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Recommendation approaches for e-learners: a survey

Authors:

Muhammad Tanvir AfzalAuthors Info & Claims

MEDES '15: Proceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems

Pages 137 - 141

https://doi.org/10.1145/2857218.2857251

Published: 25 October 2015 Publication History

Abstract

Recommending relevant content to the learner is a challenging task for any e-Learning management system. This paper has critically reviewed the literature and identified the strategies being used to recommend relevant content to learners.

This paper highlights the strength and limitations of prominent approaches and has presented challenging tasks which will be useful for the e-Learning research community to focus for the future research.

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Cited By

Niu YLin RXue H(2023)Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative FilteringApplied Sciences10.3390/app13191093313:19(10933)Online publication date: 2-Oct-2023
https://doi.org/10.3390/app131910933
Ali SHafeez YAbbas MAqib MNawaz A(2021)Enabling remote learning system for virtual personalized preferences during COVID-19 pandemicMultimedia Tools and Applications10.1007/s11042-021-11414-wOnline publication date: 17-Aug-2021
https://doi.org/10.1007/s11042-021-11414-w

Index Terms

Recommendation approaches for e-learners: a survey
1. Applied computing
  1. Education
    1. Distance learning
    2. E-learning
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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MEDES '15: Proceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems

October 2015

271 pages

ISBN:9781450334808

DOI:10.1145/2857218

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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The French Chapter of ACM Special Interest Group on Applied Computing
IFSP: Federal Institute of São Paulo

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2015

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Short-paper

Funding Sources

Graz University of Technology, Graz, Austria
Higher Education Commission of Pakistan

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MEDES '15

Sponsor:

IFSP

MEDES '15: The 7th International Conference on Management of computational and collective IntElligence in Digital EcoSystems

October 25 - 29, 2015

Caraguatatuba, Brazil

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MEDES '15 Paper Acceptance Rate 13 of 64 submissions, 20%;

Overall Acceptance Rate 267 of 682 submissions, 39%

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Cited By

Niu YLin RXue H(2023)Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative FilteringApplied Sciences10.3390/app13191093313:19(10933)Online publication date: 2-Oct-2023
https://doi.org/10.3390/app131910933
Ali SHafeez YAbbas MAqib MNawaz A(2021)Enabling remote learning system for virtual personalized preferences during COVID-19 pandemicMultimedia Tools and Applications10.1007/s11042-021-11414-wOnline publication date: 17-Aug-2021
https://doi.org/10.1007/s11042-021-11414-w

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