Ranking Learning Objects through Integration of Different Quality Indicators
The solutions used to-date for recommending learning objects have proved unsatisfactory. In an attempt to improve the situation, this document highlights the insufficiencies of the existing approaches, and identifies quality indicators that might be used to provide information on which materials to...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on learning technologies 2010-10, Vol.3 (4), p.358-363 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 363 |
|---|---|
| container_issue | 4 |
| container_start_page | 358 |
| container_title | IEEE transactions on learning technologies |
| container_volume | 3 |
| creator | Sanz-Rodríguez, Javier Dodero, Juan Manuel Sánchez-Alonso, Salvador |
| description | The solutions used to-date for recommending learning objects have proved unsatisfactory. In an attempt to improve the situation, this document highlights the insufficiencies of the existing approaches, and identifies quality indicators that might be used to provide information on which materials to recommend to users. Next, a synthesized quality indicator that can facilitate the ranking of learning objects, according to their overall quality, is proposed. In this way, explicit evaluations carried out by users or experts will be used, along with the usage data; thus, completing the information on which the recommendation is based. Taking a set of learning objects from the Merlot repository, we analyzed the relationships that exist between the different quality indicators to form an overall quality indicator that can be calculated automatically, guaranteeing that all resources will be rated. |
| doi_str_mv | 10.1109/TLT.2010.23 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_proquest_journals_1030178598</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5558894</ieee_id><sourcerecordid>2724341421</sourcerecordid><originalsourceid>FETCH-LOGICAL-c281t-1a5a46dc455b570d2267b7b99e074f143127fe96755a9f2e033f356332645ea33</originalsourceid><addsrcrecordid>eNpNkM1LwzAYxoMoOKcnj14KHqUzH03THGV-DQpDqeeQdm-2zJnMJD3sv7dlIp7e54UfzwM_hK4JnhGC5X1TNzOKh4-yEzQhksmcsIqe_svn6CLGLcYlFZJOUP2u3ad166wGHdwYlu0WuhSztAm-X2-yhUuwDjpZ7zJvskdrDARwKXvr9c6mwwCsbKeTD_ESnRm9i3D1e6fo4_mpmb_m9fJlMX-o845WJOVEc12Uq67gvOUCrygtRStaKQGLwpCCESoMyFJwrqWhgBkzjJeM0bLgoBmbottj7z747x5iUlvfBzdMKoIZJqLishqouyPVBR9jAKP2wX7pcBggNepSgy416lJ07Lw50hYA_kjOeVXJgv0AsPxklw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1030178598</pqid></control><display><type>article</type><title>Ranking Learning Objects through Integration of Different Quality Indicators</title><source>IEEE Electronic Library (IEL)</source><creator>Sanz-Rodríguez, Javier ; Dodero, Juan Manuel ; Sánchez-Alonso, Salvador</creator><creatorcontrib>Sanz-Rodríguez, Javier ; Dodero, Juan Manuel ; Sánchez-Alonso, Salvador</creatorcontrib><description>The solutions used to-date for recommending learning objects have proved unsatisfactory. In an attempt to improve the situation, this document highlights the insufficiencies of the existing approaches, and identifies quality indicators that might be used to provide information on which materials to recommend to users. Next, a synthesized quality indicator that can facilitate the ranking of learning objects, according to their overall quality, is proposed. In this way, explicit evaluations carried out by users or experts will be used, along with the usage data; thus, completing the information on which the recommendation is based. Taking a set of learning objects from the Merlot repository, we analyzed the relationships that exist between the different quality indicators to form an overall quality indicator that can be calculated automatically, guaranteeing that all resources will be rated.</description><identifier>ISSN: 1939-1382</identifier><identifier>EISSN: 1939-1382</identifier><identifier>EISSN: 2372-0050</identifier><identifier>DOI: 10.1109/TLT.2010.23</identifier><identifier>CODEN: ITLTAT</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Correlation ; Merlot ; Object oriented methods ; Object recognition ; quality ; ranking ; Search problems</subject><ispartof>IEEE transactions on learning technologies, 2010-10, Vol.3 (4), p.358-363</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Oct/Dec 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c281t-1a5a46dc455b570d2267b7b99e074f143127fe96755a9f2e033f356332645ea33</citedby><cites>FETCH-LOGICAL-c281t-1a5a46dc455b570d2267b7b99e074f143127fe96755a9f2e033f356332645ea33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5558894$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>315,781,785,797,27929,27930,54763</link.rule.ids></links><search><creatorcontrib>Sanz-Rodríguez, Javier</creatorcontrib><creatorcontrib>Dodero, Juan Manuel</creatorcontrib><creatorcontrib>Sánchez-Alonso, Salvador</creatorcontrib><title>Ranking Learning Objects through Integration of Different Quality Indicators</title><title>IEEE transactions on learning technologies</title><addtitle>TLT</addtitle><description>The solutions used to-date for recommending learning objects have proved unsatisfactory. In an attempt to improve the situation, this document highlights the insufficiencies of the existing approaches, and identifies quality indicators that might be used to provide information on which materials to recommend to users. Next, a synthesized quality indicator that can facilitate the ranking of learning objects, according to their overall quality, is proposed. In this way, explicit evaluations carried out by users or experts will be used, along with the usage data; thus, completing the information on which the recommendation is based. Taking a set of learning objects from the Merlot repository, we analyzed the relationships that exist between the different quality indicators to form an overall quality indicator that can be calculated automatically, guaranteeing that all resources will be rated.