  Knowledge tracing (KT) aims to estimate student's knowledge mastery based on
their historical interactions. Recently, the deep learning based KT (DLKT)
approaches have achieved impressive performance in the KT task. These DLKT
models heavily rely on the large number of available student interactions.
However, due to various reasons such as budget constraints and privacy
concerns, observed interactions are very limited in many real-world scenarios,
a.k.a, low-resource KT datasets. Directly training a DLKT model on a
low-resource KT dataset may lead to overfitting and it is difficult to choose
the appropriate deep neural architecture. Therefore, in this paper, we propose
a low-resource KT framework called LoReKT to address above challenges. Inspired
by the prevalent "pre-training and fine-tuning" paradigm, we aim to learn
transferable parameters and representations from rich-resource KT datasets
during the pre-training stage and subsequently facilitate effective adaptation
to low-resource KT datasets. Specifically, we simplify existing sophisticated
DLKT model architectures with purely a stack of transformer decoders. We design
an encoding mechanism to incorporate student interactions from multiple KT data
sources and develop an importance mechanism to prioritize updating parameters
with high importance while constraining less important ones during the
fine-tuning stage. We evaluate LoReKT on six public KT datasets and
experimental results demonstrate the superiority of our approach in terms of
AUC and Accuracy. To encourage reproducible research, we make our data and code
publicly available at https://anonymous.4open.science/r/LoReKT-C619.
