Educational data mining: A tutorial for the rattle package in R
Educational data mining (EDM) has been a rapidly growing research field over the last decade and enabled researchers to discover patterns and trends in education with more sophisticated methods. EDM offers promising solutions to complex educational problems. Given the rapid increase in the availabil...
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| Veröffentlicht in: | International journal of assessment tools in education 2019-01, Vol.6 (5), p.20-36 |
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| container_title | International journal of assessment tools in education |
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| creator | BULUT, Okan YAVUZ, Hatice Cigdem |
| description | Educational
data mining (EDM) has been a rapidly growing research field over the last
decade and enabled researchers to discover patterns and trends in education
with more sophisticated methods. EDM offers promising solutions to complex
educational problems. Given the rapid increase in the availability of big data
in education and software programs to analyze big data, the demand for
user-friendly, free software programs to implement EDM methods also continues
to increase. The R programming language has become a popular environment for
data mining due to its availability and flexibility. The rattle package
in R contains a set of functions to implement data mining with a graphical user
interface. This study demonstrates three widely used data mining algorithms
(classification and regression tree, random forest, and support vector machine)
in EDM using real data from the 2015 administration of the Programme for
International Student Assessment (PISA). First, a brief introduction to EDM is
provided along with the description of the selected data mining algorithms.
Then, how to perform data mining analysis using the rattle’s graphical user interface is demonstrated. The
study concludes by comparing the results of the selected data mining algorithms
and highlighting how those algorithms can be utilized in the context of
educational research.
Educational data mining (EDM) has been a rapidly growing research field over the last decade and enabled researchers to discover patterns and trends in education with more sophisticated methods. EDM offers promising solutions to complex educational problems. Given the rapid increase in the availability of big data in education and software programs to analyze big data, the demand for user-friendly, free software programs to implement EDM methods also continues to increase. The R programming language has become a popular environment for data mining due to its availability and flexibility. The rattle package in R contains a set of functions to implement data mining with a graphical user interface. This study demonstrates three widely used data mining algorithms (classification and regression tree, random forest, and support vector machine) in EDM using real data from the 2015 administration of the Programme for International Student Assessment (PISA). First, a brief introduction to EDM is provided along with the description of the selected data mining algorithms. Then, how to perform data mining analysis using the rattle’s gr |
| doi_str_mv | 10.21449/ijate.627361 |
| format | Article |
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data mining (EDM) has been a rapidly growing research field over the last
decade and enabled researchers to discover patterns and trends in education
with more sophisticated methods. EDM offers promising solutions to complex
educational problems. Given the rapid increase in the availability of big data
in education and software programs to analyze big data, the demand for
user-friendly, free software programs to implement EDM methods also continues
to increase. The R programming language has become a popular environment for
data mining due to its availability and flexibility. The rattle package
in R contains a set of functions to implement data mining with a graphical user
interface. This study demonstrates three widely used data mining algorithms
(classification and regression tree, random forest, and support vector machine)
in EDM using real data from the 2015 administration of the Programme for
International Student Assessment (PISA). First, a brief introduction to EDM is
provided along with the description of the selected data mining algorithms.
Then, how to perform data mining analysis using the rattle’s graphical user interface is demonstrated. The
study concludes by comparing the results of the selected data mining algorithms
and highlighting how those algorithms can be utilized in the context of
educational research.
