Using Artificial Neural Networks for Predictive Modeling of Graduates' Professional Choice
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Аутори
Gerasimović, MilicaStanojević, Ljiljana
Bugarić, Uglješa
Miljković, Zoran
Veljović, Alemoije
Чланак у часопису (Објављена верзија)
Метаподаци
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This paper presents the results of research carried out at the Faculty of Mechanical Engineering (FME) in Belgrade for the purpose of enrollment policy. Artificial neural networks are used in predicting graduates' professional choice, i.e. a number of secondary vocational education and training schools (VET schools) graduate students will enroll on. The assumption of graduates' professional choices was verified on a sample of 119 graduates from two Belgrade VET schools. Factors influencing the professional choice of VET school students are grouped in nine input variables. The results show that neural network algorithms present a powerful tool for predicting graduates' professional choice.
Кључне речи:
graduates' professional choice / enrollment policy / artificial neural networksИзвор:
New Educational Review, 2011, 23, 1, 175-188Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Gerasimović, Milica AU - Stanojević, Ljiljana AU - Bugarić, Uglješa AU - Miljković, Zoran AU - Veljović, Alemoije PY - 2011 UR - https://machinery.mas.bg.ac.rs/handle/123456789/1302 AB - This paper presents the results of research carried out at the Faculty of Mechanical Engineering (FME) in Belgrade for the purpose of enrollment policy. Artificial neural networks are used in predicting graduates' professional choice, i.e. a number of secondary vocational education and training schools (VET schools) graduate students will enroll on. The assumption of graduates' professional choices was verified on a sample of 119 graduates from two Belgrade VET schools. Factors influencing the professional choice of VET school students are grouped in nine input variables. The results show that neural network algorithms present a powerful tool for predicting graduates' professional choice. T2 - New Educational Review T1 - Using Artificial Neural Networks for Predictive Modeling of Graduates' Professional Choice EP - 188 IS - 1 SP - 175 VL - 23 UR - https://hdl.handle.net/21.15107/rcub_machinery_1302 ER -
@article{ author = "Gerasimović, Milica and Stanojević, Ljiljana and Bugarić, Uglješa and Miljković, Zoran and Veljović, Alemoije", year = "2011", abstract = "This paper presents the results of research carried out at the Faculty of Mechanical Engineering (FME) in Belgrade for the purpose of enrollment policy. Artificial neural networks are used in predicting graduates' professional choice, i.e. a number of secondary vocational education and training schools (VET schools) graduate students will enroll on. The assumption of graduates' professional choices was verified on a sample of 119 graduates from two Belgrade VET schools. Factors influencing the professional choice of VET school students are grouped in nine input variables. The results show that neural network algorithms present a powerful tool for predicting graduates' professional choice.", journal = "New Educational Review", title = "Using Artificial Neural Networks for Predictive Modeling of Graduates' Professional Choice", pages = "188-175", number = "1", volume = "23", url = "https://hdl.handle.net/21.15107/rcub_machinery_1302" }
Gerasimović, M., Stanojević, L., Bugarić, U., Miljković, Z.,& Veljović, A.. (2011). Using Artificial Neural Networks for Predictive Modeling of Graduates' Professional Choice. in New Educational Review, 23(1), 175-188. https://hdl.handle.net/21.15107/rcub_machinery_1302
Gerasimović M, Stanojević L, Bugarić U, Miljković Z, Veljović A. Using Artificial Neural Networks for Predictive Modeling of Graduates' Professional Choice. in New Educational Review. 2011;23(1):175-188. https://hdl.handle.net/21.15107/rcub_machinery_1302 .
Gerasimović, Milica, Stanojević, Ljiljana, Bugarić, Uglješa, Miljković, Zoran, Veljović, Alemoije, "Using Artificial Neural Networks for Predictive Modeling of Graduates' Professional Choice" in New Educational Review, 23, no. 1 (2011):175-188, https://hdl.handle.net/21.15107/rcub_machinery_1302 .