Reading ability detection using eye-tracking data with LSTM-based few-shot learning
<|reference_start|>Reading ability detection using eye-tracking data with LSTM-based few-shot learning: 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<|reference_end|>
@article{li2024reading,
    title={Reading ability detection using eye-tracking data with LSTM-based
  few-shot learning},
    author={Nanxi Li, Hongjiang Wang, Zehui Zhan},
    journal={arXiv preprint arXiv:2409.08798},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08798},
    primaryClass={cs.HC cs.AI}
}