Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks
In this study, it was aimed to predict elementary education teacher candidates’ achievements in “Science and Technology Education I and II” courses by using artificial neural networks. It was also aimed to show the independent variables importance in the prediction. In the data set used in this stud...
Gespeichert in:
| Veröffentlicht in: | International journal of assessment tools in education 2018-01, Vol.5 (3), p.491-509 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 509 |
|---|---|
| container_issue | 3 |
| container_start_page | 491 |
| container_title | International journal of assessment tools in education |
| container_volume | 5 |
| creator | Akgün, Ergün Demir, Metin |
| description | In this study, it was aimed to predict
elementary education teacher candidates’ achievements in “Science and
Technology Education I and II” courses by using artificial neural networks. It
was also aimed to show the independent variables importance in the prediction.
In the data set used in this study, variables of gender, type of education,
field of study in high school and transcript information of 14 courses
including end-of-term letter grades were collected. The fact that the
artificial neural network performance in this study was R=0.84 for the Science
and Technology Education I course, and R=0.84 for the Science and Technology
Education II course shows that the network performance overlaps with the
findings obtained from the related studies. |
| doi_str_mv | 10.21449/ijate.444073 |
| format | Article |
| fullrecord | <record><control><sourceid>eric_GA5</sourceid><recordid>TN_cdi_eric_primary_ED585240</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ericid>ED585240</ericid><sourcerecordid>ED585240</sourcerecordid><originalsourceid>FETCH-LOGICAL-c259t-dd61703adebbd738dbfe4cba24e4744b3cda8787da65225f61f868a23b01d9473</originalsourceid><addsrcrecordid>eNpNULFOwzAUtBBIVNCRjcEbU4rtOLEzRmmASgWWMluO_dK6pAmyUyr-npAgxPRO70737h1CN5QsGOU8u3d73cOCc05EfIZmw1JGgifp-T98ieYh7AkhVKQ8zugMbZ87C41rt7jojj4Azs3OwSccoO0D7mpcNiPW_guX9mh077oWb0CbHXhc6NY6O9wN-OT6Hc5972pnnG7wCxz9OPpT59_DNbqodRNg_juv0NtDuSmeovXr46rI15FhSdZH1qZUkFhbqCorYmmrGripNOPABedVbKyWQgqr04SxpE5pLVOpWVwRajMu4it0O_mCd0Z9eHcYkqtymciEcTLQdxPtLOima4fPQe2Hz9shlFoty3ytJJPJjzKalMZ3IXio_8woUWPjamxcTY3H38cJdRs</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks</title><source>ERIC - Full Text Only (Discovery)</source><creator>Akgün, Ergün ; Demir, Metin</creator><contributor>Kara,İzzet</contributor><creatorcontrib>Akgün, Ergün ; Demir, Metin ; Kara,İzzet</creatorcontrib><description>In this study, it was aimed to predict
elementary education teacher candidates’ achievements in “Science and
Technology Education I and II” courses by using artificial neural networks. It
was also aimed to show the independent variables importance in the prediction.
In the data set used in this study, variables of gender, type of education,
field of study in high school and transcript information of 14 courses
including end-of-term letter grades were collected. The fact that the
artificial neural network performance in this study was R=0.84 for the Science
and Technology Education I course, and R=0.84 for the Science and Technology
Education II course shows that the network performance overlaps with the
findings obtained from the related studies.</description><identifier>ISSN: 2148-7456</identifier><identifier>EISSN: 2148-7456</identifier><identifier>DOI: 10.21449/ijate.444073</identifier><language>eng</language><publisher>İzzet Kara</publisher><subject>Academic Achievement ; Artificial Intelligence ; Elementary School Teachers ; Felsefe & Psikoloji & Sosyoloji ; Foreign Countries ; Gender Differences ; Grades (Scholastic) ; High School Students ; Intellectual Disciplines ; Preservice Teachers ; Research Methodology ; Science Education ; Student Records ; Technology Education</subject><ispartof>International journal of assessment tools in education, 2018-01, Vol.5 (3), p.491-509</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c259t-dd61703adebbd738dbfe4cba24e4744b3cda8787da65225f61f868a23b01d9473</citedby><cites>FETCH-LOGICAL-c259t-dd61703adebbd738dbfe4cba24e4744b3cda8787da65225f61f868a23b01d9473</cites><orcidid>0000-0002-7271-6900</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,689,778,883</link.rule.ids><linktorsrc>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=ED585240$$EView_record_in_ERIC_Clearinghouse_on_Information_&_Technology$$FView_record_in_$$GERIC_Clearinghouse_on_Information_&_Technology$$Hfree_for_read</linktorsrc><backlink>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=ED585240$$DView record in ERIC$$Hfree_for_read</backlink></links><search><contributor>Kara,İzzet</contributor><creatorcontrib>Akgün, Ergün</creatorcontrib><creatorcontrib>Demir, Metin</creatorcontrib><title>Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks</title><title>International journal of assessment tools in education</title><description>In this study, it was aimed to predict
elementary education teacher candidates’ achievements in “Science and
Technology Education I and II” courses by using artificial neural networks. It
was also aimed to show the independent variables importance in the prediction.
