Abstract
In recent years, virtual learning environments are gaining more and more momentum, considering both the technologies deployed in their support and the sheer number of terminals directly or indirectly interacting with them. This essentially means that every day, more and more smart devices play an active role in this exemplary Web of Things scenario. This digital revolution, affecting education, appears clearly intertwined with the earliest forecasts of the Internet of Things, envisioning around 50 billions heterogeneous devices and gadgets to be active by 2020, considering also the deployment of the fog computing paradigm, which moves part of the computational power to the edge of the network. Moreover, these interconnected objects are expected to produce more and more significant streams of data, themselves generated at unprecedented rates, sometimes to be analyzed almost in real time. Concerning educational environments, this translates to a new type of big data stream, which can be labeled as educational big data streams. Here, pieces of information coming from different sources (such as communications between students and instructors, as well as students’ tests, etc.) require accurate analysis and mining techniques in order to retrieve fruitful and well-timed insights from them. This article presents an overview of the current state of the art of virtual learning environments and their limitations; then, it explains the main ideas behind the paradigms of big data streams and of fog computing, in order to introduce an e-learning architecture integrating both of them. Such an action aims to enhance the ability of virtual learning environments to be closer to the needs of all the actors in an educational scenario, as demonstrated by a preliminary implementation of the envisioned architecture. We believe that the proposed big stream and fog-based educational framework may pave the way towards a better understanding of students’ educational behaviors and foster new research directions in the field.
Highlights
The forecast of a worldwide network following the Internet of Everywhere (IoE) paradigm is currently becoming a reality, mainly thanks to those devices called Smart Objects (SOs)
They surely are a part of the Internet of Things (IoT) vision, and they are represented by sensors, smartphones, wearables, tablets and all other equipment allowing for new forms of interaction between the physical world, the things and the people to whom they are attached
That is why in the following, we present our proposal for a fog-based Virtual Learning Environments (VLEs) architecture, enriched with big data stream capabilities, that tries to overcome the drawbacks of cloud-based learning environments
Summary
The forecast of a worldwide network following the Internet of Everywhere (IoE) paradigm is currently becoming a reality, mainly thanks to those devices called Smart Objects (SOs). As a direct consequence of the adoption of these technologies, educational big data mining is emerging as a novel research scenario [4], wherein big data and big data stream mining techniques may be applied to the valuable information coming from different educational data sources, be they either sensor-like gadgets (e.g., wearables) or full-fledged devices (e.g., tablets and laptops) These new scenarios allow one to offer a partial solution to the most common legacy issues connected with distance learning environments and educational data mining techniques, such as the requirement for: (i) increasing scalability (considering both infrastructure and algorithms); (ii) real-time responsiveness (for quick responses towards students’ requirements and needs); and (iii) adaptivity (since workload peaks may rapidly follow more quiet periods). We offer an overview of the possible advantages of big data streams and fog computing techniques applied to a traditional distance learning scenario, and we corroborate the proposed vision with some useful conclusions, tracking the main future research lines we envision in this field, currently still in flux
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
