# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 5 Preliminary Case Study
### Future Developments and Usages  
The considered implementation is very preliminary and employed to have a proof of concept of the proposed architecture, as well as to obtain some preliminary results validating the described intuitions. As a matter of fact, various envisioned features have not been implemented: fog gateways, different from the smartphones themselves, unreliable Storm bolts located in smart sensors, orchestrating powerful bolts in the cloud, meta-learning capabilities in the cloud, the distributed data discovery and retrieval through a DHT, etc. In a future implementation, other opportune features such as the levels of drowsiness during the fruition of LOs, skipping of LOs, time spent over certain LOs, number of times an LO is accessed, number of assignments performed, connection bandwidth employed when studying, etc., could be considered. These and the already considered features could be fine-tuned according to (i) the algorithms chosen by the instructors and recommended in the cloud, as well as to (ii) the particular set or subset of students. Finally, the upper layer of the fog tier could perform more complex analyses and elaborations on the selected features, as well as on other characteristics received less frequently and directly from the spouts of the lower layers or even from the cloud. These summarize characteristics such as the average score obtained in the performed assignments, the total time spent on a certain course or a certain assignment, the average mark over different subjects, the time effectively spent in using the libraries, and so on. The proposed architecture, once entirely implemented, should enable teachers and educational managers to enact prompt retention strategies before students decide to withdraw, as well as tracking who dropped out and why. As a consequence, this information could give feedback to course authors, find relationships between educational patterns and dropouts, discover students' difficulties and \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Engineering** & **Psychology** & **Literature** \\ \hline
**Interactive exercise** & \(-51\%\) & \(-32\%\) & \(-21\%\) \\
**Quizzes** & \(-22\%\) & \(-34\%\) & \(-46\%\) \\
**Concept maps** & \(-25\%\) & \(-60\%\) & \(-55\%\) \\
**Wikis** & \(-24\%\) & \(-31\%\) & \(-22\%\) \\
**Forum** & \(-10\%\) & \(-5\%\) & \(-7\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Effectiveness in reducing dropouts of various solutions suggested by the proposed VLE architecture for students of different faculties. Figure 9: Percentage of usage of the Virtual Learning Environment (VLE) 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. Only students that used the VLE at least once a day are considered. common mistakes, guide towards the best fitting transfer model for students with special needs or that need extra help, and the like. Moreover, important knowledge about why students drop out could be mined. For example, some rules can show that if a student spends a lot of time on the wiki and on the interactive activities, then the probability of dropping out of the university decreases by a certain percentage and with a certain confidence. Conversely, another rule gained from experience could be that if a student with special needs performs less than a certain threshold of quizzes in simultaneously studied subjects, then it is very probable that he/she will fail all exams and consequently quickly drops out, or again, that some features are completely irrelevant to the prediction of withdrawals, or again that the small usage of the forum is not always an index of dropping out if it is joined with frequent access to the LOs, and so on.