# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 2 Distance Learning
### State of the Art
#### 2.1.1 Students  
Students usually enjoy VLEs for their ease of usage and communication, the presence of a friendly environment, the ubiquity allowed in the fruition of LOs, the possibility of cooperation and collaboration among students and between instructors and students themselves [6], the availability of on-line tutoring services [7], the creation of personalized learning paths, as well as of communities and groups of peers. Moreover, the contribution in [8] witnesses that students that use VLEs achieve better performances than non-users: the beneficial effects of distance learning are confirmed both by online performances and final exam outcomes. Besides, the success of VLEs is measured by the students involved, according to their usage intentions, their actual use, satisfaction and benefits in employing it [9]. However, these successful conclusions are only true under certain circumstances, i.e., a minimum effort in using the VLE is required. Moreover, the contribution in [10] points out the fact that the usageof only VLEs, on the part of the students, may lead to excessive workload and early dropouts or to the adoption of some wrong strategies. Some important characteristics that students search in modern VLEs are authenticity and reification [11]. These may regard the context of learning, specific learning tasks, the impact of the attended courses, but also the personal sphere, as well as the value of the courses as the students perceive them. LOs that allow a practical experimentation, in real life, of the knowledge they have learned are very appreciated by the students. The solutions based on the cloud computing paradigm [5] partly fulfill these requirements for online learning applications. They also foster communications and resource-sharing between the students themselves and between the students and the teachers. Cloud-based VLEs allow also for the necessary flexibility to follow dynamic personalized learning paths, as well as to participate in complex laboratory environments and for the needed scalability that guarantees a stable quality of service. Moreover, cloud-based laboratories provide actualization and re-creation of real experiments, which result in being very important for a complete involvement on the students' part. Nevertheless, they are rather complicated to enact, especially if interactive or monitoring procedures are employed, rather than simple batch processes. Moreover, the cloud may present some risks for students if their personal data are not correctly handled and stored, especially if they are managed by external organizations, unknown to the students and not complying with privacy laws. Besides, not all cloud services provide a high enough level of performance, i.e., a sustainable connection and small delays and jitters, for all LOs, and this could affect interactive and collaborative learning tasks. Despite these challenges, cloud-based virtual laboratories are still present in the recent literature. For example, in [12], the authors propose to engage students in practical activities exploiting their own mobile devices. This led to various advantages for them, such as ubiquitous and context-aware experiences in informal learning situations and the possibility to join group discussions anytime and anywhere. These, in turn, fostered problem-based learning and hands-on practical measurements and, at the same time, the motivation of the students to participate in the lab activities. The importance of mobile-based learning (m-learning) for students is highlighted also in [13], where the authors present a possible app for integrating a Learning Management System (LMS) into mobile devices, also showing how this could increase the usage of distance learning on the part of the students, especially thanks to interactive tools such as forums, reminders and message systems. In [14], m-learning is successfully extended to ubiquitous learning.