</description><subject>Correlation</subject><subject>Merlot</subject><subject>Object oriented methods</subject><subject>Object recognition</subject><subject>quality</subject><subject>ranking</subject><subject>Search problems</subject><issn>1939-1382</issn><issn>1939-1382</issn><issn>2372-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><recordid>eNpNkM1LwzAYxoMoOKcnj14KHqUzH03THGV-DQpDqeeQdm-2zJnMJD3sv7dlIp7e54UfzwM_hK4JnhGC5X1TNzOKh4-yEzQhksmcsIqe_svn6CLGLcYlFZJOUP2u3ad166wGHdwYlu0WuhSztAm-X2-yhUuwDjpZ7zJvskdrDARwKXvr9c6mwwCsbKeTD_ESnRm9i3D1e6fo4_mpmb_m9fJlMX-o845WJOVEc12Uq67gvOUCrygtRStaKQGLwpCCESoMyFJwrqWhgBkzjJeM0bLgoBmbottj7z747x5iUlvfBzdMKoIZJqLishqouyPVBR9jAKP2wX7pcBggNepSgy416lJ07Lw50hYA_kjOeVXJgv0AsPxklw</recordid><startdate>20101001</startdate><enddate>20101001</enddate><creator>Sanz-Rodríguez, Javier</creator><creator>Dodero, Juan Manuel</creator><creator>Sánchez-Alonso, Salvador</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20101001</creationdate><title>Ranking Learning Objects through Integration of Different Quality Indicators</title><author>Sanz-Rodríguez, Javier ; Dodero, Juan Manuel ; Sánchez-Alonso, Salvador</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c281t-1a5a46dc455b570d2267b7b99e074f143127fe96755a9f2e033f356332645ea33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Correlation</topic><topic>Merlot</topic><topic>Object oriented methods</topic><topic>Object recognition</topic><topic>quality</topic><topic>ranking</topic><topic>Search problems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sanz-Rodríguez, Javier</creatorcontrib><creatorcontrib>Dodero, Juan Manuel</creatorcontrib><creatorcontrib>Sánchez-Alonso, Salvador</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE transactions on learning technologies</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sanz-Rodríguez, Javier</au><au>Dodero, Juan Manuel</au><au>Sánchez-Alonso, Salvador</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ranking Learning Objects through Integration of Different Quality Indicators</atitle><jtitle>IEEE transactions on learning technologies</jtitle><stitle>TLT</stitle><date>2010-10-01</date><risdate>2010</risdate><volume>3</volume><issue>4</issue><spage>358</spage><epage>363</epage><pages>358-363</pages><issn>1939-1382</issn><eissn>1939-1382</eissn><eissn>2372-0050</eissn><coden>ITLTAT</coden><abstract>The solutions used to-date for recommending learning objects have proved unsatisfactory. In an attempt to improve the situation, this document highlights the insufficiencies of the existing approaches, and identifies quality indicators that might be used to provide information on which materials to recommend to users. Next, a synthesized quality indicator that can facilitate the ranking of learning objects, according to their overall quality, is proposed. In this way, explicit evaluations carried out by users or experts will be used, along with the usage data; thus, completing the information on which the recommendation is based. Taking a set of learning objects from the Merlot repository, we analyzed the relationships that exist between the different quality indicators to form an overall quality indicator that can be calculated automatically, guaranteeing that all resources will be rated.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TLT.2010.23</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1939-1382 |
| ispartof | IEEE transactions on learning technologies, 2010-10, Vol.3 (4), p.358-363 |
| issn | 1939-1382 1939-1382 2372-0050 |
| language | eng |
| recordid | cdi_proquest_journals_1030178598 |
| source | IEEE Electronic Library (IEL) |
| subjects | Correlation Merlot Object oriented methods Object recognition quality ranking Search problems |
| title | Ranking Learning Objects through Integration of Different Quality Indicators |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T17%3A35%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Ranking%20Learning%20Objects%20through%20Integration%20of%20Different%20Quality%20Indicators&rft.jtitle=IEEE%20transactions%20on%20learning%20technologies&rft.au=Sanz-Rodri%CC%81guez,%20Javier&rft.date=2010-10-01&rft.volume=3&rft.issue=4&rft.spage=358&rft.epage=363&rft.pages=358-363&rft.issn=1939-1382&rft.eissn=1939-1382&rft.coden=ITLTAT&rft_id=info:doi/10.1109/TLT.2010.23&rft_dat=%3Cproquest_ieee_%3E2724341421%3C/proquest_ieee_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1030178598&rft_id=info:pmid/&rft_ieee_id=5558894&rfr_iscdi=true |