who answered the question correctly did not exhibit any unexpected behavior that would invalidate the item. We consider this information helpful to examinees who incorrectly responded. If they wanted to review the question for the correct answer, we could show them the common scan-path as an extra value. To our knowledge, current DC assessments do not offer this functionality for such items during review. However, in the meta-analysis performed by [PERSON] et al., it was pointed out that the use of eye movement modeling examples was beneficial to learners' performance when non-procedural tasks were used instead of procedural tasks [102]. So, it should be further investigated to what extend the use of modeling examples in the review process of an assessment of DC might be appropriate, depending on the type of question. Finally, we also examined the fixation durations and the places where longer fixations occurred. We could confirm that successful participants undertook the longest fixations on the expected areas, and we could not identify an alternative interpretation that would undermine the assessment criteria defined. Additionally, the fixation durations' distribution validated the item design, revealing two distinct approaches--systematic and non-systematic.