From Miscue to Evidence of Difficulty: Analysis of Automatically Detected Miscues in Oral Reading for Feedback Potential
Beata Beigman Klebanov, Michael Suhan, Tenaha O’Reilly, Zuowei Wang
Abstract
This research is situated in the space between an existing NLP capability and its use(s) in an educational context. We analyze oral reading data collected with a deployed automated speech analysis software and consider how the results of automated speech analysis can be interpreted and used to inform the ideation and design of a new feature – feedback to learners and teachers. Our analysis shows how the details of the system’s performance and the details of the context of use both significantly impact the ideation process.- Anthology ID:
- 2024.bea-1.38
- Volume:
- Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Ekaterina Kochmar, Marie Bexte, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Anaïs Tack, Victoria Yaneva, Zheng Yuan
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 459–469
- Language:
- URL:
- https://aclanthology.org/2024.bea-1.38
- DOI:
- Cite (ACL):
- Beata Beigman Klebanov, Michael Suhan, Tenaha O’Reilly, and Zuowei Wang. 2024. From Miscue to Evidence of Difficulty: Analysis of Automatically Detected Miscues in Oral Reading for Feedback Potential. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 459–469, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- From Miscue to Evidence of Difficulty: Analysis of Automatically Detected Miscues in Oral Reading for Feedback Potential (Beigman Klebanov et al., BEA 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.bea-1.38.pdf