ACE-KT: Cascaded Cognitive Modeling for Stage-wise Knowledge Tracing

Teng Guo, Yubin Xia, Jinsen Ke, Mingliang Hou, Jiaqi Zheng, Zitao Liu

Abstract:

Knowledge Tracing (KT) aims to predict students’ academic performance by modeling their knowledge mastery over time, based on their historical learning interactions.
However, current KT models often oversimplify student interactions by treating them as standard time series rather than as cognitive processes.
Consequently, modeling student learning as a process of cognitive transformation rather than as a mere sequence of time-stamped events remains a fundamental challenge in KT research.
To address this issue, we propose **ACE-KT** (c**A**scaded **C**ognitive mod**E**ling for **K**nowledge **T**racing), a novel framework inspired by cognitive process theory, which shifts the focus from purely sequential modeling to cognitive representation learning.
Specifically, we design a cascaded cognitive framework inspired by human cognitive processes in three sequential stages: convolution-based rhythm perception module, Transformer encoder-based contextual structuring module, and cognitive integration module implemented via a selective structured state space model.
Extensive experiments on five real-world datasets demonstrate that **ACE-KT** consistently outperforms 20 SOTA KT baselines, demonstrating its effectiveness.
The source code is publicly available at our GitHub repository (https://github.com/AWord992/ACEKT.git).

Code Dataset Promise: Yes

Code Dataset Url: https://github.com/AWord992/ACEKT.git

Format Confirmation: I agree that I have read and followed the formatting instructions for the camera ready version.

Submission Number: 2037