The proposed approach uses the Deep Knowledge Tracing (DKT) model as its base model because DKT is a deep neural network that can easily incorporate multiple input sources and capture nonlinear dynamics among them. The approach extends DKT so that it also accounts for student forgetting, adapting its prediction of student performance using three forgetting-related features: 1) repeated time gap: the elapsed time between the current interaction and the previous interaction on the same skill id; 2) sequence time gap: the elapsed time between the current interaction and the previous interaction in the sequence, regardless of skill id; and 3) past trial count: the number of times the student has previously answered questions with the same skill id.