M Montebello
ABSTRACT
This chapter brings all the previous chapters together as they collectively and incrementally built up a crescendo to reach the highlight, namely injecting e-learning with AI to customise the education process. The proposed model makes us of all the techniques discussed in the previous chapters and endeavours to compatibly bring them together to create an intelligent personal learning environment. The evolution of e-learning was led imposed by the technology but this model proposes to conveniently employ numerous technologies and techniques to directly address specific e-learning issues. The next generation of online education is not dictated by technology but by the academic need to personalise learning together with the efficient automation offered by AI. The first e-learning issue addressed is that of isolation and Chap. 3 undertook this task with the ingenuity of crowdsourcing and the popularity of social networks. The connectivism learning theory has been associated with this phenomenon and this model makes good use of this first factor. Motivation is another e-learning issue that is addressed through the contributions from Chap. 4 as learner profiling and learning portfolios support student to be much more self-determined in their academic endeavour. The third and final issue tackles the issue of impersonality that e-learning is notoriously criticised, and Chap. 5 offers adaptive environments through the combination of a learning portfolio and supportive learning network. A truly intelligent personal learning environment backed and injected by AI techniques is being proposed as a compatible combination of all these technologies to enhance e-learning effectiveness as it leads online education to its future and the next e-learning generation. The rest of this chapter is organised as follows. The section that follows expands further the underlying rationale that led to the proposed model by analysing the contributions from the previous chapters. This is followed by the architectural setup of how these technologies come together within an online system to deliver a functional and intelligent PLE. The next section tackles all the implementation details that take place to accomplish and complete the architectural design presented before. Finally operational and pragmatic details of how the online PLE functions are covered in an effort to show how the AI-injected e-learning system will operate in reality. © 2018, Springer International Publishing AG.
Notes
Export Date: 11 December 2017