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PQSCT: Pseudo-Siamese BERT for Concept Tagging With Both Questions and Solutions

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成果类型:
期刊论文
作者:
Huang, Tao;Hu, Shengze;Yang, Huali;Geng, Jing;Liu, Sannyuya;...
通讯作者:
Yang, ZK;Yang, HL
作者机构:
[Yang, Zongkai; Liu, Sannyuya; Yang, ZK; Zhang, Hao; Huang, Tao; Geng, Jing] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.
[Hu, Shengze] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
[Yang, Huali] Wuhan Text Univ, Sch Comp Sci & Artificial Intelligence, Wuhan 430200, Peoples R China.
通讯机构:
[Yang, ZK ] C
[Yang, HL ] W
Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.
Wuhan Text Univ, Sch Comp Sci & Artificial Intelligence, Wuhan 430200, Peoples R China.
语种:
英文
关键词:
Concept tagging;exercise content;indexing methods;intelligent education
期刊:
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
ISSN:
1939-1382
年:
2023
卷:
16
期:
5
页码:
831-846
基金类别:
This work was supported in part by the State Key Program of National Natural Science of China under Grant U20A20229 and in part by the National Natural Science Foundation of China under Grant 61977033 and Grant 62077024.
机构署名:
本校为第一且通讯机构
摘要:
The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services, such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which extracts knowledge index structures and knowledge representations for exercises. Unfortunately, to the best of our knowledge, existing tagging approaches based on exercise content either ignore multiple com...

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