Predicting achievement with artificial neural networks: The case of Anadolu University Open Education system

This study aims to predict the final exam scores and pass/fail rates of the students taking the Basic Information Technologies – 1 (BIL101U) course in 2014-2015 and 2015- 2016 academic years in the Open Education System (OES) of Anadolu University, through Artificial Neural Networks (ANN). In accordance with this aim, correlational research model was adopted. In this research, data about the demographic characteristics, educational background, BIL101U course mid-term, final and success scores of 626.478 students was collected and purged. Data of 195.584 students, obtained after this process was analysed through Multilayer Perception (MLP) and Radial Basis Function (RBF) models. Sixteen different networks attained through the combination of ANN network parameters were used to predict the final exam scores and pass/fail rates of the students. As a result of the analyses, it was found out that networks established through MLPs make more exact predictions. In the prediction of the final exam scores, it was determined that there is a low level of correlation between the real scores and predicted scores. In the analyses for the prediction of pass/fail rates of the students, networks established through MLPs ensured more exact prediction results. Moreover, it was determined that the variables as mid-term exam scores, university entrance scores (OSYS) and secondary school graduation year were of highest significance in explaining the final exam scores and pass/fail rates of the students. In this study, it was found out that in the institutions serving for Open and Distance Learning, pass/fail state of the students can be predicted through ANN. Keywords: Prediction of Student Achievement, Open and Distance Learning, Artificial Neural Networks