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DyGKT: Dynamic Graph Learning for Knowledge Tracing
KE CHENG1; LINZHI PENG1; PENGYANG WANG2; JUNCHEN YE3; LEILEI SUN1; BOWEN DU3,4
2024-08
Conference NameKDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Source PublicationKDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Pages409-420
Conference DateAugust 25-29, 2024
Conference PlaceBarcelona, Spain
CountrySpain
Publication PlaceNew York, NY, USA
PublisherAssociation for Computing Machinery
Abstract

Knowledge Tracing aims to assess student learning states by predicting their performance in answering questions. Different from the existing research which utilizes fixed-length learning sequence to obtain the student states and regards KT as a static problem, this work is motivated by three dynamical characteristics: 1) The scales of students answering records are constantly growing; 2) The semantics of time intervals between the records vary; 3) The relationships between students, questions and concepts are evolving. The three dynamical characteristics above contain the great potential to revolutionize the existing knowledge tracing methods. Along this line, we propose a Dynamic Graph-based Knowledge Tracing model, namely DyGKT. In particular, a continuous-time dynamic question-answering graph for knowledge tracing is constructed to deal with the infinitely growing answering behaviors, and it is worth mentioning that it is the first time dynamic graph learning technology is used in this field. Then, a dual time encoder is proposed to capture long-term and short-term semantics among the different time intervals. Finally, a multiset indicator is utilized to model the evolving relationships between students, questions, and concepts via the graph structural feature. Numerous experiments are conducted on five real-world datasets, and the results demonstrate the superiority of our model. All the used resources are publicly available at https://github.com/PengLinzhi/DyGKT. 

KeywordDynamic Graph Educational Data Mining Graph Neural Networks Knowledge Tracing
DOI10.1145/3637528.3671773
URLView the original
Language英語English
Scopus ID2-s2.0-85203711236
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Document TypeConference paper
CollectionFaculty of Science and Technology
THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.SKLSDE Lab, Beihang University
2.SKL-IOTSC, Department of Computer and Information Science, University of Macau
3.School of Transportation Science and Engineering, Beihang University
4.Zhongguancun Laboratory
Recommended Citation
GB/T 7714
KE CHENG,LINZHI PENG,PENGYANG WANG,et al. DyGKT: Dynamic Graph Learning for Knowledge Tracing[C], New York, NY, USA:Association for Computing Machinery, 2024, 409-420.
APA KE CHENG., LINZHI PENG., PENGYANG WANG., JUNCHEN YE., LEILEI SUN., & BOWEN DU (2024). DyGKT: Dynamic Graph Learning for Knowledge Tracing. KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 409-420.
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