The paper proposes the attentive knowledge tracing (AKT) method, which uses a series of attention networks to draw connections between this question and every question the learner has responded to in the past.

The key innovations include:

• Instead of use raw question and response embeddings, the paper put raw embeddings into context and use context-aware representations of past questions and responses by taking a learner’s entire practice history into account.
• The paper proposes a novel monotonic attention mechanism that uses an exponential decay curve to down weight the importance of questions in the distant past. It also develop a context-aware measure to characterize the time distance between questions a learner has responded to in the past.
• Leveraging a series of Rasch model-based embeddings to capture individual differences among questions.