# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 2 Distance Learning
### State of the Art
#### 2.1.5 Researchers  
The aspects of VLEs, in which researchers are usually interested, regard data mining techniques and the development or the discovery of novel educational patterns.  
In [15], a recommending system for teachers is developed, and different learning object repositories were analyzed and tested. The results of a search query were listed according to both a basic relevance (based on a term frequency-inverse document frequency metric) and a local didactic relevance (based onto the relative position and the relative distance of other LOs employed in similar courses by colleagues with similar interests and ways of teaching). Nevertheless, one of the main limitation of this proposal regards the necessity of an opportune crawler to be adapted to different learning object repositories. In [16], another recommending system for teachers, integrated into a VLE, is presented. It is based both on the closeness in competences and on the abilities in using learning objects, while the instructor profile is composed of rating history, bookmark history, learning object access history and learning object creation history, combined together into an aggregation phase followed by a fuzzification step.  
Another aspect, which is usually examined in terms of data analysis and educational research, is surely cloud computing [5], as it can support computations either for heavy simulation scenarios or for machine learning and data mining techniques. By taking advantage of the cloud paradigm, in [12], the authors propose wireless communicating virtual labs. The researchers' challenge, in this situation, is a careful verification of the validity of this model of virtual labs, which the authors claim to achieve better results compared to traditional models. As a matter of fact, caution must be employed towards virtual laboratories and other forms of mobile or ubiquitous learning. Researchers have to carefully analyze the factors fostering or hindering the adoption of such technologies, on the part of the students, according to the considered learning scenario [23]. Indeed, the individual background, or the environment students are from, the social pressure and personal psychological features may not often affect the adoption of mobile devices for learning as much as innovativeness, ease of use and perceived usefulness themselves, even if the age of students results sometimes in a limit for the smallness of the screens. Moreover, the positive impact of m-learning on the students' achievements must be evaluated very carefully as well, as from the contribution in [23], it results in being marginal, in spite of the chance to study anytime and anywhere. Taking always into consideration mobile and ubiquitous learning, [PERSON] et al. [19] propose a context-based system, to monitor the students' achievements, which leads to the learning process being in the students' own environment. The proposal is interesting for researchers, as it is based on ontologies capable of modeling the context the students are in, according to the location, to the student profile, to the time dimension and to the features of the learning activities. A reasoner software layer decides which activities and LOs are the most suitable for the specific students involved, according to their personal profile, as well as to time and location of the activities and of the students themselves.  
In [14], ubiquitous learning is presented as a replacement for mobile learning. In this scenario, researchers, who are in charge of continuously evaluating the efficiency of the system both at a technical and at a pedagogical level, are fostered to discover new interesting learning patterns. This is achieved by introducing a novel meta-level in order to adaptively monitor and control actuators and perceptors and by adding introspection functionalities. Even so, this formal model is only theoretical, and its actual implementation, as well as its real effectiveness are still to be evaluated properly.