# Designing Reenacted Chatbots to Enhance Museum Experience
## 6 Analysis and Results
### Museum Experience: MES
#### 6.2.2. Chatbot and Learning Style  
We analyzed the museum experience by dividing the participants by the type of Kolb learning style, as shown in Figure 4. As a result, we found that using the same model provides different stimuli and experiences. Activist vs. Reflector:  
The analysis of users divided into activists and reflectors showed significant results in terms of engagement and emotional connection. In both factors, the QA received the lowest score, and the king received the highest score. The engagement scores were significantly different among all three models, with activists scoring higher than reflectors: the QA (\(p<0.001\)), the docent (\(p<0.01\)), and the king (\(p<0.001\)). However, in terms of emotional connection, reflectors scored higher than activists, and among the three models, only the QA (\(p<0.001\)) had significant results. Pragmatist vs. Theorist:  
The MES was analyzed for pragmatists and theorists according to how information was processed, and a significant difference was identified in terms of knowledge/learning. The docent received the highest score, followed by the QA and the king, and theorists had higher scores than pragmatists in all cases. Only the QA (\(p<0.001\)) and the king (\(p<0.05\)) were validated for significance in user-specific analysis, especially in the case of using the QA, which noticeably increased theorists' knowledge scores. According to the two aspects of learning styles, individuals were sorted into four groups: 10 activist and pragmatist, 9 activist and theorist, 8 reflector and pragmatist, and 7 reflector and theorist individuals. As shown in Figure 5, Group 1 (which represents the intersection of activist and pragmatist) and Group 2 (which represents the intersection of activist and theorist) had extremely high preferences for the king among the four groups because they were mainly influenced by the aspects of engagement and emotional connection, as discussed earlier. The group with the highest preference score for the docent was Group 4 (which represents the intersection of reflector and theorist), with an emphasis on knowledge/learning. However, given the king's MES results, Group 3 (which represents the intersection of reflector and pragmatist), which was expected to have higher preference scores for the king, unexpectedly preferred the QA and the docent. The reason for this result is that the chatbot preferences cannot be absolutely determined by the learning style, and it is highly likely that the four elements of the MES do not have the same weight independently. Figure 3: MES results on the three chatbot models. (significance \(p\)-value **: \(p<0.01\), ***: \(p<0.001\)). Analysis with Gaze and Behavior Data:  
Eye tracking data were collected during all experiments using Tobii Pro Lab software. Among the basic units of gaze data (fixation count, fixation duration, saccades), we conducted an analysis with a fixation count to assess which AOI (area of interest) received more or less attention. The AOI consists of content topic, model, selected question, answer to question, and the next questions users can choose from: theme, model, question, answer, and options. We analyzed two aspects of the visual information: where the user's focus is concentrated according to the model and which behavioral characteristics appear by learning style. The QA model tended to draw more attention to the 'answer' to questions and the  
Figure 4: MES results with respect to learning style. (upper: Activist vs. Reflector, lower: Pragmatist vs. Theorist). (significance _p_-value *: \(p\) < 0.05, **: \(p\) < 0.01, ***: \(p\) < 0.001). Figure 5: Best model preference by learning style. following questions 'options' than other AOIs. This can be interpreted in conjunction with the aforementioned MES results. As presented in Figure 6, users who used the QA, which emphasized the knowledge/learning aspect, tended to focus on 'answer'.