Official implementation of the HiSACKT model
Accepted at the 24th IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2025)
Authors: Duaa Baig, Diana Nurbakova, Baba Mbaye, and Sylvie Calabretto.
HiSACKT (Hierarchical Self-Attention and Skill Clustering for Knowledge Tracing) enhances traditional knowledge tracing models by integrating hierarchical self-attention layers and dynamic skill clustering to capture both local and global dependencies in student learning behavior.
git clone https://github.com/duaabaig/HiSACKT.git
cd HiSACKT
pip install -r requirements.txtTo train or evaluate HiSACKT on a specific dataset:
python main.py --model_name hisackt --dataset_name ASSIST2015Supported datasets: ASSIST2009, Algebra2005, Statics2011, ASSIST2015
| Model | ASSISTMENT2009 (AUC / Loss) | ALGEBRA2005 (AUC / Loss) | Statistics2011 (AUC / Loss) | ASSISTMENT2015 (AUC / Loss) |
|---|---|---|---|---|
| DKT | 79.15 / 0.60 | 80.75 / 0.56 | 77.93 / 0.55 | 72.41 / 0.60 |
| DKT+ | 80.16 / 0.64 | 81.85 / 0.58 | 77.86 / 0.57 | 71.94 / 0.61 |
| DKVMN | 72.01 / 0.55 | 81.08 / 0.42 | 74.32 / 0.44 | 70.88 / 0.52 |
| GKT-PAM | 78.24 / 0.50 | 78.36 / 0.43 | 74.33 / 0.44 | 71.88 / 0.51 |
| GKT-MHA | 78.71 / 0.50 | 73.29 / 0.49 | 71.26 / 0.46 | 72.70 / 0.51 |
| SAKT | 79.59 / 0.49 | 80.74 / 0.43 | 75.35 / 0.44 | 72.38 / 0.51 |
| HiSACKT | 82.28 / 0.44 | 91.51 / 0.26 | 76.72 / 0.42 | 93.33 / 0.24 |
| Model Variant | ASSIST2009 | Algebra2005 | Statistics2011 | ASSIST2015 | Avg AUC |
|---|---|---|---|---|---|
| HiSACKT (Full) | 82.28 | 91.50 | 76.72 | 93.33 | 85.96 |
| w/o Skill Clustering | 81.66 ↓0.62 | 83.70 ↓7.81 | 75.63 ↓1.09 | 92.30 ↓1.03 | 83.32 ↓2.64 |
| w/o Local Attention | 83.20 ↑0.92 | 89.79 ↓1.72 | 77.45 ↑0.73 | 95.19 ↑1.86 | 86.41 ↑0.45 |
| w/o Cross-Level | 81.49 ↓0.79 | 82.61 ↓8.90 | 76.63 ↓0.09 | 83.31 ↓10.02 | 81.01 ↓4.95 |
| w/o Skill-Group | 81.24 ↓1.04 | 85.30 ↓6.21 | 75.79 ↓0.93 | 82.61 ↓10.72 | 81.24 ↓4.72 |
The data loader and baseline models (DKT, DKVMN, SAKT, GKT, etc.) are adapted from
hcnoh/knowledge-tracing-collection-pytorch (MIT License).
This project is licensed under the MIT License - see the LICENSE file for details.
If you use this code, please cite:
@inproceedings{baig2025hisackt,
title={Combining Hierarchical Self-Attention and Skill Clustering to Enhance Knowledge Tracing (HiSACKT)},
author={Baig, Duaa and Nurbakova, Diana and Mbaye, Baba and Calabretto, Sylvie},
booktitle={Proceedings of the 24th IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)},
year={2025},
doi={TBA}
}