## 4 Results
### Study 1: Is It Possible to Quantify How Students Share Knowledge in a Classroom through SCOLL?
#### 4.1.2 Global Network Metrics  
We obtained a network with aggregated data. Nodes were author identifiers. Relationships were measurements of similarities between the documents generated by learners. Table 3 shows the global metrics of the network for each course. In these metrics, we observed how the density of the network was higher in the courses during 2015-2016 and 2017-2018 than in the other courses. This measure indicates the intensity of the interrelation, and it was conditioned by the number of authors and by the follow-up of the subject. Thus, in courses in 2015-2016 and 2017-2018, the number of authors was lower, and the achievement of the tasks set during the course was higher. Regarding modularity, the courses in 2015-2016 and 2016-2017 showed a lower modularity, while the course in 2016-2017 stands out for its higher modularity (0.16), i.e., for its greater ability to separate vertices into clusters. This measure illustrates the sharpness of the boundaries between clusters. The geodesic distance, the number of vertices necessary for a vertex to connect with any other SNA vertex, was between 1 and 2 for all of the courses, with the average being higher in the courses in 2016-2017 and 2017-2018, with a value of 1.73. However, the 2018-2019 course had a diameter of 4, while in the 2016-2017 course, the value was 3. We see, again, a similarity between the years 2015-2016 and 2017-2018 and the years 2016-2017 and 2018-2019. On the other hand, from the calculation of the averages of the vertex metrics in each course, we can underline that all their values were significant and reinforced the similarity of behaviour between the courses in 2015-2016 and 2017-2018 as well as between the courses in 2016-2017 and 2018-2019. However, it can also be emphasised how in the degree  
Figure 5: Frequency of aggregated data on author dyads comparison by percentile for each academic year. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Academic** \\ **Year** \\ **Density** \\ \end{tabular} } & \multicolumn{4}{c}{**Global**} & \multicolumn{4}{c}{**Average of Vertices** (Mean \(\pm\) Standard Error)**} & \multirow{2}{*}{} \\ \cline{2-3} \cline{5-10}  & \multirow{2}{*}{**Modularity**} & \multirow{2}{*}{**Average**} & \multicolumn{2}{c}{**Maximum**} & \multirow{2}{*}{**Geodesic**} & \multirow{2}{*}{**Degree**} & \multirow{2}{*}{**Betweenness**} & \multirow{2}{*}{**Closeness**} & \multirow{2}{*}{**Closeness**} & \multirow{2}{*}{**Cosification**} & \multirow{2}{*}{**Average**} \\  & & & & & & & & & & \\ \hline
2015-2016 & 0.61 & 0.06 & 1.38 & 3 & \(49.34\pm 1.68\) & \(15.93\pm 1.21\) & \(0.009\pm 0\) & \(0.74\pm 0.01\) & \(1.09\pm 0.05\) \\
2016-2017 & 0.31 & 0.16 & 1.73 & 3 & \(49.57\pm 1.93\) & \(59.31\pm 6.72\) & \(0.004\pm 0\) & \(0.55\pm 0.01\) & \(0.93\pm 0.04\) \\
2017-2018 & 0.58 & 0.05 & 1.40 & 3 & \(56.45\pm 1.88\) & \(20\pm 1.3\) & \(0.007\pm 0\) & \(0.72\pm 0.01\) & \(0.87\pm 0.04\) \\
2018-2019 & 0.33 & 0.09 & 1.73 & 4 & \(38.66\pm 1.74\) & \(43.9\pm 3.35\) & \(0.005\pm 0\) & \(0.53\pm 0.01\) & \(0.93\pm 0.04\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metrics on social networks for each course. metrics, despite the lower number of authors in the years 2015-2016 and 2017-2018, the values were higher, especially higher than in the academic year 2018-2019. These data reinforce that the follow up of the subject by the authors in the 2018-2019 course was lower than in the rest of the courses. We can observe that betweenness centrality was higher in the courses in 2016-2017 and 2018-2019, an element that undoubtedly influenced the greater number of authors and the need for a greater number of intermediaries to maintain the maximum geodesic distance at a value between 3 and 4. The eigenvector and PageRank values did not provide differential information between courses.