Deep learning based knowledge tracing(DKT), namely, modeling student knowledge state based on recurrent neural networks, have shown better results than the BKT (Bayesian Knowledge Tracing) and PFA (Performance Knowledge Tracing). (NOTE without human engineered features)

Here we transform fixed length inputs using one-hot encoding and then they are passed to the hidden layer. The hidden state can be thought as a latent knowledge state of the student. Then this is used to compute the probability of answering knowledge components correctly (the output).

As it was written in the paper: "However, since all of the information captured by the RNN lives in a same vector space in the hidden layer, the DKT model is consequently difficult to provide consistent prediction across time and therefore failing to pinpoint accurately which KCs a student is good at or unfamiliar with" Dynamic Key Value Memory Network(DKVMN) was proposed to solve this problem In DKVMN we have two types of memory: static key memory and dynamic value memory. The keys can be thought as latent concepts while values as knowledge states. The vector representation of knowledge components and knowledge states are created and then they are used to predict P(a).DKVMN outperformed DKT. Nevertheless, the vectors still lack the interpretability.