A personalized group-based recommendation approach for web search in E-learning

The unprecedented growth of the Internet, its pervasive accessibility, and ease of use have increased students' dependencies on the Web for quick search and retrieval of learning resources. However, current search engines tend to rely on the correct keywords. This excludes other characteristics...

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Main Authors: Rahman, Mohammad Mustaneer, Abdullah, Nor Aniza
Format: Article
Published: Institute of Electrical and Electronics Engineers 2018
Subjects:
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author Rahman, Mohammad Mustaneer
Abdullah, Nor Aniza
author_facet Rahman, Mohammad Mustaneer
Abdullah, Nor Aniza
author_sort Rahman, Mohammad Mustaneer
collection UM
description The unprecedented growth of the Internet, its pervasive accessibility, and ease of use have increased students' dependencies on the Web for quick search and retrieval of learning resources. However, current search engines tend to rely on the correct keywords. This excludes other characteristics, such as the individual's learning capability and readiness for specific learning materials. As a result, the same set of search-keywords delivers the same search results. This situation hinders the optimization of the Web search engines in supporting the heterogeneity of its users in their learning endeavors. This paper aims to address the issue. It attempts to augment Web search engines with personalized recommendations of search results which match students' learning competencies and behaviors. The results drawn from our experiments suggest that our novel approach can provide a notable improvement in terms of performance and satisfaction for the students.
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spelling um.eprints-209682019-04-17T08:10:58Z http://eprints.um.edu.my/20968/ A personalized group-based recommendation approach for web search in E-learning Rahman, Mohammad Mustaneer Abdullah, Nor Aniza L Education (General) QA75 Electronic computers. Computer science The unprecedented growth of the Internet, its pervasive accessibility, and ease of use have increased students' dependencies on the Web for quick search and retrieval of learning resources. However, current search engines tend to rely on the correct keywords. This excludes other characteristics, such as the individual's learning capability and readiness for specific learning materials. As a result, the same set of search-keywords delivers the same search results. This situation hinders the optimization of the Web search engines in supporting the heterogeneity of its users in their learning endeavors. This paper aims to address the issue. It attempts to augment Web search engines with personalized recommendations of search results which match students' learning competencies and behaviors. The results drawn from our experiments suggest that our novel approach can provide a notable improvement in terms of performance and satisfaction for the students. Institute of Electrical and Electronics Engineers 2018 Article PeerReviewed Rahman, Mohammad Mustaneer and Abdullah, Nor Aniza (2018) A personalized group-based recommendation approach for web search in E-learning. IEEE Access, 6. pp. 34166-34178. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2018.2850376 <https://doi.org/10.1109/ACCESS.2018.2850376>. https://doi.org/10.1109/ACCESS.2018.2850376 doi:10.1109/ACCESS.2018.2850376
spellingShingle L Education (General)
QA75 Electronic computers. Computer science
Rahman, Mohammad Mustaneer
Abdullah, Nor Aniza
A personalized group-based recommendation approach for web search in E-learning
title A personalized group-based recommendation approach for web search in E-learning
title_full A personalized group-based recommendation approach for web search in E-learning
title_fullStr A personalized group-based recommendation approach for web search in E-learning
title_full_unstemmed A personalized group-based recommendation approach for web search in E-learning
title_short A personalized group-based recommendation approach for web search in E-learning
title_sort personalized group based recommendation approach for web search in e learning
topic L Education (General)
QA75 Electronic computers. Computer science
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AT abdullahnoraniza apersonalizedgroupbasedrecommendationapproachforwebsearchinelearning
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