Predictions of academic achievements of vocational and technical high school students with artificial neural networks in science courses (physics, chemistry and biology) in Turkey and measures to be taken for their failures

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dc.authorid0000-0001-5064-6983en_US
dc.contributor.authorYağcı, Ali
dc.contributor.authorÇevik, Mustafa
dc.contributor.editorMasal, E
dc.contributor.editorÖnder, I
dc.contributor.editorBeşoluk, S
dc.contributor.editorÇalışkan, H
dc.contributor.editorDemirhan, E
dc.date.accessioned2019-12-06T21:16:06Z
dc.date.available2019-12-06T21:16:06Z
dc.date.issued2017
dc.departmentKMÜ, Eğitim Fakültesi, Temel Eğitim Bölümüen_US
dc.descriptionERPA International Congresses on Education (ERPA) -- MAY 18-21, 2017 -- Budapest, HUNGARYen_US
dc.descriptionWOS:000426428700057en_US
dc.description.abstractThe aim of this study is to predict academic achievements of vocational and technical high school (VTHS) students with artificial neural networks (ANN) in physics, chemistry and biology courses in Turkey and reveal measures to be taken for their failures. The study group consisted of 922 students studying in 10th and 11th grade in VTHS. This study was conducted with the survey method and a 34-item demographic questionnaire was developed in order to collect the data. The parameters in the questionnaire were identified as the items that were considered to influence academic achievements of the students. Opinions of 3 field specialist, 1 measurement and evaluation specialist and 2 technical teachers were taken and it was supported by the literature for the content validity and the KR20 reliability coefficient was found as.90 using SPSS 16.0 package program. The items in the questionnaire that are considered to influence academic achievements of the students were approved as independent variables/input and academic achievement mean scores of the students in physics, chemistry and biology courses in the previous year were approved as dependent variables/output. Academic achievements of the student were predicted with ANN in Matlap R2016a program using these parameters and a model was created. A successful academic achievement prediction system with an average sensitivity of %98 was developed over 922 data and the measures to be taken to prevent the failures of the students were determined at the end of the study.en_US
dc.description.sponsorshipERPAen_US
dc.identifier.citationYağcı Ali, Çevik Mustafa. (2017). Predictions of academic achievements of vocational and technical high school students with artificial neural networks in science courses (physics, chemistry and biology) in Turkey and measures to be taken for their failures. Shs Web of Conferences, 37.
dc.identifier.doi10.1051/shsconf/20173701057
dc.identifier.issn2261-2424
dc.identifier.urihttps://dx.doi.org/10.1051/shsconf/20173701057
dc.identifier.urihttps://hdl.handle.net/11492/2816
dc.identifier.volume37en_US
dc.identifier.wosWOS:000426428700057
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Sceince
dc.institutionauthorYağcı, Ali
dc.institutionauthorÇevik, Mustafa
dc.language.isoen
dc.publisherEDP Sciencesen_US
dc.relation.ispartofseriesSHS Web of Conferences
dc.relation.journalErpa Internatıonal Congresses On Education 2017 (ERPA 2017)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectVocational and Technical High Schoolsen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectAnd Science Courses Academic Achievement Predictionen_US
dc.titlePredictions of academic achievements of vocational and technical high school students with artificial neural networks in science courses (physics, chemistry and biology) in Turkey and measures to be taken for their failuresen_US
dc.typeConference Object

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