## 7 Discussion  
In order to evaluate the usability of the biofeedback system, we used the SUS evaluation method. The presented research evaluation showed a mean SUS score of 71.25. According to the participants' answers to this questionnaire, most of the participants (53.6%) had a score very close to or above the usability average (score, 68) as defined by [PERSON] [27]. There is the possibility that the perceptions of the rest 46.4% were negatively affected by the difficult and intensive conditions under which the biofeedback system was evaluated, and the minor connection problems faced by six (6) students, at the beginning \begin{table}
\begin{tabular}{c c} \hline
**Relations** & **Description** \\ \hline REL1 (HY1) & There is a positive relation between optimism and innovativeness. \\ \hline REL2 (HY2) & There is a positive relation between optimism and discomfort. \\ \hline REL3 (HY3) & Optimism has a positive effect on attitude towards using. \\ \hline REL4 (HY4) & Optimism has a positive effect on behavioral intention. \\ \hline REL5 (HY5) & Innovativeness is negatively related to discomfort. \\ \hline REL6 (HY6) & Innovativeness is negatively related to insecurity. \\ \hline REL7 (HY7) & Innovativeness has a negative effect on attitude towards using. \\ \hline REL8 (HY8) & Innovativeness has a negative effect on behavioral intention. \\ \hline REL9 (HY9) & Insecurity is positively related to discomfort. \\ \hline REL10 (HY10) & Insecurity has a negative effect on behavioral intention. \\ \hline \end{tabular}
\end{table} 
Table 2: New relationships between TRI dimensions, revealed by the derived model. Figure 4: The derived path model based on TRAM, where the symbols “_\"\"_ and “_\"\"_ indicate the level of the significance of the relation. of the activity. However, the minor connection problems, which are probable to happen in realistic conditions, were overcome easily without a remarkable delay. To our knowledge, it is the first time that the usability and acceptance of a wearable biofeedback system has been evaluated while considering a variety of factors including personality traits during real-world learning settings such as academic examinations. There are articles examining the effectiveness of learning tools utilizing a variety of sensors [17]. Moreover, there are some research articles examining wearable biofeedback systems usability, in general, resulting to high SUS mean scores. However, these articles are referring to different kinds of research, contexts, conditions, social groups and sensors of the evaluated biofeedback systems. An article utilizing biofeedback system for arm trajectory showed a mean usability score of 74.64 (above average usability score 68) [41]. Another article applied a wearable sensor-based biofeedback exercise for orthopaedic rehabilitation showing a mean SUS score of 90.8 [42]. According to [PERSON] [43], all people have different areas of emotional and intellectual sensitivity when trying to interpret and respond to current situations based on their experience. It is assumed that every participant in the presented research is a unique human being with h/her personality. Therefore, we thought that it would be interesting to examine how each subject's personality traits are affecting h/her decision and attitude about the biofeedback system usage. Moreover, analyzing all participants' behavior we tried to explore which kind of student personalities are more likely to accept the biofeedback system in learning processes choosing the examinations as the most realistic and intensive learning activity. In order to explore the direct and indirect relations between the students' personality traits and the biofeedback system's perceptions of usefulness, ease of use and behavioral intention, we examined the TRAM model. According to the confirmed path model, there were some interesting relationships between the personality traits of the TRI. Primarily, optimism was found to be positively related to both innovativeness and discomfort.