Inferring Prerequisite Knowledge Concepts in Educational Knowledge Graphs: A Multi-criteria Approach
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909773941702656 |
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| author | Alatrash, Rawaa Chatti, Mohamed Amine Wibowo, Nasha Ain, Qurat Ul |
| author_facet | Alatrash, Rawaa Chatti, Mohamed Amine Wibowo, Nasha Ain, Qurat Ul |
| contents | Educational Knowledge Graphs (EduKGs) organize various learning entities and their relationships to support structured and adaptive learning. Prerequisite relationships (PRs) are critical in EduKGs for defining the logical order in which concepts should be learned. However, the current EduKG in the MOOC platform CourseMapper lacks explicit PR links, and manually annotating them is time-consuming and inconsistent. To address this, we propose an unsupervised method for automatically inferring concept PRs without relying on labeled data. We define ten criteria based on document-based, Wikipedia hyperlink-based, graph-based, and text-based features, and combine them using a voting algorithm to robustly capture PRs in educational content. Experiments on benchmark datasets show that our approach achieves higher precision than existing methods while maintaining scalability and adaptability, thus providing reliable support for sequence-aware learning in CourseMapper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inferring Prerequisite Knowledge Concepts in Educational Knowledge Graphs: A Multi-criteria Approach Alatrash, Rawaa Chatti, Mohamed Amine Wibowo, Nasha Ain, Qurat Ul Computers and Society Artificial Intelligence Educational Knowledge Graphs (EduKGs) organize various learning entities and their relationships to support structured and adaptive learning. Prerequisite relationships (PRs) are critical in EduKGs for defining the logical order in which concepts should be learned. However, the current EduKG in the MOOC platform CourseMapper lacks explicit PR links, and manually annotating them is time-consuming and inconsistent. To address this, we propose an unsupervised method for automatically inferring concept PRs without relying on labeled data. We define ten criteria based on document-based, Wikipedia hyperlink-based, graph-based, and text-based features, and combine them using a voting algorithm to robustly capture PRs in educational content. Experiments on benchmark datasets show that our approach achieves higher precision than existing methods while maintaining scalability and adaptability, thus providing reliable support for sequence-aware learning in CourseMapper. |
| title | Inferring Prerequisite Knowledge Concepts in Educational Knowledge Graphs: A Multi-criteria Approach |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2509.05393 |