Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks
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.
Keywords
References
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Details
Primary Language
English
Subjects
Studies on Education
Journal Section
Research Article
Publication Date
September 19, 2018
Submission Date
April 16, 2018
Acceptance Date
July 3, 2018
Published in Issue
Year 2018 Volume: 5 Number: 3
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