@inproceedings{9baba72651394d25b42706fc873b15c0,
title = "Imbalanced Data for Knowledge Tracing",
abstract = "In the realm of Intelligent Tutoring Systems (ITS), Knowledge Tracing (KT) techniques play a vital role in tracking and assessing student progress and understanding of a subject. However, in practice various data classes are generally collected in a way of underrepresentation, leading to the KT performance degradation. In this work, we propose a data deduplication technique to balance the inputs to improve the KT performance. Our experimental results confirm the efficacy of the proposed scheme in addressing imbalanced data and improving KT performance.",
keywords = "data deduplication, deep learning, imbalanced data, knowledge tracing, machine learning",
author = "Chen, {Jyun Yi} and Lai, {I. Wei}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 ; Conference date: 17-07-2023 Through 19-07-2023",
year = "2023",
doi = "10.1109/ICCE-Taiwan58799.2023.10226668",
language = "English",
series = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "801--802",
booktitle = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
}