Improving Dropout Forecasting during the COVID-19 Pandemic through Feature Selection and Multilayer Perceptron Neural Network

Abstract

Nowadays, online education in universities is mature from the situation of COVID-19 spread. It has greatly changed the learning environment in the classroom and has also resulted in students’ dropout. The purpose of this study is to examine the factors that influence students’ dropout in the department of information technology of Suan Sunandha Rajabhat University. Students enrolled in the subjects in the COVID-19 pandemic situation must study online at their homes and must take online courses. This study investigated 1,650 student records, 19,450 enrollments, 16,200 grades, and 11,780 social media accounts that had access to all online courses. It was examined how to improve the model's performance by combining feature selection with a multilayer perceptron neural network method. The model was compared to student dropout predictions generated by Logistic regression, Decision Tree, Random Forest, Naive Bayes, Support Vector Machine, and Multilayer Perceptron Neural Network, with feature selection (1). The 10-Folds Cross Validation method was used to determine the efficiency of the Gain Ratio, Chi-Square, and Correlation-based Feature Selection models to compare accuracy, precision, sensitivity, F1 score, and classification error rate (e). After adjusting the modeling parameters, the multilayer perceptron neural network method combined with CFS characterization achieved an accuracy of 96.98%. The study’s findings indicate that the feature selection technique can be used to improve the neural network model's efficiency in predicting student dropout during a COVID-19 pandemic. Furthermore, the simulation can improve student dropout forecasting during spread out that persists.