Knowledge tracing consists in predicting the performance of some students on new questions given their performance on previous questions, and can be a prior step to optimizing assessment and learning. Deep knowledge tracing (DKT) is a competitive model for knowledge tracing relying on recurrent neural networks, even if some simpler models may match its performance. However, little is known about why DKT works so well. In this paper, we frame deep knowledge tracing as a encoderdecoder architecture. This viewpoint not only allows us to propose better models in terms of performance, simplicity or expressivity but also opens up promising avenues for future research directions. In particular, we show on several small and large datasets that a simpler decoder, with possibly fewer parameters than the one used by DKT, can predict student performance better.

翻译：知识追踪旨在根据学生先前答题的表现预测其在新问题上的表现，这可以作为优化评估与学习的前置步骤。深度知识追踪（DKT）是一种依赖循环神经网络的竞争性知识追踪模型，尽管某些更简洁的模型也能达到同等性能。然而，目前对DKT为何如此有效仍知之甚少。本文从编码器-解码器架构的角度重新审视深度知识追踪。这一视角不仅使我们能够提出在性能、简洁性或表达力方面更优的模型，还为未来的研究方向开辟了前景广阔的新路径。特别是在多个小型与大型数据集上，我们证明了使用比DKT更简单的解码器（其参数可能更少）能够更好地预测学生表现。