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问题的作答表现预测其在新问题上的表现，这是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，尽管某些更简单的模型也能达到其性能水平。然而，学界对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、简洁性或表达能力上更优的模型，还为未来研究方向开辟了有前景的路径。具体而言，我们在多个小型和大型数据集上证明：相比于DKT使用的解码器，一个参数可能更少的简化解码器能够更好地预测学生表现。