In a knowledge tracing task, there is a sequence of interactions for each student: $[I_1, \cdots, I_T]$, and each of these interactions consists of several features: $\{f_t^1, \cdots, f_t^n\}$. In knowledge tracing, the goal is to predict feature $f$ at time $t$, which is formulated as: p(f_t^k | { I_1, cdots, I_{t-1}, I_t, I_{t+1}, cdots, I_T } setminus Gamma(f_t^k)) where $\Gamma(f)$ is a set of features that should be masked. While knowledge tracing typically predicts the correctness of a response, assessment modeling aims to predict the assessment given the sequence of interactions: p(a_t^k | { I_1, cdots, I_{t-1}, I_t, I_{t+1}, cdots, I_T } setminus Gamma(a_t^k))