A survey on learner models in adaptive E-learning systems

E-learning became an omnipresent concept in modern education thanks to the rapid development of Information and Communication Technology for Education. Along with this exponential evolution, the learner plays a central role in both traditional and computer-assisted learning. Accordingly, the learner...

Full description

Saved in:
Bibliographic Details
Main Authors: Hlioui, Fedia, Alioui, Nadia, Gargouri, Faiez
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
adaptive E-learning system
Adaptive systems
Brain modeling
Earner model
Electronic learning
learner behavior
Lips
personalization parameters
Psychology
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 7
container_issue
container_start_page 1
container_title
container_volume
creator Hlioui, Fedia
Alioui, Nadia
Gargouri, Faiez
description E-learning became an omnipresent concept in modern education thanks to the rapid development of Information and Communication Technology for Education. Along with this exponential evolution, the learner plays a central role in both traditional and computer-assisted learning. Accordingly, the learner's individual differences have a remarkable potential to improve his/her performance and motivation. This is ensured by providing an adaptive learning support. The present paper presents a survey of learner modeling in adaptive E-learning systems while describing the main methods for extracting of the personalization parameters. The learner's modeling does not only interest researchers and developers, but also pedagogues, course' designer and tutors.
doi_str_mv 10.1109/AICCSA.2016.7945770
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_7945770</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7945770</ieee_id><sourcerecordid>7945770</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-c872d32458ba9dfe8ad0aef75c2eb387c35b4fa5af6fe625b266cae68e2ae9803</originalsourceid><addsrcrecordid>eNotj8tKw0AUQEdBsNZ-QTfzA4l3ZjKvZQhVCwUX6rrcZO7ISJKWTCzk7wXt6iwOHDiMbQWUQoB_qvdN816XEoQpra-0tXDDNt46ocFDpSTALVtJYUShlYJ79pDzN4Dy0ukV8zXPP9OFFn4aeU84jTTx4RSozzyNHAOe53Qhviv-ZBq_eF7yTEN-ZHcR-0ybK9fs83n30bwWh7eXfVMfiiSsnovOWRmUrLRr0YdIDgMgRas7Sa1ytlO6rSJqjCaSkbqVxnRIxpFE8g7Umm3_u4mIjucpDTgtx-up-gUZJEhN</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A survey on learner models in adaptive E-learning systems</title><source>IEEE Xplore All Conference Series</source><creator>Hlioui, Fedia ; Alioui, Nadia ; Gargouri, Faiez</creator><creatorcontrib>Hlioui, Fedia ; Alioui, Nadia ; Gargouri, Faiez</creatorcontrib><description>E-learning became an omnipresent concept in modern education thanks to the rapid development of Information and Communication Technology for Education. Along with this exponential evolution, the learner plays a central role in both traditional and computer-assisted learning. Accordingly, the learner's individual differences have a remarkable potential to improve his/her performance and motivation. This is ensured by providing an adaptive learning support. The present paper presents a survey of learner modeling in adaptive E-learning systems while describing the main methods for extracting of the personalization parameters. The learner's modeling does not only interest researchers and developers, but also pedagogues, course' designer and tutors.</description><identifier>EISSN: 2161-5330</identifier><identifier>EISBN: 9781509043200</identifier><identifier>EISBN: 1509043209</identifier><identifier>DOI: 10.1109/AICCSA.2016.7945770</identifier><language>eng</language><publisher>IEEE</publisher><subject>Adaptation models ; adaptive E-learning system ; Adaptive systems ; Brain modeling ; Earner model ; Electronic learning ; learner behavior ; Lips ; personalization parameters ; Psychology</subject><ispartof>2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 2016, p.1-7</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7945770$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,27902,54530,54907</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7945770$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Hlioui, Fedia</creatorcontrib><creatorcontrib>Alioui, Nadia</creatorcontrib><creatorcontrib>Gargouri, Faiez</creatorcontrib><title>A survey on learner models in adaptive E-learning systems</title><title>2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)</title><addtitle>AICCSA</addtitle><description>E-learning became an omnipresent concept in modern education thanks to the rapid development of Information and Communication Technology for Education. Along with this exponential evolution, the learner plays a central role in both traditional and computer-assisted learning. Accordingly, the learner's individual differences have a remarkable potential to improve his/her performance and motivation. This is ensured by providing an adaptive learning support. The present paper presents a survey of learner modeling in adaptive E-learning systems while describing the main methods for extracting of the personalization parameters. The learner's modeling does not only interest researchers and developers, but also pedagogues, course' designer and tutors.