Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation
Identifieur interne : 004884 ( Hal/Corpus ); précédent : 004883; suivant : 004885Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation
Auteurs : Ngoc Chan Nguyen ; Azim Roussanaly ; Anne BoyerSource :
English descriptors
Abstract
Searching and recommendation are basic functions that effectively assist learners to approach their favorite learning resources. Several searching and recommendation techniques in the Information Retrieval (IR) domain have been proposed to apply in the Technology Enhanced Learning (TEL) domain. However, few of them pay attention on particular properties of e-learning resources, which potentially improve the quality of searching and recommendation. In this paper, we propose an approach that studies relations between e-learning resources, which is a particular property existing in online educational systems, to support resource searching and recommendation. Concretely, we rank e-learning resources based on their relations by adapting the Google's PageRank algorithm. We integrate this ranking into a text-matching search engine to refine the search results. We also combine it with a content-based recommendation technique to compute the similarity between user profile and e-learning resources. Experimental results on a shared dataset showed the efficiency of our approach.
Url:
DOI: 10.5220/0005454301190129
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Hal:hal-01253030Le document en format XML
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BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation></affiliation></author></analytic><idno type="DOI">10.5220/0005454301190129</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Learning Resource Recommendation</term><term>Online Education</term><term>PageRank</term><term>Searching</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Searching and recommendation are basic functions that effectively assist learners to approach their favorite learning resources. Several searching and recommendation techniques in the Information Retrieval (IR) domain have been proposed to apply in the Technology Enhanced Learning (TEL) domain. However, few of them pay attention on particular properties of e-learning resources, which potentially improve the quality of searching and recommendation. In this paper, we propose an approach that studies relations between e-learning resources, which is a particular property existing in online educational systems, to support resource searching and recommendation. Concretely, we rank e-learning resources based on their relations by adapting the Google's PageRank algorithm. We integrate this ranking into a text-matching search engine to refine the search results. We also combine it with a content-based recommendation technique to compute the similarity between user profile and e-learning resources. Experimental results on a shared dataset showed the efficiency of our approach.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation</title><author role="aut"><persName><forename type="first">Ngoc Chan</forename><surname>Nguyen</surname></persName><email></email><idno type="halauthor">818496</idno><affiliation ref="#struct-206039"></affiliation></author><author role="aut"><persName><forename type="first">Azim</forename><surname>Roussanaly</surname></persName><email></email><idno type="halauthor">363649</idno><affiliation ref="#struct-206039"></affiliation></author><author role="aut"><persName><forename type="first">Anne</forename><surname>Boyer</surname></persName><email></email><idno type="halauthor">702989</idno><affiliation ref="#struct-206039"></affiliation></author><editor role="depositor"><persName><forename>Nguyen</forename><surname>Ngoc Chan</surname></persName><email>ngoc-chan.nguyen@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2016-01-08 15:31:56</date><date type="whenModified">2016-01-13 01:06:23</date><date type="whenReleased">2016-01-11 10:40:40</date><date type="whenProduced">2015-05-23</date><date type="whenEndEmbargoed">2016-01-08</date><ref type="file" target="https://hal.inria.fr/hal-01253030/document"><date notBefore="2016-01-08"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-01253030/file/2015%20CSEDU.pdf"><date notBefore="2016-01-08"></date></ref></edition><respStmt><resp>contributor</resp><name key="307754"><persName><forename>Nguyen</forename><surname>Ngoc Chan</surname></persName><email>ngoc-chan.nguyen@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01253030</idno><idno type="halUri">https://hal.inria.fr/hal-01253030</idno><idno type="halBibtex">nguyen:hal-01253030</idno><idno type="halRefHtml">Proceedings of the 7th International Conference on Computer Supported Education, May 2015, Lisbon, Portugal. pp.119-129, <10.5220/0005454301190129></idno><idno type="halRef">Proceedings of the 7th International Conference on Computer Supported Education, May 2015, Lisbon, Portugal. pp.119-129, <10.5220/0005454301190129></idno></publicationStmt><seriesStmt><idno type="stamp" n="TICE">Bibliothèque TICE</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="SHS">Sciences de l'Homme et de la Société</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="0">No</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation</title><author role="aut"><persName><forename type="first">Ngoc Chan</forename><surname>Nguyen</surname></persName><idno type="halAuthorId">818496</idno><affiliation ref="#struct-206039"></affiliation></author><author role="aut"><persName><forename type="first">Azim</forename><surname>Roussanaly</surname></persName><idno type="halAuthorId">363649</idno><affiliation ref="#struct-206039"></affiliation></author><author role="aut"><persName><forename type="first">Anne</forename><surname>Boyer</surname></persName><idno type="halAuthorId">702989</idno><affiliation ref="#struct-206039"></affiliation></author></analytic><monogr><meeting><title>Proceedings of the 7th International Conference on Computer Supported Education</title><date type="start">2015-05-23</date><date type="end">2015-05-25</date><settlement>Lisbon</settlement><country key="PT">Portugal</country></meeting><imprint><biblScope unit="pp">119-129</biblScope></imprint></monogr><idno type="doi">10.5220/0005454301190129</idno></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Online Education</term><term xml:lang="en">Learning Resource Recommendation</term><term xml:lang="en">Searching</term><term xml:lang="en">PageRank</term></keywords><classCode scheme="halDomain" n="info.info-tt">Computer Science [cs]/Document and Text Processing</classCode><classCode scheme="halDomain" n="shs.edu">Humanities and Social Sciences/Education</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">Searching and recommendation are basic functions that effectively assist learners to approach their favorite learning resources. Several searching and recommendation techniques in the Information Retrieval (IR) domain have been proposed to apply in the Technology Enhanced Learning (TEL) domain. However, few of them pay attention on particular properties of e-learning resources, which potentially improve the quality of searching and recommendation. In this paper, we propose an approach that studies relations between e-learning resources, which is a particular property existing in online educational systems, to support resource searching and recommendation. Concretely, we rank e-learning resources based on their relations by adapting the Google's PageRank algorithm. We integrate this ranking into a text-matching search engine to refine the search results. We also combine it with a content-based recommendation technique to compute the similarity between user profile and e-learning resources. Experimental results on a shared dataset showed the efficiency of our approach.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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