A Hybrid Deep Learning Model to Predict High-Risk Students in Virtual Learning Environments

Abstract

Online learning has accelerated with the development of the Internet and communication technology. The widely accessible open online courses are delivered using digital environments that allow students to participate at speed and location. Virtual learning environments (VLEs) have developed quickly in recent years, giving students access to high-quality digital resources. Online learning environments have numerous benefits but drawbacks, including poor engagement, high dropout rates, low engagement, and self-regulated behavior, making students define their aims. Forecasting failed students in a VLE can help organizations and teachers improve their pedagogical practices and make data-driven decisions. This work proposes a Hybrid Deep Learning (HDL) approach to predict students’ performance utilizing ECNN (Enhanced Convolution Neural Networks) Resnet model-based classification algorithms. The HDL approach is evaluated using the OULAD (Open University Learning Analytics Dataset), which provides a comprehensive and reliable assessment of the model’s performance. The hybrid DLT approaches, demonstrating superiority, exhibited greater prediction accuracy than the existing classifiers. Additionally, the models’ accuracy increases by about 95.67%, higher than other approaches are DFFNN model (93.9%) and MLP model (71.41%).

Keywords

• Academic performance of students
• virtual learning environments (VLE)
• min-max normalization
• hybrid deep learning framework
• butterfly optimization
• enhanced convolutional neural network (ECNN)