Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recently, many works present lots of special methods for applying deep neural networks to KT from different perspectives like model architecture, adversarial augmentation and etc., which make the overall algorithm and system become more and more complex. Furthermore, due to the lack of standardized evaluation protocol \citep{liu2022pykt}, there is no widely agreed KT baselines and published experimental comparisons become inconsistent and self-contradictory, i.e., the reported AUC scores of DKT on ASSISTments2009 range from 0.721 to 0.821 \citep{minn2018deep,yeung2018addressing}. Therefore, in this paper, we provide a strong but simple baseline method to deal with the KT task named \textsc{simpleKT}. Inspired by the Rasch model in psychometrics, we explicitly model question-specific variations to capture the individual differences among questions covering the same set of knowledge components that are a generalization of terms of concepts or skills needed for learners to accomplish steps in a task or a problem. Furthermore, instead of using sophisticated representations to capture student forgetting behaviors, we use the ordinary dot-product attention function to extract the time-aware information embedded in the student learning interactions. Extensive experiments show that such a simple baseline is able to always rank top 3 in terms of AUC scores and achieve 57 wins, 3 ties and 16 loss against 12 DLKT baseline methods on 7 public datasets of different domains. We believe this work serves as a strong baseline for future KT research. Code is available at \url{https://github.com/pykt-team/pykt-toolkit}\footnote{We merged our model to the \textsc{pyKT} benchmark at \url{https://pykt.org/}.}.
翻译:知识追踪 (KT) 是根据学生与智能导师系统的历史互动来预测学生未来绩效的问题。 最近, 许多工作都从模型架构、 对抗性增强等不同角度为KT提供大量特殊方法, 使整个算法和系统变得越来越复杂。 此外, 由于缺乏标准化的评价协议 \ citep{ liu2022pyk}, 没有广泛商定的 KT 基线和公布的实验性比较变得不一致, 并且自相矛盾。 例如, 所报道的 AUC 有关Asssistations2009 的 DKT 在 0. 721 至 0. 821\ citep{min2018dep,yeung2018404848mailting} 等不同角度应用深层神经神经网络网络网络网络网络。 因此, 在本文中, 我们提供了一个强大但简单的基准方法, 在 16 数学模型的启发下, 我们明确模拟 3 数据库 的变数, 来捕捉摸同一组知识组成部分之间的个别差异, 而不是简单的实验术语的简单化,, 需要 学习系统 学习 的系统, 学会 学习 的 需要 学习 的 学习 的 的 系统 的 的 的 的 直位 的 直位 的, 直位 的 的 的 的 的 的 直位 的 的 直位 直径 直径 直径 直 向 向 向 。