@inproceedings{shaka-etal-2024-error,
title = "Error Tracing in Programming: A Path to Personalised Feedback",
author = "Shaka, Martha and
Carraro, Diego and
Brown, Kenneth",
editor = {Kochmar, Ekaterina and
Bexte, Marie and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Tack, Ana{\"\i}s and
Yaneva, Victoria and
Yuan, Zheng},
booktitle = "Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bea-1.27",
pages = "330--342",
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.",
}
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%0 Conference Proceedings
%T Error Tracing in Programming: A Path to Personalised Feedback
%A Shaka, Martha
%A Carraro, Diego
%A Brown, Kenneth
%Y Kochmar, Ekaterina
%Y Bexte, Marie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%S Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F shaka-etal-2024-error
%X 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.
%U https://aclanthology.org/2024.bea-1.27
%P 330-342
Markdown (Informal)
[Error Tracing in Programming: A Path to Personalised Feedback](https://aclanthology.org/2024.bea-1.27) (Shaka et al., BEA 2024)
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.