# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 5 Preliminary Case Study
### Preliminary Results  
In this part, we show some interesting preliminary results in the employment of the envisioned architecture in its first basic implementation. The results refer to the period ranging from August 2017 (start of academic year 2017-2018) to December 2017, which encompasses the first two exam weeks for all the considered faculties. The first period of exams took place in September 2017 until the first week of October 2017, while the second period of exams took place from mid-October 2017 until the end of November 2017. Two groups of students were considered: the first one (Group A) encompasses 100 students using the traditional VLE, while the second one (Group B) regards 100 students employing the proposed architecture as described in Section 5.1.  
Figure 7: Workflow of data in the basic implementation of the proposed architecture. HRV, Heartbeat Rate Variability.  
In Figure 8, we show the percentage of dropouts in both considered groups of students versus time, in the first term of academic year 2017-2018. As can be inferred, the percentage of dropouts is considerably limited for Group B (maximum 10%), while it increases till almost 30% for Group A. As for the absolute figures, this means that, in Group B, 10 students dropped out, while, in Group A, 27 students dropped out of university at the end of the considered period. This demonstrates the successfulness of the proposed architecture, at least in this first basic implementation. The graph of Group A undergoes two sharp increases around the second half of September and the first half of November. This could be explained considering that in those periods, the Engineering exams took place and that they are usually considered by the students the most difficult ones, e.g., mathematical analysis. Consequently, dropouts could increase after or during those periods. This does not happen for students using the improved VLE architecture, whose curve undergoes a smooth increase till a maximum of 10%, almost a third compared with the level reached by Group A.  
In Figure 9, we studied the use of the VLE on the part of the students. In this way, we could evaluate whether the students appreciated the new architecture or not. The graph depicts the percentage of usage of the VLE and the improved VLE, by considering students that employed the VLE at least once a day. Particularly, in mid-December, this reached 92 for Group B and 70 for Group A. As one can see, the percentage of usage increases over time in both cases, but students of Group B seem to be more attracted toward the VLE. Moreover, as per the aforementioned absolute figures, the total number of students that employed the VLE at least once a day is greater in the case of Group B than in the case of Group A in mid-December, but this is true also considering previous months. Finally, both graphs show two sharp increases, around the first half of September and the second half of October, during exam sessions.  
In Table 1, we detail the figures in dropout reduction for each faculty and for each LO suggested by the implemented architecture. As we can see, even if all suggested solutions are effective at reducing dropouts, their effect could differ across faculties. For example, Engineering students are more influenced by interactive exercises rather than by concept maps, while the opposite takes place for students belonging to the Faculties of Psychology and Literature. Quizzes reach good results especially for Literature students, while, the usage of wikis and forums shows the least effectiveness in reducing dropouts; thus, their usage could not be considered as a proper retention strategy.  
Figure 8: Percentage of dropouts versus time in the first term of academic year 2017–2018 for two groups of students. Group A represents students using traditional VLE, while Group B represents students employing the new proposed architecture.