基于知识点会话感知的知识追踪方法.
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| Title: | 基于知识点会话感知的知识追踪方法. |
|---|---|
| Alternate Title: | Knowledge concept-aware session modeling for knowledge tracing. |
| Authors: | 王 静1 2022222231@nwnu.edu.cn, 马慧芳1,2 mahuifang@yeah.net, 张梦媛1 2023222106@nwnu.edu |
| Source: | Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Jan2026, Vol. 48 Issue 1, p180-190. 11p. |
| Subjects: | Concept learning, Mastery learning, Learning, Online education |
| Abstract (English): | Knowledge tracing (KT) aims to dynamically model learners' evolving knowledge states based on their historical learning records, and plays a significant role in online education systems. Most existing KT methods treat knowledge states as transition patterns of mastery levels of knowledge concepts from completing one exercise to completing the next, and consider learners' learning records as continuous and uniformly distributed data. However, actual learning records are considered to be divisible into different shorter sessions. To address this, a method, called knowledge concept-aware session modeling for knowledge tracing (KSMKT), is proposed to capture learners' knowledge state changes at a finer granularity. Specifically, learners' historical learning records are divided into shorter sessions from the perspective of knowledge concepts. Subsequently, a fine-grained knowledge state modeling module is proposed to capture fine-grained interaction dependencies and knowledge state changes within and across sessions. Additionally, a global knowledge proficiency modeling module is introduced to model learners' knowledge states from an overall perspective. Extensive experiments on 3 real-world datasets demonstrate that KSMKT outperforms most current baseline methods, thus proving the effectiveness of KSMKT. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 知识追踪(KT)旨在根据学习者的历史学习记录动态建模他们不断变化的知识状态,在在线 教育系统中发挥着重要作用。多数现有的KT方法将知识状态视为学习者从完成前一道习题到完成下一 道习题的知识点掌握程度的转换模式,并将学习者的学习记录视为连续且均匀分布的数据。然而,现实中 的学习记录被认为可以划分为不同的较短的会话。据此,提出了一种名为基于知识点会话感知的知识追 踪方法KSMKT,旨在以更精细的粒度捕捉学习者知识状态的变化。具体而言,首先从知识点角度将学习 者的历史学习记录划分为较短的会话。随后,提出了一个细粒度知识状态建模模块,该模块能够建模会话 内和会话间的细粒度交互依赖性和知识状态变化。此外,还引入了一个全局知识熟练度建模模块,从整体 的角度建模学习者的知识状态。在3个真实世界数据集上的大量实验结果表明,KSMKT 优于大多数当 前的基线方法,从而证明了KSMKT的有效性. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: 基于知识点会话感知的知识追踪方法. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Knowledge concept-aware session modeling for knowledge tracing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22王+静%22">王 静</searchLink><relatesTo>1</relatesTo><i> 2022222231@nwnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22马慧芳%22">马慧芳</searchLink><relatesTo>1,2</relatesTo><i> mahuifang@yeah.net</i><br /><searchLink fieldCode="AR" term="%22张梦媛%22">张梦媛</searchLink><relatesTo>1</relatesTo><i> 2023222106@nwnu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Engineering+%26+Science+%2F+Jisuanji+Gongcheng+yu+Kexue%22">Computer Engineering & Science / Jisuanji Gongcheng yu Kexue</searchLink>. Jan2026, Vol. 48 Issue 1, p180-190. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Concept+learning%22">Concept learning</searchLink><br /><searchLink fieldCode="DE" term="%22Mastery+learning%22">Mastery learning</searchLink><br /><searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: Knowledge tracing (KT) aims to dynamically model learners' evolving knowledge states based on their historical learning records, and plays a significant role in online education systems. Most existing KT methods treat knowledge states as transition patterns of mastery levels of knowledge concepts from completing one exercise to completing the next, and consider learners' learning records as continuous and uniformly distributed data. However, actual learning records are considered to be divisible into different shorter sessions. To address this, a method, called knowledge concept-aware session modeling for knowledge tracing (KSMKT), is proposed to capture learners' knowledge state changes at a finer granularity. Specifically, learners' historical learning records are divided into shorter sessions from the perspective of knowledge concepts. Subsequently, a fine-grained knowledge state modeling module is proposed to capture fine-grained interaction dependencies and knowledge state changes within and across sessions. Additionally, a global knowledge proficiency modeling module is introduced to model learners' knowledge states from an overall perspective. Extensive experiments on 3 real-world datasets demonstrate that KSMKT outperforms most current baseline methods, thus proving the effectiveness of KSMKT. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Chinese) Group: Ab Data: 知识追踪(KT)旨在根据学习者的历史学习记录动态建模他们不断变化的知识状态,在在线 教育系统中发挥着重要作用。多数现有的KT方法将知识状态视为学习者从完成前一道习题到完成下一 道习题的知识点掌握程度的转换模式,并将学习者的学习记录视为连续且均匀分布的数据。然而,现实中 的学习记录被认为可以划分为不同的较短的会话。据此,提出了一种名为基于知识点会话感知的知识追 踪方法KSMKT,旨在以更精细的粒度捕捉学习者知识状态的变化。具体而言,首先从知识点角度将学习 者的历史学习记录划分为较短的会话。随后,提出了一个细粒度知识状态建模模块,该模块能够建模会话 内和会话间的细粒度交互依赖性和知识状态变化。此外,还引入了一个全局知识熟练度建模模块,从整体 的角度建模学习者的知识状态。在3个真实世界数据集上的大量实验结果表明,KSMKT 优于大多数当 前的基线方法,从而证明了KSMKT的有效性. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3969/j.issn.1007-130X.2026.01.018 Languages: – Code: chi Text: Chinese PhysicalDescription: Pagination: PageCount: 11 StartPage: 180 Subjects: – SubjectFull: Concept learning Type: general – SubjectFull: Mastery learning Type: general – SubjectFull: Learning Type: general – SubjectFull: Online education Type: general Titles: – TitleFull: 基于知识点会话感知的知识追踪方法. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: 王 静 – PersonEntity: Name: NameFull: 马慧芳 – PersonEntity: Name: NameFull: 张梦媛 IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1007130X Numbering: – Type: volume Value: 48 – Type: issue Value: 1 Titles: – TitleFull: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue Type: main |
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