# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 7 Conclusions and Discussion  
In this article, we have proposed a big data stream and fog-based e-learning framework in order to put together various benefits in favor of distance learning institutions, instructors and students. We provided the state of the art of current VLEs, according to the different stakeholders of an educational scenario, pointing out some of their limitations, and then, we introduced big data stream mining and fog computing as technologies capable of partly solving the explained issues of current solutions. The envisioned framework has been then thoroughly described, taking advantage of a Storm master-slave structure and of a traditional layered architecture enriched with big data stream mining capabilities arising from SAMOA. Moreover, a preliminary implementation of the proposed architecture has been described together with some results demonstrating its feasibility and usefulness.  
The advantages that our proposed architecture may foster are multifaceted: real-time correct prediction of students' performances and fatigue, discovery of new didactic models according to location and times of studying, enhancement of students', or groups of students', learning abilities or their engagement in some particular topics.  
However, these will be possible provided other issues are addressed in advance such as: technologies integration, user acceptability and innovation in fog gateways, sensors, gadgets and smart objects in general. Some further theoretical issues, to be investigated in the future, regard also the security perspective, such as ownership and privacy of data.  
Future research directions may concern both theoretical and practical aspects. In the first case, we can consider the theoretical study of differentiated behaviors between groups and single individuals, as well as the accurate analysis of hidden information, as the quality of the insights is not always guaranteed, especially when crowdsourcing, based on sensing devices owned by the students themselves, is employed. In the second case, we can list a further development of the proposed proof of concept implementation, through a full-fledged development of smart gateways, or the usage of artificial-intelligence capable smartphones, endowed with full Storm and SAMOA elements, the implementation of the distributed DHT and workflow management considering also the whole capabilities of the cloud and different predictive algorithms to compare. Moreover, further metrics and analyses could be performed such as a study of the marks and performances of the students and a finer degree of personalization of the recommended LOs, exploiting long-term data in the cloud. Moreover, evaluating response times and data transmission rates towards the cloud and across different fog layers, as well as the exploitation of social relationships among students and their own devices are further research directions that could empower and enrich the presented architecture.  
The author acknowledges Prof. [PERSON] and Prof. [PERSON] for their valuable help in suggesting ideas for this paper, and Mr. [PERSON] for the careful proofreading of the paper.  
The author declares no conflict of interest.