  Transformer based knowledge tracing model is an extensively studied problem
in the field of computer-aided education. By integrating temporal features into
the encoder-decoder structure, transformers can processes the exercise
information and student response information in a natural way. However, current
state-of-the-art transformer-based variants still share two limitations. First,
extremely long temporal features cannot well handled as the complexity of
self-attention mechanism is O(n2). Second, existing approaches track the
knowledge drifts under fixed a window size, without considering different
temporal-ranges. To conquer these problems, we propose MUSE, which is equipped
with multi-scale temporal sensor unit, that takes either local or global
temporal features into consideration. The proposed model is capable to capture
the dynamic changes in users knowledge states at different temporal-ranges, and
provides an efficient and powerful way to combine local and global features to
make predictions. Our method won the 5-th place over 3,395 teams in the Riiid
AIEd Challenge 2020.
