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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.13500 |
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| _version_ | 1866910309402279936 |
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| author | Ni, Lin Wang, Sijie Zhang, Zeyu Li, Xiaoxuan Zheng, Xianda Denny, Paul Liu, Jiamou |
| author_facet | Ni, Lin Wang, Sijie Zhang, Zeyu Li, Xiaoxuan Zheng, Xianda Denny, Paul Liu, Jiamou |
| contents | Learnersourcing offers great potential for scalable education through student content creation. However, predicting student performance on learnersourced questions, which is essential for personalizing the learning experience, is challenging due to the inherent noise in student-generated data. Moreover, while conventional graph-based methods can capture the complex network of student and question interactions, they often fall short under cold start conditions where limited student engagement with questions yields sparse data. To address both challenges, we introduce an innovative strategy that synergizes the potential of integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM) embeddings. Our methodology employs a signed bipartite graph to comprehensively model student answers, complemented by a contrastive learning framework that enhances noise resilience. Furthermore, LLM's contribution lies in generating foundational question embeddings, proving especially advantageous in addressing cold start scenarios characterized by limited graph data. Validation across five real-world datasets sourced from the PeerWise platform underscores our approach's effectiveness. Our method outperforms baselines, showcasing enhanced predictive accuracy and robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_13500 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy Ni, Lin Wang, Sijie Zhang, Zeyu Li, Xiaoxuan Zheng, Xianda Denny, Paul Liu, Jiamou Machine Learning Artificial Intelligence 97P80 Learnersourcing offers great potential for scalable education through student content creation. However, predicting student performance on learnersourced questions, which is essential for personalizing the learning experience, is challenging due to the inherent noise in student-generated data. Moreover, while conventional graph-based methods can capture the complex network of student and question interactions, they often fall short under cold start conditions where limited student engagement with questions yields sparse data. To address both challenges, we introduce an innovative strategy that synergizes the potential of integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM) embeddings. Our methodology employs a signed bipartite graph to comprehensively model student answers, complemented by a contrastive learning framework that enhances noise resilience. Furthermore, LLM's contribution lies in generating foundational question embeddings, proving especially advantageous in addressing cold start scenarios characterized by limited graph data. Validation across five real-world datasets sourced from the PeerWise platform underscores our approach's effectiveness. Our method outperforms baselines, showcasing enhanced predictive accuracy and robustness. |
| title | Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy |
| topic | Machine Learning Artificial Intelligence 97P80 |
| url | https://arxiv.org/abs/2309.13500 |