Input sequence is transformed so that they would have the same fixed size n. If the size of sample is less than n than padding is applied otherwise inputs are divided and separate samples are generated. Then the interaction embedding matrix is trained $M \in \R^{2E \times d}$ which is used to get $s_i$s. Exercise embedding matrix is trained in a similar manner $E \in \R^{E \times d}$. Position encoding is used in the model in order to encode the order of sequence. Position embedding is learned in the process of training $P \in R^{n \times d}$. The $P_i$s are added to the interaction embedding matrix. Therefore as a result of embedding layer embedded interaction and exercise matrices are generated.