Educational data mining (EDM) has been a rapidly growing research field over the last decade and enabled researchers to discover patterns and trends in education with more sophisticated methods. EDM offers promising solutions to complex educational problems. Given the rapid increase in the availability of big data in education and software programs to analyze big data, the demand for user-friendly, free software programs to implement EDM methods also continues to increase. The R programming language has become a popular environment for data mining due to its availability and flexibility. The rattle package in R contains a set of functions to implement data mining with a graphical user interface. This study demonstrates three widely used data mining algorithms (classification and regression tree, random forest, and support vector machine) in EDM using real data from the 2015 administration of the Programme for International Student Assessment (PISA). First, a brief introduction to EDM is provided along with the description of the selected data mining algorithms. Then, how to perform data mining analysis using the rattle’s graphical user interface is demonstrated. The study concludes by comparing the results of the selected data mining algorithms and highlighting how those algorithms can be utilized in the context of educational research.</description><identifier>ISSN: 2148-7456</identifier><identifier>EISSN: 2148-7456</identifier><identifier>DOI: 10.21449/ijate.627361</identifier><language>eng</language><publisher>International Journal of Assessment Tools in Education</publisher><subject>Achievement Tests ; Classification ; Computer Software ; Data Analysis ; Educational Research ; Educational Researchers ; Foreign Countries ; International Assessment ; Mathematics ; Programming Languages ; Regression (Statistics) ; Scientific Literacy ; Secondary School Students ; Statistical Analysis</subject><ispartof>International journal of assessment tools in education, 2019-01, Vol.6 (5), p.20-36</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c228t-3da775cef441f2ff2c7597059a0ce9ca648b6786cd4689a9877c16938e7e735d3</citedby><cites>FETCH-LOGICAL-c228t-3da775cef441f2ff2c7597059a0ce9ca648b6786cd4689a9877c16938e7e735d3</cites><orcidid>0000-0003-2585-3686 ; 0000-0001-5853-1267</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,690,780,885</link.rule.ids><linktorsrc>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=EJ1246367$$EView_record_in_ERIC_Clearinghouse_on_Information_&_Technology$$FView_record_in_$$GERIC_Clearinghouse_on_Information_&_Technology$$Hfree_for_read</linktorsrc><backlink>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=EJ1246367$$DView record in ERIC$$Hfree_for_read</backlink></links><search><creatorcontrib>BULUT, Okan</creatorcontrib><creatorcontrib>YAVUZ, Hatice Cigdem</creatorcontrib><title>Educational data mining: A tutorial for the rattle package in R</title><title>International journal of assessment tools in education</title><description>Educational
data mining (EDM) has been a rapidly growing research field over the last
decade and enabled researchers to discover patterns and trends in education
with more sophisticated methods. EDM offers promising solutions to complex
educational problems. Given the rapid increase in the availability of big data
in education and software programs to analyze big data, the demand for
user-friendly, free software programs to implement EDM methods also continues
to increase. The R programming language has become a popular environment for
data mining due to its availability and flexibility. The rattle package
in R contains a set of functions to implement data mining with a graphical user
interface. This study demonstrates three widely used data mining algorithms
(classification and regression tree, random forest, and support vector machine)
in EDM using real data from the 2015 administration of the Programme for
International Student Assessment (PISA). First, a brief introduction to EDM is
provided along with the description of the selected data mining algorithms.
Then, how to perform data mining analysis using the rattle’s graphical user interface is demonstrated. The
study concludes by comparing the results of the selected data mining algorithms
and highlighting how those algorithms can be utilized in the context of
educational research.
Educational data mining (EDM) has been a rapidly growing research field over the last decade and enabled researchers to discover patterns and trends in education with more sophisticated methods. EDM offers promising solutions to complex educational problems. Given the rapid increase in the availability of big data in education and software programs to analyze big data, the demand for user-friendly, free software programs to implement EDM methods also continues to increase. The R programming language has become a popular environment for data mining due to its availability and flexibility. The rattle package in R contains a set of functions to implement data mining with a graphical user interface. This study demonstrates three widely used data mining algorithms (classification and regression tree, random forest, and support vector machine) in EDM using real data from the 2015 administration of the Programme for International Student Assessment (PISA). First, a brief introduction to EDM is provided along with the description of the selected data mining algorithms. Then, how to perform data mining analysis using the rattle’s graphical user interface is demonstrated. The study concludes by comparing the results of the selected data mining algorithms and highlighting how those algorithms can be utilized in the context of educational research.