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

Teng Guo, Yubin Xia, Jinsen Ke, Mingliang Hou, Jiaqi Zheng, Zitao Liu
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:4537-4545, 2026.

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 \textbf{ACE-KT} (c\textbf{A}scaded \textbf{C}ognitive mod\textbf{E}ling for \textbf{K}nowledge \textbf{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 \textbf{ACE-KT} consistently outperforms 20 SOTA KT baselines, demonstrating its effectiveness. The source code is publicly available at our GitHub repository (\url{https://github.com/AWord992/ACEKT.git).}

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-guo26b, title = { ACE-KT: Cascaded Cognitive Modeling for Stage-wise Knowledge Tracing }, author = {Guo, Teng and Xia, Yubin and Ke, Jinsen and Hou, Mingliang and Zheng, Jiaqi and Liu, Zitao}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {4537--4545}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/guo26b/guo26b.pdf}, url = {https://proceedings.mlr.press/v300/guo26b.html}, 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 \textbf{ACE-KT} (c\textbf{A}scaded \textbf{C}ognitive mod\textbf{E}ling for \textbf{K}nowledge \textbf{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 \textbf{ACE-KT} consistently outperforms 20 SOTA KT baselines, demonstrating its effectiveness. The source code is publicly available at our GitHub repository (\url{https://github.com/AWord992/ACEKT.git).} } }
Endnote
%0 Conference Paper %T ACE-KT: Cascaded Cognitive Modeling for Stage-wise Knowledge Tracing %A Teng Guo %A Yubin Xia %A Jinsen Ke %A Mingliang Hou %A Jiaqi Zheng %A Zitao Liu %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-guo26b %I PMLR %P 4537--4545 %U https://proceedings.mlr.press/v300/guo26b.html %V 300 %X 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 \textbf{ACE-KT} (c\textbf{A}scaded \textbf{C}ognitive mod\textbf{E}ling for \textbf{K}nowledge \textbf{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 \textbf{ACE-KT} consistently outperforms 20 SOTA KT baselines, demonstrating its effectiveness. The source code is publicly available at our GitHub repository (\url{https://github.com/AWord992/ACEKT.git).}
APA
Guo, T., Xia, Y., Ke, J., Hou, M., Zheng, J. & Liu, Z.. (2026). ACE-KT: Cascaded Cognitive Modeling for Stage-wise Knowledge Tracing . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:4537-4545 Available from https://proceedings.mlr.press/v300/guo26b.html.

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