  Knowledge tracing (KT) aims to leverage students' learning histories to
estimate their mastery levels on a set of pre-defined skills, based on which
the corresponding future performance can be accurately predicted. As an
important way of providing personalized experience for online education, KT has
gained increased attention in recent years. In practice, a student's learning
history comprises answers to sets of massed questions, each known as a session,
rather than merely being a sequence of independent answers. Theoretically,
within and across these sessions, students' learning dynamics can be very
different. Therefore, how to effectively model the dynamics of students'
knowledge states within and across the sessions is crucial for handling the KT
problem. Most existing KT models treat student's learning records as a single
continuing sequence, without capturing the sessional shift of students'
knowledge state. To address the above issue, we propose a novel hierarchical
transformer model, named HiTSKT, comprises an interaction(-level) encoder to
capture the knowledge a student acquires within a session, and a
session(-level) encoder to summarise acquired knowledge across the past
sessions. To predict an interaction in the current session, a knowledge
retriever integrates the summarised past-session knowledge with the previous
interactions' information into proper knowledge representations. These
representations are then used to compute the student's current knowledge state.
Additionally, to model the student's long-term forgetting behaviour across the
sessions, a power-law-decay attention mechanism is designed and deployed in the
session encoder, allowing it to emphasize more on the recent sessions.
Extensive experiments on three public datasets demonstrate that HiTSKT achieves
new state-of-the-art performance on all the datasets compared with six
state-of-the-art KT models.