</description><subject>Achievement Tests</subject><subject>Classification</subject><subject>Computer Software</subject><subject>Data Analysis</subject><subject>Educational Research</subject><subject>Educational Researchers</subject><subject>Foreign Countries</subject><subject>International Assessment</subject><subject>Mathematics</subject><subject>Programming Languages</subject><subject>Regression (Statistics)</subject><subject>Scientific Literacy</subject><subject>Secondary School Students</subject><subject>Statistical Analysis</subject><issn>2148-7456</issn><issn>2148-7456</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GA5</sourceid><recordid>eNpNkE1LAzEYhIMoWGqPHoX8ga352ryJFymlVqUgiJ6X12xSU7e7JZse_PcuXRFPM8wMc3gIueZsLrhS9jbuMPu5FiA1PyOTITQFqFKf__OXZNb3O8YYB62k5RNyv6qPDnPsWmxojRnpPrax3d7RBc3H3KU45KFLNH96mjDnxtMDui_cehpb-npFLgI2vZ_96pS8P6zelo_F5mX9tFxsCieEyYWsEaB0PijFgwhBOCgtsNIic9461Mp8aDDa1Uobi9YAOK6tNB48yLKWU3Iz_voUXXVIcY_pu1o9c6G01DD0xdi71PV98uFvw1l1AlSdAFUjIPkDMZZXQA</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>BULUT, Okan</creator><creator>YAVUZ, Hatice Cigdem</creator><general>International Journal of Assessment Tools in Education</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ERI</scope><scope>GA5</scope><orcidid>https://orcid.org/0000-0003-2585-3686</orcidid><orcidid>https://orcid.org/0000-0001-5853-1267</orcidid></search><sort><creationdate>20190101</creationdate><title>Educational data mining: A tutorial for the rattle package in R</title><author>BULUT, Okan ; YAVUZ, Hatice Cigdem</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c228t-3da775cef441f2ff2c7597059a0ce9ca648b6786cd4689a9877c16938e7e735d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Achievement Tests</topic><topic>Classification</topic><topic>Computer Software</topic><topic>Data Analysis</topic><topic>Educational Research</topic><topic>Educational Researchers</topic><topic>Foreign Countries</topic><topic>International Assessment</topic><topic>Mathematics</topic><topic>Programming Languages</topic><topic>Regression (Statistics)</topic><topic>Scientific Literacy</topic><topic>Secondary School Students</topic><topic>Statistical Analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>BULUT, Okan</creatorcontrib><creatorcontrib>YAVUZ, Hatice Cigdem</creatorcontrib><collection>CrossRef</collection><collection>ERIC</collection><collection>ERIC - Full Text Only (Discovery)</collection><jtitle>International journal of assessment tools in education</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>BULUT, Okan</au><au>YAVUZ, Hatice Cigdem</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><ericid>EJ1246367</ericid><atitle>Educational data mining: A tutorial for the rattle package in R</atitle><jtitle>International journal of assessment tools in education</jtitle><date>2019-01-01</date><risdate>2019</risdate><volume>6</volume><issue>5</issue><spage>20</spage><epage>36</epage><pages>20-36</pages><issn>2148-7456</issn><eissn>2148-7456</eissn><abstract>Educational
data mining (EDM) has been a rapidly growing research field over the last
decade and enabled researchers to discover patterns and trends in education
with more sophisticated methods. EDM offers promising solutions to complex
educational problems. Given the rapid increase in the availability of big data
in education and software programs to analyze big data, the demand for
user-friendly, free software programs to implement EDM methods also continues
to increase. The R programming language has become a popular environment for
data mining due to its availability and flexibility. The rattle package
in R contains a set of functions to implement data mining with a graphical user
interface. This study demonstrates three widely used data mining algorithms
(classification and regression tree, random forest, and support vector machine)
in EDM using real data from the 2015 administration of the Programme for
International Student Assessment (PISA). First, a brief introduction to EDM is
provided along with the description of the selected data mining algorithms.
Then, how to perform data mining analysis using the rattle’s graphical user interface is demonstrated. The
study concludes by comparing the results of the selected data mining algorithms
and highlighting how those algorithms can be utilized in the context of
educational research.
Educational data mining (EDM) has been a rapidly growing research field over the last decade and enabled researchers to discover patterns and trends in education with more sophisticated methods. EDM offers promising solutions to complex educational problems. Given the rapid increase in the availability of big data in education and software programs to analyze big data, the demand for user-friendly, free software programs to implement EDM methods also continues to increase. The R programming language has become a popular environment for data mining due to its availability and flexibility. The rattle package in R contains a set of functions to implement data mining with a graphical user interface. This study demonstrates three widely used data mining algorithms (classification and regression tree, random forest, and support vector machine) in EDM using real data from the 2015 administration of the Programme for International Student Assessment (PISA). First, a brief introduction to EDM is provided along with the description of the selected data mining algorithms. Then, how to perform data mining analysis using the rattle’s graphical user interface is demonstrated. The study concludes by comparing the results of the selected data mining algorithms and highlighting how those algorithms can be utilized in the context of educational research.</abstract><pub>International Journal of Assessment Tools in Education</pub><doi>10.21449/ijate.627361</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0003-2585-3686</orcidid><orcidid>https://orcid.org/0000-0001-5853-1267</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Achievement Tests Classification Computer Software Data Analysis Educational Research Educational Researchers Foreign Countries International Assessment Mathematics Programming Languages Regression (Statistics) Scientific Literacy Secondary School Students Statistical Analysis |
| title | Educational data mining: A tutorial for the rattle package in R |
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