  Knowledge tracing (KT) aims to assess individuals' evolving knowledge states
according to their learning interactions with different exercises in online
learning systems (OIS), which is critical in supporting decision-making for
subsequent intelligent services, such as personalized learning source
recommendation. Existing researchers have broadly studied KT and developed many
effective methods. However, most of them assume that students' historical
interactions are uniformly distributed in a continuous sequence, ignoring the
fact that actual interaction sequences are organized based on a series of
quizzes with clear boundaries, where interactions within a quiz are
consecutively completed, but interactions across different quizzes are discrete
and may be spaced over days. In this paper, we present the Quiz-based Knowledge
Tracing (QKT) model to monitor students' knowledge states according to their
quiz-based learning interactions. Specifically, as students' interactions
within a quiz are continuous and have the same or similar knowledge concepts,
we design the adjacent gate followed by a global average pooling layer to
capture the intra-quiz short-term knowledge influence. Then, as various quizzes
tend to focus on different knowledge concepts, we respectively measure the
inter-quiz knowledge substitution by the gated recurrent unit and the
inter-quiz knowledge complementarity by the self-attentive encoder with a novel
recency-aware attention mechanism. Finally, we integrate the inter-quiz
long-term knowledge substitution and complementarity across different quizzes
to output students' evolving knowledge states. Extensive experimental results
on three public real-world datasets demonstrate that QKT achieves
state-of-the-art performance compared to existing methods. Further analyses
confirm that QKT is promising in designing more effective quizzes.

Artificial Intelligence;Human-Computer Interaction