  Knowledge tracing (KT) enhances student learning by leveraging past
performance to predict future performance. Current research utilizes models
based on attention mechanisms and recurrent neural network structures to
capture long-term dependencies and correlations between exercises, aiming to
improve model accuracy. Due to the growing amount of data in smart education
scenarios, this poses a challenge in terms of time and space consumption for
knowledge tracing models. However, existing research often overlooks the
efficiency of model training and inference and the constraints of training
resources. Recognizing the significance of prioritizing model efficiency and
resource usage in knowledge tracing, we introduce Mamba4KT. This novel model is
the first to explore enhanced efficiency and resource utilization in knowledge
tracing. We also examine the interpretability of the Mamba structure both
sequence-level and exercise-level to enhance model interpretability.
Experimental findings across three public datasets demonstrate that Mamba4KT
achieves comparable prediction accuracy to state-of-the-art models while
significantly improving training and inference efficiency and resource
utilization. As educational data continues to grow, our work suggests a
promising research direction for knowledge tracing that improves model
prediction accuracy, model efficiency, resource utilization, and
interpretability simultaneously.
