SLPNet: Student Learning Performance Prediction During the COVID-19 Pandemic via a Deep Neural Network

The prediction of students’ performance has become challenging for tutors and management in education because of various factors. Many researchers have deployed different predictive models to predict students who are at risk of dropping out early or estimate grades at the end of the semester on different datasets. Nevertheless, prediction models cannot fulfill the requirements of educational management. In this paper, we propose a deep learning (DL) model named the student learning performance prediction network (SLPNet) to predict students’ grades, where we consider the Quaternaries-based Jordan University dataset, which contains demographic information, digital tools, sleep habits, social interaction, psychological state, and academic performance (assignments, quizzes, and other tasks). Additionally, we have attempted to implement other DL and machine learning (ML) models, including artificial neural networks (ANNs), support vector machines (SVMs), k nearest neighbors (K-NNs), decision trees (DTs), and random forests (RFs). Furthermore, the simulation results show that the proposed SLPNet model achieves better performance than do the ANN and ML baseline methods, with 89% accuracy, 89% precision, 88% recall, and 89% F1 score. We add three convolutional stages to our model, which improves the prediction performance and implies a new aspect of the educational dataset.