Paper ID: 2409.08798

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

Nanxi Li, Hongjiang Wang, Zehui Zhan

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 this https URL

Submitted: Sep 13, 2024