A temporal attention-based hybrid deep learning model for student performance and academic risk prediction
Adilkhan Abuov
This work presents a hybrid deep learning approach for identifying students who are likely to experience academic difficulties in virtual learning environments. The proposed framework is evaluated on the Open University Learning Analytics Dataset (OULAD) and combines two complementary types of information: temporal patterns of learner activity captured using Bidirectional Long Short-Term Memory (Bi-LSTM) networks and relatively stable student attributes modeled through a Multi-Layer Perceptron (