A survey on deep learning based knowledge tracing

Article


Song, Xiangyu, Li, Jianxin, Cai, Taotao, Yang, Shuiqiao, Yang, Tingting and Liu, Chengfei. 2022. "A survey on deep learning based knowledge tracing." Knowledge-Based Systems. 258. https://doi.org/10.1016/j.knosys.2022.110036
Article Title

A survey on deep learning based knowledge tracing

ERA Journal ID18062
Article CategoryArticle
AuthorsSong, Xiangyu, Li, Jianxin, Cai, Taotao, Yang, Shuiqiao, Yang, Tingting and Liu, Chengfei
Journal TitleKnowledge-Based Systems
Journal Citation258
Article Number110036
Number of Pages12
Year2022
PublisherElsevier
Place of PublicationNetherlands
ISSN0950-7051
1872-7409
Digital Object Identifier (DOI)https://doi.org/10.1016/j.knosys.2022.110036
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0950705122011297
Abstract“Knowledge tracing (KT)” is an emerging and popular research topic in the field of online education that seeks to assess students’ mastery of a concept based on their historical learning of relevant exercises on an online education system in order to make the most accurate prediction of student performance. Since there have been a large number of KT models, we attempt to systematically investigate, compare and discuss different aspects of KT models to find out the differences between these models in order to better assist researchers in this field. The findings of this study have made substantial contributions to the progress of online education, which is especially relevant in light of the current global pandemic. As a result of the current expansion of deep learning methods over the last decade, researchers have been tempted to include deep learning strategies into KT research with astounding results. In this paper, we evaluate current research on deep learning-based KT in the main categories listed below. In particular, we explore (1) a granular categorisation of the technological solutions presented by the mainstream Deep Learning-based KT Models. (2) a detailed analysis of techniques to KT, with a special emphasis on Deep Learning-based KT Models. (3) an analysis of the technological solutions and major improvement presented by Deep Learning-based KT models. In conclusion, we discuss possible future research directions in the field of Deep Learning-based KT.
KeywordsKnowledge Tracing; Deep learning; Educational data mining; Intelligent tutoring systems; Graph neural network
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Is supplemented byhttps://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/LP180100750
Is supplemented byhttps://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DP200103700
Is supplemented byhttps://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DP220102191
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
Public NotesFiles associated with this item cannot be displayed due to copyright restrictions.
Byline AffiliationsDeakin University
Swinburne University of Technology
Macquarie University
University of New South Wales
Peng Cheng Laboratory, China
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