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.

翻译：知识追踪（KT）通过利用学生过往表现来预测其未来表现，从而提升学习效果。当前研究主要采用基于注意力机制和循环神经网络结构的模型来捕捉练习间的长期依赖关系与相关性，旨在提高模型预测精度。随着智慧教育场景中数据量的持续增长，知识追踪模型在时间与空间消耗方面面临挑战。然而，现有研究往往忽视模型训练与推理的效率以及训练资源的限制。认识到在知识追踪中优先考虑模型效率与资源使用的重要性，我们提出了Mamba4KT。这一新颖模型首次在知识追踪领域探索了效率与资源利用的协同优化。我们还从序列层面和习题层面分析了Mamba结构的可解释性，以增强模型的可解释性。在三个公开数据集上的实验结果表明，Mamba4KT在达到与最先进模型相当的预测精度的同时，显著提升了训练与推理效率以及资源利用率。随着教育数据的持续增长，我们的工作为知识追踪指明了一个具有前景的研究方向，即同时提升模型预测精度、模型效率、资源利用率与可解释性。