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The proposed model uses an interaction-based graph neural network module to learn local academic performance representations from online interaction activities and an attribute-based graph neural network to learn global academic performance representations from attribute features of all students using dynamic graph convolution operations. The learned representations from local and global levels are combined in a local-to-global representation learning module to generate predicted academic performances. The empirical study results demonstrate that the proposed model significantly outperforms existing methods. Notably, the proposed model achieves an accuracy of 83.96% for predicting students who pass or fail and an accuracy of 90.18% for predicting students who pass or withdraw on a widely recognized public dataset. The ablation studies confirm the effectiveness and superiority of the proposed techniques.<\/jats:p>","DOI":"10.1007\/s40747-024-01344-z","type":"journal-article","created":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T14:02:08Z","timestamp":1707573728000},"page":"3557-3575","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Improving academic performance predictions with dual graph neural networks"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-6093","authenticated-orcid":false,"given":"Qionghao","family":"Huang","sequence":"first","affiliation":[]},{"given":"Yan","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,10]]},"reference":[{"issue":"3","key":"1344_CR1","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1080\/01587919.2022.2150126","volume":"44","author":"EG Oh","year":"2023","unstructured":"Oh EG, Cho M-H, Chang Y (2023) Learners\u2019 perspectives on MOOC design. 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