## 4 Results
Based on these results, we can verify the null hypotheses, which were converted from research questions:  
**Hypothesis 1 (H1). **_There is no statistically significant correlation between the student's average heart rate during the quiz and his/her score._  
**Hypothesis 2 (H2). **_There is no statistically significant correlation between the student's heart rate before testing and his/her score._  
**Hypothesis 3 (H3). **_There is no statistically significant correlation between the student's heart rate at the start of testing and his/her score._  
From the above results, we can see that (H1) and (H3) are not rejected. Thus, there is no statistically significant relationship between the result score and the average heart rate or heart rate at the start of testing. However, we also see that based on the \(p\)-value for the hypothesis 2 (H2), we can reject (H2), which means that there is a statistically significant relationship between total scores and heart rate before testing. The correlation coefficient has a value of \(-0.52\), i.e., a relatively high negative dependence. This means that if a student had a high heart rate before the quiz, then their score was low compared to a student who had a lower heart rate before testing. The dependence in the case of the variable Pretest HR against the Score is not very significant and we would classify it as a large dependence. It is also statistically significant at the 5% significance level. Higher heart rate in this case probably means higher student stress. Stress may be due to the very fact that the student will be tested or if the student fails the quiz. The results show that if the heart rate before the quiz is higher, i.e., the student is under stress, the quiz result will be worse. The data obtained from observing students' eyes when solving quizzes can tell how much the students were focused on solving the given tasks. The system recorded the direction of the student's gaze three times in 2 s. This can identify quite accurately how long the student watched which part of the screen. For this purpose, we divided the screen into zones shown in Figure 2.  
* _Question_--the area of the screen where the text of the question and answer was located,
* _Next button_--the area of the Next button that was used to switch the screen to the next question,
* _Other_--the area outside the _Question_ and _Next button_ areas,
* _Out of screen_--unrecorded points from eye tracking (since each student had 3 min to solve the question, which is 270 records, so this value was created by calculating 270--(_Question + Next button + Other_). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Valid N** & **Spearman R** & **T(N-2)** & \(p\)**-Value** \\ \hline AVG HR and Score & 15 & \(-0.404349\) & \(-1.59402\) & 0.134945 \\ Pretest HR and Score & 15 & \(-0.519935\) & \(-2.19462\) & 0.046959 \\ Start HR new and Score & 15 & \(-0.374899\) & \(-1.45806\) & 0.168556 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Spearman correlation. Table 4 contains the numbers of red dots (eye tracking records) that were included in the previously described zones. These values are listed for each student (_User ID_) separately. If the student focused mainly on the _Question_ area, we assume that this student was more focused on solving the problem compared to a student whose value was lower. The values from the variable _Other_ indicate a lower concentration rate. We assume that the _Out of screen_ value, which speaks of how much time the student took his gaze off the screen, means the lowest level of concentration. We also judge this on the basis that the students were under time pressure and therefore did not have too much time to look around. The variable _Next button_ was created so that we could also identify how much time students spent switching between questions. This information is not very revealing, as it focuses on an activity that the student has to perform but does not indicate concentration or non-concentration.