## 4 Results and Discussions
### Selection of the Best Clustering Algorithm and Development of the Software  
After choosing the best clustering algorithm, the software was developed, and the K-means algorithm was inserted into it. The clusters obtained by the software were then analyzed in order to determine the behaviors on the platform, highlighting which are the most influential features within the groupings. In this way, it is possible to profile the students who belong to a certain grouping. Figure 6: Presentation of the clusters generated by SOM algorithm. Figure 7: Silhouette score of the algorithms. Since the value is closer to 1, according to [PERSON]’s analysis, K-means was the best algorithm in comparison to the other algorithms. It was selected to implement the plugin in order to get optimal clusters. As can be seen from Table 1, \"cluster 0\" represents the least active students on the platform, with a low number of accesses (average = 5.4), low frequency of views related to video tutorials (average = 7.4), and a low number and frequency of views related to the experiments (average = 2.6). The low participation is also reflected in the delivery of the individual paper, where only 40% of the students delivered the assignment on the platform. \"Cluster 1\" represents students who are inactive or not too active in the platform. These users had an average of the number of accesses equal to 18.4, average of the number of video tutorials viewed equal to 2.5, average of the frequency of views of video tutorials equal to 11.2, average of the number of experiments viewed equal to 4.1, average of the viewing frequency of video experiments equal to 6.7. In this case, participation allowed 90% of students to carry out and deliver the final exercise. \"Cluster 2\" represents highly active students on the platform. These users had an average of the number of visits of 31.2, average of the number of video tutorials viewed equal to 3, and an average frequency of views of 16.2. The average number of experiments seen is 4.3 (5 total video experiments) while the average frequency of views is 7. This level of participation allowed all students to deliver the final paper (100% of the students). To verify if the behavior carried out by students on the e-learning platform had feedback in terms of performance, we compared each cluster with the average of the marks for the individual work done by the students belonging to the same cluster. The results obtained are very interesting. As reported in Figure 8, the level of participation in the platform of the students reflects the final grade. The most active students (\"cluster 2\"), in fact, received a higher score than the others with an average grade of 82.5. Students of \"cluster 1\", with an average activity, had a lower rating with an average of 72.04, while students of \"cluster 0\" had an average grade, equal to 53. This strong correlation highlighted by the Pearson index equal to 0.986 allowed us to confirm the differences between the students belonging to the different clusters, not only in terms of behavior but also in terms of performance. To demonstrate how student behavior affects the final student performance, ANOVA (analysis of variance) was used. ANOVA tests clusters of students grade to see if there is a difference between them [45]. A one-way ANOVA is used to test the null hypothesis that the means of multiple elements are all equal. Since after performing ANOVA, F > F crit (7.594799276 > 3.175140971), I reject the null hypothesis and accept the alternative hypothesis (at least one of the averages differs from the others). In fact, the average of the grades of the different clusters are significantly different from each other because the \(p\) value < alpha (0.001277126 < 0.05), which is the chosen significance level. Therefore, we claim that the averages of grades of the three clusters are not all the same; at least one of the means is different. Through post