Clustering Approach to Construct Knowledge Management for Statistics Concepts of University Students
The purpose of this study is to develop a methodology as to the knowledge management of concept structure for learners. Fuzzy clustering is adopted to implement classification so that learners of the same cluster have homogeneity and display common features of cognition diagnosis. In addition, fuzzy...
Gespeichert in:
Veröffentlicht in: | Applied Mechanics and Materials 2011-05, Vol.55-57, p.2197-2201 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 2201 |
---|---|
container_issue | |
container_start_page | 2197 |
container_title | Applied Mechanics and Materials |
container_volume | 55-57 |
creator | Lin, Yuan Horng Yih, Jeng Ming |
description | The purpose of this study is to develop a methodology as to the knowledge management of concept structure for learners. Fuzzy clustering is adopted to implement classification so that learners of the same cluster have homogeneity and display common features of cognition diagnosis. In addition, fuzzy clustering is based on information of concept scoring and caution index from polytomous student-problem chart. In the study, the empirical data is the assessment of statistics concepts from university students. The results show that there are four clusters and each cluster has its own cognitive characteristics. To sum up, the methodology can improve knowledge management in classroom more feasible. Finally, some recommendations and suggestions for future investigations are also discussed. |
doi_str_mv | 10.4028/www.scientific.net/AMM.55-57.2197 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1443590973</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3104813571</sourcerecordid><originalsourceid>FETCH-LOGICAL-c301t-67079026134e8d727b3135948f0b869c5752b55157dccc09ab08f4dba2123ca33</originalsourceid><addsrcrecordid>eNqNkLtOwzAUQC0eEuXxD5aYGJL6GdtjFfESrRiA2XIcpxi1SbEdKv4elyLByOThHp17fQC4wqhkiMjpdrsto_WuT77ztuxdms4Wi5LzgouSYCUOwARXFSkEk-QQXCghKaJCcqQUOfqeoUJRWp2A0xjfEKoYZnIC2no1xuSC75dwttmEwdhXmAZYD31MYbQJPvTDduXapYML05ulW-cbYDcE-JRM8jF5G3e0dZsU4dDBl95_uBB9-szE2GY6noPjzqyiu_h5z8DLzfVzfVfMH2_v69m8sBThVFQCCYVIhSlzshVENBRTrpjsUCMrZbngpOEcc9Faa5EyDZIdaxtDMKHWUHoGLvfe_I_30cWk34Yx9HmlxoxlFVJiR832lA1DjMF1ehP82oRPjZHexdY5tv6NrXNsnWNrzjUXehc7O-q9IwWTQzn7-mfVvy1fskaQig</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1443590973</pqid></control><display><type>article</type><title>Clustering Approach to Construct Knowledge Management for Statistics Concepts of University Students</title><source>Scientific.net Journals</source><creator>Lin, Yuan Horng ; Yih, Jeng Ming</creator><creatorcontrib>Lin, Yuan Horng ; Yih, Jeng Ming</creatorcontrib><description>The purpose of this study is to develop a methodology as to the knowledge management of concept structure for learners. Fuzzy clustering is adopted to implement classification so that learners of the same cluster have homogeneity and display common features of cognition diagnosis. In addition, fuzzy clustering is based on information of concept scoring and caution index from polytomous student-problem chart. In the study, the empirical data is the assessment of statistics concepts from university students. The results show that there are four clusters and each cluster has its own cognitive characteristics. To sum up, the methodology can improve knowledge management in classroom more feasible. Finally, some recommendations and suggestions for future investigations are also discussed.</description><identifier>ISSN: 1660-9336</identifier><identifier>ISSN: 1662-7482</identifier><identifier>ISBN: 9783037850992</identifier><identifier>ISBN: 303785099X</identifier><identifier>EISSN: 1662-7482</identifier><identifier>DOI: 10.4028/www.scientific.net/AMM.55-57.2197</identifier><language>eng</language><publisher>Zurich: Trans Tech Publications Ltd</publisher><subject>Knowledge management</subject><ispartof>Applied Mechanics and Materials, 2011-05, Vol.55-57, p.2197-2201</ispartof><rights>2011 Trans Tech Publications Ltd</rights><rights>Copyright Trans Tech Publications Ltd. May 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c301t-67079026134e8d727b3135948f0b869c5752b55157dccc09ab08f4dba2123ca33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttps://www.scientific.net/Image/TitleCover/1245?width=600</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Lin, Yuan Horng</creatorcontrib><creatorcontrib>Yih, Jeng Ming</creatorcontrib><title>Clustering Approach to Construct Knowledge Management for Statistics Concepts of University Students</title><title>Applied Mechanics and Materials</title><description>The purpose of this study is to develop a methodology as to the knowledge management of concept structure for learners. Fuzzy clustering is adopted to implement classification so that learners of the same cluster have homogeneity and display common features of cognition diagnosis. In addition, fuzzy clustering is based on information of concept scoring and caution index from polytomous student-problem chart. In the study, the empirical data is the assessment of statistics concepts from university students. The results show that there are four clusters and each cluster has its own cognitive characteristics. To sum up, the methodology can improve knowledge management in classroom more feasible. Finally, some recommendations and suggestions for future investigations are also discussed.