Reading ability detection using eye-tracking data with LSTM-based few-shot learning
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914948734517248 |
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| author | Li, Nanxi Wang, Hongjiang Zhan, Zehui |
| author_facet | Li, Nanxi Wang, Hongjiang Zhan, Zehui |
| contents | Reading ability detection is important in modern educational field. In this paper, a method of predicting scores of reading ability is proposed, using the eye-tracking data of a few subjects (e.g., 68 subjects). The proposed method built a regression model for the score prediction by combining Long Short Time Memory (LSTM) and light-weighted neural networks. Experiments show that with few-shot learning strategy, the proposed method achieved higher accuracy than previous methods of score prediction in reading ability detection. The code can later be downloaded at https://github.com/pumpkinLNX/LSTM-eye-tracking-pytorch.git |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08798 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reading ability detection using eye-tracking data with LSTM-based few-shot learning Li, Nanxi Wang, Hongjiang Zhan, Zehui Human-Computer Interaction Artificial Intelligence Reading ability detection is important in modern educational field. In this paper, a method of predicting scores of reading ability is proposed, using the eye-tracking data of a few subjects (e.g., 68 subjects). The proposed method built a regression model for the score prediction by combining Long Short Time Memory (LSTM) and light-weighted neural networks. Experiments show that with few-shot learning strategy, the proposed method achieved higher accuracy than previous methods of score prediction in reading ability detection. The code can later be downloaded at https://github.com/pumpkinLNX/LSTM-eye-tracking-pytorch.git |
| title | Reading ability detection using eye-tracking data with LSTM-based few-shot learning |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2409.08798 |