In the data set used in this study, variables of gender, type of education,
field of study in high school and transcript information of 14 courses
including end-of-term letter grades were collected. The fact that the
artificial neural network performance in this study was R=0.84 for the Science
and Technology Education I course, and R=0.84 for the Science and Technology
Education II course shows that the network performance overlaps with the
findings obtained from the related studies.</description><subject>Academic Achievement</subject><subject>Artificial Intelligence</subject><subject>Elementary School Teachers</subject><subject>Felsefe & Psikoloji & Sosyoloji</subject><subject>Foreign Countries</subject><subject>Gender Differences</subject><subject>Grades (Scholastic)</subject><subject>High School Students</subject><subject>Intellectual Disciplines</subject><subject>Preservice Teachers</subject><subject>Research Methodology</subject><subject>Science Education</subject><subject>Student Records</subject><subject>Technology Education</subject><issn>2148-7456</issn><issn>2148-7456</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>GA5</sourceid><recordid>eNpNULFOwzAUtBBIVNCRjcEbU4rtOLEzRmmASgWWMluO_dK6pAmyUyr-npAgxPRO70737h1CN5QsGOU8u3d73cOCc05EfIZmw1JGgifp-T98ieYh7AkhVKQ8zugMbZ87C41rt7jojj4Azs3OwSccoO0D7mpcNiPW_guX9mh077oWb0CbHXhc6NY6O9wN-OT6Hc5972pnnG7wCxz9OPpT59_DNbqodRNg_juv0NtDuSmeovXr46rI15FhSdZH1qZUkFhbqCorYmmrGripNOPABedVbKyWQgqr04SxpE5pLVOpWVwRajMu4it0O_mCd0Z9eHcYkqtymciEcTLQdxPtLOima4fPQe2Hz9shlFoty3ytJJPJjzKalMZ3IXio_8woUWPjamxcTY3H38cJdRs</recordid><startdate>20180101</startdate><enddate>20180101</enddate><creator>Akgün, Ergün</creator><creator>Demir, Metin</creator><general>İzzet Kara</general><scope>AAYXX</scope><scope>CITATION</scope><scope>IEBAR</scope><scope>ERI</scope><scope>GA5</scope><orcidid>https://orcid.org/0000-0002-7271-6900</orcidid></search><sort><creationdate>20180101</creationdate><title>Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks</title><author>Akgün, Ergün ; Demir, Metin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c259t-dd61703adebbd738dbfe4cba24e4744b3cda8787da65225f61f868a23b01d9473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Academic Achievement</topic><topic>Artificial Intelligence</topic><topic>Elementary School Teachers</topic><topic>Felsefe & Psikoloji & Sosyoloji</topic><topic>Foreign Countries</topic><topic>Gender Differences</topic><topic>Grades (Scholastic)</topic><topic>High School Students</topic><topic>Intellectual Disciplines</topic><topic>Preservice Teachers</topic><topic>Research Methodology</topic><topic>Science Education</topic><topic>Student Records</topic><topic>Technology Education</topic><toplevel>online_resources</toplevel><creatorcontrib>Akgün, Ergün</creatorcontrib><creatorcontrib>Demir, Metin</creatorcontrib><collection>CrossRef</collection><collection>Idealonline online kütüphane - Journals</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>Akgün, Ergün</au><au>Demir, Metin</au><au>Kara,İzzet</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><ericid>ED585240</ericid><atitle>Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks</atitle><jtitle>International journal of assessment tools in education</jtitle><date>2018-01-01</date><risdate>2018</risdate><volume>5</volume><issue>3</issue><spage>491</spage><epage>509</epage><pages>491-509</pages><issn>2148-7456</issn><eissn>2148-7456</eissn><abstract>In this study, it was aimed to predict
elementary education teacher candidates’ achievements in “Science and
Technology Education I and II” courses by using artificial neural networks. It
was also aimed to show the independent variables importance in the prediction.
In the data set used in this study, variables of gender, type of education,
field of study in high school and transcript information of 14 courses
including end-of-term letter grades were collected. The fact that the
artificial neural network performance in this study was R=0.84 for the Science
and Technology Education I course, and R=0.84 for the Science and Technology
Education II course shows that the network performance overlaps with the
findings obtained from the related studies.</abstract><pub>İzzet Kara</pub><doi>10.21449/ijate.444073</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0002-7271-6900</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2148-7456 |
| ispartof | International journal of assessment tools in education, 2018-01, Vol.5 (3), p.491-509 |
| issn | 2148-7456 2148-7456 |
| language | eng |
| recordid | cdi_eric_primary_ED585240 |
| source | ERIC - Full Text Only (Discovery) |
| subjects | Academic Achievement Artificial Intelligence Elementary School Teachers Felsefe & Psikoloji & Sosyoloji Foreign Countries Gender Differences Grades (Scholastic) High School Students Intellectual Disciplines Preservice Teachers Research Methodology Science Education Student Records Technology Education |
| title | Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T16%3A08%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-eric_GA5&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Modeling%20Course%20Achievements%20of%20Elementary%20Education%20Teacher%20Candidates%20with%20Artificial%20Neural%20Networks&rft.jtitle=International%20journal%20of%20assessment%20tools%20in%20education&rft.au=Akg%C3%BCn,%20Erg%C3%BCn&rft.date=2018-01-01&rft.volume=5&rft.issue=3&rft.spage=491&rft.epage=509&rft.pages=491-509&rft.issn=2148-7456&rft.eissn=2148-7456&rft_id=info:doi/10.21449/ijate.444073&rft_dat=%3Ceric_GA5%3EED585240%3C/eric_GA5%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_ericid=ED585240&rfr_iscdi=true |