Addressing Label Leakage in Knowledge Tracing Models

Yahya Badran, Yahya Badran, Christine Preisach, Christine Preisach

2025

Abstract

Knowledge Tracing (KT) is concerned with predicting students’ future performance on learning items in intelligent tutoring systems. Learning items are tagged with skill labels called knowledge concepts (KCs). Many KT models expand the sequence of item-student interactions into KC-student interactions by replacing learning items with their constituting KCs. This approach addresses the issue of sparse item-student interactions and minimises the number of model parameters. However, we identified a label leakage problem with this approach. The model’s ability to learn correlations between KCs belonging to the same item can result in the leakage of ground truth labels, which leads to decreased performance, particularly on datasets with a high number of KCs per item. In this paper, we present methods to prevent label leakage in knowledge tracing (KT) models. Our model variants that utilize these methods consistently outperform their original counterparts. This further underscores the impact of label leakage on model performance. Additionally, these methods enhance the overall performance of KT models, with one model variant surpassing all tested baselines on different benchmarks. Notably, our methods are versatile and can be applied to a wide range of KT models.

Paper Citation

in Harvard Style

Badran Y. and Preisach C. (2025). Addressing Label Leakage in Knowledge Tracing Models. In Proceedings of the 17th International Conference on Computer Supported Education - Volume 2: CSEDU; ISBN 978-989-758-746-7, SciTePress, pages 85-95. DOI: 10.5220/0013275200003932

in Bibtex Style

@conference{csedu25,
author={Yahya Badran and Christine Preisach},
title={Addressing Label Leakage in Knowledge Tracing Models},
booktitle={Proceedings of the 17th International Conference on Computer Supported Education - Volume 2: CSEDU},
year={2025},
pages={85-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013275200003932},
isbn={978-989-758-746-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Computer Supported Education - Volume 2: CSEDU
TI - Addressing Label Leakage in Knowledge Tracing Models
SN - 978-989-758-746-7
AU - Badran Y.
AU - Preisach C.
PY - 2025
SP - 85
EP - 95
DO - 10.5220/0013275200003932
PB - SciTePress