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Dergiler / International Journal of Assessment Tools in Education / 2018 / Cilt: 5 - Sayı: 3

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

Ergün Akgün
Metin DEMİR
Sayfa
491–509
Yayın Tarihi
2022-10-19
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez: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.
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Temel Bulgular & Veri: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.
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Sonuç & Katkı: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.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Elementary Education · Science and Technology Teaching · Data Mining

Özet

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

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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Bu Makaleye Atıf Yap

Akgün, E., DEMİR, M. (2022). Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks. International Journal of Assessment Tools in Education, Cilt: 5 - Sayı: 3, 491–509. https://ajindex.com/articles/540230

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Kaynakça (6)

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