Bilingual Dyslexia Analysis Algorithm
Dyslexia
Bilingualism
Machine Learning
Overview
Developing computational methods to identify and analyze dyslexia patterns in bilingual children across different language pairs. This project combines natural language processing techniques with psycholinguistic assessments to create more accurate screening tools.
Lead Researcher: Dr. Peter Awe
Timeline: 2023-2026
Funding: NSF Grant #2048153
Key Components
- Cross-linguistic comparison of dyslexia markers
- Machine learning models for early detection
- Multimodal data integration (reading, speech, eye-tracking)
Publications
- Awe, P. et al. (2024). “Cross-Linguistic Patterns in Bilingual Dyslexia.” Journal of Learning Disabilities
- Chen, M. & Awe, P. (2023). “Computational Assessment of Reading Fluency in Bilingual Children.” ACL Proceedings