## 4 Data Analysis and Results  
The collection of data resulted in a sample of 64 students who participated in the four online courses. As a first analysis to detect outliers, we realized that three values in the control group (group 1) (28 min, 14 s; 12 days, 9 h; 16 min, 10 s) and one value in group 2 (2 h, 35 min) were excessively larger than the median so they were removed from the dataset. These figures probably represent cases where the students did not answer the questionnaire in a single step and took breaks in between the activity. Therefore, the final dataset had a total of 60 students. This section shows the results of the statistical analysis of the quantitative data. Table 2 displays the main descriptive statistical elements for each group. One can observe differences between means and variability of the different groups. Group 2 presents the biggest mean (496.53 s) and SD (281.22 s) in contrast with group 1, where the mean (170.27 s) and SD (68.56 s) are the lowest. The same applies to the maximum and minimum values. A comparative analysis of the mean between the control group or group 1 and groups 2, 3, and 4 shows the following relations: 2.92, 1.88, and 1.58, respectively, indicating the amount of supplementary time that students of groups 2, 3 and 4 needed to finish the questionnaire compared to students of the control group. Figure 1 shows the variability and extreme values of the sample. Group 4 includes two outliers that possibly provide real information given the small sample for this group (6); these values are within the general range of the other observations. Therefore, we decided to keep them. Logical statistical analysis in situations where a continuous variable exists together with samples classified in independent groups includes ANOVA tests that provide comparisons of dependent variables. However, ANOVA assumes a normally distributed sample and homogeneity of variance, meaning an approximated value of the variance among independent groups. A Shapiro-Wilk test, Table 3, shows a significance larger than the benchmark 0.05 for each group. Therefore, the null hypothesis was accepted, indicating that the sample of all groups followed a normal distribution. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **N** & **Mean** & **SD** & **Median** & **MAX.** & **MIN.** \\ \hline Group 1 & 26 & 170.27 & 68.56 & 165.00 & 340.00 & 47.00 \\ \hline Group 2 & 17 & 496.53 & 281.22 & 410.00 & 1076.00 & 148.00 \\ \hline Group 3 & 11 & 320.09 & 130.51 & 331.00 & 564.00 & 132.00 \\ \hline Group 4 & 6 & 268.33 & 143.77 & 256.00 & 507.00 & 60.00 \\ \hline Total & 60 & 299.98 & 216.33 & 236.00 & 1076.00 & 47.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistical values for the groups. The picture of error bars (Figure 2) demonstrates the great variability of groups 2, 3, and 4. A Levene test (Table 4) confirmed this premise, revealing a significance of less than 0.05 in the four groups. All these reasons lead us to accept the alternative hypothesis establishing that there are variance differences in at least two groups, so it is consequently evidenced that it is not possible to implement the ANOVA test. Figure 1: Boxplots for the four groups. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**SW Statistic**} & \(\boldsymbol{p}\)**-Value** \\ \hline \multirow{4}{*}{Seconds used in completing the activity} & Group 1 & 0.966 & 0.521 \\ \cline{2-4}  & Group 2 & 0.913 & 0.113 \\ \cline{2-4}  & Group 3 & 0.975 & 0.933 \\ \cline{2-4}  & Group 4 & 0.917 & 0.486 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Normality test—Shapiro–Wilk test. Figure 2: Error bars for the groups (the last group is formed by students with reduced motor capacity without outliers). The existence of variability among the groups guided us in following the approach of applying non-parametric tests, so we used the Kruskal-Wallis system to confirm the existence of significant differences between the times of the groups.