## 6 Discussion  
### Hypothesis 1  
The first hypothesis was stated as follows:  
* _Students who use AR for learning greedy algorithms experience significantly more positive emotions than negative ones._  
The results from Emolive indicate that, at the beginning of the experience, the positive and negative emotions experienced by the students were at a similar level (varying within \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**H.**} & \multirow{2}{*}{**Null Hypothesis**} & **Sig. (U of Mann–Whitney)** & **Decision** \\ \hline \multirow{4}{*}{1} & The distribution of positive & \multirow{4}{*}{0.140} & \multirow{4}{*}{0.140} \\  & emotions before is the same across & & \\  & group categories (AEQ/Emolive) & & \\  & The distribution of negative & & \\  & The distribution of positive & & \\  & emotions before is the same across & & \\  & emotions is the same across & & \\  & emotions is the same across & & \\  & emotions is the same across & & \\  & emotions is the same across & & \\  & emotions is the same across & & \\  & emotions is the same across & & \\  & emotions is the same across & & \\  & group categories (AEQ/Emolive) & & \\  & The distribution of negative & & \\  & emotions is the same across & & \\  & emotions after is the same across & & \\  & group categories (AEQ/Emolive) & & \\  & The distribution of negative & & \\  & emotions are if the same across & & \\  & emotions after is the same across & & \\  & emotions after is the same across & & \\  & group categories (AEQ/Emolive) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hypothesis contrast of equality of means between groups. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Variable**} & \multirow{2}{*}{**Group (N)**} & **Min.** & **Max.** & **Mean** & **SD** \\ \hline \multirow{2}{*}{Positive before} & GAEQ (18) & 2.50 & 5.00 & 3.93 & 0.88 \\ \cline{2-6}  & GEmolive (30) & 0.00 & 3.50 & 2.55 & 0.98 \\ \hline \multirow{2}{*}{Negative before} & GAEQ (18) & 1.00 & 4.13 & 2.28 & 0.78 \\ \cline{2-6}  & GEmolive (30) & 1.25 & 3.75 & 2.59 & 0.78 \\ \hline \multirow{2}{*}{Positive before} & GAEQ (18) & 2.59 & 4.95 & 3.85 & 0.65 \\  & GEmolive(30) & 0.00 & 4.00 & 2.98 & 1.63 \\ \hline \multirow{2}{*}{Negative during} & GAEQ (18) & 1.00 & 4.23 & 2.03 & 0.79 \\  & GEmolive (30) & 0.50 & 2.75 & 1.69 & 0.69 \\ \hline \multirow{2}{*}{Positive after} & GAEQ (18) & 2.25 & 5.00 & 3.69 & 0.85 \\  & GEmolive (30) & 2.50 & 4.50 & 3.23 & 0.91 \\ \hline \multirow{2}{*}{Negative after} & GAEQ (18) & 1.44 & 4.19 & 2.44 & 0.85 \\  & GEmolive (30) & 0.00 & 3.00 & 1.52 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics of the emotions detected by AEQ and Emolive. 0.4 points on a scale of 1 to 5). However, as they participated in the experience, positive emotions increased while negative emotions decreased, with this difference being statistically significant at the end of the experience. Therefore, hypothesis H1 of the intervention can be affirmed, confirming that students who used AR to learn greedy algorithms experienced significantly more positive emotions than negative ones. Previous research has found evidence along these lines. For example, in a study on the relationship between activity and emotions in algorithm learning with AR [35], it was found that participants reported higher levels of positive emotions than negative emotions during the AR experience. The decrease in negative emotions (anger, disgust, fear, and sadness) found in this study is in line with the findings of other studies analyzing emotions using facial recognition. Specifically, it had been found [35] that 86% and 87% of students felt that using the AR tool was neither unpleasant nor depressing, respectively. Among negative emotions, the following finding is striking. One of the negative emotions that commonly manifests with the use of AR is anxiety [37]. Emolive does not allow for anxiety to be measured directly, but it does measure fear, which is strongly related to anxiety [10,35].