Abstract
Knowledge tracing, the process of estimating students’ mastery over concepts from their past performance and predicting future outcomes, often relies on binary pass/fail predictions. This hinders the provision of specific feedback by failing to diagnose precise errors. We present an error-tracing model for learning programming that advances traditional knowledge tracing by employing multi-label classification to forecast exact errors students may generate. Through experiments on a real student dataset, we validate our approach and compare it to two baseline knowledge-tracing methods. We demonstrate an improved ability to predict specific errors, for first attempts and for subsequent attempts at individual problems.- Anthology ID:
- 2024.bea-1.27
- 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:
- 330–342
- Language:
- URL:
- https://aclanthology.org/2024.bea-1.27
- DOI:
- Cite (ACL):
- Martha Shaka, Diego Carraro, and Kenneth Brown. 2024. Error Tracing in Programming: A Path to Personalised Feedback. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 330–342, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Error Tracing in Programming: A Path to Personalised Feedback (Shaka et al., BEA 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.bea-1.27.pdf