A traditional-learning time predictive approach for e-learning systems in challenging environments

A traditional-learning time predictive approach for e-learning systems in challenging environments

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...

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Bibliographic Details
Main Author: K. M. Belise
Format: Article
Language:English
Published: European Alliance for Innovation (EAI) 2017-11-01
Series:EAI Endorsed Transactions on e-Learning
Subjects:
challenging environment
context
e-learning
offline learning
online learning
traditional learning
prediction
recommender
content filtering
collaborative filtering
Online Access:http://eudl.eu/doi/10.4108/eai.29-11-2017.153391
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spelling doaj-cb8b602c4ec74e3a956d26005b459b932020-11-25T01:01:08ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on e-Learning2032-92532017-11-0141511410.4108/eai.29-11-2017.153391A traditional-learning time predictive approach for e-learning systems in challenging environmentsK. M. Belise0Faculty of Science, Department of Mathematics and Computer Science, LIFA, Po. Box. 67 Dschang, CameroonThe 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.http://eudl.eu/doi/10.4108/eai.29-11-2017.153391challenging environmentcontexte-learningoffline learningonline learningtraditional learningpredictionrecommendercontent filteringcollaborative filtering
collection DOAJ
language English
format Article
sources DOAJ
author K. M. Belise
spellingShingle K. M. Belise
A traditional-learning time predictive approach for e-learning systems in challenging environments
EAI Endorsed Transactions on e-Learning
challenging environment
context
e-learning
offline learning
online learning
traditional learning
prediction
recommender
content filtering
collaborative filtering
author_facet K. M. Belise
author_sort K. M. Belise
title A traditional-learning time predictive approach for e-learning systems in challenging environments
title_short A traditional-learning time predictive approach for e-learning systems in challenging environments
title_full A traditional-learning time predictive approach for e-learning systems in challenging environments
title_fullStr A traditional-learning time predictive approach for e-learning systems in challenging environments
title_full_unstemmed A traditional-learning time predictive approach for e-learning systems in challenging environments
title_sort traditional-learning time predictive approach for e-learning systems in challenging environments
publisher European Alliance for Innovation (EAI)
series EAI Endorsed Transactions on e-Learning
issn 2032-9253
publishDate 2017-11-01
description 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.
topic challenging environment
context
e-learning
offline learning
online learning
traditional learning
prediction
recommender
content filtering
collaborative filtering
url http://eudl.eu/doi/10.4108/eai.29-11-2017.153391
work_keys_str_mv AT kmbelise atraditionallearningtimepredictiveapproachforelearningsystemsinchallengingenvironments
AT kmbelise traditionallearningtimepredictiveapproachforelearningsystemsinchallengingenvironments
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