Simulated annealing for SVM parameters optimization in student’s performance prediction

Abstract

High education is an important and critical part of education all over the world. In recent months, the outbreak of the Covid-19 pandemic has turned the world increasingly to online education; therefore, the need for improving this education system became an urgent matter. Online learning systems are a primal environment for acquiring educational data which can be from different sources, especially academic institutions. These data can be mainly used to analyze and extract utilizable information to help in understanding university students’ performance and identifying factors that affect it. To extract some meaningful information from these large volumes of data, academic organizations must mine the data with high accuracy. Optimization is the process of achieving the best solution for a problem, in this work, three different real datasets were selected, pre-processed, cleaned, and filtered for applying Support Vector Machine (SVM) with Multilayer perceptron kernel (MLP kernel) and optimize its parameters using Simulated Annealing (SA) algorithm to improve the objective function value. While exploring solution space, SA offers the possibility of accepting worse neighbor solutions in a controlled manner to escape from local minima. The results show that the designed system can determine the best SVM parameters using SA and therefore presents better model evaluation.