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 ID | 18062 |
| Article Category | Article |
| Authors | Song, Xiangyu, Li, Jianxin, Cai, Taotao, Yang, Shuiqiao, Yang, Tingting and Liu, Chengfei |
| Journal Title | Knowledge-Based Systems |
| Journal Citation | 258 |
| Article Number | 110036 |
| Number of Pages | 12 |
| Year | 2022 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 0950-7051 |
| 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. |
| Keywords | Knowledge Tracing; Deep learning; Educational data mining; Intelligent tutoring systems; Graph neural network |
| Related Output | |
| Is supplemented by | https://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/LP180100750 |
| Is supplemented by | https://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DP200103700 |
| Is supplemented by | https://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DP220102191 |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 4602. Artificial intelligence |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Deakin University |
| Swinburne University of Technology | |
| Macquarie University | |
| University of New South Wales | |
| Peng Cheng Laboratory, China |
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