The proposed Self-Attentive model for Knowledge Tracing (SAKT) transforms student interaction sequences into a sequential modeling problem to predict whether a student will answer a question correctly. Its architecture consists of several key components: an embedding layer for interactions and exercises, a self-attention layer utilizing a scaled dot-product mechanism to assign weights to previous exercises, a feed-forward layer, residual connections, layer normalization, and a prediction layer.