research-article

Multi-type factors representation learning for deep learning-based knowledge tracing

Authors:

Ting WangAuthors Info & Claims

World Wide Web, Volume 25, Issue 3

Pages 1343 - 1372

https://doi.org/10.1007/s11280-022-01041-2

Published: 01 May 2022 Publication History

Abstract

Knowledge Tracing (KT) refers to the problem of predicting future learner performance given their historical interactions with e-learning platforms. Recent years, Deep Learning-based Knowledge Tracing (DLKT) methods show superior performance than traditional methods due to their strong representational ability. However, researchers usually focus on innovations in model structure, while ignoring the importance of Representation Learning (RL) for DLKT. Investigating previous studies, it is found that the mining and integration of learning-related factors can effectively improve the performance of DLKT models. This paper focuses on providing a model embedding interface for DLKT by considering multiple types of learning-related factors. We first explore and analyze four types of learning-related factors: exercise and skill, the attributes of exercise, learners’ historical performance, and learners’ forgetting behavior in the learning process. We then propose an Extensible Representation Learning (ERL) approach for DLKT to extract and integrate the representations of these four types of factors by setting five components: base embedding, auxiliary embedding, performance embedding, forgetting embedding, and embedding integration. Finally, we apply ERL into two mainstream DLKT models and comprehensively evaluate the proposed approach on several real-world benchmark datasets. Results show that ERL can significantly improve the performances of these two network on predicting future learner responses.

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Cited By

Wang WMa HZhao YLi Z(2024)Pre-training Question Embeddings for Improving Knowledge Tracing with Self-supervised Bi-graph Co-contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/363805518:4(1-20)Online publication date: 12-Feb-2024
https://dl.acm.org/doi/10.1145/3638055
He LLi XWang PTang JWang T(2024)MAN: Memory-augmented Attentive Networks for Deep Learning-based Knowledge TracingACM Transactions on Information Systems10.1145/358934042:1(1-22)Online publication date: 31-Jan-2024
https://dl.acm.org/doi/10.1145/3589340
Zu SLi LCai SShen J(2024)CIKT: Causality Inspired Knowledge TracingDatabase Systems for Advanced Applications10.1007/978-981-97-5562-2_32(485-495)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1007/978-981-97-5562-2_32
Show More Cited By

Index Terms

Multi-type factors representation learning for deep learning-based knowledge tracing
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image World Wide Web

World Wide Web Volume 25, Issue 3

May 2022

438 pages

ISSN:1386-145X

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 May 2022

Accepted: 08 March 2022

Revision received: 14 February 2022

Received: 13 September 2021

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Cited By

Wang WMa HZhao YLi Z(2024)Pre-training Question Embeddings for Improving Knowledge Tracing with Self-supervised Bi-graph Co-contrastive LearningACM Transactions on Knowledge Discovery from Data10.1145/363805518:4(1-20)Online publication date: 12-Feb-2024
https://dl.acm.org/doi/10.1145/3638055
He LLi XWang PTang JWang T(2024)MAN: Memory-augmented Attentive Networks for Deep Learning-based Knowledge TracingACM Transactions on Information Systems10.1145/358934042:1(1-22)Online publication date: 31-Jan-2024
https://dl.acm.org/doi/10.1145/3589340
Zu SLi LCai SShen J(2024)CIKT: Causality Inspired Knowledge TracingDatabase Systems for Advanced Applications10.1007/978-981-97-5562-2_32(485-495)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1007/978-981-97-5562-2_32
Li ZJacobsen MShi LZhou YWang J(2023)Broader and Deeper: A Multi-Features with Latent Relations BERT Knowledge Tracing ModelResponsive and Sustainable Educational Futures10.1007/978-3-031-42682-7_13(183-197)Online publication date: 4-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-42682-7_13

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