Prerequisite Structure Discovery in Intelligent Tutoring Systems
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911769964838912 |
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| author | Annabi, Louis Nguyen, Sao Mai |
| author_facet | Annabi, Louis Nguyen, Sao Mai |
| contents | This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems. The KS represents the relations between different Knowledge Components (KCs), while KT predicts a learner's success based on her past history. The contribution of this research includes proposing a KT model that incorporates the KS as a learnable parameter, enabling the discovery of the underlying KS from learner trajectories. The quality of the uncovered KS is assessed by using it to recommend content and evaluating the recommendation algorithm with simulated students. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01672 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prerequisite Structure Discovery in Intelligent Tutoring Systems Annabi, Louis Nguyen, Sao Mai Computers and Society Artificial Intelligence Machine Learning This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems. The KS represents the relations between different Knowledge Components (KCs), while KT predicts a learner's success based on her past history. The contribution of this research includes proposing a KT model that incorporates the KS as a learnable parameter, enabling the discovery of the underlying KS from learner trajectories. The quality of the uncovered KS is assessed by using it to recommend content and evaluating the recommendation algorithm with simulated students. |
| title | Prerequisite Structure Discovery in Intelligent Tutoring Systems |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.01672 |