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.

Çeşi̇tli̇ Yöntemler Kullanilarak İkli̇m Deği̇şi̇kli̇ği̇ Ve Çevre Sorunlarina Yöneli̇k Tutum Anali̇zi̇

“…The sample of this research is thought to be compatible with neural network analysis because there is no underlined and common rules o specify the required number of participants in the literature. There are also studies conducted with different samples showing that there is no consensus for this therefore we assume that our sample is compatible with neural network analysis (Akgün, 2017;Yaraş, 2017;Yorgancı, 2018; https://www.ibm.com/docs/en/SSLVMB_26.0.0/pdf/ en/IBM_SPSS_Neural_Network.pdf). In the network analysis, centrality measures the importance of actors in the network, showing which actors are at the center.…”

Educational Data Mining Studies in Turkey: A Systematic Review

“…Achievement prediction studies have tried to predict future success by using the data of the students during the semester, other course achievements or demographic characteristics (Akcapinar, 2014;Akgun & Demir, 2018;Bahadir, 2013;Kayri, 2015;Sengur, 2013;Turhan et al, 2013;Uysal, 2015;Yildiz, 2014). Investigation of e-learning environments was the second most studied topic in EDM studies (n = 7).…”

“…,Kose and Arslan (2016),Ayvaz et al (2017),Kose and Arslan (2017),Akgun and Demir (2018),Aybek and Okur (2018),Hussain et al (2019),Guneri and Apaydin (2004),Tekin (2014),Cifci et al …”

“…Distribution of studies according to data mining tasksOzbay (2015),Uysal (2015),Cebi (2016), Sahin (2018), Guruler et al (2010), Sohsah et al (2015), Idil et al (2016), Ayvaz et al (2017), Afacan Adanir (, Y. Aydogdu (2011), Sengur (2013), Bahadir (2013), Yildiz (2014), Akcapinar (2014), Coskun (2013), Uysal (2015), Yildiz Aybek (2016), Cebi (2016), Barngrover (2017), Yagci (2018), Yorganci (2018), Kentli and Sahin (2011), Turhan et al (2013), Akcapinar (2015), Demir (2015), Kayri (2015), Sohsah et al (2015), Bahadir (2016), Kose and Arslan (2016), Ayvaz et al(2017),Kose and Arslan (2017),Akgun and Demir (2018),Aybek and Okur (2018),Hussain et al (2019),Guneri and Apaydin (2004),Tekin (2014),Cifci et al …”