The input sequence is transformed to have a fixed size $n$. If the sample size is less than $n$, padding is applied; otherwise, inputs are divided and separate samples are generated. Then, an interaction embedding matrix $M \in \mathbb{R}^{2E imes d}$ is trained, which is used to obtain interaction embeddings $s_i$. An exercise embedding matrix $E \in \mathbb{R}^{E imes d}$ is trained in a similar manner. Position encoding is used in the model to encode the sequence order. A position embedding matrix $P \in \mathbb{R}^{n imes d}$ is learned during training. The position embeddings $P_i$ are added to the interaction embeddings. As a result of the embedding layer, embedded interaction and exercise matrices are generated.