+ t_t$.

### Masking

Inspired by the masked language model proposed in [@devlin-etal-2019-bert], we use the following method to mask the assessments in a student interaction sequence. Firstly, we mask $C$ fraction of the interaction in a sequence chosen at random. If the $t$-th interaction is chosen, we replace the corresponding input embedding with (1) $e_t + \texttt{mask}$ for $M$ fraction of the time and (2) $e_t + c_{{\mathrm{rand}}} + t_{{\mathrm{rand}}}$ for $(1-M)$ fraction of the time. Here $\texttt{mask}$ is a learned vector that represents masking, and $c_{{\mathrm{rand}}}$ and $t_{{\mathrm{rand}}}$ are embedding vectors for assessments uniformly chosen at random in the sequence. We determine $C$ and $M$ through ablation studies in Section \[sec:experiments\].

### Model Architecture

After the interactions are embedded and masked accordingly, they enter a series of Transformer encoder blocks, each consisting of a multi-head self-attention layer followed by position-wise feed-forward networks. All the inputs of layers have dimension $d_{\mathrm{model}}$. The first encoder block takes the sequence of interactions $I_1, \dots, I_T$ embedded in latent space and returns a series of vectors of the same length and dimension. For all $2 \leq i \leq N$, the $i$’th block takes the output of the $i-1$’th block as input and 