Students performance system using recurrent neural network trained by modified grey wolf optimizer

A better quality of education can be obtained in educational institutions through a system that identifies the deficiencies of students and provides an initial warning to allow intervention for the students through counseling to help them in order to address their weaknesses. Current techniques need to be updated with such new soft computing approaches. The classification of the current techniques is not satisfactory. It needs to be studied with fresh techniques especially using hybrid techniques and those techniques which mimic mechanisms from nature. This research work aims at developing an intelligent approach using a modified Grey Wolf Optimizer (GWO) that mimics hunting style of grey wolves as an optimization algorithm with the Recurrent Neural Network (RNN) that mimics neurons of human's brain to forecast outcomes of students in a particular course based on their past achievements, social settings and the academic environments. It is a two-step procedure. Firstly, the Neural Network model is trained by using training dataset and its weights and biases are optimized through using the modified GWO. In the second step, to evaluate the trained model, the designed model is tested with a predefined testing dataset. For validation procedure, a 5-fold cross validation is used to obtain the best accuracy and performance. The results show that our approach obtains the best accuracy compared with certain other algorithms. This study can help an educational system to enhance students' learning experience as well as increasing their profits.