Reading ability detection using eye-tracking data with LSTM-based few-shot learning

Nanxi Li; Hongjiang Wang; Zehui Zhan

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arXiv · 2409.08798

Reading ability detection using eye-tracking data with LSTM-based few-shot learning

Nanxi Li·Hongjiang Wang·Zehui Zhan

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Abstract

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

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Nanxi Li, Hongjiang Wang, Zehui Zhan. 2024-09-13. Reading ability detection using eye-tracking data with LSTM-based few-shot learning. https://arxiv.org/abs/2409.08798

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