</description><subject>Knowledge management</subject><issn>1660-9336</issn><issn>1662-7482</issn><issn>1662-7482</issn><isbn>9783037850992</isbn><isbn>303785099X</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqNkLtOwzAUQC0eEuXxD5aYGJL6GdtjFfESrRiA2XIcpxi1SbEdKv4elyLByOThHp17fQC4wqhkiMjpdrsto_WuT77ztuxdms4Wi5LzgouSYCUOwARXFSkEk-QQXCghKaJCcqQUOfqeoUJRWp2A0xjfEKoYZnIC2no1xuSC75dwttmEwdhXmAZYD31MYbQJPvTDduXapYML05ulW-cbYDcE-JRM8jF5G3e0dZsU4dDBl95_uBB9-szE2GY6noPjzqyiu_h5z8DLzfVzfVfMH2_v69m8sBThVFQCCYVIhSlzshVENBRTrpjsUCMrZbngpOEcc9Faa5EyDZIdaxtDMKHWUHoGLvfe_I_30cWk34Yx9HmlxoxlFVJiR832lA1DjMF1ehP82oRPjZHexdY5tv6NrXNsnWNrzjUXehc7O-q9IwWTQzn7-mfVvy1fskaQig</recordid><startdate>20110503</startdate><enddate>20110503</enddate><creator>Lin, Yuan Horng</creator><creator>Yih, Jeng Ming</creator><general>Trans Tech Publications Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>7TB</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BFMQW</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope></search><sort><creationdate>20110503</creationdate><title>Clustering Approach to Construct Knowledge Management for Statistics Concepts of University Students</title><author>Lin, Yuan Horng ; Yih, Jeng Ming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c301t-67079026134e8d727b3135948f0b869c5752b55157dccc09ab08f4dba2123ca33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Knowledge management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Yuan Horng</creatorcontrib><creatorcontrib>Yih, Jeng Ming</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Continental Europe Database</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><jtitle>Applied Mechanics and Materials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Yuan Horng</au><au>Yih, Jeng Ming</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clustering Approach to Construct Knowledge Management for Statistics Concepts of University Students</atitle><jtitle>Applied Mechanics and Materials</jtitle><date>2011-05-03</date><risdate>2011</risdate><volume>55-57</volume><spage>2197</spage><epage>2201</epage><pages>2197-2201</pages><issn>1660-9336</issn><issn>1662-7482</issn><eissn>1662-7482</eissn><isbn>9783037850992</isbn><isbn>303785099X</isbn><abstract>The purpose of this study is to develop a methodology as to the knowledge management of concept structure for learners. Fuzzy clustering is adopted to implement classification so that learners of the same cluster have homogeneity and display common features of cognition diagnosis. In addition, fuzzy clustering is based on information of concept scoring and caution index from polytomous student-problem chart. In the study, the empirical data is the assessment of statistics concepts from university students. The results show that there are four clusters and each cluster has its own cognitive characteristics. To sum up, the methodology can improve knowledge management in classroom more feasible. Finally, some recommendations and suggestions for future investigations are also discussed.</abstract><cop>Zurich</cop><pub>Trans Tech Publications Ltd</pub><doi>10.4028/www.scientific.net/AMM.55-57.2197</doi><tpages>5</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1660-9336 |
ispartof | Applied Mechanics and Materials, 2011-05, Vol.55-57, p.2197-2201 |
issn | 1660-9336 1662-7482 1662-7482 |
language | eng |
recordid | cdi_proquest_journals_1443590973 |
source | Scientific.net Journals |
subjects | Knowledge management |
title | Clustering Approach to Construct Knowledge Management for Statistics Concepts of University Students |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-11T14%3A19%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Clustering%20Approach%20to%20Construct%20Knowledge%20Management%20for%20Statistics%20Concepts%20of%20University%20Students&rft.jtitle=Applied%20Mechanics%20and%20Materials&rft.au=Lin,%20Yuan%20Horng&rft.date=2011-05-03&rft.volume=55-57&rft.spage=2197&rft.epage=2201&rft.pages=2197-2201&rft.issn=1660-9336&rft.eissn=1662-7482&rft.isbn=9783037850992&rft.isbn_list=303785099X&rft_id=info:doi/10.4028/www.scientific.net/AMM.55-57.2197&rft_dat=%3Cproquest_cross%3E3104813571%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1443590973&rft_id=info:pmid/&rfr_iscdi=true |