E-learning Behavior Analysis Based on Fuzzy Clustering
E-learning behavior analysis is an important issue to the instruction based on Internet. This paper proposed a new method to analyze the e-learning behavior. It classified e-learning behaviors into several clusters by fuzzy clustering algorithm. Behaviors in the same cluster have the most common in...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 866 |
|---|---|
| container_issue | |
| container_start_page | 863 |
| container_title | |
| container_volume | |
| creator | Jili Chen Kebin Huang Feng Wang Huixia Wang |
| description | E-learning behavior analysis is an important issue to the instruction based on Internet. This paper proposed a new method to analyze the e-learning behavior. It classified e-learning behaviors into several clusters by fuzzy clustering algorithm. Behaviors in the same cluster have the most common in characters, while behaviors between clusters have the least common. Experiments fully demonstrated that the proposed method can achieve good performance of analyzing e-learning behavior. It shows that by using cluster analysis, teachers can understand the students better in interest, personality and other informations. It also helps to develop effective educational resource and carry out the personalized instruction. |
| doi_str_mv | 10.1109/WGEC.2009.214 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5402847</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5402847</ieee_id><sourcerecordid>5402847</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-aef7f0958b824556b610f8d08c8dbf46b29c2c04351f74c26b31550fe8a2dce53</originalsourceid><addsrcrecordid>eNotjUFLwzAYQAMiKLPHnbzkD7R-SfMl6XEr3RQGXpQdR9J-0UDtJNmE7tc70NO7PN5jbCmgEgKap_22aysJ0FRSqBtWNMYKJZVCqbC-Y0XO0YPURmvUcM90V47k0hSnD76mT_cTj4mvJjfOOWa-dpkGfpz45ny5zLwdz_lE6eo-sNvgxkzFPxfsfdO9tc_l7nX70q52ZRQGT6WjYAI0aL297lF7LSDYAWxvBx-U9rLpZQ-qRhGM6qX2tUCEQNbJoSesF-zxrxuJ6PCd4pdL8wEVSKtM_QtNbkOE</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>E-learning Behavior Analysis Based on Fuzzy Clustering</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Jili Chen ; Kebin Huang ; Feng Wang ; Huixia Wang</creator><creatorcontrib>Jili Chen ; Kebin Huang ; Feng Wang ; Huixia Wang</creatorcontrib><description>E-learning behavior analysis is an important issue to the instruction based on Internet. This paper proposed a new method to analyze the e-learning behavior. It classified e-learning behaviors into several clusters by fuzzy clustering algorithm. Behaviors in the same cluster have the most common in characters, while behaviors between clusters have the least common. Experiments fully demonstrated that the proposed method can achieve good performance of analyzing e-learning behavior. It shows that by using cluster analysis, teachers can understand the students better in interest, personality and other informations. It also helps to develop effective educational resource and carry out the personalized instruction.</description><identifier>ISBN: 9781424452453</identifier><identifier>ISBN: 1424452457</identifier><identifier>ISBN: 9780769538990</identifier><identifier>ISBN: 0769538991</identifier><identifier>DOI: 10.1109/WGEC.2009.214</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Clustering algorithms ; Clustering methods ; E-learning behavior ; Educational institutions ; Educational technology ; Electronic learning ; fuzzy cluster ; Genetics ; Internet ; Pattern recognition ; Statistical analysis</subject><ispartof>2009 Third International Conference on Genetic and Evolutionary Computing, 2009, p.863-866</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/5402847$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,27929,54924</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5402847$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jili Chen</creatorcontrib><creatorcontrib>Kebin Huang</creatorcontrib><creatorcontrib>Feng Wang</creatorcontrib><creatorcontrib>Huixia Wang</creatorcontrib><title>E-learning Behavior Analysis Based on Fuzzy Clustering</title><title>2009 Third International Conference on Genetic and Evolutionary Computing</title><addtitle>WGEC</addtitle><description>E-learning behavior analysis is an important issue to the instruction based on Internet. This paper proposed a new method to analyze the e-learning behavior. It classified e-learning behaviors into several clusters by fuzzy clustering algorithm. Behaviors in the same cluster have the most common in characters, while behaviors between clusters have the least common. Experiments fully demonstrated that the proposed method can achieve good performance of analyzing e-learning behavior. It shows that by using cluster analysis, teachers can understand the students better in interest, personality and other informations. It also helps to develop effective educational resource and carry out the personalized instruction.