## 3 Results  
To check the first objective, the UBUMonitor [35] tool was used to analyse the logs of Moodle v.3.8 (UBUVirtual platform of the University of Burgos). The description of the tool can be found in Section 2.2.1. The semester started on (3 February 2020) and the initial measurement was made after two weeks (17 February 2020), the intermediate measurement after four weeks (2 March 2020) and the final measurement after eight weeks (30 March 2020). The components Assignment, Feedback, File, File submissions, Folder, Glossary, Quiz and URL were analysed. The formats chosen were boxplots (see Figure 3) and heat maps (see Figure 4). In all the visual analyses, if the teacher was positioned at the top or at the bottom, they could see the name of the student and detect the student at risk (outliers at the bottom) or the student with high performance (outliers at the top). Additionally, a frequency analysis could be performed on the heat map to visually detect those studentswhere the interaction had been marked in red. This tool allows, besides the visualisation of the data, the export of them to a.csv file. In order to check the second and third objectives and RQ2 and RQ3, firstly, the clusters were found with the three algorithms used (\(k\)-means ++, fuzzy \(k\)-means and DBSCAN) in the three periods of initial measurement (see Figure 5), intermediate measurement (see Figure 6), and final measurement (see Figure 7). These figures are the result of applying a principal component analysis (PCA). DBSCAN's plots show some grey points because they are not assigned to any cluster. Only two components were used because the third component only explained 0.04 more of the variance in the initial measurement, 0.02 in the intermediate measurement, and 0.01 in the final measurement. It was also found that the initial assessment explained 94.4% of the variance, the intermediate measurement 96.3%,  
Figure 4: Heat map of the weekly monitoring of students and detection of outliers in Moodle carried out with UBUMonitor [35]. Figure 3: Boxplot of the weekly monitoring of students and detection of outliers in Moodle carried out with UBUMonitor [35]. In addition, a goodness-of-fit index was then applied [51]. No perfect fit was found in any of the algorithms, although the DBSCAN algorithm was the best fit in the Akaike information criterion (AIC) and parsimony index (ECVI) indicators (see Table 3). In order to check whether the allocation differences between the three clusters were significant, the Friedman test for k-dependent samples was applied. No significant differences were observed between the three algorithms in the three measurements (see Table 4). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Cluster** & **Measurement** & **Mean** & **SD** & **Max** & **Min** & **MRange** & \(\chi^{2}\) & \(gl\) & \(p\) \\ \hline \multicolumn{8}{c}{Initial} \\ \(k\)-means ++ & & 13.00 & 13.46 & 2 & 28 & 1.83 & 0.55 & 2 & 0.76 \\ Fuzzy \(k\)-means & & & 9.45 & 9 & 27 & 2.33 & & & \\ DBSCAN & & 12.67 & 17.62 & 2 & 33 & 1.83 & & & \\ \multicolumn{8}{c}{Intermediate} \\ \(k\)-means ++ & & 16.33 & 17.21 & 4 & 36 & 2.17 & 0.55 & 2 & 0.76 \\ Fuzzy \(k\)-means & & 16.33 & 15.70 & 4 & 34 & 2.17 & & & \\ DBSCAN & & 14.67 & 21.08 & 2 & 39 & 1.67 & & & \\ Final & & & & & & & & \\ \multicolumn{8}{c}{\(k\)-means ++} \\ \(k\)-means ++ & & 16.33 & 23.18 & 1 & 43 & 2.33 & 2.00 & 2 & 0.37 \\ Fuzzy \(k\)-means & & 16.33 & 11.06 & 6 & 28 & 2.33 & & & \\ DBSCAN & & 13.33 & 21.39 & 0 & 38 & 1.33 & & & \\ \hline \hline \end{tabular} Note. \(p<0.05\); Initial measurement (after two weeks); Intermediate measurement (after four weeks), Final measurement (after eight weeks); SD, standard deviation; \(\chi^{2}\), Chi-squared; Max, maximum; Min, minimum; MRange, mean range.  
\end{table} 
Table 4: Analysis of the differences with the Friedman test for k-dependent samples between the results of grouping students in the three clusters in the initial, intermediate, and final measurements.