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

2024/09/13 by Nanxi Li, Li, Nanxi, Hongjiang Wang +3
Computer Science · #Artificial Intelligence (cs.AI) #Educational Technology and Assessment #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Human-Computer Interaction (cs.HC)

paper · pdf · doi:10.48550/arxiv.2409.08798

openalex publication_date 2024/09/13 · openalex created_date 2024/10/23 · openalex updated_date 2026/07/28

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

Related

• DysLexML: Screening Tool for Dyslexia Using Machine Learning
• OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responses
• Self-supervised Deep Learning for Reading Activity Classification
• An Approach to Track Reading Progression Using Eye-Gaze Fixation Points
• TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction