## 4 Results
### Study 1: Is It Possible to Quantify How Students Share Knowledge in a Classroom through SCOLL?
#### 4.1.1 Analysis of Similarities between Pairs of Authors  
The data provided by MOSS was transposed and discretised in quartiles as explained in Section 3.3.2. Table A4 shows the absolute frequencies of occurrence of each percentage of similarity transformed into quartiles. Data were separated between the highest and lower percentages (first and second author). For instance, out of the total of 26,035 pairs of documents compared, 3693 reported a similarity between 75% and 100% (p4) as the highest similarity percentage and 2614 as having the lowest similarity percentage.  
Table A5 shows the relative frequencies of _classification category_ after the application of the function _Similar_cod_ to the total pairs of documents (a total of 26,035 pairs). The largest number of document pairs (close to 50%) were unrelated because they had similarities that could be attributed to common knowledge as discussed in Section 2.2. On the other hand, a dishonest behaviour (\(\mathrm{C}_{\mathrm{copy}}\)) was only observed in 10% of the comparisons. Most  
authors were assigned a \"weak\" type of behaviour (C\({}_{\text{weak}}\)). From an inter-annual point of view, we could highlight a certain stability in the percentages, with a greater difference in the central years 2016-2018, where weak relationships increased, strong relationships decreased slightly and dishonest behaviour decreased with greater intensity. This may be a consequence of the actions adopted in the 2016-2017 academic year to avoid dishonest behaviour, after analysing the results of the 2015-2016 academic year. However, the upturn in the 2018-2019 academic year shows that there was an adaptation to the measures initially adopted, so it seems necessary to adopt additional measures to avoid dishonest behaviour.  
Figure 4 (see Table A6) shows statistics on the authors obtained by applying the _Author_behaviour_ function. Here, it can be seen that the most common behaviour (82%) was that of a \"soft\" type of relationship. This means that authors shared common study resources. It can also be observed that \"individual\" behaviour (9%) and \"hard\" behaviour (8%) were at the extremes and had very close values. It should be noted that dishonest behaviour (\"cheat\") reduced to 1%, while in the document comparison it was 10%. These data allow us to venture that most of the comparisons with dishonest behaviour were carried out by only 1% of authors. From a dynamic point of view, we can observe that soft behaviour has been increasing, to the detriment of hard and cheat behaviours. We can see in the data for the last academic year (2018-2019) that there was a shift from soft behaviour to individuals. To be able to conclude that this was a change in trend, as in the statistics of comparative documents, it would be necessary to study the subsequent courses. We do not have the data to draw such a conclusion, but it could be interesting for future research.  
Figure 5 (see Table A7) shows the aggregated data on author dyads, obtained from the _path_matrix_. If we compare these data (about authors) with those in Tables A5 and A6 (about documents), it can be seen that 1% of cheat authors were present in 6% of the pairs and 10% of the documents compared with dishonest behaviour. However, the boundary between individual and soft behaviour was fuzzier, denoting a non-stable behaviour throughout the course by these authors between one type of study and the other. Hard behaviour, on the other hand, such as cheating, seemed more defined, with 8% of the authors with this profile participating in 10% of the documents compared and 11% of the hard-type pairs.  
Figure 4: Descriptive study: frequency of _behaviour categories_ {Q\({}_{\text{individual}}\), Q\({}_{\text{soft}}\), Q\({}_{\text{hard}}\), Q\({}_{\text{cheat}}\)} for each academic year.