Error Tracing in Programming: A Path to Personalised Feedback

Martha Shaka, Diego Carraro, Kenneth Brown


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:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2024.bea-1.27.pdf