@inproceedings{guan-etal-2025-kvfkt,
title = "{KVFKT}: A New Horizon in Knowledge Tracing with Attention-Based Embedding and Forgetting Curve Integration",
author = "Guan, Quanlong and
Duan, Xiuliang and
Bian, Kaiquan and
Chen, Guanliang and
Huang, Jianbo and
Gong, Zhiguo and
Fang, Liangda",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.297/",
pages = "4399--4409",
abstract = "The knowledge tracing (KT) model based on deep learning has been proven to be superior to the traditional knowledge tracing model, eliminating the need for artificial engineering features. However, there are still problems, such as insufficient interpretability of the learning and answering processes. To address these issues, we propose a new approach in knowledge tracing with attention-based embedding and forgetting curve integration, namely KVFKT. Firstly, the embedding representation module is responsible for embedding the questions and computing the attention vector of knowledge concepts (KCs) when students answer questions and when answer time stamps are collected. Secondly, the forgetting quantification module performs the pre-prediction update of the student`s knowledge state matrix. This quantification involves calculating the interval time and associated forgetting rate of relevant KCs, following the forgetting curve. Thirdly, the answer prediction module generates responses based on students' knowledge status, guess coefficient, and question difficulty. Finally, the knowledge status update module further refines the students' knowledge status according to their answers to the questions and the characteristics of those questions. In the experiment, four real-world datasets are used to test the model. Experimental results show that KVFKT better traces students' knowledge state and outperforms state-of-the-art models."
}
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<abstract>The knowledge tracing (KT) model based on deep learning has been proven to be superior to the traditional knowledge tracing model, eliminating the need for artificial engineering features. However, there are still problems, such as insufficient interpretability of the learning and answering processes. To address these issues, we propose a new approach in knowledge tracing with attention-based embedding and forgetting curve integration, namely KVFKT. Firstly, the embedding representation module is responsible for embedding the questions and computing the attention vector of knowledge concepts (KCs) when students answer questions and when answer time stamps are collected. Secondly, the forgetting quantification module performs the pre-prediction update of the student‘s knowledge state matrix. This quantification involves calculating the interval time and associated forgetting rate of relevant KCs, following the forgetting curve. Thirdly, the answer prediction module generates responses based on students’ knowledge status, guess coefficient, and question difficulty. Finally, the knowledge status update module further refines the students’ knowledge status according to their answers to the questions and the characteristics of those questions. In the experiment, four real-world datasets are used to test the model. Experimental results show that KVFKT better traces students’ knowledge state and outperforms state-of-the-art models.</abstract>
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%0 Conference Proceedings
%T KVFKT: A New Horizon in Knowledge Tracing with Attention-Based Embedding and Forgetting Curve Integration
%A Guan, Quanlong
%A Duan, Xiuliang
%A Bian, Kaiquan
%A Chen, Guanliang
%A Huang, Jianbo
%A Gong, Zhiguo
%A Fang, Liangda
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F guan-etal-2025-kvfkt
%X The knowledge tracing (KT) model based on deep learning has been proven to be superior to the traditional knowledge tracing model, eliminating the need for artificial engineering features. However, there are still problems, such as insufficient interpretability of the learning and answering processes. To address these issues, we propose a new approach in knowledge tracing with attention-based embedding and forgetting curve integration, namely KVFKT. Firstly, the embedding representation module is responsible for embedding the questions and computing the attention vector of knowledge concepts (KCs) when students answer questions and when answer time stamps are collected. Secondly, the forgetting quantification module performs the pre-prediction update of the student‘s knowledge state matrix. This quantification involves calculating the interval time and associated forgetting rate of relevant KCs, following the forgetting curve. Thirdly, the answer prediction module generates responses based on students’ knowledge status, guess coefficient, and question difficulty. Finally, the knowledge status update module further refines the students’ knowledge status according to their answers to the questions and the characteristics of those questions. In the experiment, four real-world datasets are used to test the model. Experimental results show that KVFKT better traces students’ knowledge state and outperforms state-of-the-art models.
%U https://aclanthology.org/2025.coling-main.297/
%P 4399-4409
Markdown (Informal)
[KVFKT: A New Horizon in Knowledge Tracing with Attention-Based Embedding and Forgetting Curve Integration](https://aclanthology.org/2025.coling-main.297/) (Guan et al., COLING 2025)
ACL