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Knowledge relation rank enhanced heterogeneous learning interaction modeling for neural graph forgetting knowledge tracing

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成果类型:
期刊论文
作者:
Li, Linqing;Wang, Zhifeng
通讯作者:
Wang, ZF
作者机构:
[Li, Linqing; Wang, Zhifeng] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Peoples R China.
[Wang, Zhifeng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.
通讯机构:
[Wang, ZF ] C
Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Peoples R China.
Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.
语种:
英文
期刊:
PLOS ONE
ISSN:
1932-6203
年:
2023
卷:
18
期:
12
页码:
e0295808
基金类别:
National Natural Science Foundation of China#&#&#No. 62177022, 61901165, 61501199 AI and Faculty Empowerment Pilot Project#&#&#No. CCNUAI&FE2022-03-01 Collaborative Innovation Center for Informatization and Balanced Development of K-12 Education by MOE and Hubei Province#&#&#No. xtzd2021-005 Natural Science Foundation of Hubei Province#&#&#No. 2022CFA007
机构署名:
本校为第一且通讯机构
院系归属:
伍伦贡联合研究院
摘要:
Knowledge tracing models have gained prominence in educational data mining, with applications like the Self-Attention Knowledge Tracing model, which captures the exercise-knowledge relationship. However, conventional knowledge tracing models focus solely on static question-knowledge and knowledge-knowledge relationships, treating them with equal significance. This simplistic approach often succumbs to subjective labeling bias and lacks the depth to capture nuanced exercise-knowledge connections. In this study, we propose a novel knowledge tracing model called Knowledge Relation Rank Enhanced H...

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