Knowledge tracing machines involves IRT (item response theory), AFM (additive factor model), PFA (performance factory analysis).
For the d = 0 (when we don't have embedding):

Relation to IRT if all the features that we have are users, questions and pairs of user and answer are represented as one-hot encoders the expression for the knowledge tracing machines becomes:

$\log \frac {p(x)} {1 - p(x)} = \mu + w_i + w_{n+j} = \theta_i - d_i$

This means that the knowledge tracing machines become Rasch Model (1-PL IRT model).

Relation to AFM and PFA if we the features that we have are skills wins and fails at skill level and additionally suppose that q-matrix between questions and skills is also known. If w and x are encoded in the following way: $(\beta_1, ... \beta_s, \lambda_1, ... \lambda_s, \delta_1, delta_s)$, $(q_{j1}, ... ,1_{js}, W_{i1}, ..., W_{is}, F_{i1}, F_{is})$ - (Ws, Fs are counters for the successful /unsuccessful attempts) then Knowledge tracing machines behave exactly like AFM and PFA.

Relation to MIRT if d > 0 then knowledge tracing machine acts like MIRT.