An artificial neural network approach for predicting academic engagement based on classroom atmosphere and Academic Emotion Regulation in students

Active participation in school is very important for students' academic success. The present research was conducted with the aim of artificial neural network approach to predict academic engagement based on classroom atmosphere and Academic Emotion Regulation in students. The present research method was descriptive and correlational. The statistical population included students of the second year of secondary school in Ardabil city in 2023. 250 of these people were selected from the above statistical population using available sampling and answered the questionnaires of academic engagement, class jockey and academic emotion regulation. The data were analyzed with artificial neural network using Multilayer Perceptron (MPL) method in SPSS.25 software. The results of the analysis showed that the role of classroom atmosphere and academic emotion regulation in predicting academic engagement in adolescents with an artificial neural network has an input layer with five nodes and a hidden layer with three nodes, and the artificial neural network is able to well Predict the jumps and process of academic engagement based on the variables of classroom atmosphere and academic excitement regulation. In general, it can be said that the implementation of an educational program including teaching emotion management skills, creating positive and supportive environments by teachers and educational staff is recommended to improve academic engagement.