Research Article
A traditional-learning time predictive approach for e-learning systems in challenging environments
@ARTICLE{10.4108/eai.29-11-2017.153391, author={K. M. Belise}, title={A traditional-learning time predictive approach for e-learning systems in challenging environments}, journal={EAI Endorsed Transactions on e-Learning}, volume={4}, number={15}, publisher={EAI}, journal_a={EL}, year={2017}, month={11}, keywords={challenging environment, context, e-learning, offline learning, online learning, traditional learning, prediction, recommender, content filtering, collaborative filtering}, doi={10.4108/eai.29-11-2017.153391} }- K. M. Belise
Year: 2017
A traditional-learning time predictive approach for e-learning systems in challenging environments
EL
EAI
DOI: 10.4108/eai.29-11-2017.153391
Abstract
The explosion of world-wide-web has offered people a large number of online courses, e-classes and e-schools. Such e-learning applications contain a wide variety of learning materials which can confuse the choices of learner to select. Although the area of recommender systems has made a significant progress over the last several years to address this problem, the issue remained fairly unexplored for challenging environments. This paper proposes an approach to predict traditional-learning times for recommender systems in such environments.
Copyright © 2017 Kenmogne Edith Belise, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
