PREDICTING READING COMPREHENSION ACHIEVEMENT EMPLOYING ARTIFICIAL NEURAL NETWORKS

In this paper, the researchers used artificial neural networks to predict reading comprehension achievement, which is considered a crucial predictor of school success. The study involved 702 fourth-grade primary school students, consisting of 345 males and 357 females. A feed-forward backpropagation neural network was employed to forecast the students' reading comprehension skills, a complex ability encompassing various cognitive skills. Additionally, the researchers compared different classification methods. Hypothesis tests indicated that some variables showed statistically significant differences, while others did not. Based on the findings, the researchers drew several conclusions and made recommendations.