Imbalanced Data for Knowledge Tracing

Jyun Yi Chen; I. Wei Lai

doi:10.1109/ICCE-Taiwan58799.2023.10226668

Imbalanced Data for Knowledge Tracing

Jyun Yi Chen, I. Wei Lai^*

^*Corresponding author for this work

National Taiwan Normal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	801-802
Number of pages	2
ISBN (Electronic)	9798350324174
DOIs	https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226668
State	Published - 2023
Externally published	Yes
Event	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, Taiwan Duration: 17 07 2023 → 19 07 2023

Publication series

Name	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

Conference

Conference	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Country/Territory	Taiwan
City	Pingtung
Period	17/07/23 → 19/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

data deduplication
deep learning
imbalanced data
knowledge tracing
machine learning

Access to Document

10.1109/ICCE-Taiwan58799.2023.10226668

Cite this

Chen, J. Y., & Lai, I. W. (2023). Imbalanced Data for Knowledge Tracing. In 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings (pp. 801-802). (2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226668

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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 = "英语",

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",

address = "美国",

}

Chen, JY & Lai, IW 2023, Imbalanced Data for Knowledge Tracing. in 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings. 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 801-802, 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023, Pingtung, Taiwan, 17/07/23. https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226668

Imbalanced Data for Knowledge Tracing. / Chen, Jyun Yi; Lai, I. Wei.
2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 801-802 (2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2023

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N2 - 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.

AB - 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.

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KW - deep learning

KW - imbalanced data

KW - knowledge tracing

KW - machine learning

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BT - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

Y2 - 17 July 2023 through 19 July 2023

ER -

Imbalanced Data for Knowledge Tracing

Abstract

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Bibliographical note

Keywords

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