Error tracing in programming: a path to personalised feedback
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Date
2024
Authors
Shaka, Martha
Carraro, Diego
Brown, Kenneth N.
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Computational Linguistics
Published Version
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.
Description
Keywords
Knowledge tracing , Binary pass/fail predictions , Real student dataset
Citation
Shaka, M., Carraro, D. and Brown, K. N. (2024) 'Error tracing in programming: a path to personalised feedback', Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA). pp. 330–342. Available at: https://aclanthology.org/2024.bea-1.27/