Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation
Identifieur interne : 000529 ( Main/Exploration ); précédent : 000528; suivant : 000530Studying Relations Between E-learning Resources to Improve the Quality of Searching and Recommendation
Auteurs : Ngoc Chan Nguyen [France] ; Azim Roussanaly [France] ; Anne Boyer [France]Source :
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
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 004884
- to stream Hal, to step Curation: 004884
- to stream Hal, to step Checkpoint: 000510
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Le 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><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></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><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Nguyen, Ngoc Chan" sort="Nguyen, Ngoc Chan" uniqKey="Nguyen N" first="Ngoc Chan" last="Nguyen">Ngoc Chan Nguyen</name></region><name sortKey="Boyer, Anne" sort="Boyer, Anne" uniqKey="Boyer A" first="Anne" last="Boyer">Anne Boyer</name><name sortKey="Roussanaly, Azim" sort="Roussanaly, Azim" uniqKey="Roussanaly A" first="Azim" last="Roussanaly">Azim Roussanaly</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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