# Switching Learning Methods during the Pandemic:
## 5 Discussion  
The results presented in this section can help us understand whether the methodology and technology changes applied during the academic year 20/21 allowed students to maintain a good standard on marks and timing. To understand these aspects, we proposed a quasi-experimental study, analysing students in the academic year 20/21 as the test group, while those in the academic year 19/20 were used as the control group. From the data gathered and discussed in Section 4, an evident characteristic seen in students from both academic years was a passing rate for the assessments according to the course timetable. This led us to conclude, although this finding is limited to the case under analysis, that despite the urgent change in teaching methodology, students' time performance did not decrease. There were many previous studies that reached the same conclusions by analysing the students' preferences between on-site and online training [8]. Thus, to quantify the impact of the new teaching methods on students' performance, we focused our attention on the grades achieved during the learning path. Further discussion is needed for the results obtained in Section 4; from Figure 2(a), we notice that many pillar assessments presented a bigger standard deviation, while the average mark was almost the same for both academic years. Figure 3: The comparison between the average and the standard deviation of the pillars. (**a**) The trend of the standard deviation. (**b**) The average marks for the assessments. The limited data do not suggest much more, and we do not want to speculate on hypotheses that the analysis cannot confirm. For this reason, we decided to analyse the data more deeply. As mentioned in the previous section, there have been several changes in the setting of the master's course. Some had an impact on the teaching methodology, others on the supporting technology. One of the most impactful changes was the introduction (for the academic year 20/21) of an additional tutor able to respond promptly to students' doubts. To quantify the impact of those changes, we performed a statistical comparison between the two groups, trying to understand whether, for all the pillar assessments (or just some of them), the methodology changes had an impact on the students' performances. There are many statistical tests that could have been used to reach this goal. Most of them rely on the calculation of the average and standard deviation of two populations. However, there were some requirements regarding the distribution and the number of elements of the population that our current groups did not satisfy (e.g., a high number of elements and a normal distribution, and so on). With such a small population and considering that the most well-known parametric test was not usable, we decided to use the Wilcoxon-Mann-Whitney test (also known as the U-test) to define the differences between the two student groups. Results of the statistical analysis are reported in Table 3. The column \"Topic\" reports the topic taken into account, \"Score\" represents the score of the U-test, and \"\(p\)-value\" gives information about the acceptance of the null hypothesis. The last three columns describe the average for each topic and for each academic year and the difference between them. This analysis gives many more insights. We notice that only three pillars reported a \(p\)-value \(<\) 0.05: Teamwork on Pillar I, Teamwork on Pillar III, and Teamwork on Pillar IV. The low \(p\)-value among the two populations for these assessments suggests a low/no difference between the two groups. We propose some hypotheses about the reason behind this result:  
* Teams are composed of three to four students that have to prepare and present a common thesis. The final exam consists of a dissertation about the work that must be completed as a group rather than individually.