Year: 2016
Type: article
Abstract: Early prediction of student performance helps to take action for better achievements of students.To achieve the better education standard, several attempts have been made to predict the performance of the student, but the prediction accuracy is not acceptable.To accomplish the enhanced prediction, neural network (NN) based method is proposed.In this paper, an approach to predict student academic performance in college education based on Lion-Wolf artificial neural network is proposed.Lion algorithm and Grey Wolf optimizer is integrated to develop a Lion-wolf training algorithm to find the optimal weight for every neuron in NN.The proposed prediction model is validated based on Mean Squared Error (MSE) & Root Mean Square Error (RMSE) with the existing NN based prediction model.The experimental results show that performance of proposed prediction model is improved compared to existing prediction model with MSE of 5.25 and RMSE of 2.3.
Authors: Ramanathan L, Angelina Geetha, Khalid M, NITMAS & TNU, Swarnalatha P
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W2564757116