Sparse Binary Representation Learning for Knowledge Tracing

Yahya Badran, Yahya Badran, Christine Preisach, Christine Preisach

2025

Abstract

Knowledge tracing (KT) models aim to predict students’ future performance based on their historical interactions. Most existing KT models rely exclusively on human-defined knowledge concepts (KCs) associated with exercises. As a result, the effectiveness of these models is highly dependent on the quality and completeness of the predefined KCs. Human errors in labeling and the cost of covering all potential underlying KCs can limit model performance. In this paper, we propose a KT model, Sparse Binary Representation KT (SBRKT), that generates new KC labels, referred to as auxiliary KCs, which can augment the predefined KCs to address the limitations of relying solely on human-defined KCs. These are learned through a binary vector representation, where each bit indicates the presence (one) or absence (zero) of an auxiliary KC. The resulting discrete representation allows these auxiliary KCs to be utilized in training any KT model that incorporates KCs. Unlike pre-trained dense embeddings, which are limited to models designed to accept such vectors, our discrete representations are compatible with both classical models, such as Bayesian Knowledge Tracing (BKT), and modern deep learning approaches. To generate this discrete representation, SBRKT employs a binariza-tion method that learns a sparse representation, fully trainable via stochastic gradient descent. Additionally, SBRKT incorporates a recurrent neural network (RNN) to capture temporal dynamics and predict future student responses by effectively combining the auxiliary and predefined KCs. Experimental results demonstrate that SBRKT outperforms the tested baselines on several datasets and achieves competitive performance on others. Furthermore, incorporating the learned auxiliary KCs consistently enhances the performance of BKT across all tested datasets.

Paper Citation

in Harvard Style

Badran Y. and Preisach C. (2025). Sparse Binary Representation Learning for Knowledge Tracing. In Proceedings of the 17th International Conference on Computer Supported Education - Volume 2: CSEDU; ISBN 978-989-758-746-7, SciTePress, pages 75-84. DOI: 10.5220/0013275100003932

in Bibtex Style

@conference{csedu25,
author={Yahya Badran and Christine Preisach},
title={Sparse Binary Representation Learning for Knowledge Tracing},
booktitle={Proceedings of the 17th International Conference on Computer Supported Education - Volume 2: CSEDU},
year={2025},
pages={75-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013275100003932},
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 - Sparse Binary Representation Learning for Knowledge Tracing
SN - 978-989-758-746-7
AU - Badran Y.
AU - Preisach C.
PY - 2025
SP - 75
EP - 84
DO - 10.5220/0013275100003932
PB - SciTePress