Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks | Zendy

Ergün Akgün | Zendy; Metin Demir | Zendy

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Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks

Author(s) -

Ergün Akgün,

Metin Demir

Publication year - 2018

Publication title -

international journal of assessment tools in education

Language(s) - English

Resource type - Journals

ISSN - 2148-7456

DOI - 10.21449/ijate.444073

Subject(s) - artificial neural network , mathematics education , field (mathematics) , computer science , set (abstract data type) , science education , higher education , artificial intelligence , psychology , mathematics , pure mathematics , law , programming language , political science

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.

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