</description><subject>Algorithm design and analysis</subject><subject>Clustering algorithms</subject><subject>Clustering methods</subject><subject>E-learning behavior</subject><subject>Educational institutions</subject><subject>Educational technology</subject><subject>Electronic learning</subject><subject>fuzzy cluster</subject><subject>Genetics</subject><subject>Internet</subject><subject>Pattern recognition</subject><subject>Statistical analysis</subject><isbn>9781424452453</isbn><isbn>1424452457</isbn><isbn>9780769538990</isbn><isbn>0769538991</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjUFLwzAYQAMiKLPHnbzkD7R-SfMl6XEr3RQGXpQdR9J-0UDtJNmE7tc70NO7PN5jbCmgEgKap_22aysJ0FRSqBtWNMYKJZVCqbC-Y0XO0YPURmvUcM90V47k0hSnD76mT_cTj4mvJjfOOWa-dpkGfpz45ny5zLwdz_lE6eo-sNvgxkzFPxfsfdO9tc_l7nX70q52ZRQGT6WjYAI0aL297lF7LSDYAWxvBx-U9rLpZQ-qRhGM6qX2tUCEQNbJoSesF-zxrxuJ6PCd4pdL8wEVSKtM_QtNbkOE</recordid><startdate>200910</startdate><enddate>200910</enddate><creator>Jili Chen</creator><creator>Kebin Huang</creator><creator>Feng Wang</creator><creator>Huixia Wang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200910</creationdate><title>E-learning Behavior Analysis Based on Fuzzy Clustering</title><author>Jili Chen ; Kebin Huang ; Feng Wang ; Huixia Wang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-aef7f0958b824556b610f8d08c8dbf46b29c2c04351f74c26b31550fe8a2dce53</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Algorithm design and analysis</topic><topic>Clustering algorithms</topic><topic>Clustering methods</topic><topic>E-learning behavior</topic><topic>Educational institutions</topic><topic>Educational technology</topic><topic>Electronic learning</topic><topic>fuzzy cluster</topic><topic>Genetics</topic><topic>Internet</topic><topic>Pattern recognition</topic><topic>Statistical analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Jili Chen</creatorcontrib><creatorcontrib>Kebin Huang</creatorcontrib><creatorcontrib>Feng Wang</creatorcontrib><creatorcontrib>Huixia Wang</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 Electronic Library (IEL)</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>Jili Chen</au><au>Kebin Huang</au><au>Feng Wang</au><au>Huixia Wang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>E-learning Behavior Analysis Based on Fuzzy Clustering</atitle><btitle>2009 Third International Conference on Genetic and Evolutionary Computing</btitle><stitle>WGEC</stitle><date>2009-10</date><risdate>2009</risdate><spage>863</spage><epage>866</epage><pages>863-866</pages><isbn>9781424452453</isbn><isbn>1424452457</isbn><isbn>9780769538990</isbn><isbn>0769538991</isbn><abstract>E-learning behavior analysis is an important issue to the instruction based on Internet. This paper proposed a new method to analyze the e-learning behavior. It classified e-learning behaviors into several clusters by fuzzy clustering algorithm. Behaviors in the same cluster have the most common in characters, while behaviors between clusters have the least common. Experiments fully demonstrated that the proposed method can achieve good performance of analyzing e-learning behavior. It shows that by using cluster analysis, teachers can understand the students better in interest, personality and other informations. It also helps to develop effective educational resource and carry out the personalized instruction.</abstract><pub>IEEE</pub><doi>10.1109/WGEC.2009.214</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9781424452453 |
| ispartof | 2009 Third International Conference on Genetic and Evolutionary Computing, 2009, p.863-866 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_5402847 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Clustering algorithms Clustering methods E-learning behavior Educational institutions Educational technology Electronic learning fuzzy cluster Genetics Internet Pattern recognition Statistical analysis |
| title | E-learning Behavior Analysis Based on Fuzzy Clustering |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-16T17%3A29%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=E-learning%20Behavior%20Analysis%20Based%20on%20Fuzzy%20Clustering&rft.btitle=2009%20Third%20International%20Conference%20on%20Genetic%20and%20Evolutionary%20Computing&rft.au=Jili%20Chen&rft.date=2009-10&rft.spage=863&rft.epage=866&rft.pages=863-866&rft.isbn=9781424452453&rft.isbn_list=1424452457&rft.isbn_list=9780769538990&rft.isbn_list=0769538991&rft_id=info:doi/10.1109/WGEC.2009.214&rft_dat=%3Cieee_6IE%3E5402847%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5402847&rfr_iscdi=true |