The Self-Attentive model for Knowledge Tracing (SAKT) is a transformer-based approach designed to predict student performance. Because a student's learning process heavily depends on their past experiences and acquired skills, SAKT uses a self-attention mechanism to assign weights to previously solved items, treating individual exercises as Knowledge Components (KCs). By weighting these past interactions, the model predicts the probability of a student answering a particular question correctly. In its initial proposal, SAKT demonstrated improved predictive performance over previous knowledge tracing models.