Reading ability detection using eye-tracking data with LSTM-based few-shot learning
This work addresses reading ability detection in education, but it appears incremental as it applies existing techniques to a specific domain.
The paper tackled predicting reading ability scores from eye-tracking data using an LSTM-based few-shot learning method, achieving higher accuracy than previous methods with experiments on 68 subjects.
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