</description><subject>Adaptation models</subject><subject>adaptive E-learning system</subject><subject>Adaptive systems</subject><subject>Brain modeling</subject><subject>Earner model</subject><subject>Electronic learning</subject><subject>learner behavior</subject><subject>Lips</subject><subject>personalization parameters</subject><subject>Psychology</subject><issn>2161-5330</issn><isbn>9781509043200</isbn><isbn>1509043209</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2016</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj8tKw0AUQEdBsNZ-QTfzA4l3ZjKvZQhVCwUX6rrcZO7ISJKWTCzk7wXt6iwOHDiMbQWUQoB_qvdN816XEoQpra-0tXDDNt46ocFDpSTALVtJYUShlYJ79pDzN4Dy0ukV8zXPP9OFFn4aeU84jTTx4RSozzyNHAOe53Qhviv-ZBq_eF7yTEN-ZHcR-0ybK9fs83n30bwWh7eXfVMfiiSsnovOWRmUrLRr0YdIDgMgRas7Sa1ytlO6rSJqjCaSkbqVxnRIxpFE8g7Umm3_u4mIjucpDTgtx-up-gUZJEhN</recordid><startdate>201611</startdate><enddate>201611</enddate><creator>Hlioui, Fedia</creator><creator>Alioui, Nadia</creator><creator>Gargouri, Faiez</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201611</creationdate><title>A survey on learner models in adaptive E-learning systems</title><author>Hlioui, Fedia ; Alioui, Nadia ; Gargouri, Faiez</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c872d32458ba9dfe8ad0aef75c2eb387c35b4fa5af6fe625b266cae68e2ae9803</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Adaptation models</topic><topic>adaptive E-learning system</topic><topic>Adaptive systems</topic><topic>Brain modeling</topic><topic>Earner model</topic><topic>Electronic learning</topic><topic>learner behavior</topic><topic>Lips</topic><topic>personalization parameters</topic><topic>Psychology</topic><toplevel>online_resources</toplevel><creatorcontrib>Hlioui, Fedia</creatorcontrib><creatorcontrib>Alioui, Nadia</creatorcontrib><creatorcontrib>Gargouri, Faiez</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hlioui, Fedia</au><au>Alioui, Nadia</au><au>Gargouri, Faiez</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A survey on learner models in adaptive E-learning systems</atitle><btitle>2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)</btitle><stitle>AICCSA</stitle><date>2016-11</date><risdate>2016</risdate><spage>1</spage><epage>7</epage><pages>1-7</pages><eissn>2161-5330</eissn><eisbn>9781509043200</eisbn><eisbn>1509043209</eisbn><abstract>E-learning became an omnipresent concept in modern education thanks to the rapid development of Information and Communication Technology for Education. Along with this exponential evolution, the learner plays a central role in both traditional and computer-assisted learning. Accordingly, the learner's individual differences have a remarkable potential to improve his/her performance and motivation. This is ensured by providing an adaptive learning support. The present paper presents a survey of learner modeling in adaptive E-learning systems while describing the main methods for extracting of the personalization parameters. The learner's modeling does not only interest researchers and developers, but also pedagogues, course' designer and tutors.</abstract><pub>IEEE</pub><doi>10.1109/AICCSA.2016.7945770</doi><tpages>7</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2161-5330
ispartof 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 2016, p.1-7
issn 2161-5330
language eng
recordid cdi_ieee_primary_7945770
source IEEE Xplore All Conference Series
subjects Adaptation models
adaptive E-learning system
Adaptive systems
Brain modeling
Earner model
Electronic learning
learner behavior
Lips
personalization parameters
Psychology
title A survey on learner models in adaptive E-learning systems
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T23%3A23%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20survey%20on%20learner%20models%20in%20adaptive%20E-learning%20systems&rft.btitle=2016%20IEEE/ACS%2013th%20International%20Conference%20of%20Computer%20Systems%20and%20Applications%20(AICCSA)&rft.au=Hlioui,%20Fedia&rft.date=2016-11&rft.spage=1&rft.epage=7&rft.pages=1-7&rft.eissn=2161-5330&rft_id=info:doi/10.1109/AICCSA.2016.7945770&rft.eisbn=9781509043200&rft.eisbn_list=1509043209&rft_dat=%3Cieee_CHZPO%3E7945770%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-c872d32458ba9dfe8ad0aef75c2eb387c35b4fa5af6fe625b266cae68e2ae9803%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=7945770&rfr_iscdi=true